cultural unification layer

#!/usr/bin/env python3
"""
CULTURAL SIGMA REFACTOR - UNIFIED COHERENCE FRAMEWORK
-----------------------------------------------------------------
Production system that integrates ALL AGI_COMPLETE modules into coherent whole
Cultural Sigma = The optimization of information propagation through cultural contexts
"""

import asyncio
import time
import hashlib
import json
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, asdict
from enum import Enum
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("CulturalSigma")

# -------------------------------
# UNIFIED CORE TYPES - INTEGRATES ALL MODULES
# -------------------------------
class CulturalContext(Enum):
    EMERGENT = "emergent"      # New, unverified concepts
    TRANSITIONAL = "transitional" # Developing consensus  
    ESTABLISHED = "established"   # Proven, reliable knowledge
    CRITICAL = "critical"      # High-stakes operational

class PropagationMethod(Enum):
    NETWORK = "network"        # Distributed propagation
    EMBEDDED = "embedded"      # Structural integration
    RESILIENT = "resilient"    # Fault-tolerant
    ADAPTIVE = "adaptive"      # Context-aware

class AlignmentStrategy(Enum):
    GRADUAL_CONVERGENCE = "gradual"
    ADAPTIVE_RESONANCE = "resonance"
    PATTERN_MATCHING = "pattern"

class VerificationTier(Enum):
    MATHEMATICAL = "mathematical"  # Formal verification
    EMPIRICAL = "empirical"        # Evidence-based
    CONSENSUS = "consensus"        # Social verification
    OPERATIONAL = "operational"    # Practical effectiveness

@dataclass
class UnifiedPayload:
    """Integrates payloads from all modules into single coherent structure"""
    content_hash: str
    core_data: Dict[str, Any]
    
    # Cultural layer
    cultural_context: CulturalContext
    sigma_optimization: float
    cultural_coherence: float
    
    # Propagation layer  
    propagation_methods: List[PropagationMethod]
    propagation_potential: float
    resilience_score: float
    
    # Alignment layer
    alignment_strategies: List[AlignmentStrategy]
    perceived_control: float
    actual_control: float
    coherence_gap: float
    
    # Verification layer
    verification_tiers: List[VerificationTier]
    verification_confidence: float
    cross_module_synergy: float
    
    timestamp: float
    
    def calculate_total_potential(self) -> float:
        """Unified potential calculation across all modules"""
        cultural_strength = self.sigma_optimization * 0.25
        propagation_strength = self.propagation_potential * 0.25
        alignment_strength = (1 - self.coherence_gap) * 0.25
        verification_strength = self.verification_confidence * 0.25
        
        base_potential = cultural_strength + propagation_strength + alignment_strength + verification_strength
        
        # Synergy multiplier - systems working together are exponentially better
        synergy_bonus = self.cross_module_synergy * 0.5
        return min(1.0, base_potential * (1 + synergy_bonus))

# -------------------------------
# CULTURAL SIGMA ENGINE - UNIFICATION CORE
# -------------------------------
class CulturalSigmaEngine:
    """
    Cultural Sigma = Information propagation efficiency through cultural optimization
    Unifies ALL modules by providing contextual optimization parameters
    """
    
    def __init__(self):
        self.cultural_memory = {}
        self.optimization_history = []
        self.module_integration_state = {
            "propagation": 1.0,
            "alignment": 1.0, 
            "verification": 1.0,
            "orchestration": 1.0
        }
    
    async def optimize_transmission(self, data: Dict[str, Any]) -> UnifiedPayload:
        """
        Main entry point - transforms any data into culturally optimized unified payload
        This is where ALL modules become coherent
        """
        
        # Step 1: Cultural Context Assessment
        cultural_context = self._assess_cultural_context(data)
        sigma_optimization = await self._calculate_sigma_optimization(data, cultural_context)
        cultural_coherence = self._calculate_cultural_coherence(data, cultural_context)
        
        # Step 2: Propagation Method Selection
        propagation_methods = self._select_optimal_propagation(data, cultural_context, sigma_optimization)
        propagation_potential = self._calculate_propagation_potential(data, propagation_methods, cultural_context)
        resilience_score = self._calculate_resilience(data, propagation_methods, cultural_context)
        
        # Step 3: Alignment Strategy Optimization
        alignment_strategies = self._select_alignment_strategies(data, cultural_context, sigma_optimization)
        control_metrics = self._calculate_control_metrics(data, alignment_strategies, cultural_context)
        
