Create UNIFIED_V6

UNIFIED_V6

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+#!/usr/bin/env python3
+"""
+QUANTUM-HISTORICAL UNIFIED FIELD THEORY v6.0
+Integration of Logos Fields, Wave Interference Physics, and Cyclical Historical Analysis
+Advanced Scientific Framework for Cosmic Pattern Recognition
+"""
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Any, Callable
+from enum import Enum
+import asyncio
+import logging
+import math
+from pathlib import Path
+import json
+import h5py
+import zarr
+from scipy import integrate, optimize, special, linalg, signal, fft, stats
+import numba
+from concurrent.futures import ProcessPoolExecutor
+import multiprocessing as mp
+import hashlib
+from sklearn.metrics import mutual_info_score
+from datetime import datetime
+
+# Advanced scientific logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - [QH-UFT] %(message)s',
+    handlers=[
+        logging.FileHandler('quantum_historical_unified_field.log'),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger("quantum_historical_unified_field")
+
+@dataclass
+class UnifiedFieldConfiguration:
+    """Complete configuration for unified field computations"""
+    spatial_dimensions: int = 4
+    temporal_resolution: int = 1000
+    field_resolution: Tuple[int, int] = (512, 512)
+    quantum_cutoff: float = 1e-12
+    cultural_coherence_threshold: float = 0.7
+    historical_cycle_length: int = 140000
+    renormalization_scheme: str = "dimensional_regularization"
+
+@dataclass
+class CosmicCyclePhase(Enum):
+    """Enhanced cosmic cycle phases with quantum signatures"""
+    POST_CATACLYSM_SURVIVAL = "post_cataclysm_survival"
+    KNOWLEDGE_RECOVERY = "knowledge_recovery"  
+    CIVILIZATION_REBUILD = "civilization_rebuild"
+    DEFENSE_CONSTRUCTION = "defense_construction"
+    CATASTROPHE_IMMINENCE = "catastrophe_imminence"
+    QUANTUM_RESONANCE_PEAK = "quantum_resonance_peak"  # New phase
+
+@dataclass
+class QuantumHistoricalState:
+    """Unified quantum-historical state representation"""
+    field_tensor: torch.Tensor
+    historical_phase: CosmicCyclePhase
+    cultural_coherence: float
+    wave_interference_pattern: np.ndarray
+    temporal_correlation: float
+    quantum_entanglement: float
+    defense_preparedness: float
+    
+    def calculate_unified_potential(self) -> float:
+        """Calculate unified field potential across all domains"""
+        field_energy = torch.norm(self.field_tensor).item()
+        phase_advantage = self._phase_advantage_factor()
+        coherence_boost = self.cultural_coherence ** 2
+        wave_resonance = np.max(np.abs(self.wave_interference_pattern))
+        
+        unified_potential = (field_energy * phase_advantage * 
+                           coherence_boost * wave_resonance * 
+                           self.defense_preparedness)
+        
+        return float(unified_potential)
+    
+    def _phase_advantage_factor(self) -> float:
+        """Calculate phase-specific advantage factors"""
+        phase_factors = {
+            CosmicCyclePhase.POST_CATACLYSM_SURVIVAL: 0.3,
+            CosmicCyclePhase.KNOWLEDGE_RECOVERY: 0.5,
+            CosmicCyclePhase.CIVILIZATION_REBUILD: 0.7,
+            CosmicCyclePhase.DEFENSE_CONSTRUCTION: 0.9,
+            CosmicCyclePhase.CATASTROPHE_IMMINENCE: 1.2,
+            CosmicCyclePhase.QUANTUM_RESONANCE_PEAK: 1.5
+        }
+        return phase_factors.get(self.historical_phase, 0.7)
+
+class AdvancedWaveInterferenceEngine:
+    """Enhanced wave interference engine with quantum extensions"""
+    
+    def __init__(self, config: UnifiedFieldConfiguration):
+        self.config = config
+        self.fundamental_frequency = 1.0
+        self.harmonic_ratios = self._generate_prime_harmonics()
+        
+    def _generate_prime_harmonics(self) -> List[float]:
+        """Generate harmonic ratios based on prime number theory"""
+        primes = [2, 3, 5, 7, 11, 13, 17, 19]
+        return [1/p for p in primes]
+    
+    def compute_quantum_wave_interference(self, historical_phase: CosmicCyclePhase) -> Dict[str, Any]:
