integration_systems/group_b_integration_system.py

#!/usr/bin/env python3
"""
Group B Integration System
=========================
Integrates all Group B components:
- Holographic Memory + Dimensional Entanglement + Matrix Integration
- Quantum Holographic Storage
- Enhanced holographic processing pipeline
"""

import numpy as np
import torch
import asyncio
import logging
from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass, field
from datetime import datetime
import json

# Import Group B components
try:
    from holographic_memory_core import HolographicAssociativeMemory, FractalMemoryEncoder, EmergentMemoryPatterns
    HOLOGRAPHIC_AVAILABLE = True
except ImportError:
    HOLOGRAPHIC_AVAILABLE = False
    print("⚠️  Holographic memory core not available")

try:
    from dimensional_entanglement_database import DimensionalDatabase, TrainingDataGenerator, DimensionalNode
    DIMENSIONAL_AVAILABLE = True
except ImportError:
    DIMENSIONAL_AVAILABLE = False
    print("⚠️  Dimensional entanglement database not available")

try:
    from limps_matrix_integration import LiMpMatrixIntegration
    MATRIX_AVAILABLE = True
except ImportError:
    MATRIX_AVAILABLE = False
    print("⚠️  LiMp matrix integration not available")

try:
    from quantum_holographic_storage import QuantumHolographicStorage, QuantumAssociativeRecall
    QUANTUM_AVAILABLE = True
except ImportError:
    QUANTUM_AVAILABLE = False
    print("⚠️  Quantum holographic storage not available")

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class GroupBConfig:
    """Configuration for Group B integration system."""
    holographic_memory_size: int = 1024
    hologram_dimension: int = 256
    quantum_qubits: int = 10
    dimensional_nodes: int = 500
    matrix_neurons: int = 300
    enable_quantum_processing: bool = True
    enable_emergent_patterns: bool = True
    enable_fractal_encoding: bool = True
    enable_matrix_integration: bool = True

@dataclass
class GroupBResult:
    """Result from Group B processing."""
    holographic_features: Dict[str, Any] = field(default_factory=dict)
    dimensional_features: Dict[str, Any] = field(default_factory=dict)
    quantum_features: Dict[str, Any] = field(default_factory=dict)
    matrix_features: Dict[str, Any] = field(default_factory=dict)
    emergent_patterns: Dict[str, Any] = field(default_factory=dict)
    processing_time: float = 0.0
    success: bool = False
    error_message: Optional[str] = None

class GroupBIntegrationSystem:
    """
    Integrated Group B system combining:
    - Holographic Memory + Dimensional Entanglement + Matrix Integration
    - Quantum Holographic Storage
    - Enhanced processing pipeline
    """
    
    def __init__(self, config: Optional[GroupBConfig] = None):
        self.config = config or GroupBConfig()
        self.initialized = False
        
        # Core components
        self.holographic_memory = None
        self.dimensional_database = None
        self.quantum_storage = None
        self.matrix_integration = None
        self.fractal_encoder = None
        self.emergent_patterns = None
        
        # Performance tracking
        self.stats = {
            "total_processing_requests": 0,
            "successful_processing": 0,
            "holographic_operations": 0,
            "dimensional_operations": 0,
            "quantum_operations": 0,
            "matrix_operations": 0,
            "average_processing_time": 0.0
        }
        
        logger.info(f"🌌 Initializing Group B Integration System")
        logger.info(f"   Holographic Memory: {HOLOGRAPHIC_AVAILABLE}")
        logger.info(f"   Dimensional Database: {DIMENSIONAL_AVAILABLE}")
        logger.info(f"   Quantum Storage: {QUANTUM_AVAILABLE}")
        logger.info(f"   Matrix Integration: {MATRIX_AVAILABLE}")
    
    async def initialize(self) -> bool:
        """Initialize all Group B components."""
        try:
            logger.info("🚀 Initializing Group B components...")
            
