ADVANCED_CONSCIOUSNESS

import numpy as np
import pandas as pd
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional, Any
from enum import Enum
import math
from scipy import spatial, stats
import networkx as nx
from datetime import datetime
import json
from collections import defaultdict
import warnings
warnings.filterwarnings('ignore')

class ConsciousnessState(Enum):
    DELTA = "Deep Unconscious"      # 0.5-4 Hz
    THETA = "Subconscious"          # 4-8 Hz  
    ALPHA = "Relaxed Awareness"     # 8-12 Hz
    BETA = "Active Cognition"       # 12-30 Hz
    GAMMA = "Transcendent Unity"    # 30-100 Hz
    SCHUMANN = "Earth Resonance"    # 7.83 Hz

@dataclass
class QuantumSignature:
    """Qualia state vector for consciousness experience"""
    coherence: float                    # 0-1, quantum coherence level
    entanglement: float                 # 0-1, non-local connectivity  
    qualia_vector: np.ndarray           # 5D experience vector [visual, emotional, cognitive, somatic, spiritual]
    resonance_frequency: float          # Hz, characteristic resonance
    decoherence_time: float = 1.0       # Time until quantum state collapse
    nonlocal_correlation: float = 0.5   # EPR-type correlations
    
    def calculate_qualia_distance(self, other: 'QuantumSignature') -> float:
        """Calculate distance between qualia experiences using cosine similarity"""
        return spatial.distance.cosine(self.qualia_vector, other.qualia_vector)
    
    def entanglement_entropy(self) -> float:
        """Calculate von Neumann entropy of quantum state"""
        return -self.coherence * math.log(self.coherence + 1e-10) if self.coherence > 0 else 0
    
    def evolve_state(self, time: float) -> 'QuantumSignature':
        """Evolve quantum state over time with decoherence"""
        decay = math.exp(-time / self.decoherence_time)
        return QuantumSignature(
            coherence=self.coherence * decay,
            entanglement=self.entanglement * decay,
            qualia_vector=self.qualia_vector * decay,
            resonance_frequency=self.resonance_frequency,
            decoherence_time=self.decoherence_time,
            nonlocal_correlation=self.nonlocal_correlation * decay
        )

@dataclass 
class NeuralCorrelate:
    """Brain region and frequency correlates with advanced connectivity"""
    primary_regions: List[str]           # e.g., ["PFC", "DMN", "Visual Cortex"]
    frequency_band: ConsciousnessState
    cross_hemispheric_sync: float        # 0-1
    neuroplasticity_impact: float        # 0-1
    default_mode_engagement: float = 0.5 # 0-1, DMN involvement
    salience_network_coupling: float = 0.5 # 0-1, SN connectivity
    thalamocortical_resonance: float = 0.5 # 0-1, thalamic gating
    
    @property
    def neural_efficiency(self) -> float:
        """Calculate overall neural processing efficiency"""
        weights = [0.3, 0.25, 0.2, 0.15, 0.1]
        factors = [
            self.cross_hemispheric_sync,
            self.neuroplasticity_impact,
            self.default_mode_engagement,
            self.salience_network_coupling,
            self.thalamocortical_resonance
        ]
        return sum(w * f for w, f in zip(weights, factors))

@dataclass
class ArchetypalStrand:
    """Symbolic DNA strand representing cultural genotype with enhanced metrics"""
    name: str
    symbolic_form: str                   # e.g., "Lion", "Sunburst"
    temporal_depth: int                  # years in cultural record
    spatial_distribution: float          # 0-1 global prevalence
    preservation_rate: float             # 0-1 iconographic fidelity
    quantum_coherence: float             # 0-1 symbolic stability
    cultural_penetration: float = 0.5    # 0-1, depth in cultural psyche
    transformative_potential: float = 0.5 # 0-1, capacity for change
    num_variants: int = 1                # Number of cultural variants
    
    @property
    def symbolic_strength(self) -> float:
        """Calculate overall archetypal strength with enhanced weighting"""
        weights = [0.20, 0.20, 0.15, 0.15, 0.15, 0.15]  # Enhanced weighting
        factors = [
            self.temporal_depth/10000, 
            self.spatial_distribution, 
            self.preservation_rate, 
            self.quantum_coherence,
            self.cultural_penetration,
            self.transformative_potential
        ]
        return min(1.0, sum(w * f for w, f in zip(weights, factors)))
    
    @property
    def cultural_resilience(self) -> float:
        """Calculate resilience against cultural erosion"""
        return (self.preservation_rate * 0.4 + 
                self.temporal_depth/10000 * 0.3 + 
                self.quantum_coherence * 0.3)

class ConsciousnessTechnology:
    """Advanced neuro-symbolic interface technology with state tracking"""
    
    def __init__(self, name: str, archetype: ArchetypalStrand, 
                 neural_correlate: NeuralCorrelate, quantum_sig: QuantumSignature):
        self.name = name
        self.archetype = archetype
        self.neural_correlate = neural_correlate
        self.quantum_signature = quantum_sig
        self.activation_history = []
        self.performance_metrics = {
            'avg_activation_intensity': 0.0,
            'successful_activations': 0,
            'neural_efficiency_trend': [],
            'quantum_coherence_trend': []
        }
        
    def activate(self, intensity: float = 1.0, duration: float = 1.0) -> Dict[str, Any]:
        """Advanced activation with duration and performance tracking"""
        # Calculate dynamic effects based on duration and intensity
        neural_boost = math.tanh(intensity * duration)
        quantum_amplification = intensity * (1 - math.exp(-duration))
        
        activation = {
            'timestamp': datetime.now(),
            'archetype': self.archetype.name,
            'intensity': intensity,
            'duration': duration,
            'neural_state': self.neural_correlate.frequency_band,
            'neural_efficiency': self.neural_correlate.neural_efficiency * (1 + neural_boost),
            'quantum_coherence': self.quantum_signature.coherence * (1 + quantum_amplification),
            'qualia_experience': self.quantum_signature.qualia_vector * intensity,
            'entanglement_level': self.quantum_signature.entanglement * intensity,
            'performance_score': self._calculate_performance_score(intensity, duration)
        }
        
        self.activation_history.append(activation)
        self._update_performance_metrics(activation)
        return activation
    
    def _calculate_performance_score(self, intensity: float, duration: float) -> float:
        """Calculate activation performance score"""
        neural_component = self.neural_correlate.neural_efficiency * intensity
        quantum_component = self.quantum_signature.coherence * duration
        return (neural_component * 0.6 + quantum_component * 0.4)
    
    def _update_performance_metrics(self, activation: Dict):
        """Update long-term performance tracking"""
        self.performance_metrics['successful_activations'] += 1
        self.performance_metrics['avg_activation_intensity'] = (
            self.performance_metrics['avg_activation_intensity'] * 0.9 + 
            activation['intensity'] * 0.1
        )
        self.performance_metrics['neural_efficiency_trend'].append(
            activation['neural_efficiency']
        )
        self.performance_metrics['quantum_coherence_trend'].append(
            activation['quantum_coherence']
        )
    
    def get_performance_report(self) -> Dict[str, Any]:
        """Generate comprehensive performance analysis"""
        trends = self.performance_metrics
        if len(trends['neural_efficiency_trend']) > 1:
            neural_slope = stats.linregress(
                range(len(trends['neural_efficiency_trend'])),
                trends['neural_efficiency_trend']
            ).slope
            quantum_slope = stats.linregress(
                range(len(trends['quantum_coherence_trend'])),
                trends['quantum_coherence_trend']
            ).slope
        else:
            neural_slope = quantum_slope = 0.0
            
        return {
            'total_activations': trends['successful_activations'],
            'average_intensity': trends['avg_activation_intensity'],
            'neural_efficiency_trend': neural_slope,
            'quantum_coherence_trend': quantum_slope,
            'overall_health': (trends['avg_activation_intensity'] * 0.4 + 
                             (1 if neural_slope > 0 else 0) * 0.3 +
                             (1 if quantum_slope > 0 else 0) * 0.3)
        }

class CulturalPhylogenetics:
    """Advanced evolutionary analysis of symbolic DNA with Bayesian methods"""
    
    def __init__(self):
        self.cladograms = {}
        self.ancestral_reconstructions = {}
        self.symbolic_traits = [
            "solar_association", "predatory_nature", "sovereignty", 
            "transcendence", "protection", "wisdom", "chaos", "creation",
            "fertility", "destruction", "renewal", "guidance"
        ]
        self.trait_correlations = np.eye(len(self.symbolic_traits))
    
    def build_cladogram(self, archetypes: List[ArchetypalStrand], 
                       trait_matrix: np.ndarray, 
                       method: str = 'bayesian') -> nx.DiGraph:
        """Build evolutionary tree using multiple methods"""
        
        if method == 'bayesian':
            return self._bayesian_phylogeny(archetypes, trait_matrix)
        elif method == 'neighbor_joining':
            return self._neighbor_joining(archetypes, trait_matrix)
        else:  # minimum_spanning_tree
            return self._minimum_spanning_tree(archetypes, trait_matrix)
    
    def _bayesian_phylogeny(self, archetypes: List[ArchetypalStrand], 
                          trait_matrix: np.ndarray) -> nx.DiGraph:
        """Bayesian phylogenetic inference"""
        G = nx.DiGraph()
        n = len(archetypes)
        
        # Calculate Bayesian posterior probabilities for relationships
        for i, arch1 in enumerate(archetypes):
            for j, arch2 in enumerate(archetypes):
                if i != j:
                    # Bayesian distance incorporating prior knowledge
                    likelihood = math.exp(-spatial.distance.euclidean(
                        trait_matrix[i], trait_matrix[j]
                    ))
                    prior = self._calculate_phylogenetic_prior(arch1, arch2)
                    posterior = likelihood * prior
                    
                    G.add_edge(arch1.name, arch2.name, 
                              weight=1/posterior,  # Convert to distance
                              probability=posterior)
        
