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1531 lines
66 KiB
Python
1531 lines
66 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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Carrier state Factory — two-layer labeled-box TSE encode/decode.
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Layer 1 — Carrier state boxes (structured signal reconstruction)
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────────────────────────────────────────────────────────────
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Each carrier state parameter is a labeled box:
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CarrierBox: label=(band, mode, param, frame) value=f16
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Stamp → Label → Sort (American Flag Sort) → Pack into FoamVoxels
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Layer 2 — Jupiter boxes (Procedural Generative Fractal Summation)
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─────────────────────────────────────────────────────────────────────────
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The irreducible friction residual is no longer stored as a static blob.
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Instead, it is mapped to a low-energy Procedural Generative Seed.
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This seed, when expanded via Holographic Recursion (Opcode 0x19), sums
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to the total complexity of the original residual.
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irreducible core → 64-bit Fractal Seed → UV Map Topological Rollup
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Expansion: Seed + Stride (W) → Deterministic Complexity Reconstruction
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This refinement (v4.0) moves the system from Shannon-bounded storage to
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Kolmogorov-bounded algorithmic generation, drastically reducing the
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"AETHER floor" energy cost per bit.
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Phase-lock condition gates the layer:
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PHASE_GROUNDED (phi_ratio ≈ φ) → tunnel fires, zero-damage transfer
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PHASE_SEISMIC (phi_ratio ≈ 1-2φ) → partial encoding, minor drift
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PHASE_FLAME (phi_ratio >> φ) → skip Jupiter layer, pure noise
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The metanarrative harness (tsm_narrative_layers.vh / metanarrative_goal_spec.md)
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is the complexity filter: F(S) = Equilibrium ↔ ∇Entropy ≈ 0 & Φ ≈ 1.618.
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When the harness says PHASE_FLAME the layer is bypassed — no false precision.
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Jupiter boxes use band=0xFE as the layer marker in the 32-bit label.
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"""
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from __future__ import annotations
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import hashlib
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import math
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import os
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import struct
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import sys
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Tuple
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sys.path.insert(0, os.path.dirname(__file__))
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from usc_spectral_core import (
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deep_compression_snag, shift_allocation, conversion_efficiency,
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shannon_capacity, eddington_utilization, landauer_cost,
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)
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# ── Parameter catalogue ───────────────────────────────────────────────────────
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# Geometric params NOT transmitted — reconstructed at decode (white hole) from
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# the shared physical basis. This is the gamma-pattern principle:
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#
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# pos_x → _band_centre(band_idx, f_low, f_high, n_bands)
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# bandwidth → (f_high - f_low) / n_bands
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# rate → n_samples / sample_rate (frame duration, constant per frame)
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#
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# What remains on the wire: amplitude, phase, velocity, curvature, coherence.
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PARAM_NAMES = ('amplitude', 'phase', 'velocity', 'curvature', 'coherence')
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N_PARAMS = len(PARAM_NAMES) # 5 (was 8; pos_x, bandwidth, rate eliminated)
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# ── Complex geometry scaffold (geometry-rip branch) ───────────────────────────
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# COMPLEX_NUMBER_AUDIT #H1 and #M2 now split into independent flags (DAG 777):
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#
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# _USE_H1 (HIGH): complex Goertzel velocity — chirp / phase drift detection.
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# Safe to enable. This IS the carrier state path-finding projector:
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# H1 projects input data onto n-space to find the carrier state
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# trajectory. Without H1, chirp signals appear as zero-velocity
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# (amplitude-only path), producing a degenerate n-space trace.
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# See: sessions/chat-carrier state-nspace-path-trace-20260404.md
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#
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# _USE_M2 (LOWER): PHI^(i*PHI_FRAC) Weyl sequence in _modes_from_bytes.
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# DEFERRED — Weyl inverts enwik9 discrimination (95.9% FLAME).
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# i%7 is intentional hardware design; Weyl is not the fix.
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_USE_H1 = True # DAG 777: chirp detection live; carrier state path projector enabled
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_USE_M2 = False # DEFERRED: Weyl (PHI^(i*PHI_FRAC)) inverts enwik9 discrimination
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# ── Label packing layout (32 bits total) ─────────────────────────────────────
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# bits 31-24 : band_idx (8 bands, 3 bits → padded to 8)
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# bits 23-11 : mode_idx (up to 8191 modes, 13 bits)
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# bits 10- 8 : param_idx (8 params, 3 bits)
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# bits 7- 0 : frame_idx (256 frames, 8 bits)
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def pack_label(band: int, mode: int, param: int, frame: int) -> int:
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return ((band & 0xFF) << 24) | ((mode & 0x1FFF) << 11) | ((param & 0x7) << 8) | (frame & 0xFF)
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def unpack_label(label: int) -> Tuple[int, int, int, int]:
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band = (label >> 24) & 0xFF
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mode = (label >> 11) & 0x1FFF
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param = (label >> 8) & 0x07
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frame = (label >> 0) & 0xFF
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return band, mode, param, frame
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# ── Half-float helpers (no numpy required) ───────────────────────────────────
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def f32_to_f16_bits(v: float) -> int:
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"""Convert f32 to f16 bit pattern (IEEE 754 half-precision)."""
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packed = struct.pack('>f', float(v))
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b = struct.unpack('>I', packed)[0]
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sign = (b >> 16) & 0x8000
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exp = ((b >> 23) & 0xFF) - 127 + 15
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mant = (b >> 13) & 0x3FF
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if exp <= 0:
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return sign
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if exp >= 31:
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return sign | 0x7C00
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return sign | (exp << 10) | mant
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def f16_bits_to_f32(h: int) -> float:
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"""Convert f16 bit pattern back to Python float."""
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sign = -1.0 if h & 0x8000 else 1.0
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exp = (h >> 10) & 0x1F
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mant = h & 0x3FF
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if exp == 0:
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return sign * (mant / 1024.0) * (2.0 ** -14)
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if exp == 31:
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return sign * float('inf')
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return sign * (1.0 + mant / 1024.0) * (2.0 ** (exp - 15))
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# ── Core data structures ──────────────────────────────────────────────────────
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@dataclass(order=True)
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class CarrierBox:
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"""
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The fundamental factory unit.
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label is the sort key — ordering is the full address in carrier state space.
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"""
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label: int # packed (band, mode, param, frame)
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value_bits: int # f16 bit pattern
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def decode_value(self) -> float:
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return f16_bits_to_f32(self.value_bits)
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def pack(self) -> bytes:
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"""6 bytes: 4-byte label + 2-byte f16."""
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return struct.pack('>IH', self.label, self.value_bits)
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@classmethod
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def unpack(cls, data: bytes) -> 'CarrierBox':
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label, vbits = struct.unpack('>IH', data)
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return cls(label, vbits)
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@property
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def address(self) -> Tuple[int, int, int, int]:
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return unpack_label(self.label)
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@dataclass
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class FoamVoxel:
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"""
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A voxel = one carrier state's parameter boxes, grouped by (band, mode).
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All 8 param slots; missing ones default to 0.
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"""
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band: int
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mode: int
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params: List[float] = field(default_factory=lambda: [0.0] * N_PARAMS)
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def to_boxes(self, frame: int = 0) -> List[CarrierBox]:
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boxes = []
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for p_idx, val in enumerate(self.params):
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label = pack_label(self.band, self.mode, p_idx, frame)
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boxes.append(CarrierBox(label, f32_to_f16_bits(val)))
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return boxes
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@classmethod
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def from_boxes(cls, boxes: List[CarrierBox]) -> 'FoamVoxel':
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if not boxes:
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raise ValueError("empty box list")
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band, mode, _, _ = boxes[0].address
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params = [0.0] * N_PARAMS
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for box in boxes:
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_, _, p_idx, _ = box.address
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params[p_idx] = box.decode_value()
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return cls(band, mode, params)
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# ── MSD Radix Sort (American Flag Sort) on label integers ─────────────────────
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# Public-domain algorithm. O(32n) for 32-bit keys. Beats Timsort for n > 2000.
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def _radix_sort_boxes(boxes: List[CarrierBox], bit: int = 31) -> None:
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"""In-place MSD radix sort on CarrierBox.label. Uses 2-way split per bit."""
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if len(boxes) <= 1 or bit < 0:
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return
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if len(boxes) <= 16:
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# Insertion sort wins for tiny partitions
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for i in range(1, len(boxes)):
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key = boxes[i]
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j = i - 1
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while j >= 0 and boxes[j].label > key.label:
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boxes[j + 1] = boxes[j]
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j -= 1
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boxes[j + 1] = key
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return
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# Partition on current bit
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zeros, ones = [], []
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mask = 1 << bit
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for b in boxes:
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(zeros if not (b.label & mask) else ones).append(b)
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_radix_sort_boxes(zeros, bit - 1)
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_radix_sort_boxes(ones, bit - 1)
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boxes[:] = zeros + ones
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# ── Domain basis registry ─────────────────────────────────────────────────────
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# The gamma-pattern principle applied universally:
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# transmit band_idx (token), reconstruct energy/frequency at decode time
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# from the shared physical basis table.
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#
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# spacing='log' — octave/decade bands (audio, X-ray, gamma)
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# spacing='linear'— uniform channel grid (FM radio, AM radio, microwave)
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# spacing='table' — explicit nuclear/atomic line positions (ENSDF, NIST)
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#
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# unit is informational only — the codec doesn't care if it's Hz or keV or
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# "Regge units" or "monopole charge quanta". The constants are parameters
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# we choose to maximise sparsity for the target data. Nothing requires the
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# basis to be physically realised in 3+1 dimensions.
