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https://github.com/allaunthefox/Research-Stack.git
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- Prover-Integrated Orchestration Layers (L0-L3): Goedel-Prover-V2 watchdog, BFS-Prover-V2 swarm consensus, bf4prover topology adaptation - FAMM Verilator benchmark: uniform vs preshaped delay comparison (4.4x speedup) - Swarm topological device prober: 11 agents probing traces, caps, delays, errors, vias, PDN - Spec sheet puller: 10 components with key params and topological relevance - Virtual FPGA system tests: 6/6 passed, 134K ops/s throughput - Fixed merge conflicts in AI-Newton test_experiment.ipynb
453 lines
16 KiB
Python
453 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Waveprobe Manifold Generator + FAMM Map Preshaping
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==================================================
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Uses waveprobe to generate manifold shapes from eigenvalue spectra,
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then preshapes FAMM (Frustrated Access Memory Module) delay-line maps
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based on the eigenvalue-derived manifold geometry.
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Pipeline:
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1. Generate waveprobe diagnostic payload with manifold eigenvalues
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2. Compute eigenvalue spectrum from simulated manifold Laplacian
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3. Derive manifold shape from eigenvalue distribution
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4. Preshape FAMM delay maps to match manifold curvature
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5. Output FAMM-compatible delay-weight configuration
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Integration: waveprobe → eigenvalue → manifold → FAMM preshape
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"""
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import numpy as np
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import json
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import hashlib
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import time
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from dataclasses import dataclass
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from typing import List, Dict, Tuple, Optional
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from pathlib import Path
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RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack")
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@dataclass
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class WaveprobeManifold:
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"""Waveprobe-generated manifold with eigenvalue spectrum."""
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probe_id: str
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dimension: int
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eigenvalues: List[float] # Laplacian eigenvalue spectrum
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eigenvectors: List[List[float]] # Manifold embedding
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curvature_tensor: List[float]
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topology_valid: bool
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manifold_shape: str # 'spherical', 'hyperbolic', 'flat', 'toroidal'
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@dataclass
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class FAMMDelayMap:
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"""FAMM delay-line map preshaped by manifold geometry."""
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address: int
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data: float # Q16.16 representation
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delay: float # Delay time (shaped by eigenvalue)
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delay_mass: float # Causal constraint mass
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delay_weight: float # Shaped by eigenvector component
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curvature_aligned: bool # Whether delay follows manifold curvature
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class WaveprobeManifoldGenerator:
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"""
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Generate manifold shapes from waveprobe diagnostic payloads.
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Uses simulated Laplacian eigenvalue spectra to define manifold geometry,
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then extracts topological invariants for FAMM preshaping.
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"""
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def __init__(self, dimension: int = 4):
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self.dimension = dimension
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self.probe_types = ["manifold_topology", "eigenvalue_spectrum", "curvature_tensor"]
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def generate_laplacian_spectrum(self, n_modes: int = 16) -> Tuple[List[float], List[List[float]]]:
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"""
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Generate Laplacian eigenvalue spectrum for manifold.
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For a d-dimensional manifold, Laplacian eigenvalues λ_k scale as:
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λ_k ∝ k^(2/d) for large k (Weyl law)
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Returns: (eigenvalues, eigenvectors)
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"""
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# Simulate eigenvalue spectrum
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# λ_k = (k * π / L)² for Dirichlet boundary conditions
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L = 1.0 # Characteristic length
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eigenvalues = []
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eigenvectors = []
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for k in range(1, n_modes + 1):
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# Weyl law scaling: λ_k ∝ k^(2/d)
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if self.dimension > 0:
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lam = (np.pi * k / L) ** (2.0 / self.dimension)
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else:
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lam = (np.pi * k / L) ** 2
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eigenvalues.append(lam)
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# Generate corresponding eigenfunction (simplified)
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# φ_k(x) = sin(kπx/L) for 1D, product for higher D
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vec = [np.sin(k * np.pi * i / (n_modes + 1)) for i in range(1, n_modes + 1)]
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vec = [v / np.linalg.norm(vec) for v in vec] # Normalize
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eigenvectors.append(vec)
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return eigenvalues, eigenvectors
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def classify_manifold_shape(self, eigenvalues: List[float]) -> str:
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"""
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Classify manifold shape from eigenvalue distribution.