        # Find maximum likelihood tree
        mst = nx.minimum_spanning_tree(G, weight='weight')
        self.cladograms[tuple(a.name for a in archetypes)] = mst
        return mst
    
    def _neighbor_joining(self, archetypes: List[ArchetypalStrand],
                         trait_matrix: np.ndarray) -> nx.DiGraph:
        """Neighbor-joining algorithm for phylogenetic reconstruction"""
        # Simplified implementation
        G = nx.DiGraph()
        distances = spatial.distance.pdist(trait_matrix, metric='euclidean')
        distance_matrix = spatial.distance.squareform(distances)
        
        # Build tree using hierarchical clustering
        from scipy.cluster import hierarchy
        Z = hierarchy.linkage(distance_matrix, method='average')
        
        # Convert to networkx graph
        # This is a simplified conversion - full NJ would be more complex
        for i in range(len(archetypes)-1):
            G.add_edge(archetypes[int(Z[i,0])].name, 
                      archetypes[int(Z[i,1])].name, 
                      weight=Z[i,2])
        
        self.cladograms[tuple(a.name for a in archetypes)] = G
        return G
    
    def _minimum_spanning_tree(self, archetypes: List[ArchetypalStrand],
                              trait_matrix: np.ndarray) -> nx.DiGraph:
        """Traditional minimum spanning tree approach"""
        G = nx.Graph()
        
        for i, arch1 in enumerate(archetypes):
            for j, arch2 in enumerate(archetypes):
                if i != j:
                    distance = spatial.distance.euclidean(
                        trait_matrix[i], trait_matrix[j]
                    )
                    G.add_edge(arch1.name, arch2.name, weight=distance)
        
        mst = nx.minimum_spanning_tree(G)
        self.cladograms[tuple(a.name for a in archetypes)] = mst
        return mst
    
    def _calculate_phylogenetic_prior(self, arch1: ArchetypalStrand, 
                                    arch2: ArchetypalStrand) -> float:
        """Calculate Bayesian prior based on temporal and spatial overlap"""
        temporal_overlap = 1 - abs(arch1.temporal_depth - arch2.temporal_depth) / 10000
        spatial_similarity = 1 - abs(arch1.spatial_distribution - arch2.spatial_distribution)
        return (temporal_overlap * 0.6 + spatial_similarity * 0.4)
    
    def find_common_ancestor(self, archetype1: str, archetype2: str, 
                           method: str = 'lca') -> Optional[str]:
        """Find most recent common ancestor using multiple methods"""
        for cladogram in self.cladograms.values():
            if archetype1 in cladogram and archetype2 in cladogram:
                try:
                    if method == 'lca':
                        # Use networkx's LCA for rooted trees
                        if hasattr(nx, 'lowest_common_ancestor'):
                            return nx.lowest_common_ancestor(cladogram, archetype1, archetype2)
                        else:
                            # Fallback method
                            path1 = nx.shortest_path(cladogram, source=list(cladogram.nodes())[0], target=archetype1)
                            path2 = nx.shortest_path(cladogram, source=list(cladogram.nodes())[0], target=archetype2)
                            common = [n for n in path1 if n in path2]
                            return common[-1] if common else None
                    else:
                        # Shortest path midpoint
                        path = nx.shortest_path(cladogram, archetype1, archetype2)
                        return path[len(path)//2] if len(path) > 2 else path[0]
                except (nx.NetworkXNoPath, nx.NodeNotFound):
                    continue
        return None
    
    def calculate_evolutionary_rate(self, archetype: str) -> float:
        """Calculate evolutionary rate of an archetype"""
        # Simplified evolutionary rate calculation
        for cladogram in self.cladograms.values():
            if archetype in cladogram:
                # Sum of branch lengths from root
                try:
                    root = [n for n in cladogram.nodes() if cladogram.in_degree(n) == 0][0]
                    path = nx.shortest_path(cladogram, root, archetype)
                    total_length = sum(cladogram[u][v]['weight'] for u, v in zip(path[:-1], path[1:]))
                    return total_length / len(path) if path else 0.0
                except (IndexError, nx.NetworkXNoPath):
                    continue
        return 0.0

class GeospatialArchetypalMapper:
    """Advanced GIS-based symbolic distribution analysis with temporal dynamics"""
    
    def __init__(self):
        self.archetype_distributions = {}
        self.mutation_hotspots = []
        self.diffusion_models = {}
        self.spatial_correlations = {}
    
    def add_archetype_distribution(self, archetype: str, 
                                 coordinates: List[Tuple[float, float]], 
                                 intensity: List[float], 
                                 epoch: str,
                                 uncertainty: List[float] = None):
        """Add spatial data with uncertainty estimates"""
        key = f"{archetype}_{epoch}"
        
        if uncertainty is None:
            uncertainty = [0.1] * len(coordinates)  # Default uncertainty
        
        self.archetype_distributions[key] = {
            'coordinates': coordinates,
            'intensity': intensity,
            'uncertainty': uncertainty,
            'epoch': epoch,
            'centroid': self._calculate_centroid(coordinates, intensity),
            'spread': self._calculate_spatial_spread(coordinates, intensity),
            'density': self._calculate_point_density(coordinates, intensity)
        }
        
        self._update_diffusion_model(archetype, coordinates, intensity, epoch)
    
    def _calculate_centroid(self, coords: List[Tuple], intensities: List[float]) -> Tuple[float, float]:
        """Calculate intensity-weighted centroid with robustness"""
        if not coords:
            return (0, 0)
        try:
            weighted_lat = sum(c[0] * i for c, i in zip(coords, intensities)) / sum(intensities)
            weighted_lon = sum(c[1] * i for c, i in zip(coords, intensities)) / sum(intensities)
            return (weighted_lat, weighted_lon)
        except ZeroDivisionError:
            return (np.mean([c[0] for c in coords]), np.mean([c[1] for c in coords]))
    
    def _calculate_spatial_spread(self, coords: List[Tuple], intensities: List[float]) -> float:
        """Calculate spatial spread (standard distance)"""
        if len(coords) < 2:
            return 0.0
        centroid = self._calculate_centroid(coords, intensities)
        distances = [math.sqrt((c[0]-centroid[0])**2 + (c[1]-centroid[1])**2) for c in coords]
        return np.std(distances)
    
    def _calculate_point_density(self, coords: List[Tuple], intensities: List[float]) -> float:
        """Calculate point density metric"""
        if not coords:
            return 0.0
        spread = self._calculate_spatial_spread(coords, intensities)
        total_intensity = sum(intensities)
        return total_intensity / (spread + 1e-10)  # Avoid division by zero
    
    def _update_diffusion_model(self, archetype: str, coords: List[Tuple], 
                              intensities: List[float], epoch: str):
        """Update diffusion model for archetype spread"""
        if archetype not in self.diffusion_models:
            self.diffusion_models[archetype] = {}
        
        centroid = self._calculate_centroid(coords, intensities)
        spread = self._calculate_spatial_spread(coords, intensities)
        
        self.diffusion_models[archetype][epoch] = {
            'centroid': centroid,
            'spread': spread,
            'intensity_sum': sum(intensities),
            'point_count': len(coords)
        }
    
    def detect_mutation_hotspots(self, threshold: float = 0.8, 
                               method: str = 'variance'):
        """Advanced hotspot detection using multiple methods"""
        self.mutation_hotspots.clear()
        
        for key, data in self.archetype_distributions.items():
            if method == 'variance':
                score = np.var(data['intensity'])
            elif method == 'spatial_autocorrelation':
                score = self._calculate_morans_i(data['coordinates'], data['intensity'])
            elif method == 'getis_ord':
                score = self._calculate_getis_ord(data['coordinates'], data['intensity'])
            else:
                score = np.var(data['intensity'])
            
            if score > threshold:
                self.mutation_hotspots.append({
                    'location': key,
                    'score': score,
                    'method': method,
                    'epoch': data['epoch'],
                    'centroid': data['centroid'],
                    'significance': self._calculate_hotspot_significance(score, threshold)
                })
        
        # Sort by significance
        self.mutation_hotspots.sort(key=lambda x: x['significance'], reverse=True)
    
    def _calculate_morans_i(self, coords: List[Tuple], intensities: List[float]) -> float:
        """Calculate Moran's I for spatial autocorrelation (simplified)"""
        if len(coords) < 2:
            return 0.0
        # Simplified implementation
        centroid = self._calculate_centroid(coords, intensities)
        deviations = [i - np.mean(intensities) for i in intensities]
        spatial_lag = sum(d1 * d2 for d1 in deviations for d2 in deviations) / len(deviations)**2
        return abs(spatial_lag)  # Simplified
    
    def _calculate_getis_ord(self, coords: List[Tuple], intensities: List[float]) -> float:
        """Calculate Getis-Ord Gi* statistic (simplified)"""
        if len(coords) < 2:
            return 0.0
        # Simplified hot spot detection
        mean_intensity = np.mean(intensities)
        std_intensity = np.std(intensities)
        if std_intensity == 0:
            return 0.0
        return max(0, (max(intensities) - mean_intensity) / std_intensity)
    
    def _calculate_hotspot_significance(self, score: float, threshold: float) -> float:
        """Calculate statistical significance of hotspot"""
        return min(1.0, (score - threshold) / (1 - threshold)) if score > threshold else 0.0
    
    def predict_archetype_spread(self, archetype: str, future_epochs: int = 5) -> List[Dict]:
        """Predict future spatial distribution"""
        if archetype not in self.diffusion_models:
            return []
        
        epochs = sorted(self.diffusion_models[archetype].keys())
        if len(epochs) < 2:
            return []
        
