Create module 50_reality interface

module 50_reality interface

@@ -0,0 +1,388 @@
+#!/usr/bin/env python3
+"""
+FACT ENGINE - Empirical Historical Analysis System
+Cross-domain pattern detection with statistical verification
+"""
+
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import hashlib
+from typing import Dict, List, Any, Tuple
+from scipy import stats
+import logging
+from dataclasses import dataclass
+from enum import Enum
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class DataDomain(Enum):
+    ARCHAEOLOGICAL = "archaeological"
+    GEOLOGICAL = "geological" 
+    ASTRONOMICAL = "astronomical"
+    HISTORICAL = "historical"
+    MYTHOLOGICAL = "mythological"
+    GENETIC = "genetic"
+
+@dataclass
+class EmpiricalFact:
+    """A verified fact with statistical confidence"""
+    domain: DataDomain
+    description: str
+    data_source: str
+    confidence: float
+    statistical_significance: float
+    supporting_evidence: List[str]
+    timestamp: datetime
+
+class ArchaeologicalAnalyzer:
+    """Analyze archaeological site data for patterns"""
+    
+    def analyze_site_clusters(self, sites_data: np.ndarray) -> Dict[str, float]:
+        """Analyze temporal and spatial clustering of archaeological sites"""
+        if len(sites_data) < 3:
+            return {'cluster_confidence': 0.0}
+        
+        # Temporal clustering analysis
+        dates = sites_data[:, 0]  # Assumes first column is dating
+        temporal_clustering = self._analyze_temporal_clustering(dates)
+        
+        # Spatial clustering analysis  
+        coordinates = sites_data[:, 1:3]  # Assumes lat/long
+        spatial_clustering = self._analyze_spatial_clustering(coordinates)
+        
+        return {
+            'temporal_cluster_strength': temporal_clustering,
+            'spatial_cluster_strength': spatial_clustering,
+            'cluster_confidence': (temporal_clustering + spatial_clustering) / 2
+        }
+    
+    def _analyze_temporal_clustering(self, dates: np.ndarray) -> float:
+        """Calculate temporal clustering using kernel density"""
+        if len(dates) < 3:
+            return 0.0
+        
+        # Normalize dates
+        normalized_dates = (dates - np.min(dates)) / (np.max(dates) - np.min(dates))
+        
+        # Calculate clustering using nearest neighbor distances in time
+        sorted_dates = np.sort(normalized_dates)
+        time_gaps = np.diff(sorted_dates)
+        
+        if np.mean(time_gaps) == 0:
+            return 0.0
+            
+        # Clustering index (lower gaps = more clustering)
+        clustering_index = 1 - (np.mean(time_gaps) / (1 / len(time_gaps)))
+        return float(max(0, clustering_index))
+
+class GeologicalEventAnalyzer:
+    """Analyze geological event patterns"""
+    
+    def analyze_catastrophe_clusters(self, event_data: np.ndarray) -> Dict[str, float]:
+        """Analyze temporal clustering of geological catastrophe events"""
+        if len(event_data) < 3:
+            return {'catastrophe_cluster_confidence': 0.0}
+        
+        # Event timing analysis
+        event_times = event_data[:, 0]
+        cluster_strength = self._calculate_event_clustering(event_times)
+        
+        # Magnitude correlation analysis
+        if event_data.shape[1] > 1:
+            magnitudes = event_data[:, 1]
+            magnitude_trend = self._analyze_magnitude_trend(event_times, magnitudes)
+        else:
+            magnitude_trend = 0.0
+        
+        return {
+            'temporal_clustering': cluster_strength,
+            'magnitude_correlation': magnitude_trend,
+            'catastrophe_cluster_confidence': (cluster_strength + magnitude_trend) / 2
+        }
+    
+    def _calculate_event_clustering(self, event_times: np.ndarray) -> float:
+        """Calculate Poisson deviation for event clustering"""
+        if len(event_times) < 3:
+            return 0.0
+            
+        time_gaps = np.diff(np.sort(event_times))
+        expected_gap = np.mean(time_gaps)
+        
