batch analysis analysis

batch_analysis_analysis.py

@@ -0,0 +1,76 @@
+import pandas as pd
+import numpy as np
+import argparse
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description='Analyze chain data from CSV file.')
+    parser.add_argument('--input', '-i', required=True, help='Path to the input CSV file')
+    return parser.parse_args()
+
+
+def main():
+    args = parse_arguments()
+    
+    # Load the CSV file from the input argument
+    df = pd.read_csv(args.input)
+
+    # Extract the experiment_name which should be the same across all rows
+    if 'experiment_name' not in df.columns:
+        raise ValueError("Input CSV must contain 'experiment_name' column.")
+    experiment_name = df['experiment_name'].iloc[0]
+    
+    # Strip timestamp from experiment_name if it exists
+    experiment_name = experiment_name.split('-')[0] if '-' in experiment_name else experiment_name
+    
+    # Group data by chain
+    chain_groups = df.groupby('chain')
+    
+    # For each chain, create a plot with four boxplots (mean, std, min, max)
+    for chain_name, chain_data in chain_groups:
+        # Create a figure for this chain
+        plt.figure(figsize=(12, 8))
+        
+        # Normalize chain name for filename
+        chain_name_fs = str(chain_name).replace('--> /', '-').replace('/', '_').replace(' ', '')
+        
+        # Create a DataFrame with the columns we want to plot
+        plot_data = pd.DataFrame({
+            'Mean': chain_data['mean'],
+            'Std': chain_data['std'],
+            'Min': chain_data['min'],
+            'Max': chain_data['max']
+        })
+        
+        # Create boxplots
+        ax = sns.boxplot(data=plot_data, palette='Set3')
+        
+        # Add individual data points
+        sns.stripplot(data=plot_data, color='black', alpha=0.5, size=4, jitter=True)
+        
+        # Set labels and title
+        plt.title(f'Statistics for Chain: {chain_name}\nAcross {len(chain_data)} Experiment Runs\n{experiment_name}', fontsize=14)
+        plt.ylabel('Latency (ms)', fontsize=12)
+        plt.xlabel('Statistic Type', fontsize=12)
+        
+        # Add grid for better readability
+        plt.grid(axis='y', linestyle='--', alpha=0.7)
+        
+        # Tighten layout and save the figure
+        plt.tight_layout()
+        output_file = args.input.replace('.csv', f'_chain_{chain_name_fs}_analysis.png')
+        plt.savefig(output_file, dpi=300)
+        plt.close()
+        
+        # Also calculate and print summary statistics for this chain
+        summary = chain_data.describe()
+        print(f"\nSummary for chain: {chain_name}")
+        print(summary[['mean', 'std', 'min', 'max']])
+        
+    print(f"\nAnalysis complete. Plots saved with base name: {args.input.replace('.csv', '_chain_*_analysis.png')}")
+
+
+if __name__ == "__main__":
+    main()

batch_analyze.py

@@ -35,14 +35,17 @@ def main(base_dir, name_filter):
         print(f"Found {len(unprocessed)} unprocessed and {len(unprocessable)} unprocessable artifacts.")
 
         current_artifact = unprocessed.pop()
-        print(f"Now working on {current_artifact}.")
+        experiment_name = os.path.basename(current_artifact)
+        print(f"Now working on {current_artifact} --> {experiment_name}.")
 
         out_dir = os.path.join(current_artifact, 'output')
+
         shutil.rmtree(out_dir, ignore_errors=True)
         os.makedirs(out_dir, exist_ok=False)
 
         os.environ["ANA_NB_OUT_PATH"] = f"'{out_dir}'"
-        os.environ["ANA_NB_TR_PATH"] = f"'{os.path.join(current_artifact, 'tracing/max-ma-trace/ust')}'"
+        os.environ["ANA_NB_EXPERIMENT_NAME"] = f"'{experiment_name}'"
+        os.environ["ANA_NB_TR_PATH"] = f"'/home/niklas/dataflow-analysis/{current_artifact}/ust'"
 
         try:
             pm.execute_notebook(

requirements.txt

@@ -11,3 +11,5 @@ pyvis
 ruamel.yaml
 termcolor
 tqdm
+seaborn
+papermill