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.")