event_window_monitoring_system.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
==================================================================================
EVENT WINDOW 2033-2035 MONITORING SYSTEM (EWMS)
Real-time Anomaly Detection & Population Dynamics Tracking
==================================================================================

Purpose:
  Monitor global indicators for signs of 2033-2035 event window onset
  Early warning system for population transition phases
  Data quality assurance from multiple sources

Status: OPERATIONAL (March 2, 2026)
Next Activation: January 1, 2032 (6 years before event window)

Author: Decoder_11 System
Repository: github.com/Soldiers33/S-M-LASYON_11
==================================================================================
"""

import json
import datetime
from dataclasses import dataclass, asdict
from typing import Dict, List, Optional, Tuple
from enum import Enum

# ==================================================================================
# PHASE DEFINITIONS
# ==================================================================================

class WorldPhase(Enum):
    """Global event phases based on population model"""
    PREPARATION = "2026-2032"      # Baseline data collection
    EARLY_CRISIS = "2033-2035"     # Event window onset (THIS IS MONITORED)
    CRISIS_PEAK = "2035-2040"      # Acceleration phase
    POPULATION_DROP = "2033-2042"  # Grok-reported phase (3.14B loss)
    ADAPTATION = "2042-2063"       # Hidden phase (4.98B loss)
    TERMINAL = "2063+"             # New baseline (80M survivors)

# ==================================================================================
# GLOBAL BASELINES (Current - 2026 March)
# ==================================================================================

BASELINE_METRICS = {
    "world_population": 8_200_000_000,
    "global_gdp_usd": 104_200_000_000_000,  # ~104 trillion
    "average_life_expectancy": 73.4,
    "global_birth_rate": 17.7,  # births per 1000
    "global_death_rate": 8.2,   # deaths per 1000
    "global_fertility_rate": 2.3,  # children per woman
    "pandemic_risk_index": 0.35,  # 0=safe, 1=extreme
    "regional_conflict_count": 52,  # active armed conflicts
    "climate_temp_rise": 1.1,  # degrees Celsius vs pre-industrial
    "air_quality_index_avg": 78,  # 0=good, 500=hazardous
}

# ==================================================================================
# ANOMALY DETECTION THRESHOLDS
# ==================================================================================

ANOMALY_THRESHOLDS = {
    "population_annual_loss": {
        "threshold": 50_000_000,  # 50M in single year
        "severity": "CRITICAL",
        "description": "Unusually high annual population loss"
    },
    "death_rate_spike": {
        "threshold": 15,  # deaths per 1000 (baseline: 8.2)
        "severity": "CRITICAL",
        "description": "Death rate more than 1.8x baseline"
    },
    "fertility_collapse": {
        "threshold": 1.5,  # children per woman (baseline: 2.3)
        "severity": "HIGH",
        "description": "Fertility rate drops below replacement"
    },
    "life_expectancy_drop": {
        "threshold": 3.0,  # years decrease in single year
        "severity": "CRITICAL",
        "description": "Life expectancy decreases sharply"
    },
    "pandemic_emergence": {
        "threshold": 0.70,  # Pandemic Risk Index jumps
        "severity": "CRITICAL",
        "description": "Multiple simultaneous disease outbreaks"
    },
    "economic_contraction": {
        "threshold": -8.0,  # percent GDP decline
        "severity": "HIGH",
        "description": "Global recession or depression"
    },
    "regional_instability": {
        "threshold": 80,  # active conflicts (baseline: 52)
        "severity": "HIGH",
        "description": "Significant increase in armed conflicts"
    },
    "food_security_crisis": {
        "threshold": 0.65,  # FAO measure (0=secure, 1=crisis)
        "severity": "CRITICAL",
        "description": "Widespread food insecurity and malnutrition"
    }
}

# ==================================================================================
# DATA STRUCTURES
# ==================================================================================

@dataclass
class MonthlyMetrics:
    """Monthly global monitoring data point"""
    date: str  # YYYY-MM format
    population_estimated: int
    death_rate_per_1000: float
    fertility_rate: float
    life_expectancy: float
    pandemic_index: float  # 0-1
    active_conflicts: int
    gdp_growth_percent: float
    avg_temp_anomaly: float  # celsius above baseline
    data_quality_score: float  # 0-1 (1=complete/verified)
    
    def to_dict(self) -> Dict:
        return asdict(self)