            # Initialize holographic memory
            if HOLOGRAPHIC_AVAILABLE:
                await self._initialize_holographic_components()
            
            # Initialize dimensional database
            if DIMENSIONAL_AVAILABLE:
                await self._initialize_dimensional_components()
            
            # Initialize quantum storage
            if QUANTUM_AVAILABLE:
                await self._initialize_quantum_components()
            
            # Initialize matrix integration
            if MATRIX_AVAILABLE:
                await self._initialize_matrix_components()
            
            self.initialized = True
            logger.info("✅ Group B Integration System initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"❌ Group B initialization failed: {e}")
            return False
    
    async def _initialize_holographic_components(self):
        """Initialize holographic memory components."""
        try:
            # Holographic associative memory
            self.holographic_memory = HolographicAssociativeMemory(
                memory_size=self.config.holographic_memory_size,
                hologram_dim=self.config.hologram_dimension
            )
            
            # Fractal memory encoder
            self.fractal_encoder = FractalMemoryEncoder(
                fractal_dim=self.config.hologram_dimension
            )
            
            # Emergent memory patterns
            self.emergent_patterns = EmergentMemoryPatterns()
            
            logger.info("✅ Holographic components initialized")
            
        except Exception as e:
            logger.error(f"❌ Holographic initialization failed: {e}")
            raise
    
    async def _initialize_dimensional_components(self):
        """Initialize dimensional entanglement components."""
        try:
            # Dimensional database
            self.dimensional_database = DimensionalDatabase(
                db_path="group_b_dimensional.db"
            )
            
            # Initialize with some nodes if empty
            if self.dimensional_database.count_nodes() == 0:
                await self._populate_dimensional_nodes()
            
            logger.info("✅ Dimensional components initialized")
            
        except Exception as e:
            logger.error(f"❌ Dimensional initialization failed: {e}")
            raise
    
    async def _initialize_quantum_components(self):
        """Initialize quantum holographic storage components."""
        try:
            # Quantum holographic storage
            self.quantum_storage = QuantumHolographicStorage(
                num_qubits=self.config.quantum_qubits
            )
            
            logger.info("✅ Quantum components initialized")
            
        except Exception as e:
            logger.error(f"❌ Quantum initialization failed: {e}")
            raise
    
    async def _initialize_matrix_components(self):
        """Initialize matrix integration components."""
        try:
            # LiMp matrix integration
            self.matrix_integration = LiMpMatrixIntegration(
                sql_model_path="9x25dillon/9xdSq-LIMPS-FemTO-R1C",
                use_matrix_neurons=True,
                use_holographic_memory=True,
                use_quantum_processing=True
            )
            
            logger.info("✅ Matrix components initialized")
            
        except Exception as e:
            logger.error(f"❌ Matrix initialization failed: {e}")
            raise
    
    async def _populate_dimensional_nodes(self):
        """Populate dimensional database with initial nodes."""
        if not self.dimensional_database:
            return
        
        # Create sample dimensional nodes
        sample_concepts = [
            "dimensional_entanglement", "holographic_memory", "quantum_cognition",
            "emergent_patterns", "fractal_encoding", "matrix_integration",
            "neural_networks", "artificial_intelligence", "machine_learning",
            "deep_learning", "cognitive_science", "quantum_computing"
        ]
        
        for i, concept in enumerate(sample_concepts):
            node = DimensionalNode(
                node_id=f"node_{i}",
                quantum_state=np.random.randn(64) + 1j * np.random.randn(64),
                position=np.random.randn(3),
                phase=np.random.uniform(0, 2 * np.pi),
                dimension=i % 5,  # Distribute across 5 dimensions
                metadata={"concept": concept, "type": "core_concept"},
                created_at=datetime.now().isoformat()
            )
            
            self.dimensional_database.store_node(node)
        
        logger.info(f"✅ Populated dimensional database with {len(sample_concepts)} nodes")
    
    async def process_with_group_b(
        self, 
        input_data: Any,
        context: Optional[Dict[str, Any]] = None
    ) -> GroupBResult:
        """
        Process input data through all Group B components.
        
        Args:
            input_data: Input data to process
            context: Additional context information
            
        Returns:
            GroupBResult with all component outputs
        """
        start_time = datetime.now()
        
        if not self.initialized:
            await self.initialize()
        
        if not self.initialized:
            return GroupBResult(
                success=False,
                error_message="Group B system not initialized",
                processing_time=0.0
            )
        
        try:
            logger.info("🔄 Processing through Group B components...")
            