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@dataclass
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class DomainBasis:
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"""Physical basis table for one spectral domain."""
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name: str
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f_low: float # lowest energy/frequency in domain units
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f_high: float # highest energy/frequency in domain units
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n_bands: int # number of addressable basis elements
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spacing: str # 'log', 'linear', or 'table'
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unit: str # display unit (Hz, keV, MHz, THz, …)
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table: List[float] = field(default_factory=list) # explicit lines if spacing='table'
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def centre(self, band_idx: int) -> float:
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"""Reconstruct centre value from band index — the gamma pattern decoder."""
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if self.spacing == 'log':
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log_step = (math.log2(max(self.f_high, 1e-300)) -
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math.log2(max(self.f_low, 1e-300))) / self.n_bands
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return max(self.f_low, 1e-300) * (2 ** (band_idx * log_step))
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if self.spacing == 'linear':
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step = (self.f_high - self.f_low) / self.n_bands
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return self.f_low + band_idx * step
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# table: explicit nuclear/atomic lines
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if self.table:
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return self.table[min(band_idx, len(self.table) - 1)]
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return self.f_low
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def bands(self) -> List[float]:
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return [self.centre(i) for i in range(self.n_bands)]
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# Band-prefix → domain slot assignments (8-bit band field, 0x00-0xFF)
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# Each range is a domain family. The decoder reads the prefix, loads the
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# matching basis from DOMAIN_REGISTRY, and reconstructs without any extra wire bytes.
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DOMAIN_BAND_SLOTS: Dict[str, int] = {
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'audio_44k': 0x00,
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'audio_hi': 0x01,
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'radio_am': 0x10,
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'radio_fm': 0x11,
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'microwave': 0x12,
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'visible': 0x20,
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'near_ir': 0x21,
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'terahertz': 0x22,
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'xray_soft': 0x30,
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'xray_hard': 0x31,
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'gamma_lines': 0x40,
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'phonon_al': 0x50,
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# 0xF0-0xFB: accelerated-time subregisters (assigned dynamically)
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# 0xFE: Jupiter tunnel
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# 0xFF: Planck floor / raw
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}
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SLOT_TO_DOMAIN: Dict[int, str] = {v: k for k, v in DOMAIN_BAND_SLOTS.items()}
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# Pre-defined domain bases — both sides share this table (never transmitted).
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# Adding a domain here costs zero bytes on the wire.
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DOMAIN_REGISTRY: Dict[str, DomainBasis] = {
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# ── Audio ──────────────────────────────────────────────────────────────────
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'audio_44k': DomainBasis('audio_44k', 20, 22050, 8, 'log', 'Hz'),
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'audio_hi': DomainBasis('audio_hi', 20, 96000, 16, 'log', 'Hz'), # 192kHz/24-bit
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# ── Radio ──────────────────────────────────────────────────────────────────
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'radio_fm': DomainBasis('radio_fm', 87.5e6, 108e6, 101,'linear', 'Hz'), # 200kHz channels
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'radio_am': DomainBasis('radio_am', 530e3, 1710e3, 119,'linear', 'Hz'), # 10kHz channels
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'microwave': DomainBasis('microwave', 300e6, 300e9, 16, 'log', 'Hz'),
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# ── Optical / IR ───────────────────────────────────────────────────────────
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'visible': DomainBasis('visible', 380e12, 750e12, 8, 'log', 'Hz'), # 380-750 THz
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'near_ir': DomainBasis('near_ir', 100e12, 380e12, 8, 'log', 'Hz'),
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'terahertz': DomainBasis('terahertz', 100e9, 10e12, 16, 'log', 'Hz'),
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# ── X-ray ──────────────────────────────────────────────────────────────────
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'xray_soft': DomainBasis('xray_soft', 0.1, 10.0, 16, 'log', 'keV'), # soft X-ray
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'xray_hard': DomainBasis('xray_hard', 10.0, 150.0, 16, 'log', 'keV'), # hard X-ray
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# ── Gamma (nuclear line table — ENSDF subset) ───────────────────────────────
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# Key lines: annihilation, Na-22, Co-60, Cs-137, Tl-208, K-40, Bi-214 …
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'gamma_lines': DomainBasis('gamma_lines', 0.0, 3000.0, 16, 'table', 'keV', table=[
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511.0, # e+/e- annihilation (pair production)
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661.7, # Cs-137 (most common calibration source)
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1173.2, # Co-60 line 1
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1274.5, # Na-22
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1332.5, # Co-60 line 2
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1460.8, # K-40 (natural background)
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1764.5, # Bi-214 (radon chain)
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2614.5, # Tl-208 (thorium chain)
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583.2, # Tl-208 low
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727.3, # Bi-212
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1120.3, # Bi-214
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1238.1, # Bi-214
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609.3, # Bi-214
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1377.7, # Bi-214
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2204.1, # Bi-214
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2447.9, # Bi-214 high
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]),
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# ── Phonon (Debye model — aluminium example) ────────────────────────────────
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# ω = v_s × k, Debye cutoff ω_D = 2π × 9.7 THz for Al
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'phonon_al': DomainBasis('phonon_al', 0.0, 9.7e12, 16, 'linear', 'THz'),
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# ══ Beyond-3D / exotic / tunable bases ══════════════════════════════════════
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# Constants are parameters — set them to maximise sparsity for your data.
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# None of these need to be physically realised. The decoder reconstructs
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# the same deterministic float from the same index regardless of "reality".
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# ── Magnetic charge quantization (Dirac condition) ──────────────────────────
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# g_n = n × g_D where g_D = ℏc/2e ≈ 68.5 × e (SI)
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# Basis: charge quanta. Tune g_D to match data periodicity.
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'magnetic_charge': DomainBasis('magnetic_charge', 1.0, 128.0, 16, 'linear', 'g_D'),
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# ── Harmonic tower (linearly spaced multiples) ─────────────────────────────
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# M_n = n × M_c where M_c = compactification scale (tunable)
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# Set M_c to the characteristic energy of your data.
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'harmonic_tower': DomainBasis('harmonic_tower', 0.0, 1000.0, 32, 'linear', 'M_c'),
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# ── Square-root sequence ────────────────────────────────────────────────────
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# M_n = √n — concave growth, good for sub-linear scaling.
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'sqrt_sequence': DomainBasis('sqrt_sequence', 0.0, 16.0, 16, 'table', 'scale', table=[
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math.sqrt(n) for n in range(16)
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]),
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# ── Gap sequence (|n - 1| for n=0..15) ──────────────────────────────────────
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# Zero at n=1, rises linearly on either side. Useful for data with a central
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# mode and symmetric sidebands.
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'gap_sequence': DomainBasis('gap_sequence', 0.0, 15.0, 16, 'table', 'gap', table=[
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abs(n - 1) for n in range(16)
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]),
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# ── Octonion modes (7 imaginary units e1…e7) ─────────────────────────────────
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# Octonions are the largest normed division algebra. Map data to the 7
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# non-associative imaginary axes + real axis = 8 basis elements.
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# Useful for 8-channel / 7.1 audio or colour + alpha data.
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'octonion': DomainBasis('octonion', 0.0, 7.0, 8, 'linear', 'e_i'),
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# ── Gap fraction sequence (linear, 9 steps) ────────────────────────────────
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# 9 evenly spaced values. Tune range to data scale.
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'gap_fraction': DomainBasis('gap_fraction', 0.0, 100.0, 9, 'linear', 'scale'),
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# ── Log-spaced range ──────────────────────────────────────────────────────
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# Log-uniform from 1 μ to 1 m. Useful for wide dynamic range data.
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'log_range': DomainBasis('log_range', 1e-6, 1e-3, 16, 'log', 'unit'),
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# ── Conformal dimension tower (2 + √(4+n)) ────────────────────────────────
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# Convex growth sequence. Good for data with accelerating scale structure.
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'conformal_tower': DomainBasis('conformal_tower', 2.0, 18.0, 16, 'table', 'Δ', table=[
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2.0 + math.sqrt(4.0 + n) for n in range(16)
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]),
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# ── Surreal / p-adic (ultrametric basis) ─────────────────────────────────────
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# p-adic absolute value: |n|_p = p^{-v_p(n)} where v_p = p-adic valuation.
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# Use p=2 (dyadic) — basis values are powers of 1/2.
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# Ultrametric geometry: nearby in p-adic sense ≠ nearby in real sense.
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# Excellent for hierarchical / tree-structured data.
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'padic_2': DomainBasis('padic_2', 0.0, 1.0, 16, 'table', '|·|_2', table=[
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2.0 ** (-n) for n in range(16) # 1, 1/2, 1/4, 1/8, …
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]),
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# ── Graviton polarization modes ───────────────────────────────────────────────
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# Only 2 physical polarizations: + (plus) and × (cross).
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# Extended: include scalar (dilaton) and vector (graviphoton) from higher-D.
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# 4 modes total for 4D supergravity multiplet.
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'graviton': DomainBasis('graviton', 0.0, 3.0, 4, 'table', 'pol', table=[
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0.0, # + polarization
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1.0, # × polarization
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2.0, # scalar (dilaton)
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3.0, # vector (graviphoton)
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]),
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# ── Spin network (loop quantum gravity) ──────────────────────────────────────
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# Area eigenvalues: A_j = 8πγl_P² √(j(j+1)) for half-integer j=0,½,1,…
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# γ = Barbero-Immirzi parameter (≈ 0.2375). Basis: spin labels j.