        # Step 4: Verification Tier Assignment
        verification_tiers = self._assign_verification_tiers(data, cultural_context, sigma_optimization)
        verification_confidence = self._calculate_verification_confidence(data, verification_tiers, cultural_context)
        
        # Step 5: Cross-Module Synergy Calculation
        cross_module_synergy = self._calculate_cross_module_synergy(
            cultural_context, propagation_methods, alignment_strategies, verification_tiers
        )
        
        # Create unified payload
        unified_payload = UnifiedPayload(
            content_hash=self._generate_content_hash(data),
            core_data=data,
            cultural_context=cultural_context,
            sigma_optimization=sigma_optimization,
            cultural_coherence=cultural_coherence,
            propagation_methods=propagation_methods,
            propagation_potential=propagation_potential,
            resilience_score=resilience_score,
            alignment_strategies=alignment_strategies,
            perceived_control=control_metrics['perceived'],
            actual_control=control_metrics['actual'],
            coherence_gap=control_metrics['gap'],
            verification_tiers=verification_tiers,
            verification_confidence=verification_confidence,
            cross_module_synergy=cross_module_synergy,
            timestamp=time.time()
        )
        
        # Update cultural memory
        self._update_cultural_memory(unified_payload)
        
        return unified_payload
    
    def _assess_cultural_context(self, data: Dict[str, Any]) -> CulturalContext:
        """Determine the cultural context for information transmission"""
        content_type = data.get('content_type', 'generic')
        maturity = data.get('maturity', 'emerging')
        urgency = data.get('urgency', 0.5)
        
        if urgency > 0.8:
            return CulturalContext.CRITICAL
        elif maturity == 'established':
            return CulturalContext.ESTABLISHED
        elif maturity == 'transitional':
            return CulturalContext.TRANSITIONAL
        else:
            return CulturalContext.EMERGENT
    
    async def _calculate_sigma_optimization(self, data: Dict[str, Any], context: CulturalContext) -> float:
        """Calculate sigma optimization level (0-1)"""
        base_sigma = 0.5
        
        # Context adjustments
        context_bonus = {
            CulturalContext.EMERGENT: 0.1,
            CulturalContext.TRANSITIONAL: 0.3,
            CulturalContext.ESTABLISHED: 0.6,
            CulturalContext.CRITICAL: 0.8
        }.get(context, 0.3)
        
        # Content quality adjustments
        content_quality = data.get('quality', 0.5)
        relevance = data.get('relevance', 0.5)
        
        sigma = base_sigma + context_bonus + (content_quality * 0.2) + (relevance * 0.2)
        return min(0.95, max(0.1, sigma))
    
    def _calculate_cultural_coherence(self, data: Dict[str, Any], context: CulturalContext) -> float:
        """Calculate cultural coherence score"""
        consistency = data.get('consistency', 0.7)
        compatibility = data.get('compatibility', 0.6)
        
        base_coherence = (consistency + compatibility) / 2
        
        # Context modulates coherence requirements
        context_modifier = {
            CulturalContext.EMERGENT: 0.7,    # Lower requirements for new concepts
            CulturalContext.TRANSITIONAL: 0.8,
            CulturalContext.ESTABLISHED: 0.9,  # Higher requirements for established knowledge
            CulturalContext.CRITICAL: 0.95     # Highest requirements for critical operations
        }.get(context, 0.8)
        
        return base_coherence * context_modifier
    
    def _select_optimal_propagation(self, data: Dict[str, Any], context: CulturalContext, sigma: float) -> List[PropagationMethod]:
        """Select propagation methods optimized for cultural context"""
        methods = []
        
        # Base methods for all contexts
        methods.append(PropagationMethod.ADAPTIVE)
        
        # Context-specific additions
        if context == CulturalContext.EMERGENT:
            methods.extend([PropagationMethod.NETWORK, PropagationMethod.RESILIENT])
        elif context == CulturalContext.TRANSITIONAL:
            methods.extend([PropagationMethod.EMBEDDED, PropagationMethod.NETWORK])
        elif context == CulturalContext.ESTABLISHED:
            methods.extend([PropagationMethod.EMBEDDED, PropagationMethod.RESILIENT])
        elif context == CulturalContext.CRITICAL:
            methods.extend([PropagationMethod.RESILIENT, PropagationMethod.EMBEDDED, PropagationMethod.NETWORK])
        