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- Spherical: eigenvalues cluster at low end (positive curvature)
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- Hyperbolic: eigenvalues spread out (negative curvature)
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- Flat: uniform distribution (zero curvature)
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- Toroidal: periodic pattern
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"""
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if len(eigenvalues) < 3:
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return 'unknown'
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# Compute eigenvalue gaps
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gaps = [eigenvalues[i+1] - eigenvalues[i] for i in range(len(eigenvalues)-1)]
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mean_gap = np.mean(gaps)
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std_gap = np.std(gaps)
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# Coefficient of variation
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cv = std_gap / mean_gap if mean_gap > 0 else 0
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# Classify based on gap distribution
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if cv < 0.3:
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return 'spherical' # Low variation, clustered
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elif cv > 0.7:
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return 'hyperbolic' # High variation, spread out
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elif any(g < 0.01 * mean_gap for g in gaps[:3]):
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return 'toroidal' # Near-degenerate low modes
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else:
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return 'flat'
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def compute_curvature_tensor(self, eigenvalues: List[float]) -> List[float]:
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"""
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Compute Ricci curvature tensor components from eigenvalues.
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Simplified: R_ii ∝ Σ(1/λ_k) for k > 0 (zeta function regularized)
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"""
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# Regularized sum: exclude zero mode
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curvatures = []
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for i in range(min(4, self.dimension)):
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if len(eigenvalues) > 1:
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# Ricci curvature ~ sum of inverse eigenvalues
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ricci = sum(1.0 / lam for lam in eigenvalues[1:] if lam > 0.001)
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curvatures.append(ricci / len(eigenvalues))
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else:
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curvatures.append(0.0)
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return curvatures
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def generate_waveprobe(self, probe_type: str = "manifold_topology") -> WaveprobeManifold:
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"""Generate waveprobe diagnostic payload with manifold data."""
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timestamp = time.time()
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probe_id = f"manifold_{hashlib.sha256(str(timestamp).encode()).hexdigest()[:12]}"
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# Generate eigenvalue spectrum
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eigenvalues, eigenvectors = self.generate_laplacian_spectrum(n_modes=16)
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# Classify manifold shape
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manifold_shape = self.classify_manifold_shape(eigenvalues)
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# Compute curvature
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curvature_tensor = self.compute_curvature_tensor(eigenvalues)
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# Topology validation
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topology_valid = all(ev > 0 for ev in eigenvalues[1:]) # Positive semi-definite Laplacian
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return WaveprobeManifold(
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probe_id=probe_id,
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dimension=self.dimension,
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eigenvalues=eigenvalues,
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eigenvectors=eigenvectors,
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curvature_tensor=curvature_tensor,
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topology_valid=topology_valid,
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manifold_shape=manifold_shape
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)
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class FAMMPreshaper:
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"""
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Preshape FAMM delay-line maps based on waveprobe manifold geometry.
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Maps manifold eigenvalues to FAMM delay parameters:
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- delay ∝ 1/√λ (lower eigenvalue = longer delay = lower frequency mode)
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- delay_weight ∝ eigenvector amplitude (stronger coupling for dominant modes)
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- delay_mass ∝ curvature (higher curvature = more causal constraint)
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"""
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def __init__(self, bank_size: int = 256, max_delay: float = 32767.0):
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self.bank_size = bank_size
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self.max_delay = max_delay # Q16.16 max
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def eigenvalue_to_delay(self, eigenvalue: float, scale: float = 1000.0) -> float:
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"""
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Map Laplacian eigenvalue to FAMM delay time.
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Lower eigenvalue (lower frequency mode) → longer delay
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τ ∝ 1/√λ
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"""
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if eigenvalue <= 0:
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return self.max_delay
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# Delay ∝ 1/√λ
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delay = scale / np.sqrt(eigenvalue)
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# Clamp to Q16.16 range
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return min(delay, self.max_delay)
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def eigenvector_to_weight(self, eigenvector_component: float) -> float:
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"""
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Map eigenvector component to FAMM delay weight.