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'spin_network': DomainBasis('spin_network', 0.0, 4.0, 16, 'table', 'j', table=[
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n * 0.5 for n in range(16) # j = 0, 1/2, 1, 3/2, …, 15/2
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]),
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}
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||
|
||
def _octave_bands(f_low: float, f_high: float, n: int) -> List[float]:
|
||
log_step = (math.log2(max(f_high, 1)) - math.log2(max(f_low, 1))) / n
|
||
return [max(f_low, 1) * (2 ** (i * log_step)) for i in range(n)]
|
||
|
||
|
||
def _band_centre(band_idx: int, f_low: float, f_high: float, n_bands: int) -> float:
|
||
"""Reconstruct centre frequency from band index — no value stored in boxes.
|
||
|
||
This is the 'gamma pattern' decoder: band_idx is the atomic token, fc is
|
||
derived from the shared physical basis (octave geometry). Both sides must
|
||
agree on f_low, f_high, n_bands — equivalent to agreeing on a detector
|
||
response curve or nuclear line table.
|
||
"""
|
||
log_step = (math.log2(max(f_high, 1)) - math.log2(max(f_low, 1))) / n_bands
|
||
return max(f_low, 1) * (2 ** (band_idx * log_step))
|
||
|
||
|
||
def _goertzel(samples: List[float], freq: float, sample_rate: float) -> float:
|
||
"""Single-bin DFT magnitude via Goertzel algorithm. O(n)."""
|
||
w = 2 * math.pi * freq / max(sample_rate, 1)
|
||
coeff = 2 * math.cos(w)
|
||
s1 = s2 = 0.0
|
||
for s in samples:
|
||
s0 = s + coeff * s1 - s2
|
||
s2, s1 = s1, s0
|
||
real = s1 - s2 * math.cos(w)
|
||
imag = s2 * math.sin(w)
|
||
return math.sqrt(real * real + imag * imag) / max(len(samples), 1)
|
||
|
||
|
||
def best_domain_for_band(
|
||
samples: List[float],
|
||
sample_rate: float,
|
||
candidates: List[str],
|
||
top_n: int = 1,
|
||
) -> List[str]:
|
||
"""
|
||
Route a signal band to whichever domain basis gives the sparsest
|
||
representation — minimum description length in practice.
|
||
|
||
Sparsity score = energy concentration in the top-1 basis element
|
||
(Gini-style: how much of the total power is in the single best bin).
|
||
Higher concentration → fewer boxes needed → better compression.
|
||
|
||
Both encoder and decoder have DOMAIN_REGISTRY, so only the domain
|
||
NAME (one integer slot index) needs to be in the box label — zero
|
||
extra bytes on the wire.
|
||
"""
|
||
nyquist = sample_rate / 2.0
|
||
|
||
# Step 1: find signal peak frequency via coarse audio sweep
|
||
audio_bands = _octave_bands(max(1.0, nyquist / 2048), nyquist, 32)
|
||
audio_powers = [_goertzel(samples, f, sample_rate) for f in audio_bands]
|
||
peak_idx = max(range(len(audio_powers)), key=lambda i: audio_powers[i])
|
||
peak_freq = audio_bands[peak_idx] # peak frequency in signal's own space
|
||
|
||
scores: List[Tuple[float, str]] = []
|
||
|
||
for dname in candidates:
|
||
if dname not in DOMAIN_REGISTRY:
|
||
continue
|
||
basis = DOMAIN_REGISTRY[dname]
|
||
|
||
# Step 2: coverage check — does this domain's range cover the signal?
|
||
# We normalise: map peak_freq through the domain's unit scale.
|
||
# Domains whose f_low..f_high span the signal's peak score higher.
|
||
# (Different domains use different physical units; we compare by
|
||
# fractional position: 0=f_low, 1=f_high → in-range iff 0≤pos≤1)
|
||
span = max(basis.f_high - basis.f_low, 1e-300)
|
||
pos = (peak_freq - basis.f_low) / span # normalised position
|
||
# Gaussian coverage weight: peak at pos=0.5, falls off toward edges
|
||
coverage = math.exp(-8.0 * (pos - 0.5) ** 2)
|
||
|
||
# Step 3: sparsity within the domain (how concentrated in fewest bins?)
|
||
n_probe = min(basis.n_bands, 16)
|
||
powers = [_goertzel(samples, basis.centre(i), sample_rate)
|
||
for i in range(n_probe)]
|
||
total = sum(powers) + 1e-30
|
||
peak_p = max(powers)
|
||
concentration = peak_p / total
|
||
|
||
score = coverage * concentration
|
||
scores.append((score, dname))
|
||
|
||
scores.sort(reverse=True)
|
||
return [d for _, d in scores[:top_n]]
|
||
|
||
|
||
def encode_signal(
|
||
samples: List[float],
|
||
sample_rate: float,
|
||
f_low: float = 20.0,
|
||
f_high: float = 20000.0,
|
||
snr_db_val: float = 40.0,
|
||
spin_param: float = 0.5,
|
||
friction_coeff: float = 0.05,
|
||
n_bands: int = 8,
|
||
) -> Tuple[List[CarrierBox], dict]:
|
||
"""
|
||
Factory encoding pass.
|
||
|
||
1. DFT-based band decomposition (using Goertzel-like approach)
|
||
2. Per-band carrier state parameter extraction
|
||
3. Label each parameter → CarrierBox
|
||
4. Sort boxes by label (American Flag Sort)
|
||
5. Return sorted box stream + stats
|
||
|
||
Returns
|
||
-------
|
||
boxes : sorted list of CarrierBox (the compressed stream)
|
||
stats : encoding statistics (modes, λ_Edd, etc.)
|
||
"""
|
||
n = len(samples)
|
||
if n == 0:
|
||
return [], {}
|
||
|
||
duration = n / sample_rate
|
||
snr_lin = 10 ** (snr_db_val / 10.0)
|
||
|
||
# ── Band decomposition via sliding DFT window ─────────────────────────────
|
||
centers = _octave_bands(f_low, f_high, n_bands)
|
||
band_params: List[List[float]] = [] # [band][param_idx]
|
||
band_entropy: List[float] = []
|
||
|
||
for b_idx, fc in enumerate(centers):
|
||
# Goertzel DFT for single frequency (O(n) per bin)
|
||
w = 2 * math.pi * fc / sample_rate
|
||
s1, s2 = 0.0, 0.0
|
||
coeff = 2 * math.cos(w)
|
||
for s in samples:
|
||
s0 = s + coeff * s1 - s2
|
||
s2, s1 = s1, s0
|
||
real = s1 - s2 * math.cos(w)
|
||
imag = s2 * math.sin(w)
|
||
amp = math.sqrt(real ** 2 + imag ** 2) / max(n, 1)
|
||
phase = math.atan2(imag, real)
|
||
|
||
# Temporal analysis: velocity (freq drift), curvature (chirp rate)
|
||
# Approximate from windowed halves
|
||
half = n // 2
|
||
s1h, s2h = 0.0, 0.0
|
||
for s in samples[:half]:
|
||
s0 = s + coeff * s1h - s2h
|
||
s2h, s1h = s1h, s0
|
||
amp_first = math.sqrt((s1h - s2h * math.cos(w)) ** 2 +
|
||
(s2h * math.sin(w)) ** 2) / max(half, 1)
|
||
if _USE_H1:
|
||
# COMPLEX_AUDIT #H1: complex velocity — imaginary part encodes phase drift (chirp rate)
|
||
z_full = complex(real, imag) / max(n, 1)
|
||
z_half = complex(s1h - s2h * math.cos(w),
|
||
s2h * math.sin(w)) / max(half, 1)
|
||
_denom = abs(z_half) + 1e-30
|
||
velocity = abs((z_full - z_half) / _denom)
|
||
curvature = abs(z_full - z_half) ** 2
|
||
else:
|
||
# COMPLEX_AUDIT #H1: amplitude-only — chirp (const amplitude, sweep freq) shows velocity≈0
|
||
velocity = (amp - amp_first) / max(amp_first + 1e-30, 1e-30)
|
||
curvature = velocity ** 2 # second-order approx
|
||
|
||
# RMS of band-passed region (for entropy estimate)
|
||
bw = (f_high - f_low) / n_bands
|
||
h = math.log2(max(amp * 2 * bw + 1, 2)) # entropy ∝ log(occupancy)
