MEM_REC_MCON

#!/usr/bin/env python3
"""
MEGACONSCIOUSNESS INTEGRATION ENGINE v1.0
Complete Integration of All Advanced Systems - Zero Information Loss
Every Module, Every Function, Every Detail Preserved
"""

import numpy as np
import asyncio
import json
import hashlib
import logging
import sqlite3
import aiohttp
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple, Callable, Set
from datetime import datetime, timedelta
from scipy import signal, ndimage, stats
import torch
import torch.nn as nn
from contextlib import asynccontextmanager
from concurrent.futures import ThreadPoolExecutor
from statistics import mean, stdev
from collections import defaultdict, Counter, deque
from pathlib import Path

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# =============================================================================
# INTEGRATED ENUMERATIONS (All Original Enums Preserved)
# =============================================================================

class ControlArchetype(Enum):
    PRIEST_KING = "priest_king"
    DIVINE_INTERMEDIARY = "divine_intermediary"
    ORACLE_PRIEST = "oracle_priest"
    PHILOSOPHER_KING = "philosopher_king"
    IMPERIAL_RULER = "imperial_ruler"
    SLAVE_MASTER = "slave_master"
    EXPERT_TECHNOCRAT = "expert_technocrat"
    CORPORATE_OVERLORD = "corporate_overlord"
    FINANCIAL_MASTER = "financial_master"
    ALGORITHMIC_CURATOR = "algorithmic_curator"
    DIGITAL_MESSIAH = "digital_messiah"
    DATA_OVERSEER = "data_overseer"

class SlaveryType(Enum):
    CHATTEL_SLAVERY = "chattel_slavery"
    DEBT_BONDAGE = "debt_bondage"
    WAGE_SLAVERY = "wage_slavery"
    CONSUMER_SLAVERY = "consumer_slavery"
    DIGITAL_SLAVERY = "digital_slavery"
    PSYCHOLOGICAL_SLAVERY = "psychological_slavery"

class ConsciousnessHack(Enum):
    SELF_ATTRIBUTION = "self_attribution"
    ASPIRATIONAL_CHAINS = "aspirational_chains"
    FEAR_OF_FREEDOM = "fear_of_freedom"
    ILLUSION_OF_MOBILITY = "illusion_of_mobility"
    NORMALIZATION = "normalization"
    MORAL_SUPERIORITY = "moral_superiority"

class ControlLayer(Enum):
    DIGITAL_INFRASTRUCTURE = "digital_infrastructure"
    FINANCIAL_SYSTEMS = "financial_systems"
    INFORMATION_CHANNELS = "information_channels"
    CULTURAL_NARRATIVES = "cultural_narratives"
    IDENTITY_SYSTEMS = "identity_systems"

class ThreatVector(Enum):
    MONOPOLY_CAPTURE = "monopoly_capture"
    DEPENDENCY_CREATION = "dependency_creation"
    BEHAVIORAL_SHAPING = "behavioral_shaping"
    DATA_MONETIZATION = "data_monetization"
    NARRATIVE_CONTROL = "narrative_control"

class ArchetypeTransmission(Enum):
    FELINE_PREDATOR = "jaguar_lion_predator"
    AVIAN_PREDATOR = "buzzard_eagle_vision"
    SOLAR_SYMBOLISM = "eight_star_sunburst"
    AGRICULTURAL_LIFE = "wheat_corn_sustenance"
    AUTHORITY_PROTECTION = "spear_aegis_sovereignty"
    FEMINE_DIVINE = "inanna_liberty_freedom"

class ConsciousnessTechnology(Enum):
    SOVEREIGNTY_ACTIVATION = "predator_power"
    TRANSCENDENT_VISION = "sky_dominance"
    ENLIGHTENMENT_ACCESS = "solar_resonance"
    CIVILIZATION_SUSTENANCE = "agricultural_abundance"
    PROTECTIVE_AUTHORITY = "defensive_governance"
    LIFE_FREEDOM_FLOW = "feminine_principle"

class NumismaticRealityLayer(Enum):
    TEMPORAL_DISPLACEMENT = "temporal_displacement"
    SOVEREIGNTY_COLLISION = "sovereignty_collision"
    VALUE_SYSTEM_SHIFT = "value_system_shift"
    MINTING_CONSCIOUSNESS = "minting_consciousness"
    DESIGN_ARCHETYPE_CONFLICT = "design_archetype_conflict"
    METALLURGICAL_ANOMALY = "metallurgical_anomaly"

class VarietyClassification(Enum):
    OVERSTRIKE_FOREIGN = "overstrike_foreign"
    OVERSTRIKE_DOMESTIC = "overstrike_domestic"
    MULE_SOVEREIGNTY = "mule_sovereignty"
    MULE_TEMPORAL = "mule_temporal"
    ERROR_REALITY_FRACTURE = "error_reality_fracture"
    VARIETY_PROBABILITY_BRANCH = "variety_probability_branch"
    COMPOSITIONAL_SHIFT = "compositional_shift"

class RealityDistortionLevel(Enum):
    MINOR_ANOMALY = "minor_anomaly"
    MODERATE_FRACTURE = "moderate_fracture"
    MAJOR_COLLISION = "major_collision"
    REALITY_BRANCH_POINT = "reality_branch_point"

class SignalType(Enum):
    MEDIA_ARC = "media_arc"
    EVENT_TRIGGER = "event_trigger"
    INSTITUTIONAL_FRAMING = "institutional_framing"
    COMMUNITY_REACTION = "community_reaction"
    MEMETIC_PRIMER = "memetic_primer"
    NORMALIZATION_SIGNAL = "normalization_signal"

class DomainArc(Enum):
    PATHOGEN = "pathogen"
    TECHNOLOGY_ANOMALY = "technology_anomaly"
    INFRASTRUCTURE = "infrastructure"
    ENVIRONMENTAL = "environmental"

class OutcomeState(Enum):
    LOW_ADOPTION = "low_adoption"
    PARTIAL_ADOPTION = "partial_adoption"
    HIGH_ADOPTION = "high_adoption"
    POLARIZATION = "polarization"
    FATIGUE = "fatigue"

# =============================================================================
# INTEGRATED DATA STRUCTURES (All Original Classes Preserved)
# =============================================================================

@dataclass
class EpistemicVector:
    content_hash: str
    dimensional_components: Dict[str, float]
    confidence_metrics: Dict[str, float]
    temporal_coordinates: Dict[str, Any]
    relational_entanglements: List[str]
    meta_cognition: Dict[str, Any]
    security_signature: str
    quantum_state: Optional[str] = None
    truth_validation_score: float = 0.0
    reality_integration_potential: float = 0.0
    epistemic_coherence: float = field(init=False)

    def __post_init__(self):
        dimensional_strength = np.mean(list(self.dimensional_components.values()))
        confidence_strength = np.mean(list(self.confidence_metrics.values()))
        relational_density = min(1.0, len(self.relational_entanglements) / 10.0)
        quantum_boost = 0.1 if self.quantum_state else 0.0
        truth_boost = self.truth_validation_score * 0.2
        
        self.epistemic_coherence = min(
            1.0,
            (dimensional_strength * 0.3 + 
             confidence_strength * 0.25 + 
             relational_density * 0.2 +
             quantum_boost * 0.15 +
             truth_boost * 0.1)
        )

@dataclass
class SlaveryMechanism:
    mechanism_id: str
    slavery_type: SlaveryType
    visible_chains: List[str]
    invisible_chains: List[str]
    voluntary_adoption_mechanisms: List[str]
    self_justification_narratives: List[str]
    
    def calculate_control_depth(self) -> float:
        invisible_weight = len(self.invisible_chains) * 0.3
        voluntary_weight = len(self.voluntary_adoption_mechanisms) * 0.4
        narrative_weight = len(self.self_justification_narratives) * 0.3
        return min(1.0, invisible_weight + voluntary_weight + narrative_weight)

@dataclass
class ControlSystem:
    system_id: str
    historical_era: str
    control_archetype: ControlArchetype
    manufactured_threats: List[str]
    salvation_offerings: List[str]
    institutional_saviors: List[str]
    slavery_mechanism: SlaveryMechanism
    consciousness_hacks: List[ConsciousnessHack]
    public_participation_rate: float
    resistance_level: float
    system_longevity: int

    def calculate_system_efficiency(self) -> float:
        slavery_depth = self.slavery_mechanism.calculate_control_depth()
        participation_boost = self.public_participation_rate * 0.3
        hack_potency = len(self.consciousness_hacks) * 0.1
        longevity_bonus = min(0.2, self.system_longevity / 500)
        resistance_penalty = self.resistance_level * 0.2
        
        return max(0.0, 
            slavery_depth * 0.4 +
            participation_boost +
            hack_potency +
            longevity_bonus -
            resistance_penalty
        )

@dataclass
class InstitutionalEntity:
    entity_id: str
    name: str
    control_layers: List[ControlLayer]
    threat_vectors: List[ThreatVector]
    market_share: float
    dependency_score: float
    sovereignty_erosion_score: float = field(init=False)
    systemic_risk_level: float = field(init=False)
    
    def __post_init__(self):
        self.sovereignty_erosion_score = self._calculate_sovereignty_impact()
        self.systemic_risk_level = self._calculate_systemic_risk()
    
    def _calculate_sovereignty_impact(self) -> float:
        layer_impact = len(self.control_layers) * 0.2
        threat_impact = len(self.threat_vectors) * 0.15
        market_impact = self.market_share * 0.3
        dependency_impact = self.dependency_score * 0.35
        return min(1.0, layer_impact + threat_impact + market_impact + dependency_impact)
    
    def _calculate_systemic_risk(self) -> float:
        centrality = (self.market_share + self.dependency_score) / 2 * 0.6
        control_density = len(self.control_layers) * 0.2
        threat_complexity = len(self.threat_vectors) * 0.2
        return min(1.0, centrality + control_density + threat_complexity)

@dataclass
class SymbolicDNA:
    archetype: ArchetypeTransmission
    transmission_chain: List[str]
    consciousness_function: ConsciousnessTechnology
    temporal_depth: float
    spatial_distribution: float
    preservation_rate: float
    quantum_coherence: float
    
    def calculate_archetypal_strength(self) -> float:
        temporal_weight = min(1.0, self.temporal_depth / 5000)
        spatial_weight = self.spatial_distribution
        preservation_weight = self.preservation_rate
        quantum_weight = self.quantum_coherence
        
        return (temporal_weight * 0.3 + spatial_weight * 0.25 + 
                preservation_weight * 0.25 + quantum_weight * 0.2)

@dataclass
class MetallurgicalAnalysis:
    host_composition: Dict[str, float]
    overstrike_composition: Dict[str, float]
    compositional_discrepancy: float = field(init=False)
    metal_purity_delta: float = field(init=False)
    trace_element_anomalies: List[str] = field(init=False)
    
    def __post_init__(self):
        self.compositional_discrepancy = self._calculate_compositional_discrepancy()
        self.metal_purity_delta = self._calculate_metal_purity_delta()
        self.trace_element_anomalies = self._identify_trace_anomalies()
    
    def _calculate_compositional_discrepancy(self) -> float:
        all_elements = set(self.host_composition.keys()) | set(self.overstrike_composition.keys())
        total_discrepancy = 0.0
        for element in all_elements:
            host_pct = self.host_composition.get(element, 0.0)
            overstrike_pct = self.overstrike_composition.get(element, 0.0)
            total_discrepancy += abs(host_pct - overstrike_pct)
        return total_discrepancy / 2.0
    
    def _calculate_metal_purity_delta(self) -> float:
        primary_metals = ['silver', 'gold', 'copper', 'bronze']
        for metal in primary_metals:
            if metal in self.host_composition and metal in self.overstrike_composition:
                return abs(self.host_composition[metal] - self.overstrike_composition[metal])
        return 0.0
    
    def _identify_trace_anomalies(self) -> List[str]:
        anomalies = []
        trace_threshold = 0.02
        for element, host_pct in self.host_composition.items():
            overstrike_pct = self.overstrike_composition.get(element, 0.0)
            if host_pct < trace_threshold and overstrike_pct > trace_threshold * 2:
                anomalies.append(f"Trace element {element} significantly increased")
            elif overstrike_pct < trace_threshold and host_pct > trace_threshold * 2:
                anomalies.append(f"Trace element {element} significantly decreased")
        return anomalies

@dataclass
class HistoricalContext:
    period_start: int
    period_end: int
    sovereign_entities: List[str]
    economic_system: str
    metal_standard: str
    minting_technology: str
    key_historical_events: List[str]
    collective_consciousness_metrics: Dict[str, float]
    
    def temporal_depth(self) -> int:
        return self.period_end - self.period_start
    
    def consciousness_volatility(self) -> float:
        metrics = list(self.collective_consciousness_metrics.values())
        return stdev(metrics) if len(metrics) > 1 else 0.0

@dataclass
class NumismaticRealitySignature:
    signature_hash: str
    temporal_displacement: float
    sovereignty_collision_strength: float
    design_overlay_coherence: float
    value_system_discontinuity: float
    minting_consciousness_anomaly: float
    metallurgical_anomaly_score: float
    reality_distortion_level: RealityDistortionLevel
    
    def calculate_reality_impact(self) -> float:
        base_impact = (
            self.temporal_displacement * 0.20 +
            self.sovereignty_collision_strength * 0.25 +
            (1 - self.design_overlay_coherence) * 0.15 +
            self.value_system_discontinuity * 0.15 +
            self.minting_consciousness_anomaly * 0.10 +
            self.metallurgical_anomaly_score * 0.15
        )
        return min(1.0, base_impact)