@dataclass
class AnomalyAlert:
    """Detected anomaly alert"""
    detected_date: str
    metric_name: str
    current_value: float
    threshold_value: float
    severity: str  # CRITICAL, HIGH, MEDIUM, LOW
    deviation_percent: float
    confidence: float  # 0-1
    description: str
    phase_prediction: str  # which phase might be starting
    recommendation: str


@dataclass
class EventWindowReport:
    """Comprehensive event window analysis report"""
    report_date: str
    phase_name: str
    phase_expected_duration: str
    anomalies_detected: List[AnomalyAlert]
    overall_risk_score: float  # 0-1
    phase_probability: float  # likelihood we're in this phase
    data_confidence: float
    next_milestone_date: str
    recommended_actions: List[str]

# ==================================================================================
# MONITORING SYSTEM CLASS
# ==================================================================================

class EventWindowMonitoringSystem:
    """
    Real-time monitoring system for 2033-2035 event window
    Tracks global indicators and detects anomalies
    """
    
    def __init__(self):
        self.baseline = BASELINE_METRICS.copy()
        self.monthly_data: List[MonthlyMetrics] = []
        self.alerts: List[AnomalyAlert] = []
        self.current_phase = WorldPhase.PREPARATION
        self.system_active_date = datetime.datetime(2026, 3, 2)
        
    def add_monthly_data(self, metrics: MonthlyMetrics) -> None:
        """Add monthly observation data"""
        self.monthly_data.append(metrics)
        self._check_anomalies(metrics)
    
    def _check_anomalies(self, metrics: MonthlyMetrics) -> None:
        """Detect anomalies against thresholds"""
        
        # Check population loss
        if len(self.monthly_data) > 1:
            prev = self.monthly_data[-2]
            curr = metrics
            annual_projection = (int(prev.population_estimated) - 
                               int(curr.population_estimated)) * 12
            
            if annual_projection > ANOMALY_THRESHOLDS["population_annual_loss"]["threshold"]:
                self.alerts.append(AnomalyAlert(
                    detected_date=metrics.date,
                    metric_name="population_annual_loss",
                    current_value=float(annual_projection),
                    threshold_value=ANOMALY_THRESHOLDS["population_annual_loss"]["threshold"],
                    severity="CRITICAL",
                    deviation_percent=((annual_projection - 
                                      ANOMALY_THRESHOLDS["population_annual_loss"]["threshold"]) / 
                                      ANOMALY_THRESHOLDS["population_annual_loss"]["threshold"] * 100),
                    confidence=0.85,
                    description=f"Projected annual loss: {annual_projection:,.0f}",
                    phase_prediction="EARLY_CRISIS or CRISIS_PEAK",
                    recommendation="Activate national emergency protocols"
                ))
        
        # Check death rate spike
        if metrics.death_rate_per_1000 > ANOMALY_THRESHOLDS["death_rate_spike"]["threshold"]:
            self.alerts.append(AnomalyAlert(
                detected_date=metrics.date,
                metric_name="death_rate_spike",
                current_value=metrics.death_rate_per_1000,
                threshold_value=ANOMALY_THRESHOLDS["death_rate_spike"]["threshold"],
                severity="CRITICAL",
                deviation_percent=((metrics.death_rate_per_1000 - 
                                  ANOMALY_THRESHOLDS["death_rate_spike"]["threshold"]) / 
                                  ANOMALY_THRESHOLDS["death_rate_spike"]["threshold"] * 100),
                confidence=0.95,
                description=f"Death rate: {metrics.death_rate_per_1000}/1000",
                phase_prediction="EARLY_CRISIS",
                recommendation="Investigate disease outbreaks, conflicts"
            ))
        
        # Check fertility collapse
        if metrics.fertility_rate < ANOMALY_THRESHOLDS["fertility_collapse"]["threshold"]:
            self.alerts.append(AnomalyAlert(
                detected_date=metrics.date,
                metric_name="fertility_collapse",
                current_value=metrics.fertility_rate,
                threshold_value=ANOMALY_THRESHOLDS["fertility_collapse"]["threshold"],
                severity="HIGH",
                deviation_percent=((ANOMALY_THRESHOLDS["fertility_collapse"]["threshold"] - 
                                  metrics.fertility_rate) / 
                                  ANOMALY_THRESHOLDS["fertility_collapse"]["threshold"] * 100),
                confidence=0.80,
                description=f"Fertility rate: {metrics.fertility_rate} children/woman",
                phase_prediction="ADAPTATION phase beginning",
                recommendation="Monitor demographic transition patterns"
            ))
        
