Update visualization.py

visualization.py

@@ -200,24 +200,26 @@ def plot_posture(df, posture_scores, color='blue', anomaly_threshold=3):
     plt.close()
     return fig
 
-def create_kdeplot(df):
+def create_kdeplot(df, mse_embeddings, mse_posture):
     plt.figure(figsize=(10, 6), dpi=300)
+    df['MSE_Combined'] = (mse_embeddings + mse_posture) / 2
     scatter = plt.scatter(df['Timecode_Seconds'], df['Timecode_Seconds'], 
                           c=df['MSE_Combined'], cmap='YlOrRd', alpha=0.5)
     plt.colorbar(scatter, label='Combined MSE')
-    plt.xlabel('Facial Features Timecode')
-    plt.ylabel('Body Posture Timecode')
+    plt.xlabel('Facial Features Timecode (seconds)')
+    plt.ylabel('Body Posture Timecode (seconds)')
     plt.title('Scatter Plot of Facial Features vs Body Posture Timecodes (Color: Combined MSE)')
     plt.close()
     return plt.gcf()
 
-def create_jointplot(df):
+def create_jointplot(df, mse_embeddings, mse_posture):
     plt.figure(figsize=(10, 6), dpi=300)
+    df['MSE_Combined'] = (mse_embeddings + mse_posture) / 2
     joint_plot = sns.jointplot(data=df, x='Timecode_Seconds', y='Timecode_Seconds', 
                                kind='scatter', hue='MSE_Combined', palette='YlOrRd', 
                                joint_kws={'alpha': 0.5})
     joint_plot.fig.suptitle('Joint Plot of Facial Features vs Body Posture Timecodes (Color: Combined MSE)', y=1.02)
-    plt.xlabel('Facial Features Timecode')
-    plt.ylabel('Body Posture Timecode')
+    plt.xlabel('Facial Features Timecode (seconds)')
+    plt.ylabel('Body Posture Timecode (seconds)')
     plt.close()
     return joint_plot.fig