added count, beautified boxplot output
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3 changed files with 71 additions and 43 deletions
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@ -1,19 +1,16 @@
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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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@ -21,56 +18,85 @@ def main():
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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 each chain, create a figure with five subplots for boxplots (mean, std, min, max, count)
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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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fig, axs = plt.subplots(1, 5, figsize=(18, 6), constrained_layout=True)
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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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plot_data = chain_data[['mean', 'std', 'min', 'max', 'count']].copy()
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plot_data.columns = ['Mean', 'Std', 'Min', 'Max', 'Count']
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# Make all plots have the same color palette
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palette = sns.color_palette("husl", 4)
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# Add a distinct color for the 'Count' plot, as it is a different metric
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colors = palette + ['lightcoral']
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for idx, (col, color) in enumerate(zip(['Mean', 'Std', 'Min', 'Max', 'Count'], colors)):
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ax = axs[idx]
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# Create boxplots
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sns.boxplot(data=plot_data[col], ax=ax, color=color, showfliers=True, width=0.4)
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# Add individual data points
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sns.swarmplot(data=plot_data[col], ax=ax, color='black', size=3, alpha=0.6)
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# Set labels and title
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ax.set_title(f'{col} Distribution', fontsize=14, fontweight='bold')
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ax.set_xticks([]) # Remove x-ticks for clarity
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ax.set_xlabel('') # No x-label needed
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ax.set_ylabel('Latency (ms)' if col != 'Count' else 'Count', fontsize=12)
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# Calculate statistics of the statistics
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data_values = plot_data[col]
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stats_text = (
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f"Mean: {data_values.mean():.2f}\n"
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f"Std: {data_values.std():.2f}\n"
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f"Min: {data_values.min():.2f}\n"
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f"Max: {data_values.max():.2f}"
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)
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# --- Place legend in the top right using axes fraction coordinates ---
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ax.text(
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0.95, 0.98, # axes fraction: 95% right, 98% up
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stats_text,
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transform=ax.transAxes,
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verticalalignment='top',
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horizontalalignment='right',
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fontsize=10,
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bbox=dict(facecolor='white', alpha=0.9, boxstyle='round,pad=0.3', edgecolor='gray')
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)
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# Add grid for better readability
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ax.grid(axis='y', linestyle='--', alpha=0.4)
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# Set the overall title for the figure
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plt.suptitle(
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f'Statistics for Chain: {chain_name}\nAcross {len(chain_data)} Experiment Runs - {experiment_name}',
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fontsize=18, fontweight='bold'
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)
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# Save the figure with a filename that includes the chain name
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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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# Print summary statistics for the 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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print(summary[['mean', 'std', 'min', 'max', 'count']])
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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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@ -51,7 +51,7 @@ def main(base_dir, name_filter):
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pm.execute_notebook(
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"./trace-analysis.ipynb",
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os.path.join(current_artifact, "output", "trace-analysis.ipynb"),
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log_output=True
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log_output=False
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)
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except Exception as e:
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LOGGER.exception(e)
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@ -683,6 +683,7 @@
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" std_latency = np.std(e2e_latencies)\n",
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" min_latency = np.min(e2e_latencies)\n",
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" max_latency = np.max(e2e_latencies)\n",
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" count_latencies = len(e2e_latencies)\n",
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" ax.axvline(mean_latency, c=\"red\", linewidth=2)\n",
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" _, max_ylim = ax.get_ylim()\n",
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" # Create a multi-line string with all stats\n",
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@ -690,7 +691,8 @@
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" f\"Mean: {mean_latency:.2f} ms\\n\"\n",
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" f\"Std: {std_latency:.2f} ms\\n\"\n",
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" f\"Min: {min_latency:.2f} ms\\n\"\n",
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" f\"Max: {max_latency:.2f} ms\"\n",
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" f\"Max: {max_latency:.2f} ms\\n\"\n",
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" f\"Count: {count_latencies}\"\n",
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" )\n",
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" # Place text near top right of plot\n",
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" ax.text(\n",
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@ -703,10 +705,10 @@
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" bbox=dict(facecolor='white', alpha=0.7, boxstyle='round,pad=0.3')\n",
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" )\n",
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" plt.savefig(os.path.join(OUT_PATH, f\"plot_e2es_{name}.png\"))\n",
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" result_strings.append(f\"Chain {topics[0]} --> {topics[-1]} E2E stats: Mean: {mean_latency:.2f} ms, Std: {std_latency:.2f} ms, Min: {min_latency:.2f} ms, Max: {max_latency:.2f} ms\")\n",
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" result_strings.append(f\"Chain {topics[0]} --> {topics[-1]} E2E stats: Mean: {mean_latency:.2f} ms, Std: {std_latency:.2f} ms, Min: {min_latency:.2f} ms, Max: {max_latency:.2f} ms, Count: {count_latencies}\")\n",
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" # also do it as csv of order: exepriment_name, chain, mean, std, min, max\n",
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" result_strings_csv.append(\n",
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" f\"{EXPERIMENT_NAME},{topics[0]} --> {topics[-1]},{mean_latency:.2f},{std_latency:.2f},{min_latency:.2f},{max_latency:.2f}\"\n",
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" f\"{EXPERIMENT_NAME},{topics[0]} --> {topics[-1]},{mean_latency:.2f},{std_latency:.2f},{min_latency:.2f},{max_latency:.2f},{count_latencies}\"\n",
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" )\n",
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"\n",
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" ##################################################\n",
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