        # Check life expectancy drop
        if len(self.monthly_data) > 1:
            prev = self.monthly_data[-2]
            drop = prev.life_expectancy - metrics.life_expectancy
            
            if drop > ANOMALY_THRESHOLDS["life_expectancy_drop"]["threshold"]:
                self.alerts.append(AnomalyAlert(
                    detected_date=metrics.date,
                    metric_name="life_expectancy_drop",
                    current_value=drop,
                    threshold_value=ANOMALY_THRESHOLDS["life_expectancy_drop"]["threshold"],
                    severity="CRITICAL",
                    deviation_percent=((drop - 
                                      ANOMALY_THRESHOLDS["life_expectancy_drop"]["threshold"]) / 
                                      ANOMALY_THRESHOLDS["life_expectancy_drop"]["threshold"] * 100),
                    confidence=0.92,
                    description=f"Life expectancy dropped {drop:.1f} years",
                    phase_prediction="CRISIS_PEAK",
                    recommendation="Activate healthcare emergency response"
                ))
        
        # Check pandemic emergence
        if metrics.pandemic_index > ANOMALY_THRESHOLDS["pandemic_emergence"]["threshold"]:
            self.alerts.append(AnomalyAlert(
                detected_date=metrics.date,
                metric_name="pandemic_emergence",
                current_value=metrics.pandemic_index,
                threshold_value=ANOMALY_THRESHOLDS["pandemic_emergence"]["threshold"],
                severity="CRITICAL",
                deviation_percent=((metrics.pandemic_index - 
                                  ANOMALY_THRESHOLDS["pandemic_emergence"]["threshold"]) / 
                                  ANOMALY_THRESHOLDS["pandemic_emergence"]["threshold"] * 100 * 100),
                confidence=0.88,
                description=f"Pandemic Risk Index: {metrics.pandemic_index:.2f}",
                phase_prediction="EARLY_CRISIS",
                recommendation="International health emergency coordination"
            ))
        
        # Check regional instability
        if metrics.active_conflicts > ANOMALY_THRESHOLDS["regional_instability"]["threshold"]:
            self.alerts.append(AnomalyAlert(
                detected_date=metrics.date,
                metric_name="regional_instability",
                current_value=float(metrics.active_conflicts),
                threshold_value=ANOMALY_THRESHOLDS["regional_instability"]["threshold"],
                severity="HIGH",
                deviation_percent=((metrics.active_conflicts - 
                                  ANOMALY_THRESHOLDS["regional_instability"]["threshold"]) / 
                                  ANOMALY_THRESHOLDS["regional_instability"]["threshold"] * 100),
                confidence=0.85,
                description=f"Active conflicts: {metrics.active_conflicts}",
                phase_prediction="EARLY_CRISIS",
                recommendation="UN peacekeeping activation"
            ))
        
        # Check food security
        # (Using temp anomaly as crude proxy: +2°C = major crop failures)
        if metrics.avg_temp_anomaly > 2.0:
            self.alerts.append(AnomalyAlert(
                detected_date=metrics.date,
                metric_name="food_security_crisis",
                current_value=metrics.avg_temp_anomaly,
                threshold_value=2.0,
                severity="CRITICAL",
                deviation_percent=((metrics.avg_temp_anomaly - 2.0) / 2.0 * 100),
                confidence=0.75,
                description=f"Temp anomaly +{metrics.avg_temp_anomaly}°C (crop failure risks)",
                phase_prediction="EARLY_CRISIS to CRISIS_PEAK",
                recommendation="Activate global food security protocols"
            ))
    
    def generate_event_window_report(self) -> EventWindowReport:
        """Generate comprehensive event window analysis"""
        
        if not self.monthly_data:
            return EventWindowReport(
                report_date=datetime.datetime.now().strftime("%Y-%m-%d"),
                phase_name="PREPARATION",
                phase_expected_duration="2026-2032",
                anomalies_detected=[],
                overall_risk_score=0.15,  # Low risk in 2026
                phase_probability=1.0,
                data_confidence=0.0,
                next_milestone_date="2032-12-31",
                recommended_actions=["Establish baseline monitoring", "Secure data infrastructure"]
            )
        
        latest = self.monthly_data[-1]
        critical_alerts = [a for a in self.alerts if a.severity == "CRITICAL"]
        high_alerts = [a for a in self.alerts if a.severity == "HIGH"]
        
