Scruff AI
9302a86ea8
New docs: - docs/em_spectrum_overlay.html: Full EM spectrum with Khra/Gixx lines, physics markers, cell-size slider - docs/2026-03-28_170500_cto-report_fractal-echo-analysis.txt: CTO fractal echo analysis report New scripts (Beast sweep infrastructure + analyzers): - scripts/nuclear_magic_analyzer.py: shell structure, peak clustering, mode counting, GUE tests - scripts/physics_domain_analysis.py: 4-domain physics (nuclear shells, Brillouin, band gaps, GUE) - scripts/direct_zmq_sweep.py: ZMQ direct sweep driver - scripts/sweep_real.py: real sweep execution - scripts/analyze_sweep.py, comprehensive_analysis.py: sweep analysis tools - scripts/execute_prime_mapping.py: prime lattice mapping - scripts/extrapolate_findings.py, check_extrapolation.py: extrapolation tools - scripts/eta_calc.py, time_estimate.py, updated_estimate.py, show_pattern.py: utilities Prime analysis (root): - navigator_prime_analysis.py, navigator_prime_analysis_v2.py Updated: - navigator/mock_lbm_daemon.py, navigator/telemetry_server.py
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
import csv
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from collections import defaultdict
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CSV = '/mnt/d/Resonance_Engine/sweep_results/em_direct_sweep_20260327_080716.csv'
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data = []
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with open(CSV) as f:
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reader = csv.DictReader(f)
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for row in reader:
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data.append(row)
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cohs = [float(r['coherence']) for r in data]
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print(f'Total points: {len(data)}')
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print(f'Coherence range: {min(cohs):.4f} - {max(cohs):.4f}')
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print(f'Mean coherence: {sum(cohs)/len(cohs):.4f}')
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by_omega = defaultdict(list)
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for r in data:
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by_omega[float(r['omega'])].append(r)
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print()
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print('=== Best coherence per omega ===')
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for omega in sorted(by_omega.keys()):
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rows = by_omega[omega]
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best = max(rows, key=lambda r: float(r['coherence']))
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print(f' Omega={omega:.1f}: Coh={float(best["coherence"]):.4f} K={best["khra_amp"]} G={best["gixx_amp"]} Asym={float(best["asymmetry"]):.4f}')
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print()
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print('=== Top 10 parameter combos (by coherence) ===')
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sorted_data = sorted(data, key=lambda r: float(r['coherence']), reverse=True)
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for i, r in enumerate(sorted_data[:10]):
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print(f' #{i+1}: Omega={r["omega"]} K={r["khra_amp"]} G={r["gixx_amp"]} -> Coh={r["coherence"]} Asym={r["asymmetry"]} Vort={r["vorticity_mean"]}')
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print()
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print('=== Bottom 5 parameter combos (by coherence) ===')
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for i, r in enumerate(sorted_data[-5:]):
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print(f' Omega={r["omega"]} K={r["khra_amp"]} G={r["gixx_amp"]} -> Coh={r["coherence"]} Asym={r["asymmetry"]}')
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print()
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print('=== Asymmetry at coherence extremes ===')
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top20 = sorted_data[:20]
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bot20 = sorted_data[-20:]
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print(f' Top 20 coh avg asymmetry: {sum(float(r["asymmetry"]) for r in top20)/20:.4f}')
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print(f' Bottom 20 coh avg asymmetry: {sum(float(r["asymmetry"]) for r in bot20)/20:.4f}')
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print()
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print('=== Coherence by khra (averaged across all omega/gixx) ===')
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by_khra = defaultdict(list)
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for r in data:
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by_khra[r['khra_amp']].append(float(r['coherence']))
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for k in sorted(by_khra.keys()):
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vals = by_khra[k]
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print(f' K={k}: avg_coh={sum(vals)/len(vals):.4f} (n={len(vals)})')
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print()
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print('=== Coherence by gixx (averaged across all omega/khra) ===')
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by_gixx = defaultdict(list)
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for r in data:
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by_gixx[r['gixx_amp']].append(float(r['coherence']))
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for g in sorted(by_gixx.keys()):
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vals = by_gixx[g]
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print(f' G={g}: avg_coh={sum(vals)/len(vals):.4f} (n={len(vals)})')
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