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}")