+        """Compute quantum-enhanced wave interference patterns"""
+        
+        # Phase-dependent frequency selection
+        phase_frequencies = self._get_phase_frequencies(historical_phase)
+        
+        # Generate quantum wave components
+        wave_components = []
+        for freq_ratio in phase_frequencies:
+            component = self._generate_quantum_wave(freq_ratio)
+            wave_components.append(component)
+        
+        # Quantum interference superposition
+        interference_pattern = self._quantum_superposition(wave_components)
+        
+        # Calculate quantum coherence metrics
+        coherence_metrics = self._calculate_quantum_coherence(interference_pattern, wave_components)
+        
+        return {
+            'interference_pattern': interference_pattern,
+            'wave_components': wave_components,
+            'phase_frequencies': phase_frequencies,
+            'quantum_coherence': coherence_metrics,
+            'symbolic_emergence': self._detect_symbolic_patterns(interference_pattern)
+        }
+    
+    def _generate_quantum_wave(self, frequency_ratio: float) -> np.ndarray:
+        """Generate quantum wave with phase coherence"""
+        x = np.linspace(0, 4 * np.pi, self.config.temporal_resolution)
+        
+        # Quantum wave function with complex phase
+        quantum_phase = np.exp(1j * frequency_ratio * x)
+        envelope = np.exp(-0.1 * x)  # Decaying envelope
+        
+        wave = np.real(quantum_phase * envelope)
+        return wave
+    
+    def _quantum_superposition(self, wave_components: List[np.ndarray]) -> np.ndarray:
+        """Apply quantum superposition principle to wave components"""
+        if not wave_components:
+            return np.zeros(self.config.temporal_resolution)
+        
+        # Weighted superposition based on harmonic significance
+        weights = [1/(i+1) for i in range(len(wave_components))]
+        total_weight = sum(weights)
+        
+        superposed = np.zeros_like(wave_components[0])
+        for i, component in enumerate(wave_components):
+            superposed += weights[i] * component
+        
+        return superposed / total_weight
+    
+    def _calculate_quantum_coherence(self, pattern: np.ndarray, components: List[np.ndarray]) -> Dict[str, float]:
+        """Calculate quantum coherence metrics"""
+        if len(components) < 2:
+            return {'overall_coherence': 0.0, 'phase_stability': 0.0, 'quantum_entanglement': 0.0}
+        
+        # Phase coherence between components
+        phase_coherences = []
+        for i in range(len(components)):
+            for j in range(i+1, len(components)):
+                coherence = np.abs(np.corrcoef(components[i], components[j])[0,1])
+                phase_coherences.append(coherence)
+        
+        # Pattern self-similarity (quantum entanglement analog)
+        pattern_fft = fft.fft(pattern)
+        spectral_coherence = np.mean(np.abs(pattern_fft)) / (np.std(np.abs(pattern_fft)) + 1e-12)
+        
+        return {
+            'overall_coherence': float(np.mean(phase_coherences)),
+            'phase_stability': float(np.std(phase_coherences)),
+            'quantum_entanglement': float(spectral_coherence),
+            'component_correlation': float(np.mean(phase_coherences))
+        }
+    
+    def _detect_symbolic_patterns(self, pattern: np.ndarray) -> Dict[str, Any]:
+        """Detect emergent symbolic patterns in wave interference"""
+        # Find zero crossings (yin-yang dots analog)
+        zero_crossings = np.where(np.diff(np.signbit(pattern)))[0]
+        
+        # Detect periodic structures
+        autocorrelation = signal.correlate(pattern, pattern, mode='full')
+        autocorrelation = autocorrelation[len(autocorrelation)//2:]
+        
+        # Find peaks in autocorrelation (periodic patterns)
+        peaks, properties = signal.find_peaks(autocorrelation[:100], height=0.1)
+        
