import os import json import glob import numpy as np import matplotlib.pyplot as plt from typing import Dict, List, Any, Optional from matplotlib.figure import Figure from datetime import datetime class SimulationVisualizer: """Visualizes results from cognitive simulations""" def __init__(self, log_directory: str = "simulation_logs"): self.log_directory = log_directory def find_simulation_logs(self, simulation_id: Optional[str] = None) -> List[str]: """Find all simulation logs or logs for a specific simulation ID""" pattern = f"{simulation_id}_*.json" if simulation_id else "*.json" return glob.glob(os.path.join(self.log_directory, pattern)) def get_latest_simulation_id(self) -> Optional[str]: """Get the ID of the most recent simulation""" log_files = glob.glob(os.path.join(self.log_directory, "sim_*_final.json")) if not log_files: return None # Sort by modification time (newest first) log_files.sort(key=os.path.getmtime, reverse=True) # Extract simulation ID from filename (sim_1234567890_final.json -> sim_1234567890) latest_file = os.path.basename(log_files[0]) return latest_file.split('_final.json')[0] def load_simulation_data(self, simulation_id: str) -> Dict[str, Any]: """Load data for a specific simulation""" data = { "iterations": [], "energy_levels": [], "obstacles": [], "rewards": [], "environment_conditions": [], "performance_metrics": {}, "behavior_weights": {}, "final_state": None } # Load all log files for this simulation log_files = self.find_simulation_logs(simulation_id) log_files.sort(key=lambda x: int(os.path.basename(x).split('_')[-1].split('.')[0]) if not os.path.basename(x).endswith('final.json') else float('inf')) for log_file in log_files: try: with open(log_file, 'r') as f: log_data = json.load(f) # Store iteration data if "iteration" in log_data: data["iterations"].append(log_data["iteration"]) # Energy levels if "lifeform" in log_data and "energy_percentage" in log_data["lifeform"]: data["energy_levels"].append(log_data["lifeform"]["energy_percentage"]) # Environment state if "environment" in log_data: env = log_data["environment"] data["obstacles"].append(env.get("obstacles", 0)) data["rewards"].append(env.get("rewards", 0)) data["environment_conditions"].append(env.get("environmental_condition", 0)) # Performance metrics if "lifeform" in log_data and "performance_metrics" in log_data["lifeform"]: metrics = log_data["lifeform"]["performance_metrics"] for key, value in metrics.items(): if key not in data["performance_metrics"]: data["performance_metrics"][key] = [] data["performance_metrics"][key].append(value) # Behavior weights if "lifeform" in log_data and "behavior_weights" in log_data["lifeform"]: weights = log_data["lifeform"]["behavior_weights"] for key, value in weights.items(): if key not in data["behavior_weights"]: data["behavior_weights"][key] = [] data["behavior_weights"][key].append(value) # Store final state data if os.path.basename(log_file).endswith('final.json'): data["final_state"] = log_data except Exception as e: print(f"Error loading log file {log_file}: {str(e)}") return data def plot_energy_levels(self, data: Dict[str, Any], show: bool = True) -> Figure: """Plot energy levels over time""" fig, ax = plt.subplots(figsize=(10, 6)) if data["iterations"] and data["energy_levels"]: ax.plot(data["iterations"], data["energy_levels"], 'b-', label='Energy Level') ax.set_xlabel('Iteration') ax.set_ylabel('Energy Level (%)') ax.set_title('Lifeform Energy Levels Over Time') ax.grid(True, alpha=0.3) ax.set_ylim(0, 1.05) # Add a horizontal line at 20% energy as a "danger zone" ax.axhline(y=0.2, color='r', linestyle='--', alpha=0.5, label='Low Energy Warning') ax.legend() else: ax.text(0.5, 0.5, 'No energy data available', ha='center', va='center') if show: plt.tight_layout() plt.show() return fig def plot_environment_conditions(self, data: Dict[str, Any], show: bool = True) -> Figure: """Plot environmental conditions over time""" fig, ax = plt.subplots(figsize=(10, 6)) if data["iterations"]: if data["obstacles"]: ax.plot(data["iterations"], data["obstacles"], 'r-', label='Obstacles') if data["rewards"]: ax.plot(data["iterations"], data["rewards"], 'g-', label='Rewards') if data["environment_conditions"]: ax.plot(data["iterations"], data["environment_conditions"], 'b-', label='Environmental Conditions') ax.set_xlabel('Iteration') ax.set_ylabel('Intensity') ax.set_title('Environmental Conditions Over Time') ax.grid(True, alpha=0.3) ax.set_ylim(0, 1.05) ax.legend() else: ax.text(0.5, 0.5, 'No environment data available', ha='center', va='center') if show: plt.tight_layout() plt.show() return fig def plot_performance_metrics(self, data: Dict[str, Any], show: bool = True) -> Figure: """Plot performance metrics over time""" metrics = data["performance_metrics"] if not metrics or not data["iterations"]: fig, ax = plt.subplots(figsize=(10, 6)) ax.text(0.5, 0.5, 'No performance metrics available', ha='center', va='center') return fig # Create a multi-line chart for all metrics fig, ax = plt.subplots(figsize=(10, 6)) colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] color_idx = 0 for