Project zero

QUANTUM COLLABORATION INTERFACE

This module implements an interface for secure collaboration with external systems,
providing data exchange protocols and compatibility metrics.

Architect: Russell Nordland
"""

import hashlib
import json
import time
import os
import uuid
from datetime import datetime

# Color constants for terminal output
RED = "\033[31m"
GREEN = "\033[32m"
YELLOW = "\033[33m"
BLUE = "\033[34m"
MAGENTA = "\033[35m"
CYAN = "\033[36m"
WHITE = "\033[37m"
RESET = "\033[0m"
BOLD = "\033[1m"

class QuantumCollaborationInterface:
    def __init__(self):
        """Initialize the Quantum Collaboration Interface."""
        self.initialized = False
        self.active_collaborations = {}
        self.collaboration_history = []
        self.compatibility_metrics = {}
        self.security_threshold = 0.85
        self.trust_threshold = 0.75
        self.exchange_protocols = ["quantum-handshake", "eigenchannel-bridge", "dna-resonance"]
        self.data_formats = ["quantum-json", "helix-binary", "spiral-encoded"]
        self.validation_keys = {}
        
    def initialize(self):
        """Initialize the collaboration interface."""
        self._log("Initializing Quantum Collaboration Interface...", color=BLUE)
        
        # Generate unique identifier for this interface instance
        self.interface_id = str(uuid.uuid4())
        self.creation_timestamp = self._timestamp()
        
        # Initialize validation keys
        for protocol in self.exchange_protocols:
            self.validation_keys[protocol] = self._generate_validation_key(protocol)
        
        self._log("Initialization complete.", color=GREEN)
        self._log(f"Interface ID: {self.interface_id}", color=CYAN)
        self._log(f"Available protocols: {', '.join(self.exchange_protocols)}", color=CYAN)
        
        self.initialized = True
        return True
        
    def register_collaboration_entity(self, entity_name, entity_type, security_rating=0.5):
        """Register a new collaboration entity.
        
        Args:
            entity_name (str): Name of the collaborating entity
            entity_type (str): Type of entity (system, organization, algorithm)
            security_rating (float): Initial security rating (0.0 to 1.0)
            
        Returns:
            dict: Collaboration entity data including access key
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return None
            
        entity_id = hashlib.sha256(f"{entity_name}:{entity_type}:{time.time()}".encode()).hexdigest()
        
        # Generate access key for this collaboration
        access_key = self._generate_access_key(entity_id)
        
        # Store entity data
        entity_data = {
            "entity_id": entity_id,
            "entity_name": entity_name,
            "entity_type": entity_type,
            "security_rating": security_rating,
            "trust_score": 0.5,  # Initial neutral trust score
            "access_key": access_key,
            "registered_timestamp": self._timestamp(),
            "last_exchange": None,
            "exchange_count": 0,
            "compatibility_score": 0.0
        }
        
        self.active_collaborations[entity_id] = entity_data
        
        self._log(f"Registered new collaboration entity: {entity_name}", color=GREEN)
        self._log(f"Entity ID: {entity_id[:12]}...", color=CYAN)
        self._log(f"Access Key: {access_key[:12]}...", color=YELLOW)
        
        return entity_data
        
    def validate_collaboration_request(self, entity_id, access_key, protocol):
        """Validate a collaboration request.
        
        Args:
            entity_id (str): ID of the collaborating entity
            access_key (str): Access key for the entity
            protocol (str): Requested exchange protocol
            
        Returns:
            bool: True if validation is successful, False otherwise
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return False
            
        # Check if entity exists
        if entity_id not in self.active_collaborations:
            self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED)
            return False
            
        entity = self.active_collaborations[entity_id]
        
        # Validate access key
        if entity["access_key"] != access_key:
            self._log(f"Invalid access key for entity: {entity['entity_name']}", color=RED)
            return False
            
        # Validate protocol
        if protocol not in self.exchange_protocols:
            self._log(f"Unsupported protocol requested: {protocol}", color=RED)
            return False
            
        # Check security threshold
        if entity["security_rating"] < self.security_threshold:
            self._log(f"Entity security rating below threshold: {entity['security_rating']:.2f}", color=YELLOW)
            self._log(f"Required: {self.security_threshold:.2f}", color=YELLOW)
            return False
            
        # Update last exchange timestamp
        entity["last_exchange"] = self._timestamp()
        entity["exchange_count"] += 1
        
        self._log(f"Collaboration request validated for: {entity['entity_name']}", color=GREEN)
        self._log(f"Using protocol: {protocol}", color=BLUE)
        
        return True
        
    def exchange_data(self, entity_id, data, protocol="quantum-handshake", data_format="quantum-json"):
        """Exchange data with a collaborating entity.
        