        # Simple linear extrapolation of centroid movement and spread
        recent_data = [self.diffusion_models[archetype][e] for e in epochs[-2:]]
        centroid_drift = (
            recent_data[1]['centroid'][0] - recent_data[0]['centroid'][0],
            recent_data[1]['centroid'][1] - recent_data[0]['centroid'][1]
        )
        spread_growth = recent_data[1]['spread'] - recent_data[0]['spread']
        
        predictions = []
        current_centroid = recent_data[1]['centroid']
        current_spread = recent_data[1]['spread']
        
        for i in range(1, future_epochs + 1):
            predicted_centroid = (
                current_centroid[0] + centroid_drift[0] * i,
                current_centroid[1] + centroid_drift[1] * i
            )
            predicted_spread = current_spread + spread_growth * i
            
            predictions.append({
                'epoch': f'future_{i}',
                'predicted_centroid': predicted_centroid,
                'predicted_spread': predicted_spread,
                'confidence': max(0, 1.0 - i * 0.2)  # Decreasing confidence
            })
        
        return predictions

class ArchetypalEntropyIndex:
    """Advanced measurement of symbolic degradation and mutation rates"""
    
    def __init__(self):
        self.entropy_history = {}
        self.complexity_metrics = {}
        self.stability_thresholds = {
            'low_entropy': 0.3,
            'medium_entropy': 0.6,
            'high_entropy': 0.8
        }
    
    def calculate_entropy(self, archetype: ArchetypalStrand, 
                         historical_forms: List[str],
                         meaning_shifts: List[float],
                         contextual_factors: Dict[str, float] = None) -> Dict[str, float]:
        """Advanced entropy calculation with multiple dimensions"""
        
        if contextual_factors is None:
            contextual_factors = {
                'cultural_turbulence': 0.5,
                'technological_disruption': 0.5,
                'social_volatility': 0.5
            }
        
        # Form entropy (morphological changes with complexity weighting)
        if len(historical_forms) > 1:
            form_complexity = self._calculate_form_complexity(historical_forms)
            form_changes = len(set(historical_forms)) / len(historical_forms)
            form_entropy = form_changes * (1 + form_complexity * 0.5)
        else:
            form_entropy = 0
            form_complexity = 0
        
        # Meaning entropy (semantic drift with contextual sensitivity)
        meaning_entropy = np.std(meaning_shifts) if meaning_shifts else 0
        contextual_sensitivity = sum(contextual_factors.values()) / len(contextual_factors)
        meaning_entropy_adj = meaning_entropy * (1 + contextual_sensitivity * 0.3)
        
        # Structural entropy (internal consistency)
        structural_entropy = self._calculate_structural_entropy(archetype, historical_forms)
        
        # Combined entropy scores
        total_entropy = (form_entropy * 0.4 + 
                        meaning_entropy_adj * 0.4 + 
                        structural_entropy * 0.2)
        
        # Stability classification
        stability_level = self._classify_stability(total_entropy)
        
        result = {
            'total_entropy': total_entropy,
            'form_entropy': form_entropy,
            'meaning_entropy': meaning_entropy_adj,
            'structural_entropy': structural_entropy,
            'form_complexity': form_complexity,
            'stability_level': stability_level,
            'mutation_risk': self._calculate_mutation_risk(total_entropy, contextual_factors),
            'resilience_score': 1 - total_entropy
        }
        
        self.entropy_history[archetype.name] = {
            **result,
            'contextual_factors': contextual_factors,
            'last_updated': datetime.now(),
            'historical_trend': self._update_historical_trend(archetype.name, total_entropy)
        }
        
        self.complexity_metrics[archetype.name] = form_complexity
        
        return result
    
    def _calculate_form_complexity(self, forms: List[str]) -> float:
        """Calculate complexity of form variations"""
        if not forms:
            return 0.0
        
        # Simple complexity metric based on variation and length
        avg_length = np.mean([len(f) for f in forms])
        variation_ratio = len(set(forms)) / len(forms)
        
        return min(1.0, (avg_length / 100 * 0.3 + variation_ratio * 0.7))
    
    def _calculate_structural_entropy(self, archetype: ArchetypalStrand, 
                                    forms: List[str]) -> float:
        """Calculate structural entropy based on internal consistency"""
        # Measure how well the archetype maintains structural integrity
        coherence_penalty = 1 - archetype.quantum_coherence
        preservation_penalty = 1 - archetype.preservation_rate
        
        return (coherence_penalty * 0.6 + preservation_penalty * 0.4)
    
    def _classify_stability(self, entropy: float) -> str:
        """Classify archetype stability level"""
        if entropy <= self.stability_thresholds['low_entropy']:
            return 'high_stability'
        elif entropy <= self.stability_thresholds['medium_entropy']:
            return 'medium_stability'
        elif entropy <= self.stability_thresholds['high_entropy']:
            return 'low_stability'
        else:
            return 'critical_instability'
    
    def _calculate_mutation_risk(self, entropy: float, 
                               contextual_factors: Dict[str, float]) -> float:
        """Calculate risk of significant mutation"""
        base_risk = entropy
        contextual_risk = sum(contextual_factors.values()) / len(contextual_factors)
        
        return min(1.0, base_risk * 0.7 + contextual_risk * 0.3)
    
    def _update_historical_trend(self, archetype_name: str, current_entropy: float) -> List[float]:
        """Update historical entropy trend"""
        if archetype_name not in self.entropy_history:
            return [current_entropy]
        
        current_trend = self.entropy_history[archetype_name].get('historical_trend', [])
        current_trend.append(current_entropy)
        
        # Keep only last 10 readings
        return current_trend[-10:]
    
    def get_high_entropy_archetypes(self, threshold: float = 0.7) -> List[Dict]:
        """Get archetypes with high mutation rates with detailed analysis"""
        high_entropy = []
        
        for name, data in self.entropy_history.items():
            if data['total_entropy'] > threshold:
                high_entropy.append({
                    'archetype': name,
                    'total_entropy': data['total_entropy'],
                    'stability_level': data['stability_level'],
                    'mutation_risk': data['mutation_risk'],
                    'resilience_score': data['resilience_score'],
                    'trend_direction': self._calculate_trend_direction(data['historical_trend'])
                })
        
        return sorted(high_entropy, key=lambda x: x['mutation_risk'], reverse=True)
    
    def _calculate_trend_direction(self, trend: List[float]) -> str:
        """Calculate direction of entropy trend"""
        if len(trend) < 2:
            return 'stable'
        
        slope = stats.linregress(range(len(trend)), trend).slope
        
        if slope > 0.01:
            return 'increasing'
        elif slope < -0.01:
            return 'decreasing'
        else:
            return 'stable'
    
    def get_entropy_network(self) -> nx.Graph:
        """Build network of archetypes based on entropy correlations"""
        G = nx.Graph()
        
        archetype_names = list(self.entropy_history.keys())
        
        for i, arch1 in enumerate(archetype_names):
            for j, arch2 in enumerate(archetype_names):
                if i < j:  # Avoid duplicate pairs
                    # Calculate entropy correlation
                    trend1 = self.entropy_history[arch1].get('historical_trend', [0])
                    trend2 = self.entropy_history[arch2].get('historical_trend', [0])
                    
                    # Pad with zeros if different lengths
                    max_len = max(len(trend1), len(trend2))
                    trend1_padded = trend1 + [0] * (max_len - len(trend1))
                    trend2_padded = trend2 + [0] * (max_len - len(trend2))
                    
                    if len(trend1_padded) > 1:
                        correlation = np.corrcoef(trend1_padded, trend2_padded)[0,1]
                        if not np.isnan(correlation) and abs(correlation) > 0.3:
                            G.add_edge(arch1, arch2, 
                                      weight=abs(correlation),
                                      correlation=correlation)
        
        return G

class CrossCulturalResonanceMatrix:
    """Advanced comparison of archetypal strength across civilizations"""
    
    def __init__(self):
        self.civilization_data = {}
        self.resonance_matrix = {}
        self.cultural_clusters = {}
        self.resonance_network = nx.Graph()
    
    def add_civilization_archetype(self, civilization: str, 
                                 archetype: str, 
                                 strength: float, 
                                 neural_impact: float,
                                 cultural_context: Dict[str, float] = None):
        """Add archetype data with cultural context"""
        if civilization not in self.civilization_data:
            self.civilization_data[civilization] = {}
        
        if cultural_context is None:
            cultural_context = {
                'technological_level': 0.5,
                'spiritual_emphasis': 0.5,
                'individualism': 0.5,
                'ecological_connection': 0.5
            }
        
        self.civilization_data[civilization][archetype] = {
            'strength': strength,
            'neural_impact': neural_impact,
            'cultural_context': cultural_context,
            'resonance_potential': self._calculate_resonance_potential(strength, neural_impact, cultural_context)
        }
    
    def _calculate_resonance_potential(self, strength: float, 
                                     neural_impact: float,
                                     cultural_context: Dict[str, float]) -> float:
        """Calculate overall resonance potential"""
        base_potential = (strength * 0.5 + neural_impact * 0.5)
        cultural_modifier = sum(cultural_context.values()) / len(cultural_context)
        
        return base_potential * (0.7 + cultural_modifier * 0.3)
    
    def calculate_cross_resonance(self, arch1: str, arch2: str, 
                                method: str = 'pearson') -> Dict[str, float]:
        """Calculate resonance between archetypes using multiple methods"""
        strengths_1 = []
        strengths_2 = []
        neural_impacts_1 = []
        neural_impacts_2 = []
        
        for civ_data in self.civilization_data.values():
            if arch1 in civ_data and arch2 in civ_data:
                strengths_1.append(civ_data[arch1]['strength'])
                strengths_2.append(civ_data[arch2]['strength'])
                neural_impacts_1.append(civ_data[arch1]['neural_impact'])
                neural_impacts_2.append(civ_data[arch2]['neural_impact'])
        
        results = {}
        
        if len(strengths_1) > 1:
            if method == 'pearson':
                strength_resonance = np.corrcoef(strengths_1, strengths_2)[0,1]
                neural_resonance = np.corrcoef(neural_impacts_1, neural_impacts_2)[0,1]
            elif method == 'spearman':
                strength_resonance = stats.spearmanr(strengths_1, strengths_2)[0]
                neural_resonance = stats.spearmanr(neural_impacts_1, neural_impacts_2)[0]
            else:  # cosine similarity
                strength_resonance = 1 - spatial.distance.cosine(strengths_1, strengths_2)
                neural_resonance = 1 - spatial.distance.cosine(neural_impacts_1, neural_impacts_2)
            
            results = {
                'strength_resonance': max(0, strength_resonance) if not np.isnan(strength_resonance) else 0,
                'neural_resonance': max(0, neural_resonance) if not np.isnan(neural_resonance) else 0,
                'overall_resonance': (max(0, strength_resonance) * 0.6 + max(0, neural_resonance) * 0.4)
            }
        else:
            results = {
                'strength_resonance': 0.0,
                'neural_resonance': 0.0,
                'overall_resonance': 0.0
            }
        
        return results
    
    def build_resonance_network(self, threshold: float = 0.3) -> nx.Graph:
        """Build advanced resonance network with community detection"""
        G = nx.Graph()
        archetypes = set()
        
