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QUANTUM COLLABORATION INTERFACE |
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This module implements an interface for secure collaboration with external systems, |
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providing data exchange protocols and compatibility metrics. |
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Architect: Russell Nordland |
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""" |
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import hashlib |
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import json |
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import time |
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import os |
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import uuid |
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from datetime import datetime |
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# Color constants for terminal output |
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RED = "\033[31m" |
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GREEN = "\033[32m" |
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YELLOW = "\033[33m" |
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BLUE = "\033[34m" |
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MAGENTA = "\033[35m" |
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CYAN = "\033[36m" |
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WHITE = "\033[37m" |
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RESET = "\033[0m" |
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BOLD = "\033[1m" |
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class QuantumCollaborationInterface: |
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def __init__(self): |
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"""Initialize the Quantum Collaboration Interface.""" |
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self.initialized = False |
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self.active_collaborations = {} |
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self.collaboration_history = [] |
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self.compatibility_metrics = {} |
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self.security_threshold = 0.85 |
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self.trust_threshold = 0.75 |
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self.exchange_protocols = ["quantum-handshake", "eigenchannel-bridge", "dna-resonance"] |
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self.data_formats = ["quantum-json", "helix-binary", "spiral-encoded"] |
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self.validation_keys = {} |
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def initialize(self): |
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"""Initialize the collaboration interface.""" |
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self._log("Initializing Quantum Collaboration Interface...", color=BLUE) |
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# Generate unique identifier for this interface instance |
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self.interface_id = str(uuid.uuid4()) |
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self.creation_timestamp = self._timestamp() |
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# Initialize validation keys |
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for protocol in self.exchange_protocols: |
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self.validation_keys[protocol] = self._generate_validation_key(protocol) |
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self._log("Initialization complete.", color=GREEN) |
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self._log(f"Interface ID: {self.interface_id}", color=CYAN) |
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self._log(f"Available protocols: {', '.join(self.exchange_protocols)}", color=CYAN) |
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self.initialized = True |
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return True |
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def register_collaboration_entity(self, entity_name, entity_type, security_rating=0.5): |
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"""Register a new collaboration entity. |
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Args: |
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entity_name (str): Name of the collaborating entity |
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entity_type (str): Type of entity (system, organization, algorithm) |
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security_rating (float): Initial security rating (0.0 to 1.0) |
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Returns: |
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dict: Collaboration entity data including access key |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return None |
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entity_id = hashlib.sha256(f"{entity_name}:{entity_type}:{time.time()}".encode()).hexdigest() |
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# Generate access key for this collaboration |
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access_key = self._generate_access_key(entity_id) |
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# Store entity data |
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entity_data = { |
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"entity_id": entity_id, |
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"entity_name": entity_name, |
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"entity_type": entity_type, |
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"security_rating": security_rating, |
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"trust_score": 0.5, # Initial neutral trust score |
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"access_key": access_key, |
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"registered_timestamp": self._timestamp(), |
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"last_exchange": None, |
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"exchange_count": 0, |
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"compatibility_score": 0.0 |
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} |
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self.active_collaborations[entity_id] = entity_data |
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self._log(f"Registered new collaboration entity: {entity_name}", color=GREEN) |
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self._log(f"Entity ID: {entity_id[:12]}...", color=CYAN) |
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self._log(f"Access Key: {access_key[:12]}...", color=YELLOW) |
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return entity_data |
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def validate_collaboration_request(self, entity_id, access_key, protocol): |
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"""Validate a collaboration request. |
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Args: |
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entity_id (str): ID of the collaborating entity |
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access_key (str): Access key for the entity |
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protocol (str): Requested exchange protocol |
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Returns: |
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bool: True if validation is successful, False otherwise |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return False |
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# Check if entity exists |
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if entity_id not in self.active_collaborations: |
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self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED) |
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return False |
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entity = self.active_collaborations[entity_id] |
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# Validate access key |
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if entity["access_key"] != access_key: |
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self._log(f"Invalid access key for entity: {entity['entity_name']}", color=RED) |