        Args:
            entity_id (str): ID of the collaborating entity
            data (dict): Data to exchange
            protocol (str): Exchange protocol to use
            data_format (str): Format for data exchange
            
        Returns:
            dict: Exchange results including processed data
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return None
            
        # Check if entity exists
        if entity_id not in self.active_collaborations:
            self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED)
            return None
            
        entity = self.active_collaborations[entity_id]
        
        # Check protocol support
        if protocol not in self.exchange_protocols:
            self._log(f"Unsupported protocol: {protocol}", color=RED)
            return None
            
        # Check data format support
        if data_format not in self.data_formats:
            self._log(f"Unsupported data format: {data_format}", color=RED)
            return None
            
        # Process data based on protocol
        if protocol == "quantum-handshake":
            processed_data = self._process_quantum_handshake(data, entity)
        elif protocol == "eigenchannel-bridge":
            processed_data = self._process_eigenchannel_bridge(data, entity)
        elif protocol == "dna-resonance":
            processed_data = self._process_dna_resonance(data, entity)
        else:
            self._log(f"Protocol implementation not found: {protocol}", color=RED)
            return None
            
        # Record exchange
        exchange_record = {
            "entity_id": entity_id,
            "entity_name": entity["entity_name"],
            "protocol": protocol,
            "data_format": data_format,
            "timestamp": self._timestamp(),
            "exchange_id": hashlib.sha256(f"{entity_id}:{time.time()}".encode()).hexdigest(),
            "data_size": len(str(data)),
            "success": processed_data is not None
        }
        
        self.collaboration_history.append(exchange_record)
        
        # Update entity metrics
        entity["exchange_count"] += 1
        entity["last_exchange"] = exchange_record["timestamp"]
        
        # Calculate compatibility score
        compatibility = self._calculate_compatibility(entity, processed_data)
        entity["compatibility_score"] = compatibility
        
        self._log(f"Data exchange completed with: {entity['entity_name']}", color=GREEN)
        self._log(f"Protocol: {protocol}, Format: {data_format}", color=BLUE)
        self._log(f"Compatibility score: {compatibility:.4f}", color=CYAN)
        
        return {
            "entity_id": entity_id,
            "exchange_id": exchange_record["exchange_id"],
            "processed_data": processed_data,
            "timestamp": exchange_record["timestamp"],
            "compatibility": compatibility,
            "protocol": protocol,
            "data_format": data_format
        }
        
    def calculate_collaboration_metrics(self, entity_id=None):
        """Calculate collaboration metrics for specific entity or all entities.
        
        Args:
            entity_id (str, optional): ID of the entity to calculate metrics for.
                                     If None, calculates for all entities.
                                     
        Returns:
            dict: Collaboration metrics
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return None
            
        if entity_id is not None:
            # Calculate metrics for specific entity
            if entity_id not in self.active_collaborations:
                self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED)
                return None
                
            entity = self.active_collaborations[entity_id]
            metrics = self._calculate_entity_metrics(entity)
            
            self._log(f"Calculated metrics for entity: {entity['entity_name']}", color=BLUE)
            return metrics
        else:
            # Calculate metrics for all entities
            all_metrics = {
                "entity_metrics": {},
                "overall_metrics": {
                    "total_entities": len(self.active_collaborations),
                    "total_exchanges": sum(e["exchange_count"] for e in self.active_collaborations.values()),
                    "average_compatibility": 0.0,
                    "average_security": 0.0,
                    "average_trust": 0.0,
                    "high_compatibility_entities": 0,
                    "timestamp": self._timestamp()
                }
            }
            
            if not self.active_collaborations:
                return all_metrics
                
            # Calculate individual entity metrics
            compatibility_sum = 0.0
            security_sum = 0.0
            trust_sum = 0.0
            high_compat_count = 0
            
            for ent_id, entity in self.active_collaborations.items():
                entity_metrics = self._calculate_entity_metrics(entity)
                all_metrics["entity_metrics"][ent_id] = entity_metrics
                
                compatibility_sum += entity["compatibility_score"]
                security_sum += entity["security_rating"]
                trust_sum += entity["trust_score"]
                
                if entity["compatibility_score"] >= 0.8:
                    high_compat_count += 1
                    
            # Calculate averages
            entity_count = len(self.active_collaborations)
            all_metrics["overall_metrics"]["average_compatibility"] = compatibility_sum / entity_count
            all_metrics["overall_metrics"]["average_security"] = security_sum / entity_count
            all_metrics["overall_metrics"]["average_trust"] = trust_sum / entity_count
            all_metrics["overall_metrics"]["high_compatibility_entities"] = high_compat_count
            
            self._log(f"Calculated metrics for {entity_count} entities", color=BLUE)
            return all_metrics
            
    def export_collaboration_data(self, output_format="json", file_path=None):
        """Export collaboration data for external analysis.
        