+        if expected_gap == 0:
+            return 0.0
+            
+        # Coefficient of variation (clustered events have CV > 1)
+        cv = np.std(time_gaps) / expected_gap
+        clustering_strength = min(1.0, (cv - 1) / 2)  # Normalize to 0-1
+        return max(0.0, clustering_strength)
+
+class MythologicalPatternAnalyzer:
+    """Analyze cross-cultural mythological patterns"""
+    
+    def analyze_myth_correlations(self, myth_data: Dict[str, List[str]]) -> Dict[str, float]:
+        """Analyze correlation between mythological themes across cultures"""
+        if len(myth_data) < 2:
+            return {'myth_correlation_confidence': 0.0}
+        
+        cultures = list(myth_data.keys())
+        correlation_matrix = np.zeros((len(cultures), len(cultures)))
+        
+        # Calculate theme overlap between cultures
+        for i, culture1 in enumerate(cultures):
+            for j, culture2 in enumerate(cultures):
+                if i != j:
+                    themes1 = set(myth_data[culture1])
+                    themes2 = set(myth_data[culture2])
+                    overlap = len(themes1.intersection(themes2)) / len(themes1.union(themes2))
+                    correlation_matrix[i, j] = overlap
+        
+        np.fill_diagonal(correlation_matrix, 0)
+        
+        return {
+            'average_cross_cultural_correlation': float(np.mean(correlation_matrix)),
+            'maximum_correlation': float(np.max(correlation_matrix)),
+            'myth_correlation_confidence': float(np.mean(correlation_matrix))
+        }
+
+class StatisticalAnalyzer:
+    """Core statistical analysis methods"""
+    
+    def calculate_confidence_interval(self, data: np.ndarray, confidence: float = 0.95) -> Tuple[float, float]:
+        """Calculate confidence interval for data"""
+        if len(data) < 2:
+            return (0.0, 0.0)
+        
+        mean = np.mean(data)
+        sem = stats.sem(data)
+        ci = stats.t.interval(confidence, len(data)-1, loc=mean, scale=sem)
+        return (float(ci[0]), float(ci[1]))
+    
+    def test_significance(self, data1: np.ndarray, data2: np.ndarray) -> float:
+        """Test statistical significance between two datasets"""
+        if len(data1) < 3 or len(data2) < 3:
+            return 0.0
+        
+        t_stat, p_value = stats.ttest_ind(data1, data2)
+        significance = 1 - p_value  # Convert to confidence
+        return float(max(0.0, significance))
+
+class FactEngine:
+    """
+    Main fact analysis engine - cross-domain pattern detection
+    """
+    
+    def __init__(self):
+        self.archaeological_analyzer = ArchaeologicalAnalyzer()
+        self.geological_analyzer = GeologicalEventAnalyzer()
+        self.mythological_analyzer = MythologicalPatternAnalyzer()
+        self.stats_analyzer = StatisticalAnalyzer()
+        self.verified_facts: List[EmpiricalFact] = []
+        
+    def analyze_civilization_cycles(self, 
+                                  archaeological_data: np.ndarray,
+                                  geological_data: np.ndarray,
+                                  mythological_data: Dict[str, List[str]]) -> Dict[str, Any]:
+        """Cross-domain analysis of civilization cycle patterns"""
+        
+        # Archaeological analysis
+        arch_results = self.archaeological_analyzer.analyze_site_clusters(archaeological_data)
+        
+        # Geological analysis  
+        geo_results = self.geological_analyzer.analyze_catastrophe_clusters(geological_data)
+        
+        # Mythological analysis
+        myth_results = self.mythological_analyzer.analyze_myth_correlations(mythological_data)
+        
+        # Cross-domain correlation
+        domain_correlations = self._calculate_domain_correlations(
+            arch_results, geo_results, myth_results
+        )
+        
+        # Overall confidence calculation
+        overall_confidence = np.mean([
+            arch_results['cluster_confidence'],
+            geo_results['catastrophe_cluster_confidence'], 
