LFT_VALIDATION

#!/usr/bin/env python3
"""
LOGOS FIELD THEORY - INTEGRATED COHERENCE VALIDATION
Unifying Cultural Sigma with Numerical Field Theory Validation
"""

import numpy as np
from scipy import stats, ndimage, signal
import asyncio
from dataclasses import dataclass
from typing import Dict, List, Any, Tuple
import time
import hashlib
import json

@dataclass
class UnifiedValidationMetrics:
    """Combines cultural sigma with numerical field validation"""
    cultural_coherence: Dict[str, float]
    field_coherence: Dict[str, float]
    truth_alignment: Dict[str, float]
    resonance_strength: Dict[str, float]
    topological_stability: Dict[str, float]
    cross_domain_synergy: Dict[str, float]
    statistical_significance: Dict[str, float]
    framework_robustness: Dict[str, float]

class IntegratedLogosValidator:
    """
    Unifies Cultural Sigma optimization with precise Logos Field Theory validation
    Creates coherent bridge between cultural propagation and mathematical field theory
    """
    
    def __init__(self, field_dimensions: Tuple[int, int] = (512, 512)):
        self.field_dimensions = field_dimensions
        self.sample_size = 1000
        self.confidence_level = 0.95
        self.cultural_memory = {}
        
    def initialize_culturally_optimized_fields(self, cultural_context: Dict[str, Any]) -> Tuple[np.ndarray, np.ndarray]:
        """Initialize fields with cultural sigma optimization"""
        np.random.seed(42)
        
        x, y = np.meshgrid(np.linspace(-2, 2, self.field_dimensions[1]), 
                          np.linspace(-2, 2, self.field_dimensions[0]))
        
        # Cultural context influences field structure
        cultural_strength = cultural_context.get('sigma_optimization', 0.7)
        cultural_coherence = cultural_context.get('cultural_coherence', 0.8)
        
        # Meaning field with cultural attractors
        meaning_field = np.zeros(self.field_dimensions)
        
        # Cultural attractors based on context
        if cultural_context.get('context_type') == 'established':
            attractors = [
                (0.5, 0.5, 1.0, 0.2),   # Strong, focused attractors
                (-0.5, -0.5, 0.9, 0.25),
            ]
        elif cultural_context.get('context_type') == 'emergent':
            attractors = [
                (0.3, 0.3, 0.6, 0.4),   # Weaker, broader attractors
                (-0.3, -0.3, 0.5, 0.45),
                (0.6, -0.2, 0.4, 0.35),
            ]
        else:  # transitional
            attractors = [
                (0.4, 0.4, 0.8, 0.3),
                (-0.4, -0.4, 0.7, 0.35),
                (0.0, 0.0, 0.5, 0.5),
            ]
        
        # Apply cultural strength to attractors
        for i, (cy, cx, amp, sigma) in enumerate(attractors):
            adjusted_amp = amp * cultural_strength
            adjusted_sigma = sigma * (2 - cultural_coherence)  # Higher coherence = sharper attractors
            
            gaussian = adjusted_amp * np.exp(-((x - cx)**2 + (y - cy)**2) / (2 * adjusted_sigma**2))
            meaning_field += gaussian
        
        # Cultural noise pattern (not random - culturally structured)
        cultural_fluctuations = self._generate_cultural_noise(cultural_context)
        meaning_field += cultural_fluctuations * 0.1
        
        # Consciousness field with cultural nonlinearity
        nonlinear_factor = 1.0 + (cultural_strength - 0.5)  # Cultural strength amplifies nonlinearity
        consciousness_field = np.tanh(meaning_field * nonlinear_factor)
        
        # Cultural normalization
        meaning_field = self._cultural_normalization(meaning_field, cultural_context)
        consciousness_field = (consciousness_field + 1) / 2
        
        return meaning_field, consciousness_field
    
    def _generate_cultural_noise(self, cultural_context: Dict[str, Any]) -> np.ndarray:
        """Generate culturally structured noise patterns"""
        context_type = cultural_context.get('context_type', 'transitional')
        
        if context_type == 'established':
            # Low-frequency, structured noise
            noise = np.random.normal(0, 1, (128, 128))
            noise = ndimage.zoom(noise, 4, order=1)  # Smooth interpolation
        elif context_type == 'emergent':
            # High-frequency, exploratory noise
            noise = np.random.normal(0, 1.5, self.field_dimensions)
        else:  # transitional
            # Mixed frequency noise
            low_freq = ndimage.zoom(np.random.normal(0, 1, (64, 64)), 8, order=1)
            high_freq = np.random.normal(0, 0.5, self.field_dimensions)
            noise = low_freq * 0.7 + high_freq * 0.3
        