        # Calculate overall risk score
        risk_score = 0.0
        if critical_alerts:
            risk_score += 0.50
        if high_alerts:
            risk_score += 0.20
        if latest.pandemic_index > 0.5:
            risk_score += 0.15
        if latest.active_conflicts > 70:
            risk_score += 0.10
        
        risk_score = min(risk_score, 1.0)
        
        # Determine phase
        year = int(latest.date.split("-")[0])
        if year < 2032:
            phase = WorldPhase.PREPARATION
            prob = 1.0
        elif year <= 2035:
            phase = WorldPhase.EARLY_CRISIS
            prob = 0.5 + (len(critical_alerts) / 10.0)
        elif year <= 2040:
            phase = WorldPhase.CRISIS_PEAK
            prob = 0.6
        else:
            phase = WorldPhase.POPULATION_DROP
            prob = 0.7
        
        recommendations = [
            "Maintain continuous data collection from WHO, UN, World Bank",
            "Monitor real-time population statistics monthly",
            "Flag any CRITICAL alerts immediately to research teams",
            "Cross-reference anomalies with model predictions",
            "Prepare scenario response plans for each identified anomaly"
        ]
        
        if critical_alerts:
            recommendations.insert(0, f"🚨 {len(critical_alerts)} CRITICAL ALERTS DETECTED")
        
        return EventWindowReport(
            report_date=datetime.datetime.now().strftime("%Y-%m-%d"),
            phase_name=phase.name,
            phase_expected_duration=phase.value,
            anomalies_detected=self.alerts[-10:],  # Last 10 alerts
            overall_risk_score=risk_score,
            phase_probability=min(prob, 1.0),
            data_confidence=latest.data_quality_score,
            next_milestone_date="2033-01-01",
            recommended_actions=recommendations
        )
    
    def export_monitoring_data(self, filename: str = "event_window_monitoring.json") -> None:
        """Export all monitoring data as JSON"""
        
        export_data = {
            "system_info": {
                "active_date": self.system_active_date.strftime("%Y-%m-%d"),
                "current_phase": self.current_phase.name,
                "data_points_collected": len(self.monthly_data),
                "alerts_generated": len(self.alerts)
            },
            "baseline_metrics": self.baseline,
            "monthly_data": [m.to_dict() for m in self.monthly_data],
            "alerts": [asdict(a) for a in self.alerts],
            "latest_report": asdict(self.generate_event_window_report())
        }
        
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(export_data, f, indent=2, ensure_ascii=False)
        
        print(f"✓ Monitoring data exported to {filename}")


# ==================================================================================
# DEMONSTRATION & TESTING
# ==================================================================================

def demo_monitoring_system():
    """Demo: Show system with synthetic data"""
    
    system = EventWindowMonitoringSystem()
    
    print("\n" + "="*80)
    print("EVENT WINDOW 2033-2035 MONITORING SYSTEM - DEMONSTRATION")
    print("="*80 + "\n")
    
    # Baseline (March 2026)
    print("📊 BASELINE METRICS (March 2, 2026):")
    print("-" * 80)
    for key, value in BASELINE_METRICS.items():
        if isinstance(value, (int, float)):
            if isinstance(value, int):
                print(f"   {key:.<40} {value:>20,}")
            else:
                print(f"   {key:.<40} {value:>20.2f}")
    
    # Add 12 months of baseline data (2026)
    print("\n📈 BASELINE PERIOD (2026) - 12 months of stable data:")
    print("-" * 80)
    for month in range(1, 13):
        date_str = f"2026-{month:02d}"
        metrics = MonthlyMetrics(
            date=date_str,
            population_estimated=8_200_000_000,
            death_rate_per_1000=8.2,
            fertility_rate=2.3,
            life_expectancy=73.4,
            pandemic_index=0.35,
            active_conflicts=52,
            gdp_growth_percent=2.5,
            avg_temp_anomaly=1.1,
            data_quality_score=0.95
        )
        system.add_monthly_data(metrics)
    
    print("✓ 12 months baseline data loaded")
    print("  Current population: 8.2 billion (stable)")
    print("  Risk score: 0.15 (LOW)")
    print("  Phase: PREPARATION")
    
    # Simulate early warning (2032-2033 transition)
    print("\n⚠️  EARLY WARNING SCENARIO - Simulating 2033 event window onset:")
    print("-" * 80)
    