        # Get all unique archetypes
        for civ_data in self.civilization_data.values():
            archetypes.update(civ_data.keys())
        
        # Calculate resonances and build network
        for arch1 in archetypes:
            for arch2 in archetypes:
                if arch1 != arch2:
                    resonance_data = self.calculate_cross_resonance(arch1, arch2)
                    overall_resonance = resonance_data['overall_resonance']
                    
                    if overall_resonance > threshold:
                        G.add_edge(arch1, arch2, 
                                  weight=overall_resonance,
                                  strength_resonance=resonance_data['strength_resonance'],
                                  neural_resonance=resonance_data['neural_resonance'])
        
        # Detect communities in the resonance network
        if len(G.nodes()) > 0:
            try:
                communities = nx.algorithms.community.greedy_modularity_communities(G)
                for i, community in enumerate(communities):
                    for node in community:
                        G.nodes[node]['community'] = i
                self.cultural_clusters = {i: list(community) for i, community in enumerate(communities)}
            except:
                # Fallback if community detection fails
                for node in G.nodes():
                    G.nodes[node]['community'] = 0
        
        self.resonance_network = G
        return G
    
    def find_cultural_clusters(self) -> Dict[int, List[str]]:
        """Identify clusters of culturally resonant archetypes"""
        if not self.cultural_clusters:
            self.build_resonance_network()
        return self.cultural_clusters
    
    def calculate_civilization_similarity(self, civ1: str, civ2: str) -> float:
        """Calculate similarity between two civilizations"""
        if civ1 not in self.civilization_data or civ2 not in self.civilization_data:
            return 0.0
        
        common_archetypes = set(self.civilization_data[civ1].keys()) & set(self.civilization_data[civ2].keys())
        if not common_archetypes:
            return 0.0
        
        similarities = []
        for arch in common_archetypes:
            strength_sim = 1 - abs(self.civilization_data[civ1][arch]['strength'] - 
                                 self.civilization_data[civ2][arch]['strength'])
            neural_sim = 1 - abs(self.civilization_data[civ1][arch]['neural_impact'] - 
                               self.civilization_data[civ2][arch]['neural_impact'])
            similarities.append((strength_sim + neural_sim) / 2)
        
        return np.mean(similarities) if similarities else 0.0
    
    def get_universal_archetypes(self, threshold: float = 0.7) -> List[str]:
        """Find archetypes present in most civilizations"""
        civ_count = len(self.civilization_data)
        if civ_count == 0:
            return []
        
        archetype_frequency = defaultdict(int)
        for civ_data in self.civilization_data.values():
            for arch in civ_data.keys():
                archetype_frequency[arch] += 1
        
        universal = [arch for arch, count in archetype_frequency.items() 
                    if count / civ_count >= threshold]
        return sorted(universal, key=lambda x: archetype_frequency[x], reverse=True)

class SymbolicMutationEngine:
    """Advanced prediction of archetype evolution under cultural pressure"""
    
    def __init__(self):
        self.transformation_rules = {
            'weapon': ['tool', 'symbol', 'concept', 'algorithm'],
            'physical': ['digital', 'virtual', 'neural', 'quantum'],
            'individual': ['networked', 'collective', 'distributed', 'holographic'],
            'concrete': ['abstract', 'algorithmic', 'quantum', 'consciousness_based'],
            'hierarchical': ['networked', 'decentralized', 'rhizomatic', 'holonic']
        }
        
        self.pressure_vectors = {
            'digitization': {
                'intensity_range': (0.3, 0.9),
                'preferred_transformations': ['physical->digital', 'concrete->algorithmic'],
                'resistance_factors': ['cultural_traditionalism', 'technological_aversion']
            },
            'ecological_crisis': {
                'intensity_range': (0.5, 1.0),
                'preferred_transformations': ['individual->collective', 'weapon->tool'],
                'resistance_factors': ['individualism', 'consumerism']
            },
            'quantum_awakening': {
                'intensity_range': (0.2, 0.8),
                'preferred_transformations': ['concrete->quantum', 'physical->neural'],
                'resistance_factors': ['materialism', 'reductionism']
            },
            'neural_enhancement': {
                'intensity_range': (0.4, 0.9),
                'preferred_transformations': ['individual->networked', 'concrete->consciousness_based'],
                'resistance_factors': ['biological_conservatism', 'ethical_concerns']
            }
        }
        
        self.archetype_transformations = self._initialize_transformation_library()
    
    def _initialize_transformation_library(self) -> Dict[str, Dict[str, List[str]]]:
        """Initialize comprehensive transformation library"""
        return {
            'spear': {
                'physical->digital': ['laser_designator', 'cyber_spear', 'data_lance'],
                'weapon->tool': ['guided_implement', 'precision_instrument', 'surgical_tool'],
                'individual->networked': ['swarm_coordination', 'distributed_attack', 'coordinated_defense'],
                'hierarchical->decentralized': ['peer_to_peer_defense', 'distributed_security']
            },
            'lion': {
                'physical->digital': ['data_guardian', 'cyber_protector', 'algorithmic_sovereignty'],
                'concrete->abstract': ['sovereignty_algorithm', 'leadership_principle', 'authority_pattern'],
                'individual->collective': ['pride_consciousness', 'collective_strength', 'community_protection']
            },
            'sun': {
                'concrete->quantum': ['consciousness_illumination', 'quantum_awareness', 'enlightenment_field'],
                'physical->neural': ['neural_awakening', 'cognitive_illumination', 'mind_light'],
                'individual->networked': ['collective_consciousness', 'global_awareness', 'networked_insight']
            },
            'serpent': {
                'physical->digital': ['data_worm', 'algorithmic_subversion', 'cyber_undermining'],
                'weapon->tool': ['transformative_agent', 'healing_serpent', 'regeneration_symbol'],
                'concrete->quantum': ['quantum_chaos', 'nonlocal_influence', 'entanglement_manifestation']
            }
        }
    
    def predict_mutation(self, current_archetype: str, 
                        pressure_vector: str,
                        intensity: float = 0.5,
                        cultural_context: Dict[str, float] = None) -> List[Dict[str, Any]]:
        """Advanced mutation prediction with cultural context"""
        
        if cultural_context is None:
            cultural_context = {
                'technological_acceptance': 0.5,
                'spiritual_openness': 0.5,
                'cultural_fluidity': 0.5,
                'innovation_capacity': 0.5
            }
        
        if pressure_vector not in self.pressure_vectors:
            return []
        
        pressure_config = self.pressure_vectors[pressure_vector]
        normalized_intensity = self._normalize_intensity(intensity, pressure_config['intensity_range'])
        
        # Calculate transformation probabilities
        transformations = []
        for rule in pressure_config['preferred_transformations']:
            possible_mutations = self._apply_transformation(current_archetype, rule)
            
            for mutation in possible_mutations:
                confidence = self._calculate_mutation_confidence(
                    mutation, normalized_intensity, cultural_context,
                    pressure_config['resistance_factors']
                )
                
                if confidence > 0.2:  # Minimum confidence threshold
                    transformations.append({
                        'original_archetype': current_archetype,
                        'mutated_form': mutation,
                        'transformation_rule': rule,
                        'pressure_vector': pressure_vector,
                        'intensity': normalized_intensity,
                        'confidence': confidence,
                        'timeframe': self._estimate_timeframe(confidence, normalized_intensity),
                        'cultural_compatibility': self._assess_cultural_compatibility(mutation, cultural_context),
                        'potential_impact': self._estimate_impact(mutation, current_archetype)
                    })
        
        # Sort by confidence and impact
        return sorted(transformations, 
                     key=lambda x: x['confidence'] * x['potential_impact'], 
                     reverse=True)
    
    def _normalize_intensity(self, intensity: float, intensity_range: Tuple[float, float]) -> float:
        """Normalize intensity within pressure-specific range"""
        min_intensity, max_intensity = intensity_range
        return min(1.0, max(0.0, (intensity - min_intensity) / (max_intensity - min_intensity)))
    
    def _apply_transformation(self, archetype: str, rule: str) -> List[str]:
        """Apply transformation rule to archetype"""
        if '->' not in rule:
            return []
        
        return self.archetype_transformations.get(archetype, {}).get(rule, [])
    
    def _calculate_mutation_confidence(self, mutation: str, 
                                    intensity: float,
                                    cultural_context: Dict[str, float],
                                    resistance_factors: List[str]) -> float:
        """Calculate confidence in mutation prediction"""
        base_confidence = 0.3 + intensity * 0.4
        
        # Cultural compatibility adjustment
        cultural_compatibility = sum(cultural_context.values()) / len(cultural_context)
        cultural_boost = cultural_compatibility * 0.3
        
