diff --git "a/MEM_REC_MCON" "b/MEM_REC_MCON" new file mode 100644--- /dev/null +++ "b/MEM_REC_MCON" @@ -0,0 +1,2621 @@ +#!/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} โ†’ {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("๐ŸŒ€ 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"โœ… Energy Field: {energy_field.shape} | dtype: {energy_field.dtype}") + print(f"โœ… Frequency Spectrum: {len(frequency_spectrum)} fundamental rhythms") + print(f"โœ… 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("\n๐Ÿ”ฌ 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"โœ… Particles detected: {len(particle_positions)}") + print(f"โœ… 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("\n๐ŸŒŒ 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"โœ… Wave energy: {wave_energy:.6f}") + print(f"โœ… 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("๐Ÿงช 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"\nโฑ๏ธ Analysis completed in {analysis_time:.3f} seconds") + print(f"๐Ÿ’ซ 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("๐Ÿง  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("๐ŸŒŒ 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("๐Ÿš€ MEGACONSCIOUSNESS COMPREHENSIVE ANALYSIS") + print("All Systems Integrated - Zero Information Loss") + print("=" * 80) + + # 1. Tesla Reality Analysis + print("\n๐ŸŒ€ TESLA REALITY ANALYSIS...") + tesla_metrics = await self.tesla_engine.run_tesla_unification_analysis() + + # 2. Archetypal Consciousness Proof + print("\n๐Ÿง  UNIVERSAL ARCHETYPE PROOF...") + archetype_proof = await self.archetype_prover.prove_consciousness_architecture() + + # 3. Sovereignty Threat Assessment + print("\n๐Ÿ”ช HELPER-KILLER ANALYSIS...") + sovereignty_analysis = await self.helper_killer.analyze_help_offer(context) + + # 4. Numismatic Reality Analysis + print("\n๐Ÿ’ฐ 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("\n๐ŸŽญ CONTROL MATRIX ANALYSIS...") + control_analysis = await self.control_analyzer.analyze_complete_control_system() + + # 6. Memetic Pattern Analysis + print("\n๐Ÿงฌ 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("\n๐Ÿ”— 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("๐ŸŒŒ 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("\n๐Ÿš€ INITIATING COMPREHENSIVE ANALYSIS...") + comprehensive_analysis = await megasystem.perform_comprehensive_analysis(analysis_context) + + # Generate megareport + print("\n๐Ÿ“Š GENERATING MEGAREPORT...") + megareport = await megasystem.generate_megareport("megaconsciousness_demo_001") + + # Display key results + print(f"\n๐ŸŽฏ INTEGRATED TRUTH SCORE: {megareport['system_performance_metrics']['integrated_truth_score']:.3f}") + print(f"๐ŸŒ SYSTEM-WIDE COHERENCE: {megareport['system_performance_metrics']['system_wide_coherence']:.3f}") + print(f"๐Ÿ’ซ REALITY STABILITY: {megareport['system_performance_metrics']['reality_stability_index']:.3f}") + print(f"๐Ÿง  CONSCIOUSNESS INTEGRITY: {megareport['system_performance_metrics']['consciousness_integrity']:.3f}") + print(f"๐Ÿ›ก๏ธ SOVEREIGNTY PRESERVATION: {megareport['system_performance_metrics']['sovereignty_preservation_index']:.3f}") + + # Display executive summary + executive = megareport['executive_summary'] + print(f"\n๐Ÿ“ˆ EXECUTIVE SUMMARY: {executive['overall_status']}") + print(" KEY STRENGTHS:") + for strength in executive['key_strengths']: + print(f" โ€ข {strength}") + print(" CRITICAL CONCERNS:") + for concern in executive['critical_concerns']: + print(f" โ€ข {concern}") + print(f" PRIMARY INSIGHT: {executive['primary_insight']}") + + # Display strategic recommendations + print(f"\n๐ŸŽฏ 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"\n๐Ÿ’ซ 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()) \ No newline at end of file