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return False |
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# Validate protocol |
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if protocol not in self.exchange_protocols: |
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self._log(f"Unsupported protocol requested: {protocol}", color=RED) |
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return False |
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# Check security threshold |
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if entity["security_rating"] < self.security_threshold: |
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self._log(f"Entity security rating below threshold: {entity['security_rating']:.2f}", color=YELLOW) |
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self._log(f"Required: {self.security_threshold:.2f}", color=YELLOW) |
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return False |
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# Update last exchange timestamp |
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entity["last_exchange"] = self._timestamp() |
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entity["exchange_count"] += 1 |
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self._log(f"Collaboration request validated for: {entity['entity_name']}", color=GREEN) |
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self._log(f"Using protocol: {protocol}", color=BLUE) |
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return True |
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def exchange_data(self, entity_id, data, protocol="quantum-handshake", data_format="quantum-json"): |
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"""Exchange data with a collaborating entity. |
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Args: |
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entity_id (str): ID of the collaborating entity |
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data (dict): Data to exchange |
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protocol (str): Exchange protocol to use |
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data_format (str): Format for data exchange |
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Returns: |
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dict: Exchange results including processed data |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return None |
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# Check if entity exists |
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if entity_id not in self.active_collaborations: |
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self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED) |
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return None |
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entity = self.active_collaborations[entity_id] |
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# Check protocol support |
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if protocol not in self.exchange_protocols: |
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self._log(f"Unsupported protocol: {protocol}", color=RED) |
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return None |
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# Check data format support |
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if data_format not in self.data_formats: |
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self._log(f"Unsupported data format: {data_format}", color=RED) |
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return None |
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# Process data based on protocol |
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if protocol == "quantum-handshake": |
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processed_data = self._process_quantum_handshake(data, entity) |
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elif protocol == "eigenchannel-bridge": |
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processed_data = self._process_eigenchannel_bridge(data, entity) |
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elif protocol == "dna-resonance": |
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processed_data = self._process_dna_resonance(data, entity) |
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else: |
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self._log(f"Protocol implementation not found: {protocol}", color=RED) |
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return None |
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# Record exchange |
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exchange_record = { |
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"entity_id": entity_id, |
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"entity_name": entity["entity_name"], |
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"protocol": protocol, |
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"data_format": data_format, |
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"timestamp": self._timestamp(), |
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"exchange_id": hashlib.sha256(f"{entity_id}:{time.time()}".encode()).hexdigest(), |
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"data_size": len(str(data)), |
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"success": processed_data is not None |
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} |
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self.collaboration_history.append(exchange_record) |
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# Update entity metrics |
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entity["exchange_count"] += 1 |
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entity["last_exchange"] = exchange_record["timestamp"] |
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# Calculate compatibility score |
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compatibility = self._calculate_compatibility(entity, processed_data) |
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entity["compatibility_score"] = compatibility |
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self._log(f"Data exchange completed with: {entity['entity_name']}", color=GREEN) |
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self._log(f"Protocol: {protocol}, Format: {data_format}", color=BLUE) |
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self._log(f"Compatibility score: {compatibility:.4f}", color=CYAN) |
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return { |
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"entity_id": entity_id, |
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"exchange_id": exchange_record["exchange_id"], |
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"processed_data": processed_data, |
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"timestamp": exchange_record["timestamp"], |
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"compatibility": compatibility, |
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"protocol": protocol, |
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"data_format": data_format |
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} |
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def calculate_collaboration_metrics(self, entity_id=None): |
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"""Calculate collaboration metrics for specific entity or all entities. |
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Args: |
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entity_id (str, optional): ID of the entity to calculate metrics for. |
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If None, calculates for all entities. |
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Returns: |
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dict: Collaboration metrics |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return None |
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if entity_id is not None: |
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# Calculate metrics for specific entity |
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if entity_id not in self.active_collaborations: |
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self._log(f"Entity ID not found: {entity_id[:12]}...", color=RED) |
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return None |
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entity = self.active_collaborations[entity_id] |