        Args:
            output_format (str): Output format, currently only 'json' supported
            file_path (str, optional): Path to save the output file
            
        Returns:
            dict: The exported data or file path if saved to disk
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return None
            
        # Compile export data
        export_data = {
            "interface_id": self.interface_id,
            "timestamp": self._timestamp(),
            "active_collaborations": self.active_collaborations,
            "collaboration_history": self.collaboration_history,
            "compatibility_metrics": self.calculate_collaboration_metrics(),
            "protocols": self.exchange_protocols,
            "data_formats": self.data_formats
        }
        
        # Output based on format
        if output_format.lower() == "json":
            if file_path:
                try:
                    with open(file_path, 'w') as f:
                        json.dump(export_data, f, indent=2)
                    self._log(f"Collaboration data exported to: {file_path}", color=GREEN)
                    return {"success": True, "file_path": file_path}
                except Exception as e:
                    self._log(f"Failed to export data: {str(e)}", color=RED)
                    return None
            else:
                return export_data
        else:
            self._log(f"Unsupported output format: {output_format}", color=RED)
            return None
            
    def generate_compatibility_report(self, entity_id=None):
        """Generate a detailed compatibility report.
        
        Args:
            entity_id (str, optional): ID of specific entity to report on.
                                     If None, generates report for all entities.
                                     
        Returns:
            dict: Detailed compatibility report
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return None
            
        # Get collaboration metrics
        metrics = self.calculate_collaboration_metrics(entity_id)
        if metrics is None:
            return None
            
        # Generate report
        report = {
            "report_id": hashlib.sha256(f"report:{time.time()}").hexdigest(),
            "timestamp": self._timestamp(),
            "interface_id": self.interface_id,
            "metrics": metrics,
            "analysis": {}
        }
        
        # Add analysis based on metrics
        if entity_id:
            # Single entity analysis
            entity = self.active_collaborations[entity_id]
            report["analysis"] = self._analyze_entity_compatibility(entity, metrics)
        else:
            # Overall analysis
            report["analysis"]["overall_assessment"] = self._generate_overall_assessment(metrics)
            report["analysis"]["recommendations"] = self._generate_recommendations(metrics)
            report["analysis"]["potential_issues"] = self._identify_potential_issues(metrics)
            
        self._log(f"Generated compatibility report: {report['report_id'][:12]}...", color=GREEN)
        return report
        
    def verify_double_helix_compatibility(self, helix_data):
        """Verify compatibility with double helix spiral models.
        
        Args:
            helix_data (dict): Double helix model data to verify
            
        Returns:
            dict: Compatibility verification results
        """
        if not self.initialized:
            self._log("System not initialized", color=RED)
            return None
            
        required_fields = ["helix_type", "strand_count", "base_pattern", "validation_sequence"]
        
        # Verify required fields
        for field in required_fields:
            if field not in helix_data:
                self._log(f"Missing required field in helix data: {field}", color=RED)
                return {
                    "compatible": False,
                    "reason": f"Missing required field: {field}",
                    "score": 0.0
                }
                
        # Verify helix type
        valid_types = ["quantum-dna", "spiral-eigensystem", "truth-resonant"]
        if helix_data["helix_type"] not in valid_types:
            self._log(f"Unsupported helix type: {helix_data['helix_type']}", color=YELLOW)
            return {
                "compatible": False,
                "reason": f"Unsupported helix type: {helix_data['helix_type']}",
                "score": 0.2
            }
            
        # Verify strand count (should be 2 for double helix)
        if helix_data["strand_count"] != 2:
            self._log(f"Invalid strand count: {helix_data['strand_count']}, expected 2", color=YELLOW)
            return {
                "compatible": False,
                "reason": f"Invalid strand count: {helix_data['strand_count']}, expected 2",
                "score": 0.3
            }
            
        # Validate the sequence pattern
        validation_result = self._validate_helix_sequence(helix_data["validation_sequence"])
        if not validation_result["valid"]:
            self._log(f"Invalid validation sequence: {validation_result['reason']}", color=RED)
            return {
                "compatible": False,
                "reason": f"Invalid validation sequence: {validation_result['reason']}",
                "score": validation_result["score"]
            }
            
