Scruff AI
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""Quick sweep analysis - Windows native paths"""
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import json
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import glob
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import re
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from collections import defaultdict
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# Read all status files
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files = glob.glob('sweep_results/run_*_status.json')
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print(f"Found {len(files)} status files")
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# Collect data
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data_points = []
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for f in files:
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try:
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with open(f) as fp:
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data = json.load(fp)
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match = re.search(r'run_(\d+)', f)
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if match:
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run_id = int(match.group(1))
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coherence = data.get('coherence')
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asymmetry = data.get('asymmetry')
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if coherence and asymmetry and 10 < asymmetry < 20:
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data_points.append({
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'run_id': run_id,
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'coherence': coherence,
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'asymmetry': asymmetry,
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'cycle': data.get('cycle', 0)
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})
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except:
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pass
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print(f"Valid data points: {len(data_points)}")
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if data_points:
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# Basic stats
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coherences = [d['coherence'] for d in data_points]
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asymmetries = [d['asymmetry'] for d in data_points]
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print(f"\nCoherence: {min(coherences):.4f} to {max(coherences):.4f}, mean={sum(coherences)/len(coherences):.4f}")
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print(f"Asymmetry: {min(asymmetries):.4f} to {max(asymmetries):.4f}, mean={sum(asymmetries)/len(asymmetries):.4f}")
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# Correlation
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import math
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n = len(data_points)
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mean_c = sum(coherences)/n
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mean_a = sum(asymmetries)/n
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cov = sum((c - mean_c) * (a - mean_a) for c, a in zip(coherences, asymmetries))
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var_c = sum((c - mean_c)**2 for c in coherences)
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var_a = sum((a - mean_a)**2 for a in asymmetries)
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if var_c > 0 and var_a > 0:
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correlation = cov / math.sqrt(var_c * var_a)
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print(f"\nCorrelation (coherence vs asymmetry): {correlation:.4f}")
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# Run progression
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print("\n=== RUN PROGRESSION ===")
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by_run = defaultdict(list)
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for d in data_points:
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by_run[d['run_id']].append(d['asymmetry'])
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# Show first 10 runs
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for run_id in sorted(by_run.keys())[:10]:
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asymms = by_run[run_id]
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print(f"Run {run_id}: {len(asymms)} samples, asymmetry {min(asymms):.2f}-{max(asymms):.2f}")
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print("\nAnalysis complete.")
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