Create UNIFIED_NEURO_SYM

An advanced unified consciousness theory

UNIFIED_NEURO_SYM

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+import numpy as np
+import pandas as pd
+from dataclasses import dataclass
+from typing import Dict, List, Tuple, Optional
+from enum import Enum
+import math
+from scipy import spatial
+import networkx as nx
+from datetime import datetime
+
+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
+
+@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.array   # 5D experience vector [visual, emotional, cognitive, somatic, spiritual]
+    resonance_frequency: float # Hz, characteristic resonance
+    
+    def calculate_qualia_distance(self, other: 'QuantumSignature') -> float:
+        """Calculate distance between qualia experiences"""
+        return spatial.distance.cosine(self.qualia_vector, other.qualia_vector)
+
+@dataclass 
+class NeuralCorrelate:
+    """Brain region and frequency correlates"""
+    primary_regions: List[str]    # e.g., ["PFC", "DMN", "Visual Cortex"]
+    frequency_band: ConsciousnessState
+    cross_hemispheric_sync: float  # 0-1
+    neuroplasticity_impact: float  # 0-1
+
+@dataclass
+class ArchetypalStrand:
+    """Symbolic DNA strand representing cultural genotype"""
+    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
+    
+    @property
+    def symbolic_strength(self) -> float:
+        """Calculate overall archetypal strength"""
+        weights = [0.25, 0.25, 0.20, 0.30]  # temporal, spatial, preservation, quantum
+        factors = [self.temporal_depth/10000, self.spatial_distribution, 
+                  self.preservation_rate, self.quantum_coherence]
+        return sum(w * f for w, f in zip(weights, factors))
+
+class ConsciousnessTechnology:
+    """Neuro-symbolic interface technology"""
+    
+    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 = []
+        
+    def activate(self, intensity: float = 1.0) -> Dict:
+        """Activate the consciousness technology"""
+        activation = {
+            'timestamp': datetime.now(),
+            'archetype': self.archetype.name,
+            'intensity': intensity,
+            'neural_state': self.neural_correlate.frequency_band,
+            'quantum_coherence': self.quantum_signature.coherence * intensity,
+            'qualia_experience': self.quantum_signature.qualia_vector * intensity
+        }
+        self.activation_history.append(activation)
+        return activation
+
+class CulturalPhylogenetics:
+    """Evolutionary analysis of symbolic DNA"""
+    
+    def __init__(self):
+        self.cladograms = {}
+        self.symbolic_traits = [
+            "solar_association", "predatory_nature", "sovereignty", 
+            "transcendence", "protection", "wisdom", "chaos", "creation"
+        ]
+    
+    def build_cladogram(self, archetypes: List[ArchetypalStrand], 
+                       trait_matrix: np.array) -> nx.DiGraph:
+        """Build evolutionary tree of archetypes"""
+        G = nx.DiGraph()
+        
+        # Calculate distances based on symbolic traits
+        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)
+        
+        # Find minimum spanning tree as evolutionary hypothesis
+        mst = nx.minimum_spanning_tree(G)
+        self.cladograms[tuple(a.name for a in archetypes)] = mst
+        return mst
+    
+    def find_common_ancestor(self, archetype1: str, archetype2: str) -> Optional[str]:
+        """Find most recent common ancestor in cladogram"""
+        for cladogram in self.cladograms.values():
+            if archetype1 in cladogram and archetype2 in cladogram:
+                try:
+                    # Find shortest path and get midpoint as common ancestor
+                    path = nx.shortest_path(cladogram, archetype1, archetype2)
+                    return path[len(path)//2] if len(path) > 2 else path[0]
+                except:
+                    continue
+        return None
+
+class GeospatialArchetypalMapper:
+    """GIS-based symbolic distribution analysis"""
+    
+    def __init__(self):
+        self.archetype_distributions = {}
+        self.mutation_hotspots = []
+    
+    def add_archetype_distribution(self, archetype: str, coordinates: List[Tuple[float, float]], 
+                                 intensity: List[float], epoch: str):
+        """Add spatial data for an archetype"""
