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aureon-os / scripts /diagnostics /check_coherence_lambda.py

Deploy Aureon OS (deploy/selfhost stack: nginx edge + fusion API x2 + console + HNC organ + redis + postgres; paper mode) (part 19)

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#!/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('\n📊 BATTEN MATRIX - COHERENCE × LAMBDA × PROBABILITY READINGS:\n')
    print('=' * 80)
    
    if not state:
        print('\n⚠️  SUBSYSTEM_STATE is empty - system may still be initializing')
        print('    The Probability Nexus collects data over time before generating metrics.')
    else:
        print(f'\n✅ 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'  🌊 Coherence: {coherence:.4f}')
            print(f'  ⚡ Lambda:    {lambda_stability:.4f}')
            print(f'  🎲 Confidence: {confidence:.4f}')
            print(f'  📊 SCORE:     {score:.6f} {"🟢 PASS" if score >= 0.618 else "🔴 FAIL"}')
            print(f'  💎 Clarity: {clarity:.2f} | Chaos: {chaos:.4f} ({chaos_trend})')
            print()
    
    print('=' * 80)
    print('\n💡 Note: Batten Matrix requires Score ≥ 0.618 (φ golden ratio) for 4th pass execution')
    print('   Formula: Score = Coherence × Lambda × Confidence\n')
    
except Exception as e:
    print(f'\n❌ Error accessing Probability Nexus state: {e}')
    print('\n💡 The system may need more runtime to collect coherence/lambda metrics.')
    print('   These values build up as the scanner processes market data.\n')

	#!/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('\n📊 BATTEN MATRIX - COHERENCE × LAMBDA × PROBABILITY READINGS:\n')
	print('=' * 80)

	if not state:
	print('\n⚠️ SUBSYSTEM_STATE is empty - system may still be initializing')
	print(' The Probability Nexus collects data over time before generating metrics.')
	else:
	print(f'\n✅ 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' 🌊 Coherence: {coherence:.4f}')
	print(f' ⚡ Lambda: {lambda_stability:.4f}')
	print(f' 🎲 Confidence: {confidence:.4f}')
	print(f' 📊 SCORE: {score:.6f} {"🟢 PASS" if score >= 0.618 else "🔴 FAIL"}')
	print(f' 💎 Clarity: {clarity:.2f} \| Chaos: {chaos:.4f} ({chaos_trend})')
	print()

	print('=' * 80)
	print('\n💡 Note: Batten Matrix requires Score ≥ 0.618 (φ golden ratio) for 4th pass execution')
	print(' Formula: Score = Coherence × Lambda × Confidence\n')

	except Exception as e:
	print(f'\n❌ Error accessing Probability Nexus state: {e}')
	print('\n💡 The system may need more runtime to collect coherence/lambda metrics.')
	print(' These values build up as the scanner processes market data.\n')