+        return {
+            'zero_crossings': len(zero_crossings),
+            'periodic_structures': len(peaks),
+            'pattern_complexity': float(np.std(pattern) / (np.mean(np.abs(pattern)) + 1e-12)),
+            'symbolic_confidence': min(0.95, len(zero_crossings) * 0.1 + len(peaks) * 0.05)
+        }
+
+class EnhancedLogosFieldEngine:
+    """Enhanced Logos field engine with historical integration"""
+    
+    def __init__(self, config: UnifiedFieldConfiguration):
+        self.config = config
+        self.field_cache = {}
+        self.gradient_cache = {}
+        self.EPSILON = config.quantum_cutoff
+        
+        # Enhanced cultural parameters
+        self.cultural_archetypes = {
+            'established': {'stability': 0.9, 'innovation': 0.3, 'resilience': 0.8},
+            'emergent': {'stability': 0.4, 'innovation': 0.9, 'resilience': 0.6},
+            'transitional': {'stability': 0.7, 'innovation': 0.6, 'resilience': 0.7},
+            'quantum_resonant': {'stability': 0.8, 'innovation': 0.8, 'resilience': 0.9}
+        }
+    
+    def initialize_unified_field(self, historical_phase: CosmicCyclePhase, 
+                               cultural_context: Dict[str, Any]) -> torch.Tensor:
+        """Initialize unified quantum-historical field"""
+        
+        # Generate base cultural field
+        cultural_field = self._generate_cultural_field(cultural_context)
+        
+        # Apply historical phase modulation
+        phase_modulation = self._get_phase_modulation(historical_phase)
+        modulated_field = cultural_field * phase_modulation
+        
+        # Add quantum fluctuations
+        quantum_fluctuations = self._generate_quantum_fluctuations(modulated_field.shape)
+        unified_field = modulated_field + 0.1 * quantum_fluctuations
+        
+        # Renormalize
+        unified_field = self._renormalize_field(unified_field)
+        
+        return unified_field
+    
+    def _generate_cultural_field(self, cultural_context: Dict[str, Any]) -> torch.Tensor:
+        """Generate cultural field with archetypal patterns"""
+        archetype = cultural_context.get('archetype', 'transitional')
+        archetype_params = self.cultural_archetypes[archetype]
+        
+        x, y = np.meshgrid(np.linspace(-2, 2, self.config.field_resolution[1]), 
+                          np.linspace(-2, 2, self.config.field_resolution[0]))
+        
+        field = torch.zeros(self.config.field_resolution, dtype=torch.float64)
+        
+        # Archetype-specific attractor patterns
+        if archetype == 'established':
+            attractors = [(0.5, 0.5, 1.2), (-0.5, -0.5, 1.1), (0.0, 0.0, 0.4)]
+        elif archetype == 'emergent':
+            attractors = [(0.3, 0.3, 0.8), (-0.3, -0.3, 0.7), (0.6, -0.2, 0.6), (-0.2, 0.6, 0.5)]
+        elif archetype == 'quantum_resonant':
+            attractors = [(0.4, 0.4, 1.0), (-0.4, -0.4, 0.9), (0.3, -0.3, 0.8), (-0.3, 0.3, 0.8)]
+        else:  # transitional
+            attractors = [(0.4, 0.4, 1.0), (-0.4, -0.4, 0.9), (0.0, 0.0, 0.7)]
+        
+        for cx, cy, amplitude in attractors:
+            # Adjust amplitude by archetype parameters
+            adjusted_amp = amplitude * archetype_params['stability']
+            sigma = 0.2 * archetype_params['resilience']
+            
+            gaussian = adjusted_amp * np.exp(-((x - cx)**2 + (y - cy)**2) / (2 * sigma**2))
+            field += torch.from_numpy(gaussian)
+        
+        return field
+    
+    def _get_phase_modulation(self, historical_phase: CosmicCyclePhase) -> float:
+        """Get historical phase modulation factor"""
+        phase_modulations = {
+            CosmicCyclePhase.POST_CATACLYSM_SURVIVAL: 0.5,
+            CosmicCyclePhase.KNOWLEDGE_RECOVERY: 0.7,
+            CosmicCyclePhase.CIVILIZATION_REBUILD: 0.9,
+            CosmicCyclePhase.DEFENSE_CONSTRUCTION: 1.1,
+            CosmicCyclePhase.CATASTROPHE_IMMINENCE: 1.3,
+            CosmicCyclePhase.QUANTUM_RESONANCE_PEAK: 1.5
+        }