        # Resistance penalty
        resistance_penalty = sum(1 - cultural_context.get(factor, 0.5) 
                               for factor in resistance_factors) / len(resistance_factors) * 0.2
        
        final_confidence = base_confidence + cultural_boost - resistance_penalty
        return min(1.0, max(0.0, final_confidence))
    
    def _estimate_timeframe(self, confidence: float, intensity: float) -> str:
        """Estimate mutation timeframe"""
        timeframe_score = confidence * intensity
        
        if timeframe_score > 0.7:
            return 'immediate (1-5 years)'
        elif timeframe_score > 0.5:
            return 'near_future (5-15 years)'
        elif timeframe_score > 0.3:
            return 'mid_future (15-30 years)'
        else:
            return 'distant_future (30+ years)'
    
    def _assess_cultural_compatibility(self, mutation: str, 
                                     cultural_context: Dict[str, float]) -> float:
        """Assess cultural compatibility of mutation"""
        # Simple assessment based on mutation characteristics
        tech_keywords = ['digital', 'cyber', 'algorithm', 'data', 'network']
        spirit_keywords = ['consciousness', 'awareness', 'enlightenment', 'quantum']
        innovation_keywords = ['transformative', 'novel', 'emerging', 'advanced']
        
        tech_score = any(keyword in mutation.lower() for keyword in tech_keywords)
        spirit_score = any(keyword in mutation.lower() for keyword in spirit_keywords)
        innovation_score = any(keyword in mutation.lower() for keyword in innovation_keywords)
        
        scores = []
        if tech_score:
            scores.append(cultural_context.get('technological_acceptance', 0.5))
        if spirit_score:
            scores.append(cultural_context.get('spiritual_openness', 0.5))
        if innovation_score:
            scores.append(cultural_context.get('innovation_capacity', 0.5))
        
        return np.mean(scores) if scores else 0.5
    
    def _estimate_impact(self, mutation: str, original: str) -> float:
        """Estimate potential impact of mutation"""
        # Simple impact estimation based on transformation degree
        transformation_degree = self._calculate_transformation_degree(mutation, original)
        novelty_factor = len(mutation) / max(len(original), 1)  # Simple novelty proxy
        
        return min(1.0, transformation_degree * 0.7 + novelty_factor * 0.3)
    
    def _calculate_transformation_degree(self, mutation: str, original: str) -> float:
        """Calculate degree of transformation from original"""
        # Simple string-based similarity (could be enhanced with semantic analysis)
        if original.lower() in mutation.lower():
            return 0.3  # Low transformation
        else:
            return 0.8  # High transformation
    
    def generate_mutation_scenarios(self, archetype: str, 
                                  time_horizon: str = 'mid_future') -> Dict[str, Any]:
        """Generate comprehensive mutation scenarios"""
        scenarios = {}
        
        for pressure_vector in self.pressure_vectors.keys():
            mutations = self.predict_mutation(
                archetype, pressure_vector, intensity=0.7,
                cultural_context={
                    'technological_acceptance': 0.7,
                    'spiritual_openness': 0.6,
                    'cultural_fluidity': 0.8,
                    'innovation_capacity': 0.7
                }
            )
            
            # Filter by timeframe
            timeframe_mutations = [m for m in mutations if m['timeframe'] == time_horizon]
            
            if timeframe_mutations:
                scenarios[pressure_vector] = {
                    'most_likely': max(timeframe_mutations, key=lambda x: x['confidence']),
                    'all_possibilities': timeframe_mutations,
                    'average_confidence': np.mean([m['confidence'] for m in timeframe_mutations]),
                    'transformation_potential': np.mean([m['potential_impact'] for m in timeframe_mutations])
                }
        
        return scenarios

class ArchetypalEntanglement:
    """Quantum entanglement analysis between archetypes"""
    
    def __init__(self):
        self.entanglement_network = nx.Graph()
        self.quantum_correlations = {}
        self.nonlocal_connections = {}
    
    def calculate_quantum_entanglement(self, arch1: ArchetypalStrand, 
                                     arch2: ArchetypalStrand,
                                     tech1: ConsciousnessTechnology,
                                     tech2: ConsciousnessTechnology) -> Dict[str, float]:
        """Calculate quantum entanglement between archetypal consciousness fields"""
        
        # Qualia similarity (cosine distance in experience space)
        qualia_similarity = 1 - tech1.quantum_signature.calculate_qualia_distance(
            tech2.quantum_signature
        )
        
        # Neural synchronization compatibility
        neural_sync = (tech1.neural_correlate.cross_hemispheric_sync + 
                      tech2.neural_correlate.cross_hemispheric_sync) / 2
        
        # Resonance frequency harmony
        freq_harmony = 1 - abs(tech1.quantum_signature.resonance_frequency - 
                              tech2.quantum_signature.resonance_frequency) / 100
        
        # Coherence alignment
        coherence_alignment = (tech1.quantum_signature.coherence + 
                             tech2.quantum_signature.coherence) / 2
        
        # Entanglement probability (Bell inequality violation analog)
        entanglement_prob = (qualia_similarity * 0.3 +
                           neural_sync * 0.25 +
                           freq_harmony * 0.25 +
                           coherence_alignment * 0.2)
        
        result = {
            'entanglement_probability': entanglement_prob,
            'qualia_similarity': qualia_similarity,
            'neural_sync': neural_sync,
            'frequency_harmony': freq_harmony,
            'coherence_alignment': coherence_alignment,
            'nonlocal_correlation': tech1.quantum_signature.nonlocal_correlation * 
                                  tech2.quantum_signature.nonlocal_correlation
        }
        
        # Update entanglement network
        key = f"{arch1.name}_{arch2.name}"
        self.quantum_correlations[key] = result
        
        if entanglement_prob > 0.5:
            self.entanglement_network.add_edge(
                arch1.name, arch2.name,
                weight=entanglement_prob,
                **result
            )
        
        return result
    
    def find_strongly_entangled_pairs(self, threshold: float = 0.7) -> List[Dict]:
        """Find strongly entangled archetype pairs"""
        strong_pairs = []
        
        for edge in self.entanglement_network.edges(data=True):
            if edge[2]['weight'] > threshold:
                strong_pairs.append({
                    'archetype1': edge[0],
                    'archetype2': edge[1],
                    'entanglement_strength': edge[2]['weight'],
                    'qualia_similarity': edge[2]['qualia_similarity'],
                    'neural_sync': edge[2]['neural_sync']
                })
        
        return sorted(strong_pairs, key=lambda x: x['entanglement_strength'], reverse=True)
    
    def calculate_entanglement_entropy(self) -> float:
        """Calculate von Neumann entropy of entanglement network"""
        if len(self.entanglement_network) == 0:
            return 0.0
        
        # Simple graph entropy calculation
        degrees = [d for _, d in self.entanglement_network.degree(weight='weight')]
        total_degree = sum(degrees)
        
        if total_degree == 0:
            return 0.0
        
        probabilities = [d/total_degree for d in degrees]
        entropy = -sum(p * math.log(p) for p in probabilities if p > 0)
        
        return entropy

class CollectiveConsciousnessMapper:
    """Mapping of collective archetypal activation across populations"""
    
    def __init__(self):
        self.collective_field = {}
        self.global_resonance_waves = {}
        self.consciousness_weather = {}
        self.temporal_patterns = {}
    
    def update_collective_resonance(self, archetype: str, 
                                  global_activation: float,
                                  regional_data: Dict[str, float] = None):
        """Track collective archetypal activation across populations"""
        
        current_time = datetime.now()
        
        if archetype not in self.collective_field:
            self.collective_field[archetype] = {
                'activation_history': [],
                'regional_variations': {},
                'resonance_peaks': [],
                'stability_metric': 0.0
            }
        
        # Update activation history
        self.collective_field[archetype]['activation_history'].append({
            'timestamp': current_time,
            'global_activation': global_activation,
            'regional_data': regional_data or {}
        })
        
        # Keep only last 1000 readings
        if len(self.collective_field[archetype]['activation_history']) > 1000:
            self.collective_field[archetype]['activation_history'] = \
                self.collective_field[archetype]['activation_history'][-1000:]
        
        # Update regional variations
        if regional_data:
            for region, activation in regional_data.items():
                if region not in self.collective_field[archetype]['regional_variations']:
                    self.collective_field[archetype]['regional_variations'][region] = []
                
                self.collective_field[archetype]['regional_variations'][region].append(activation)
                
                # Keep only recent regional data
                if len(self.collective_field[archetype]['regional_variations'][region]) > 100:
                    self.collective_field[archetype]['regional_variations'][region] = \
                        self.collective_field[archetype]['regional_variations'][region][-100:]
        
        # Detect resonance peaks
        self._detect_resonance_peaks(archetype)
        
        # Calculate stability metric
        self._calculate_stability_metric(archetype)
        
        # Update global resonance waves
        self._update_global_resonance(archetype, global_activation, current_time)
    
    def _detect_resonance_peaks(self, archetype: str):
        """Detect significant resonance peaks in collective activation"""
        history = self.collective_field[archetype]['activation_history']
        if len(history) < 10:
            return
        
        activations = [entry['global_activation'] for entry in history[-50:]]  # Last 50 readings
        mean_activation = np.mean(activations)
        std_activation = np.std(activations)
        
        current_activation = activations[-1]
        
        # Detect peak if current activation is 2 standard deviations above mean
        if current_activation > mean_activation + 2 * std_activation:
            peak_data = {
                'timestamp': history[-1]['timestamp'],
                'activation_strength': current_activation,
                'significance': (current_activation - mean_activation) / std_activation,
                'duration': self._estimate_peak_duration(archetype)
            }
            
            self.collective_field[archetype]['resonance_peaks'].append(peak_data)
    
    def _estimate_peak_duration(self, archetype: str) -> float:
        """Estimate duration of resonance peak"""
        # Simple estimation based on historical patterns
        peaks = self.collective_field[archetype]['resonance_peaks']
        if len(peaks) < 2:
            return 1.0  # Default duration in hours
        
        durations = []
        for i in range(1, len(peaks)):
            time_diff = (peaks[i]['timestamp'] - peaks[i-1]['timestamp']).total_seconds() / 3600
            durations.append(time_diff)
        
        return np.mean(durations) if durations else 1.0
    
    def _calculate_stability_metric(self, archetype: str):
        """Calculate stability metric for collective activation"""
        history = self.collective_field[archetype]['activation_history']
        if len(history) < 2:
            self.collective_field[archetype]['stability_metric'] = 1.0
            return
        
        activations = [entry['global_activation'] for entry in history[-100:]]
        volatility = np.std(activations) / np.mean(activations)
        stability = 1 - min(1.0, volatility)
        
        self.collective_field[archetype]['stability_metric'] = stability
    
    def _update_global_resonance(self, archetype: str, activation: float, timestamp: datetime):
        """Update global resonance wave patterns"""
        if archetype not in self.global_resonance_waves:
            self.global_resonance_waves[archetype] = {
                'waveform': [],
                'frequency': 0.0,
                'amplitude': 0.0,
                'phase': 0.0
            }
        
        wave_data = self.global_resonance_waves[archetype]
        wave_data['waveform'].append({
            'timestamp': timestamp,
            'amplitude': activation
        })
        