+            myth_results['myth_correlation_confidence'],
+            domain_correlations['cross_domain_alignment']
+        ])
+        
+        result = {
+            'timestamp': datetime.now().isoformat(),
+            'domain_results': {
+                'archaeological': arch_results,
+                'geological': geo_results,
+                'mythological': myth_results
+            },
+            'cross_domain_analysis': domain_correlations,
+            'overall_confidence': float(overall_confidence),
+            'civilization_cycle_hypothesis_supported': overall_confidence > 0.7
+        }
+        
+        # Create empirical fact if confidence is high
+        if overall_confidence > 0.7:
+            fact = EmpiricalFact(
+                domain=DataDomain.HISTORICAL,
+                description="Evidence for cyclical civilization patterns across archaeological, geological, and mythological domains",
+                data_source="Multi-domain correlation analysis",
+                confidence=overall_confidence,
+                statistical_significance=domain_correlations['statistical_significance'],
+                supporting_evidence=[
+                    f"Archaeological clustering: {arch_results['cluster_confidence']:.3f}",
+                    f"Geological event correlation: {geo_results['catastrophe_cluster_confidence']:.3f}",
+                    f"Mythological cross-cultural alignment: {myth_results['myth_correlation_confidence']:.3f}"
+                ],
+                timestamp=datetime.now()
+            )
+            self.verified_facts.append(fact)
+        
+        return result
+    
+    def _calculate_domain_correlations(self, arch_results: Dict, geo_results: Dict, myth_results: Dict) -> Dict[str, float]:
+        """Calculate correlations between different domain results"""
+        
+        # Extract key confidence metrics
+        arch_confidence = arch_results['cluster_confidence']
+        geo_confidence = geo_results['catastrophe_cluster_confidence']
+        myth_confidence = myth_results['myth_correlation_confidence']
+        
+        confidences = [arch_confidence, geo_confidence, myth_confidence]
+        
+        # Calculate alignment (how well domains support each other)
+        alignment = 1 - (np.std(confidences) / 0.5)  # Normalize
+        
+        return {
+            'cross_domain_alignment': float(max(0.0, alignment)),
+            'domain_consistency': float(1 - np.std(confidences)),
+            'statistical_significance': float(np.mean(confidences))
+        }
+    
+    def get_verified_facts(self, min_confidence: float = 0.7) -> List[EmpiricalFact]:
+        """Get facts that meet confidence threshold"""
+        return [fact for fact in self.verified_facts if fact.confidence >= min_confidence]
+    
+    def export_fact_report(self) -> Dict[str, Any]:
+        """Export comprehensive fact report"""
+        high_confidence_facts = self.get_verified_facts(0.8)
+        medium_confidence_facts = self.get_verified_facts(0.6)
+        
+        return {
+            'report_timestamp': datetime.now().isoformat(),
+            'total_facts_verified': len(self.verified_facts),
+            'high_confidence_facts': len(high_confidence_facts),
+            'medium_confidence_facts': len(medium_confidence_facts),
+            'fact_breakdown_by_domain': self._get_domain_breakdown(),
+            'confidence_distribution': self._get_confidence_distribution(),
+            'facts': [
+                {
+                    'description': fact.description,
+                    'domain': fact.domain.value,
+                    'confidence': fact.confidence,
+                    'significance': fact.statistical_significance,
+                    'evidence': fact.supporting_evidence
+                }
+                for fact in high_confidence_facts
+            ]
+        }
+    
+    def _get_domain_breakdown(self) -> Dict[str, int]:
+        """Get fact count by domain"""
+        breakdown = {}
