script for gen images

flake.nix

@@ -186,7 +186,7 @@
         };
 
         devShells.default = let
-          python = pkgs.python3.withPackages (ps: [ps.torch ps.torchvision ps.psutil]);
+          python = pkgs.python3.withPackages (ps: [ps.torch ps.torchvision ps.psutil ps.pandas ps.matplotlib ps.seaborn]);
         in
           craneLib.devShell {
             # Inherit inputs from checks.

scripts/test.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def read_and_visualize(csv_file):
+    # Read the CSV file
+    df = pd.read_csv(csv_file)
+
+    # Display the first few rows of the dataframe
+    print(df.head())
+
+    # Identify all the properties (assuming they are all columns except for 'runtime')
+    properties = [col for col in df.columns if col != 'speedup' and col != 'time' and col != 'stddev']
+
+    # Pairplot to see general pairwise relationships, may help to understand the overall relationship between properties and runtime
+    sns.pairplot(df)
+    plt.suptitle('Pairplot of Properties and Runtime', y=1.02)
+    plt.show()
+
+    # Create scatter plots for each property against runtime
+    for prop in properties:
+        plt.figure(figsize=(10, 6))
+        sns.scatterplot(x=df[prop], y=df['runtime'])
+        plt.title(f'Impact of {prop} on Runtime')
+        plt.xlabel(prop)
+        plt.ylabel('Runtime')
+        plt.show()
+
+    # Create box plots for categorical properties if any (e.g., difficulty level or type) against runtime
+    for prop in properties:
+        if df[prop].dtype == 'object':
+            plt.figure(figsize=(10, 6))
+            sns.boxplot(x=df[prop], y=df['runtime'])
+            plt.title(f'Impact of {prop} on Runtime')
+            plt.xlabel(prop)
+            plt.ylabel('Runtime')
+            plt.show()
+
+# Example usage
+csv_file = 'all.csv'  # Replace with your actual CSV file path
+read_and_visualize(csv_file)