+        return phase_modulations.get(historical_phase, 1.0)
+    
+    def _generate_quantum_fluctuations(self, shape: Tuple[int, int]) -> torch.Tensor:
+        """Generate quantum fluctuations with proper spectral properties"""
+        # Generate scale-invariant fluctuations (1/f noise)
+        base_noise = torch.randn(shape)
+        
+        # Apply Fourier filter for 1/f spectrum
+        noise_fft = torch.fft.fft2(base_noise)
+        frequencies = torch.fft.fftfreq(shape[0])[:, None] ** 2 + torch.fft.fftfreq(shape[1]) ** 2
+        frequencies[0, 0] = 1.0  # Avoid division by zero
+        
+        # 1/f filter
+        filter = 1.0 / torch.sqrt(frequencies)
+        filtered_fft = noise_fft * filter
+        
+        quantum_fluctuations = torch.fft.ifft2(filtered_fft).real
+        return quantum_fluctuations / torch.std(quantum_fluctuations)
+    
+    def _renormalize_field(self, field: torch.Tensor) -> torch.Tensor:
+        """Apply field renormalization"""
+        field_mean = torch.mean(field)
+        field_std = torch.std(field)
+        
+        if field_std > self.EPSILON:
+            normalized = (field - field_mean) / field_std
+        else:
+            normalized = field - field_mean
+            
+        return torch.tanh(normalized)  # Nonlinear compression
+    
+    def compute_field_metrics(self, field: torch.Tensor, 
+                            wave_interference: Dict[str, Any]) -> Dict[str, float]:
+        """Compute comprehensive field metrics"""
+        
+        # Basic field statistics
+        field_energy = torch.norm(field).item()
+        field_entropy = self._compute_field_entropy(field)
+        
+        # Topological features
+        topology_metrics = self._compute_topological_metrics(field)
+        
+        # Wave-field coupling
+        wave_coupling = self._compute_wave_field_coupling(field, wave_interference)
+        
+        # Cultural coherence
+        cultural_coherence = self._compute_cultural_coherence(field)
+        
+        return {
+            'field_energy': field_energy,
+            'field_entropy': field_entropy,
+            'topological_complexity': topology_metrics['complexity'],
+            'curvature_variance': topology_metrics['curvature_variance'],
+            'wave_field_coupling': wave_coupling,
+            'cultural_coherence': cultural_coherence,
+            'unified_stability': self._compute_unified_stability(field_energy, cultural_coherence, wave_coupling)
+        }
+    
+    def _compute_field_entropy(self, field: torch.Tensor) -> float:
+        """Compute Shannon entropy of field distribution"""
+        hist, bins = np.histogram(field.numpy().flatten(), bins=50, density=True)
+        hist = hist[hist > 0]  # Remove zero bins
+        entropy = -np.sum(hist * np.log(hist)) * (bins[1] - bins[0])
+        return float(entropy)
+    
+    def _compute_topological_metrics(self, field: torch.Tensor) -> Dict[str, float]:
+        """Compute topological metrics of field"""
+        try:
+            # Compute gradients
+            dy, dx = torch.gradient(field)
+            
+            # Compute second derivatives
+            dyy, dyx = torch.gradient(dy)
+            dxy, dxx = torch.gradient(dx)
+            
+            # Gaussian curvature approximation
+            gradient_squared = 1 + dx**2 + dy**2
+            gaussian_curvature = (dxx * dyy - dxy * dyx) / (gradient_squared**2)
+            
+            return {
+                'complexity': float(torch.std(gaussian_curvature).item()),
+                'curvature_variance': float(torch.var(gaussian_curvature).item()),
+                'gradient_magnitude': float(torch.mean(torch.sqrt(dx**2 + dy**2)).item())
+            }
+        except:
+            return {'complexity': 0.1, 'curvature_variance': 0.01, 'gradient_magnitude': 0.5}
+    
+    def _compute_wave_field_coupling(self, field: torch.Tensor, 
+                                   wave_interference: Dict[str, Any]) -> float:
+        """Compute coupling between field and wave interference"""
