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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() |