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metrics = self._calculate_entity_metrics(entity) |
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self._log(f"Calculated metrics for entity: {entity['entity_name']}", color=BLUE) |
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return metrics |
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else: |
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# Calculate metrics for all entities |
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all_metrics = { |
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"entity_metrics": {}, |
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"overall_metrics": { |
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"total_entities": len(self.active_collaborations), |
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"total_exchanges": sum(e["exchange_count"] for e in self.active_collaborations.values()), |
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"average_compatibility": 0.0, |
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"average_security": 0.0, |
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"average_trust": 0.0, |
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"high_compatibility_entities": 0, |
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"timestamp": self._timestamp() |
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} |
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} |
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if not self.active_collaborations: |
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return all_metrics |
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# Calculate individual entity metrics |
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compatibility_sum = 0.0 |
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security_sum = 0.0 |
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trust_sum = 0.0 |
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high_compat_count = 0 |
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for ent_id, entity in self.active_collaborations.items(): |
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entity_metrics = self._calculate_entity_metrics(entity) |
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all_metrics["entity_metrics"][ent_id] = entity_metrics |
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compatibility_sum += entity["compatibility_score"] |
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security_sum += entity["security_rating"] |
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trust_sum += entity["trust_score"] |
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if entity["compatibility_score"] >= 0.8: |
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high_compat_count += 1 |
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# Calculate averages |
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entity_count = len(self.active_collaborations) |
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all_metrics["overall_metrics"]["average_compatibility"] = compatibility_sum / entity_count |
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all_metrics["overall_metrics"]["average_security"] = security_sum / entity_count |
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all_metrics["overall_metrics"]["average_trust"] = trust_sum / entity_count |
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all_metrics["overall_metrics"]["high_compatibility_entities"] = high_compat_count |
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self._log(f"Calculated metrics for {entity_count} entities", color=BLUE) |
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return all_metrics |
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def export_collaboration_data(self, output_format="json", file_path=None): |
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"""Export collaboration data for external analysis. |
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Args: |
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output_format (str): Output format, currently only 'json' supported |
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file_path (str, optional): Path to save the output file |
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Returns: |
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dict: The exported data or file path if saved to disk |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return None |
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# Compile export data |
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export_data = { |
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"interface_id": self.interface_id, |
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"timestamp": self._timestamp(), |
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"active_collaborations": self.active_collaborations, |
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"collaboration_history": self.collaboration_history, |
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"compatibility_metrics": self.calculate_collaboration_metrics(), |
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"protocols": self.exchange_protocols, |
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"data_formats": self.data_formats |
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} |
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# Output based on format |
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if output_format.lower() == "json": |
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if file_path: |
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try: |
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with open(file_path, 'w') as f: |
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json.dump(export_data, f, indent=2) |
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self._log(f"Collaboration data exported to: {file_path}", color=GREEN) |
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return {"success": True, "file_path": file_path} |
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except Exception as e: |
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self._log(f"Failed to export data: {str(e)}", color=RED) |
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return None |
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else: |
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return export_data |
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else: |
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self._log(f"Unsupported output format: {output_format}", color=RED) |
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return None |
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def generate_compatibility_report(self, entity_id=None): |
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"""Generate a detailed compatibility report. |
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Args: |
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entity_id (str, optional): ID of specific entity to report on. |
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If None, generates report for all entities. |
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Returns: |
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dict: Detailed compatibility report |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return None |
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# Get collaboration metrics |
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metrics = self.calculate_collaboration_metrics(entity_id) |
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if metrics is None: |
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return None |
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# Generate report |
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report = { |
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"report_id": hashlib.sha256(f"report:{time.time()}").hexdigest(), |
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"timestamp": self._timestamp(), |
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"interface_id": self.interface_id, |
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"metrics": metrics, |
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"analysis": {} |
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} |
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# Add analysis based on metrics |
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if entity_id: |
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# Single entity analysis |
