culture sigma refactor

#!/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())