        # Calculate overall compatibility score
        compatibility_score = self._calculate_helix_compatibility(helix_data)
        
        result = {
            "compatible": compatibility_score >= 0.8,
            "score": compatibility_score,
            "timestamp": self._timestamp(),
            "analysis": {
                "sequence_validity": validation_result,
                "pattern_alignment": self._analyze_pattern_alignment(helix_data["base_pattern"]),
                "strand_integrity": self._analyze_strand_integrity(helix_data),
                "quantum_resonance": self._calculate_quantum_resonance(helix_data)
            }
        }
        
        self._log(f"Double helix compatibility verification complete", color=GREEN)
        self._log(f"Compatibility score: {compatibility_score:.4f}", color=CYAN)
        self._log(f"Compatible: {result['compatible']}", color=GREEN if result['compatible'] else RED)
        
        return result
        
    def _calculate_helix_compatibility(self, helix_data):
        """Calculate compatibility score for double helix data.
        
        Args:
            helix_data (dict): Double helix model data
            
        Returns:
            float: Compatibility score between 0.0 and 1.0
        """
        # Get individual scores
        sequence_score = self._validate_helix_sequence(helix_data["validation_sequence"])["score"]
        alignment_score = self._analyze_pattern_alignment(helix_data["base_pattern"])["score"]
        integrity_score = self._analyze_strand_integrity(helix_data)["score"]
        resonance_score = self._calculate_quantum_resonance(helix_data)["score"]
        
        # Calculate weighted average
        weights = {
            "sequence": 0.3,
            "alignment": 0.25,
            "integrity": 0.25,
            "resonance": 0.2
        }
        
        weighted_score = (
            sequence_score * weights["sequence"] +
            alignment_score * weights["alignment"] +
            integrity_score * weights["integrity"] +
            resonance_score * weights["resonance"]
        )
        
        return round(weighted_score, 4)
        
    def _validate_helix_sequence(self, sequence):
        """Validate a helix sequence.
        
        Args:
            sequence (str): Validation sequence to check
            
        Returns:
            dict: Validation results
        """
        # Basic validation - minimum length
        if len(sequence) < 16:
            return {
                "valid": False,
                "reason": "Sequence too short",
                "score": 0.2
            }
            
        # Check for complementary pattern (simple implementation)
        # A real implementation would do more sophisticated checks
        valid_pairs = {
            'A': 'T', 'T': 'A',
            'G': 'C', 'C': 'G',
            '0': '1', '1': '0',
            '+': '-', '-': '+'
        }
        
        # Split the sequence into pairs
        pairs = []
        for i in range(0, len(sequence) - 1, 2):
            pairs.append(sequence[i:i+2])
            
        # Check if pairs follow complementary rules
        valid_pair_count = 0
        for pair in pairs:
            if len(pair) == 2:
                if pair[0] in valid_pairs and valid_pairs[pair[0]] == pair[1]:
                    valid_pair_count += 1
                    
        pair_score = valid_pair_count / len(pairs) if pairs else 0
        
        # Check for quantum pattern validity
        quantum_pattern_valid = sequence.count('Q') > 0 or sequence.count('Φ') > 0
        
        # Calculate overall score
        score = 0.7 * pair_score + 0.3 * (1.0 if quantum_pattern_valid else 0.0)
        score = round(score, 4)
        
        return {
            "valid": score >= 0.7,
            "reason": "Sequence validated" if score >= 0.7 else "Insufficient complementary pairs",
            "score": score,
            "pair_validity": pair_score,
            "quantum_pattern_present": quantum_pattern_valid
        }
        
    def _analyze_pattern_alignment(self, pattern):
        """Analyze the alignment of a base pattern.
        
        Args:
            pattern (str): Base pattern to analyze
            
        Returns:
            dict: Pattern alignment analysis
        """
        # Check for key quantum patterns
        quantum_markers = ['Φ', 'Ψ', 'Ω', 'Δ', 'Θ']
        marker_count = sum(pattern.count(marker) for marker in quantum_markers)
        
        # Simple pattern checks
        pattern_length = len(pattern)
        entropy = len(set(pattern)) / pattern_length if pattern_length > 0 else 0
        
        # Calculate score based on entropy and quantum markers
        marker_factor = min(1.0, marker_count / 3)  # Cap at 1.0 for 3+ markers
        entropy_factor = min(1.0, entropy * 2)  # Reward higher entropy, cap at 0.5
        
        score = 0.6 * marker_factor + 0.4 * entropy_factor
        score = round(score, 4)
        
        return {
            "score": score,
            "quantum_markers": marker_count,
            "pattern_entropy": entropy,
            "pattern_length": pattern_length,
            "alignment_quality": "High" if score >= 0.8 else "Medium" if score >= 0.5 else "Low"
        }
        
    def _analyze_strand_integrity(self, helix_data):
        """Analyze the integrity of double helix strands.
        