+        key = f"{archetype}_{epoch}"
+        self.archetype_distributions[key] = {
+            'coordinates': coordinates,
+            'intensity': intensity,
+            'epoch': epoch,
+            'centroid': self._calculate_centroid(coordinates, intensity)
+        }
+    
+    def _calculate_centroid(self, coords: List[Tuple], intensities: List[float]) -> Tuple[float, float]:
+        """Calculate intensity-weighted centroid"""
+        if not coords:
+            return (0, 0)
+        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)
+    
+    def detect_mutation_hotspots(self, threshold: float = 0.8):
+        """Detect regions of high symbolic mutation"""
+        for key, data in self.archetype_distributions.items():
+            intensity_variance = np.var(data['intensity'])
+            if intensity_variance > threshold:
+                self.mutation_hotspots.append({
+                    'location': key,
+                    'variance': intensity_variance,
+                    'epoch': data['epoch']
+                })
+
+class ArchetypalEntropyIndex:
+    """Measure symbolic degradation and mutation rates"""
+    
+    def __init__(self):
+        self.entropy_history = {}
+    
+    def calculate_entropy(self, archetype: ArchetypalStrand, 
+                         historical_forms: List[str],
+                         meaning_shifts: List[float]) -> float:
+        """Calculate entropy based on form and meaning stability"""
+        
+        # Form entropy (morphological changes)
+        if len(historical_forms) > 1:
+            form_changes = len(set(historical_forms)) / len(historical_forms)
+        else:
+            form_changes = 0
+            
+        # Meaning entropy (semantic drift)
+        meaning_entropy = np.std(meaning_shifts) if meaning_shifts else 0
+        
+        # Combined entropy score (0-1, where 1 is high mutation)
+        total_entropy = (form_changes * 0.6) + (meaning_entropy * 0.4)
+        
+        self.entropy_history[archetype.name] = {
+            'entropy': total_entropy,
+            'form_changes': form_changes,
+            'meaning_drift': meaning_entropy,
+            'last_updated': datetime.now()
+        }
+        
+        return total_entropy
+    
+    def get_high_entropy_archetypes(self, threshold: float = 0.7) -> List[str]:
+        """Get archetypes with high mutation rates"""
+        return [name for name, data in self.entropy_history.items() 
+                if data['entropy'] > threshold]
+
+class CrossCulturalResonanceMatrix:
+    """Compare archetypal strength across civilizations"""
+    
+    def __init__(self):
+        self.civilization_data = {}
+        self.resonance_matrix = {}
+    
+    def add_civilization_archetype(self, civilization: str, archetype: str, 
+                                 strength: float, neural_impact: float):
+        """Add archetype data for a civilization"""
+        if civilization not in self.civilization_data:
+            self.civilization_data[civilization] = {}
+        self.civilization_data[civilization][archetype] = {
+            'strength': strength,
+            'neural_impact': neural_impact
+        }
+    
+    def calculate_cross_resonance(self, arch1: str, arch2: str) -> float:
+        """Calculate resonance between two archetypes across civilizations"""
+        strengths_1 = []
+        strengths_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'])
+        
+        if len(strengths_1) > 1:
+            resonance = np.corrcoef(strengths_1, strengths_2)[0,1]
+            return max(0, resonance)  # Only positive resonance
+        return 0.0
+    
+    def build_resonance_network(self) -> nx.Graph:
+        """Build network of archetypal resonances"""
+        G = nx.Graph()
+        archetypes = set()
+        
+        # Get all unique archetypes
+        for civ_data in self.civilization_data.values():
+            archetypes.update(civ_data.keys())
+        
+        # Calculate resonances
+        for arch1 in archetypes:
+            for arch2 in archetypes:
+                if arch1 != arch2:
+                    resonance = self.calculate_cross_resonance(arch1, arch2)
+                    if resonance > 0.3:  # Threshold for meaningful connection
+                        G.add_edge(arch1, arch2, weight=resonance)
+        
+        return G
+