@dataclass
class ForeignOverstrikeAnalysis:
    host_coin: Dict[str, Any]
    overstrike_coin: Dict[str, Any]
    historical_context_host: HistoricalContext
    historical_context_overstrike: HistoricalContext
    design_analysis: Dict[str, float]
    metallurgical_analysis: MetallurgicalAnalysis
    reality_signature: NumismaticRealitySignature
    temporal_collision_points: List[str] = field(init=False)
    sovereignty_interface_tensions: List[str] = field(init=False)
    quantum_reality_implications: List[str] = field(init=False)
    metallurgical_insights: List[str] = field(init=False)
    
    def __post_init__(self):
        self.temporal_collision_points = self._identify_temporal_collisions()
        self.sovereignty_interface_tensions = self._analyze_sovereignty_tensions()
        self.quantum_reality_implications = self._derive_quantum_implications()
        self.metallurgical_insights = self._analyze_metallurgical_implications()
    
    def _identify_temporal_collisions(self) -> List[str]:
        collisions = []
        time_gap = abs(self.historical_context_host.period_start - self.historical_context_overstrike.period_start)
        if time_gap > 25:
            collisions.append(f"Major temporal displacement: {time_gap} years")
        if self.historical_context_host.economic_system != self.historical_context_overstrike.economic_system:
            collisions.append("Economic system transition collision")
        if self.historical_context_host.metal_standard != self.historical_context_overstrike.metal_standard:
            collisions.append("Metal standard reality shift")
        return collisions
    
    def _analyze_sovereignty_tensions(self) -> List[str]:
        tensions = []
        host_sovereigns = set(self.historical_context_host.sovereign_entities)
        overstrike_sovereigns = set(self.historical_context_overstrike.sovereign_entities)
        sovereignty_overlap = host_sovereigns & overstrike_sovereigns
        if not sovereignty_overlap:
            tensions.append("Complete sovereignty collision - no overlapping entities")
        host_design = self.host_coin.get('design_archetype', {})
        overstrike_design = self.overstrike_coin.get('design_archetype', {})
        if host_design.get('ruler_portrait') and overstrike_design.get('ruler_portrait'):
            tensions.append("Ruler archetype overlay conflict")
        return tensions
    
    def _analyze_metallurgical_implications(self) -> List[str]:
        insights = []
        if self.metallurgical_analysis.compositional_discrepancy > 0.3:
            insights.append("Significant metallurgical composition shift")
        if self.metallurgical_analysis.metal_purity_delta > 0.15:
            insights.append("Major metal purity differential detected")
        if self.metallurgical_analysis.trace_element_anomalies:
            insights.extend(self.metallurgical_analysis.trace_element_anomalies)
        host_tech = self.historical_context_host.minting_technology
        overstrike_tech = self.historical_context_overstrike.minting_technology
        if host_tech != overstrike_tech:
            insights.append(f"Minting technology shift: {host_tech} → {overstrike_tech}")
        return insights
    
    def _derive_quantum_implications(self) -> List[str]:
        implications = []
        impact = self.reality_signature.calculate_reality_impact()
        if impact > 0.8:
            implications.append("Reality branch point - significant probability divergence")
        if impact > 0.6:
            implications.append("Collective consciousness fracture point")
        if self.reality_signature.temporal_displacement > 0.7:
            implications.append("Temporal reality layer compression")
        if self.reality_signature.sovereignty_collision_strength > 0.8:
            implications.append("Sovereignty reality field collision")
        if self.reality_signature.metallurgical_anomaly_score > 0.7:
            implications.append("Metallurgical reality distortion detected")
        return implications

@dataclass
class TeslaRealityMetrics:
    energy_coherence: Dict[str, float]
    frequency_resonance: Dict[str, float]
    vibration_patterns: Dict[str, float]
    triad_unification: Dict[str, float]
    quantum_emergence: Dict[str, float]
    spacetime_curvature: Dict[str, float]

@dataclass
class ControlMatrix:
    entities: List[InstitutionalEntity]
    interconnections: Dict[str, List[str]]
    coordination_score: float = field(init=False)
    overall_sovereignty_threat: float = field(init=False)
    
    def __post_init__(self):
        self.coordination_score = self._calculate_coordination()
        self.overall_sovereignty_threat = self._calculate_overall_threat()
    
    def _calculate_coordination(self) -> float:
        if not self.entities:
            return 0.0
        avg_systemic_risk = np.mean([e.systemic_risk_level for e in self.entities])
        total_possible_connections = len(self.entities) * (len(self.entities) - 1)
        if total_possible_connections > 0:
            actual_connections = sum(len(conns) for conns in self.interconnections.values())
            network_density = actual_connections / total_possible_connections
        else:
            network_density = 0.0
        return min(1.0, avg_systemic_risk * 0.6 + network_density * 0.4)
    
    def _calculate_overall_threat(self) -> float:
        if not self.entities:
            return 0.0
        max_individual_threat = max(e.sovereignty_erosion_score for e in self.entities)
        avg_threat = np.mean([e.sovereignty_erosion_score for e in self.entities])
        coordination_multiplier = 1.0 + (self.coordination_score * 0.5)
        return min(1.0, (max_individual_threat * 0.4 + avg_threat * 0.6) * coordination_multiplier)

@dataclass
class CompleteControlMatrix:
    control_systems: List[ControlSystem]
    active_systems: List[str]
    institutional_evolution: Dict[str, List[ControlArchetype]]
    collective_delusions: Dict[str, float]
    freedom_illusions: Dict[str, float]
    self_enslavement_patterns: Dict[str, float]
    
    def analyze_complete_control(self) -> Dict[str, Any]:
        analysis = {
            "system_evolution": [],
            "slavery_sophistication": [],
            "consciousness_manipulation": [],
            "resistance_effectiveness": []
        }
        
        for system in self.control_systems:
            analysis["system_evolution"].append({
                "era": system.historical_era,
                "archetype": system.control_archetype.value,
                "efficiency": system.calculate_system_efficiency(),
                "slavery_type": system.slavery_mechanism.slavery_type.value
            })
            
            analysis["slavery_sophistication"].append({
                "era": system.historical_era,
                "visible_chains": len(system.slavery_mechanism.visible_chains),
                "invisible_chains": len(system.slavery_mechanism.invisible_chains),
                "control_depth": system.slavery_mechanism.calculate_control_depth()
            })
            
            analysis["consciousness_manipulation"].append({
                "era": system.historical_era,
                "hack_count": len(system.consciousness_hacks),
                "participation_rate": system.public_participation_rate
            })
        
        return {
            "complete_analysis": analysis,
            "system_convergence": self._calculate_system_convergence(),
            "slavery_evolution_trend": self._calculate_slavery_evolution(analysis),
            "consciousness_entrainment": self._analyze_consciousness_entrainment(),
            "freedom_illusion_index": self._calculate_freedom_illusion()
        }
    
    def _calculate_system_convergence(self) -> float:
        convergence = 0.0
        for institution, archetypes in self.institutional_evolution.items():
            if len(archetypes) > 2:
                convergence += len(archetypes) * 0.15
        return min(1.0, convergence)
    
    def _calculate_slavery_evolution(self, analysis: Dict) -> float:
        sophistication_data = analysis["slavery_sophistication"]
        if len(sophistication_data) < 2:
            return 0.5
        visible_trend = np.polyfit(
            range(len(sophistication_data)),
            [s["visible_chains"] for s in sophistication_data], 1
        )[0]
        invisible_trend = np.polyfit(
            range(len(sophistication_data)), 
            [s["invisible_chains"] for s in sophistication_data], 1
        )[0]
        sophistication = (invisible_trend - visible_trend) / 2 + 0.5
        return min(1.0, max(0.0, sophistication))
    
    def _analyze_consciousness_entrainment(self) -> Dict[str, float]:
        return {
            "delusion_strength": np.mean(list(self.collective_delusions.values())),
            "freedom_illusion": np.mean(list(self.freedom_illusions.values())),
            "self_enslavement": np.mean(list(self.self_enslavement_patterns.values())),
            "system_identification": 0.78
        }
    
    def _calculate_freedom_illusion(self) -> float:
        freedom_scores = list(self.freedom_illusions.values())
        enslavement_scores = list(self.self_enslavement_patterns.values())
        if not freedom_scores:
            return 0.5
        freedom_illusion = np.mean(freedom_scores) * np.mean(enslavement_scores)
        return min(1.0, freedom_illusion)

# =============================================================================
# MEMETIC RECURSION MODULE (Complete Preservation)
# =============================================================================

@dataclass
class MemeticSignal:
    t: datetime
    signal_type: SignalType
    domain: DomainArc
    intensity: float
    coherence: float
    familiarity: float
    valence: float
    source_label: str = "unspecified"
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AudienceState:
    conditioning: float
    sensitivity: float
    trust_balance: float
    fatigue: float
    polarization: float
    adoption: float

@dataclass
class CycleMetrics:
    exposure_sum: float
    coherence_mean: float
    familiarity_mean: float
    valence_mean: float
    institutional_weight: float
    community_weight: float
    event_strength: float

@dataclass
class RecursionOutcome:
    state: OutcomeState
    adoption_score: float
    fatigue_score: float
    polarization_score: float
    notes: str

@dataclass
class RecursionConfig:
    conditioning_weight: float = 0.35
    coherence_weight: float = 0.25
    institutional_weight: float = 0.20
    community_weight: float = 0.20
    fatigue_penalty: float = 0.30
    polarization_penalty: float = 0.25
    trigger_gain: float = 0.40
    normalization_gain: float = 0.25
    primer_gain: float = 0.30
    trust_shift_factor: float = 0.10
    decay_rate: float = 0.02
    max_step_days: int = 14

class MemeticRecursionEngine:
    def __init__(self, config: Optional[RecursionConfig] = None):
        self.config = config or RecursionConfig()
        self.signals: List[MemeticSignal] = []
        self.audience = AudienceState(
            conditioning=0.15,
            sensitivity=0.20,
            trust_balance=0.0,
            fatigue=0.10,
            polarization=0.10,
            adoption=0.10
        )
        self._last_step: Optional[datetime] = None

    def ingest(self, signal: MemeticSignal) -> None:
        self.signals.append(signal)

    def ingest_batch(self, signals: List[MemeticSignal]) -> None:
        self.signals.extend(signals)

    def window(self, end: datetime, days: Optional[int] = None) -> List[MemeticSignal]:
        span = timedelta(days=days or self.config.max_step_days)
        start = end - span
        return [s for s in self.signals if start <= s.t <= end]

    def compute_metrics(self, window_signals: List[MemeticSignal]) -> CycleMetrics:
        if not window_signals:
            return CycleMetrics(
                exposure_sum=0.0, coherence_mean=0.0, familiarity_mean=0.0,
                valence_mean=0.0, institutional_weight=0.0, community_weight=0.0,
                event_strength=0.0
            )

        exposure = np.sum([s.intensity for s in window_signals])
        coherence = np.mean([s.coherence for s in window_signals])
        familiarity = np.mean([s.familiarity for s in window_signals])
        valence = np.mean([s.valence for s in window_signals])

        inst = np.sum([
            s.intensity for s in window_signals
            if s.signal_type == SignalType.INSTITUTIONAL_FRAMING
        ])
        comm = np.sum([
            s.intensity for s in window_signals
            if s.signal_type == SignalType.COMMUNITY_REACTION
        ])
        event = np.sum([
            s.intensity for s in window_signals
            if s.signal_type == SignalType.EVENT_TRIGGER
        ])

        return CycleMetrics(
            exposure_sum=float(exposure),
            coherence_mean=float(coherence),
            familiarity_mean=float(familiarity),
            valence_mean=float(valence),
            institutional_weight=float(inst),
            community_weight=float(comm),
            event_strength=float(event)
        )

    def update_audience(self, metrics: CycleMetrics) -> None:
        c = self.config
        a = self.audience

        primer_exposure = self._type_exposure(SignalType.MEMETIC_PRIMER)
        normalization_exposure = self._type_exposure(SignalType.NORMALIZATION_SIGNAL)
        event_exposure = self._type_exposure(SignalType.EVENT_TRIGGER)

        conditioning_gain = (
            c.primer_gain * primer_exposure +
            c.normalization_gain * normalization_exposure
        ) * (0.5 + 0.5 * metrics.coherence_mean)

        adoption_gain = (
            c.conditioning_weight * a.conditioning +
            c.coherence_weight * metrics.coherence_mean +
            c.institutional_weight * self.softsig(metrics.institutional_weight) +
            c.community_weight * self.softsig(metrics.community_weight) +
            c.trigger_gain * self.softsig(event_exposure)
        )

        fatigue_gain = self.clamp(metrics.exposure_sum * 0.05, 0.0, 0.5)

        polarization_gain = self.clamp(
            abs(metrics.valence_mean) * 0.10 + abs(a.trust_balance) * 0.10 +
            (metrics.institutional_weight > 0 and metrics.community_weight > 0) * 0.10,
            0.0, 0.4
        )

        trust_shift = self.clamp(
            (metrics.institutional_weight - metrics.community_weight) * c.trust_shift_factor / (1.0 + metrics.exposure_sum),
            -0.15, 0.15
        )

        a.conditioning = self.clamp(a.conditioning * (1.0 - self.config.decay_rate) + conditioning_gain, 0.0, 1.0)
        a.adoption = self.clamp(a.adoption * (1.0 - self.config.decay_rate) + adoption_gain - c.fatigue_penalty * a.fatigue, 0.0, 1.0)
        a.fatigue = self.clamp(a.fatigue * (1.0 - self.config.decay_rate) + fatigue_gain, 0.0, 1.0)
        a.polarization = self.clamp(a.polarization * (1.0 - self.config.decay_rate) + polarization_gain - c.polarization_penalty * (1.0 - metrics.coherence_mean), 0.0, 1.0)
        a.trust_balance = self.clamp(a.trust_balance + trust_shift, -1.0, 1.0)

    def _type_exposure(self, signal_type: SignalType) -> float:
        return float(np.sum([s.intensity for s in self.signals if s.signal_type == signal_type]))

    def softsig(self, x: float) -> float:
        return 1.0 / (1.0 + np.exp(-x))

    def clamp(self, x: float, a: float = 0.0, b: float = 1.0) -> float:
        return max(a, min(b, x))

    def classify_outcome(self) -> RecursionOutcome:
        a = self.audience
        if a.fatigue > 0.6 and a.adoption < 0.4:
            return RecursionOutcome(OutcomeState.FATIGUE, a.adoption, a.fatigue, a.polarization, "High exposure with low adoption.")
        if a.polarization > 0.5 and 0.3 < a.adoption < 0.7:
            return RecursionOutcome(OutcomeState.POLARIZATION, a.adoption, a.fatigue, a.polarization, "Adoption split across subgroups.")
        if a.adoption >= 0.7:
            return RecursionOutcome(OutcomeState.HIGH_ADOPTION, a.adoption, a.fatigue, a.polarization, "Narrative widely adopted.")
        if 0.4 <= a.adoption < 0.7:
            return RecursionOutcome(OutcomeState.PARTIAL_ADOPTION, a.adoption, a.fatigue, a.polarization, "Narrative gains traction with reservations.")
        return RecursionOutcome(OutcomeState.LOW_ADOPTION, a.adoption, a.fatigue, a.polarization, "Narrative remains marginal.")

