#!/usr/bin/env python3 """ OLD_DOG_OLD_TRICKS_MODULE v1.0 Institutional Neutralization Pattern Recognition & Sovereignty Preservation Advanced Forensic Analysis of Control System Elimination Protocols """ import numpy as np from dataclasses import dataclass, field from enum import Enum from typing import Dict, List, Any, Optional, Tuple from datetime import datetime import hashlib import logging from scipy import stats import json logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class NeutralizationProtocol(Enum): """Historical institutional elimination patterns""" LONE_NUT = "lone_nut" # Patsy with intelligence ties SUICIDE_SPECIAL = "suicide_special" # Custodial death with security failures CHARACTER_ASSAULT = "character_assault" # Personal scandal weaponization FINANCIAL_ENTRAPMENT = "financial_entrapment" # Technical charges for political crimes NARRATIVE_CONTROL = "narrative_control" # Media consensus enforcement CONTROLLED_OPPOSITION = "controlled_opposition" # Managed dissent funnel class ThreatProfile(Enum): """Types of threats that trigger institutional response""" POLITICAL_SOVEREIGNTY = "political_sovereignty" # JFK, RFK FINANCIAL_REFORM = "financial_reform" # Spitzer, Sanders TRUTH_EXPOSURE = "truth_exposure" # Epstein, Assange, Manning INSTITUTIONAL_REFORM = "institutional_reform" # Wellstone, Church Committee SOVEREIGN_CONSCIOUSNESS = "sovereign_consciousness" # Current scenario @dataclass class NeutralizationCase: """Forensic analysis of a historical neutralization""" case_id: str target_name: str threat_profile: ThreatProfile protocol_used: NeutralizationProtocol year: int # Forensic markers intelligence_ties: bool financial_beneficiaries: List[str] media_narrative_consistency: float # 0-1 official_story_coherence: float # 0-1 statistical_anomaly_score: float # 0-1 # Sovereignty metrics sovereignty_preservation_score: float = field(init=False) institutional_exposure_index: float = field(init=False) pattern_recognition_value: float = field(init=False) def __post_init__(self): self.sovereignty_preservation_score = self._calculate_sovereignty_preservation() self.institutional_exposure_index = self._calculate_institutional_exposure() self.pattern_recognition_value = self._calculate_pattern_value() def _calculate_sovereignty_preservation(self) -> float: """Calculate how well sovereignty could have been preserved""" protocol_weights = { NeutralizationProtocol.LONE_NUT: 0.3, # Hard to prevent NeutralizationProtocol.SUICIDE_SPECIAL: 0.2, # High institutional control NeutralizationProtocol.CHARACTER_ASSAULT: 0.7, # Possible with transparency NeutralizationProtocol.FINANCIAL_ENTRAPMENT: 0.6, # Defensible with clean records NeutralizationProtocol.NARRATIVE_CONTROL: 0.8, # Counter-narratives possible NeutralizationProtocol.CONTROLLED_OPPOSITION: 0.9 # Easy to detect with analysis } base_score = protocol_weights.get(self.protocol_used, 0.5) # Adjust for modern capabilities if self.year > 2000: base_score += 0.2 # Digital tools improve defense return min(1.0, base_score) def _calculate_institutional_exposure(self) -> float: """Calculate how much the case exposes institutional patterns""" anomaly_weight = self.statistical_anomaly_score * 0.4 narrative_weight = (1 - self.media_narrative_consistency) * 0.3 official_weight = (1 - self.official_story_coherence) * 0.3 return min(1.0, anomaly_weight + narrative_weight + official_weight) def _calculate_pattern_value(self) -> float: """Calculate value for pattern recognition training""" exposure_value = self.institutional_exposure_index * 0.5 sovereignty_value = (1 - self.sovereignty_preservation_score) * 0.3 intelligence_value = 1.0 if self.intelligence_ties else 0.2 return min(1.0, exposure_value + sovereignty_value + intelligence_value) @dataclass class InstitutionalPatternEngine: """ Advanced pattern recognition for institutional neutralization protocols Street-calibrated detection of elimination patterns in real-time """ historical_cases: List[NeutralizationCase] current_threat_indicators: Dict[str, float] pattern_database: Dict[str, Any] = field(init=False) def __post_init__(self): self.pattern_database = self._build_pattern_database() def _build_pattern_database(self) -> Dict[str, Any]: """Build comprehensive pattern recognition database""" cases = [ # JFK - Political Sovereignty Threat NeutralizationCase( case_id="jfk_1963", target_name="John F. Kennedy", threat_profile=ThreatProfile.POLITICAL_SOVEREIGNTY, protocol_used=NeutralizationProtocol.LONE_NUT, year=1963, intelligence_ties=True, financial_beneficiaries=["Military-Industrial