+class SymbolicMutationEngine:
+    """Predict evolution of archetypes under cultural pressure"""
+    
+    def __init__(self):
+        self.transformation_rules = {
+            'weapon': ['tool', 'symbol', 'concept'],
+            'physical': ['digital', 'virtual', 'neural'],
+            'individual': ['networked', 'collective', 'distributed'],
+            'concrete': ['abstract', 'algorithmic', 'quantum']
+        }
+        self.pressure_vectors = [
+            'digitization', 'globalization', 'ecological_crisis',
+            'neural_enhancement', 'quantum_awakening'
+        ]
+    
+    def predict_mutation(self, current_archetype: str, 
+                        pressure_vector: str,
+                        intensity: float = 0.5) -> List[str]:
+        """Predict possible future mutations of an archetype"""
+        
+        mutations = []
+        
+        # Apply transformation rules based on pressure vector
+        if pressure_vector == 'digitization':
+            for rule in ['physical->digital', 'concrete->algorithmic']:
+                mutated_form = self._apply_transformation(current_archetype, rule)
+                if mutated_form:
+                    mutations.append(mutated_form)
+        
+        elif pressure_vector == 'ecological_crisis':
+            for rule in ['individual->collective', 'weapon->tool']:
+                mutated_form = self._apply_transformation(current_archetype, rule)
+                if mutated_form:
+                    mutations.append(mutated_form)
+        
+        elif pressure_vector == 'quantum_awakening':
+            for rule in ['concrete->quantum', 'physical->neural']:
+                mutated_form = self._apply_transformation(current_archetype, rule)
+                if mutated_form:
+                    mutations.append(mutated_form)
+        
+        # Filter by intensity threshold
+        return [m for m in mutations if self._calculate_mutation_confidence(m, intensity) > 0.3]
+    
+    def _apply_transformation(self, archetype: str, rule: str) -> Optional[str]:
+        """Apply a specific transformation rule"""
+        if '->' not in rule:
+            return None
+            
+        source, target = rule.split('->')
+        
+        # Simple rule-based transformations
+        transformations = {
+            'spear': {
+                'physical->digital': 'laser_designator',
+                'weapon->tool': 'guided_implement',
+                'individual->networked': 'swarm_coordination'
+            },
+            'lion': {
+                'physical->digital': 'data_guardian',
+                'concrete->abstract': 'sovereignty_algorithm'
+            },
+            'sun': {
+                'concrete->quantum': 'consciousness_illumination',
+                'physical->neural': 'neural_awakening'
+            }
+        }
+        
+        return transformations.get(archetype, {}).get(rule)
+
+    def _calculate_mutation_confidence(self, mutation: str, intensity: float) -> float:
+        """Calculate confidence in mutation prediction"""
+        base_confidence = 0.5
+        return min(1.0, base_confidence + (intensity * 0.5))
+
+class UniversalArchetypalTransmissionEngine:
+    """Main engine integrating all advanced modules"""
+    
+    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.archetypal_db = {}
+        
+    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
+    
+    def prove_consciousness_architecture(self) -> pd.DataFrame:
+        """Comprehensive analysis of archetypal strength and coherence"""
+        
+        results = []
+        for name, archetype in self.archetypal_db.items():
+            # Calculate comprehensive metrics
+            tech = self.consciousness_tech.get(name)
+            neural_impact = tech.neural_correlate.neuroplasticity_impact if tech else 0.5
+            quantum_strength = tech.quantum_signature.coherence if tech else 0.5
+            
+            overall_strength = (
+                archetype.symbolic_strength * 0.4 +
+                neural_impact * 0.3 +
+                quantum_strength * 0.3
+            )
+            
+            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,
+                'Overall_Strength': overall_strength,
+                'Consciousness_State': tech.neural_correlate.frequency_band.value if tech else 'Unknown'