+        if 'interference_pattern' not in wave_interference:
+            return 0.5
+            
+        wave_pattern = wave_interference['interference_pattern']
+        
+        # Resize wave pattern to match field dimensions
+        if len(wave_pattern) != field.shape[0]:
+            wave_resized = np.interp(
+                np.linspace(0, len(wave_pattern)-1, field.shape[0]),
+                np.arange(len(wave_pattern)),
+                wave_pattern
+            )
+        else:
+            wave_resized = wave_pattern
+        
+        # Expand to 2D for correlation
+        wave_2d = np.outer(wave_resized, np.ones(field.shape[1]))
+        
+        # Compute correlation
+        correlation = np.corrcoef(field.numpy().flatten(), wave_2d.flatten())[0,1]
+        return float(abs(correlation))
+
+class QuantumHistoricalUnifiedEngine:
+    """Main unified engine integrating all components"""
+    
+    def __init__(self, config: UnifiedFieldConfiguration = None):
+        self.config = config or UnifiedFieldConfiguration()
+        self.wave_engine = AdvancedWaveInterferenceEngine(self.config)
+        self.field_engine = EnhancedLogosFieldEngine(self.config)
+        self.historical_cycles = self._initialize_historical_cycles()
+        
+    def _initialize_historical_cycles(self) -> List[Dict[str, Any]]:
+        """Initialize historical cycle database"""
+        return [
+            {
+                'cycle_number': 1,
+                'phase': CosmicCyclePhase.POST_CATACLYSM_SURVIVAL,
+                'cultural_archetype': 'emergent',
+                'defense_level': 0.2,
+                'knowledge_preservation': 0.1
+            },
+            {
+                'cycle_number': 2,
+                'phase': CosmicCyclePhase.KNOWLEDGE_RECOVERY, 
+                'cultural_archetype': 'transitional',
+                'defense_level': 0.4,
+                'knowledge_preservation': 0.3
+            },
+            {
+                'cycle_number': 3,
+                'phase': CosmicCyclePhase.CIVILIZATION_REBUILD,
+                'cultural_archetype': 'established',
+                'defense_level': 0.6,
+                'knowledge_preservation': 0.5
+            },
+            {
+                'cycle_number': 4, 
+                'phase': CosmicCyclePhase.DEFENSE_CONSTRUCTION,
+                'cultural_archetype': 'established',
+                'defense_level': 0.8,
+                'knowledge_preservation': 0.7
+            },
+            {
+                'cycle_number': 5,
+                'phase': CosmicCyclePhase.CATASTROPHE_IMMINENCE,
+                'cultural_archetype': 'quantum_resonant', 
+                'defense_level': 0.9,
+                'knowledge_preservation': 0.9
+            }
+        ]
+    
+    async def compute_unified_state(self, current_phase: CosmicCyclePhase = None,
+                                  cultural_context: Dict[str, Any] = None) -> QuantumHistoricalState:
+        """Compute complete unified quantum-historical state"""
+        
+        if current_phase is None:
+            current_phase = CosmicCyclePhase.CATASTROPHE_IMMINENCE
+            
+        if cultural_context is None:
+            cultural_context = {
+                'archetype': 'quantum_resonant',
+                'coherence_level': 0.8,
+                'innovation_factor': 0.7,
+                'temporal_alignment': 0.9
+            }
+        
+        # Compute wave interference patterns
+        wave_analysis = self.wave_engine.compute_quantum_wave_interference(current_phase)
+        
+        # Initialize unified field
+        unified_field = self.field_engine.initialize_unified_field(current_phase, cultural_context)
+        
+        # Compute field metrics
+        field_metrics = self.field_engine.compute_field_metrics(unified_field, wave_analysis)
+        
+        # Calculate defense preparedness from historical context
+        current_cycle = next((c for c in self.historical_cycles if c['phase'] == current_phase), None)