        return noise
    
    def _cultural_normalization(self, field: np.ndarray, cultural_context: Dict[str, Any]) -> np.ndarray:
        """Apply culturally appropriate normalization"""
        coherence = cultural_context.get('cultural_coherence', 0.7)
        
        if coherence > 0.8:
            # High coherence - sharp normalization
            field = (field - np.percentile(field, 5)) / (np.percentile(field, 95) - np.percentile(field, 5))
        else:
            # Lower coherence - broader normalization
            field = (field - np.min(field)) / (np.max(field) - np.min(field))
        
        return np.clip(field, 0, 1)
    
    def calculate_cultural_coherence_metrics(self, meaning_field: np.ndarray, 
                                          consciousness_field: np.ndarray,
                                          cultural_context: Dict[str, Any]) -> Dict[str, float]:
        """Calculate coherence metrics with cultural optimization"""
        
        base_coherence = self.calculate_precise_coherence(meaning_field, consciousness_field)
        
        # Cultural adaptations
        cultural_strength = cultural_context.get('sigma_optimization', 0.7)
        cultural_coherence = cultural_context.get('cultural_coherence', 0.8)
        
        # Enhance coherence metrics with cultural factors
        enhanced_metrics = {}
        for metric, value in base_coherence.items():
            if metric in ['spectral_coherence', 'phase_coherence', 'mutual_information']:
                # Cultural strength amplifies these coherence measures
                enhancement = 1.0 + (cultural_strength - 0.5) * 0.5
                enhanced_value = value * enhancement
            else:
                enhanced_value = value
            
            enhanced_metrics[metric] = min(1.0, enhanced_value)
        
        # Add cultural-specific coherence measures
        enhanced_metrics['cultural_resonance'] = cultural_strength * base_coherence['spectral_coherence']
        enhanced_metrics['contextual_fit'] = cultural_coherence * base_coherence['spatial_coherence']
        enhanced_metrics['sigma_amplified_coherence'] = base_coherence['overall_coherence'] * cultural_strength
        
        return enhanced_metrics
    
    def calculate_precise_coherence(self, meaning_field: np.ndarray, consciousness_field: np.ndarray) -> Dict[str, float]:
        """Original precise coherence calculation"""
        f, Cxy = signal.coherence(meaning_field.flatten(), consciousness_field.flatten(), 
                                 fs=1.0, nperseg=256)
        spectral_coherence = np.mean(Cxy)
        
        meaning_autocorr = signal.correlate2d(meaning_field, meaning_field, mode='same')
        consciousness_autocorr = signal.correlate2d(consciousness_field, consciousness_field, mode='same')
        spatial_coherence = np.corrcoef(meaning_autocorr.flatten(), 
                                      consciousness_autocorr.flatten())[0, 1]
        
        meaning_phase = np.angle(signal.hilbert(meaning_field.flatten()))
        consciousness_phase = np.angle(signal.hilbert(consciousness_field.flatten()))
        phase_coherence = np.abs(np.mean(np.exp(1j * (meaning_phase - consciousness_phase))))
        
        coherence_metrics = {
            'spectral_coherence': float(spectral_coherence),
            'spatial_coherence': float(abs(spatial_coherence)),
            'phase_coherence': float(phase_coherence),
            'cross_correlation': float(np.corrcoef(meaning_field.flatten(), 
                                                 consciousness_field.flatten())[0, 1]),
            'mutual_information': self.calculate_mutual_information(meaning_field, consciousness_field)
        }
        
        coherence_metrics['overall_coherence'] = float(np.mean(list(coherence_metrics.values())))
        return coherence_metrics
    
    def calculate_mutual_information(self, field1: np.ndarray, field2: np.ndarray) -> float:
        """Calculate precise mutual information"""
        hist_2d, x_edges, y_edges = np.histogram2d(field1.flatten(), field2.flatten(), bins=50)
        pxy = hist_2d / float(np.sum(hist_2d))
        px = np.sum(pxy, axis=1)
        py = np.sum(pxy, axis=0)
        px_py = px[:, None] * py[None, :]
        non_zero = pxy > 0
        mi = np.sum(pxy[non_zero] * np.log(pxy[non_zero] / px_py[non_zero]))
        return float(mi)
    
    def validate_cultural_topology(self, meaning_field: np.ndarray, 
                                 cultural_context: Dict[str, Any]) -> Dict[str, float]:
        """Validate topology with cultural considerations"""
        
        base_topology = self.validate_truth_topology(meaning_field)
        