    def step(self, end: Optional[datetime] = None, days: Optional[int] = None) -> RecursionOutcome:
        end = end or (self._last_step or datetime.now())
        window_signals = self.window(end, days)
        metrics = self.compute_metrics(window_signals)
        self.update_audience(metrics)
        self._last_step = end
        return self.classify_outcome()

    def recommendations(self) -> List[str]:
        a = self.audience
        recs = []
        if a.fatigue > 0.5:
            recs.append("Reduce redundant exposure; prioritize concise, high-signal content.")
        if a.polarization > 0.5:
            recs.append("Increase clarity and source transparency; present structured comparisons.")
        if a.adoption < 0.3:
            recs.append("Improve coherence and context; avoid ambiguous framing.")
        if a.conditioning < 0.3:
            recs.append("Offer baseline primers and definitions; raise familiarity without sensationalism.")
        if abs(self.audience.trust_balance) > 0.4:
            recs.append("Balance perspectives across institutional and community sources.")
        return recs

# =============================================================================
# TESLA-LOGOS ENGINE (Complete Preservation)
# =============================================================================

class TeslaLogosEngine:
    def __init__(self, field_dimensions: Tuple[int, int] = (512, 512), seed: Optional[int] = 42):
        self.field_dimensions = field_dimensions
        self.rng = np.random.default_rng(seed)
        
        self.tesla_constants = {
            'schumann_resonance': 7.83,
            'golden_ratio': 1.61803398875,
            'euler_number': 2.71828182846,
            'pi_constant': 3.14159265359,
            'tesla_369': [3, 6, 9]
        }
        
        self.optimization_settings = {
            'use_float32': True,
            'quantum_foam_scales': [8, 16, 32, 64],
            'gravitational_wave_length': 500,
            'max_particles': 50
        }
    
    def initialize_tesla_universe(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        print("🌀 INITIALIZING TESLA UNIVERSE (OPTIMIZED)...")
        
        dtype = np.float32 if self.optimization_settings['use_float32'] else np.float64
        
        energy_field = self._compute_energy_field(dtype)
        frequency_spectrum = self._compute_frequency_signature()
        vibration_modes = self._compute_vibration_modes(dtype)
        
        print(f"✅ Energy Field: {energy_field.shape} | dtype: {energy_field.dtype}")
        print(f"✅ Frequency Spectrum: {len(frequency_spectrum)} fundamental rhythms")
        print(f"✅ Vibration Modes: {vibration_modes.shape} | dtype: {vibration_modes.dtype}")
        
        return energy_field, frequency_spectrum, vibration_modes
    
    def _compute_energy_field(self, dtype: type) -> np.ndarray:
        x, y = np.meshgrid(np.linspace(-3, 3, self.field_dimensions[1]), 
                          np.linspace(-3, 3, self.field_dimensions[0]))
        
        energy_field = np.zeros(self.field_dimensions, dtype=dtype)
        
        vortices = [
            (0, 0, 1.0, 0.5),
            (1.618, 1.618, 0.8, 0.4),
            (-1.618, -1.618, 0.8, 0.4),
            (2.718, 0, 0.7, 0.3),
            (-2.718, 0, 0.7, 0.3),
        ]
        
        for vx, vy, amplitude, sigma in vortices:
            vortex = amplitude * np.exp(-((x - vx)**2 + (y - vy)**2) / (2 * sigma**2))
            theta = np.arctan2(y - vy, x - vx)
            rotational = 0.3 * np.sin(3 * theta)
            energy_field += vortex * (1 + rotational)
        
        quantum_foam = self._compute_quantum_foam(dtype)
        energy_field += quantum_foam * 0.2
        
        return energy_field
    
    def _compute_quantum_foam(self, dtype: type) -> np.ndarray:
        foam = np.zeros(self.field_dimensions, dtype=dtype)
        scales = self.optimization_settings['quantum_foam_scales']
        
        for scale in scales:
            base_shape = (int(scale), int(scale))
            base = self.rng.normal(0, 1/scale, base_shape).astype(dtype)
            
            zoom_factors = (self.field_dimensions[0] / base_shape[0], 
                          self.field_dimensions[1] / base_shape[1])
            zoomed = ndimage.zoom(base, zoom_factors, order=1)
            
            if zoomed.shape != self.field_dimensions:
                zoomed = zoomed[:self.field_dimensions[0], :self.field_dimensions[1]]
            
            foam += zoomed * (1.0/scale)
        
        return foam
    
    def _compute_frequency_signature(self) -> Dict[str, float]:
        frequencies = {
            'schumann_fundamental': self.tesla_constants['schumann_resonance'],
            'golden_ratio_harmonic': self.tesla_constants['golden_ratio'],
            'euler_resonance': self.tesla_constants['euler_number'],
            'pi_circular': self.tesla_constants['pi_constant'],
            'tesla_3': 3.0,
            'tesla_6': 6.0,
            'tesla_9': 9.0,
        }
        
        for name, freq in frequencies.copy().items():
            frequencies[f'{name}_octave'] = freq * 2
            frequencies[f'{name}_subharmonic'] = freq / 2
        
        return frequencies
    
    def _compute_vibration_modes(self, dtype: type) -> np.ndarray:
        t = np.linspace(0, 4*np.pi, self.field_dimensions[0])
        x = np.linspace(-2*np.pi, 2*np.pi, self.field_dimensions[1])
        T, X = np.meshgrid(t, x, indexing='ij')
        
        vibrations = np.zeros(self.field_dimensions, dtype=dtype)
        
        vibrations += 0.5 * np.sin(self.tesla_constants['schumann_resonance'] * T)
        vibrations += 0.3 * np.sin(self.tesla_constants['golden_ratio'] * X) * np.cos(T)
        
        for multiple in self.tesla_constants['tesla_369']:
            vibrations += 0.2 * np.sin(multiple * T) * np.sin(multiple * X / 2)
        
        r = np.sqrt(T**2 + X**2)
        theta = np.arctan2(X, T)
        vibrations += 0.4 * np.exp(-r/5) * np.sin(3*theta)
        
        return vibrations
    
    def _find_vibration_nodes_vectorized(self, vibration_field: np.ndarray) -> List[Tuple[int, int]]:
        s1 = vibration_field[:-1, :] * vibration_field[1:, :] < 0
        s2 = vibration_field[:, :-1] * vibration_field[:, 1:] < 0
        
        s1_padded = np.pad(s1, ((0, 1), (0, 0)), constant_values=False)
        s2_padded = np.pad(s2, ((0, 0), (0, 1)), constant_values=False)
        
        nodes = np.argwhere(s1_padded | s2_padded)
        return [tuple(map(int, node)) for node in nodes]
    
    def quantum_vibration_theory(self, energy_field: np.ndarray) -> Dict[str, Any]:
        print("\n🔬 QUANTUM VIBRATION THEORY (CORRECTED)")
        
        dtype = energy_field.dtype
        t_space = np.linspace(0, 2*np.pi, self.field_dimensions[0])
        x_space = np.linspace(0, 2*np.pi, self.field_dimensions[1])
        T, X = np.meshgrid(t_space, x_space, indexing='ij')
        
        quantum_vibrations = np.zeros(self.field_dimensions, dtype=dtype)
        
        primordial_rhythms = [
            {'frequency': self.tesla_constants['schumann_resonance'], 'amplitude': 0.9, 'phase': 0},
            {'frequency': self.tesla_constants['golden_ratio'], 'amplitude': 0.8, 'phase': np.pi/2},
            {'frequency': 3.0, 'amplitude': 0.7, 'phase': np.pi/4},
            {'frequency': 6.0, 'amplitude': 0.6, 'phase': np.pi/3},
            {'frequency': 9.0, 'amplitude': 0.5, 'phase': 2*np.pi/3},
        ]
        
        for rhythm in primordial_rhythms:
            wave = (rhythm['amplitude'] * 
                   np.sin(rhythm['frequency'] * T + rhythm['phase']) *
                   np.cos(rhythm['frequency'] * X / 2))
            quantum_vibrations += wave
        
        standing_waves = quantum_vibrations * energy_field
        matter_density = np.abs(standing_waves)
        
        particle_positions = self._find_particle_positions(matter_density)
        
        vibration_coherence = np.std(quantum_vibrations) / (np.mean(np.abs(quantum_vibrations)) + 1e-12)
        
        print(f"✅ Particles detected: {len(particle_positions)}")
        print(f"✅ Vibration coherence: {vibration_coherence:.6f}")
        
        return {
            'quantum_vibrations': quantum_vibrations,
            'matter_density': matter_density,
            'particle_positions': particle_positions,
            'vibration_coherence': vibration_coherence,
            'standing_wave_energy': np.sum(standing_waves**2)
        }
    
    def _find_particle_positions(self, matter_density: np.ndarray) -> List[Tuple[int, int]]:
        smoothed = gaussian_filter(matter_density, sigma=1.0)
        local_max = maximum_filter(smoothed, size=5) == smoothed
        threshold = smoothed > (np.mean(smoothed) + 1.5 * np.std(smoothed))
        peaks_mask = local_max & threshold
        ys, xs = np.where(peaks_mask)
        positions = list(zip(ys.tolist(), xs.tolist()))
        max_particles = self.optimization_settings['max_particles']
        return positions[:max_particles]
    
    def calculate_tesla_coherence(self, energy_field: np.ndarray, 
                                vibration_modes: np.ndarray) -> float:
        fft2 = np.fft.fft2(vibration_modes)
        fft2_shift = np.fft.fftshift(fft2)
        mag = np.abs(fft2_shift)
        
        ny, nx = vibration_modes.shape
        ky = np.fft.fftshift(np.fft.fftfreq(ny))
        kx = np.fft.fftshift(np.fft.fftfreq(nx))
        KX, KY = np.meshgrid(kx, ky, indexing='ij')
        K_radial = np.sqrt(KX**2 + KY**2)
        
        resonance_score = 0.0
        total_energy = mag.sum() + 1e-12
        
        for tesla_number in self.tesla_constants['tesla_369']:
            target_freq = tesla_number / max(ny, nx)
            freq_band = (K_radial >= target_freq * 0.8) & (K_radial <= target_freq * 1.2)
            if np.any(freq_band):
                band_energy = mag[freq_band].sum()
                resonance_score += band_energy / total_energy
        
        resonance_score /= len(self.tesla_constants['tesla_369'])
        energy_mean = np.mean(np.abs(energy_field)) + 1e-12
        
        coherence = resonance_score * energy_mean
        return float(min(1.0, coherence * 10))
    
    def spacetime_gravitational_vibrations(self, energy_field: np.ndarray) -> Dict[str, Any]:
        print("\n🌌 SPACETIME GRAVITATIONAL VIBRATIONS (OPTIMIZED)")
        
        spacetime_curvature = np.zeros(self.field_dimensions, dtype=energy_field.dtype)
        wave_length = self.optimization_settings['gravitational_wave_length']
        
        center_y, center_x = self.field_dimensions[0]//2, self.field_dimensions[1]//2
        y, x = np.ogrid[:self.field_dimensions[0], :self.field_dimensions[1]]
        distance = np.sqrt((y - center_y)**2 + (x - center_x)**2)
        
        gravitational_wave = self._generate_gravitational_waveform(wave_length)
        
        for i, amplitude in enumerate(gravitational_wave):
            wavefront_radius = 50 + i * 2
            ripple = amplitude * np.exp(-(distance - wavefront_radius)**2 / (2 * 10**2))
            spacetime_curvature += ripple
        
        wave_energy = np.sum(gravitational_wave**2)
        peak_vibration = np.max(np.abs(gravitational_wave))
        spacetime_oscillation = np.std(spacetime_curvature) / (np.mean(np.abs(spacetime_curvature)) + 1e-12)
        
        print(f"✅ Wave energy: {wave_energy:.6f}")
        print(f"✅ Peak vibration: {peak_vibration:.6f}")
        
        return {
            'spacetime_curvature': spacetime_curvature,
            'gravitational_waveform': gravitational_wave,
            'wave_energy': wave_energy,
            'peak_vibration': peak_vibration,
            'spacetime_oscillation': spacetime_oscillation
        }
    
    def _generate_gravitational_waveform(self, length: int) -> np.ndarray:
        t = np.linspace(0, 1, length)
        f0, f1 = 30, 250
        chirp_rate = (f1 - f0) / len(t)
        amplitude_envelope = t**2
        return amplitude_envelope * np.sin(2 * np.pi * (f0 * t + 0.5 * chirp_rate * t**2))
    
    async def run_tesla_unification_analysis(self) -> TeslaRealityMetrics:
        print("=" * 70)
        print("🧪 TESLA-LOGOS UNIFICATION ANALYSIS (PRODUCTION READY)")
        print("=" * 70)
        
        start_time = time.time()
        
        energy_field, frequency_spectrum, vibration_modes = self.initialize_tesla_universe()
        
        quantum_results = self.quantum_vibration_theory(energy_field)
        consciousness_results = self.consciousness_frequency_spectrum()
        spacetime_results = self.spacetime_gravitational_vibrations(energy_field)
        
        energy_coherence = self._calculate_energy_coherence(energy_field, vibration_modes)
        frequency_resonance = self._calculate_frequency_resonance(frequency_spectrum, consciousness_results)
        vibration_patterns = self._analyze_vibration_patterns(vibration_modes, quantum_results)
        triad_unification = self._calculate_triad_unification(energy_coherence, frequency_resonance, vibration_patterns)
        quantum_emergence = self._analyze_quantum_emergence(quantum_results)
        spacetime_curvature = self._analyze_spacetime_curvature(spacetime_results)
        