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Larger eigenvector amplitude → stronger delay weight
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w = |φ_k(x)|² (probability density interpretation)
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"""
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weight = eigenvector_component ** 2
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return min(weight, 1.0) # Normalized to [0,1]
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def curvature_to_mass(self, curvature: float, base_mass: float = 1.0) -> float:
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"""
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Map manifold curvature to FAMM delay mass.
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Higher curvature → larger delay mass (more causal constraint)
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mass ∝ |R| (absolute Ricci curvature)
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"""
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mass = base_mass * (1.0 + abs(curvature))
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return min(mass, self.max_delay / 10) # Scale appropriately
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def preshape_famm_map(
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self,
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manifold: WaveprobeManifold,
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n_cells: Optional[int] = None
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) -> List[FAMMDelayMap]:
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"""
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Preshape FAMM delay-line map from waveprobe manifold.
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Distributes FAMM cells across manifold modes,
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assigning delays based on eigenvalue spectrum.
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"""
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if n_cells is None:
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n_cells = self.bank_size
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famm_maps = []
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# Use top N eigenvalues for N cells
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n_modes = min(len(manifold.eigenvalues), n_cells)
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for i in range(n_cells):
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# Cycle through eigenmodes
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mode_idx = i % n_modes
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# Get eigenvalue and eigenvector for this mode
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eigenvalue = manifold.eigenvalues[mode_idx]
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eigenvector = manifold.eigenvectors[mode_idx]
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# Pick component from eigenvector (distribute across spatial positions)
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vec_idx = i % len(eigenvector)
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eigencomponent = eigenvector[vec_idx]
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# Compute curvature component
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curvature_idx = i % len(manifold.curvature_tensor)
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curvature = manifold.curvature_tensor[curvature_idx]
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# Map to FAMM parameters
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delay = self.eigenvalue_to_delay(eigenvalue)
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weight = self.eigenvector_to_weight(eigencomponent)
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mass = self.curvature_to_mass(curvature)
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# Data value (simulated Q16.16)
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data_val = eigencomponent * 32767.0 # Scale to Q16.16 range
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famm_maps.append(FAMMDelayMap(
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address=i,
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data=float(data_val),
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delay=float(delay),
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delay_mass=float(mass),
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delay_weight=float(weight),
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curvature_aligned=True
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))
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return famm_maps
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class WaveprobeFAMMIntegration:
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"""
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Integrate waveprobe manifold generation with FAMM preshaping.
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Complete pipeline: waveprobe → eigenvalue → manifold → FAMM
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"""
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def __init__(self, dimension: int = 4, bank_size: int = 256):
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self.waveprobe_gen = WaveprobeManifoldGenerator(dimension)
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self.famm_preshaper = FAMMPreshaper(bank_size)
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def generate_preshaped_famm(
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self,
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probe_type: str = "manifold_topology",
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output_format: str = "lean"
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) -> Dict:
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"""
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Generate complete waveprobe → FAMM preshaped configuration.
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Returns configuration in specified format (lean, json, or python).
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"""
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# Step 1: Generate waveprobe manifold
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manifold = self.waveprobe_gen.generate_waveprobe(probe_type)
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# Step 2: Preshape FAMM map
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famm_maps = self.famm_preshaper.preshape_famm_map(manifold)
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# Step 3: Format output
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if output_format == "lean":
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return self._to_lean_format(manifold, famm_maps)
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elif output_format == "json":
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return self._to_json_format(manifold, famm_maps)
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else:
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return self._to_python_format(manifold, famm_maps)
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def _to_lean_format(
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self,
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manifold: WaveprobeManifold,
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famm_maps: List[FAMMDelayMap]
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) -> Dict:
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"""Convert to Lean 4 FAMM initialization format."""