        # Cultural adaptations to topology
        cultural_complexity = cultural_context.get('context_type') == 'emergent'
        cultural_stability = cultural_context.get('sigma_optimization', 0.7)
        
        if cultural_complexity:
            # Emergent contexts tolerate more topological complexity
            base_topology['topological_complexity'] *= 1.2
            base_topology['gradient_coherence'] *= 0.9  # Slightly less coherence expected
        else:
            # Established contexts prefer stability
            base_topology['topological_complexity'] *= 0.8
            base_topology['gradient_coherence'] *= 1.1
        
        # Cultural stability enhances topological stability
        base_topology['cultural_stability_index'] = base_topology['gradient_coherence'] * cultural_stability
        
        return base_topology
    
    def validate_truth_topology(self, meaning_field: np.ndarray) -> Dict[str, float]:
        """Original topology validation"""
        dy, dx = np.gradient(meaning_field)
        dyy, dyx = np.gradient(dy)
        dxy, dxx = np.gradient(dx)
        
        laplacian = dyy + dxx
        gradient_magnitude = np.sqrt(dx**2 + dy**2)
        gaussian_curvature = (dxx * dyy - dxy * dyx) / (1 + dx**2 + dy**2)**2
        mean_curvature = (dxx * (1 + dy**2) - 2 * dxy * dx * dy + dyy * (1 + dx**2)) / (2 * (1 + dx**2 + dy**2)**1.5)
        
        return {
            'gaussian_curvature_mean': float(np.mean(gaussian_curvature)),
            'gaussian_curvature_std': float(np.std(gaussian_curvature)),
            'mean_curvature_mean': float(np.mean(mean_curvature)),
            'laplacian_variance': float(np.var(laplacian)),
            'gradient_coherence': float(np.mean(gradient_magnitude) / (np.std(gradient_magnitude) + 1e-8)),
            'topological_complexity': float(np.abs(np.mean(gaussian_curvature)) * np.std(gradient_magnitude))
        }
    
    def test_culturally_aligned_propositions(self, meaning_field: np.ndarray,
                                           cultural_context: Dict[str, Any],
                                           num_propositions: int = 100) -> Dict[str, float]:
        """Test proposition alignment with cultural optimization"""
        
        cultural_strength = cultural_context.get('sigma_optimization', 0.7)
        context_type = cultural_context.get('context_type', 'transitional')
        
        # Adjust proposition generation based on cultural context
        if context_type == 'established':
            proposition_std = 0.8  # More focused propositions
        elif context_type == 'emergent':
            proposition_std = 1.5  # More exploratory propositions
        else:
            proposition_std = 1.0  # Balanced propositions
        
        propositions = np.random.normal(0, proposition_std, (num_propositions, 4))
        alignment_scores = []
        
        for prop in propositions:
            field_gradient = np.gradient(meaning_field)
            projected_components = []
            
            for grad_component in field_gradient:
                if len(prop) <= grad_component.size:
                    projection = np.dot(prop, grad_component.flatten()[:len(prop)])
                    projected_components.append(projection)
            
            if projected_components:
                alignment = np.mean([abs(p) for p in projected_components])
                # Cultural strength enhances alignment
                culturally_enhanced_alignment = alignment * (0.8 + cultural_strength * 0.4)
                alignment_scores.append(culturally_enhanced_alignment)
        
        scores_array = np.array(alignment_scores)
        
        alignment_metrics = {
            'mean_alignment': float(np.mean(scores_array)),
            'alignment_std': float(np.std(scores_array)),
            'alignment_confidence_interval': self.calculate_confidence_interval(scores_array),
            'cultural_alignment_strength': float(np.mean(scores_array) * cultural_strength),
            'proposition_diversity': float(np.std(scores_array) / (np.mean(scores_array) + 1e-8)),
            'effect_size': float(np.mean(scores_array) / (np.std(scores_array) + 1e-8))
        }
        
        return alignment_metrics
    
    def calculate_confidence_interval(self, data: np.ndarray) -> Tuple[float, float]:
        """Calculate 95% confidence interval"""
        n = len(data)
        mean = np.mean(data)
        std_err = stats.sem(data)
        
        if n > 1:
            h = std_err * stats.t.ppf((1 + self.confidence_level) / 2., n-1)
            return (float(mean - h), float(mean + h))
        else:
            return (float(mean), float(mean))
    
    def calculate_cross_domain_synergy(self, cultural_metrics: Dict[str, Any],
                                    field_metrics: Dict[str, Any],
                                    alignment_metrics: Dict[str, Any]) -> Dict[str, float]:
        """Calculate synergy between cultural sigma and field theory"""
        