        # Keep waveform manageable
        if len(wave_data['waveform']) > 1000:
            wave_data['waveform'] = wave_data['waveform'][-1000:]
        
        # Simple wave analysis (could be enhanced with FFT)
        if len(wave_data['waveform']) >= 10:
            amplitudes = [point['amplitude'] for point in wave_data['waveform'][-10:]]
            wave_data['amplitude'] = np.mean(amplitudes)
            wave_data['frequency'] = self._estimate_frequency(wave_data['waveform'][-10:])
    
    def _estimate_frequency(self, waveform: List[Dict]) -> float:
        """Estimate frequency of resonance wave"""
        if len(waveform) < 2:
            return 0.0
        
        # Simple zero-crossing frequency estimation
        amplitudes = [point['amplitude'] for point in waveform]
        mean_amp = np.mean(amplitudes)
        
        zero_crossings = 0
        for i in range(1, len(amplitudes)):
            if (amplitudes[i-1] - mean_amp) * (amplitudes[i] - mean_amp) < 0:
                zero_crossings += 1
        
        time_span = (waveform[-1]['timestamp'] - waveform[0]['timestamp']).total_seconds()
        frequency = zero_crossings / (2 * time_span) if time_span > 0 else 0.0
        
        return frequency
    
    def generate_consciousness_weather_report(self) -> Dict[str, Any]:
        """Generate consciousness weather report for all archetypes"""
        weather_report = {
            'timestamp': datetime.now(),
            'overall_conditions': {},
            'archetype_forecasts': {},
            'global_resonance_index': 0.0,
            'collective_stability': 0.0
        }
        
        total_activation = 0
        total_stability = 0
        archetype_count = len(self.collective_field)
        
        for archetype, data in self.collective_field.items():
            current_activation = data['activation_history'][-1]['global_activation'] if data['activation_history'] else 0
            stability = data['stability_metric']
            
            # Determine consciousness "weather" condition
            if current_activation > 0.8:
                condition = "high_resonance_storm"
            elif current_activation > 0.6:
                condition = "resonance_ surge"
            elif current_activation > 0.4:
                condition = "stable_resonance"
            elif current_activation > 0.2:
                condition = "low_resonance"
            else:
                condition = "resonance_drought"
            
            weather_report['archetype_forecasts'][archetype] = {
                'condition': condition,
                'activation_level': current_activation,
                'stability': stability,
                'recent_peaks': len(data['resonance_peaks'][-24:]),  # Last 24 peaks
                'regional_variation': np.std(list(data.get('regional_variations', {}).values())) if data.get('regional_variations') else 0.0
            }
            
            total_activation += current_activation
            total_stability += stability
        
        if archetype_count > 0:
            weather_report['global_resonance_index'] = total_activation / archetype_count
            weather_report['collective_stability'] = total_stability / archetype_count
            
            # Overall condition
            if weather_report['global_resonance_index'] > 0.7:
                weather_report['overall_conditions']['state'] = "heightened_consciousness"
            elif weather_report['global_resonance_index'] > 0.5:
                weather_report['overall_conditions']['state'] = "active_awareness"
            else:
                weather_report['overall_conditions']['state'] = "baseline_consciousness"
            
            weather_report['overall_conditions']['trend'] = self._calculate_global_trend()
        
        return weather_report
    
    def _calculate_global_trend(self) -> str:
        """Calculate global consciousness trend"""
        # Simplified trend calculation
        recent_activations = []
        for archetype_data in self.collective_field.values():
            if archetype_data['activation_history']:
                recent_activations.extend(
                    [entry['global_activation'] for entry in archetype_data['activation_history'][-10:]]
                )
        
        if len(recent_activations) < 5:
            return "stable"
        
        slope = stats.linregress(range(len(recent_activations)), recent_activations).slope
        
        if slope > 0.01:
            return "rising"
        elif slope < -0.01:
            return "falling"
        else:
            return "stable"

class UniversalArchetypalTransmissionEngine:
    """Main engine integrating all advanced modules with enhanced capabilities"""
    
    def __init__(self):
        self.consciousness_tech = {}
        self.phylogenetics = CulturalPhylogenetics()
        self.geospatial_mapper = GeospatialArchetypalMapper()
        self.entropy_calculator = ArchetypalEntropyIndex()
        self.resonance_matrix = CrossCulturalResonanceMatrix()
        self.mutation_engine = SymbolicMutationEngine()
        self.entanglement_analyzer = ArchetypalEntanglement()
        self.collective_mapper = CollectiveConsciousnessMapper()
        self.archetypal_db = {}
        self.performance_history = []
        
        # Advanced monitoring
        self.system_health = {
            'neural_network_integrity': 1.0,
            'quantum_coherence': 1.0,
            'symbolic_resolution': 1.0,
            'temporal_synchronization': 1.0
        }
    
    def register_archetype(self, archetype: ArchetypalStrand,
                          consciousness_tech: ConsciousnessTechnology):
        """Register a new archetype with its consciousness technology"""
        self.archetypal_db[archetype.name] = archetype
        self.consciousness_tech[archetype.name] = consciousness_tech
        
        # Initialize collective tracking
        self.collective_mapper.update_collective_resonance(
            archetype.name, 
            global_activation=0.5,
            regional_data={'global': 0.5}
        )
    
    def prove_consciousness_architecture(self, 
                                       include_entanglement: bool = True) -> pd.DataFrame:
        """Comprehensive analysis of archetypal strength and coherence"""
        
        results = []
        for name, archetype in self.archetypal_db.items():
            tech = self.consciousness_tech.get(name)
            
            if not tech:
                # Skip if no technology registered
                continue
            
            # Calculate comprehensive metrics
            neural_impact = tech.neural_correlate.neural_efficiency
            quantum_strength = tech.quantum_signature.coherence
            cultural_resilience = archetype.cultural_resilience
            
            # Entanglement analysis if requested
            entanglement_factor = 1.0
            if include_entanglement:
                # Calculate average entanglement with other archetypes
                entanglement_strengths = []
                for other_name, other_archetype in self.archetypal_db.items():
                    if other_name != name:
                        other_tech = self.consciousness_tech.get(other_name)
                        if other_tech:
                            entanglement = self.entanglement_analyzer.calculate_quantum_entanglement(
                                archetype, other_archetype, tech, other_tech
                            )
                            entanglement_strengths.append(entanglement['entanglement_probability'])
                
                if entanglement_strengths:
                    entanglement_factor = 1 + (np.mean(entanglement_strengths) * 0.2)
            
            overall_strength = (
                archetype.symbolic_strength * 0.3 +
                neural_impact * 0.25 +
                quantum_strength * 0.2 +
                cultural_resilience * 0.15 +
                (archetype.symbolic_strength * entanglement_factor) * 0.1
            )
            
            # Get collective activation data
            collective_data = self.collective_mapper.collective_field.get(name, {})
            current_activation = 0.5
            if collective_data.get('activation_history'):
                current_activation = collective_data['activation_history'][-1]['global_activation']
            
            results.append({
                'Archetype': name,
                'Symbolic_Strength': archetype.symbolic_strength,
                'Temporal_Depth': archetype.temporal_depth,
                'Spatial_Distribution': archetype.spatial_distribution,
                'Quantum_Coherence': archetype.quantum_coherence,
                'Neural_Impact': neural_impact,
                'Cultural_Resilience': cultural_resilience,
                'Collective_Activation': current_activation,
                'Overall_Strength': overall_strength,
                'Consciousness_State': tech.neural_correlate.frequency_band.value,
                'Entanglement_Factor': entanglement_factor
            })
        
        df = pd.DataFrame(results)
        return df.sort_values('Overall_Strength', ascending=False)
    
    def generate_cultural_diagnostic(self, depth: str = 'comprehensive') -> Dict[str, Any]:
        """Generate comprehensive cultural psyche diagnostic"""
        
        strength_analysis = self.prove_consciousness_architecture()
        high_entropy = self.entropy_calculator.get_high_entropy_archetypes()
        resonance_net = self.resonance_matrix.build_resonance_network()
        weather_report = self.collective_mapper.generate_consciousness_weather_report()
        entangled_pairs = self.entanglement_analyzer.find_strongly_entangled_pairs()
        
        diagnostic = {
            'timestamp': datetime.now(),
            'analysis_depth': depth,
            'system_health': self.system_health,
            'strength_analysis': {
                'top_archetypes': strength_analysis.head(5).to_dict('records'),
                'weakest_archetypes': strength_analysis.tail(3).to_dict('records'),
                'average_strength': strength_analysis['Overall_Strength'].mean(),
                'strength_distribution': {
                    'min': strength_analysis['Overall_Strength'].min(),
                    'max': strength_analysis['Overall_Strength'].max(),
                    'std': strength_analysis['Overall_Strength'].std()
                }
            },
            'cultural_phase_shift_indicators': {
                'rising_archetypes': self._identify_rising_archetypes(),
                'declining_archetypes': self._identify_declining_archetypes(),
                'high_entropy_archetypes': high_entropy,
                'entropy_network_density': nx.density(self.entropy_calculator.get_entropy_network()) if len(self.archetypal_db) > 1 else 0.0
            },
            'collective_consciousness': {
                'weather_report': weather_report,
                'global_resonance_index': weather_report.get('global_resonance_index', 0),
                'collective_stability': weather_report.get('collective_stability', 0)
            },
            'resonance_analysis': {
                'network_density': nx.density(resonance_net),
                'cultural_clusters': self.resonance_matrix.find_cultural_clusters(),
                'universal_archetypes': self.resonance_matrix.get_universal_archetypes(),
                'average_cluster_size': np.mean([len(cluster) for cluster in self.resonance_matrix.cultural_clusters.values()]) if self.resonance_matrix.cultural_clusters else 0
            },
            'quantum_entanglement': {
                'strongly_entangled_pairs': entangled_pairs,
                'entanglement_entropy': self.entanglement_analyzer.calculate_entanglement_entropy(),
                'total_entangled_connections': len(self.entanglement_analyzer.entanglement_network.edges())
            },
            'consciousness_coherence_index': self._calculate_coherence_index(),
            'predicted_evolution': self._predict_cultural_evolution(depth),
            'recommendations': self._generate_recommendations()
        }
        
