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da0ce7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | #!/usr/bin/env python
"""Check current Batten Matrix readings from live system."""
import json
# Try to read from global state if it exists
try:
import aureon_probability_nexus
state = getattr(aureon_probability_nexus, 'SUBSYSTEM_STATE', {})
print('\nπ BATTEN MATRIX - COHERENCE Γ LAMBDA Γ PROBABILITY READINGS:\n')
print('=' * 80)
if not state:
print('\nβ οΈ SUBSYSTEM_STATE is empty - system may still be initializing')
print(' The Probability Nexus collects data over time before generating metrics.')
else:
print(f'\nβ
Found metrics for {len(state)} symbols:\n')
for symbol, metrics in list(state.items())[:10]:
clarity = metrics.get('avg_clarity', 0)
coherence = metrics.get('avg_coherence', 0)
chaos = metrics.get('chaos', 0)
chaos_trend = metrics.get('chaos_trend', 'stable')
price = metrics.get('latest_price', 0)
# Calculate confidence (same as in make_predictions)
confidence = (clarity / 5.0) * 0.5 + coherence * 0.5
# Estimate lambda (stability) - inverse of chaos
lambda_stability = max(0, 1 - chaos) if chaos > 0 else 0.5
# Batten Matrix Score
score = coherence * lambda_stability * confidence
print(f'{symbol:12s}')
print(f' π Coherence: {coherence:.4f}')
print(f' β‘ Lambda: {lambda_stability:.4f}')
print(f' π² Confidence: {confidence:.4f}')
print(f' π SCORE: {score:.6f} {"π’ PASS" if score >= 0.618 else "π΄ FAIL"}')
print(f' π Clarity: {clarity:.2f} | Chaos: {chaos:.4f} ({chaos_trend})')
print()
print('=' * 80)
print('\nπ‘ Note: Batten Matrix requires Score β₯ 0.618 (Ο golden ratio) for 4th pass execution')
print(' Formula: Score = Coherence Γ Lambda Γ Confidence\n')
except Exception as e:
print(f'\nβ Error accessing Probability Nexus state: {e}')
print('\nπ‘ The system may need more runtime to collect coherence/lambda metrics.')
print(' These values build up as the scanner processes market data.\n')
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