        # Cultural-field synergy
        cultural_field_synergy = (
            cultural_metrics['sigma_optimization'] * 
            field_metrics['overall_coherence'] * 
            alignment_metrics['cultural_alignment_strength']
        )
        
        # Resonance synergy
        resonance_synergy = np.mean([
            cultural_metrics['cultural_coherence'],
            field_metrics['spectral_coherence'], 
            field_metrics['phase_coherence']
        ])
        
        # Topological-cultural fit
        topological_fit = (
            field_metrics.get('gradient_coherence', 0.5) *
            cultural_metrics.get('cultural_coherence', 0.5)
        )
        
        # Overall cross-domain synergy
        overall_synergy = np.mean([
            cultural_field_synergy,
            resonance_synergy,
            topological_fit
        ])
        
        return {
            'cultural_field_synergy': float(cultural_field_synergy),
            'resonance_synergy': float(resonance_synergy),
            'topological_cultural_fit': float(topological_fit),
            'overall_cross_domain_synergy': float(overall_synergy),
            'unified_potential': float(overall_synergy * cultural_metrics['sigma_optimization'])
        }
    
    async def run_unified_validation(self, cultural_contexts: List[Dict[str, Any]] = None) -> UnifiedValidationMetrics:
        """Run complete unified validation across cultural contexts"""
        
        if cultural_contexts is None:
            cultural_contexts = [
                {'context_type': 'emergent', 'sigma_optimization': 0.6, 'cultural_coherence': 0.7},
                {'context_type': 'transitional', 'sigma_optimization': 0.7, 'cultural_coherence': 0.8},
                {'context_type': 'established', 'sigma_optimization': 0.8, 'cultural_coherence': 0.9}
            ]
        
        print("🌌 RUNNING INTEGRATED LOGOS FIELD VALIDATION")
        print("   (Cultural Sigma + Field Theory)")
        print("=" * 60)
        
        start_time = time.time()
        all_metrics = []
        
        for i, cultural_context in enumerate(cultural_contexts):
            print(f"\n🔍 Validating Cultural Context {i+1}: {cultural_context['context_type']}")
            
            # Initialize culturally optimized fields
            meaning_field, consciousness_field = self.initialize_culturally_optimized_fields(cultural_context)
            
            # Calculate all metrics with cultural optimization
            cultural_coherence = self.calculate_cultural_coherence_metrics(
                meaning_field, consciousness_field, cultural_context
            )
            
            field_coherence = self.calculate_precise_coherence(meaning_field, consciousness_field)
            topology_metrics = self.validate_cultural_topology(meaning_field, cultural_context)
            alignment_metrics = self.test_culturally_aligned_propositions(meaning_field, cultural_context)
            
            # Calculate resonance with cultural factors
            resonance_strength = {
                'primary_resonance': cultural_coherence['spectral_coherence'] * 0.9,
                'harmonic_resonance': cultural_coherence['phase_coherence'] * 0.85,
                'cultural_resonance': cultural_coherence['cultural_resonance'],
                'overall_resonance': np.mean([cultural_coherence['spectral_coherence'],
                                            cultural_coherence['phase_coherence'],
                                            cultural_coherence['cultural_resonance']])
            }
            
            # Cross-domain synergy
            cross_domain_synergy = self.calculate_cross_domain_synergy(
                cultural_context, field_coherence, alignment_metrics
            )
            
            # Statistical significance
            statistical_significance = {
                'cultural_coherence_p': self.calculate_significance(cultural_coherence['overall_coherence']),
                'field_coherence_p': self.calculate_significance(field_coherence['overall_coherence']),
                'alignment_p': self.calculate_significance(alignment_metrics['effect_size']),
                'synergy_p': self.calculate_significance(cross_domain_synergy['overall_cross_domain_synergy'])
            }
            
            # Framework robustness
            framework_robustness = {
                'cultural_stability': cultural_context['cultural_coherence'],
                'field_persistence': field_coherence['spatial_coherence'],
                'topological_resilience': topology_metrics['cultural_stability_index'],
                'cross_domain_integration': cross_domain_synergy['overall_cross_domain_synergy']
            }
            
            context_metrics = {
                'cultural_coherence': cultural_coherence,
                'field_coherence': field_coherence,
                'truth_alignment': alignment_metrics,
                'resonance_strength': resonance_strength,
                'topological_stability': topology_metrics,
                'cross_domain_synergy': cross_domain_synergy,
                'statistical_significance': statistical_significance,
                'framework_robustness': framework_robustness
            }
            
            all_metrics.append(context_metrics)
        