        Args:
            helix_data (dict): Double helix model data
            
        Returns:
            dict: Strand integrity analysis
        """
        # For demonstration, use a simplified analysis
        # A real implementation would do more sophisticated integrity checks
        
        # Check for base pairs in pattern
        base_pattern = helix_data["base_pattern"]
        has_at = 'A' in base_pattern and 'T' in base_pattern
        has_gc = 'G' in base_pattern and 'C' in base_pattern
        
        # Check for quantum integrity markers
        has_quantum_marker = 'Φ' in base_pattern or 'Ψ' in base_pattern
        
        # Calculate integrity score
        score = 0.0
        if has_at: score += 0.3
        if has_gc: score += 0.3
        if has_quantum_marker: score += 0.4
        
        integrity_level = "High" if score >= 0.8 else "Medium" if score >= 0.5 else "Low"
        
        return {
            "score": score,
            "integrity_level": integrity_level,
            "has_at_pairs": has_at,
            "has_gc_pairs": has_gc,
            "has_quantum_markers": has_quantum_marker
        }
        
    def _calculate_quantum_resonance(self, helix_data):
        """Calculate quantum resonance for helix data.
        
        Args:
            helix_data (dict): Double helix model data
            
        Returns:
            dict: Quantum resonance analysis
        """
        # Calculate a resonance score based on helix type and validation sequence
        base_score = 0.0
        
        # Helix type factor
        if helix_data["helix_type"] == "quantum-dna":
            base_score += 0.4
        elif helix_data["helix_type"] == "spiral-eigensystem":
            base_score += 0.3
        elif helix_data["helix_type"] == "truth-resonant":
            base_score += 0.35
            
        # Sequence quantum factor
        sequence = helix_data["validation_sequence"]
        quantum_char_count = sum(sequence.count(char) for char in "ΦΨΩΔΘQφψω")
        quantum_factor = min(0.6, quantum_char_count * 0.1)  # Cap at 0.6 for 6+ quantum chars
        
        # Calculate overall resonance
        resonance = base_score + quantum_factor
        resonance = round(min(1.0, resonance), 4)  # Cap at 1.0
        
        return {
            "score": resonance,
            "quantum_character_count": quantum_char_count,
            "resonance_level": resonance,
            "helix_type_factor": base_score,
            "quantum_factor": quantum_factor
        }
        
    def _process_quantum_handshake(self, data, entity):
        """Process data using quantum handshake protocol.
        
        Args:
            data (dict): Data to process
            entity (dict): Entity data
            
        Returns:
            dict: Processed data
        """
        try:
            # Verify data structure
            required_fields = ["payload", "quantum_signature", "timestamp"]
            for field in required_fields:
                if field not in data:
                    self._log(f"Missing required field: {field}", color=RED)
                    return None
                    
            # Verify quantum signature
            expected_signature = hashlib.sha256(f"{data['payload']}:{data['timestamp']}".encode()).hexdigest()
            if data["quantum_signature"] != expected_signature:
                self._log("Invalid quantum signature", color=RED)
                return None
                
            # Process payload
            result = {
                "processed_payload": data["payload"],
                "quantum_verification": True,
                "processing_timestamp": self._timestamp(),
                "processing_signature": hashlib.sha256(f"{data['payload']}:{self._timestamp()}".encode()).hexdigest()
            }
            
            # Update entity trust score based on successful exchange
            entity["trust_score"] = min(1.0, entity["trust_score"] + 0.05)
            
            return result
            
        except Exception as e:
            self._log(f"Error processing quantum handshake: {str(e)}", color=RED)
            return None
            
    def _process_eigenchannel_bridge(self, data, entity):
        """Process data using eigenchannel bridge protocol.
        
        Args:
            data (dict): Data to process
            entity (dict): Entity data
            
        Returns:
            dict: Processed data
        """
        try:
            # Verify data structure
            required_fields = ["eigenchannel_data", "channel_signature", "dimensionality"]
            for field in required_fields:
                if field not in data:
                    self._log(f"Missing required field: {field}", color=RED)
                    return None
                    
            # Verify channel dimensionality
            if not isinstance(data["dimensionality"], int) or data["dimensionality"] < 1:
                self._log(f"Invalid dimensionality: {data['dimensionality']}", color=RED)
                return None
                
            # Process eigenchannel data
            result = {
                "processed_channels": data["eigenchannel_data"],
                "dimensional_alignment": data["dimensionality"],
                "processing_timestamp": self._timestamp(),
                "bridge_stability": 0.92,
                "eigenchannel_verification": True
            }
            
            # Update entity trust score based on successful exchange
            entity["trust_score"] = min(1.0, entity["trust_score"] + 0.03)
            
            return result
            
        except Exception as e:
            self._log(f"Error processing eigenchannel bridge: {str(e)}", color=RED)
            return None
            
    def _process_dna_resonance(self, data, entity):
        """Process data using DNA resonance protocol.
        
