OLD_DOG_OLD_TRICKS

#!/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())