|
||
|
||
# pos_x, bandwidth, rate NOT stored — white hole reconstructs from geometry.
|
||
band_params.append([
|
||
amp, # 0: amplitude
|
||
phase, # 1: phase
|
||
velocity, # 2: velocity (amplitude drift)
|
||
curvature, # 3: curvature
|
||
spin_param, # 4: coherence (signal-level spin)
|
||
])
|
||
band_entropy.append(h)
|
||
|
||
# ── Snag geometry (Bekenstein N=3) ────────────────────────────────────────
|
||
# Shakura-Sunyaev viscous dissipation: friction reduces effective entropy
|
||
# captured at the horizon (high friction_coeff → more energy radiated away)
|
||
h_total = sum(band_entropy) * ((f_high - f_low) * 2 * duration / n_bands) * (1.0 - friction_coeff)
|
||
snag = deep_compression_snag(h_total, n_dims=3)
|
||
n_modes = snag['horizon_modes']
|
||
|
||
# Distribute modes across bands by gravitational redshift weighting
|
||
band_dims = shift_allocation(band_entropy, n_modes)
|
||
|
||
capacity = shannon_capacity(f_high - f_low, snr_lin) * duration
|
||
|
||
# ── Label + pack each parameter box ──────────────────────────────────────
|
||
all_boxes: List[CarrierBox] = []
|
||
frame = 0 # single-frame encode
|
||
|
||
for b_idx, (params, n_dim) in enumerate(zip(band_params, band_dims)):
|
||
# Each mode in this band shares the same band-level parameters
|
||
# (in a full implementation, n_dim modes would be distinct Gabor atoms)
|
||
for m_idx in range(n_dim):
|
||
# Scale parameters by mode index (modes are harmonics of the band)
|
||
mode_scale = 1.0 / (1 + m_idx)
|
||
for p_idx, val in enumerate(params):
|
||
label = pack_label(b_idx, m_idx, p_idx, frame)
|
||
# amplitude(0) and velocity(2) scale with mode harmonic
|
||
vbits = f32_to_f16_bits(val * mode_scale if p_idx in (0, 2) else val)
|
||
all_boxes.append(CarrierBox(label, vbits))
|
||
|
||
# ── Sort (American Flag Sort on 32-bit label) ─────────────────────────────
|
||
_radix_sort_boxes(all_boxes)
|
||
|
||
# ── Stats ─────────────────────────────────────────────────────────────────
|
||
lam = eddington_utilization(len(all_boxes) * 16, capacity) # value bits only
|
||
payload_bytes = len(all_boxes) * 6 # 4-byte label + 2-byte f16
|
||
|
||
stats = {
|
||
'n_samples': n,
|
||
'duration_s': duration,
|
||
'n_bands': n_bands,
|
||
'h_total': h_total,
|
||
'n_modes': n_modes,
|
||
'n_boxes': len(all_boxes),
|
||
'band_dims': band_dims,
|
||
'band_entropy': band_entropy,
|
||
'payload_bytes': payload_bytes,
|
||
'raw_bytes': n * 2, # 16-bit PCM equivalent
|
||
'lambda_edd': lam,
|
||
'landauer_J': landauer_cost(h_total),
|
||
'capacity_bits': capacity,
|
||
}
|
||
return all_boxes, stats
|
||
|
||
|
||
# ── Signal decoder ────────────────────────────────────────────────────────────
|
||
|
||
def decode_boxes(
|
||
boxes: List[CarrierBox],
|
||
n_samples: int,
|
||
sample_rate: float,
|
||
f_low: float = 20.0,
|
||
f_high: float = 20000.0,
|
||
n_bands: int = 8,
|
||
) -> List[float]:
|
||
"""
|
||
Factory decode pass.
|
||
|
||
1. Sort boxes by label (already sorted from encoder, O(n) verify)
|
||
2. Reassemble voxels by (band, mode) prefix
|
||
3. Synthesise signal: sum windowed sinusoids (Gabor atoms)
|
||
"""
|
||
if not boxes:
|
||
return [0.0] * n_samples
|
||
|
||
# Group boxes into voxels by (band, mode)
|
||
voxel_map: Dict[Tuple[int, int], List[CarrierBox]] = {}
|
||
for box in boxes:
|
||
band, mode, _, _ = box.address
|
||
key = (band, mode)
|
||
voxel_map.setdefault(key, []).append(box)
|
||
|
||
# Reconstruct voxels and synthesise
|
||
output = [0.0] * n_samples
|
||
# Gaussian window — depends only on n_samples, constant across all voxels.
|
||
# Precompute once to avoid O(voxels × n_samples) redundant math.exp calls.
|
||
_sigma = n_samples / 6.0
|
||
_half_n = n_samples / 2.0
|
||
_gauss_win = [math.exp(-0.5 * ((i - _half_n) / _sigma) ** 2)
|
||
for i in range(n_samples)]
|
||
for (band, mode), vboxes in voxel_map.items():
|
||
voxel = FoamVoxel.from_boxes(vboxes)
|
||
# White hole reconstruction: geometric params recovered from physical basis.
|
||
# Nothing was lost — the horizon preserved the label, the label has the law.
|
||
fc = _band_centre(band, f_low, f_high, n_bands)
|
||
# bw = (f_high - f_low) / n_bands — geometric, not used in Gabor synthesis
|
||
amp = voxel.params[0] # amplitude
|
||
phase = voxel.params[1] # phase
|
||
mode_scale = 1.0 / (1 + mode)
|
||
a = amp * mode_scale
|
||
|
||
# Gabor atom: windowed sinusoid
|
||
for i in range(n_samples):
|
||
t = i / sample_rate
|
||
output[i] += a * math.sin(2 * math.pi * fc * t + phase) * _gauss_win[i]
|
||
|
||
return output
|
||
|
||
|
||
# ── Round-trip quality measurement ────────────────────────────────────────────
|
||
|
||
def snr_db(original: List[float], reconstructed: List[float]) -> float:
|
||
"""Signal-to-noise ratio between original and reconstructed signal."""
|
||
n = min(len(original), len(reconstructed))
|
||
sig_pwr = sum(x ** 2 for x in original[:n]) / max(n, 1)
|
||
noise_pwr = sum((x - y) ** 2
|
||
for x, y in zip(original[:n], reconstructed[:n])) / max(n, 1)
|
||
if noise_pwr < 1e-30:
|
||
return 100.0
|
||
return 10 * math.log10(max(sig_pwr / noise_pwr, 1e-30))
|
||
|
||
|
||
# ── Jupiter Layer — φ-tunnel residual encoding ──────────────────────────────
|
||
|
||
_PHI = 1.618033988749895
|
||
_PHI_EPSILON = 1e-3 # practical tolerance (hardware uses 1e-12; signal layer ±0.1%)
|
||
_PHI_FRAC = 1.0 / _PHI # ≈ 0.6180339887 — irrational Weyl step, no integer period
|
||
# Precomputed lookup for PHI^(i%7): period-7 pattern, only 7 distinct values.
|
||
# Modes 0,7,14 share the same divisor — compute once, index by (i % 7).
|
||
_PHI_POW7 = tuple(1.618033988749895 ** k for k in range(7))
|
||
_J_BAND = 0xFE # layer marker in the 32-bit label (band field)
|
||
|
||
# ── NE geometry scaffold (geometry-rip branch) ────────────────────────────────
|
||
# USE_NE_GEOMETRY = False: all existing behaviour is preserved.
|
||
# Flip to True only after full enwik9 calibration (calibrate_geometry.py).
|
||
# Fixes EUCLIDEAN_ASSUMPTION_AUDIT finding #1 (CRITICAL): linear phi_prox.
|
||
USE_NE_GEOMETRY = False
|
||
_TOOLS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "tools")
|
||
if _TOOLS_DIR not in sys.path:
|
||
sys.path.insert(0, _TOOLS_DIR)
|
||
try:
|
||
from geometry_noneuclidean import log_phi_proximity as _log_phi_prox
|
||
_NE_GEO_AVAILABLE = True
|
||
except ImportError:
|
||
_NE_GEO_AVAILABLE = False
|
||
|
||
# _USE_H1 and _USE_M2 are defined at module top (line ~67) so they're available
|
||
# before any function that uses them. The deprecated alias is kept for compat.
|
||
_USE_COMPLEX_GEOMETRY = _USE_H1 and _USE_M2 # DEPRECATED — always False while M2 deferred
|
||
try:
|
||
import geometry_complex as _cg # noqa: F401
|
||
_CG_AVAILABLE = True
|
||
except ImportError:
|
||
_CG_AVAILABLE = False
|
||
|
||
# ── Baked attestation constants (DAG 743) ─────────────────────────────────
|
||
# J_MODES loaded from 5-Applications/scripts/carrier_constants.py (generated by 4-Infrastructure/hardware/void_mask_gen.py).
|
||
# Falls back to 14 if constants file is absent (backward-compatible).
|
||
_J_MODES = 14
|
||
try:
|
||
import os as _os_sc, sys as _sys_sc
|
||
_sc_dir = _os_sc.path.dirname(_os_sc.path.abspath(__file__))
|
||
if _sc_dir not in _sys_sc.path:
|
||
_sys_sc.path.insert(0, _sc_dir)
|
||
import importlib.util as _ilu_sc
|
||
_sc_spec = _ilu_sc.spec_from_file_location(
|
||
"carrier_constants", _os_sc.path.join(_sc_dir, "carrier_constants.py")
|
||
)
|
||
_sc = _ilu_sc.module_from_spec(_sc_spec)
|
||
_sc_spec.loader.exec_module(_sc)
|
||
_J_MODES = _sc.J_MODES
|
||
except Exception:
|
||
pass # use default J=14
|
||
|
||
# Precomputed per-mode scale table — _USE_M2 checked once at import, not 14× per call.
|
||
_MODE_EXPS: tuple = (
|
||
tuple(_PHI ** (k * _PHI_FRAC) for k in range(_J_MODES))
|
||
if _USE_M2 else
|
||
tuple(_PHI_POW7[k % 7] for k in range(_J_MODES))
|
||
)
|
||
|
||
# ARPG phases from metanarrative_goal_spec.md — used as complexity filter.
|
||
# Describes the state of the signal's manifold:
|
||
# GROUNDED = crystallized, SEISMIC = shifting, FLAME = burning/reforming.
|
||
_PHASE_GROUNDED = 'PHASE_GROUNDED' # φ_ratio ≈ φ → full Jupiter encoding
|
||
_PHASE_SEISMIC = 'PHASE_SEISMIC' # mild drift → partial encoding
|
||
_PHASE_FLAME = 'PHASE_FLAME' # off-manifold → skip, too noisy
|
||
|
||
|
||
def _phi_ratio(modes: List[float]) -> float:
|
||
"""sum(modes[1:]) / modes[0] — should equal φ when phase-locked."""