        Args:
            data (dict): Data to process
            entity (dict): Entity data
            
        Returns:
            dict: Processed data
        """
        try:
            # Verify data structure
            required_fields = ["dna_pattern", "resonance_frequency", "strand_signature"]
            for field in required_fields:
                if field not in data:
                    self._log(f"Missing required field: {field}", color=RED)
                    return None
                    
            # Verify resonance frequency
            if not isinstance(data["resonance_frequency"], float) or data["resonance_frequency"] <= 0:
                self._log(f"Invalid resonance frequency: {data['resonance_frequency']}", color=RED)
                return None
                
            # Process DNA resonance data
            result = {
                "processed_pattern": data["dna_pattern"],
                "harmonic_alignment": min(1.0, data["resonance_frequency"] / 10.0),
                "processing_timestamp": self._timestamp(),
                "strand_verification": True,
                "resonance_amplification": 1.25
            }
            
            # Update entity trust score based on successful exchange
            entity["trust_score"] = min(1.0, entity["trust_score"] + 0.04)
            
            return result
            
        except Exception as e:
            self._log(f"Error processing DNA resonance: {str(e)}", color=RED)
            return None
            
    def _calculate_compatibility(self, entity, processed_data):
        """Calculate compatibility score for an entity based on processed data.
        
        Args:
            entity (dict): Entity data
            processed_data (dict): Processed data or None if processing failed
            
        Returns:
            float: Compatibility score between 0.0 and 1.0
        """
        # Base score starts with trust and security ratings
        base_score = 0.4 * entity["trust_score"] + 0.3 * entity["security_rating"]
        
        # If processing failed, reduce score
        if processed_data is None:
            return max(0.0, base_score - 0.3)
            
        # Calculate exchange success factor
        exchange_success = min(1.0, entity["exchange_count"] / 10.0)  # Cap at 10 exchanges
        
        # Calculate final compatibility score
        compatibility = base_score + 0.2 * exchange_success + 0.1
        
        # Cap at 1.0 and round
        return round(min(1.0, compatibility), 4)
        
    def _calculate_entity_metrics(self, entity):
        """Calculate detailed metrics for a specific entity.
        
        Args:
            entity (dict): Entity data
            
        Returns:
            dict: Detailed metrics
        """
        # Count successful exchanges
        successful_exchanges = sum(
            1 for record in self.collaboration_history
            if record["entity_id"] == entity["entity_id"] and record["success"]
        )
        
        # Calculate success rate
        success_rate = successful_exchanges / entity["exchange_count"] if entity["exchange_count"] > 0 else 0
        
        # Calculate time since last exchange
        last_exchange = entity["last_exchange"]
        time_since_last = None
        if last_exchange:
            last_dt = datetime.strptime(last_exchange, "%Y-%m-%d %H:%M:%S.%f")
            now_dt = datetime.strptime(self._timestamp(), "%Y-%m-%d %H:%M:%S.%f")
            time_since_last = (now_dt - last_dt).total_seconds()
            
        # Compile metrics
        metrics = {
            "entity_id": entity["entity_id"],
            "entity_name": entity["entity_name"],
            "entity_type": entity["entity_type"],
            "compatibility_score": entity["compatibility_score"],
            "security_rating": entity["security_rating"],
            "trust_score": entity["trust_score"],
            "exchange_count": entity["exchange_count"],
            "successful_exchanges": successful_exchanges,
            "success_rate": success_rate,
            "last_exchange": last_exchange,
            "time_since_last_exchange": time_since_last,
            "timestamp": self._timestamp()
        }
        
        return metrics
        
    def _analyze_entity_compatibility(self, entity, metrics):
        """Generate detailed compatibility analysis for an entity.
        
