import argparse import matplotlib.dates as mdates import matplotlib.pyplot as plt import pandas as pd def plot_power_data(csv_path, output_path, plot_types): """ Reads power data from a CSV file and generates a plot image. Args: csv_path (str): The path to the input CSV file. output_path (str): The path to save the output plot image. plot_types (list): A list of strings indicating which plots to generate (e.g., ['power', 'voltage', 'current']). """ try: # Read the CSV file into a pandas DataFrame # The 'timestamp' column is parsed as dates df = pd.read_csv(csv_path, parse_dates=['timestamp']) print(f"Successfully loaded {len(df)} records from '{csv_path}'") except FileNotFoundError: print(f"Error: The file '{csv_path}' was not found.") return except Exception as e: print(f"An error occurred while reading the CSV file: {e}") return # --- Plotting Configuration --- plot_configs = { 'power': {'title': 'Power Consumption', 'ylabel': 'Power (W)', 'cols': ['vin_power', 'main_power', 'usb_power']}, 'voltage': {'title': 'Voltage', 'ylabel': 'Voltage (V)', 'cols': ['vin_voltage', 'main_voltage', 'usb_voltage']}, 'current': {'title': 'Current', 'ylabel': 'Current (A)', 'cols': ['vin_current', 'main_current', 'usb_current']} } channel_labels = ['VIN', 'MAIN', 'USB'] channel_colors = ['red', 'green', 'blue'] num_plots = len(plot_types) if num_plots == 0: print("No plot types selected. Exiting.") return # Create a figure and a set of subplots based on the number of selected plot types. # sharex=True makes all subplots share the same x-axis (time) # squeeze=False ensures that 'axes' is always a 2D array, even if num_plots is 1. fig, axes = plt.subplots(num_plots, 1, figsize=(15, 6 * num_plots), sharex=True, squeeze=False) axes = axes.flatten() # Flatten the 2D array to 1D for easier iteration # --- Loop through selected plot types and generate plots --- for i, plot_type in enumerate(plot_types): ax = axes[i] config = plot_configs[plot_type] for j, col_name in enumerate(config['cols']): ax.plot(df['timestamp'], df[col_name], label=channel_labels[j], color=channel_colors[j]) ax.set_title(config['title']) ax.set_ylabel(config['ylabel']) ax.legend() ax.grid(True, which='both', linestyle='--', linewidth=0.5) # --- Formatting the x-axis (Time) --- # Improve date formatting on the x-axis # Apply formatting to the last subplot's x-axis last_ax = axes[-1] last_ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S')) last_ax.xaxis.set_major_locator(plt.MaxNLocator(15)) # Limit the number of ticks plt.xlabel('Time') plt.xticks(rotation=45) # Add a main title to the figure start_time = df['timestamp'].iloc[0].strftime('%Y-%m-%d %H:%M:%S') end_time = df['timestamp'].iloc[-1].strftime('%H:%M:%S') fig.suptitle(f'ODROID Power Log ({start_time} to {end_time})', fontsize=16, y=0.95) # Adjust layout to prevent titles/labels from overlapping plt.tight_layout(rect=[0, 0, 1, 0.94]) # --- Save the plot to a file --- try: plt.savefig(output_path, dpi=150) print(f"Plot successfully saved to '{output_path}'") except Exception as e: print(f"An error occurred while saving the plot: {e}") def main(): parser = argparse.ArgumentParser(description="Generate a plot from an Odroid PowerMate CSV log file.") parser.add_argument("input_csv", help="Path to the input CSV log file.") parser.add_argument("output_image", help="Path to save the output plot image (e.g., plot.png).") parser.add_argument( "-t", "--type", nargs='+', choices=['power', 'voltage', 'current'], default=['power', 'voltage', 'current'], help="Types of plots to generate. Choose from 'power', 'voltage', 'current'. " "Default is to generate all three." ) args = parser.parse_args() plot_power_data(args.input_csv, args.output_image, args.type) if __name__ == "__main__": main()