key, values in metrics.items(): if len(values) == len(data["iterations"]): ax.plot(data["iterations"], values, f'{colors[color_idx]}-', label=key.capitalize()) color_idx = (color_idx + 1) % len(colors) ax.set_xlabel('Iteration') ax.set_ylabel('Score') ax.set_title('Performance Metrics Over Time') ax.grid(True, alpha=0.3) ax.set_ylim(0, 1.05) ax.legend() if show: plt.tight_layout() plt.show() return fig def plot_behavior_weights(self, data: Dict[str, Any], show: bool = True) -> Figure: """Plot behavior weights over time to show adaptation""" weights = data["behavior_weights"] if not weights or not data["iterations"]: fig, ax = plt.subplots(figsize=(10, 6)) ax.text(0.5, 0.5, 'No behavior weight data available', ha='center', va='center') return fig fig, ax = plt.subplots(figsize=(10, 6)) colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] color_idx = 0 # Sample the iterations to reduce visual clutter if there are many if len(data["iterations"]) > 50: sample_rate = len(data["iterations"]) // 50 sample_indices = range(0, len(data["iterations"]), sample_rate) sampled_iterations = [data["iterations"][i] for i in sample_indices] else: sample_indices = range(len(data["iterations"])) sampled_iterations = data["iterations"] for key, values in weights.items(): if len(values) == len(data["iterations"]): sampled_values = [values[i] for i in sample_indices] ax.plot(sampled_iterations, sampled_values, f'{colors[color_idx]}-', label=key) color_idx = (color_idx + 1) % len(colors) ax.set_xlabel('Iteration') ax.set_ylabel('Weight') ax.set_title('Behavior Weights Over Time (Adaptation)') ax.grid(True, alpha=0.3) ax.legend() if show: plt.tight_layout() plt.show() return fig def generate_summary_report(self, simulation_id: Optional[str] = None, show_plots: bool = True) -> None: """Generate a comprehensive summary report of a simulation""" if simulation_id is None: simulation_id = self.get_latest_simulation_id() if simulation_id is None: print("No simulation logs found.") return print(f"\n{'='*60}") print(f"SIMULATION SUMMARY REPORT - {simulation_id}") print(f"{'='*60}") data = self.load_simulation_data(simulation_id) if not data["iterations"]: print("No data available for this simulation.") return # Print basic information final_state = data["final_state"] if final_state: print(f"\n--- Simulation Overview ---") sim_time = datetime.fromtimestamp(final_state.get("timestamp", 0)) print(f"Date: {sim_time.strftime('%Y-%m-%d %H:%M:%S')}") if "lifeform" in final_state: lifeform = final_state["lifeform"] print(f"Lifeform: {lifeform.get('name', 'Unknown')}") print(f"Total Iterations: {final_state.get('iteration', 0)}") print(f"Actions Taken: {lifeform.get('actions_taken', 0)}") print(f"Energy Consumed: {lifeform.get('lifetime_energy_consumed', 0):.2f}") # Calculate survival ratio energy_percent = lifeform.get('energy_percentage', 0) * 100 print(f"Final Energy: {energy_percent:.1f}%") if "final_statistics" in final_state: stats = final_state["final_statistics"] print(f"\n--- Final Statistics ---") print(f"Survival Time: {stats.get('survival_time', 0)} iterations") # Calculate average energy if "avg_energy" in stats: print(f"Average Energy Level: {stats.get('avg_energy', 0) * 100:.1f}%") if "lifeform" in final_state and "performance_metrics" in final_state["lifeform"]: print(f"\n--- Performance Metrics ---") for key, value in final_state["lifeform"]["performance_metrics"].items(): print(f"{key.capitalize()}: {value:.2f}") # Generate plots if show_plots: self.plot_energy_levels(data) self.plot_environment_conditions(data) self.plot_performance_metrics(data) self.plot_behavior_weights(data) print(f"\n{'='*60}") print(f"END OF REPORT - {simulation_id}") print(f"{'='*60}\n") def save_report_plots(self, simulation_id: Optional[str] = None, output_dir: Optional[str] = None) -> None: """Save all plots for a simulation to files""" if simulation_id is None: simulation_id = self.get_latest_simulation_id() if simulation_id is None: print("No simulation logs found.") return if output_dir is None: output_dir = os.path.join(self.log_directory, f"{simulation_id}_plots") os.makedirs(output_dir, exist_ok=True) data = self.load_simulation_data(simulation_id) if not data["iterations"]: print("No data available for this simulation.") return # Generate and save plots plots = [ ("energy", self.plot_energy_levels(data, show=False)), ("environment", self.plot_environment_conditions(data, show=False)), ("performance", self.plot_performance_metrics(data, show=False)), ("behavior", self.plot_behavior_weights(data, show=False)) ] for name, fig in plots: filename = os.path.join(output_dir, f"{simulation_id}_{name}.png") fig.savefig(filename, dpi=300, bbox_inches='tight') plt.close(fig) print(f"Saved plot to {filename}") def main(): """Run the visualization tool on the latest simulation""" visualizer = SimulationVisualizer() latest_sim_id = visualizer.get_latest_simulation_id() if latest_sim_id: print(f"Analyzing latest simulation: {latest_sim_id}") visualizer.generate_summary_report(latest_sim_id) else: print("No simulation logs found.") if __name__ == "__main__": main()