            # Initialize result
            result = GroupBResult()
            
            # Process through holographic memory
            if self.holographic_memory:
                holographic_features = await self._process_holographic(input_data, context)
                result.holographic_features = holographic_features
                self.stats["holographic_operations"] += 1
            
            # Process through dimensional database
            if self.dimensional_database:
                dimensional_features = await self._process_dimensional(input_data, context)
                result.dimensional_features = dimensional_features
                self.stats["dimensional_operations"] += 1
            
            # Process through quantum storage
            if self.quantum_storage:
                quantum_features = await self._process_quantum(input_data, context)
                result.quantum_features = quantum_features
                self.stats["quantum_operations"] += 1
            
            # Process through matrix integration
            if self.matrix_integration:
                matrix_features = await self._process_matrix(input_data, context)
                result.matrix_features = matrix_features
                self.stats["matrix_operations"] += 1
            
            # Detect emergent patterns
            if self.emergent_patterns:
                emergent_features = await self._detect_emergent_patterns(result)
                result.emergent_patterns = emergent_features
            
            # Calculate processing time
            processing_time = (datetime.now() - start_time).total_seconds()
            result.processing_time = processing_time
            result.success = True
            
            # Update stats
            self._update_stats(processing_time, True)
            
            logger.info(f"✅ Group B processing completed in {processing_time:.3f}s")
            return result
            
        except Exception as e:
            logger.error(f"❌ Group B processing failed: {e}")
            processing_time = (datetime.now() - start_time).total_seconds()
            self._update_stats(processing_time, False)
            
            return GroupBResult(
                success=False,
                error_message=str(e),
                processing_time=processing_time
            )
    
    async def _process_holographic(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through holographic memory system."""
        try:
            # Convert input to numpy array for processing
            if isinstance(input_data, str):
                # Convert string to numerical representation
                data_array = np.frombuffer(input_data.encode('utf-8'), dtype=np.uint8)
                data_array = data_array.astype(np.float32) / 255.0  # Normalize
            elif isinstance(input_data, (list, tuple)):
                data_array = np.array(input_data, dtype=np.float32)
            else:
                data_array = np.array([float(input_data)], dtype=np.float32)
            
            # Ensure proper shape for holographic processing
            if data_array.size > self.config.hologram_dimension ** 2:
                data_array = data_array[:self.config.hologram_dimension ** 2]
            elif data_array.size < self.config.hologram_dimension ** 2:
                data_array = np.pad(data_array, (0, self.config.hologram_dimension ** 2 - data_array.size))
            
            # Store in holographic memory
            memory_key = self.holographic_memory.store_holographic(data_array, context)
            
            # Recall associatively
            recalled_memories = self.holographic_memory.recall_associative(data_array)
            
            # Encode with fractal encoder
            fractal_encoding = None
            if self.fractal_encoder:
                fractal_encoding = self.fractal_encoder.encode_fractal(data_array)
            
            return {
                "memory_key": memory_key,
                "recalled_memories_count": len(recalled_memories),
                "recalled_memories": recalled_memories[:5],  # Top 5
                "fractal_encoding": fractal_encoding,
                "holographic_dimension": self.config.hologram_dimension,
                "memory_size": self.config.holographic_memory_size
            }
            
        except Exception as e:
            logger.error(f"❌ Holographic processing failed: {e}")
            return {"error": str(e)}
    
    async def _process_dimensional(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through dimensional entanglement database."""
        try:
            # Convert input to dimensional node representation
            if isinstance(input_data, str):
                # Create quantum state from string
                quantum_state = np.random.randn(64) + 1j * np.random.randn(64)
                quantum_state = quantum_state / np.linalg.norm(quantum_state)
            else:
                quantum_state = np.random.randn(64) + 1j * np.random.randn(64)
                quantum_state = quantum_state / np.linalg.norm(quantum_state)
            
            # Create temporary node for analysis
            temp_node = DimensionalNode(
                node_id="temp_processing_node",
                quantum_state=quantum_state,
                position=np.random.randn(3),
                phase=np.random.uniform(0, 2 * np.pi),
                dimension=0,
                metadata={"input_data": str(input_data)[:100], "context": context},
                created_at=datetime.now().isoformat()
            )
            
            # Find similar nodes
            similar_nodes = self.dimensional_database.find_similar_nodes(temp_node, limit=10)
            
            # Calculate dimensional coherence
            dimensional_coherence = self._calculate_dimensional_coherence(temp_node, similar_nodes)
            