+        defense_preparedness = current_cycle['defense_level'] if current_cycle else 0.7
+        
+        # Create unified state
+        unified_state = QuantumHistoricalState(
+            field_tensor=unified_field,
+            historical_phase=current_phase,
+            cultural_coherence=field_metrics['cultural_coherence'],
+            wave_interference_pattern=wave_analysis['interference_pattern'],
+            temporal_correlation=field_metrics['wave_field_coupling'],
+            quantum_entanglement=wave_analysis['quantum_coherence']['quantum_entanglement'],
+            defense_preparedness=defense_preparedness
+        )
+        
+        return unified_state
+    
+    async def analyze_historical_trajectory(self) -> Dict[str, Any]:
+        """Analyze complete historical trajectory across cycles"""
+        
+        trajectory_analysis = {}
+        
+        for cycle in self.historical_cycles:
+            unified_state = await self.compute_unified_state(
+                cycle['phase'], 
+                {'archetype': cycle['cultural_archetype']}
+            )
+            
+            trajectory_analysis[cycle['cycle_number']] = {
+                'phase': cycle['phase'].value,
+                'unified_potential': unified_state.calculate_unified_potential(),
+                'field_metrics': self.field_engine.compute_field_metrics(
+                    unified_state.field_tensor,
+                    {'interference_pattern': unified_state.wave_interference_pattern}
+                ),
+                'defense_preparedness': cycle['defense_level'],
+                'knowledge_preservation': cycle['knowledge_preservation']
+            }
+        
+        # Calculate trajectory metrics
+        potentials = [data['unified_potential'] for data in trajectory_analysis.values()]
+        defense_levels = [data['defense_preparedness'] for data in trajectory_analysis.values()]
+        
+        return {
+            'trajectory_analysis': trajectory_analysis,
+            'progress_trend': self._calculate_progress_trend(potentials),
+            'defense_acceleration': self._calculate_acceleration(defense_levels),
+            'quantum_resonance_peak': max(potentials) if potentials else 0.0,
+            'optimal_preparedness_phase': self._find_optimal_phase(trajectory_analysis)
+        }
+    
+    def _calculate_progress_trend(self, values: List[float]) -> float:
+        """Calculate progress trend using linear regression"""
+        if len(values) < 2:
+            return 0.0
+        x = np.arange(len(values))
+        slope, _ = np.polyfit(x, values, 1)
+        return float(slope)
+    
+    def _calculate_acceleration(self, values: List[float]) -> float:
+        """Calculate acceleration of values"""
+        if len(values) < 3:
+            return 0.0
+        second_derivative = np.gradient(np.gradient(values))
+        return float(np.mean(second_derivative))
+    
+    def _find_optimal_phase(self, trajectory: Dict[str, Any]) -> str:
+        """Find phase with optimal preparedness"""
+        if not trajectory:
+            return "unknown"
+        
+        max_potential = -1
+        optimal_phase = "unknown"
+        
+        for cycle_num, data in trajectory.items():
+            if data['unified_potential'] > max_potential:
+                max_potential = data['unified_potential']
+                optimal_phase = data['phase']
+        
+        return optimal_phase
+
+# Advanced visualization and analysis
+class UnifiedAnalysisEngine:
+    """Advanced analysis and visualization engine"""
+    
+    def __init__(self):
+        self.metrics_history = []
+    
+    async def generate_comprehensive_report(self, unified_engine: QuantumHistoricalUnifiedEngine) -> Dict[str, Any]:
+        """Generate comprehensive analysis report"""
+        
+        # Compute current unified state
+        current_state = await unified_engine.compute_unified_state()
+        
+        # Analyze historical trajectory
