Python parse/visualize script uses event system
visualize_log.py should auto-locate log files on both Windows and Mac systemsmaster
Daniel Lovell
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05400f318a
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99d617f142
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import os
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import glob
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import argparse
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import platform
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import pandas as pd
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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def get_logs_directory():
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if platform.system() == "Windows":
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return os.path.expandvars(r"%USERPROFILE%\AppData\LocalLow\BAMLAB\micromissiles\Telemetry\Logs")
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elif platform.system() == "Darwin": # macOS
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return os.path.expanduser("~/Library/Application Support/BAMLAB/micromissiles/Telemetry/Logs")
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else:
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raise NotImplementedError(f"Unsupported platform: {platform.system()}")
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def find_latest_file(directory, file_pattern):
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list_of_files = glob.glob(os.path.join(directory, file_pattern))
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if not list_of_files:
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print(f"No files matching '{file_pattern}' found in {directory}")
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return None
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latest_file = max(list_of_files, key=os.path.getctime)
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print(f"Using latest file: {latest_file}")
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return latest_file
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def find_latest_telemetry_file():
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logs_dir = get_logs_directory()
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latest_log_dir = max(glob.glob(os.path.join(logs_dir, "*")), key=os.path.getctime)
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return find_latest_file(latest_log_dir, 'sim_telemetry_*.csv')
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def find_latest_event_log():
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latest_telemetry_file = find_latest_telemetry_file()
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if latest_telemetry_file:
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return latest_telemetry_file.replace('sim_telemetry_', 'sim_events_')
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else:
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return None
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def plot_telemetry(telemetry_file_path, event_file_path):
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# Read the telemetry CSV file
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df = pd.read_csv(telemetry_file_path)
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# Read the event CSV file
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event_df = pd.read_csv(event_file_path)
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# Sanitize the 'Event' column to ensure consistency
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event_df['Event'] = event_df['Event'].str.upper().str.strip()
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# Debugging: Print unique event types to verify correct parsing
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unique_events = event_df['Event'].unique()
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print(f"Unique Events Found: {unique_events}")
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# Create a 3D plot
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fig = plt.figure(figsize=(14, 10))
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ax = fig.add_subplot(111, projection='3d')
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# Define colors for different agent types
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colors = {'T': 'red', 'M': 'blue'}
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# Group data by AgentID
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agent_types = set()
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for agent_id, agent_data in df.groupby('AgentID'):
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agent_type = agent_data['AgentType'].iloc[0]
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color = colors.get(agent_type, 'black')
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downsampled = agent_data.iloc[::10]
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ax.plot(
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downsampled['AgentX'],
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downsampled['AgentZ'],
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downsampled['AgentY'],
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color=color,
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alpha=0.5,
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linewidth=0.5,
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label=f"Agent Type: {agent_type}" # Optional: More descriptive labels
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)
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agent_types.add(agent_type)
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# Define event markers with higher zorder for visibility
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event_markers = {
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'HIT': ('o', 'green', 'Hit'),
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'MISS': ('x', 'red', 'Miss'),
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'NEW_THREAT': ('^', 'orange', 'New Threat'),
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'NEW_INTERCEPTOR': ('s', 'blue', 'New Interceptor')
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}
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# Plot events
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for event_type, (marker, color, label) in event_markers.items():
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event_data = event_df[event_df['Event'] == event_type]
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if not event_data.empty:
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ax.scatter(
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event_data['PositionX'],
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event_data['PositionZ'],
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event_data['PositionY'],
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c=color,
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marker=marker,
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s=100, # Increased marker size for better visibility
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label=label,
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edgecolors='k', # Adding black edges for contrast
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depthshade=True,
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zorder=5 # Ensure markers are on top
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)
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# Set labels
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ax.set_xlabel('X (m)', fontsize=12)
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ax.set_ylabel('Z (m)', fontsize=12)
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ax.set_zlabel('Y (m)', fontsize=12)
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# Set view angle for better visualization
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ax.view_init(elev=20, azim=45)
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# Add a ground plane for reference
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x_min, x_max = ax.get_xlim()
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z_min, z_max = ax.get_ylim()
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xx, zz = np.meshgrid(np.linspace(x_min, x_max, 2), np.linspace(z_min, z_max, 2))
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yy = np.zeros_like(xx)
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ax.plot_surface(xx, zz, yy, alpha=0.2, color='green')
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plt.title('Agents Trajectories and Events (X: Right, Z: Forward, Y: Up)', fontsize=14)
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# Optimize legend to prevent overcrowding
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handles, labels = ax.get_legend_handles_labels()
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# Remove duplicate labels
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unique = dict(zip(labels, handles))
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ax.legend(unique.values(), unique.keys(), loc='upper left', bbox_to_anchor=(1, 1), fontsize=10)
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plt.tight_layout()
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plt.show()
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def print_summary(telemetry_file_path, event_file_path):
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# Read the telemetry CSV file
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df = pd.read_csv(telemetry_file_path)
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# Read the event CSV file
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event_df = pd.read_csv(event_file_path)
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# Sanitize the 'Event' column to ensure consistency
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event_df['Event'] = event_df['Event'].str.upper().str.strip()
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# Print total number of events
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total_events = len(event_df)
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print(f"Total number of events: {total_events}")
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# Print counts of each event type
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event_counts = event_df['Event'].value_counts()
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print("\nEvent Counts:")
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for event_type, count in event_counts.items():
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print(f" {event_type}: {count}")
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# Calculate the time duration of the events
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if 'Time' in event_df.columns:
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start_time = event_df['Time'].min()
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end_time = event_df['Time'].max()
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duration = end_time - start_time
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print(f"\nTotal duration of events: {duration:.2f} seconds (from {start_time:.2f} to {end_time:.2f})")
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else:
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print("\n'Time' column not found in event data.")