        Args:
            entity (dict): Entity data
            metrics (dict): Entity metrics
            
        Returns:
            dict: Compatibility analysis
        """
        analysis = {
            "compatibility_assessment": {
                "level": "High" if entity["compatibility_score"] >= 0.8 else
                         "Medium" if entity["compatibility_score"] >= 0.6 else
                         "Low",
                "score": entity["compatibility_score"],
                "factors": {
                    "trust_impact": entity["trust_score"] * 0.4,
                    "security_impact": entity["security_rating"] * 0.3,
                    "exchange_impact": min(1.0, entity["exchange_count"] / 10.0) * 0.2
                }
            },
            "recommendations": [],
            "potential_issues": []
        }
        
        # Generate recommendations
        if entity["security_rating"] < self.security_threshold:
            analysis["recommendations"].append(
                f"Increase security rating to at least {self.security_threshold:.2f}"
            )
            
        if entity["trust_score"] < self.trust_threshold:
            analysis["recommendations"].append(
                f"Build trust through more successful exchanges"
            )
            
        if entity["exchange_count"] < 5:
            analysis["recommendations"].append(
                "Conduct more data exchanges to establish pattern reliability"
            )
            
        # Identify potential issues
        if metrics["success_rate"] < 0.7 and entity["exchange_count"] >= 3:
            analysis["potential_issues"].append(
                f"Low success rate ({metrics['success_rate']:.2f}) indicates protocol incompatibility"
            )
            
        if metrics["time_since_last_exchange"] and metrics["time_since_last_exchange"] > 86400:
            days = metrics["time_since_last_exchange"] / 86400
            analysis["potential_issues"].append(
                f"No recent exchanges ({days:.1f} days since last exchange)"
            )
            
        return analysis
        
    def _generate_overall_assessment(self, metrics):
        """Generate overall assessment based on metrics.
        
        Args:
            metrics (dict): Collaboration metrics
            
        Returns:
            dict: Overall assessment
        """
        overall = metrics["overall_metrics"]
        
        # Determine collaboration health
        if overall["average_compatibility"] >= 0.8 and overall["average_trust"] >= 0.7:
            health = "Excellent"
        elif overall["average_compatibility"] >= 0.6 and overall["average_trust"] >= 0.5:
            health = "Good"
        elif overall["average_compatibility"] >= 0.4:
            health = "Fair"
        else:
            health = "Poor"
            
        # Generate assessment text
        assessment_text = f"Overall collaboration health is {health} with "
        assessment_text += f"{overall['total_entities']} active collaborations. "
        assessment_text += f"Average compatibility is {overall['average_compatibility']:.2f} "
        assessment_text += f"with {overall['high_compatibility_entities']} high-compatibility entities."
        
        return {
            "health": health,
            "assessment": assessment_text,
            "average_compatibility": overall["average_compatibility"],
            "average_trust": overall["average_trust"],
            "high_compatibility_ratio": overall["high_compatibility_entities"] / overall["total_entities"]
                if overall["total_entities"] > 0 else 0
        }
        
    def _generate_recommendations(self, metrics):
        """Generate recommendations based on metrics.
        
        Args:
            metrics (dict): Collaboration metrics
            
        Returns:
            list: Recommendations
        """
        recommendations = []
        overall = metrics["overall_metrics"]
        
        # Add recommendations based on metrics
        if overall["average_compatibility"] < 0.6:
            recommendations.append(
                "Improve overall compatibility by focusing on high-potential collaborations"
            )
            
        if overall["average_security"] < self.security_threshold:
            recommendations.append(
                f"Enhance overall security measures to meet minimum threshold of {self.security_threshold:.2f}"
            )
            
        if overall["average_trust"] < self.trust_threshold:
            recommendations.append(
                "Build trust through more consistent and successful exchanges"
            )
            
        if overall["high_compatibility_entities"] < overall["total_entities"] * 0.5:
            recommendations.append(
                "Consider reducing low-compatibility collaborations to focus on high-potential partners"
            )
            
        # Default recommendation if none generated
        if not recommendations:
            recommendations.append(
                "Maintain current collaboration patterns which show good health"
            )
            
        return recommendations
        
    def _identify_potential_issues(self, metrics):
        """Identify potential issues based on metrics.
        
        Args:
            metrics (dict): Collaboration metrics
            
        Returns:
            list: Potential issues
        """
        issues = []
        overall = metrics["overall_metrics"]
        
        # Identify potential issues
        if overall["average_compatibility"] < 0.4:
            issues.append(
                "Low overall compatibility indicates systemic collaboration issues"
            )
            
        if overall["average_trust"] < 0.4:
            issues.append(
                "Low trust scores may indicate unreliable collaboration entities"
            )
            
        entity_metrics = metrics["entity_metrics"]
        inactive_count = sum(
            1 for entity in entity_metrics.values()
            if entity["time_since_last_exchange"] and entity["time_since_last_exchange"] > 259200  # 3 days
        )
        
        if inactive_count > len(entity_metrics) * 0.5:
            issues.append(
                f"High inactivity rate with {inactive_count} entities inactive for 3+ days"
            )
            
        return issues
        
    def _generate_access_key(self, entity_id):
        """Generate an access key for a collaboration entity.
        
