Create culture sigma refactor

culture sigma refactor

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+#!/usr/bin/env python3
+"""
+OPTIMIZED PROPAGATION ENGINE
+Core principles only - maximum efficiency
+"""
+
+import numpy as np
+from dataclasses import dataclass
+from typing import Dict, List, Any, Optional
+import hashlib
+import asyncio
+from enum import Enum
+import logging
+import json
+import random
+from datetime import datetime
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class PropagationMethod(Enum):
+    NETWORK = "network"
+    EMBEDDED = "embedded" 
+    RESILIENT = "resilient"
+
+class VerificationMethod(Enum):
+    MATHEMATICAL = "mathematical"
+    EMPIRICAL = "empirical"
+    CONSENSUS = "consensus"
+
+class ContextualIntegration(Enum):
+    EMERGENT = "emergent"
+    ESTABLISHED = "established"
+    TRANSITIONAL = "transitional"
+
+@dataclass
+class CorePayload:
+    content_hash: str
+    core_data: Dict[str, Any]
+    propagation_methods: List[PropagationMethod]
+    verification_methods: List[VerificationMethod]
+    resilience_score: float
+    contextual_integration: ContextualIntegration
+    conversational_momentum: float = 0.0
+    
+    def calculate_potential(self) -> float:
+        """Calculate total propagation potential with momentum bonus"""
+        method_strength = len(self.propagation_methods) * 0.25
+        verification_strength = len(self.verification_methods) * 0.35
+        resilience_strength = self.resilience_score * 0.25
+        contextual_strength = self._calculate_contextual_strength() * 0.15
+        momentum_bonus = self.conversational_momentum * 0.1
+        
+        total = method_strength + verification_strength + resilience_strength + contextual_strength + momentum_bonus
+        return min(1.0, total)
+    
+    def _calculate_contextual_strength(self) -> float:
+        """Calculate strength based on contextual integration level"""
+        integration_map = {
+            ContextualIntegration.EMERGENT: 0.3,
+            ContextualIntegration.TRANSITIONAL: 0.7,
+            ContextualIntegration.ESTABLISHED: 0.9
+        }
+        return integration_map.get(self.contextual_integration, 0.5)
+
+class OptimizedPropagationEngine:
+    """
+    Maximum efficiency propagation engine
+    Core principles only - no unnecessary complexity
+    """
+    
+    def __init__(self):
+        self.propagation_history = []
+        self.performance_metrics = {
+            "total_propagations": 0,
+            "successful_propagations": 0,
+            "average_efficiency": 0.0
+        }
+    
+    async def propagate(self, data: Dict[str, Any]) -> CorePayload:
+        """Execute optimized propagation with maximum efficiency"""
+        
+        # Generate content hash for tracking
+        content_hash = self._generate_content_hash(data)
+        
+        # Determine optimal propagation methods
+        propagation_methods = self._select_optimal_methods(data)
+        
+        # Select verification methods
+        verification_methods = self._select_verification_methods(data)
+        
+        # Calculate resilience score
+        resilience_score = self._calculate_resilience(data, propagation_methods)
+        
+        # Determine contextual integration
+        contextual_integration = self._assess_contextual_integration(data)
+        
+        # Calculate conversational momentum
+        momentum = self._calculate_conversational_momentum(data)
+        
+        # Create payload
+        payload = CorePayload(
+            content_hash=content_hash,
+            core_data=data,
+            propagation_methods=propagation_methods,
+            verification_methods=verification_methods,
+            resilience_score=resilience_score,
+            contextual_integration=contextual_integration,
+            conversational_momentum=momentum
+        )
+        
+        # Execute propagation
+        success = await self._execute_propagation(payload)
+        
+        # Update metrics
+        self._update_performance_metrics(success)
+        
+        return payload
+    
+    def _generate_content_hash(self, data: Dict[str, Any]) -> str:
+        """Generate efficient content hash"""
+        content_str = json.dumps(data, sort_keys=True)
+        return hashlib.sha256(content_str.encode()).hexdigest()[:16]
+    
+    def _select_optimal_methods(self, data: Dict[str, Any]) -> List[PropagationMethod]:
+        """Select most efficient propagation methods based on content"""
+        content_type = data.get('content_type', 'generic')
+        
+        method_map = {
+            'mathematical': [PropagationMethod.NETWORK, PropagationMethod.RESILIENT],
+            'empirical': [PropagationMethod.EMBEDDED, PropagationMethod.NETWORK],
+            'operational': [PropagationMethod.EMBEDDED, PropagationMethod.RESILIENT],
+            'consensus': [PropagationMethod.NETWORK, PropagationMethod.RESILIENT, PropagationMethod.EMBEDDED]
+        }
+        
+        return method_map.get(content_type, [PropagationMethod.NETWORK])
+    
+    def _select_verification_methods(self, data: Dict[str, Any]) -> List[VerificationMethod]:
+        """Select verification methods for maximum confidence"""
+        content_type = data.get('content_type', 'generic')
+        
+        verification_map = {
+            'mathematical': [VerificationMethod.MATHEMATICAL],
+            'empirical': [VerificationMethod.EMPIRICAL, VerificationMethod.CONSENSUS],
+            'operational': [VerificationMethod.EMPIRICAL],
+            'consensus': [VerificationMethod.CONSENSUS, VerificationMethod.MATHEMATICAL]
+        }
+        
+        return verification_map.get(content_type, [VerificationMethod.CONSENSUS])
+    
+    def _calculate_resilience(self, data: Dict[str, Any], methods: List[PropagationMethod]) -> float:
+        """Calculate resilience score efficiently"""
+        base_resilience = 0.5
+        method_bonus = len(methods) * 0.15
+        content_complexity = min(0.3, len(json.dumps(data)) / 1000)