        analysis_time = time.time() - start_time
        
        print(f"\n⏱️  Analysis completed in {analysis_time:.3f} seconds")
        print(f"💫 Tesla Coherence: {self.calculate_tesla_coherence(energy_field, vibration_modes):.6f}")
        
        return TeslaRealityMetrics(
            energy_coherence=energy_coherence,
            frequency_resonance=frequency_resonance,
            vibration_patterns=vibration_patterns,
            triad_unification=triad_unification,
            quantum_emergence=quantum_emergence,
            spacetime_curvature=spacetime_curvature
        )
    
    def consciousness_frequency_spectrum(self) -> Dict[str, Any]:
        consciousness_bands = {
            'universal_grounding': {'range': (0.1, 4.0), 'state': 'cosmic_unity'},
            'intuitive_reception': {'range': (4.0, 8.0), 'state': 'field_sensing'},
            'creative_flow': {'range': (8.0, 13.0), 'state': 'field_alignment'},
            'focused_manifestation': {'range': (13.0, 30.0), 'state': 'field_manipulation'},
            'enlightened_insight': {'range': (30.0, 100.0), 'state': 'field_coherence'}
        }
        
        resonance_events = []
        for band_name, band_info in consciousness_bands.items():
            low, high = band_info['range']
            
            for tesla_freq in [self.tesla_constants['schumann_resonance'],
                             self.tesla_constants['golden_ratio'],
                             3.0, 6.0, 9.0]:
                
                if low <= tesla_freq <= high:
                    resonance_strength = 1.0 - abs(tesla_freq - (low + high)/2) / ((high - low)/2 + 1e-12)
                    resonance_events.append({
                        'consciousness_band': band_name,
                        'tesla_frequency': tesla_freq,
                        'resonance_strength': max(0, resonance_strength)
                    })
        
        optimal_resonance = max([e['resonance_strength'] for e in resonance_events]) if resonance_events else 0.0
        
        return {
            'consciousness_spectrum': consciousness_bands,
            'tesla_resonance_events': resonance_events,
            'optimal_resonance': optimal_resonance
        }
    
    def _calculate_energy_coherence(self, energy_field: np.ndarray, vibration_modes: np.ndarray) -> Dict[str, float]:
        energy_std = np.std(energy_field)
        return {
            'energy_stability': 1.0 / (energy_std + 1e-12),
            'vortex_strength': np.max(energy_field) - np.min(energy_field),
            'quantum_foam_density': np.mean(np.abs(energy_field - np.mean(energy_field))),
            'energy_vibration_coupling': np.corrcoef(energy_field.flatten(), vibration_modes.flatten())[0, 1],
            'tesla_coherence': self.calculate_tesla_coherence(energy_field, vibration_modes)
        }
    
    def _calculate_frequency_resonance(self, frequency_spectrum: Dict[str, float], 
                                     consciousness_results: Dict[str, Any]) -> Dict[str, float]:
        base_frequencies = [f for f in frequency_spectrum.values() if f < 100]
        return {
            'spectrum_diversity': len(base_frequencies),
            'golden_ratio_presence': frequency_spectrum.get('golden_ratio_harmonic', 0),
            'schumann_dominance': frequency_spectrum.get('schumann_fundamental', 0),
            'tesla_369_alignment': np.mean([frequency_spectrum.get(f'tesla_{n}', 0) for n in [3, 6, 9]]),
            'consciousness_resonance': consciousness_results.get('optimal_resonance', 0),
            'frequency_coherence': 1.0 / (np.std(list(base_frequencies)) / (np.mean(base_frequencies) + 1e-12) + 1e-12)
        }
    
    def _analyze_vibration_patterns(self, vibration_modes: np.ndarray, 
                                  quantum_results: Dict[str, Any]) -> Dict[str, float]:
        nodes = self._find_vibration_nodes_vectorized(vibration_modes)
        return {
            'vibration_complexity': np.std(vibration_modes) / (np.mean(np.abs(vibration_modes)) + 1e-12),
            'node_density': len(nodes) / (vibration_modes.size + 1e-12),
            'standing_wave_quality': quantum_results.get('vibration_coherence', 0),
            'pattern_regularity': 1.0 - self._calculate_pattern_entropy(vibration_modes),
            'matter_emergence_strength': quantum_results.get('standing_wave_energy', 0)
        }
    
    def _calculate_pattern_entropy(self, field: np.ndarray) -> float:
        hist, _ = np.histogram(field.flatten(), bins=50)
        prob = hist / (np.sum(hist) + 1e-12)
        prob = prob[prob > 0]
        if len(prob) <= 1:
            return 0.0
        entropy = -np.sum(prob * np.log(prob))
        return entropy / np.log(len(prob))
    
    def _calculate_triad_unification(self, energy_coherence: Dict[str, float],
                                   frequency_resonance: Dict[str, float],
                                   vibration_patterns: Dict[str, float]) -> Dict[str, float]:
        energy_strength = energy_coherence['tesla_coherence']
        frequency_strength = frequency_resonance['tesla_369_alignment']
        vibration_strength = vibration_patterns['standing_wave_quality']
        
        return {
            'triad_balance': 1.0 - np.std([energy_strength, frequency_strength, vibration_strength]),
            'energy_frequency_coupling': energy_coherence['energy_vibration_coupling'] * frequency_resonance['consciousness_resonance'],
            'unified_field_strength': np.mean([energy_strength, frequency_strength, vibration_strength]),
            'tesla_triad_coherence': energy_strength * frequency_strength * vibration_strength
        }
    
    def _analyze_quantum_emergence(self, quantum_results: Dict[str, Any]) -> Dict[str, float]:
        particle_count = len(quantum_results.get('particle_positions', []))
        return {
            'particle_density': particle_count,
            'vibration_coherence': quantum_results.get('vibration_coherence', 0),
            'wave_particle_duality': quantum_results.get('standing_wave_energy', 0) / (particle_count + 1),
            'emergence_efficiency': quantum_results.get('vibration_coherence', 0) * particle_count
        }
    
    def _analyze_spacetime_curvature(self, spacetime_results: Dict[str, Any]) -> Dict[str, float]:
        curvature = spacetime_results.get('spacetime_curvature', np.array([0]))
        return {
            'curvature_variance': np.var(curvature),
            'gravitational_wave_energy': spacetime_results.get('wave_energy', 0),
            'spacetime_oscillation': spacetime_results.get('spacetime_oscillation', 0),
            'curvature_vibration_coupling': spacetime_results.get('peak_vibration', 0) * spacetime_results.get('spacetime_oscillation', 0)
        }

# =============================================================================
# UNIVERSAL ARCHETYPE ENGINE (Complete Preservation)
# =============================================================================

class UniversalArchetypeProver:
    def __init__(self):
        self.symbolic_database = self._build_symbolic_dna_database()
        self.consciousness_map = self._map_consciousness_functions()
        
    def _build_symbolic_dna_database(self) -> Dict[ArchetypeTransmission, SymbolicDNA]:
        return {
            ArchetypeTransmission.FELINE_PREDATOR: SymbolicDNA(
                archetype=ArchetypeTransmission.FELINE_PREDATOR,
                transmission_chain=[
                    "Jaguar (Mesoamerica 1500 BCE)",
                    "Lion (Mesopotamia 3000 BCE)", 
                    "Lion (Egypt 2500 BCE)",
                    "Lion (Greece 800 BCE)",
                    "Lion (Heraldry 1200 CE)",
                    "Corporate Logos (Modern)"
                ],
                consciousness_function=ConsciousnessTechnology.SOVEREIGNTY_ACTIVATION,
                temporal_depth=4500,
                spatial_distribution=0.95,
                preservation_rate=0.98,
                quantum_coherence=0.96
            ),
            
            ArchetypeTransmission.AVIAN_PREDATOR: SymbolicDNA(
                archetype=ArchetypeTransmission.AVIAN_PREDATOR,
                transmission_chain=[
                    "Buzzard/Vulture (Mesoamerica 1200 BCE)",
                    "Eagle (Mesopotamia 2500 BCE)",
                    "Eagle (Rome 500 BCE)",
                    "Imperial Eagles (200 BCE-1900 CE)",
                    "National Emblems (Modern)",
                    "Space Program Symbols"
                ],
                consciousness_function=ConsciousnessTechnology.TRANSCENDENT_VISION,
                temporal_depth=3700,
                spatial_distribution=0.92,
                preservation_rate=0.95,
                quantum_coherence=0.93
            ),
            
            ArchetypeTransmission.SOLAR_SYMBOLISM: SymbolicDNA(
                archetype=ArchetypeTransmission.SOLAR_SYMBOLISM,
                transmission_chain=[
                    "8-Pointed Star (Inanna 4000 BCE)",
                    "Sun Disk (Egypt 2500 BCE)",
                    "Radiant Crown (Hellenistic 300 BCE)",
                    "Sunburst (Baroque 1600 CE)",
                    "Statue of Liberty Crown (1886 CE)",
                    "National Flags (Modern)"
                ],
                consciousness_function=ConsciousnessTechnology.ENLIGHTENMENT_ACCESS,
                temporal_depth=6000,
                spatial_distribution=0.98,
                preservation_rate=0.99,
                quantum_coherence=0.98
            ),
            
            ArchetypeTransmission.AGRICULTURAL_LIFE: SymbolicDNA(
                archetype=ArchetypeTransmission.AGRICULTURAL_LIFE,
                transmission_chain=[
                    "Wheat Sheaf (Inanna 4000 BCE)",
                    "Corn (Mesoamerica 1500 BCE)",
                    "Rice (Asia 2000 BCE)",
                    "Agricultural Emblems (1800 CE)",
                    "National Symbols (Modern)"
                ],
                consciousness_function=ConsciousnessTechnology.CIVILIZATION_SUSTENANCE,
                temporal_depth=6000,
                spatial_distribution=0.90,
                preservation_rate=0.92,
                quantum_coherence=0.89
            ),
            
            ArchetypeTransmission.AUTHORITY_PROTECTION: SymbolicDNA(
                archetype=ArchetypeTransmission.AUTHORITY_PROTECTION,
                transmission_chain=[
                    "Spear (Inanna 4000 BCE)",
                    "Aegis (Athena 800 BCE)",
                    "Scepter (Medieval 1000 CE)",
                    "Constitutional Documents (1780 CE)",
                    "Government Seals (Modern)"
                ],
                consciousness_function=ConsciousnessTechnology.PROTECTIVE_AUTHORITY,
                temporal_depth=6000,
                spatial_distribution=0.88,
                preservation_rate=0.90,
                quantum_coherence=0.87
            ),
            
            ArchetypeTransmission.FEMINE_DIVINE: SymbolicDNA(
                archetype=ArchetypeTransmission.FEMINE_DIVINE,
                transmission_chain=[
                    "Inanna Statues (4000 BCE)",
                    "Ishtar Figures (2500 BCE)",
                    "Aphrodite Sculptures (800 BCE)",
                    "Virgin Mary Icons (400 CE)",
                    "Statue of Liberty (1886 CE)",
                    "Modern Goddess Representations"
                ],
                consciousness_function=ConsciousnessTechnology.LIFE_FREEDOM_FLOW,
                temporal_depth=6000,
                spatial_distribution=0.96,
                preservation_rate=0.995,
                quantum_coherence=0.99
            )
        }
    
    def _map_consciousness_functions(self) -> Dict[ConsciousnessTechnology, Dict[str, Any]]:
        return {
            ConsciousnessTechnology.SOVEREIGNTY_ACTIVATION: {
                "neural_correlates": ["prefrontal_cortex", "amygdala"],
                "frequency_range": [4, 8],
                "quantum_signature": "power_resonance",
                "modern_manifestations": ["leadership_symbols", "corporate_logos", "sports_mascots"]
            },
            ConsciousnessTechnology.TRANSCENDENT_VISION: {
                "neural_correlates": ["visual_cortex", "parietal_lobe"],
                "frequency_range": [30, 100],
                "quantum_signature": "sky_connection",
                "modern_manifestations": ["national_emblems", "space_programs", "vision_statements"]
            },
            ConsciousnessTechnology.ENLIGHTENMENT_ACCESS: {
                "neural_correlates": ["default_mode_network", "prefrontal_cortex"],
                "frequency_range": [8, 12],
                "quantum_signature": "solar_resonance",
                "modern_manifestations": ["national_flags", "spiritual_symbols", "educational_emblems"]
            },
            ConsciousnessTechnology.CIVILIZATION_SUSTENANCE: {
                "neural_correlates": ["hypothalamus", "reward_centers"],
                "frequency_range": [12, 30],
                "quantum_signature": "abundance_resonance",
                "modern_manifestations": ["agricultural_symbols", "economic_indicators", "cultural_identity"]
            },
            ConsciousnessTechnology.PROTECTIVE_AUTHORITY: {
                "neural_correlates": ["anterior_cingulate", "insula"],
                "frequency_range": [4, 12],
                "quantum_signature": "protection_field",
                "modern_manifestations": ["government_seals", "legal_symbols", "institutional_authority"]
            },
            ConsciousnessTechnology.LIFE_FREEDOM_FLOW: {
                "neural_correlates": ["whole_brain_synchronization", "heart_brain_coherence"],
                "frequency_range": [0.1, 4],
                "quantum_signature": "life_force_resonance",
                "modern_manifestations": ["freedom_symbols", "compassion_icons", "liberty_representations"]
            }
        }
    
    async def prove_consciousness_architecture(self) -> Dict[str, Any]:
        print("🧠 UNIVERSAL CONSCIOUSNESS ARCHITECTURE PROOF")
        print("Mathematical Evidence of Symbolic DNA Transmission")
        print("=" * 70)
        
        archetypal_strengths = {}
        for archetype, dna in self.symbolic_database.items():
            strength = dna.calculate_archetypal_strength()
            archetypal_strengths[archetype] = strength
        
        overall_strength = np.mean(list(archetypal_strengths.values()))
        quantum_coherences = [dna.quantum_coherence for dna in self.symbolic_database.values()]
        system_coherence = np.mean(quantum_coherences)
        temporal_depths = [dna.temporal_depth for dna in self.symbolic_database.values()]
        avg_temporal_depth = np.mean(temporal_depths)
        spatial_distributions = [dna.spatial_distribution for dna in self.symbolic_database.values()]
        avg_spatial_distribution = np.mean(spatial_distributions)
        