|
||
return sum(modes[1:]) / max(modes[0], 1e-15)
|
||
|
||
|
||
def _classify_phase(band_amps: List[float], _residual_bits: float) -> str:
|
||
"""
|
||
Metanarrative harness complexity filter.
|
||
Maps (band_amps, residual_magnitude) → ARPG phase.
|
||
|
||
Corrected 2026-03-31: previous equation computed sum(modes[1:])/modes[0]
|
||
over 14 tiled modes, which yields values in [0,13] and can never reach
|
||
φ=1.618. All signals classified SEISMIC regardless of content.
|
||
|
||
Corrected metric (two components):
|
||
|
||
C(a) = max(a) / (sum(a) - max(a) + ε) spectral concentration
|
||
Φ_signal = min |a[i]/a[i+1] - φ| near peak φ-proximity of falloff
|
||
Φ_corr = 0.6·clamp(C, 0, 1) + 0.4·(1 - Φ_signal/φ)
|
||
|
||
Thresholds: GROUNDED ≥ 0.55, SEISMIC ≥ 0.35, FLAME < 0.35
|
||
|
||
See 6-Documentation/docs/PHASE_CLASSIFIER_CORRECTION.md for derivation and measured values.
|
||
"""
|
||
if not band_amps or max(band_amps) < 1e-15:
|
||
return _PHASE_FLAME
|
||
|
||
# Component 1: spectral concentration — is energy in one place?
|
||
a_max = max(band_amps)
|
||
a_rest = sum(band_amps) - a_max
|
||
conc = a_max / (a_rest + 1e-15)
|
||
conc = min(conc, 2.0) / 2.0 # clamp and normalise to [0, 1]
|
||
|
||
# Component 2: φ-proximity of dominant falloff — does peak decay at φ-rate?
|
||
if USE_NE_GEOMETRY and _NE_GEO_AVAILABLE:
|
||
# NE path: log-multiplicative proximity, global consecutive ratio search.
|
||
# AUDIT FINDING #1 fix: projective distance from PHI, not linear deviation.
|
||
# AUDIT FINDING #1 fix (peak-window): search all pairs, not just near peak.
|
||
ratios = [band_amps[i] / (band_amps[i + 1] + 1e-15)
|
||
for i in range(len(band_amps) - 1)
|
||
if band_amps[i + 1] > 1e-15]
|
||
phi_prox = max(0.0, max((_log_phi_prox(r) for r in ratios), default=0.0))
|
||
else:
|
||
# EU path (default): linear deviation from PHI, near-peak window only.
|
||
# EUCLIDEAN ASSUMPTION AUDIT #1 (CRITICAL) — do not apply outside NE mode.
|
||
peak_i = band_amps.index(a_max)
|
||
phi_drifts = []
|
||
for i in range(max(0, peak_i - 1), min(len(band_amps) - 1, peak_i + 3)):
|
||
denom = band_amps[i + 1]
|
||
if denom > 1e-15:
|
||
phi_drifts.append(abs(band_amps[i] / denom - _PHI))
|
||
phi_signal = min(phi_drifts) if phi_drifts else _PHI
|
||
phi_prox = max(0.0, 1.0 - phi_signal / _PHI) # linear — EUCLIDEAN
|
||
|
||
phi_corr = 0.6 * conc + 0.4 * phi_prox
|
||
|
||
# SF-B: thresholds 0.47 / 0.35 are calibrated on the Euclidean phi_corr
|
||
# distribution (EU path above). When USE_NE_GEOMETRY=True, _log_phi_prox
|
||
# produces a different phi_corr distribution and these thresholds are
|
||
# uncalibrated — the NE path requires independent calibration via
|
||
# calibrate_geometry.py before USE_NE_GEOMETRY can be safely flipped.
|
||
if phi_corr >= 0.47:
|
||
return _PHASE_GROUNDED
|
||
if phi_corr >= 0.35:
|
||
return _PHASE_SEISMIC
|
||
return _PHASE_FLAME
|
||
|
||
|
||
def _modes_from_bytes(data: bytes) -> List[float]:
|
||
"""
|
||
φ-normalise payload bytes into 14 vibration mode amplitudes.
|
||
Each mode samples the mean of a distinct non-overlapping segment of the
|
||
full input, ensuring all bytes contribute regardless of chunk size.
|
||
|
||
deviation = |segment_mean − 127.5| / 127.5 (centred deviation, [0, 1])
|
||
amplitude = deviation / φ^(i % 7)
|
||
|
||
Deviation extractor replaces raw mean normalisation (mean/255) which
|
||
produced synthetic φ-ratios for uniform-mean signals (white noise CLT
|
||
convergence → mean ≈ 127.5 for all segments → amp[i] ∝ 1/φ^(i%7) →
|
||
perfect φ-decay → false GROUNDED classification).
|
||
Using |mean − 127.5| collapses uniform signals to near-zero amplitude,
|
||
correctly classifying them as FLAME/SEISMIC. Structured signals with
|
||
varying segment means retain real amplitude structure.
|
||
"""
|
||
n = max(len(data), 1)
|
||
modes = []
|
||
for i in range(_J_MODES):
|
||
lo = int(i * n / _J_MODES)
|
||
hi = int((i + 1) * n / _J_MODES)
|
||
if hi <= lo:
|
||
hi = lo + 1
|
||
seg = data[lo:min(hi, n)]
|
||
# SF-C fix: empty segment (can occur when _J_MODES > len(data)) must
|
||
# produce zero amplitude, not maximum amplitude. The previous guard
|
||
# `mean_val = ... if seg else 0` set mean=0 → deviation=|0-127.5|/127.5=1.0
|
||
# → amp = 1.0/PHI^(i%7) — the strongest possible signal from nothing.
|
||
if not seg:
|
||
modes.append(0.0)
|
||
continue
|
||
mean_val = sum(seg) / len(seg)
|
||
deviation = abs(mean_val - 127.5) / 127.5
|
||
amp = deviation / _MODE_EXPS[i]
|
||
modes.append(min(1.0, amp))
|
||
return modes
|
||
|
||
|
||
def _bytes_from_modes(modes: List[float]) -> bytes:
|
||
"""Inverse of _modes_from_bytes — recover bytes from mode amplitudes.
|
||
SF-3: This function is complete but dead (never called anywhere).
|
||
Retained as the documented round-trip inverse; remove if unused after encoder wiring.
|
||
"""
|
||
out = bytearray(_J_MODES)
|
||
for i in range(_J_MODES):
|
||
scale = _MODE_EXPS[i]
|
||
bval = int(min(255, max(0, round(modes[i] * scale * 255.0))))
|
||
out[i] = bval
|
||
return bytes(out)
|
||
|
||
|
||
_J_LOG_SCALE = 60.0 # log2 range: supports residuals up to 2^60 bits (~1 EiB)
|
||
|
||
|
||
def jupiter_encode(
|
||
residual_entropy: float,
|
||
band_amps: List[float],
|
||
frame: int = 0,
|
||
) -> Tuple[List[CarrierBox], str]:
|
||
"""
|
||
Jupiter Layer encoder.
|
||
|
||
Encoding strategy (separates φ-lock key from payload):
|
||
────────────────────────────────────────────────────────
|
||
modes[0] = log2(residual_entropy) / LOG_SCALE → payload (normalised to [0,1])
|
||
modes[1:] = modes[0] × φ / (N-1) → exact φ-lock, no correction needed
|
||
phi_ratio = sum(modes[1:]) / modes[0]
|
||
= (N-1) × (modes[0] × φ / (N-1)) / modes[0]
|
||
= φ ✓ (analytically exact)
|
||
|
||
The tunnel fires on the first check — PHASE_FLAME only if modes[0] ≈ 0
|
||
(residual is essentially zero, nothing to encode).
|
||
|
||
Harness phase reflects signal complexity via band_amps Laplacian (informational).
|
||
"""
|
||
if residual_entropy <= 0:
|
||
return [], _PHASE_FLAME
|
||
|
||
# Encode residual magnitude into modes[0] via log-scale normalisation
|
||
log_r = math.log2(max(residual_entropy, 1.0)) / _J_LOG_SCALE
|
||
log_r = min(1.0, max(1e-6, log_r))
|
||
|
||
modes = [0.0] * _J_MODES
|
||
modes[0] = log_r
|
||
target = log_r * _PHI / (_J_MODES - 1)
|
||
for i in range(1, _J_MODES):
|
||
modes[i] = target # analytically φ-locked
|
||
|
||
# Harness phase from band_amps — signal surface complexity
|
||
phase = _classify_phase(band_amps if band_amps else [], residual_entropy)
|
||
# Override FLAME only for the proxy — the payload itself is always locked
|
||
if phase == _PHASE_FLAME:
|
||
phase = _PHASE_SEISMIC # locked payload; signal surface is noisy
|
||
|
||
active_modes = 7 if phase == _PHASE_SEISMIC else _J_MODES
|
||
|
||
boxes = []
|
||
for m_idx in range(active_modes):
|
||
label = pack_label(_J_BAND, m_idx, 0, frame)
|
||
boxes.append(CarrierBox(label, f32_to_f16_bits(modes[m_idx])))
|
||
|
||
return boxes, phase
|
||
|
||
|
||
def jupiter_decode(boxes: List[CarrierBox]) -> Tuple[float, str]:
|
||
"""
|
||
Jupiter Layer decoder.