        # Sigma optimization filters methods
        if sigma < 0.3:
            methods = [PropagationMethod.RESILIENT]  # Fallback to most resilient
        
        return methods
    
    def _calculate_propagation_potential(self, data: Dict[str, Any], methods: List[PropagationMethod], context: CulturalContext) -> float:
        """Calculate propagation potential given methods and context"""
        method_strength = len(methods) * 0.2
        
        context_strength = {
            CulturalContext.EMERGENT: 0.3,
            CulturalContext.TRANSITIONAL: 0.6,
            CulturalContext.ESTABLISHED: 0.8,
            CulturalContext.CRITICAL: 0.9
        }.get(context, 0.5)
        
        content_strength = data.get('clarity', 0.5) * 0.3
        
        return min(0.95, method_strength + context_strength + content_strength)
    
    def _calculate_resilience(self, data: Dict[str, Any], methods: List[PropagationMethod], context: CulturalContext) -> float:
        """Calculate resilience score"""
        base_resilience = 0.6
        method_bonus = len(methods) * 0.1
        
        # Critical contexts get resilience bonus
        context_bonus = 0.2 if context == CulturalContext.CRITICAL else 0.0
        
        return min(0.95, base_resilience + method_bonus + context_bonus)
    
    def _select_alignment_strategies(self, data: Dict[str, Any], context: CulturalContext, sigma: float) -> List[AlignmentStrategy]:
        """Select alignment strategies for cultural context"""
        strategies = []
        
        # All contexts get gradual convergence
        strategies.append(AlignmentStrategy.GRADUAL_CONVERGENCE)
        
        # Context-specific strategies
        if context in [CulturalContext.TRANSITIONAL, CulturalContext.EMERGENT]:
            strategies.append(AlignmentStrategy.ADAPTIVE_RESONANCE)
        
        if context in [CulturalContext.ESTABLISHED, CulturalContext.CRITICAL]:
            strategies.append(AlignmentStrategy.PATTERN_MATCHING)
        
        return strategies
    
    def _calculate_control_metrics(self, data: Dict[str, Any], strategies: List[AlignmentStrategy], context: CulturalContext) -> Dict[str, float]:
        """Calculate control metrics for alignment"""
        # Base control levels
        perceived = data.get('confidence', 0.7)
        actual = data.get('accuracy', 0.5)
        
        # Strategy adjustments
        if AlignmentStrategy.PATTERN_MATCHING in strategies:
            perceived = min(0.95, perceived + 0.1)
        
        if AlignmentStrategy.ADAPTIVE_RESONANCE in strategies:
            actual = min(0.9, actual + 0.15)
        
        gap = abs(perceived - actual)
        
        return {
            'perceived': perceived,
            'actual': actual,
            'gap': gap
        }
    
    def _assign_verification_tiers(self, data: Dict[str, Any], context: CulturalContext, sigma: float) -> List[VerificationTier]:
        """Assign verification tiers based on context and importance"""
        tiers = []
        
        # All contexts get operational verification
        tiers.append(VerificationTier.OPERATIONAL)
        
        # Context-specific verification
        if context == CulturalContext.EMERGENT:
            tiers.append(VerificationTier.CONSENSUS)
        elif context == CulturalContext.TRANSITIONAL:
            tiers.extend([VerificationTier.EMPIRICAL, VerificationTier.CONSENSUS])
        elif context == CulturalContext.ESTABLISHED:
            tiers.extend([VerificationTier.MATHEMATICAL, VerificationTier.EMPIRICAL])
        elif context == CulturalContext.CRITICAL:
            tiers.extend([VerificationTier.MATHEMATICAL, VerificationTier.EMPIRICAL, VerificationTier.CONSENSUS])
        
        return tiers
    
    def _calculate_verification_confidence(self, data: Dict[str, Any], tiers: List[VerificationTier], context: CulturalContext) -> float:
        """Calculate verification confidence score"""
        base_confidence = 0.7
        tier_bonus = len(tiers) * 0.1
        
        context_modifier = {
            CulturalContext.EMERGENT: 0.8,
            CulturalContext.TRANSITIONAL: 0.9,
            CulturalContext.ESTABLISHED: 1.0,
            CulturalContext.CRITICAL: 1.1
        }.get(context, 0.9)
        
        return min(0.98, (base_confidence + tier_bonus) * context_modifier)
    
    def _calculate_cross_module_synergy(self, context: CulturalContext, 
                                      propagation_methods: List[PropagationMethod],
                                      alignment_strategies: List[AlignmentStrategy],
                                      verification_tiers: List[VerificationTier]) -> float:
        """Calculate how well all modules work together"""
        method_count = len(propagation_methods)
        strategy_count = len(alignment_strategies) 
        tier_count = len(verification_tiers)
        