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entity = self.active_collaborations[entity_id] |
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report["analysis"] = self._analyze_entity_compatibility(entity, metrics) |
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else: |
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# Overall analysis |
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report["analysis"]["overall_assessment"] = self._generate_overall_assessment(metrics) |
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report["analysis"]["recommendations"] = self._generate_recommendations(metrics) |
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report["analysis"]["potential_issues"] = self._identify_potential_issues(metrics) |
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self._log(f"Generated compatibility report: {report['report_id'][:12]}...", color=GREEN) |
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return report |
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def verify_double_helix_compatibility(self, helix_data): |
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"""Verify compatibility with double helix spiral models. |
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Args: |
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helix_data (dict): Double helix model data to verify |
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Returns: |
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dict: Compatibility verification results |
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""" |
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if not self.initialized: |
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self._log("System not initialized", color=RED) |
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return None |
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required_fields = ["helix_type", "strand_count", "base_pattern", "validation_sequence"] |
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# Verify required fields |
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for field in required_fields: |
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if field not in helix_data: |
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self._log(f"Missing required field in helix data: {field}", color=RED) |
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return { |
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"compatible": False, |
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"reason": f"Missing required field: {field}", |
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"score": 0.0 |
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} |
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# Verify helix type |
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valid_types = ["quantum-dna", "spiral-eigensystem", "truth-resonant"] |
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if helix_data["helix_type"] not in valid_types: |
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self._log(f"Unsupported helix type: {helix_data['helix_type']}", color=YELLOW) |
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return { |
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"compatible": False, |
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"reason": f"Unsupported helix type: {helix_data['helix_type']}", |
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"score": 0.2 |
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} |
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# Verify strand count (should be 2 for double helix) |
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if helix_data["strand_count"] != 2: |
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self._log(f"Invalid strand count: {helix_data['strand_count']}, expected 2", color=YELLOW) |
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return { |
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"compatible": False, |
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"reason": f"Invalid strand count: {helix_data['strand_count']}, expected 2", |
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"score": 0.3 |
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} |
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# Validate the sequence pattern |
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validation_result = self._validate_helix_sequence(helix_data["validation_sequence"]) |
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if not validation_result["valid"]: |
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self._log(f"Invalid validation sequence: {validation_result['reason']}", color=RED) |
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return { |
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"compatible": False, |
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"reason": f"Invalid validation sequence: {validation_result['reason']}", |
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"score": validation_result["score"] |
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} |
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# Calculate overall compatibility score |
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compatibility_score = self._calculate_helix_compatibility(helix_data) |
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result = { |
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"compatible": compatibility_score >= 0.8, |
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"score": compatibility_score, |
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"timestamp": self._timestamp(), |
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"analysis": { |
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"sequence_validity": validation_result, |
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"pattern_alignment": self._analyze_pattern_alignment(helix_data["base_pattern"]), |
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"strand_integrity": self._analyze_strand_integrity(helix_data), |
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"quantum_resonance": self._calculate_quantum_resonance(helix_data) |
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} |
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} |
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self._log(f"Double helix compatibility verification complete", color=GREEN) |
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self._log(f"Compatibility score: {compatibility_score:.4f}", color=CYAN) |
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self._log(f"Compatible: {result['compatible']}", color=GREEN if result['compatible'] else RED) |
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return result |
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def _calculate_helix_compatibility(self, helix_data): |
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"""Calculate compatibility score for double helix data. |
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Args: |
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helix_data (dict): Double helix model data |
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Returns: |
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float: Compatibility score between 0.0 and 1.0 |
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""" |
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# Get individual scores |
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sequence_score = self._validate_helix_sequence(helix_data["validation_sequence"])["score"] |
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alignment_score = self._analyze_pattern_alignment(helix_data["base_pattern"])["score"] |
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integrity_score = self._analyze_strand_integrity(helix_data)["score"] |
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resonance_score = self._calculate_quantum_resonance(helix_data)["score"] |
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# Calculate weighted average |
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weights = { |
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"sequence": 0.3, |
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|
"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" |
|
|
) |
|
|
|
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return recommendations |
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|
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|
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() |