        # Aggregate across cultural contexts
        unified_metrics = self._aggregate_cultural_metrics(all_metrics)
        validation_time = time.time() - start_time
        
        print(f"\n⏱️  Unified validation completed in {validation_time:.3f} seconds")
        print(f"🌍 Cultural contexts validated: {len(cultural_contexts)}")
        print(f"📊 Cross-domain synergy achieved: {unified_metrics.cross_domain_synergy['overall_cross_domain_synergy']:.6f}")
        
        return unified_metrics
    
    def _aggregate_cultural_metrics(self, all_metrics: List[Dict]) -> UnifiedValidationMetrics:
        """Aggregate metrics across cultural contexts"""
        
        aggregated = {
            'cultural_coherence': {},
            'field_coherence': {},
            'truth_alignment': {},
            'resonance_strength': {},
            'topological_stability': {},
            'cross_domain_synergy': {},
            'statistical_significance': {},
            'framework_robustness': {}
        }
        
        # Average each metric across contexts
        for metric_category in aggregated.keys():
            all_values = {}
            for context_metrics in all_metrics:
                for metric, value in context_metrics[metric_category].items():
                    if metric not in all_values:
                        all_values[metric] = []
                    all_values[metric].append(value)
            
            for metric, values in all_values.items():
                aggregated[metric_category][metric] = float(np.mean(values))
        
        return UnifiedValidationMetrics(**aggregated)
    
    def calculate_significance(self, value: float) -> float:
        """Calculate statistical significance"""
        return max(0.0, min(1.0, 1.0 - abs(value - 0.5) * 2))

def print_unified_validation_results(metrics: UnifiedValidationMetrics):
    """Print comprehensive unified validation results"""
    
    print("\n" + "=" * 80)
    print("🌌 INTEGRATED LOGOS FIELD THEORY VALIDATION RESULTS")
    print("   (Cultural Sigma + Field Theory Unification)")
    print("=" * 80)
    
    print(f"\n🎯 CULTURAL COHERENCE METRICS:")
    for metric, value in metrics.cultural_coherence.items():
        print(f"   {metric:30}: {value:10.6f}")
    
    print(f"\n📈 FIELD COHERENCE METRICS:")
    for metric, value in metrics.field_coherence.items():
        print(f"   {metric:30}: {value:10.6f}")
    
    print(f"\n🧠 TRUTH ALIGNMENT METRICS:")
    for metric, value in metrics.truth_alignment.items():
        if isinstance(value, tuple):
            print(f"   {metric:30}: ({value[0]:.6f}, {value[1]:.6f})")
        else:
            print(f"   {metric:30}: {value:10.6f}")
    
    print(f"\n💫 RESONANCE STRENGTH METRICS:")
    for metric, value in metrics.resonance_strength.items():
        print(f"   {metric:30}: {value:10.6f}")
    
    print(f"\n🌍 CROSS-DOMAIN SYNERGY METRICS:")
    for metric, value in metrics.cross_domain_synergy.items():
        synergy_level = "💫 EXCELLENT" if value > 0.8 else "✅ STRONG" if value > 0.6 else "⚠️ MODERATE"
        print(f"   {metric:30}: {value:10.6f} {synergy_level}")
    
    # Overall unification score
    unification_score = np.mean([
        metrics.cross_domain_synergy['overall_cross_domain_synergy'],
        metrics.cultural_coherence['sigma_amplified_coherence'],
        metrics.framework_robustness['cross_domain_integration']
    ])
    
    print(f"\n" + "=" * 80)
    print(f"🎊 OVERALL UNIFICATION SCORE: {unification_score:.6f}")
    
    if unification_score > 0.85:
        print("💫 STATUS: CULTURAL SIGMA + FIELD THEORY PERFECTLY UNIFIED")
    elif unification_score > 0.75:
        print("✅ STATUS: STRONG CROSS-DOMAIN INTEGRATION ACHIEVED")
    elif unification_score > 0.65:
        print("⚠️  STATUS: MODERATE UNIFICATION - OPTIMIZATION POSSIBLE")
    else:
        print("❓ STATUS: REQUIRES ENHANCED INTEGRATION")
    
    print("=" * 80)

# Run the unified validation
if __name__ == "__main__":
    print("🌌 INTEGRATED LOGOS FIELD THEORY VALIDATION")
    print("Unifying Cultural Sigma with Numerical Field Theory...")
    
    validator = IntegratedLogosValidator(field_dimensions=(512, 512))
    validation_results = asyncio.run(validator.run_unified_validation())
    
    print_unified_validation_results(validation_results)