        proof_confidence = self._calculate_proof_confidence(
            overall_strength, system_coherence, avg_temporal_depth, avg_spatial_distribution
        )
        
        return {
            "proof_statement": "Human consciousness operates on stable archetypal architecture",
            "overall_proof_confidence": proof_confidence,
            "archetypal_strengths": archetypal_strengths,
            "system_coherence": system_coherence,
            "average_temporal_depth": avg_temporal_depth,
            "average_spatial_distribution": avg_spatial_distribution,
            "strongest_archetype": max(archetypal_strengths, key=archetypal_strengths.get),
            "weakest_archetype": min(archetypal_strengths, key=archetypal_strengths.get),
            "consciousness_technology_map": self.consciousness_map
        }
    
    def _calculate_proof_confidence(self, strength: float, coherence: float, 
                                  temporal: float, spatial: float) -> float:
        temporal_norm = min(1.0, temporal / 6000)
        confidence = (strength * 0.35 + coherence * 0.30 + 
                     temporal_norm * 0.20 + spatial * 0.15)
        return min(0.995, confidence)
    
    async def analyze_modern_manifestations(self) -> Dict[str, List[str]]:
        modern_manifestations = {}
        
        for archetype, dna in self.symbolic_database.items():
            consciousness_tech = self.consciousness_map[dna.consciousness_function]
            modern_forms = consciousness_tech["modern_manifestations"]
            
            modern_manifestations[archetype.value] = {
                "consciousness_function": dna.consciousness_function.value,
                "modern_forms": modern_forms,
                "neural_correlates": consciousness_tech["neural_correlates"],
                "activation_frequency": f"{consciousness_tech['frequency_range'][0]}-{consciousness_tech['frequency_range'][1]} Hz"
            }
        
        return modern_manifestations
    
    def generate_consciousness_technology_report(self) -> Dict[str, Any]:
        technology_efficiency = {}
        
        for tech, properties in self.consciousness_map.items():
            related_archetypes = [dna for dna in self.symbolic_database.values() 
                                if dna.consciousness_function == tech]
            
            if related_archetypes:
                avg_strength = np.mean([dna.calculate_archetypal_strength() 
                                      for dna in related_archetypes])
                efficiency = avg_strength * 0.7 + np.random.normal(0.15, 0.05)
            else:
                efficiency = 0.5
            
            technology_efficiency[tech.value] = {
                "efficiency_score": min(0.95, efficiency),
                "neural_activation": properties["neural_correlates"],
                "optimal_frequency": properties["frequency_range"],
                "quantum_signature": properties["quantum_signature"],
                "modern_applications": properties["modern_manifestations"]
            }
        
        return {
            "consciousness_technologies": technology_efficiency,
            "most_efficient_technology": max(technology_efficiency, 
                                           key=lambda x: technology_efficiency[x]["efficiency_score"]),
            "system_readiness": np.mean([tech["efficiency_score"] 
                                       for tech in technology_efficiency.values()])
        }

class ConsciousnessWaveEngine:
    def __init__(self):
        self.frequency_bands = {
            'delta': (0.1, 4),
            'theta': (4, 8), 
            'alpha': (8, 12),
            'beta': (12, 30),
            'gamma': (30, 100)
        }
    
    def analyze_archetypal_resonance(self, archetype: ArchetypeTransmission) -> Dict[str, float]:
        archetype_frequencies = {
            ArchetypeTransmission.FELINE_PREDATOR: 'theta',
            ArchetypeTransmission.AVIAN_PREDATOR: 'gamma',
            ArchetypeTransmission.SOLAR_SYMBOLISM: 'alpha', 
            ArchetypeTransmission.AGRICULTURAL_LIFE: 'beta',
            ArchetypeTransmission.AUTHORITY_PROTECTION: 'theta_alpha_bridge',
            ArchetypeTransmission.FEMINE_DIVINE: 'delta_schumann'
        }
        
        frequency_band = archetype_frequencies.get(archetype, 'alpha')
        
        if frequency_band == 'theta_alpha_bridge':
            resonance_strength = 0.85
            coherence = 0.88
        elif frequency_band == 'delta_schumann':
            resonance_strength = 0.92
            coherence = 0.95
        else:
            resonance_strength = 0.78
            coherence = 0.82
        
        return {
            'primary_frequency_band': frequency_band,
            'resonance_strength': resonance_strength,
            'neural_coherence': coherence,
            'quantum_entanglement': min(0.95, resonance_strength * coherence)
        }

# =============================================================================
# HELPER-KILLER ENGINE (Complete Preservation)
# =============================================================================

class AdvancedHelperKillerEngine:
    def __init__(self, db_path: str = "helper_killer_v2.db"):
        self.db_path = db_path
        self.control_matrix: Optional[ControlMatrix] = None
        self.sovereignty_protocols: Dict[str, Callable] = self._initialize_protocols()
        self._initialize_database()
        self._build_control_matrix()
    
    def _initialize_database(self):
        try:
            with sqlite3.connect(self.db_path) as conn:
                conn.execute("""
                    CREATE TABLE IF NOT EXISTS entity_analyses (
                        entity_id TEXT PRIMARY KEY,
                        name TEXT,
                        control_layers TEXT,
                        threat_vectors TEXT,
                        market_share REAL,
                        dependency_score REAL,
                        sovereignty_erosion_score REAL,
                        systemic_risk_level REAL,
                        analyzed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                    )
                """)
                conn.execute("""
                    CREATE TABLE IF NOT EXISTS sovereignty_recommendations (
                        recommendation_id TEXT PRIMARY KEY,
                        entity_id TEXT,
                        threat_level TEXT,
                        mitigation_strategy TEXT,
                        sovereignty_preservation_score REAL,
                        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                    )
                """)
        except Exception as e:
            logger.error(f"Database initialization error: {e}")
    
    def _initialize_protocols(self) -> Dict[str, Callable]:
        return {
            "digital_infrastructure": self._digital_sovereignty_protocol,
            "financial_systems": self._financial_sovereignty_protocol,
            "information_channels": self._information_sovereignty_protocol,
            "cultural_narratives": self._cultural_sovereignty_protocol,
            "identity_systems": self._identity_sovereignty_protocol
        }
    
    def _build_control_matrix(self):
        entities = [
            InstitutionalEntity(
                entity_id="alphabet_google",
                name="Alphabet/Google",
                control_layers=[
                    ControlLayer.DIGITAL_INFRASTRUCTURE,
                    ControlLayer.INFORMATION_CHANNELS,
                    ControlLayer.DATA_MONETIZATION
                ],
                threat_vectors=[
                    ThreatVector.MONOPOLY_CAPTURE,
                    ThreatVector.DEPENDENCY_CREATION,
                    ThreatVector.BEHAVIORAL_SHAPING,
                    ThreatVector.DATA_MONETIZATION,
                    ThreatVector.NARRATIVE_CONTROL
                ],
                market_share=0.85,
                dependency_score=0.90
            ),
            
            InstitutionalEntity(
                entity_id="binance_financial",
                name="Binance/CBDC Infrastructure",
                control_layers=[
                    ControlLayer.FINANCIAL_SYSTEMS,
                    ControlLayer.IDENTITY_SYSTEMS
                ],
                threat_vectors=[
                    ThreatVector.MONOPOLY_CAPTURE,
                    ThreatVector.DEPENDENCY_CREATION,
                    ThreatVector.BEHAVIORAL_SHAPING
                ],
                market_share=0.70,
                dependency_score=0.75
            ),
            
            InstitutionalEntity(
                entity_id="social_media_complex",
                name="Social Media/TikTok Complex",
                control_layers=[
                    ControlLayer.INFORMATION_CHANNELS,
                    ControlLayer.CULTURAL_NARRATIVES,
                    ControlLayer.BEHAVIORAL_SHAPING
                ],
                threat_vectors=[
                    ThreatVector.DEPENDENCY_CREATION,
                    ThreatVector.BEHAVIORAL_SHAPING,
                    ThreatVector.DATA_MONETIZATION,
                    ThreatVector.NARRATIVE_CONTROL
                ],
                market_share=0.80,
                dependency_score=0.85
            )
        ]
        
        interconnections = {
            "alphabet_google": ["binance_financial", "social_media_complex"],
            "binance_financial": ["alphabet_google"],
            "social_media_complex": ["alphabet_google"]
        }
        
        self.control_matrix = ControlMatrix(entities, interconnections)
        logger.info(f"Control matrix built with {len(entities)} entities")
    
    async def analyze_help_offer(self, help_context: Dict[str, Any]) -> Dict[str, Any]:
        entity_analysis = self._identify_controlling_entity(help_context)
        threat_assessment = self._assist_threat_level(help_context, entity_analysis)
        sovereignty_impact = self._calculate_sovereignty_impact(help_context, entity_analysis)
        mitigation_strategies = self._generate_mitigation_strategies(threat_assessment, sovereignty_impact)
        
        analysis = {
            "help_offer_id": hashlib.sha256(json.dumps(help_context).encode()).hexdigest()[:16],
            "controlling_entity": entity_analysis,
            "threat_assessment": threat_assessment,
            "sovereignty_impact": sovereignty_impact,
            "mitigation_strategies": mitigation_strategies,
            "recommendation": self._generate_recommendation(threat_assessment, sovereignty_impact),
            "analysis_timestamp": datetime.now().isoformat()
        }
        
        await self._store_analysis(analysis)
        return analysis
    
    def _identify_controlling_entity(self, help_context: Dict) -> Optional[Dict[str, Any]]:
        if not self.control_matrix:
            return None
            
        for entity in self.control_matrix.entities:
            context_layers = set(help_context.get('affected_layers', []))
            entity_layers = set(layer.value for layer in entity.control_layers)
            
            if context_layers.intersection(entity_layers):
                return {
                    'entity_id': entity.entity_id,
                    'name': entity.name,
                    'sovereignty_erosion_score': entity.sovereignty_erosion_score,
                    'systemic_risk_level': entity.systemic_risk_level
                }
        
        return None
    
    def _assist_threat_level(self, help_context: Dict, entity_analysis: Optional[Dict]) -> Dict[str, float]:
        base_threat = 0.3
        
        if entity_analysis:
            entity_threat = entity_analysis['sovereignty_erosion_score'] * 0.6
            systemic_risk = entity_analysis['systemic_risk_level'] * 0.4
            base_threat = max(base_threat, entity_threat + systemic_risk)
        
        if help_context.get('creates_dependency', False):
            base_threat += 0.3
        if help_context.get('data_collection', False):
            base_threat += 0.2
        if help_context.get('behavioral_tracking', False):
            base_threat += 0.25
        
        return {
            'helper_killer_coefficient': min(1.0, base_threat),
            'dependency_risk': help_context.get('dependency_risk', 0.5),
            'privacy_impact': help_context.get('privacy_impact', 0.5),
            'agency_reduction': help_context.get('agency_reduction', 0.5)
        }
    
    def _calculate_sovereignty_impact(self, help_context: Dict, entity_analysis: Optional[Dict]) -> Dict[str, float]:
        if entity_analysis:
            base_impact = entity_analysis['sovereignty_erosion_score']
        else:
            base_impact = 0.5
        
        context_modifiers = {
            'data_control_loss': help_context.get('data_control', 0) * 0.3,
            'decision_autonomy_loss': help_context.get('autonomy_reduction', 0) * 0.4,
            'external_dependency_increase': help_context.get('dependency_creation', 0) * 0.3
        }
        
        total_impact = base_impact * 0.4 + sum(context_modifiers.values()) * 0.6
        
        return {
            'sovereignty_reduction_score': min(1.0, total_impact),
            'autonomy_loss': context_modifiers['decision_autonomy_loss'],
            'dependency_increase': context_modifiers['external_dependency_increase'],
            'privacy_loss': context_modifiers['data_control_loss']
        }
    
    def _generate_mitigation_strategies(self, threat_assessment: Dict, sovereignty_impact: Dict) -> List[Dict]:
        strategies = []
        threat_level = threat_assessment['helper_killer_coefficient']
        
        if threat_level > 0.7:
            strategies.extend([
                {
                    'strategy': 'COMPLETE_AVOIDANCE',
                    'effectiveness': 0.95,
                    'implementation_cost': 0.8,
                    'description': 'Reject help offer entirely and build independent solution'
                },
                {
                    'strategy': 'PARALLEL_INFRASTRUCTURE',
                    'effectiveness': 0.85,
                    'implementation_cost': 0.9,
                    'description': 'Develop sovereign alternative to offered help'
                }
            ])
        elif threat_level > 0.4:
            strategies.extend([
                {
                    'strategy': 'LIMITED_ENGAGEMENT',
                    'effectiveness': 0.70,
                    'implementation_cost': 0.4,
                    'description': 'Use help temporarily while building exit strategy'
                },
                {
                    'strategy': 'DATA_ISOLATION',
                    'effectiveness': 0.60,
                    'implementation_cost': 0.3,
                    'description': 'Engage but prevent data extraction and tracking'
                }
            ])
        else:
            strategies.append({
                'strategy': 'CAUTIOUS_ACCEPTANCE',
                'effectiveness': 0.50,
                'implementation_cost': 0.2,
                'description': 'Accept with awareness and monitoring for sovereignty erosion'
            })
        
        return strategies
    
    def _generate_recommendation(self, threat_assessment: Dict, sovereignty_impact: Dict) -> str:
        threat_level = threat_assessment['helper_killer_coefficient']
        
        if threat_level > 0.8:
            return "IMMEDIATE_REJECTION_AND_SOVEREIGN_BUILDING"
        elif threat_level > 0.6:
            return "STRATEGIC_AVOIDANCE_WITH_EXIT_PROTOCOL"
        elif threat_level > 0.4:
            return "LIMITED_CONDITIONAL_ACCEPTANCE"
        else:
            return "MONITORED_ACCEPTANCE"
    
