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https://github.com/allaunthefox/Research-Stack.git
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83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Linux Kernel RGFlow Audit
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Converts kernel source to Unified Compression format and audits lawfulness.
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"""
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import os
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import sys
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import numpy as np
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from pathlib import Path
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import logging
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure"))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism"))
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from scripts.commoncrawl_waveprobe_ingestion import UnifiedAdaptationEquation, AdaptationState, UnifiedCompressor
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logging.basicConfig(level=logging.INFO, format='%(levelname)s:KernelAudit:%(message)s')
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logger = logging.getLogger(__name__)
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def audit_kernel_file(filepath: Path):
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if not filepath.exists():
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logger.error(f"File not found: {filepath}")
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return
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logger.info(f"Auditing Linux Kernel subsystem: {filepath.name}")
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with open(filepath, 'rb') as f:
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data = f.read()
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# 1. Convert to Unified Compression format
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compressor = UnifiedCompressor()
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compressed_bytes, ratio = compressor.compress_bytestream(data)
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logger.info(f"Unified Compression Ratio: {ratio:.3f}")
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# 2. Extract Adaptation State from the compressed "Genome"
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adaptation_eq = UnifiedAdaptationEquation()
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# Heuristic mapping for Kernel Code
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# Entropy of compressed data (information density)
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entropy = -np.sum(np.histogram(np.frombuffer(compressed_bytes, dtype=np.uint8), bins=256, density=True)[0] * np.log2(np.histogram(np.frombuffer(compressed_bytes, dtype=np.uint8), bins=256, density=True)[0] + 1e-9))
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norm_entropy = min(entropy / 8.0, 1.0)
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mu_q = 0.001 # Kernel code is extremely stable
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rho_q = 0.9 # High refresh (frequent patches)
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C_fac = 0.8 # High connectance (tightly coupled)
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M_fac = 0.7 # Modularity (subsystems)
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n_e = 1.0 # Maximum observer mass (millions of users/devs)
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sigma_q = 1.0 + norm_entropy # SNR based on compressed info density
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state = AdaptationState(mu_q, rho_q, C_fac, M_fac, n_e, sigma_q)
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# 3. Evaluate RGFlow trajectory
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(lawful_now, lawful_under_flow, reaches_attractor, flows_to_noise,
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flows_to_sabotage, cost, margin, rg_depth, attractor_id, failure_mask) = \
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adaptation_eq.evaluate_state(state)
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print("\n" + "="*80)
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print(f"Linux Kernel Audit Report: {filepath.name}")
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print("="*80)
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print(f"Status: {'✓ LAWFUL' if lawful_under_flow else '✗ NON-LAWFUL'}")
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print(f"RG Depth: {rg_depth}/10")
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print(f"Attractor: {attractor_id}")
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print(f"Stability Margin: {margin:.4f}")
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print(f"Compression Ratio: {ratio:.3f}")
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print("-" * 80)
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print(f"Genome Logic: mu={mu_q}, rho={rho_q}, C={C_fac}, M={M_fac}, ne={n_e}, sigma={sigma_q:.3f}")
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if not lawful_under_flow:
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print(f"Failure Mask: {failure_mask} (1:Drake, 2:Drift, 4:Error)")
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print("="*80 + "\n")
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if __name__ == "__main__":
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# Targeting the core scheduler
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target = Path("/usr/src/linux-cachyos/kernel/sched/core.c")
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if not target.exists():
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# Fallback to any file in the kernel source if core.c is not specifically there
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sources = list(Path("/usr/src/linux-cachyos").rglob("*.c"))
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if sources:
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target = sources[0]
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audit_kernel_file(target)
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