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