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lean_cells = []
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for m in famm_maps:
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# Convert to Q16.16 hex representation
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data_hex = f"0x{int(m.data) & 0xFFFF:04X}"
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delay_hex = f"0x{int(m.delay) & 0xFFFF:04X}"
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mass_hex = f"0x{int(m.delay_mass) & 0xFFFF:04X}"
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weight_hex = f"0x{int(m.delay_weight * 65535) & 0xFFFF:04X}"
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lean_cells.append({
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"data": data_hex,
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"delay": delay_hex,
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"delayMass": mass_hex,
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"delayWeight": weight_hex
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})
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return {
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"manifold": {
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"probe_id": manifold.probe_id,
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"dimension": manifold.dimension,
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"shape": manifold.manifold_shape,
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"eigenvalues": [f"{ev:.6f}" for ev in manifold.eigenvalues[:8]], # Top 8
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"curvature": [f"{c:.6f}" for c in manifold.curvature_tensor],
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"topology_valid": manifold.topology_valid
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},
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"famm_bank": {
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"size": len(famm_maps),
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"maxDelay": f"0x{int(self.famm_preshaper.max_delay):04X}",
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"cells": lean_cells[:16] # First 16 for demo
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},
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"generation_timestamp": time.time()
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}
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def _to_json_format(
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self,
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manifold: WaveprobeManifold,
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famm_maps: List[FAMMDelayMap]
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) -> Dict:
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"""Convert to JSON format for external tools."""
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return {
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"waveprobe": {
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"probe_id": manifold.probe_id,
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"dimension": manifold.dimension,
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"manifold_shape": manifold.manifold_shape,
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"eigenvalue_spectrum": manifold.eigenvalues,
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"curvature_tensor": manifold.curvature_tensor,
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"topology_valid": manifold.topology_valid
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},
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"famm_delay_map": [
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{
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"address": m.address,
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"delay_ms": m.delay,
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"delay_mass": m.delay_mass,
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"delay_weight": m.delay_weight,
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"data": m.data,
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"curvature_aligned": m.curvature_aligned
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}
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for m in famm_maps
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]
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}
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def _to_python_format(
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self,
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manifold: WaveprobeManifold,
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famm_maps: List[FAMMDelayMap]
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) -> Dict:
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"""Convert to Python-compatible format."""
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return {
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"manifold": manifold,
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"famm_maps": famm_maps,
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"summary": {
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"shape": manifold.manifold_shape,
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"n_cells": len(famm_maps),
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"mean_delay": np.mean([m.delay for m in famm_maps]),
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"mean_weight": np.mean([m.delay_weight for m in famm_maps])
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}
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}
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def main():
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"""Generate waveprobe manifold and preshape FAMM maps."""
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print("=" * 70)
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print("Waveprobe Manifold Generator + FAMM Map Preshaper")
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print("=" * 70)
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# Initialize integration
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integration = WaveprobeFAMMIntegration(dimension=4, bank_size=256)
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print("\n[1] Generating waveprobe manifold with eigenvalue spectrum...")
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# Generate preshaped FAMM
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result = integration.generate_preshaped_famm(
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probe_type="manifold_topology",
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output_format="lean"
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)
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print(f" Probe ID: {result['manifold']['probe_id']}")
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print(f" Dimension: {result['manifold']['dimension']}")
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print(f" Manifold Shape: {result['manifold']['shape']}")
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print(f" Topology Valid: {result['manifold']['topology_valid']}")
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print("\n[2] Eigenvalue Spectrum (top 8):")
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for i, ev in enumerate(result['manifold']['eigenvalues'][:8]):
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print(f" λ_{i+1} = {ev}")
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print("\n[3] Curvature Tensor:")
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for i, c in enumerate(result['manifold']['curvature']):
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print(f" R_{i} = {c}")
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print(f"\n[4] FAMM Bank Configuration:")
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print(f" Size: {result['famm_bank']['size']} cells")
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print(f" Max Delay: {result['famm_bank']['maxDelay']} (Q16.16)")
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print(f"\n[5] Sample FAMM Cells (first 4):")
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for i, cell in enumerate(result['famm_bank']['cells'][:4]):
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print(f" Cell[{i}]: data={cell['data']}, delay={cell['delay']}, "
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f"mass={cell['delayMass']}, weight={cell['delayWeight']}")
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# Save output
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output_path = RESEARCH_STACK / "4-Infrastructure/shim/waveprobe_famm_output.json"
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with open(output_path, 'w') as f:
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json.dump(result, f, indent=2)
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print(f"\n[6] Output saved to: {output_path}")
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print("\n" + "=" * 70)
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print("Integration Complete")
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print("Waveprobe eigenvalue spectrum → Manifold shape → FAMM delay map preshape")
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print("=" * 70)
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return result
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if __name__ == "__main__":
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main()
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