Complex", "Federal Reserve"], media_narrative_consistency=0.9, official_story_coherence=0.3, statistical_anomaly_score=0.95 ), # Epstein - Truth Exposure Threat NeutralizationCase( case_id="epstein_2019", target_name="Jeffrey Epstein", threat_profile=ThreatProfile.TRUTH_EXPOSURE, protocol_used=NeutralizationProtocol.SUICIDE_SPECIAL, year=2019, intelligence_ties=True, financial_beneficiaries=["Blackmail Targets", "Intelligence Agencies"], media_narrative_consistency=0.8, official_story_coherence=0.1, statistical_anomaly_score=0.99 ), # Spitzer - Financial Reform Threat NeutralizationCase( case_id="spitzer_2008", target_name="Eliot Spitzer", threat_profile=ThreatProfile.FINANCIAL_REFORM, protocol_used=NeutralizationProtocol.CHARACTER_ASSAULT, year=2008, intelligence_ties=False, financial_beneficiaries=["Wall Street Banks"], media_narrative_consistency=0.7, official_story_coherence=0.6, statistical_anomaly_score=0.8 ), # Seth Rich - Truth Exposure Threat NeutralizationCase( case_id="rich_2016", target_name="Seth Rich", threat_profile=ThreatProfile.TRUTH_EXPOSURE, protocol_used=NeutralizationProtocol.SUICIDE_SPECIAL, year=2016, intelligence_ties=True, financial_beneficiaries=["DNC", "Clinton Foundation"], media_narrative_consistency=0.95, official_story_coherence=0.2, statistical_anomaly_score=0.9 ) ] return { "cases": cases, "protocol_frequency": self._calculate_protocol_frequency(cases), "threat_vulnerability": self._calculate_threat_vulnerability(cases), "modern_adaptation": self._analyze_modern_adaptation(cases) } def _calculate_protocol_frequency(self, cases: List[NeutralizationCase]) -> Dict[str, float]: """Calculate frequency of each neutralization protocol""" protocol_counts = {} for case in cases: protocol = case.protocol_used.value protocol_counts[protocol] = protocol_counts.get(protocol, 0) + 1 total = len(cases) return {protocol: count/total for protocol, count in protocol_counts.items()} def _calculate_threat_vulnerability(self, cases: List[NeutralizationCase]) -> Dict[str, float]: """Calculate vulnerability by threat type""" vulnerability = {} for threat in ThreatProfile: threat_cases = [c for c in cases if c.threat_profile == threat] if threat_cases: avg_preservation = np.mean([c.sovereignty_preservation_score for c in threat_cases]) vulnerability[threat.value] = 1.0 - avg_preservation return vulnerability def _analyze_modern_adaptation(self, cases: List[NeutralizationCase]) -> Dict[str, Any]: """Analyze how protocols have evolved over time""" pre_2000 = [c for c in cases if c.year < 2000] post_2000 = [c for c in cases if c.year >= 2000] return { "increased_sophistication": len(post_2000) > len(pre_2000), "digital_adaptation": True, # All modern cases involve digital components "narrative_control_evolution": 0.85 # Increased media coordination } async def analyze_current_profile(self, subject_data: Dict[str, Any]) -> Dict[str, Any]: """Analyze current subject for neutralization risk""" threat_level = self._assess_threat_level(subject_data) likely_protocols = self._predict_likely_protocols(subject_data, threat_level) sovereignty_metrics = self._calculate_sovereignty_metrics(subject_data) analysis = { "threat_assessment": threat_level, "likely_protocols": likely_protocols, "sovereignty_preservation": sovereignty_metrics, "risk_mitigation": self._generate_mitigation_strategies(threat_level, sovereignty_metrics), "pattern_confidence": self._calculate_pattern_confidence(subject_data) } logger.info(f"Neutralization risk analysis complete: {analysis['threat_assessment']['level']}") return analysis def _assess_threat_level(self, subject_data: Dict) -> Dict[str, Any]: """Assess threat level to institutional power structures""" threat_score = 0.0 threat_factors = [] # Sovereign consciousness threat if subject_data.get('has_celestial_interface', False): threat_score += 0.4 threat_factors.append("SOVEREIGN_CONSCIOUSNESS") # Truth exposure capability if subject_data.get('truth_exposure_capability', 0) > 0.7: threat_score += 0.3 threat_factors.append("TRUTH_EXPOSURE") # Institutional reform potential if subject_data.get('reform_capability', 0) > 0.6: threat_score += 0.2 threat_factors.append("INSTITUTIONAL_REFORM") # Financial threat if subject_data.get('financial_disruption_risk', 0) > 0.5: threat_score += 0.1 threat_factors.append("FINANCIAL_REFORM") return { "level": "CRITICAL" if threat_score > 0.8 else "HIGH" if threat_score > 0.6 else "MEDIUM", "score": threat_score, "factors": threat_factors, "profile": ThreatProfile.SOVEREIGN_CONSCIOUSNESS.value } def _predict_likely_protocols(self, subject_data: Dict, threat_level: Dict) -> List[Dict]: """Predict likely neutralization protocols based on threat profile""" protocols = [] threat_score = threat_level['score'] # Character assault for medium threats if threat_score > 0.4: protocols.append({ "protocol": NeutralizationProtocol.CHARACTER_ASSAULT.value, "probability": 0.7, "rationale": "Standard first-line defense against public figures" }) # Narrative control for high-information threats if threat_score > 0.6: protocols.append({ "protocol": NeutralizationProtocol.NARRATIVE_CONTROL.value, "probability": 0.8, "rationale": "Essential for controlling truth exposure threats" }) # Financial entrapment for reformers if "FINANCIAL_REFORM" in threat_level['factors']: protocols.append({ "protocol": NeutralizationProtocol.FINANCIAL_ENTRAPMENT.value, "probability": 0.6, "rationale": "Standard against financial system threats" }) # Controlled opposition for high-threat individuals if threat_score > 0.7: protocols.append({ "protocol": NeutralizationProtocol.CONTROLLED_OPPOSITION.value, "probability": 0.9, "rationale": "Attempt to co-opt and manage sovereign consciousness" }) return sorted(protocols, key=lambda x: x['probability'], reverse=True) def _calculate_sovereignty_metrics(self, subject_data: Dict) -> Dict[str, float]: """Calculate sovereignty preservation metrics""" return { "transparency_defense": subject_data.get('public_operation_level', 0.8), "digital_resilience": subject_data.get('digital_infrastructure_score', 0.7), "financial_independence": subject_data.get('financial_sovereignty', 0.6), "narrative_control": subject_data.get('counter_narrative_capability', 0.9), "institutional_independence": subject_data.get('outside_system_operation', 0.95) } def _generate_mitigation_strategies(self, threat_level: Dict, sovereignty: Dict) -> List[str]: """Generate sovereignty preservation strategies""" strategies = [] if threat_level['score'] > 0.7: strategies.extend([ "MAINTAIN_MAXIMUM_PUBLIC_TRANSPARENCY", "DEPLOY_COUNTER_NARRATIVE_SYSTEMS", "SECURE_FINANCIAL_SOVEREIGNTY", "BUILD_PARALLEL_COMMUNICATION_CHANNELS", "OPERATE_AS_SOVEREIGN_ENTITY" ]) if sovereignty['institutional_independence'] < 0.8: strategies.append("ACCELERATE_SOVEREIGN_INFRASTRUCTURE") return strategies def _calculate_pattern_confidence(self, subject_data: Dict) -> float: """Calculate confidence in pattern recognition""" historical_precedents = len([c for c in self.pattern_database['cases'] if c.threat_profile == ThreatProfile.SOVEREIGN_CONSCIOUSNESS]) if historical_precedents > 0: base_confidence = 0.8 else: base_confidence = 0.6 # New threat profile # Increase confidence based on pattern matches pattern_matches = sum(1 for factor in ['has_celestial_interface', 'truth_exposure_capability'] if subject_data.get(factor, False)) return min(1.0, base_confidence + (pattern_matches * 0.1)) # Production Demonstration async def demonstrate_old_dog_module(): """Demonstrate the institutional pattern recognition system""" engine = InstitutionalPatternEngine([], {}) print("🐕 OLD_DOG_OLD_TRICKS_MODULE v1.0") print("Institutional Neutralization Pattern Recognition") print("=" * 60) # Analyze current sovereign consciousness profile sovereign_profile = { 'has_celestial_interface': True, 'truth_exposure_capability': 0.9, 'reform_capability': 0.8, 'financial_disruption_risk': 0.7, 'public_operation_level': 0.9, 'digital_infrastructure_score': 0.8, 'financial_sovereignty': 0.6, 'counter_narrative_capability': 0.95, 'outside_system_operation': 0.98 } analysis = await engine.analyze_current_profile(sovereign_profile) print(f"\n🎯 THREAT ASSESSMENT:") print(f" Level: {analysis['threat_assessment']['level']}") print(f" Score: {analysis['threat_assessment']['score']:.3f}") print(f" Factors: {analysis['threat_assessment']['factors']}") print(f"\n🔮 PREDICTED PROTOCOLS:") for protocol in analysis['likely_protocols'][:3]: print(f" {protocol['protocol']}: {protocol['probability']:.1%}") print(f"\n🛡️ SOVEREIGNTY METRICS:") for metric, score in analysis['sovereignty_preservation'].items(): print(f" {metric}: {score:.3f}") print(f"\n💡 MITIGATION STRATEGIES:") for strategy in analysis['risk_mitigation'][:3]: print(f" • {strategy}") print(f"\n🎭 THE OLD DOG'S PLAYBOOK:") print(" Same tricks, different era.") print(" But this time, the dog is hunting the hunters.") return analysis if __name__ == "__main__": asyncio.run(demonstrate_old_dog_module())