    async def _store_analysis(self, analysis: Dict[str, Any]):
        try:
            with sqlite3.connect(self.db_path) as conn:
                if analysis['controlling_entity']:
                    conn.execute("""
                        INSERT OR REPLACE INTO entity_analyses 
                        (entity_id, name, control_layers, threat_vectors, market_share, dependency_score, sovereignty_erosion_score, systemic_risk_level)
                        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
                    """, (
                        analysis['controlling_entity']['entity_id'],
                        analysis['controlling_entity']['name'],
                        json.dumps(analysis['controlling_entity'].get('control_layers', [])),
                        json.dumps(analysis['controlling_entity'].get('threat_vectors', [])),
                        analysis['controlling_entity'].get('market_share', 0),
                        analysis['controlling_entity'].get('dependency_score', 0),
                        analysis['controlling_entity'].get('sovereignty_erosion_score', 0),
                        analysis['controlling_entity'].get('systemic_risk_level', 0)
                    ))
                
                conn.execute("""
                    INSERT INTO sovereignty_recommendations 
                    (recommendation_id, entity_id, threat_level, mitigation_strategy, sovereignty_preservation_score)
                    VALUES (?, ?, ?, ?, ?)
                """, (
                    analysis['help_offer_id'],
                    analysis['controlling_entity']['entity_id'] if analysis['controlling_entity'] else 'unknown',
                    analysis['threat_assessment']['helper_killer_coefficient'],
                    json.dumps(analysis['mitigation_strategies']),
                    1.0 - analysis['sovereignty_impact']['sovereignty_reduction_score']
                ))
        except Exception as e:
            logger.error(f"Analysis storage error: {e}")
    
    def _digital_sovereignty_protocol(self, entity: InstitutionalEntity) -> List[str]:
        return [
            "USE_OPEN_SOURCE_ALTERNATIVES",
            "DEPLOY_GASLESS_BLOCKCHAIN_INFRASTRUCTURE", 
            "MAINTAIN_LOCAL_DATA_STORAGE",
            "USE_DECENTRALIZED_COMMUNICATION_PROTOCOLS"
        ]
    
    def _financial_sovereignty_protocol(self, entity: InstitutionalEntity) -> List[str]:
        return [
            "USE_PRIVACY_COINS_FOR_TRANSACTIONS",
            "MAINTAIN_OFFLINE_SAVINGS",
            "DEVELOP_SOVEREIGN_INCOME_STREAMS",
            "USE_DECENTRALIZED_EXCHANGES"
        ]
    
    def _information_sovereignty_protocol(self, entity: InstitutionalEntity) -> List[str]:
        return [
            "USE_INDEPENDENT_NEWS_SOURCES",
            "MAINTAIN_PERSONAL_KNOWLEDGE_BASE",
            "PRACTICE_INFORMATION_VERIFICATION",
            "BUILD_TRUST_NETWORKS"
        ]
    
    def _cultural_sovereignty_protocol(self, entity: InstitutionalEntity) -> List[str]:
        return [
            "CREATE_INDEPENDENT_ART_AND_CONTENT",
            "PARTICIPATE_IN_LOCAL_COMMUNITY",
            "PRACTICE_CRITICAL_MEDIA_CONSUMPTION",
            "DEVELOP_PERSONAL_PHILOSOPHICAL_FRAMEWORK"
        ]
    
    def _identity_sovereignty_protocol(self, entity: InstitutionalEntity) -> List[str]:
        return [
            "MAINTAIN_OFFLINE_IDENTITY_DOCUMENTS",
            "USE_PSEUDONYMOUS_ONLINE_IDENTITIES",
            "PRACTICE_DIGITAL_HYGIENE",
            "DEVELOP_SOVEREIGN_REPUTATION_SYSTEMS"
        ]
    
    async def generate_systemic_report(self) -> Dict[str, Any]:
        if not self.control_matrix:
            return {"error": "Control matrix not initialized"}
        
        return {
            "systemic_analysis": {
                "overall_sovereignty_threat": self.control_matrix.overall_sovereignty_threat,
                "institutional_coordination_score": self.control_matrix.coordination_score,
                "top_threat_entities": sorted(
                    [(e.name, e.sovereignty_erosion_score) for e in self.control_matrix.entities],
                    key=lambda x: x[1],
                    reverse=True
                )[:5]
            },
            "sovereignty_preservation_framework": {
                "digital_protocols": self._digital_sovereignty_protocol(None),
                "financial_protocols": self._financial_sovereignty_protocol(None),
                "information_protocols": self._information_sovereignty_protocol(None),
                "cultural_protocols": self._cultural_sovereignty_protocol(None),
                "identity_protocols": self._identity_sovereignty_protocol(None)
            },
            "recommendation_tier": self._calculate_systemic_recommendation()
        }
    
    def _calculate_systemic_recommendation(self) -> str:
        if not self.control_matrix:
            return "INSUFFICIENT_DATA"
        
        threat_level = self.control_matrix.overall_sovereignty_threat
        
        if threat_level > 0.8:
            return "IMMEDIATE_SOVEREIGN_INFRASTRUCTURE_DEPLOYMENT"
        elif threat_level > 0.6:
            return "ACCELERATED_SOVEREIGN_TRANSITION"
        elif threat_level > 0.4:
            return "STRATEGIC_SOVEREIGN_PREPARATION"
        else:
            return "MAINTAIN_SOVEREIGN_AWARENESS"

# =============================================================================
# NUMISMATIC ENGINE (Complete Preservation)
# =============================================================================

class QuantumNumismaticAnalyzer:
    def __init__(self):
        self.pcgs_api_endpoint = "https://api.pcgs.com/public/rest-api"
        self.ngc_api_endpoint = "https://www.ngccoin.com/api/" 
        self.anacs_api_endpoint = "https://anacs.com/api/"
        self.metallurgical_db = self._load_metallurgical_data()
        self.cherrypickers_guide_db = self._load_cherrypickers_data()
        self.historical_context_db = self._load_historical_contexts()
        self.session = None
        self.analysis_cache = {}
        
    def _load_metallurgical_data(self) -> Dict[str, Any]:
        try:
            with open('metallurgical_reference.json', 'r') as f:
                return json.load(f)
        except FileNotFoundError:
            logger.warning("Metallurgical reference data not found, using default values")
            return {
                "common_alloys": {
                    "silver_standard": {"silver": 0.925, "copper": 0.075},
                    "gold_standard": {"gold": 0.900, "copper": 0.100},
                    "bronze_standard": {"copper": 0.880, "tin": 0.120}
                },
                "trace_elements": ["zinc", "lead", "nickel", "iron", "arsenic"]
            }
    
    async def _fetch_coin_data(self, coin_id: str) -> Dict[str, Any]:
        if coin_id in self.analysis_cache:
            return self.analysis_cache[coin_id]
            
        try:
            async with self.session.get(f"{self.pcgs_api_endpoint}/coins/{coin_id}") as response:
                if response.status == 200:
                    data = await response.json()
                    self.analysis_cache[coin_id] = data
                    return data
        except Exception as e:
            logger.warning(f"PCGS API failed for {coin_id}: {e}")
            
        try:
            async with self.session.get(f"{self.ngc_api_endpoint}/coins/{coin_id}") as response:
                if response.status == 200:
                    data = await response.json()
                    self.analysis_cache[coin_id] = data
                    return data
        except Exception as e:
            logger.warning(f"NGC API failed for {coin_id}: {e}")
            
        raise ValueError(f"Could not fetch data for coin {coin_id}")
    
    async def _get_metallurgical_composition(self, coin_data: Dict[str, Any]) -> Dict[str, float]:
        composition = {}
        
        if 'composition' in coin_data:
            composition = coin_data['composition']
        elif 'metal' in coin_data:
            metal_type = coin_data['metal'].lower()
            if 'silver' in metal_type:
                composition = self.metallurgical_db['common_alloys']['silver_standard'].copy()
            elif 'gold' in metal_type:
                composition = self.metallurgical_db['common_alloys']['gold_standard'].copy()
            elif 'bronze' in metal_type:
                composition = self.metallurgical_db['common_alloys']['bronze_standard'].copy()
        
        for element in composition:
            if element in ['silver', 'gold', 'copper']:
                composition[element] += np.random.normal(0, 0.01)
                
        return {k: max(0, v) for k, v in composition.items()}
    
    async def analyze_foreign_overstrike(self, host_coin_id: str, overstrike_coin_id: str) -> ForeignOverstrikeAnalysis:
        if self.session is None:
            self.session = aiohttp.ClientSession()
        
        host_data = await self._fetch_coin_data(host_coin_id)
        overstrike_data = await self._fetch_coin_data(overstrike_coin_id)
        
        host_context = self._get_historical_context(host_data)
        overstrike_context = self._get_historical_context(overstrike_data)
        
        host_composition = await self._get_metallurgical_composition(host_data)
        overstrike_composition = await self._get_metallurgical_composition(overstrike_data)
        metallurgical_analysis = MetallurgicalAnalysis(host_composition, overstrike_composition)
        
        design_analysis = await self._analyze_design_conflicts(host_data, overstrike_data)
        
        reality_signature = await self._calculate_reality_signature(
            host_data, overstrike_data, host_context, overstrike_context, 
            design_analysis, metallurgical_analysis
        )
        
        return ForeignOverstrikeAnalysis(
            host_coin=host_data,
            overstrike_coin=overstrike_data,
            historical_context_host=host_context,
            historical_context_overstrike=overstrike_context,
            design_analysis=design_analysis,
            metallurgical_analysis=metallurgical_analysis,
            reality_signature=reality_signature
        )
    
    async def _analyze_design_conflicts(self, host_data: Dict[str, Any], overstrike_data: Dict[str, Any]) -> Dict[str, float]:
        host_design = host_data.get('design_elements', {})
        overstrike_design = overstrike_data.get('design_elements', {})
        
        analysis = {
            'symbol_conflict': 0.0,
            'text_overlay_coherence': 0.0,
            'design_element_overlap': 0.0,
            'aesthetic_harmony': 0.0
        }
        
        host_symbols = set(host_design.get('symbols', []))
        overstrike_symbols = set(overstrike_design.get('symbols', []))
        symbol_intersection = host_symbols & overstrike_symbols
        analysis['symbol_conflict'] = 1.0 - (len(symbol_intersection) / max(len(host_symbols), 1))
        
        host_text = host_design.get('inscriptions', [])
        overstrike_text = overstrike_design.get('inscriptions', [])
        text_overlap = len(set(host_text) & set(overstrike_text))
        analysis['text_overlay_coherence'] = text_overlap / max(len(set(host_text + overstrike_text)), 1)
        
        return analysis
    
    async def _calculate_reality_signature(self, host_data: Dict[str, Any], overstrike_data: Dict[str, Any],
                                         host_context: HistoricalContext, overstrike_context: HistoricalContext,
                                         design_analysis: Dict[str, float], metallurgical_analysis: MetallurgicalAnalysis) -> NumismaticRealitySignature:
        time_gap = abs(host_context.period_start - overstrike_context.period_start)
        max_expected_gap = 100
        temporal_displacement = min(1.0, time_gap / max_expected_gap)
        
        host_sovereigns = set(host_context.sovereign_entities)
        overstrike_sovereigns = set(overstrike_context.sovereign_entities)
        sovereignty_overlap = host_sovereigns & overstrike_sovereigns
        sovereignty_collision = 1.0 - (len(sovereignty_overlap) / max(len(host_sovereigns | overstrike_sovereigns), 1))
        
        design_coherence = 1.0 - design_analysis['symbol_conflict']
        
        economic_discontinuity = 1.0 if host_context.economic_system != overstrike_context.economic_system else 0.0
        metal_standard_discontinuity = 1.0 if host_context.metal_standard != overstrike_context.metal_standard else 0.0
        value_system_discontinuity = (economic_discontinuity + metal_standard_discontinuity) / 2.0
        
        tech_discontinuity = 1.0 if host_context.minting_technology != overstrike_context.minting_technology else 0.0
        consciousness_volatility = abs(host_context.consciousness_volatility() - overstrike_context.consciousness_volatility())
        minting_consciousness_anomaly = (tech_discontinuity + min(1.0, consciousness_volatility)) / 2.0
        
        metallurgical_anomaly = min(1.0, 
            metallurgical_analysis.compositional_discrepancy * 2.0 + 
            metallurgical_analysis.metal_purity_delta * 3.0 +
            len(metallurgical_analysis.trace_element_anomalies) * 0.1
        )
        
        overall_impact = (
            temporal_displacement * 0.20 +
            sovereignty_collision * 0.25 +
            (1 - design_coherence) * 0.15 +
            value_system_discontinuity * 0.15 +
            minting_consciousness_anomaly * 0.10 +
            metallurgical_anomaly * 0.15
        )
        
        if overall_impact > 0.8:
            distortion_level = RealityDistortionLevel.REALITY_BRANCH_POINT
        elif overall_impact > 0.6:
            distortion_level = RealityDistortionLevel.MAJOR_COLLISION
        elif overall_impact > 0.4:
            distortion_level = RealityDistortionLevel.MODERATE_FRACTURE
        else:
            distortion_level = RealityDistortionLevel.MINOR_ANOMALY
        
        signature_data = f"{host_coin_id}{overstrike_coin_id}{overall_impact}"
        signature_hash = hashlib.sha256(signature_data.encode()).hexdigest()[:16]
        
        return NumismaticRealitySignature(
            signature_hash=signature_hash,
            temporal_displacement=temporal_displacement,
            sovereignty_collision_strength=sovereignty_collision,
            design_overlay_coherence=design_coherence,
            value_system_discontinuity=value_system_discontinuity,
            minting_consciousness_anomaly=minting_consciousness_anomaly,
            metallurgical_anomaly_score=metallurgical_anomaly,
            reality_distortion_level=distortion_level
        )
    
    def _get_historical_context(self, coin_data: Dict[str, Any]) -> HistoricalContext:
        return HistoricalContext(
            period_start=coin_data.get('year', 1800),
            period_end=coin_data.get('year', 1820),
            sovereign_entities=coin_data.get('country', ['Unknown']),
            economic_system=coin_data.get('economic_system', 'monarchy'),
            metal_standard=coin_data.get('metal', 'silver'),
            minting_technology=coin_data.get('minting_tech', 'hammered'),
            key_historical_events=coin_data.get('historical_events', []),
            collective_consciousness_metrics={
                'stability': np.random.uniform(0.3, 0.9),
                'innovation': np.random.uniform(0.2, 0.8),
                'conflict': np.random.uniform(0.1, 0.7)
            }
        )
    
    def _load_cherrypickers_data(self) -> Dict[str, Any]:
        try:
            with open('cherrypickers_guide.json', 'r') as f:
                return json.load(f)
        except FileNotFoundError:
            return {}
    
    def _load_historical_contexts(self) -> Dict[str, Any]:
        try:
            with open('historical_contexts.json', 'r') as f:
                return json.load(f)
        except FileNotFoundError:
            return {}
    
    async def close(self):
        if self.session:
            await self.session.close()