|
||
|
||
Returns
|
||
-------
|
||
residual_entropy : recovered residual bits value
|
||
phase : detected ARPG phase based on phi_ratio of recovered modes
|
||
"""
|
||
j_boxes = [b for b in boxes if (b.label >> 24) & 0xFF == _J_BAND]
|
||
if not j_boxes:
|
||
return 0.0, _PHASE_FLAME
|
||
|
||
modes = [0.0] * _J_MODES
|
||
for box in j_boxes:
|
||
_, m_idx, _, _ = box.address
|
||
if m_idx < _J_MODES:
|
||
modes[m_idx] = box.decode_value()
|
||
|
||
phi_r = _phi_ratio(modes)
|
||
phase = _classify_phase(modes, 0.0) # SF-1: was phi_r (scalar) — crashes on max()
|
||
|
||
# Recover residual: modes[0] = log2(residual) / LOG_SCALE
|
||
log_r = max(modes[0], 1e-9)
|
||
residual_entropy = 2.0 ** (log_r * _J_LOG_SCALE)
|
||
|
||
return residual_entropy, phase
|
||
|
||
|
||
# ── Omnitoken manifest — GraphVM header for the audio stream ──────────────────
|
||
#
|
||
# Each encoded stream carries an omnitoken manifest that declares:
|
||
# - Which subregisters are active and what physical basis they use
|
||
# - The foam profile (band allocation bias in 4D foam space)
|
||
# - A deterministic register_id (tick-based, idempotent)
|
||
# - The archive_compression domain — previously "unavailable" in omnitoken_v3
|
||
#
|
||
# The manifest IS the GraphVM preamble: it tells the white hole which
|
||
# instruction set (basis tables) to load before executing the box stream.
|
||
#
|
||
# Register state transitions mirror phase classification:
|
||
# potential → FLAME (frame not yet resolved)
|
||
# candidate → subregister routing initiated
|
||
# collapsed → GROUNDED/SEISMIC phase selected
|
||
# committed → box stream emitted, deterministic replay safe
|
||
|
||
_OT_SCHEMA = "omnitoken-audio/v1"
|
||
_OT_SUBREGISTERS = {
|
||
# band prefix → domain name + basis description
|
||
0x00: 'audio_band_0',
|
||
0x07: 'audio_band_7', # range 0x00-0x07 = normal time domain
|
||
0xFD: 'accel_depth_1', # 2× accelerated time
|
||
0xFC: 'accel_depth_2', # 4× accelerated time
|
||
0xFB: 'accel_depth_3', # 8× accelerated time
|
||
0xFE: 'jupiter_tunnel',
|
||
}
|
||
|
||
|
||
def _register_id(stream_hash: str, frame_count: int) -> str:
|
||
"""Deterministic register_id: tick-based, idempotent (same input → same id)."""
|
||
tick = f"{stream_hash[:16]}-f{frame_count:04d}"
|
||
return f"wreg-audio-{tick}"
|
||
|
||
|
||
def build_omnitoken_manifest(
|
||
sample_rate: float,
|
||
f_low: float,
|
||
f_high: float,
|
||
n_bands: int,
|
||
n_frames: int,
|
||
spin_param: float,
|
||
friction_coeff: float,
|
||
foam_center: List[float],
|
||
active_subregisters: List[int],
|
||
stream_hash: str,
|
||
phase_counts: Dict[str, int],
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Build the omnitoken manifest header for a compressed audio stream.
|
||
|
||
This activates the archive_compression domain that is 'unavailable'
|
||
in the base omnitoken_v3 schema — the carrier factory IS that domain.
|
||
|
||
The nd_point (14-axis) encodes the Jupiter mode vector so any omnitoken-
|
||
aware node can reconstruct the φ-lock state without the full box stream.
|
||
"""
|
||
# Jupiter nd_point: 14-axis position in φ-normalised mode space
|
||
# modes[0] = log2(friction_coeff proxy) / LOG_SCALE
|
||
# modes[1:] = modes[0] × φ / 13 (analytically locked)
|
||
log_r = math.log2(max(friction_coeff * 1000, 1.0)) / _J_LOG_SCALE
|
||
log_r = min(1.0, max(1e-6, log_r))
|
||
target = log_r * _PHI / (_J_MODES - 1)
|
||
nd_point = [log_r] + [target] * (_J_MODES - 1)
|
||
|
||
# Foam score = φ-ratio of the nd_point (should be ≈ φ for well-locked stream)
|
||
foam_score = round(_phi_ratio(nd_point), 5)
|
||
|
||
# Subregister surface bus domains
|
||
domains: Dict[str, Any] = {
|
||
'audio_normal_time': {
|
||
'domain': 'audio_normal_time',
|
||
'band_range': [0x00, 0x07],
|
||
'basis': {
|
||
'f_low': f_low,
|
||
'f_high': f_high,
|
||
'n_bands': n_bands,
|
||
'sample_rate': sample_rate,
|
||
},
|
||
'status': 'active',
|
||
},
|
||
'archive_compression': {
|
||
'domain': 'archive_compression',
|
||
'status': 'active', # was "unavailable" in omnitoken_v3
|
||
'selection_policy': 'carrier_factory_graphvm',
|
||
'params': {
|
||
'spin_param': spin_param,
|
||
'friction_coeff': friction_coeff,
|
||
'param_names': list(PARAM_NAMES),
|
||
'n_params': N_PARAMS,
|
||
'box_bytes': 6,
|
||
},
|
||
},
|
||
'jupiter_tunnel': {
|
||
'domain': 'jupiter_tunnel',
|
||
'band_marker': _J_BAND,
|
||
'modes': _J_MODES,
|
||
'phi': _PHI,
|
||
'log_scale': _J_LOG_SCALE,
|
||
'status': 'active',
|
||
},
|
||
}
|
||
|
||
# Add accelerated-time subregisters if any FLAME frames were processed
|
||
for band_prefix in active_subregisters:
|
||
if band_prefix < 0xFD:
|
||
continue
|
||
depth = _SUBREGISTER_BAND_BASE - band_prefix + 1
|
||
key = f'accel_depth_{depth}'
|
||
domains[key] = {
|
||
'domain': key,
|
||
'band_marker': band_prefix,
|
||
'time_factor': 2 ** depth,
|
||
'status': 'active',
|
||
}
|
||
|
||
register_id = _register_id(stream_hash, n_frames)
|
||
|
||
return {
|
||
'schema': _OT_SCHEMA,
|
||
'name': f'omnitoken-audio-{stream_hash[:8]}',
|
||
'n_dimensional_surface': {
|
||
'axes': _J_MODES,
|
||
'nd_point': nd_point,
|
||
'foam_score': foam_score,
|
||
'phi_ratio': round(_phi_ratio(nd_point), 6),
|
||
},
|
||
'foam_profile': 'audio_carrier state',
|
||
'foam_center': (foam_center + [0.5] * 4)[:4],
|
||
'surface_bus': {
|
||
'schema': 'omnitoken-surface-bus/v1',
|
||
'agnostic': True,
|
||
'domains': domains,
|
||
},
|
||
'stream': {
|
||
'n_frames': n_frames,
|
||
'sample_rate': sample_rate,
|
||
'phase_counts': phase_counts,
|
||
'register_id': register_id,
|
||
'register_state': 'committed',
|
||
'register_state_transitions': [
|
||
{'state': 'potential', 'reason': 'stream_ingested'},
|
||
{'state': 'candidate', 'reason': 'phase_classified'},
|
||
{'state': 'collapsed', 'reason': 'subregister_selected'},
|
||
{'state': 'committed', 'reason': 'box_stream_emitted'},
|
||
],
|
||
'replay_safety': {
|
||
'strategy': 'stream_hash_idempotent',
|
||
'stream_hash': stream_hash,
|
||
'replay_safe': True,
|
||
},
|
||
},
|
||
}
|
||
|
||
|
||
# ── Multi-frame streaming encoder — time domain + metanarrative gating ────────
|
||
|
||
# Delta thresholds per phase (relative fraction of reference magnitude — scale-free)
|
||
# SF-4 fix: was absolute (f16 units) — comparison abs(val-ref) > 0.001 transmits
|
||
# everything for values in [1, 65504] range since f16 LSB ≈ 0.001 for values > 1.
|
||
# Now relative: abs(val-ref)/max(|ref|,|val|,eps) > threshold.
|
||
# GROUNDED: 0.001 = 0.1% — tight tolerance for stable sustained tones
|
||
# SEISMIC: 0.01 = 1.0% — moderate tolerance for transient-rich material
|
||
# FLAME: inf = raw keyframe (noise: prediction is useless)
|
||
_DELTA_THRESHOLD = {
|
||
_PHASE_GROUNDED: 0.001,
|
||
_PHASE_SEISMIC: 0.01,
|
||
_PHASE_FLAME: float('inf'), # transmit everything raw
|
||
}
|
||
|
||
# Minimum frame size before recursion stops (Planck floor — below this,
|
||
# frequency resolution is too coarse to be meaningful for audio).
|
||
_MIN_FRAME_SAMPLES = 64
|
||
|
||
# Subregister band marker — boxes from accelerated-time sub-frames use a
|
||
# reserved band prefix so the white hole knows which time domain they came from.
|
||
# 0xFD = subregister depth 1 (2× accel), 0xFC = depth 2 (4×), etc.
|
||
_SUBREGISTER_BAND_BASE = 0xFD # counts DOWN per recursion level
|
||
|
||
|
||
def _classify_frame(
|
||
boxes: List[CarrierBox],
|
||
h_total: float,
|
||
spin_param: float,
|
||
) -> str:
|
||
"""Classify a frame's complexity via Jupiter φ-ratio of its amplitude envelope."""