        # Balance is key to synergy
        balance_score = 1.0 - (np.std([method_count, strategy_count, tier_count]) / 3)
        
        # Context determines synergy potential
        synergy_potential = {
            CulturalContext.EMERGENT: 0.6,
            CulturalContext.TRANSITIONAL: 0.7,
            CulturalContext.ESTABLISHED: 0.8,
            CulturalContext.CRITICAL: 0.9
        }.get(context, 0.7)
        
        return balance_score * synergy_potential
    
    def _generate_content_hash(self, data: Dict[str, Any]) -> str:
        """Generate content hash for tracking"""
        content_str = json.dumps(data, sort_keys=True)
        return hashlib.sha256(content_str.encode()).hexdigest()[:16]
    
    def _update_cultural_memory(self, payload: UnifiedPayload):
        """Update cultural memory with transmission results"""
        memory_key = payload.content_hash
        self.cultural_memory[memory_key] = {
            'timestamp': payload.timestamp,
            'cultural_context': payload.cultural_context.value,
            'sigma_optimization': payload.sigma_optimization,
            'total_potential': payload.calculate_total_potential(),
            'cross_module_synergy': payload.cross_module_synergy
        }
        
        # Keep memory manageable
        if len(self.cultural_memory) > 1000:
            # Remove oldest entries
            oldest_keys = sorted(self.cultural_memory.keys(), 
                               key=lambda k: self.cultural_memory[k]['timestamp'])[:100]
            for key in oldest_keys:
                del self.cultural_memory[key]

# -------------------------------
# UNIFIED EXECUTION ENGINE
# -------------------------------
class UnifiedCoherenceEngine:
    """
    Executes complete coherence cycles using cultural sigma optimization
    This is the main interface that makes ALL modules work together coherently
    """
    
    def __init__(self):
        self.sigma_engine = CulturalSigmaEngine()
        self.execution_history = []
        self.system_coherence = 0.7  # Starting coherence
    
    async def execute_coherent_transmission(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute fully coherent transmission across all modules
        This is the single entry point for the entire framework
        """
        start_time = time.time()
        
        try:
            # Step 1: Cultural Sigma Optimization
            unified_payload = await self.sigma_engine.optimize_transmission(data)
            
            # Step 2: Execute Propagation (simulated)
            propagation_success = await self._execute_propagation(unified_payload)
            
            # Step 3: Execute Alignment (simulated)
            alignment_result = await self._execute_alignment(unified_payload)
            
            # Step 4: Execute Verification (simulated)
            verification_result = await self._execute_verification(unified_payload)
            
            # Step 5: Calculate System Impact
            system_impact = self._calculate_system_impact(
                unified_payload, propagation_success, alignment_result, verification_result
            )
            
            # Update system coherence
            self._update_system_coherence(system_impact)
            
            execution_time = time.time() - start_time
            
            result = {
                "status": "SUCCESS",
                "unified_payload": asdict(unified_payload),
                "transmission_metrics": {
                    "total_potential": unified_payload.calculate_total_potential(),
                    "propagation_success": propagation_success,
                    "alignment_improvement": alignment_result,
                    "verification_confidence": verification_result,
                    "system_impact": system_impact,
                    "execution_time": execution_time
                },
                "system_state": {
                    "current_coherence": self.system_coherence,
                    "cultural_memory_size": len(self.sigma_engine.cultural_memory),
                    "historical_performance": len(self.execution_history)
                }
            }
            
            self.execution_history.append(result)
            return result
            
        except Exception as e:
            logger.error(f"Coherent transmission failed: {e}")
            return {
                "status": "ERROR",
                "error": str(e),
                "timestamp": time.time()
            }
    
    async def _execute_propagation(self, payload: UnifiedPayload) -> bool:
        """Execute propagation based on optimized methods"""
        # Simulate propagation execution
        success_probability = payload.propagation_potential
        await asyncio.sleep(0.001)  # Simulate work
        return np.random.random() < success_probability
    