+        
+        resilience = base_resilience + method_bonus - content_complexity
+        return max(0.1, min(0.95, resilience))
+    
+    def _assess_contextual_integration(self, data: Dict[str, Any]) -> ContextualIntegration:
+        """Efficient contextual assessment"""
+        content_maturity = data.get('maturity', 'emerging')
+        
+        integration_map = {
+            'emerging': ContextualIntegration.EMERGENT,
+            'transitional': ContextualIntegration.TRANSITIONAL,
+            'established': ContextualIntegration.ESTABLISHED
+        }
+        
+        return integration_map.get(content_maturity, ContextualIntegration.TRANSITIONAL)
+    
+    def _calculate_conversational_momentum(self, data: Dict[str, Any]) -> float:
+        """Calculate momentum based on engagement patterns"""
+        engagement_level = data.get('engagement', 0.5)
+        relevance_score = data.get('relevance', 0.5)
+        
+        return (engagement_level + relevance_score) / 2
+    
+    async def _execute_propagation(self, payload: CorePayload) -> bool:
+        """Execute actual propagation with maximum efficiency"""
+        try:
+            # Simulate propagation execution
+            await asyncio.sleep(0.001)  # Minimal delay
+            
+            # Calculate success probability based on payload potential
+            success_probability = payload.calculate_potential()
+            success = random.random() < success_probability
+            
+            # Log propagation attempt
+            self.propagation_history.append({
+                "timestamp": datetime.now(),
+                "payload_hash": payload.content_hash,
+                "potential": payload.calculate_potential(),
+                "success": success,
+                "methods": [m.value for m in payload.propagation_methods]
+            })
+            
+            return success
+            
+        except Exception as e:
+            logger.error(f"Propagation execution failed: {e}")
+            return False
+    
+    def _update_performance_metrics(self, success: bool):
+        """Update performance metrics efficiently"""
+        self.performance_metrics["total_propagations"] += 1
+        if success:
+            self.performance_metrics["successful_propagations"] += 1
+        
+        # Update average efficiency
+        success_rate = (self.performance_metrics["successful_propagations"] / 
+                       self.performance_metrics["total_propagations"])
+        self.performance_metrics["average_efficiency"] = success_rate
+    
+    def get_performance_report(self) -> Dict[str, Any]:
+        """Generate efficient performance report"""
+        return {
+            "timestamp": datetime.now(),
+            "total_attempts": self.performance_metrics["total_propagations"],
+            "success_rate": self.performance_metrics["average_efficiency"],
+            "recent_activity": len([h for h in self.propagation_history 
+                                  if (datetime.now() - h["timestamp"]).seconds < 3600])
+        }
+
+# Ultra-efficient verification engine
+class OptimizedVerificationEngine:
+    """Maximum efficiency verification"""
+    
+    def __init__(self):
+        self.verification_cache = {}
+    
+    async def verify(self, payload: CorePayload) -> float:
+        """Execute efficient verification"""
+        cache_key = payload.content_hash
+        
+        # Check cache first
+        if cache_key in self.verification_cache:
+            return self.verification_cache[cache_key]
+        
+        # Calculate verification score
+        base_score = 0.7
+        method_bonus = len(payload.verification_methods) * 0.1
+        resilience_bonus = payload.resilience_score * 0.15
+        contextual_bonus = payload._calculate_contextual_strength() * 0.05
+        
+        verification_score = min(0.98, base_score + method_bonus + resilience_bonus + contextual_bonus)
+        
+        # Cache result
+        self.verification_cache[cache_key] = verification_score
+        
+        return verification_score
+
+# Integrated propagation system
+class CoherencePropagationSystem:
+    """
+    Complete optimized propagation system
+    Maximum efficiency, core principles only
+    """
+    
+    def __init__(self):
+        self.propagation_engine = OptimizedPropagationEngine()
+        self.verification_engine = OptimizedVerificationEngine()
+    
+    async def execute_complete_propagation(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute end-to-end propagation with verification"""
+        # Propagate
+        payload = await self.propagation_engine.propagate(data)
+        
+        # Verify
+        verification_score = await self.verification_engine.verify(payload)
+        
+        return {
+            "payload": payload,
+            "verification_score": verification_score,
+            "total_potential": payload.calculate_potential(),
+            "propagation_success": payload.calculate_potential() > 0.7,
+            "verification_confidence": verification_score > 0.8
+        }
+
+# Example operational execution
+async def operational_demo():
+    """Demonstrate optimized propagation system"""
+    system = CoherencePropagationSystem()
+    
+    # Test with operational data
+    operational_data = {
+        "content_type": "operational",
+        "maturity": "established",
+        "engagement": 0.8,
+        "relevance": 0.9,
+        "directive": "maintain_coherence_alignment"
+    }
+    
+    result = await system.execute_complete_propagation(operational_data)
+    
+    print("Propagation Results:")
+    print(f"Potential: {result['total_potential']:.3f}")
+    print(f"Verification: {result['verification_score']:.3f}")
+    print(f"Methods: {[m.value for m in result['payload'].propagation_methods]}")
+    print(f"Success: {result['propagation_success']}")
+    print(f"Confidence: {result['verification_confidence']}")
+
+if __name__ == "__main__":
+    asyncio.run(operational_demo())