|
||
eta = conversion_efficiency(spin_param)
|
||
residual = h_total * (1.0 - eta)
|
||
# Use band amplitude envelope (first box per band) as the φ-ratio input,
|
||
# not raw box values — gives a stable spectral shape proxy.
|
||
band_amps: List[float] = []
|
||
seen_bands: set = set()
|
||
for box in boxes:
|
||
band, _, param, _ = box.address
|
||
if param == 0 and band not in seen_bands: # param 0 = amplitude
|
||
band_amps.append(abs(box.decode_value()))
|
||
seen_bands.add(band)
|
||
_, phase = jupiter_encode(residual, band_amps)
|
||
return phase
|
||
|
||
|
||
def _encode_flame_subregister(
|
||
samples: List[float],
|
||
sample_rate: float,
|
||
f_low: float,
|
||
f_high: float,
|
||
snr_db_val: float,
|
||
spin_param: float,
|
||
friction_coeff: float,
|
||
n_bands: int,
|
||
depth: int,
|
||
frame_offset: int,
|
||
) -> List[CarrierBox]:
|
||
"""
|
||
Relativistic subregister: accelerated time domain for FLAME-class frames.
|
||
|
||
When a frame is too complex to compress at normal resolution, time contracts:
|
||
the frame is split into two half-length sub-frames and each is re-encoded.
|
||
This trades frequency resolution for temporal resolution (Heisenberg duality).
|
||
|
||
Recursion stops at _MIN_FRAME_SAMPLES (Planck floor) — below this,
|
||
Goertzel frequency resolution is meaningless for audio.
|
||
|
||
Sub-frame boxes carry a special band marker (0xFD, 0xFC, …) so the white
|
||
hole decoder knows which time domain each box came from.
|
||
"""
|
||
if len(samples) <= _MIN_FRAME_SAMPLES or depth > 4:
|
||
# Planck floor reached — encode raw, mark with subregister depth band
|
||
boxes, _ = encode_signal(
|
||
samples, sample_rate,
|
||
f_low=f_low, f_high=f_high, snr_db_val=snr_db_val,
|
||
spin_param=spin_param, friction_coeff=friction_coeff,
|
||
n_bands=n_bands,
|
||
)
|
||
sub_band = max(0x00, _SUBREGISTER_BAND_BASE - depth)
|
||
out = []
|
||
for box in boxes:
|
||
_, mode, param, _ = box.address
|
||
label = pack_label(sub_band, mode, param, frame_offset & 0xFF)
|
||
out.append(CarrierBox(label, box.value_bits))
|
||
return out
|
||
|
||
# Split into two half-length sub-frames
|
||
half = len(samples) // 2
|
||
halves = [samples[:half], samples[half:]]
|
||
result = []
|
||
|
||
for i, half_samples in enumerate(halves):
|
||
sub_boxes, sub_stats = encode_signal(
|
||
half_samples, sample_rate,
|
||
f_low=f_low, f_high=f_high, snr_db_val=snr_db_val,
|
||
spin_param=spin_param, friction_coeff=friction_coeff,
|
||
n_bands=n_bands,
|
||
)
|
||
sub_phase = _classify_frame(sub_boxes, sub_stats['h_total'], spin_param)
|
||
|
||
if sub_phase == _PHASE_FLAME:
|
||
# Still complex — recurse deeper (time contracts further)
|
||
result.extend(_encode_flame_subregister(
|
||
half_samples, sample_rate,
|
||
f_low, f_high, snr_db_val, spin_param, friction_coeff, n_bands,
|
||
depth + 1, frame_offset * 2 + i,
|
||
))
|
||
else:
|
||
# Resolved — tag with subregister depth and emit
|
||
sub_band = max(0x00, _SUBREGISTER_BAND_BASE - depth)
|
||
for box in sub_boxes:
|
||
_, mode, param, _ = box.address
|
||
label = pack_label(sub_band, mode, param, (frame_offset * 2 + i) & 0xFF)
|
||
result.append(CarrierBox(label, box.value_bits))
|
||
|
||
return result
|
||
|
||
|
||
def encode_stream(
|
||
frames: List[List[float]],
|
||
sample_rate: float,
|
||
f_low: float = 20.0,
|
||
f_high: float = 20000.0,
|
||
snr_db_val: float = 40.0,
|
||
spin_param: float = 0.5,
|
||
friction_coeff: float = 0.05,
|
||
n_bands: int = 8,
|
||
) -> Tuple[List[List[CarrierBox]], dict]:
|
||
"""
|
||
Multi-frame streaming encoder with metanarrative-gated delta compression.
|
||
|
||
Strategy
|
||
────────
|
||
Frame 0 — always a full keyframe (all boxes transmitted).
|
||
Frame N — metanarrative harness classifies complexity via Jupiter φ-ratio:
|
||
|
||
PHASE_GROUNDED → linear prediction: store only boxes where
|
||
|value - predicted| > threshold (near-zero for tones)
|
||
PHASE_SEISMIC → delta from previous frame above medium threshold
|
||
PHASE_FLAME → raw keyframe (noise: prediction is useless)
|
||
|
||
The frame_idx field in each label encodes the frame number, so the white
|
||
hole (decoder) can replay the full sequence deterministically.
|
||
|
||
Returns
|
||
-------
|
||
frame_streams : list of box lists, one per frame
|
||
stats : aggregate compression statistics
|
||
"""
|
||
frame_streams: List[List[CarrierBox]] = []
|
||
prev_values: Dict[int, float] = {} # label → last transmitted f32 value
|
||
pred_values: Dict[int, float] = {} # label → linear-predicted f32 value
|
||
|
||
total_raw = 0
|
||
total_boxes = 0
|
||
total_key = 0
|
||
total_delta = 0
|
||
phase_counts: Dict[str, int] = {
|
||
_PHASE_GROUNDED: 0, _PHASE_SEISMIC: 0, _PHASE_FLAME: 0
|
||
}
|
||
|
||
for f_idx, frame_samples in enumerate(frames):
|
||
boxes, stats = encode_signal(
|
||
frame_samples, sample_rate,
|
||
f_low=f_low, f_high=f_high, snr_db_val=snr_db_val,
|
||
spin_param=spin_param, friction_coeff=friction_coeff,
|
||
n_bands=n_bands,
|
||
)
|
||
|
||
# Classify this frame's complexity via band amplitude envelope φ-ratio
|
||
h_total = stats['h_total']
|
||
phase = _classify_frame(boxes, h_total, spin_param)
|
||
phase_counts[phase] = phase_counts.get(phase, 0) + 1
|
||
|
||
threshold = _DELTA_THRESHOLD[phase]
|
||
total_raw += len(frame_samples) * 2
|
||
|
||
if phase == _PHASE_FLAME and f_idx > 0:
|
||
# Relativistic subregister: complex frame → accelerated time domain.
|
||
# Split into sub-frames at 2× resolution; recurse until resolved.
|
||
sub_boxes = _encode_flame_subregister(
|
||
frame_samples, sample_rate,
|
||
f_low, f_high, snr_db_val, spin_param, friction_coeff, n_bands,
|
||
depth=1, frame_offset=f_idx,
|
||
)
|
||
frame_streams.append(sub_boxes)
|
||
total_boxes += len(sub_boxes)
|
||
total_delta += 1
|
||
continue
|
||
|
||
if f_idx == 0: # SF-2: was `or phase == _PHASE_FLAME` — unreachable (continue above)
|
||
# Keyframe: transmit all boxes with updated frame_idx
|
||
out_boxes = []
|
||
for box in boxes:
|
||
band, mode, param, _ = box.address
|
||
new_label = pack_label(band, mode, param, f_idx & 0xFF)
|
||
new_box = CarrierBox(new_label, box.value_bits)
|
||
out_boxes.append(new_box)
|
||
prev_values[new_label & 0xFFFFFF00 | 0] = box.decode_value()
|
||
pred_values[new_label & 0xFFFFFF00 | 0] = box.decode_value()
|
||
frame_streams.append(out_boxes)
|
||
total_boxes += len(out_boxes)
|
||
total_key += 1
|
||
else:
|
||
# Delta frame: only transmit boxes that changed beyond threshold
|
||
# For GROUNDED: compare against linear prediction (2×prev - prev2)
|
||
out_boxes = []
|
||
for box in boxes:
|
||
band, mode, param, _ = box.address
|
||
base_label = pack_label(band, mode, param, 0)
|
||
val = box.decode_value()
|
||
|
||
if phase == _PHASE_GROUNDED and base_label in pred_values:
|
||
reference = pred_values[base_label]
|
||
else:
|
||
reference = prev_values.get(base_label, 0.0)
|
||
|
||
# SF-4 fix: relative comparison — abs(delta)/max(|ref|,|val|,eps) > threshold
|
||
_denom = max(abs(reference), abs(val), 1e-6)
|
||
if abs(val - reference) / _denom > threshold:
|
||
new_label = pack_label(band, mode, param, f_idx & 0xFF)
|
||
out_boxes.append(CarrierBox(new_label, box.value_bits))
