diff --git a/Data/show_plots.py b/Data/show_plots.py
index 7fa28f0..5aff54c 100644
--- a/Data/show_plots.py
+++ b/Data/show_plots.py
@@ -571,6 +571,61 @@ plt.show()
 
 
 # %% Time Plot
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from scipy import stats
 
+# Load the TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Extract the inference_time_sec column
+inference_times = df['inference_time_sec'].dropna()  # Remove NaN values
+
+# Calculate statistics
+mean_time = inference_times.mean()
+std_time = inference_times.std()
+median_time = np.median(inference_times)
+
+# Create the histogram
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Create histogram with bins of 1 second width
+min_time = int(inference_times.min())
+max_time = int(inference_times.max()) + 1
+bins = np.arange(min_time, max_time + 1, 1)  # Bins of 1 second width
+
+# Create histogram with counts (not probability density)
+n, bins, patches = ax.hist(inference_times, bins=bins, color='lightblue', alpha=0.7, edgecolor='black', linewidth=0.5)
+
+# Generate Gaussian curve for fit
+x = np.linspace(inference_times.min(), inference_times.max(), 100)
+# Scale Gaussian to match histogram counts
+gaussian_counts = stats.norm.pdf(x, mean_time, std_time) * len(inference_times) * (bins[1] - bins[0])
+
+# Plot Gaussian fit
+ax.plot(x, gaussian_counts, color='red', linewidth=2, label=f'Gaussian Fit (μ={mean_time:.1f}s, σ={std_time:.1f}s)')
+
+# Add vertical lines for mean and median
+ax.axvline(mean_time, color='blue', linestyle='--', linewidth=2, label=f'Mean = {mean_time:.1f}s')
+ax.axvline(median_time, color='green', linestyle='--', linewidth=2, label=f'Median = {median_time:.1f}s')
+
+# Add standard deviation as vertical lines
+ax.axvline(mean_time + std_time, color='saddlebrown', linestyle=':', linewidth=1, alpha=0.7, label=f'+1σ = {mean_time + std_time:.1f}s')
+ax.axvline(mean_time - std_time, color='saddlebrown', linestyle=':', linewidth=1, alpha=0.7, label=f'-1σ = {mean_time - std_time:.1f}s')
+
+ax.set_xlabel('Inference Time (seconds)')
+ax.set_ylabel('Frequency')
+ax.set_title('Inference Time Distribution with Gaussian Fit')
+ax.legend()
+ax.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
 
 ##
+
+
+
+