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()