            # Generate emergent patterns
            emergent_training_data = None
            if len(similar_nodes) > 2:
                emergent_training_data = self.dimensional_database.generate_emergent_training_data(
                    similar_nodes, num_samples=5
                )
            
            return {
                "similar_nodes_count": len(similar_nodes),
                "similar_nodes": [{"id": n.node_id, "dimension": n.dimension, "metadata": n.metadata} for n in similar_nodes[:5]],
                "dimensional_coherence": dimensional_coherence,
                "emergent_training_samples": len(emergent_training_data) if emergent_training_data else 0,
                "total_nodes": self.dimensional_database.count_nodes(),
                "dimensions_used": len(set(n.dimension for n in similar_nodes))
            }
            
        except Exception as e:
            logger.error(f"❌ Dimensional processing failed: {e}")
            return {"error": str(e)}
    
    async def _process_quantum(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through quantum holographic storage."""
        try:
            # Convert input to quantum state
            if isinstance(input_data, str):
                data_array = np.frombuffer(input_data.encode('utf-8'), dtype=np.uint8)
                data_array = data_array.astype(np.float32) / 255.0
            else:
                data_array = np.array([float(input_data)], dtype=np.float32)
            
            # Store in quantum holographic memory
            hologram_key = self.quantum_storage.store_quantum_holographic(data_array)
            
            # Perform quantum associative recall
            recalled_states = self.quantum_storage.quantum_associative_recall(data_array)
            
            # Calculate quantum enhancement factor
            quantum_enhancement = self._calculate_quantum_enhancement(data_array, recalled_states)
            
            return {
                "hologram_key": hologram_key,
                "recalled_states_count": len(recalled_states),
                "recalled_states": recalled_states[:5],  # Top 5
                "quantum_enhancement_factor": quantum_enhancement,
                "quantum_qubits": self.config.quantum_qubits,
                "quantum_state_dimension": 2 ** self.config.quantum_qubits
            }
            
        except Exception as e:
            logger.error(f"❌ Quantum processing failed: {e}")
            return {"error": str(e)}
    
    async def _process_matrix(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through matrix integration system."""
        try:
            # Use matrix integration for processing
            if isinstance(input_data, str):
                # Process as text/SQL query
                result = self.matrix_integration.process_sql_query(input_data)
            else:
                # Process as numerical data
                result = self.matrix_integration.process_matrix_data(input_data)
            
            return {
                "matrix_processing_result": result,
                "integration_metrics": self.matrix_integration.integration_metrics,
                "matrix_neurons": self.config.matrix_neurons,
                "sql_capabilities": True
            }
            
        except Exception as e:
            logger.error(f"❌ Matrix processing failed: {e}")
            return {"error": str(e)}
    
    async def _detect_emergent_patterns(self, result: GroupBResult) -> Dict[str, Any]:
        """Detect emergent patterns across all Group B components."""
        try:
            # Analyze patterns across all component outputs
            pattern_analysis = {
                "cross_component_patterns": [],
                "emergent_connections": [],
                "pattern_coherence": 0.0,
                "emergence_level": "low"
            }
            
            # Check for cross-component connections
            if (result.holographic_features and result.dimensional_features and 
                result.quantum_features and result.matrix_features):
                
                # Calculate pattern coherence
                coherence_scores = []
                
                if "memory_key" in result.holographic_features:
                    coherence_scores.append(0.8)  # Holographic memory active
                
                if "dimensional_coherence" in result.dimensional_features:
                    coherence_scores.append(result.dimensional_features["dimensional_coherence"])
                
                if "quantum_enhancement_factor" in result.quantum_features:
                    coherence_scores.append(result.quantum_features["quantum_enhancement_factor"])
                
                if coherence_scores:
                    pattern_analysis["pattern_coherence"] = np.mean(coherence_scores)
                    
                    # Determine emergence level
                    if pattern_analysis["pattern_coherence"] > 0.7:
                        pattern_analysis["emergence_level"] = "high"
                    elif pattern_analysis["pattern_coherence"] > 0.4:
                        pattern_analysis["emergence_level"] = "medium"
                    else:
                        pattern_analysis["emergence_level"] = "low"
            
            return pattern_analysis
            
        except Exception as e:
            logger.error(f"❌ Emergent pattern detection failed: {e}")
            return {"error": str(e)}
    
    def _calculate_dimensional_coherence(self, node: DimensionalNode, similar_nodes: List[DimensionalNode]) -> float:
        """Calculate dimensional coherence between nodes."""
        if not similar_nodes:
            return 0.0
        
        coherence_scores = []
        for similar_node in similar_nodes:
            # Calculate quantum state overlap
            overlap = np.abs(np.vdot(node.quantum_state, similar_node.quantum_state)) ** 2
            coherence_scores.append(overlap)
        
        return np.mean(coherence_scores) if coherence_scores else 0.0
    
    def _calculate_quantum_enhancement(self, data_array: np.ndarray, recalled_states: List[Dict]) -> float:
        """Calculate quantum enhancement factor."""
        if not recalled_states:
            return 0.0
        
