76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
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import pandas as pd
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import numpy as np
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import argparse
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import seaborn as sns
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import matplotlib.pyplot as plt
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def parse_arguments():
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parser = argparse.ArgumentParser(description='Analyze chain data from CSV file.')
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parser.add_argument('--input', '-i', required=True, help='Path to the input CSV file')
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return parser.parse_args()
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def main():
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args = parse_arguments()
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# Load the CSV file from the input argument
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df = pd.read_csv(args.input)
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# Extract the experiment_name which should be the same across all rows
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if 'experiment_name' not in df.columns:
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raise ValueError("Input CSV must contain 'experiment_name' column.")
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experiment_name = df['experiment_name'].iloc[0]
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# Strip timestamp from experiment_name if it exists
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experiment_name = experiment_name.split('-')[0] if '-' in experiment_name else experiment_name
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# Group data by chain
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chain_groups = df.groupby('chain')
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# For each chain, create a plot with four boxplots (mean, std, min, max)
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for chain_name, chain_data in chain_groups:
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# Create a figure for this chain
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plt.figure(figsize=(12, 8))
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# Normalize chain name for filename
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chain_name_fs = str(chain_name).replace('--> /', '-').replace('/', '_').replace(' ', '')
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# Create a DataFrame with the columns we want to plot
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plot_data = pd.DataFrame({
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'Mean': chain_data['mean'],
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'Std': chain_data['std'],
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'Min': chain_data['min'],
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'Max': chain_data['max']
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})
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# Create boxplots
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ax = sns.boxplot(data=plot_data, palette='Set3')
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# Add individual data points
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sns.stripplot(data=plot_data, color='black', alpha=0.5, size=4, jitter=True)
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# Set labels and title
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plt.title(f'Statistics for Chain: {chain_name}\nAcross {len(chain_data)} Experiment Runs\n{experiment_name}', fontsize=14)
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plt.ylabel('Latency (ms)', fontsize=12)
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plt.xlabel('Statistic Type', fontsize=12)
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# Add grid for better readability
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plt.grid(axis='y', linestyle='--', alpha=0.7)
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# Tighten layout and save the figure
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plt.tight_layout()
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output_file = args.input.replace('.csv', f'_chain_{chain_name_fs}_analysis.png')
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plt.savefig(output_file, dpi=300)
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plt.close()
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# Also calculate and print summary statistics for this chain
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summary = chain_data.describe()
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print(f"\nSummary for chain: {chain_name}")
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print(summary[['mean', 'std', 'min', 'max']])
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print(f"\nAnalysis complete. Plots saved with base name: {args.input.replace('.csv', '_chain_*_analysis.png')}")
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if __name__ == "__main__":
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main()
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