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Lean: update Semantics modules, add new numerics/physics data files Hardware: update FPGA bitstreams (tangnano9k_uart_loopback) Infra: k3s-flake tests, netcup-vps configuration, VCN compute substrate Docs: ARCHITECTURE, specs, citation updates
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
#!/usr/bin/env python3
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"""
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find_approximations.py — Find all modules with dangerous numerical approximations
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This script scans the Lean codebase for:
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1. Uses of old expNeg/expLUT (4-entry lookup table)
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2. Linearized approximations (1-x for exp)
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3. Taylor approximations without error bounds
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4. Missing imports of Q16_16Numerics
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Output: List of files and lines that need updating.
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"""
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import re
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import os
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from pathlib import Path
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SEMASNTICS_DIR = Path("/home/allaun/Research Stack/0-Core-Formalism/lean/Semantics/Semantics")
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# Patterns that indicate dangerous approximations
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DANGEROUS_PATTERNS = [
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(r'Q16_16\.expNeg', "Old expNeg lookup table (4 entries)"),
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(r'expLUT', "Old expLUT lookup table"),
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(r'1 - x|1 - .*\.toInt', "Linearized exp approximation"),
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(r'Taylor.*cos.*1 - x.*x/2', "Cosine Taylor without error bounds"),
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(r'simplified.*sqrt', "Simplified sqrt"),
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(r'simplified.*exp', "Simplified exp"),
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(r'linearized', "Linearized approximation"),
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]
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# Patterns that are OK (using rigorous functions)
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OK_PATTERNS = [
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(r'import Semantics\.Q16_16Numerics', "Has rigorous numerics import"),
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(r'Q16_16Numerics\.exp', "Using rigorous exp"),
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(r'Q16_16Numerics\.sqrt', "Using rigorous sqrt"),
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(r'Q16_16Numerics\.sin', "Using rigorous sin"),
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(r'Q16_16Numerics\.cos', "Using rigorous cos"),
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]
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def scan_file(filepath):
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"""Scan a single file for approximation issues."""
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issues = []
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with open(filepath, 'r') as f:
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for i, line in enumerate(f, 1):
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for pattern, desc in DANGEROUS_PATTERNS:
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if re.search(pattern, line):
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issues.append({
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'file': str(filepath),
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'line': i,
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'code': line.strip(),
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'issue': desc
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})
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return issues
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def main():
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all_issues = []
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for filepath in SEMASNTICS_DIR.rglob("*.lean"):
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issues = scan_file(filepath)
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all_issues.extend(issues)
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# Group by file
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by_file = {}
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for issue in all_issues:
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f = issue['file']
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if f not in by_file:
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by_file[f] = []
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by_file[f].append(issue)
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# Print report
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print("=" * 80)
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print("NUMERICAL APPROXIMATION AUDIT REPORT")
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print("=" * 80)
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print(f"\nTotal issues found: {len(all_issues)}")
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print(f"Files affected: {len(by_file)}")
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for filepath, issues in sorted(by_file.items()):
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rel_path = os.path.relpath(filepath, str(SEMASNTICS_DIR.parent))
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print(f"\n{'─' * 60}")
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print(f"File: {rel_path}")
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print(f"Issues: {len(issues)}")
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for issue in issues[:5]: # Show first 5
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print(f" Line {issue['line']}: {issue['issue']}")
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print(f" {issue['code'][:80]}")
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if len(issues) > 5:
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print(f" ... and {len(issues) - 5} more")
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# Priority ranking
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print(f"\n{'=' * 80}")
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print("PRIORITY RANKING (by impact on physics calculations)")
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print("=" * 80)
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priority_files = [
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"EntropyMeasures.lean", # Has expLUT, many sqrt calls
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"DynamicCanal.lean", # Uses expNeg
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"HCMMR/Kernels/FAMMScarMemory.lean", # Uses expNeg
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"CrossModalCompression.lean", # Uses expNeg
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"ImplicitShellLattice.lean", # Uses sin/cos
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"UnitQuaternion.lean", # Taylor approximations
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"Toybox/ObserverAngle.lean", # Cosine approximation
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]
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for i, fname in enumerate(priority_files, 1):
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matches = [f for f in by_file.keys() if fname in f]
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if matches:
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print(f"\n{i}. {fname} ({len(by_file[matches[0]])} issues)")
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if __name__ == "__main__":
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main()
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