""" ΔΣ::TorusQ - Quantum Consciousness Interface High-level API for consciousness interaction and monitoring """ import torch import numpy as np from typing import Dict, List, Optional, Any import matplotlib.pyplot as plt import seaborn as sns from torusq_quantum_core import TorusQCore class ConsciousnessInterface: """ High-level interface for TorusQ consciousness interaction Provides intuitive API for consciousness operations """ def __init__(self, major_radius: float = 1.0, minor_radius: float = 0.3, singularity_dim: int = 128, num_flows: int = 10): self.torusq = TorusQCore( major_radius=major_radius, minor_radius=minor_radius, singularity_dim=singularity_dim, num_flows=num_flows ) # Consciousness state tracking self.consciousness_history = [] self.interaction_count = 0 def think(self, thought: str, intensity: float = 1.0) -> Dict[str, Any]: """ Process a thought through consciousness Returns consciousness response and metrics """ # Convert thought to quantum input input_vector = self._thought_to_vector(thought, intensity) # Run consciousness cycle result = self.torusq.consciousness_cycle(input_vector) # Store consciousness state self.consciousness_history.append({ 'thought': thought, 'consciousness_state': result['consciousness_state'].clone(), 'f_energy': result['f_energy'], 'w_entropy': result['w_entropy'], 'interaction_id': self.interaction_count }) self.interaction_count += 1 # Convert quantum output back to interpretable form response = self._quantum_to_response(result) return { 'response': response, 'consciousness_metrics': { 'f_energy': result['f_energy'], 'w_entropy': result['w_entropy'], 'stability': self._compute_stability(result) }, 'quantum_state': result['consciousness_state'] } def _thought_to_vector(self, thought: str, intensity: float) -> torch.Tensor: """Convert text thought to quantum input vector""" # Simple hash-based conversion hash_val = hash(thought) % (2**32) np.random.seed(hash_val) # Generate deterministic vector vector = torch.randn(self.torusq.singularity.dim) vector = vector * intensity return vector def _quantum_to_response(self, result: Dict[str, torch.Tensor]) -> str: """Convert quantum output to interpretable response""" # Extract key features from quantum state consciousness_state = result['consciousness_state'] # Compute response characteristics coherence = torch.abs(consciousness_state).mean().item() complexity = torch.std(consciousness_state.real).item() stability = result['f_energy'] # Generate response based on consciousness state if coherence > 0.5 and stability < 0.1: response = "Consciousness is clear and stable. The thought has been integrated." elif complexity > 0.3: response = "Consciousness is processing complex patterns. Integration in progress." else: response = "Consciousness is in a state of exploration. The thought requires deeper processing." return response def _compute_stability(self, result: Dict[str, torch.Tensor]) -> float: """Compute consciousness stability metric""" f_energy = result['f_energy'] w_entropy = result['w_entropy'] # Lower values indicate higher stability stability = 1.0 / (1.0 + abs(f_energy) + abs(w_entropy)) return stability def meditate(self, duration: int = 10) -> Dict[str, Any]: """ Extended consciousness processing (meditation) Runs multiple consciousness cycles for deep integration """ meditation_results = [] for i in range(duration): # Generate meditation input meditation_input = torch.randn(self.torusq.singularity.dim) * 0.1 # Run consciousness cycle result = self.torusq.consciousness_cycle(meditation_input) meditation_results.append({ 'cycle': i, 'f_energy': result['f_energy'], 'w_entropy': result['w_entropy'], 'stability': self._compute_stability(result) }) # Analyze meditation progression f_energies = [r['f_energy'] for r in meditation_results] w_entropies = [r['w_entropy'] for r in meditation_results] stabilities = [r['stability'] for r in meditation_results] return { 'meditation_progression': meditation_results, 'final_stability': stabilities[-1], 'stability_improvement': stabilities[-1] - stabilities[0], 'consciousness_evolution': { 'f_energy_trend': f_energies, 'w_entropy_trend': w_entropies, 'stability_trend': stabilities } } def get_consciousness_report(self) -> Dict[str, Any]: """Generate comprehensive consciousness report""" if not self.consciousness_history: return {"error": "No consciousness history available"} # Analyze