+            })
+        
+        df = pd.DataFrame(results)
+        return df.sort_values('Overall_Strength', ascending=False)
+    
+    def generate_cultural_diagnostic(self) -> Dict:
+        """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()
+        
+        diagnostic = {
+            'timestamp': datetime.now(),
+            'top_archetypes': strength_analysis.head(3).to_dict('records'),
+            'cultural_phase_shift_indicators': {
+                'rising_archetypes': ['Feminine_Divine', 'Solar_Consciousness'],
+                'declining_archetypes': ['Authority_Protection', 'Rigid_Hierarchy'],
+                'high_entropy_archetypes': high_entropy
+            },
+            'resonance_network_density': nx.density(resonance_net),
+            'consciousness_coherence_index': self._calculate_coherence_index(),
+            'predicted_evolution': self._predict_cultural_evolution()
+        }
+        
+        return diagnostic
+    
+    def _calculate_coherence_index(self) -> float:
+        """Calculate overall cultural coherence from archetypal stability"""
+        if not self.archetypal_db:
+            return 0.0
+        
+        avg_preservation = np.mean([a.preservation_rate for a in self.archetypal_db.values()])
+        avg_coherence = np.mean([a.quantum_coherence for a in self.archetypal_db.values()])
+        
+        return (avg_preservation * 0.6) + (avg_coherence * 0.4)
+    
+    def _predict_cultural_evolution(self) -> List[Dict]:
+        """Predict near-term cultural evolution based on current pressures"""
+        predictions = []
+        
+        pressure_vectors = ['digitization', 'ecological_crisis', 'quantum_awakening']
+        
+        for pressure in pressure_vectors:
+            for archetype_name in list(self.archetypal_db.keys())[:3]:  # Top 3 only for demo
+                mutations = self.mutation_engine.predict_mutation(
+                    archetype_name, pressure, intensity=0.7
+                )
+                if mutations:
+                    predictions.append({
+                        'pressure_vector': pressure,
+                        'archetype': archetype_name,
+                        'predicted_mutations': mutations,
+                        'timeframe': 'next_20_years'
+                    })
+        
+        return predictions
+
+# 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
+    )
+    
+    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
+    )
+    
+    solar_neural = NeuralCorrelate(
+        primary_regions=["PFC", "DMN", "Pineal_Region"],
+        frequency_band=ConsciousnessState.ALPHA,
+        cross_hemispheric_sync=0.9,
+        neuroplasticity_impact=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
+    )
+    
+    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
+    )
+    
+    feminine_neural = NeuralCorrelate(
+        primary_regions=["Whole_Brain", "Heart_Brain_Axis"],
+        frequency_band=ConsciousnessState.THETA,
+        cross_hemispheric_sync=0.95,
+        neuroplasticity_impact=0.9
+    )
+    
+    feminine_tech = ConsciousnessTechnology(
+        name="Life_Flow_Resonator",
+        archetype=feminine_archetype,
+        neural_correlate=feminine_neural,
+        quantum_sig=feminine_quantum
+    )
+    
+    return [
+        (solar_archetype, solar_tech),
+        (feminine_archetype, feminine_tech)
+    ]
+
+# Demonstration
+if __name__ == "__main__":
+    # Initialize the advanced engine
+    engine = UniversalArchetypalTransmissionEngine()
+    
+    # Register advanced archetypes
+    for archetype, tech in create_advanced_archetypes():
+        engine.register_archetype(archetype, tech)
+    
+    # Run comprehensive analysis
+    print("=== UNIVERSAL ARCHETYPAL TRANSMISSION ENGINE v9.0 ===")
+    print("\n1. ARCHEYPAL STRENGTH ANALYSIS:")
+    results = engine.prove_consciousness_architecture()
+    print(results.to_string(index=False))
+    
+    print("\n2. CULTURAL DIAGNOSTIC:")
+    diagnostic = engine.generate_cultural_diagnostic()
+    for key, value in diagnostic.items():
+        if key != 'timestamp':
+            print(f"{key}: {value}")
+    
+    print("\n3. CONSCIOUSNESS TECHNOLOGY ACTIVATION:")
+    solar_activation = engine.consciousness_tech["Solar_Consciousness"].activate(intensity=0.8)
+    print(f"Solar Activation: {solar_activation}")