#!/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())