+        for fact in self.verified_facts:
+            domain = fact.domain.value
+            breakdown[domain] = breakdown.get(domain, 0) + 1
+        return breakdown
+    
+    def _get_confidence_distribution(self) -> Dict[str, int]:
+        """Get confidence level distribution"""
+        distribution = {
+            'very_high': len([f for f in self.verified_facts if f.confidence >= 0.9]),
+            'high': len([f for f in self.verified_facts if 0.8 <= f.confidence < 0.9]),
+            'medium': len([f for f in self.verified_facts if 0.7 <= f.confidence < 0.8]),
+            'low': len([f for f in self.verified_facts if f.confidence < 0.7])
+        }
+        return distribution
+
+# DEMONSTRATION WITH SYNTHETIC DATA
+def demonstrate_fact_engine():
+    """Demonstrate the fact engine with realistic synthetic data"""
+    
+    print("FACT ENGINE - Empirical Historical Analysis")
+    print("=" * 60)
+    
+    engine = FactEngine()
+    
+    # Synthetic archaeological data (site dates in years BP)
+    archaeological_data = np.array([
+        [12600, 37.2, 38.9],  # Göbekli Tepe timeframe
+        [11500, 29.9, 31.1],   # Giza water erosion hypothesis  
+        [20000, -6.99, 107.05], # Gunung Padang controversial dating
+        [12800, 37.2, 38.9],   # Younger Dryas impact timeframe
+        [11000, 29.9, 31.1]    # Post-catastrophe rebuilding
+    ])
+    
+    # Synthetic geological event data (time BP, magnitude)
+    geological_data = np.array([
+        [12800, 8.5],  # Younger Dryas impact
+        [11400, 7.2],  # Meltwater pulse
+        [8200, 6.8],   # 8.2 kiloyear event
+        [4200, 6.5],   # 4.2 kiloyear event
+        [3200, 6.2]    # 3.2 kiloyear event
+    ])
+    
+    # Synthetic mythological theme data
+    mythological_data = {
+        'sumerian': ['great_flood', 'dragon_battle', 'golden_age', 'gods_war'],
+        'biblical': ['great_flood', 'leviathan', 'eden', 'apocalypse'],
+        'greek': ['deucalion_flood', 'typhon', 'golden_age', 'olympian_war'],
+        'norse': ['ragnarok', 'jormungandr', 'golden_age', 'aesir_vanir_war'],
+        'hindu': ['manu_flood', 'vritra', 'satya_yuga', 'deva_asura_war']
+    }
+    
+    # Run cross-domain analysis
+    print("\n🔍 ANALYZING CIVILIZATION CYCLE PATTERNS...")
+    results = engine.analyze_civilization_cycles(
+        archaeological_data, geological_data, mythological_data
+    )
+    
+    print(f"\n📊 RESULTS:")
+    print(f"Overall Confidence: {results['overall_confidence']:.3f}")
+    print(f"Hypothesis Supported: {results['civilization_cycle_hypothesis_supported']}")
+    
+    print(f"\n🏛️  ARCHAEOLOGICAL:")
+    arch = results['domain_results']['archaeological']
+    print(f"  Site Clustering: {arch['cluster_confidence']:.3f}")
+    
+    print(f"\n🌋 GEOLOGICAL:")
+    geo = results['domain_results']['geological']
+    print(f"  Event Clustering: {geo['catastrophe_cluster_confidence']:.3f}")
+    
+    print(f"\n📖 MYTHOLOGICAL:")
+    myth = results['domain_results']['mythological']
+    print(f"  Cross-Cultural Correlation: {myth['myth_correlation_confidence']:.3f}")
+    
+    print(f"\n🔗 CROSS-DOMAIN:")
+    cross = results['cross_domain_analysis']
+    print(f"  Domain Alignment: {cross['cross_domain_alignment']:.3f}")
+    
+    # Export fact report
+    report = engine.export_fact_report()
+    
+    print(f"\n📈 FACT REPORT:")
+    print(f"Total Verified Facts: {report['total_facts_verified']}")
+    print(f"High Confidence Facts: {report['high_confidence_facts']}")
+    
+    if report['high_confidence_facts'] > 0:
+        print(f"\n💎 HIGH CONFIDENCE FINDINGS:")
+        for fact in report['facts']:
+            print(f"  • {fact['description']}")
+            print(f"    Confidence: {fact['confidence']:.3f}")
+            print(f"    Domain: {fact['domain']}")
+    
+    print(f"\n🎯 ENGINE STATUS: OPERATIONAL")
+    print("Method: Empirical multi-domain pattern correlation")
+    print("Output: Statistically verified historical facts")
+
+if __name__ == "__main__":
+    demonstrate_fact_engine()