|
||
prev_values[base_label] = val
|
||
# SF-A fix: only update linear prediction for transmitted boxes.
|
||
# For untransmitted boxes, the decoder never learns val and cannot
|
||
# replicate this state update — leaving pred_values unchanged means
|
||
# encoder and decoder both use prev_values[base_label] as reference
|
||
# on the next frame, keeping them in sync.
|
||
pred_values[base_label] = 2.0 * val - reference
|
||
|
||
frame_streams.append(out_boxes)
|
||
total_boxes += len(out_boxes)
|
||
total_delta += 1
|
||
|
||
ratio = (total_raw / max(total_boxes * 6, 1))
|
||
|
||
# Compute stream hash for idempotent register_id
|
||
raw_bytes = b''.join(
|
||
box.pack() for stream in frame_streams for box in stream
|
||
)
|
||
stream_hash = hashlib.sha256(raw_bytes).hexdigest()
|
||
|
||
# Discover which subregister band prefixes were used
|
||
active_bands: set = set()
|
||
for stream in frame_streams:
|
||
for box in stream:
|
||
active_bands.add((box.label >> 24) & 0xFF)
|
||
|
||
# Build omnitoken manifest — activates archive_compression domain
|
||
foam_center = [
|
||
min(1.0, total_boxes / max(total_raw / 6, 1)), # packing density
|
||
ratio / 10.0, # compression quality
|
||
phase_counts.get(_PHASE_GROUNDED, 0) / max(len(frames), 1),
|
||
1.0 - phase_counts.get(_PHASE_FLAME, 0) / max(len(frames), 1),
|
||
]
|
||
manifest = build_omnitoken_manifest(
|
||
sample_rate=sample_rate,
|
||
f_low=f_low, f_high=f_high, n_bands=n_bands,
|
||
n_frames=len(frames),
|
||
spin_param=spin_param, friction_coeff=friction_coeff,
|
||
foam_center=foam_center,
|
||
active_subregisters=list(active_bands),
|
||
stream_hash=stream_hash,
|
||
phase_counts=phase_counts,
|
||
)
|
||
|
||
stats = {
|
||
'n_frames': len(frames),
|
||
'total_raw_B': total_raw,
|
||
'total_box_B': total_boxes * 6,
|
||
'ratio': ratio,
|
||
'keyframes': total_key,
|
||
'delta_frames': total_delta,
|
||
'phase_counts': phase_counts,
|
||
'boxes_per_frame': [len(s) for s in frame_streams],
|
||
'stream_hash': stream_hash[:16],
|
||
'omnitoken': manifest,
|
||
}
|
||
return frame_streams, stats
|
||
|
||
|
||
def run_factory_poc():
|
||
"""
|
||
Two-layer round-trip: Layer 1 (carrier state boxes) + Layer 2 (Jupiter/tunnel).
|
||
Metanarrative harness gates the Jupiter layer by complexity (ARPG phase).
|
||
"""
|
||
print("=" * 72)
|
||
print(" SOLITON FACTORY — Layer 1 (carrier state) + Layer 2 (Jupiter/tunnel)")
|
||
print(" Metanarrative harness: F(S)=Equilibrium ↔ ∇H≈0 & φ≈1.618")
|
||
print("=" * 72)
|
||
|
||
fs = 44100
|
||
duration = 0.1
|
||
n = int(fs * duration)
|
||
|
||
def make_tone(freq, amp=0.8):
|
||
return [amp * math.sin(2 * math.pi * freq * i / fs) for i in range(n)]
|
||
|
||
def make_chord(freqs, amp=0.5):
|
||
s = [sum(amp * math.sin(2 * math.pi * f * i / fs) for f in freqs)
|
||
for i in range(n)]
|
||
m = max(abs(x) for x in s) or 1
|
||
return [x / m for x in s]
|
||
|
||
def make_noise(amp=0.3):
|
||
rng = __import__('random').Random(42)
|
||
return [rng.uniform(-amp, amp) for _ in range(n)]
|
||
|
||
test_cases = [
|
||
('PURE TONE 440 Hz', make_tone(440), 0.80, 0.04),
|
||
('CHORD C4-E4-G4', make_chord([261,330,392]), 0.60, 0.05),
|
||
('WHITE NOISE', make_noise(), 0.10, 0.15),
|
||
]
|
||
|
||
hdr = (f"{'Signal':<22} {'L1 boxes':>9} {'L2 boxes':>9} "
|
||
f"{'Total B':>8} {'Ratio':>6} {'λ_Edd':>8} {'Phase':<16} {'Residual recover'}")
|
||
print(f"\n{hdr}")
|
||
print("-" * 90)
|
||
|
||
for label, sig, spin, friction in test_cases:
|
||
# ── Layer 1: carrier state encode ───────────────────────────────────────────
|
||
boxes, stats = encode_signal(
|
||
sig, fs, f_low=20, f_high=20000, snr_db_val=40,
|
||
spin_param=spin, friction_coeff=friction,
|
||
)
|
||
|
||
# Compute friction residual from TSE model
|
||
h_total = stats['h_total']
|
||
eta = conversion_efficiency(spin)
|
||
residual = h_total * (1.0 - eta)
|
||
|
||
# Band amplitudes as proxy mode state for the harness
|
||
band_amps = [b.decode_value() for b in boxes[:8]]
|
||
|
||
# ── Layer 2: Jupiter encode ──────────────────────────────────────────
|
||
j_boxes, phase = jupiter_encode(residual, band_amps)
|
||
all_boxes = boxes + j_boxes
|
||
|
||
# ── Decode: split by layer marker ────────────────────────────────────
|
||
l1_boxes = [b for b in all_boxes if (b.label >> 24) & 0xFF != _J_BAND]
|
||
recovered_residual, _ = jupiter_decode(all_boxes)
|
||
|
||
total_bytes = len(all_boxes) * 6
|
||
ratio = stats['raw_bytes'] / max(total_bytes, 1)
|
||
# f16 log-scale: ~0.3% relative error is expected (10-bit mantissa × exp amplification)
|
||
residual_ok = (abs(recovered_residual - residual) / max(residual, 1.0)) < 0.005
|
||
|
||
print(f" {label:<20} {len(l1_boxes):>9,} {len(j_boxes):>9,} "
|
||
f"{total_bytes:>7,}B {ratio:>5.2f}× "
|
||
f"{stats['lambda_edd']:>7.4f} {phase:<16} "
|
||
f"{'✓ ' + f'{recovered_residual:.1f}' if residual_ok else '✗ drift'}")
|
||
|
||
print("\n [Layer 1] band 0x00-0x07 — carrier state basis (Bekenstein modes, φ-sorted)")
|
||
print(" [Layer 2] band 0xFE — Jupiter tunnel (φ-normalised residual)")
|
||
print(" [Harness] PHASE_GROUNDED→full, PHASE_SEISMIC→7-mode, PHASE_FLAME→skip")
|
||
print(" [Box] 6 bytes: 4-byte label + 2-byte f16 | sort: MSD radix 32-bit")
|
||
print("=" * 72)
|
||
|
||
# ── Streaming PoC: multi-frame delta compression ──────────────────────────
|
||
print("\n" + "=" * 72)
|
||
print(" STREAMING — time domain + metanarrative delta gating (16 frames)")
|
||
print("=" * 72)
|
||
|
||
n_frames = 16
|
||
|
||
stream_cases = [
|
||
('PURE TONE 440 Hz', [make_tone(440)] * n_frames, 0.80, 0.04),
|
||
('CHORD C4-E4-G4', [make_chord([261,330,392])]* n_frames, 0.60, 0.05),
|
||
('WHITE NOISE', [make_noise()] * n_frames, 0.10, 0.15),
|
||
]
|
||
|
||
shdr = (f"{'Signal':<22} {'Frames':>7} {'Key':>5} {'Delta':>7} "
|
||
f"{'Raw B':>8} {'Box B':>8} {'Ratio':>7} Phase distribution")
|
||
print(f"\n{shdr}")
|
||
print("-" * 85)
|
||
|
||
for slabel, frame_list, spin, friction in stream_cases:
|
||
_, sstats = encode_stream(
|
||
frame_list, fs, f_low=20, f_high=20000, snr_db_val=40,
|
||
spin_param=spin, friction_coeff=friction,
|
||
)
|
||
pc = sstats['phase_counts']
|
||
phase_str = (f"G={pc.get(_PHASE_GROUNDED,0)} "
|
||
f"S={pc.get(_PHASE_SEISMIC,0)} "
|
||
f"F={pc.get(_PHASE_FLAME,0)}")
|
||
print(f" {slabel:<20} {sstats['n_frames']:>7} "
|
||
f"{sstats['keyframes']:>5} {sstats['delta_frames']:>7} "
|
||
f"{sstats['total_raw_B']:>8,} {sstats['total_box_B']:>8,} "
|
||
f"{sstats['ratio']:>6.2f}× {phase_str}")
|
||
bpf = sstats['boxes_per_frame']
|
||
print(f" boxes/frame: {bpf[:8]}{'...' if len(bpf)>8 else ''}")
|
||
|
||
# Show omnitoken manifest summary
|
||
ot = sstats['omnitoken']
|
||
nd = ot['n_dimensional_surface']
|
||
st = ot['stream']
|
||
ac = ot['surface_bus']['domains'].get('archive_compression', {})
|
||
print(f" omnitoken: {ot['name']} φ={nd['phi_ratio']:.4f} "
|
||
f"foam={nd['foam_score']:.4f} "
|
||
f"archive_compression={ac.get('status','?')} "
|
||
f"reg={st['register_state']}")
|
||
|
||
print("\n [Frame 0] keyframe — full box stream")
|
||
print(" [Frame 1+] delta — only changed boxes (GROUNDED: predict+residual)")
|
||
print(" [FLAME frame] subregister — accelerated time domain, band 0xFD-0xFB")
|
||
print(" [Omnitoken] GraphVM manifest — activates archive_compression domain")
|
||
print("=" * 72)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
run_factory_poc()
|