--- a
+++ b/run_exp_2.py
@@ -0,0 +1,74 @@
+import os
+import sys
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from run import run
+
+from matplotlib.colors import LinearSegmentedColormap
+
+
+def run_experiment2(data_path):
+    '''
+    Function that conducts a series of experiments with varying:
+     - number of segments used for training and testing (N)
+     - segment length (T)
+
+    Parameters:
+    data_path (str): path to the preprocessed dataset.
+
+    Saves:
+    - CSV files for accuracy, training time, and evaluation time grids, and 
+    - a heatmap plot of model accuracy in the 'exp2_results' directory.
+    '''
+
+    segments_range = [500,350,200,100,50]
+    seconds_range = [1,2,5,7,10]
+    leads = [0,1]
+
+    accuracy_grid = np.zeros((len(segments_range), len(seconds_range)))
+    train_time_grid = np.zeros_like(accuracy_grid)
+    eval_time_grid = np.zeros_like(accuracy_grid)
+
+
+    for i, num_segments in enumerate(segments_range):
+        for j, num_seconds in enumerate(seconds_range):
+            print(f'Training for {num_segments} segments, {num_seconds} s long')
+            accuracy, total_train_time, total_eval_time = run(NUM_SEGMENTS=num_segments, 
+                                                              NUM_SECONDS=num_seconds, 
+                                                              NUM_BATCH=16, 
+                                                              LEADS=leads, 
+                                                              NUM_EPOCHS=200, 
+                                                              DATA_PATH = data_path, 
+                                                              FS=128)
+            accuracy_grid[i, j] = accuracy
+            train_time_grid[i,j] = total_train_time
+            eval_time_grid[i,j] = total_eval_time
+
+
+    os.makedirs('exp2_results', exist_ok=True)
+    np.savetxt('exp2_results/accuracy_grid.csv', accuracy_grid, delimiter=',', fmt='%.2f')
+    np.savetxt('exp2_results/train_time_grid.csv', train_time_grid, delimiter=',', fmt='%.2f')
+    np.savetxt('exp2_results/eval_time_grid.csv', eval_time_grid, delimiter=',', fmt='%.2f')
+
+    color_list = ["#FCDDD9", "#FABEB7", "#FAA99F", "#FA968A", "#F87F71",
+                "#DC7366", "#B15F56", "#884C45", "#593633", "#2E2322"]
+    custom_colormap = LinearSegmentedColormap.from_list("custom_salmon", color_list)
+
+    yticklabels = [int(w/2) for w in segments_range]
+    plt.figure(figsize=(10, 8), dpi=100)
+    sns.heatmap(accuracy_grid, annot=True, fmt=".2f", xticklabels=seconds_range, yticklabels=yticklabels, 
+                cmap=custom_colormap, cbar_kws={'label': 'Accuracy %'})
+    plt.xlabel('NUMBER OF SECONDS (T)')
+    plt.ylabel('TRAIN SAMPLES PER SUBJECT (N)')
+    plt.savefig('exp2_results/fig_exp2.png')
+    plt.close()
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python run_exp_2.py <data_path>")
+        sys.exit(1)
+
+    data_path = sys.argv[1]
+
+    run_experiment2(data_path=data_path)