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# Provide some insightful data about the hits and misses
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if 'Time' in event_df.columns:
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hits = event_df[event_df['Event'] == 'HIT']
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misses = event_df[event_df['Event'] == 'MISS']
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if not hits.empty:
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first_hit_time = hits['Time'].min()
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last_hit_time = hits['Time'].max()
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print(f"\nFirst hit at {first_hit_time:.2f} seconds, last hit at {last_hit_time:.2f} seconds")
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else:
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print("\nNo hits recorded.")
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if not misses.empty:
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first_miss_time = misses['Time'].min()
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last_miss_time = misses['Time'].max()
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print(f"First miss at {first_miss_time:.2f} seconds, last miss at {last_miss_time:.2f} seconds")
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else:
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print("No misses recorded.")
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else:
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print("\n'Time' column not found in event data.")
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# Update the main function to pass both telemetry and event file paths
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Visualize telemetry data and events.')
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parser.add_argument('telemetry_file', nargs='?', default=None, help='Path to telemetry CSV file.')
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parser.add_argument('event_file', nargs='?', default=None, help='Path to event CSV file.')
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args = parser.parse_args()
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if args.telemetry_file and args.event_file:
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telemetry_file_path = args.telemetry_file
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event_file_path = args.event_file
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else:
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telemetry_file_path = find_latest_telemetry_file()
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event_file_path = find_latest_event_log()
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if telemetry_file_path is None or event_file_path is None:
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exit(1)
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print_summary(telemetry_file_path, event_file_path)
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plot_telemetry(telemetry_file_path, event_file_path)
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@ -1,82 +0,0 @@
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import os
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import glob
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import argparse
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import pandas as pd
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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def find_latest_telemetry_file(directory='./Logs/'):
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list_of_files = glob.glob(os.path.join(directory, 'sim_telemetry_*.csv'))
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if not list_of_files:
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print(f"No telemetry files found in {directory}")
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return None
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latest_file = max(list_of_files, key=os.path.getctime)
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print(f"Using latest telemetry file: {latest_file}")
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return latest_file
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def plot_telemetry(file_path):
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# Read the telemetry CSV file
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df = pd.read_csv(file_path)
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# Create a 3D plot
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fig = plt.figure(figsize=(12, 8))
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ax = fig.add_subplot(111, projection='3d')
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# Define colors for different agent types
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colors = {'T': 'red', 'M': 'blue'}
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# Group data by AgentID
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for agent_id, agent_data in df.groupby('AgentID'):
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agent_type = agent_data['AgentType'].iloc[0]
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color = colors.get(agent_type, 'black')
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downsampled = agent_data.iloc[::10]
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ax.plot(
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downsampled['AgentX'],
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downsampled['AgentZ'],
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downsampled['AgentY'],
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color=color,
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alpha=0.5,
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linewidth=0.5,
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label=f"{agent_type}"
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)
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ax.set_xlabel('X (m)')
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ax.set_ylabel('Z (m)')
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ax.set_zlabel('Y (m)')
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ax.view_init(elev=20, azim=45)
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# Add a ground plane
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x_min, x_max = ax.get_xlim()
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z_min, z_max = ax.get_ylim()
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xx, zz = np.meshgrid(np.linspace(x_min, x_max, 2), np.linspace(z_min, z_max, 2))
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yy = np.zeros_like(xx)
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ax.plot_surface(xx, zz, yy, alpha=0.2, color='green')
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plt.title('Agents Trajectories (X: Right, Z: Forward, Y: Up)')
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legend = [
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plt.Line2D([0], [0], color='red', lw=2, label='Threat'),
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plt.Line2D([0], [0], color='blue', lw=2, label='Interceptor')
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]
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plt.legend(handles=legend)
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plt.tight_layout()
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plt.show()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Visualize telemetry data.')
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parser.add_argument('file', nargs='?', default=None, help='Path to telemetry CSV file.')
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args = parser.parse_args()
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if args.file:
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file_path = args.file
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else:
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file_path = find_latest_telemetry_file()
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if file_path is None:
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exit(1)
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plot_telemetry(file_path)
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