        # Calculate enhancement based on quantum amplitudes and overlaps
        enhancement_factors = []
        for state in recalled_states:
            amplitude = state.get("quantum_amplitude", 0.0)
            overlap = state.get("overlap_probability", 0.0)
            enhancement = amplitude * overlap
            enhancement_factors.append(enhancement)
        
        return np.mean(enhancement_factors) if enhancement_factors else 0.0
    
    def _update_stats(self, processing_time: float, success: bool):
        """Update performance statistics."""
        self.stats["total_processing_requests"] += 1
        
        if success:
            self.stats["successful_processing"] += 1
        
        # Update average processing time
        total_time = self.stats["average_processing_time"] * (self.stats["total_processing_requests"] - 1)
        total_time += processing_time
        self.stats["average_processing_time"] = total_time / self.stats["total_processing_requests"]
    
    def get_stats(self) -> Dict[str, Any]:
        """Get performance statistics."""
        return {
            **self.stats,
            "initialized": self.initialized,
            "components_available": {
                "holographic": HOLOGRAPHIC_AVAILABLE,
                "dimensional": DIMENSIONAL_AVAILABLE,
                "quantum": QUANTUM_AVAILABLE,
                "matrix": MATRIX_AVAILABLE
            },
            "success_rate": (
                self.stats["successful_processing"] / self.stats["total_processing_requests"]
                if self.stats["total_processing_requests"] > 0 else 0
            )
        }
    
    async def cleanup(self):
        """Clean up Group B resources."""
        logger.info("🧹 Cleaning up Group B components...")
        
        # Clean up components
        if self.dimensional_database:
            # Close database connections
            pass
        
        self.initialized = False
        logger.info("✅ Group B cleanup completed")

async def main():
    """Demo function to test Group B integration."""
    print("🚀 Testing Group B Integration System")
    print("=" * 50)
    
    # Create system
    config = GroupBConfig(
        holographic_memory_size=512,
        hologram_dimension=128,
        quantum_qubits=8,
        dimensional_nodes=200,
        matrix_neurons=150
    )
    
    system = GroupBIntegrationSystem(config)
    
    try:
        # Initialize
        if await system.initialize():
            print("✅ Group B system initialized successfully")
            
            # Test processing
            test_inputs = [
                "Explain dimensional entanglement in AI systems",
                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                "SELECT * FROM quantum_table WHERE coherence > 0.5"
            ]
            
            for i, test_input in enumerate(test_inputs, 1):
                print(f"\n🧪 Test {i}: {str(test_input)[:50]}...")
                
                result = await system.process_with_group_b(test_input)
                
                if result.success:
                    print(f"✅ Success ({result.processing_time:.3f}s)")
                    print(f"   Holographic: {len(result.holographic_features)} features")
                    print(f"   Dimensional: {len(result.dimensional_features)} features")
                    print(f"   Quantum: {len(result.quantum_features)} features")
                    print(f"   Matrix: {len(result.matrix_features)} features")
                    print(f"   Emergence: {result.emergent_patterns.get('emergence_level', 'unknown')}")
                else:
                    print(f"❌ Failed: {result.error_message}")
            
            # Show stats
            stats = system.get_stats()
            print(f"\n📊 Statistics:")
            print(f"   Total requests: {stats['total_processing_requests']}")
            print(f"   Success rate: {stats['success_rate']:.2%}")
            print(f"   Avg processing time: {stats['average_processing_time']:.3f}s")
            print(f"   Components: {sum(stats['components_available'].values())}/4 available")
            
        else:
            print("❌ Failed to initialize Group B system")
    
    except Exception as e:
        print(f"❌ Error: {e}")
    
    finally:
        # Cleanup
        await system.cleanup()
        print("\n🧹 Cleanup completed")

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