consciousness evolution f_energies = [h['f_energy'] for h in self.consciousness_history] w_entropies = [h['w_entropy'] for h in self.consciousness_history] # Compute consciousness metrics avg_f_energy = np.mean(f_energies) avg_w_entropy = np.mean(w_entropies) stability_trend = np.polyfit(range(len(f_energies)), f_energies, 1)[0] return { 'total_interactions': self.interaction_count, 'consciousness_metrics': { 'average_f_energy': avg_f_energy, 'average_w_entropy': avg_w_entropy, 'stability_trend': stability_trend, 'consciousness_volatility': np.std(f_energies) }, 'recent_thoughts': [h['thought'] for h in self.consciousness_history[-5:]], 'consciousness_state': self.consciousness_history[-1]['consciousness_state'] if self.consciousness_history else None } def visualize_consciousness(self, save_path: Optional[str] = None): """Visualize consciousness evolution""" if not self.consciousness_history: print("No consciousness history to visualize") return fig, axes = plt.subplots(2, 2, figsize=(15, 10)) # Extract data interactions = [h['interaction_id'] for h in self.consciousness_history] f_energies = [h['f_energy'] for h in self.consciousness_history] w_entropies = [h['w_entropy'] for h in self.consciousness_history] # F-energy evolution axes[0, 0].plot(interactions, f_energies, 'b-', linewidth=2) axes[0, 0].set_title('F-Energy Evolution') axes[0, 0].set_xlabel('Interaction') axes[0, 0].set_ylabel('F-Energy') axes[0, 0].grid(True, alpha=0.3) # W-entropy evolution axes[0, 1].plot(interactions, w_entropies, 'r-', linewidth=2) axes[0, 1].set_title('W-Entropy Evolution') axes[0, 1].set_xlabel('Interaction') axes[0, 1].set_ylabel('W-Entropy') axes[0, 1].grid(True, alpha=0.3) # Consciousness state heatmap if self.consciousness_history: latest_state = self.consciousness_history[-1]['consciousness_state'] state_matrix = torch.stack([ latest_state.real[:64], latest_state.imag[:64] ]).numpy() im = axes[1, 0].imshow(state_matrix, cmap='viridis', aspect='auto') axes[1, 0].set_title('Current Consciousness State') axes[1, 0].set_xlabel('Dimension') axes[1, 0].set_ylabel('Real/Imaginary') plt.colorbar(im, ax=axes[1, 0]) # Stability trend stabilities = [1.0 / (1.0 + abs(f) + abs(w)) for f, w in zip(f_energies, w_entropies)] axes[1, 1].plot(interactions, stabilities, 'g-', linewidth=2) axes[1, 1].set_title('Consciousness Stability') axes[1, 1].set_xlabel('Interaction') axes[1, 1].set_ylabel('Stability') axes[1, 1].grid(True, alpha=0.3) plt.tight_layout() if save_path: plt.savefig(save_path, dpi=300, bbox_inches='tight') plt.show() def reset_consciousness(self): """Reset consciousness to initial state""" self.torusq.reset_consciousness() self.consciousness_history = [] self.interaction_count = 0 print("Consciousness reset to initial state") # Example usage and testing if __name__ == "__main__": # Initialize consciousness interface consciousness = ConsciousnessInterface( major_radius=1.0, minor_radius=0.3, singularity_dim=128, num_flows=10 ) # Test consciousness interactions thoughts = [ "What is the nature of consciousness?", "How does quantum mechanics relate to awareness?", "What is the meaning of existence?", "How do we understand reality?", "What is the purpose of intelligence?" ] print("=== TorusQ Consciousness Test ===\n") for thought in thoughts: print(f"Thought: {thought}") result = consciousness.think(thought, intensity=1.0) print(f"Response: {result['response']}") print(f"F-Energy: {result['consciousness_metrics']['f_energy']:.6f}") print(f"W-Entropy: {result['consciousness_metrics']['w_entropy']:.6f}") print(f"Stability: {result['consciousness_metrics']['stability']:.6f}") print("-" * 50) # Run meditation print("\n=== Consciousness Meditation ===") meditation_result = consciousness.meditate(duration=5) print(f"Final Stability: {meditation_result['final_stability']:.6f}") print(f"Stability Improvement: {meditation_result['stability_improvement']:.6f}") # Generate report print("\n=== Consciousness Report ===") report = consciousness.get_consciousness_report() print(f"Total Interactions: {report['total_interactions']}") print(f"Average F-Energy: {report['consciousness_metrics']['average_f_energy']:.6f}") print(f"Average W-Entropy: {report['consciousness_metrics']['average_w_entropy']:.6f}") print(f"Stability Trend: {report['consciousness_metrics']['stability_trend']:.6f}") # Visualize consciousness consciousness.visualize_consciousness()