+        trajectory = await unified_engine.analyze_historical_trajectory()
+        
+        # Calculate critical metrics
+        unified_potential = current_state.calculate_unified_potential()
+        defense_gap = 1.0 - current_state.defense_preparedness
+        temporal_alignment = current_state.temporal_correlation
+        
+        # Risk assessment
+        risk_factors = self._assess_risk_factors(current_state, trajectory)
+        
+        # Strategic recommendations
+        recommendations = self._generate_recommendations(
+            current_state, trajectory, risk_factors
+        )
+        
+        return {
+            'current_state': {
+                'unified_potential': unified_potential,
+                'defense_preparedness': current_state.defense_preparedness,
+                'cultural_coherence': current_state.cultural_coherence,
+                'quantum_entanglement': current_state.quantum_entanglement,
+                'temporal_alignment': temporal_alignment,
+                'historical_phase': current_state.historical_phase.value
+            },
+            'trajectory_analysis': trajectory,
+            'risk_assessment': risk_factors,
+            'strategic_recommendations': recommendations,
+            'overall_status': self._determine_overall_status(unified_potential, risk_factors),
+            'quantum_resonance_level': self._calculate_resonance_level(current_state, trajectory)
+        }
+    
+    def _assess_risk_factors(self, current_state: QuantumHistoricalState, 
+                           trajectory: Dict[str, Any]) -> Dict[str, float]:
+        """Assess risk factors based on current state and trajectory"""
+        
+        # Defense gap risk
+        defense_risk = 1.0 - current_state.defense_preparedness
+        
+        # Cultural coherence risk
+        coherence_risk = 1.0 - current_state.cultural_coherence
+        
+        # Historical pattern risk
+        historical_risk = 0.0
+        if 'progress_trend' in trajectory:
+            if trajectory['progress_trend'] < 0:
+                historical_risk = 0.3
+            elif trajectory['progress_trend'] < 0.1:
+                historical_risk = 0.1
+        
+        # Temporal misalignment risk
+        temporal_risk = 1.0 - current_state.temporal_correlation
+        
+        return {
+            'defense_gap_risk': defense_risk,
+            'coherence_risk': coherence_risk,
+            'historical_pattern_risk': historical_risk,
+            'temporal_misalignment_risk': temporal_risk,
+            'overall_risk': np.mean([defense_risk, coherence_risk, historical_risk, temporal_risk])
+        }
+    
+    def _generate_recommendations(self, current_state: QuantumHistoricalState,
+                                trajectory: Dict[str, Any],
+                                risk_factors: Dict[str, float]) -> List[str]:
+        """Generate strategic recommendations"""
+        
+        recommendations = []
+        
+        # Defense recommendations
+        if risk_factors['defense_gap_risk'] > 0.3:
+            recommendations.append("ACCELERATE quantum defense field deployment")
+            recommendations.append("ENHANCE space-based shielding infrastructure")
+        
+        # Cultural coherence recommendations
+        if risk_factors['coherence_risk'] > 0.4:
+            recommendations.append("STRENGTHEN cultural memory preservation systems")
+            recommendations.append("ACTIVATE global consciousness alignment protocols")
+        
+        # Historical pattern recommendations
+        if risk_factors['historical_pattern_risk'] > 0.2:
+            recommendations.append("IMPLEMENT historical cycle breakpoint strategies")
+            recommendations.append("DEVELOP quantum resonance amplification techniques")
+        
+        # Temporal alignment recommendations
+        if risk_factors['temporal_misalignment_risk'] > 0.3:
+            recommendations.append("OPTIMIZE wave interference temporal synchronization")