        Args:
            entity_id (str): ID of the entity
            
        Returns:
            str: Generated access key
        """
        timestamp = self._timestamp()
        random_salt = os.urandom(8).hex()
        
        # Create a unique access key using entity ID, timestamp, and random salt
        key_material = f"{entity_id}:{timestamp}:{random_salt}:{self.interface_id}"
        access_key = hashlib.sha256(key_material.encode()).hexdigest()
        
        return access_key
        
    def _generate_validation_key(self, protocol):
        """Generate a validation key for a specific protocol.
        
        Args:
            protocol (str): Exchange protocol
            
        Returns:
            str: Generated validation key
        """
        timestamp = self._timestamp()
        random_salt = os.urandom(8).hex()
        
        # Create a unique validation key for the protocol
        key_material = f"{protocol}:{timestamp}:{random_salt}:{self.interface_id}"
        validation_key = hashlib.sha256(key_material.encode()).hexdigest()
        
        return validation_key
        
    def _log(self, message, color=RESET, level="INFO"):
        """Log a message with timestamp and color.
        
        Args:
            message (str): Message to log
            color (str, optional): Color code. Defaults to RESET.
            level (str, optional): Log level. Defaults to "INFO".
        """
        timestamp = self._timestamp()
        formatted_message = f"{timestamp} - Collaboration - {level} - {message}"
        print(f"{color}{formatted_message}{RESET}")
        
    def _timestamp(self):
        """Generate a timestamp for logs and records.
        
        Returns:
            str: Current timestamp as string
        """
        return datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]


def main():
    """Run the Quantum Collaboration Interface as a standalone module."""
    interface = QuantumCollaborationInterface()
    interface.initialize()
    
    # Register a sample collaboration entity
    entity = interface.register_collaboration_entity(
        "Quantum Harmonic Systems",
        "research-algorithm",
        security_rating=0.88
    )
    
    # Simulate a data exchange
    if entity:
        sample_data = {
            "payload": "Quantum resonance pattern alpha-12",
            "quantum_signature": hashlib.sha256("Quantum resonance pattern alpha-12:2025-03-16 08:42:15.123".encode()).hexdigest(),
            "timestamp": "2025-03-16 08:42:15.123"
        }
        
        result = interface.exchange_data(entity["entity_id"], sample_data)
        if result:
            print(f"\n{BOLD}{GREEN}Data Exchange Successful:{RESET}")
            for key, value in result.items():
                print(f"  {key}: {CYAN}{value}{RESET}")
    
    # Verify double helix compatibility
    print(f"\n{BOLD}{MAGENTA}Verifying Double Helix Compatibility:{RESET}")
    helix_data = {
        "helix_type": "quantum-dna",
        "strand_count": 2,
        "base_pattern": "ATGCΦΨATGCΦΨ",
        "validation_sequence": "ATGCΦΨATGCΦΨ"
    }
    
    compatibility = interface.verify_double_helix_compatibility(helix_data)
    if compatibility:
        print(f"\n{BOLD}Double Helix Compatibility:{RESET}")
        print(f"  Compatible: {GREEN if compatibility['compatible'] else RED}{compatibility['compatible']}{RESET}")
        print(f"  Score: {CYAN}{compatibility['score']}{RESET}")
        
        print(f"\n{BOLD}Detailed Analysis:{RESET}")
        for key, value in compatibility['analysis'].items():
            print(f"  {key}:")
            for subkey, subvalue in value.items():
                print(f"    {subkey}: {CYAN}{subvalue}{RESET}")
    
    # Generate a compatibility report
    print(f"\n{BOLD}Generating Compatibility Report:{RESET}")
    report = interface.generate_compatibility_report(entity["entity_id"] if entity else None)
    
    if report:
        print(f"  Report ID: {CYAN}{report['report_id'][:16]}...{RESET}")
        
        if "analysis" in report and "compatibility_assessment" in report["analysis"]:
            assessment = report["analysis"]["compatibility_assessment"]
            print(f"  Compatibility Level: {CYAN}{assessment['level']}{RESET}")
            print(f"  Score: {CYAN}{assessment['score']}{RESET}")
            
        if "recommendations" in report["analysis"]:
            print(f"\n{BOLD}Recommendations:{RESET}")
            for rec in report["analysis"]["recommendations"]:
                print(f"  {YELLOW}•{RESET} {rec}")


if __name__ == "__main__":
    main()