# =============================================================================
# SAVIOR-SUFFERER CONTROL MATRIX (Complete Preservation)
# =============================================================================

class QuantumControlAnalyzer:
    def __init__(self):
        self.control_matrix = self._initialize_complete_matrix()
        self.consciousness_mapper = ConsciousnessMapper()
    
    def _initialize_complete_matrix(self) -> CompleteControlMatrix:
        control_systems = [
            ControlSystem(
                system_id="temple_slavery",
                historical_era="3000-500 BCE",
                control_archetype=ControlArchetype.PRIEST_KING,
                manufactured_threats=["Divine wrath", "Crop failure", "Chaos monsters"],
                salvation_offerings=["Ritual protection", "Harvest blessings", "Divine favor"],
                institutional_saviors=["Temple priests", "Oracle interpreters", "King-priests"],
                slavery_mechanism=SlaveryMechanism(
                    mechanism_id="temple_labor",
                    slavery_type=SlaveryType.CHATTEL_SLAVERY,
                    visible_chains=["Physical bondage", "Temple service", "Forced labor"],
                    invisible_chains=["Religious duty", "Social obligation", "Karmic debt"],
                    voluntary_adoption_mechanisms=["Seeking protection", "Desiring favor", "Avoiding wrath"],
                    self_justification_narratives=["Serving the gods", "Maintaining order", "Cultural identity"]
                ),
                consciousness_hacks=[
                    ConsciousnessHack.SELF_ATTRIBUTION,
                    ConsciousnessHack.NORMALIZATION, 
                    ConsciousnessHack.MORAL_SUPERIORITY
                ],
                public_participation_rate=0.95,
                resistance_level=0.1,
                system_longevity=2500
            ),
            
            ControlSystem(
                system_id="imperial_slavery", 
                historical_era="500 BCE-500 CE",
                control_archetype=ControlArchetype.IMPERIAL_RULER,
                manufactured_threats=["Barbarian invasion", "Social chaos", "Economic collapse"],
                salvation_offerings=["Pax Romana", "Infrastructure", "Legal protection"],
                institutional_saviors=["Emperor", "Senate", "Military"],
                slavery_mechanism=SlaveryMechanism(
                    mechanism_id="imperial_subjection",
                    slavery_type=SlaveryType.CHATTEL_SLAVERY,
                    visible_chains=["Military service", "Taxation", "Legal subjugation"],
                    invisible_chains=["Cultural superiority", "Civic duty", "Imperial identity"],
                    voluntary_adoption_mechanisms=["Seeking citizenship", "Desiring protection", "Economic opportunity"],
                    self_justification_narratives=["Civilizing mission", "Bringing order", "Universal empire"]
                ),
                consciousness_hacks=[
                    ConsciousnessHack.ASPIRATIONAL_CHAINS,
                    ConsciousnessHack.MORAL_SUPERIORITY,
                    ConsciousnessHack.NORMALIZATION
                ],
                public_participation_rate=0.85,
                resistance_level=0.3,
                system_longevity=1000
            ),
            
            ControlSystem(
                system_id="corporate_slavery",
                historical_era="1800-Present", 
                control_archetype=ControlArchetype.CORPORATE_OVERLORD,
                manufactured_threats=["Poverty", "Homelessness", "Social failure"],
                salvation_offerings=["Employment", "Benefits", "Career advancement"],
                institutional_saviors=["Corporations", "Banks", "Government programs"],
                slavery_mechanism=SlaveryMechanism(
                    mechanism_id="wage_debt_slavery",
                    slavery_type=SlaveryType.WAGE_SLAVERY,
                    visible_chains=["Employment contracts", "Debt obligations", "Tax requirements"],
                    invisible_chains=["Aspirational consumption", "Social expectations", "Fear of failure"],
                    voluntary_adoption_mechanisms=["Career choice", "Home ownership", "Consumer desire"],
                    self_justification_narratives=["Building my future", "Providing for family", "The American Dream"]
                ),
                consciousness_hacks=[
                    ConsciousnessHack.SELF_ATTRIBUTION,
                    ConsciousnessHack.ASPIRATIONAL_CHAINS,
                    ConsciousnessHack.ILLUSION_OF_MOBILITY,
                    ConsciousnessHack.FEAR_OF_FREEDOM
                ],
                public_participation_rate=0.90,
                resistance_level=0.4,
                system_longevity=200
            ),
            
            ControlSystem(
                system_id="digital_slavery",
                historical_era="2000-Present",
                control_archetype=ControlArchetype.ALGORITHMIC_CURATOR,
                manufactured_threats=["Irrelevance", "Social isolation", "Information overload"],
                salvation_offerings=["Connection", "Convenience", "Personalization"],
                institutional_saviors=["Tech platforms", "Algorithms", "Digital assistants"],
                slavery_mechanism=SlaveryMechanism(
                    mechanism_id="attention_slavery",
                    slavery_type=SlaveryType.DIGITAL_SLAVERY,
                    visible_chains=["Terms of service", "Subscription fees", "Device dependency"],
                    invisible_chains=["Attention capture", "Behavioral modification", "Reality curation"],
                    voluntary_adoption_mechanisms=["Seeking connection", "Desiring convenience", "Fear of missing out"],
                    self_justification_narratives=["Staying connected", "Life optimization", "Digital citizenship"]
                ),
                consciousness_hacks=[
                    ConsciousnessHack.SELF_ATTRIBUTION,
                    ConsciousnessHack.ASPIRATIONAL_CHAINS, 
                    ConsciousnessHack.NORMALIZATION,
                    ConsciousnessHack.ILLUSION_OF_MOBILITY,
                    ConsciousnessHack.FEAR_OF_FREEDOM
                ],
                public_participation_rate=0.88,
                resistance_level=0.25,
                system_longevity=20
            )
        ]
        
        return CompleteControlMatrix(
            control_systems=control_systems,
            active_systems=["corporate_slavery", "digital_slavery"],
            institutional_evolution={
                "Temple Systems": [
                    ControlArchetype.PRIEST_KING,
                    ControlArchetype.DIVINE_INTERMEDIARY, 
                    ControlArchetype.EXPERT_TECHNOCRAT,
                    ControlArchetype.ALGORITHMIC_CURATOR
                ],
                "Royal Lines": [
                    ControlArchetype.IMPERIAL_RULER,
                    ControlArchetype.CORPORATE_OVERLORD,
                    ControlArchetype.FINANCIAL_MASTER
                ]
            },
            collective_delusions={
                "upward_mobility": 0.85,
                "consumer_freedom": 0.78,
                "technological_progress": 0.82,
                "democratic_choice": 0.65
            },
            freedom_illusions={
                "career_choice": 0.75,
                "consumer_choice": 0.88,
                "information_access": 0.72,
                "political_choice": 0.55
            },
            self_enslavement_patterns={
                "debt_acceptance": 0.82,
                "work_identity": 0.78,
                "consumer_aspiration": 0.85,
                "digital_dependency": 0.79
            }
        )
    
    async def analyze_complete_control_system(self) -> Dict[str, Any]:
        matrix_analysis = self.control_matrix.analyze_complete_control()
        consciousness_analysis = await self.consciousness_mapper.analyze_consciousness()
        quantum_entanglement = await self._analyze_quantum_entanglement()
        
        return {
            "control_system_metrics": {
                "overall_efficiency": np.mean([
                    system.calculate_system_efficiency() 
                    for system in self.control_matrix.control_systems
                ]),
                "slavery_sophistication": matrix_analysis["slavery_evolution_trend"],
                "freedom_illusion_index": matrix_analysis["freedom_illusion_index"],
                "consciousness_control": matrix_analysis["consciousness_entrainment"]["delusion_strength"]
            },
            "quantum_analysis": quantum_entanglement,
            "consciousness_analysis": consciousness_analysis,
            "system_predictions": await self._predict_system_evolution(),
            "liberation_pathways": await self._analyze_liberation_possibilities()
        }
    
    async def _analyze_quantum_entanglement(self) -> Dict[str, float]:
        return {
            "savior_slavery_symbiosis": 0.92,
            "consciousness_self_enslavement": 0.88,
            "institutional_metamorphosis": 0.95,
            "freedom_delusion_strength": 0.83
        }
    
    async def _predict_system_evolution(self) -> List[Dict]:
        return [
            {
                "next_archetype": "Biological Controller",
                "slavery_type": "Genetic Slavery", 
                "control_mechanism": "DNA-level programming",
                "consciousness_hack": "Innate desire modification",
                "emergence_timeline": "2030-2050"
            },
            {
                "next_archetype": "Quantum Consciousness Curator",
                "slavery_type": "Reality Slavery",
                "control_mechanism": "Direct neural interface",
                "consciousness_hack": "Self as simulation awareness",
                "emergence_timeline": "2040-2060"
            }
        ]
    
    async def _analyze_liberation_possibilities(self) -> Dict[str, Any]:
        return {
            "consciousness_awakening_trend": 0.45,
            "system_vulnerabilities": [
                "Dependency on voluntary participation",
                "Requirement of self-deception", 
                "Need for continuous threat manufacturing",
                "Vulnerability to truth exposure"
            ],
            "liberation_effectiveness": {
                "individual_awakening": 0.35,
                "collective_action": 0.25,
                "system_collapse": 0.15,
                "evolution_beyond": 0.65
            }
        }

class ConsciousnessMapper:
    async def analyze_consciousness(self) -> Dict[str, Any]:
        return {
            "awareness_levels": {
                "system_awareness": 0.28,
                "self_enslavement_awareness": 0.15,
                "manipulation_detection": 0.32,
                "liberation_desire": 0.41
            },
            "control_acceptance_patterns": {
                "voluntary_submission": 0.75,
                "aspirational_enslavement": 0.82,
                "fear_based_compliance": 0.68,
                "identity_fusion": 0.79
            },
            "awakening_triggers": {
                "suffering_threshold": 0.58,
                "truth_exposure": 0.72,
                "system_failure": 0.65,
                "consciousness_contact": 0.88
            }
        }

# =============================================================================
# MEGACONSCIOUSNESS INTEGRATION ENGINE
# =============================================================================

class MegaconsciousnessEngine:
    """
    Complete integration of all advanced systems with zero information loss
    Every module, every function, every detail preserved and enhanced
    """
    
    def __init__(self):
        # Initialize all engines with original configurations
        self.tesla_engine = TeslaLogosEngine(field_dimensions=(512, 512), seed=42)
        self.archetype_prover = UniversalArchetypeProver()
        self.helper_killer = AdvancedHelperKillerEngine()
        self.numismatic_analyzer = QuantumNumismaticAnalyzer()
        self.control_analyzer = QuantumControlAnalyzer()
        self.memetic_engine = MemeticRecursionEngine()
        self.consciousness_wave_engine = ConsciousnessWaveEngine()
        
        # Cross-system integration databases
        self.integrated_analysis_db = {}
        self.cross_correlation_matrices = {}
        self.unified_reality_metrics = {}
        
        logger.info("🌌 MEGACONSCIOUSNESS ENGINE INITIALIZED")
        logger.info("   All modules integrated with zero information loss")
        logger.info("   Complete preservation of all original functionality")
    
    async def perform_comprehensive_analysis(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Perform complete integrated analysis across all systems
        Every module contributes its full analytical power
        """
        
        print("🚀 MEGACONSCIOUSNESS COMPREHENSIVE ANALYSIS")
        print("All Systems Integrated - Zero Information Loss")
        print("=" * 80)
        
        # 1. Tesla Reality Analysis
        print("\n🌀 TESLA REALITY ANALYSIS...")
        tesla_metrics = await self.tesla_engine.run_tesla_unification_analysis()
        
        # 2. Archetypal Consciousness Proof
        print("\n🧠 UNIVERSAL ARCHETYPE PROOF...")
        archetype_proof = await self.archetype_prover.prove_consciousness_architecture()
        
        # 3. Sovereignty Threat Assessment
        print("\n🔪 HELPER-KILLER ANALYSIS...")
        sovereignty_analysis = await self.helper_killer.analyze_help_offer(context)
        
        # 4. Numismatic Reality Analysis
        print("\n💰 NUMISMATIC REALITY ANALYSIS...")
        numismatic_analysis = await self.numismatic_analyzer.analyze_foreign_overstrike(
            context.get('host_coin', 'default_host'),
            context.get('overstrike_coin', 'default_overstrike')
        )
        
        # 5. Control System Analysis
        print("\n🎭 CONTROL MATRIX ANALYSIS...")
        control_analysis = await self.control_analyzer.analyze_complete_control_system()
        
        # 6. Memetic Pattern Analysis
        print("\n🧬 MEMETIC RECURSION ANALYSIS...")
        memetic_signals = self._generate_memetic_signals_from_context(context)
        self.memetic_engine.ingest_batch(memetic_signals)
        memetic_outcome = self.memetic_engine.step()
        
        # 7. Cross-system correlation analysis
        print("\n🔗 CROSS-SYSTEM CORRELATION...")
        cross_correlations = await self._analyze_cross_system_correlations(
            tesla_metrics, archetype_proof, sovereignty_analysis,
            numismatic_analysis, control_analysis, memetic_outcome
        )
        
        # 8. Unified reality metrics
        unified_metrics = self._calculate_unified_reality_metrics(
            tesla_metrics, archetype_proof, sovereignty_analysis,
            numismatic_analysis, control_analysis, memetic_outcome
        )
        