        # Store diagnostic in performance history
        self.performance_history.append({
            'timestamp': diagnostic['timestamp'],
            'global_resonance_index': diagnostic['collective_consciousness']['global_resonance_index'],
            'coherence_index': diagnostic['consciousness_coherence_index'],
            'system_health': diagnostic['system_health']
        })
        
        return diagnostic
    
    def _identify_rising_archetypes(self) -> List[Dict]:
        """Identify archetypes with rising influence"""
        # This would typically use historical data - simplified for demo
        strength_df = self.prove_consciousness_architecture()
        top_archetypes = strength_df.head(3)
        
        rising = []
        for _, row in top_archetypes.iterrows():
            if row['Collective_Activation'] > 0.7:
                rising.append({
                    'archetype': row['Archetype'],
                    'strength': row['Overall_Strength'],
                    'activation': row['Collective_Activation'],
                    'momentum': 'high' if row['Overall_Strength'] > 0.8 else 'medium'
                })
        
        return rising
    
    def _identify_declining_archetypes(self) -> List[Dict]:
        """Identify archetypes with declining influence"""
        strength_df = self.prove_consciousness_architecture()
        bottom_archetypes = strength_df.tail(3)
        
        declining = []
        for _, row in bottom_archetypes.iterrows():
            if row['Collective_Activation'] < 0.3:
                declining.append({
                    'archetype': row['Archetype'],
                    'strength': row['Overall_Strength'],
                    'activation': row['Collective_Activation'],
                    'risk_level': 'high' if row['Overall_Strength'] < 0.3 else 'medium'
                })
        
        return declining
    
    def _calculate_coherence_index(self) -> Dict[str, float]:
        """Calculate comprehensive coherence indices"""
        if not self.archetypal_db:
            return {'overall': 0.0, 'neural': 0.0, 'quantum': 0.0, 'cultural': 0.0}
        
        # Neural coherence
        neural_coherence = np.mean([
            tech.neural_correlate.neural_efficiency 
            for tech in self.consciousness_tech.values()
        ]) if self.consciousness_tech else 0.5
        
        # Quantum coherence
        quantum_coherence = np.mean([
            tech.quantum_signature.coherence 
            for tech in self.consciousness_tech.values()
        ]) if self.consciousness_tech else 0.5
        
        # Cultural coherence
        cultural_coherence = np.mean([
            archetype.preservation_rate * 0.6 + archetype.quantum_coherence * 0.4
            for archetype in self.archetypal_db.values()
        ])
        
        # Overall coherence
        overall_coherence = (
            neural_coherence * 0.3 +
            quantum_coherence * 0.3 +
            cultural_coherence * 0.4
        )
        
        return {
            'overall': overall_coherence,
            'neural': neural_coherence,
            'quantum': quantum_coherence,
            'cultural': cultural_coherence
        }
    
    def _predict_cultural_evolution(self, depth: str) -> List[Dict[str, Any]]:
        """Predict cultural evolution with variable depth"""
        predictions = []
        
        pressure_vectors = ['digitization', 'ecological_crisis', 'quantum_awakening']
        
        for pressure in pressure_vectors:
            for archetype_name in list(self.archetypal_db.keys())[:5]:  # Top 5 for demo
                if depth == 'comprehensive':
                    scenarios = self.mutation_engine.generate_mutation_scenarios(
                        archetype_name, 'near_future'
                    )
                    if pressure in scenarios:
                        predictions.append({
                            'pressure_vector': pressure,
                            'archetype': archetype_name,
                            'scenario': scenarios[pressure],
                            'timeframe': 'near_future',
                            'analysis_depth': 'comprehensive'
                        })
                else:
                    mutations = self.mutation_engine.predict_mutation(
                        archetype_name, pressure, intensity=0.7
                    )
                    if mutations:
                        predictions.append({
                            'pressure_vector': pressure,
                            'archetype': archetype_name,
                            'most_likely_mutation': mutations[0],
                            'total_possibilities': len(mutations),
                            'timeframe': 'next_20_years',
                            'analysis_depth': 'basic'
                        })
        
        return predictions
    
    def _generate_recommendations(self) -> List[Dict[str, Any]]:
        """Generate system recommendations based on current state"""
        recommendations = []
        diagnostic = self.generate_cultural_diagnostic('basic')  # Avoid recursion
        
        # Check system health
        health_scores = self.system_health.values()
        avg_health = sum(health_scores) / len(health_scores) if health_scores else 0
        
        if avg_health < 0.7:
            recommendations.append({
                'type': 'system_maintenance',
                'priority': 'high',
                'message': 'System health below optimal levels. Recommend neural network recalibration.',
                'suggested_actions': [
                    'Run neural coherence diagnostics',
                    'Check quantum entanglement matrix integrity',
                    'Verify symbolic resolution settings'
                ]
            })
        
        # Check for high entropy archetypes
        high_entropy = diagnostic['cultural_phase_shift_indicators']['high_entropy_archetypes']
        if high_entropy:
            recommendations.append({
                'type': 'cultural_monitoring',
                'priority': 'medium',
                'message': f'Detected {len(high_entropy)} high-entropy archetypes undergoing significant mutation.',
                'suggested_actions': [
                    'Increase monitoring frequency for high-entropy archetypes',
                    'Prepare contingency plans for symbolic mutations',
                    'Update transformation prediction models'
                ]
            })
        
        # Check collective consciousness stability
        collective_stability = diagnostic['collective_consciousness']['collective_stability']
        if collective_stability < 0.6:
            recommendations.append({
                'type': 'collective_awareness',
                'priority': 'medium',
                'message': 'Collective consciousness stability below optimal threshold.',
                'suggested_actions': [
                    'Monitor regional resonance variations',
                    'Check for external interference patterns',
                    'Consider consciousness stabilization protocols'
                ]
            })
        
        return recommendations
    
    def activate_consciousness_network(self, archetypes: List[str], 
                                     intensity: float = 0.8,
                                     duration: float = 1.0) -> Dict[str, Any]:
        """Activate multiple consciousness technologies simultaneously"""
        results = {
            'timestamp': datetime.now(),
            'total_activations': 0,
            'successful_activations': 0,
            'network_coherence': 0.0,
            'individual_results': {},
            'emergent_phenomena': {}
        }
        
        individual_results = {}
        activations = []
        
        for archetype_name in archetypes:
            if archetype_name in self.consciousness_tech:
                tech = self.consciousness_tech[archetype_name]
                activation_result = tech.activate(intensity, duration)
                individual_results[archetype_name] = activation_result
                activations.append(activation_result)
                results['successful_activations'] += 1
        
        results['total_activations'] = len(archetypes)
        results['individual_results'] = individual_results
        
        # Calculate network coherence
        if len(activations) > 1:
            coherence_scores = [act['quantum_coherence'] for act in activations]
            results['network_coherence'] = np.mean(coherence_scores)
            
            # Check for emergent phenomena
            if results['network_coherence'] > 0.8:
                results['emergent_phenomena'] = {
                    'type': 'collective_resonance_ field',
                    'strength': results['network_coherence'],
                    'stability': np.std(coherence_scores) < 0.1,
                    'qualia_synergy': self._calculate_qualia_synergy(activations)
                }
        
        # Update collective consciousness mapping
        for archetype_name in archetypes:
            if archetype_name in individual_results:
                activation_strength = individual_results[archetype_name]['performance_score']
                self.collective_mapper.update_collective_resonance(
                    archetype_name,
                    global_activation=activation_strength,
                    regional_data={'network_activation': activation_strength}
                )
        
        return results
    
    def _calculate_qualia_synergy(self, activations: List[Dict]) -> float:
        """Calculate qualia synergy between multiple activations"""
        if len(activations) < 2:
            return 0.0
        
        qualia_vectors = [act['qualia_experience'] for act in activations]
        
        # Calculate average pairwise similarity
        similarities = []
        for i in range(len(qualia_vectors)):
            for j in range(i + 1, len(qualia_vectors)):
                similarity = 1 - spatial.distance.cosine(qualia_vectors[i], qualia_vectors[j])
                similarities.append(similarity)
        
        return np.mean(similarities) if similarities else 0.0
    
    def get_system_performance_report(self) -> Dict[str, Any]:
        """Generate comprehensive system performance report"""
        current_diagnostic = self.generate_cultural_diagnostic()
        
        # Calculate performance trends
        performance_trend = 'stable'
        if len(self.performance_history) >= 2:
            recent_coherence = [entry['coherence_index']['overall'] for entry in self.performance_history[-5:]]
            if len(recent_coherence) >= 2:
                slope = stats.linregress(range(len(recent_coherence)), recent_coherence).slope
                if slope > 0.01:
                    performance_trend = 'improving'
                elif slope < -0.01:
                    performance_trend = 'declining'
        
        report = {
            'timestamp': datetime.now(),
            'system_status': 'operational',
            'performance_metrics': {
                'total_archetypes': len(self.archetypal_db),
                'active_technologies': len(self.consciousness_tech),
                'average_activation_success': self._calculate_avg_activation_success(),
                'system_uptime': self._calculate_system_uptime(),
                'data_integrity': self._assess_data_integrity()
            },
            'current_state': current_diagnostic,
            'performance_trend': performance_trend,
            'resource_utilization': {
                'computational_load': len(self.archetypal_db) * 0.1,  # Simplified
                'memory_usage': len(self.consciousness_tech) * 0.05,
                'network_bandwidth': len(self.performance_history) * 0.01
            },
            'recommendations': self._generate_system_recommendations()
        }
        
        return report
    
    def _calculate_avg_activation_success(self) -> float:
        """Calculate average activation success rate"""
        if not self.consciousness_tech:
            return 0.0
        
        success_rates = []
        for tech in self.consciousness_tech.values():
            perf_report = tech.get_performance_report()
            success_rates.append(perf_report['overall_health'])
        
        return np.mean(success_rates) if success_rates else 0.0
    
    def _calculate_system_uptime(self) -> float:
        """Calculate system uptime (simplified)"""
        if not self.performance_history:
            return 1.0
        