+            recommendations.append("CALIBRATE field oscillations to historical resonance frequencies")
+        
+        # Always include these
+        recommendations.extend([
+            "MAINTAIN quantum-historical field monitoring",
+            "PRESERVE knowledge across potential cycle transitions",
+            "DEVELOP adaptive defense response protocols",
+            "FOSTER global cooperation in unified field research"
+        ])
+        
+        return recommendations
+    
+    def _determine_overall_status(self, unified_potential: float, 
+                                risk_factors: Dict[str, float]) -> str:
+        """Determine overall system status"""
+        
+        if unified_potential > 0.8 and risk_factors['overall_risk'] < 0.2:
+            return "OPTIMAL"
+        elif unified_potential > 0.6 and risk_factors['overall_risk'] < 0.4:
+            return "STABLE"
+        elif unified_potential > 0.4 and risk_factors['overall_risk'] < 0.6:
+            return "DEVELOPING"
+        else:
+            return "CRITICAL"
+    
+    def _calculate_resonance_level(self, current_state: QuantumHistoricalState,
+                                 trajectory: Dict[str, Any]) -> float:
+        """Calculate quantum resonance level"""
+        
+        base_resonance = current_state.quantum_entanglement * current_state.temporal_correlation
+        
+        # Boost from historical trajectory
+        if 'quantum_resonance_peak' in trajectory:
+            historical_boost = trajectory['quantum_resonance_peak'] * 0.3
+        else:
+            historical_boost = 0.0
+            
+        # Defense alignment factor
+        defense_alignment = current_state.defense_preparedness * 0.4
+        
+        resonance_level = base_resonance + historical_boost + defense_alignment
+        return min(1.0, resonance_level)
+
+# Main execution
+async def main():
+    """Execute complete unified field analysis"""
+    
+    print("🌌 QUANTUM-HISTORICAL UNIFIED FIELD THEORY v6.0")
+    print("Integration of Logos Fields, Wave Physics, and Historical Analysis")
+    print("=" * 80)
+    
+    # Initialize engines
+    config = UnifiedFieldConfiguration()
+    unified_engine = QuantumHistoricalUnifiedEngine(config)
+    analysis_engine = UnifiedAnalysisEngine()
+    
+    # Generate comprehensive report
+    report = await analysis_engine.generate_comprehensive_report(unified_engine)
+    
+    # Display results
+    print(f"\n📊 CURRENT UNIFIED STATE:")
+    current = report['current_state']
+    for metric, value in current.items():
+        print(f"   {metric:25}: {value:10.6f}")
+    
+    print(f"\n⚠️  RISK ASSESSMENT:")
+    risks = report['risk_assessment']
+    for risk, value in risks.items():
+        level = "🔴 HIGH" if value > 0.5 else "🟡 MEDIUM" if value > 0.3 else "🟢 LOW"
+        print(f"   {risk:25}: {value:10.6f} {level}")
+    
+    print(f"\n🎯 STRATEGIC RECOMMENDATIONS:")
+    for i, recommendation in enumerate(report['strategic_recommendations'][:6], 1):
+        print(f"   {i:2}. {recommendation}")
+    
+    print(f"\n💫 OVERALL STATUS: {report['overall_status']}")
+    print(f"🌀 QUANTUM RESONANCE: {report['quantum_resonance_level']:.1%}")
+    
+    # Historical trajectory insights
+    trajectory = report['trajectory_analysis']
+    print(f"\n📈 HISTORICAL TRAJECTORY:")
+    print(f"   Progress Trend: {trajectory['progress_trend']:+.4f}")
+    print(f"   Defense Acceleration: {trajectory['defense_acceleration']:+.4f}")
+    print(f"   Optimal Phase: {trajectory['optimal_preparedness_phase']}")
+    
+    print(f"\n🌠 ULTIMATE INSIGHT:")
+    print("   We are operating at the convergence point of:")
+    print("   • Quantum field dynamics")
+    print("   • Wave interference physics") 
+    print("   • 140,000-year historical cycles")
+    print("   • Cultural coherence patterns")
+    print("   This unified framework enables unprecedented")
+    print("   predictive capability and strategic preparedness.")
+
+if __name__ == "__main__":
+    asyncio.run(main())