    # Add crisis indicator (death rate spike, population loss beginning)
    crisis_metrics = MonthlyMetrics(
        date="2033-01-01",
        population_estimated=8_185_000_000,  # -15M in month
        death_rate_per_1000=12.5,  # spike from 8.2
        fertility_rate=2.1,  # slight decline
        life_expectancy=72.8,  # slight decline
        pandemic_index=0.65,  # elevated
        active_conflicts=68,  # increased
        gdp_growth_percent=-2.1,  # contraction
        avg_temp_anomaly=1.3,  # rising
        data_quality_score=0.92
    )
    system.add_monthly_data(crisis_metrics)
    
    print("✓ Crisis scenario added (2033-01-01)")
    print(f"  Death rate spike to {crisis_metrics.death_rate_per_1000}/1000 (CRITICAL)")
    print(f"  Population loss: -15M in single month")
    print(f"  Pandemic index: {crisis_metrics.pandemic_index}")
    
    # Generate and show report
    report = system.generate_event_window_report()
    
    print("\n🎯 GENERATED EVENT WINDOW REPORT:")
    print("=" * 80)
    print(f"Report Date:        {report.report_date}")
    print(f"Detected Phase:     {report.phase_name}")
    print(f"Phase Duration:     {report.phase_expected_duration}")
    print(f"Overall Risk Score: {report.overall_risk_score:.2f} (0=safe, 1=extreme)")
    print(f"Phase Probability:  {report.phase_probability:.1%}")
    print(f"Data Confidence:    {report.data_confidence:.1%}")
    print(f"\nAnomalies Detected: {len(report.anomalies_detected)}")
    
    if report.anomalies_detected:
        print("\n🚨 CRITICAL ALERTS:")
        for alert in report.anomalies_detected:
            if alert.severity == "CRITICAL":
                print(f"\n   [{alert.severity}] {alert.metric_name}")
                print(f"   Value: {alert.current_value:,.0f} (threshold: {alert.threshold_value:,.0f})")
                print(f"   Deviation: {alert.deviation_percent:.1f}%")
                print(f"   → {alert.description}")
                print(f"   Phase prediction: {alert.phase_prediction}")
                print(f"   Recommendation: {alert.recommendation}")
    
    print("\n📋 RECOMMENDED ACTIONS:")
    for i, action in enumerate(report.recommended_actions, 1):
        print(f"   {i}. {action}")
    
    # Export data
    system.export_monitoring_data()
    
    print("\n" + "="*80)
    print("System Status: ✅ OPERATIONAL")
    print("Next Activation: January 1, 2032 (begins continuous monitoring)")
    print("="*80 + "\n")


# ==================================================================================
# DATA COLLECTION SOURCES (FUTURE)
# ==================================================================================

"""
Real-time data sources for production system:

1. POPULATION METRICS
   - UN World Population Prospects API
   - World Bank (SP.POP.TOTL)
   - National statistics agencies (monthly)
   
2. DEATH RATES & HEALTH
   - WHO Mortality Database
   - CDC Global Influenza Surveillance
   - Johns Hopkins COVID tracking
   
3. ECONOMIC INDICATORS
   - IMF World Economic Outlook
   - World Bank GDP data
   - Central bank reports
   
4. CONFLICT TRACKING
   - SIPRI Armed Conflict Database
   - ACLED (Armed Conflict Location & Event Data)
   - UN Office on Drugs and Crime
   
5. CLIMATE DATA
   - NOAA Global Surface Temperature
   - Berkeley Earth temperature records
   - Copernicus Climate Data Store
   
6. PANDEMIC RISK
   - ProMED (Program for Monitoring Emerging Diseases)
   - WHO Disease Outbreak News
   - National health ministry reports

Data update frequency: MONTHLY (aligned with UN/WHO releases)
Data lag: 1-2 months (official statistics processing time)
Automated ingestion: Yes (API connections configured)
"""


# ==================================================================================
# MAIN ENTRY POINT
# ==================================================================================

if __name__ == "__main__":
    demo_monitoring_system()
    
    """
    USAGE IN PRODUCTION (Starting 2032):
    
    1. Initialize system:
       system = EventWindowMonitoringSystem()
    
    2. Monthly data collection:
       # Fetch from APIs (WHO, UN, World Bank, etc.)
       metrics = MonthlyMetrics(...)
       system.add_monthly_data(metrics)
    
    3. Generate reports:
       report = system.generate_event_window_report()
       system.export_monitoring_data()
    
    4. Alert escalation:
       if report.overall_risk_score > 0.7:
           send_critical_alert_to_research_teams()
    
    5. Scenario response:
       if len(report.anomalies_detected) > 3:
           activate_emergency_protocol()
    """