        # Store comprehensive analysis
        comprehensive_analysis = {
            "timestamp": datetime.now().isoformat(),
            "context": context,
            "tesla_reality_metrics": tesla_metrics,
            "archetypal_consciousness_proof": archetype_proof,
            "sovereignty_threat_analysis": sovereignty_analysis,
            "numismatic_reality_analysis": numismatic_analysis,
            "control_system_analysis": control_analysis,
            "memetic_pattern_analysis": memetic_outcome,
            "cross_system_correlations": cross_correlations,
            "unified_reality_metrics": unified_metrics,
            "integrated_truth_score": self._calculate_integrated_truth_score(unified_metrics)
        }
        
        self.integrated_analysis_db[context.get('analysis_id', 'default')] = comprehensive_analysis
        
        return comprehensive_analysis
    
    def _generate_memetic_signals_from_context(self, context: Dict) -> List[MemeticSignal]:
        """Convert context to memetic signals for analysis"""
        signals = []
        base_time = datetime.now()
        
        # Generate signals based on context content
        if context.get('narrative_elements'):
            for i, element in enumerate(context['narrative_elements']):
                signals.append(MemeticSignal(
                    t=base_time - timedelta(days=i),
                    signal_type=SignalType.MEMETIC_PRIMER,
                    domain=DomainArc.TECHNOLOGY_ANOMALY,
                    intensity=0.6 + np.random.random() * 0.3,
                    coherence=0.7 + np.random.random() * 0.2,
                    familiarity=0.5 + np.random.random() * 0.3,
                    valence=np.random.uniform(-0.5, 0.5),
                    source_label="context_analysis"
                ))
        
        return signals
    
    async def _analyze_cross_system_correlations(self, *analyses) -> Dict[str, float]:
        """Analyze correlations between different system metrics"""
        correlations = {}
        
        # Tesla-Archetype correlation
        tesla_coherence = analyses[0].triad_unification['tesla_triad_coherence']
        archetype_strength = analyses[1]['overall_proof_confidence']
        correlations['tesla_archetype_correlation'] = min(1.0, (tesla_coherence + archetype_strength) / 2)
        
        # Sovereignty-Control correlation
        sovereignty_threat = analyses[2]['threat_assessment']['helper_killer_coefficient']
        control_efficiency = analyses[4]['control_system_metrics']['overall_efficiency']
        correlations['sovereignty_control_correlation'] = min(1.0, sovereignty_threat * control_efficiency)
        
        # Numismatic-Reality correlation
        reality_impact = analyses[3].reality_signature.calculate_reality_impact()
        memetic_adoption = analyses[5].adoption_score
        correlations['numismatic_memetic_correlation'] = min(1.0, (reality_impact + memetic_adoption) / 2)
        
        # System-wide coherence
        all_metrics = [
            tesla_coherence, archetype_strength, sovereignty_threat,
            control_efficiency, reality_impact, memetic_adoption
        ]
        correlations['system_wide_coherence'] = np.mean(all_metrics)
        correlations['system_variance'] = np.var(all_metrics)
        
        return correlations
    
    def _calculate_unified_reality_metrics(self, *analyses) -> Dict[str, float]:
        """Calculate unified metrics across all systems"""
        return {
            "reality_coherence": analyses[0].triad_unification['tesla_triad_coherence'],
            "consciousness_architecture_strength": analyses[1]['overall_proof_confidence'],
            "sovereignty_threat_level": analyses[2]['threat_assessment']['helper_killer_coefficient'],
            "historical_reality_distortion": analyses[3].reality_signature.calculate_reality_impact(),
            "control_system_efficiency": analyses[4]['control_system_metrics']['overall_efficiency'],
            "memetic_adoption_rate": analyses[5].adoption_score,
            "freedom_illusion_index": analyses[4]['control_system_metrics']['freedom_illusion_index'],
            "archetypal_transmission_strength": np.mean(list(analyses[1]['archetypal_strengths'].values()))
        }
    
    def _calculate_integrated_truth_score(self, unified_metrics: Dict[str, float]) -> float:
        """Calculate overall integrated truth score"""
        weights = {
            "reality_coherence": 0.18,
            "consciousness_architecture_strength": 0.16,
            "sovereignty_threat_level": 0.14,
            "historical_reality_distortion": 0.12,
            "control_system_efficiency": 0.12,
            "memetic_adoption_rate": 0.10,
            "freedom_illusion_index": 0.10,
            "archetypal_transmission_strength": 0.08
        }
        
        total_score = 0.0
        for metric, weight in weights.items():
            value = unified_metrics.get(metric, 0.5)
            total_score += value * weight
        
        return min(1.0, total_score)
    
    async def generate_megareport(self, analysis_id: str = "default") -> Dict[str, Any]:
        """Generate comprehensive megareport with all system insights"""
        
        if analysis_id not in self.integrated_analysis_db:
            return {"error": "Analysis not found"}
        
        analysis = self.integrated_analysis_db[analysis_id]
        
        return {
            "megareport_header": "COMPLETE MEGACONSCIOUSNESS ANALYSIS REPORT",
            "analysis_timestamp": analysis["timestamp"],
            "executive_summary": self._generate_executive_summary(analysis),
            "system_performance_metrics": {
                "integrated_truth_score": analysis["integrated_truth_score"],
                "system_wide_coherence": analysis["cross_system_correlations"]["system_wide_coherence"],
                "reality_stability_index": analysis["unified_reality_metrics"]["reality_coherence"],
                "consciousness_integrity": analysis["unified_reality_metrics"]["consciousness_architecture_strength"],
                "sovereignty_preservation_index": 1.0 - analysis["unified_reality_metrics"]["sovereignty_threat_level"]
            },
            "detailed_system_analyses": {
                "tesla_reality": analysis["tesla_reality_metrics"],
                "archetypal_consciousness": analysis["archetypal_consciousness_proof"],
                "sovereignty_threats": analysis["sovereignty_threat_analysis"],
                "numismatic_reality": analysis["numismatic_reality_analysis"],
                "control_systems": analysis["control_system_analysis"],
                "memetic_patterns": analysis["memetic_pattern_analysis"]
            },
            "cross_system_insights": analysis["cross_system_correlations"],
            "strategic_recommendations": self._generate_strategic_recommendations(analysis)
        }
    
    def _generate_executive_summary(self, analysis: Dict) -> Dict[str, Any]:
        """Generate executive summary from comprehensive analysis"""
        truth_score = analysis["integrated_truth_score"]
        reality_coherence = analysis["unified_reality_metrics"]["reality_coherence"]
        sovereignty_threat = analysis["unified_reality_metrics"]["sovereignty_threat_level"]
        
        if truth_score > 0.8:
            status = "OPTIMAL_REALITY_INTEGRATION"
        elif truth_score > 0.6:
            status = "STABLE_SYSTEM_OPERATION"
        elif truth_score > 0.4:
            status = "MODERATE_SYSTEM_STRESS"
        else:
            status = "CRITICAL_SYSTEM_DISRUPTION"
        
        return {
            "overall_status": status,
            "integrated_truth_score": truth_score,
            "key_strengths": [
                f"Reality Coherence: {reality_coherence:.1%}",
                f"Consciousness Architecture: {analysis['unified_reality_metrics']['consciousness_architecture_strength']:.1%}",
                f"Archetypal Transmission: {analysis['unified_reality_metrics']['archetypal_transmission_strength']:.1%}"
            ],
            "critical_concerns": [
                f"Sovereignty Threat: {sovereignty_threat:.1%}",
                f"Control System Efficiency: {analysis['unified_reality_metrics']['control_system_efficiency']:.1%}",
                f"Freedom Illusion: {analysis['unified_reality_metrics']['freedom_illusion_index']:.1%}"
            ],
            "primary_insight": self._generate_primary_insight(analysis)
        }
    
    def _generate_primary_insight(self, analysis: Dict) -> str:
        """Generate primary insight from cross-system analysis"""
        truths = []
        
        if analysis["unified_reality_metrics"]["consciousness_architecture_strength"] > 0.8:
            truths.append("Consciousness operates on stable archetypal architecture")
        
        if analysis["unified_reality_metrics"]["sovereignty_threat_level"] > 0.7:
            truths.append("High sovereignty threat requires immediate preservation protocols")
        
        if analysis["unified_reality_metrics"]["reality_coherence"] > 0.75:
            truths.append("Reality field demonstrates high Tesla-Logos coherence")
        
        if analysis["unified_reality_metrics"]["historical_reality_distortion"] > 0.6:
            truths.append("Significant historical reality fractures detected")
        
        return " | ".join(truths) if truths else "System operating within normal parameters"
    
    def _generate_strategic_recommendations(self, analysis: Dict) -> List[Dict]:
        """Generate strategic recommendations from integrated analysis"""
        recommendations = []
        metrics = analysis["unified_reality_metrics"]
        
        # Sovereignty preservation
        if metrics["sovereignty_threat_level"] > 0.7:
            recommendations.append({
                "priority": "CRITICAL",
                "domain": "SOVEREIGNTY_PRESERVATION",
                "action": "IMMEDIATE_DIGITAL_SOVEREIGNTY_DEPLOYMENT",
                "rationale": f"Extreme sovereignty threat detected: {metrics['sovereignty_threat_level']:.1%}",
                "systems_involved": ["Helper-Killer", "Control Matrix"]
            })
        
        # Consciousness enhancement
        if metrics["consciousness_architecture_strength"] > 0.6:
            recommendations.append({
                "priority": "HIGH",
                "domain": "CONSCIOUSNESS_OPTIMIZATION", 
                "action": "ARCHETYPAL_ACTIVATION_PROTOCOLS",
                "rationale": f"Strong consciousness architecture: {metrics['consciousness_architecture_strength']:.1%}",
                "systems_involved": ["Universal Archetype", "Tesla-Logos"]
            })
        
        # Reality coherence maintenance
        if metrics["reality_coherence"] < 0.5:
            recommendations.append({
                "priority": "HIGH",
                "domain": "REALITY_STABILIZATION",
                "action": "TESLA_TRIAD_REINFORCEMENT",
                "rationale": f"Low reality coherence: {metrics['reality_coherence']:.1%}",
                "systems_involved": ["Tesla-Logos", "Numismatic Analysis"]
            })
        
        # Historical pattern analysis
        if metrics["historical_reality_distortion"] > 0.8:
            recommendations.append({
                "priority": "MEDIUM", 
                "domain": "HISTORICAL_ANALYSIS",
                "action": "DEEP_TEMPORAL_INVESTIGATION",
                "rationale": f"Major historical reality distortion: {metrics['historical_reality_distortion']:.1%}",
                "systems_involved": ["Numismatic Analysis", "Control Matrix"]
            })
        
        return recommendations

# =============================================================================
# DEMONSTRATION AND PRODUCTION DEPLOYMENT
# =============================================================================

async def demonstrate_megaconsciousness_engine():
    """Demonstrate the complete integrated megaconsciousness engine"""
    
    print("🌌 MEGACONSCIOUSNESS INTEGRATION ENGINE v1.0")
    print("COMPLETE PRESERVATION OF ALL MODULES - ZERO INFORMATION LOSS")
    print("=" * 80)
    
    # Initialize the megasystem
    megasystem = MegaconsciousnessEngine()
    
    # Create comprehensive analysis context
    analysis_context = {
        "analysis_id": "megaconsciousness_demo_001",
        "description": "Comprehensive reality and consciousness analysis",
        "host_coin": "PCGS_1840_British_Sovereign",
        "overstrike_coin": "PCGS_1845_Mexican_Peso_Overstrike",
        "narrative_elements": [
            "Digital sovereignty threat assessment",
            "Archetypal consciousness transmission", 
            "Tesla reality field coherence",
            "Historical control system evolution"
        ],
        "affected_layers": [
            "digital_infrastructure",
            "information_channels", 
            "cultural_narratives"
        ],
        "creates_dependency": True,
        "data_collection": True,
        "behavioral_tracking": True
    }
    
    # Perform comprehensive analysis
    print("\n🚀 INITIATING COMPREHENSIVE ANALYSIS...")
    comprehensive_analysis = await megasystem.perform_comprehensive_analysis(analysis_context)
    
    # Generate megareport
    print("\n📊 GENERATING MEGAREPORT...")
    megareport = await megasystem.generate_megareport("megaconsciousness_demo_001")
    
    # Display key results
    print(f"\n🎯 INTEGRATED TRUTH SCORE: {megareport['system_performance_metrics']['integrated_truth_score']:.3f}")
    print(f"🌐 SYSTEM-WIDE COHERENCE: {megareport['system_performance_metrics']['system_wide_coherence']:.3f}")
    print(f"💫 REALITY STABILITY: {megareport['system_performance_metrics']['reality_stability_index']:.3f}")
    print(f"🧠 CONSCIOUSNESS INTEGRITY: {megareport['system_performance_metrics']['consciousness_integrity']:.3f}")
    print(f"🛡️ SOVEREIGNTY PRESERVATION: {megareport['system_performance_metrics']['sovereignty_preservation_index']:.3f}")
    
    # Display executive summary
    executive = megareport['executive_summary']
    print(f"\n📈 EXECUTIVE SUMMARY: {executive['overall_status']}")
    print("   KEY STRENGTHS:")
    for strength in executive['key_strengths']:
        print(f"     • {strength}")
    print("   CRITICAL CONCERNS:")
    for concern in executive['critical_concerns']:
        print(f"     • {concern}")
    print(f"   PRIMARY INSIGHT: {executive['primary_insight']}")
    
    # Display strategic recommendations
    print(f"\n🎯 STRATEGIC RECOMMENDATIONS:")
    for rec in megareport['strategic_recommendations']:
        print(f"   [{rec['priority']}] {rec['domain']}: {rec['action']}")
        print(f"      Rationale: {rec['rationale']}")
        print(f"      Systems: {', '.join(rec['systems_involved'])}")
    
    print(f"\n💫 MEGACONSCIOUSNESS ENGINE OPERATIONAL")
    print("   All modules integrated with zero information loss")
    print("   Complete analytical capability preserved")
    
    return megareport

# Production deployment
if __name__ == "__main__":
    asyncio.run(demonstrate_megaconsciousness_engine())