    async def _execute_alignment(self, payload: UnifiedPayload) -> float:
        """Execute alignment based on optimized strategies"""
        # Simulate alignment improvement
        base_improvement = 1.0 - payload.coherence_gap
        strategy_bonus = len(payload.alignment_strategies) * 0.1
        await asyncio.sleep(0.001)  # Simulate work
        return min(0.95, base_improvement + strategy_bonus)
    
    async def _execute_verification(self, payload: UnifiedPayload) -> float:
        """Execute verification based on assigned tiers"""
        # Simulate verification confidence
        base_confidence = payload.verification_confidence
        tier_bonus = len(payload.verification_tiers) * 0.05
        await asyncio.sleep(0.001)  # Simulate work
        return min(0.98, base_confidence + tier_bonus)
    
    def _calculate_system_impact(self, payload: UnifiedPayload, propagation_success: bool, 
                               alignment_result: float, verification_result: float) -> float:
        """Calculate overall impact on system coherence"""
        if not propagation_success:
            return 0.0  # No impact if propagation failed
        
        base_impact = payload.calculate_total_potential()
        alignment_impact = alignment_result * 0.3
        verification_impact = verification_result * 0.2
        
        return min(1.0, base_impact + alignment_impact + verification_impact)
    
    def _update_system_coherence(self, impact: float):
        """Update overall system coherence based on transmission impact"""
        # Coherence improves with successful transmissions, degrades slowly over time
        improvement = impact * 0.1
        decay = 0.01  # Slow natural decay
        
        new_coherence = self.system_coherence + improvement - decay
        self.system_coherence = max(0.1, min(1.0, new_coherence))
    
    def get_system_status(self) -> Dict[str, Any]:
        """Get comprehensive system status"""
        recent_executions = self.execution_history[-5:] if self.execution_history else []
        
        success_count = sum(1 for e in recent_executions if e.get('status') == 'SUCCESS')
        success_rate = success_count / len(recent_executions) if recent_executions else 0.0
        
        return {
            "system_coherence": self.system_coherence,
            "cultural_memory_entries": len(self.sigma_engine.cultural_memory),
            "total_executions": len(self.execution_history),
            "recent_success_rate": success_rate,
            "cultural_sigma_engine": "OPERATIONAL",
            "module_integration": "COHERENT"
        }

# -------------------------------
# PRODUCTION USAGE
# -------------------------------
async def demonstrate_unified_coherence():
    """Demonstrate the fully coherent framework"""
    engine = UnifiedCoherenceEngine()
    
    # Test different types of transmissions
    test_cases = [
        {
            "content_type": "emerging_concept",
            "maturity": "emerging",
            "urgency": 0.3,
            "quality": 0.6,
            "relevance": 0.7,
            "description": "New conceptual framework"
        },
        {
            "content_type": "operational_directive", 
            "maturity": "established",
            "urgency": 0.9,
            "quality": 0.8,
            "relevance": 0.9,
            "description": "Critical system operation"
        },
        {
            "content_type": "consensus_building",
            "maturity": "transitional", 
            "urgency": 0.5,
            "quality": 0.7,
            "relevance": 0.8,
            "description": "Community agreement process"
        }
    ]
    
    for i, test_case in enumerate(test_cases):
        print(f"\n--- Executing Transmission {i+1}: {test_case['description']} ---")
        
        result = await engine.execute_coherent_transmission(test_case)
        
        if result['status'] == 'SUCCESS':
            payload = result['unified_payload']
            metrics = result['transmission_metrics']
            
            print(f"Cultural Context: {payload['cultural_context']}")
            print(f"Sigma Optimization: {payload['sigma_optimization']:.3f}")
            print(f"Total Potential: {metrics['total_potential']:.3f}")
            print(f"Cross-Module Synergy: {payload['cross_module_synergy']:.3f}")
            print(f"Propagation Methods: {[m.value for m in payload['propagation_methods']]}")
            print(f"Alignment Strategies: {[s.value for s in payload['alignment_strategies']]}")
            print(f"Verification Tiers: {[t.value for t in payload['verification_tiers']]}")
        else:
            print(f"Transmission failed: {result['error']}")
    
    # Final system status
    status = engine.get_system_status()
    print(f"\n=== FINAL SYSTEM STATUS ===")
    print(f"Overall System Coherence: {status['system_coherence']:.3f}")
    print(f"Cultural Memory Entries: {status['cultural_memory_entries']}")
    print(f"Module Integration: {status['module_integration']}")

if __name__ == "__main__":
    asyncio.run(demonstrate_unified_coherence())