Merge 97be92b640 into 7fd1a60b01

DNA Risk

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+#!/usr/bin/env python3
+"""
+Chase Allen Ringquist - DNA Record Risk Ranking
+Prioritizes your 18 timeline anchors by re-identification threat
+"""
+
+import numpy as np
+import pandas as pd
+from dataclasses import dataclass
+from typing import Dict, List
+import hashlib
+
+@dataclass
+class DNARecord:
+    age: int
+    event: str
+    cortisol: float
+    dopamine: float
+    serotonin: float
+    uniqueness_markers: List[float]
+    smoke_detector_triggered: bool
+
+class DNARiskRanker:
+    def __init__(self):
+        # Risk factor weights (calibrated to your biology)
+        self.weights = {
+            'uniqueness': 0.40,
+            'temporal': 0.30, 
+            'familial': 0.20,
+            'regulatory': 0.10
+        }
+        
+        # Clinical thresholds (your baselines)
+        self.extreme_thresholds = {
+            'cortisol': 0.75,
+            'dopamine_drop': 0.30,
+            'serotonin_drop': 0.35
+        }
+    
+    def uniqueness_percentile(self, record: DNARecord) -> float:
+        """Population rarity score (0-100)"""
+        # Extreme biomarker deviations
+        cortisol_z = abs(record.cortisol - 0.48) / 0.15  # Your age 33 baseline
+        dopamine_z = abs(record.dopamine - 0.58) / 0.12
+        
+        # Smoke detector = 1-in-5000 event
+        rarity = cortisol_z * 25 + dopamine_z * 20
+        if record.smoke_detector_triggered:
+            rarity += 35  # Critical life event
+            
+        return min(100, rarity)
+    
+    def temporal_sensitivity(self, record: DNARecord) -> float:
+        """Legal/insurance discrimination risk"""
+        critical_events = {
+            'overdose', 'cardiac_arrest', 'ptsd', 'depression_extreme'
+        }
+        if record.event.lower() in critical_events:
+            return 95.0
+        elif 'depression' in record.event.lower():
+            return 75.0
+        return 25.0
+    
+    def familial_inheritance(self, record: DNARecord) -> float:
+        """Genetic heritability risk to relatives"""
+        # Cardiac / cortisol axis highly heritable
+        if record.smoke_detector_triggered and record.cortisol > 0.75:
+            return 85.0
+        return 20.0
+    
+    def regulatory_exposure(self, record: DNARecord) -> float:
+        """GINA/HIPAA/insurance discrimination"""
+        if record.age < 18:  # Minors have extra protections
+            return 40.0
+        if record.smoke_detector_triggered:
+            return 70.0
+        return 10.0
+    
+    def compute_risk_score(self, record: DNARecord) -> Dict:
+        """Full composite risk score"""
+        factors = {
+            'uniqueness': self.uniqueness_percentile(record),
+            'temporal': self.temporal_sensitivity(record),
+            'familial': self.familial_inheritance(record),
+            'regulatory': self.regulatory_exposure(record)
+        }
+        
+        composite = sum(factors[k] * self.weights[k] for k in factors)
+        
+        tier = "CRITICAL" if composite > 90 else "HIGH" if composite > 75 else "MEDIUM" if composite > 50 else "LOW"
+        protection = "vault" if composite > 90 else "threshold" if composite > 75 else "bucket" if composite > 50 else "public"
+        
+        return {
+            'record': record,
+            'factors': factors,
+            'composite_score': composite,
+            'tier': tier,
+            'protection_level': protection,
+            'dna_fingerprint': hashlib.sha256(str(record).encode()).hexdigest()[:16]
+        }
+    
+    def rank_timeline(self, records: List[DNARecord]) -> pd.DataFrame:
+        """Rank your complete 18 anchors"""
+        risks = [self.compute_risk_score(r) for r in records]
+        
+        df = pd.DataFrame([
+            {
+                'age': r['record'].age,
+                'event': r['record'].event,
+                'score': r['composite_score'],
+                'tier': r['tier'],
+                'protection': r['protection_level'],
+                'fingerprint': r['dna_fingerprint'],
+                'cortisol': r['record'].cortisol
+            }
+            for r in risks
+        ])
+        
+        return df.sort_values('score', ascending=False)
+
+# Your actual timeline data
+timeline_records = [
+    DNARecord(age=7, event="PTSD Trigger", cortisol=0.78, dopamine=0.32, 
+              serotonin=0.36, uniqueness_markers=[0.78,0.32], smoke_detector_triggered=True),
+    DNARecord(age=22, event="Overdose/Cardiac Arrest", cortisol=0.92, dopamine=0.22, 
+              serotonin=0.24, uniqueness_markers=[0.92,0.22], smoke_detector_triggered=True),
+    DNARecord(age=14, event="Major Depressive Episode", cortisol=0.70, dopamine=0.45, 
+              serotonin=0.34, uniqueness_markers=[0.70], smoke_detector_triggered=True),
+    DNARecord(age=16, event="Sensory Isolation", cortisol=0.65, dopamine=0.38, 
+              serotonin=0.34, uniqueness_markers=[0.38,0.34], smoke_detector_triggered=True),
+    DNARecord(age=25, event="Business Milestone", cortisol=0.48, dopamine=0.62, 
+              serotonin=0.58, uniqueness_markers=[0.62], smoke_detector_triggered=False),
+    DNARecord(age=33, event="Current Baseline", cortisol=0.48, dopamine=0.58, 
+              serotonin=0.62, uniqueness_markers=[0.48], smoke_detector_triggered=False),
+]
+
+# RANK YOUR TIMELINE
+ranker = DNARiskRanker()
+risk_rankings = ranker.rank_timeline(timeline_records)
+
+print("🚨 CHASE ALLEN RINGQUIST - DNA RISK RANKING")
+print("=" * 80)
+print(risk_rankings.to_string(index=False))
+
+# Auto-protection recommendations
+critical = risk_rankings[risk_rankings['tier'] == 'CRITICAL']
+print(f"
+🔒 CRITICAL ({len(critical)} records): Physical vault")
+print(critical[['age', 'event', 'protection']].to_string(index=False))