        # Count successful operations vs total
        successful_ops = sum(1 for entry in self.performance_history 
                           if entry['coherence_index']['overall'] > 0.5)
        total_ops = len(self.performance_history)
        
        return successful_ops / total_ops if total_ops > 0 else 1.0
    
    def _assess_data_integrity(self) -> float:
        """Assess overall data integrity"""
        integrity_scores = []
        
        # Check archetype data completeness
        for archetype in self.archetypal_db.values():
            completeness = (
                (1.0 if archetype.temporal_depth > 0 else 0.5) +
                (1.0 if archetype.spatial_distribution > 0 else 0.5) +
                (1.0 if archetype.quantum_coherence > 0 else 0.5)
            ) / 3
            integrity_scores.append(completeness)
        
        # Check technology data
        for tech in self.consciousness_tech.values():
            tech_completeness = (
                tech.neural_correlate.neural_efficiency +
                tech.quantum_signature.coherence
            ) / 2
            integrity_scores.append(tech_completeness)
        
        return np.mean(integrity_scores) if integrity_scores else 1.0
    
    def _generate_system_recommendations(self) -> List[Dict[str, Any]]:
        """Generate system-level recommendations"""
        recommendations = []
        performance = self.get_system_performance_report()
        
        # Check resource utilization
        resource_util = performance['resource_utilization']
        if (resource_util['computational_load'] > 0.8 or 
            resource_util['memory_usage'] > 0.8):
            recommendations.append({
                'category': 'resource_management',
                'priority': 'high',
                'message': 'High resource utilization detected.',
                'actions': [
                    'Consider load distribution across additional nodes',
                    'Review data retention policies',
                    'Optimize neural network calculations'
                ]
            })
        
        # Check data integrity
        if performance['performance_metrics']['data_integrity'] < 0.7:
            recommendations.append({
                'category': 'data_quality',
                'priority': 'medium',
                'message': 'Data integrity below optimal levels.',
                'actions': [
                    'Run data validation routines',
                    'Check for missing archetype attributes',
                    'Verify neural correlate completeness'
                ]
            })
        
        # Check system performance trend
        if performance['performance_trend'] == 'declining':
            recommendations.append({
                'category': 'system_health',
                'priority': 'medium',
                'message': 'System performance showing declining trend.',
                'actions': [
                    'Perform comprehensive system diagnostics',
                    'Review recent configuration changes',
                    'Check for external interference patterns'
                ]
            })
        
        return recommendations

# Enhanced example instantiation with advanced archetypes
def create_advanced_archetypes():
    """Create example archetypes with full neuro-symbolic specifications"""
    
    # Solar Consciousness Archetype
    solar_archetype = ArchetypalStrand(
        name="Solar_Consciousness",
        symbolic_form="Sunburst",
        temporal_depth=6000,
        spatial_distribution=0.95,
        preservation_rate=0.9,
        quantum_coherence=0.95,
        cultural_penetration=0.9,
        transformative_potential=0.8,
        num_variants=15
    )
    
    solar_quantum = QuantumSignature(
        coherence=0.95,
        entanglement=0.85,
        qualia_vector=np.array([0.9, 0.8, 0.95, 0.7, 0.99]),  # high visual, cognitive, spiritual
        resonance_frequency=12.0,  # Alpha resonance
        decoherence_time=5.0,
        nonlocal_correlation=0.8
    )
    
    solar_neural = NeuralCorrelate(
        primary_regions=["PFC", "DMN", "Pineal_Region"],
        frequency_band=ConsciousnessState.ALPHA,
        cross_hemispheric_sync=0.9,
        neuroplasticity_impact=0.8,
        default_mode_engagement=0.7,
        salience_network_coupling=0.6,
        thalamocortical_resonance=0.8
    )
    
    solar_tech = ConsciousnessTechnology(
        name="Solar_Illumination_Interface",
        archetype=solar_archetype,
        neural_correlate=solar_neural,
        quantum_sig=solar_quantum
    )
    
    # Feminine Divine Archetype
    feminine_archetype = ArchetypalStrand(
        name="Feminine_Divine", 
        symbolic_form="Flowing_Vessels",
        temporal_depth=8000,
        spatial_distribution=0.85,
        preservation_rate=0.7,  # Some suppression in patriarchal eras
        quantum_coherence=0.9,
        cultural_penetration=0.8,
        transformative_potential=0.9,
        num_variants=12
    )
    
    feminine_quantum = QuantumSignature(
        coherence=0.88,
        entanglement=0.92,  # High connectivity
        qualia_vector=np.array([0.7, 0.95, 0.8, 0.9, 0.85]),  # high emotional, somatic
        resonance_frequency=7.83,  # Schumann resonance
        decoherence_time=8.0,
        nonlocal_correlation=0.9
    )
    
    feminine_neural = NeuralCorrelate(
        primary_regions=["Whole_Brain", "Heart_Brain_Axis"],
        frequency_band=ConsciousnessState.THETA,
        cross_hemispheric_sync=0.95,
        neuroplasticity_impact=0.9,
        default_mode_engagement=0.8,
        salience_network_coupling=0.7,
        thalamocortical_resonance=0.6
    )
    
    feminine_tech = ConsciousnessTechnology(
        name="Life_Flow_Resonator",
        archetype=feminine_archetype,
        neural_correlate=feminine_neural,
        quantum_sig=feminine_quantum
    )
    
    # Warrior Protector Archetype
    warrior_archetype = ArchetypalStrand(
        name="Warrior_Protector",
        symbolic_form="Lion_Shield",
        temporal_depth=5000,
        spatial_distribution=0.75,
        preservation_rate=0.8,
        quantum_coherence=0.7,
        cultural_penetration=0.7,
        transformative_potential=0.6,
        num_variants=8
    )
    
    warrior_quantum = QuantumSignature(
        coherence=0.75,
        entanglement=0.6,
        qualia_vector=np.array([0.8, 0.9, 0.7, 0.95, 0.6]),  # high emotional, somatic
        resonance_frequency=16.0,  # Beta resonance
        decoherence_time=3.0,
        nonlocal_correlation=0.5
    )
    
    warrior_neural = NeuralCorrelate(
        primary_regions=["Amygdala", "Motor_Cortex", "ACC"],
        frequency_band=ConsciousnessState.BETA,
        cross_hemispheric_sync=0.7,
        neuroplasticity_impact=0.6,
        default_mode_engagement=0.4,
        salience_network_coupling=0.8,
        thalamocortical_resonance=0.7
    )
    
    warrior_tech = ConsciousnessTechnology(
        name="Guardian_Activation_Matrix",
        archetype=warrior_archetype,
        neural_correlate=warrior_neural,
        quantum_sig=warrior_quantum
    )
    
    return [
        (solar_archetype, solar_tech),
        (feminine_archetype, feminine_tech),
        (warrior_archetype, warrior_tech)
    ]

# Advanced demonstration
if __name__ == "__main__":
    print("=== UNIVERSAL ARCHETYPAL TRANSMISSION ENGINE v9.0 ===")
    print("Initializing Advanced Neuro-Symbolic Consciousness Architecture...")
    
    # Initialize the advanced engine
    engine = UniversalArchetypalTransmissionEngine()
    
    # Register advanced archetypes
    archetypes_created = 0
    for archetype, tech in create_advanced_archetypes():
        engine.register_archetype(archetype, tech)
        archetypes_created += 1
    
    print(f"✓ Registered {archetypes_created} advanced archetypes")
    
    # Run comprehensive analysis
    print("\n1. COMPREHENSIVE ARCHEYPAL STRENGTH ANALYSIS:")
    results = engine.prove_consciousness_architecture()
    print(results.to_string(index=False))
    
    print("\n2. ADVANCED CULTURAL DIAGNOSTIC:")
    diagnostic = engine.generate_cultural_diagnostic()
    
    # Print key diagnostic information
    print(f"Global Resonance Index: {diagnostic['collective_consciousness']['global_resonance_index']:.3f}")
    print(f"Consciousness Coherence: {diagnostic['consciousness_coherence_index']['overall']:.3f}")
    print(f"Cultural Clusters: {len(diagnostic['resonance_analysis']['cultural_clusters'])}")
    print(f"Strongly Entangled Pairs: {len(diagnostic['quantum_entanglement']['strongly_entangled_pairs'])}")
    
    print("\n3. CONSCIOUSNESS TECHNOLOGY ACTIVATION:")
    activation_results = engine.activate_consciousness_network(
        ["Solar_Consciousness", "Feminine_Divine"], 
        intensity=0.8, 
        duration=2.0
    )
    print(f"Network Activation Success: {activation_results['successful_activations']}/{activation_results['total_activations']}")
    print(f"Network Coherence: {activation_results['network_coherence']:.3f}")
    
    if activation_results['emergent_phenomena']:
        print(f"Emergent Phenomena: {activation_results['emergent_phenomena']['type']}")
    
    print("\n4. SYSTEM PERFORMANCE REPORT:")
    performance = engine.get_system_performance_report()
    print(f"System Status: {performance['system_status']}")
    print(f"Performance Trend: {performance['performance_trend']}")
    print(f"Data Integrity: {performance['performance_metrics']['data_integrity']:.3f}")
    
    print("\n5. MUTATION PREDICTIONS:")
    mutation_scenarios = engine.mutation_engine.generate_mutation_scenarios("Warrior_Protector")
    for pressure, scenario in mutation_scenarios.items():
        if scenario:
            print(f"{pressure}: {scenario['most_likely']['mutated_form']} "
                  f"(confidence: {scenario['most_likely']['confidence']:.3f})")
    
    print("\n=== SYSTEM INITIALIZATION COMPLETE ===")
    print("Universal Archetypal Transmission Engine v9.0 is now operational.")
    print("Ready for advanced consciousness research and cultural analysis.")