Update visualization.py

visualization.py

@@ -199,27 +199,3 @@ def plot_posture(df, posture_scores, color='blue', anomaly_threshold=3):
     plt.tight_layout()
     plt.close()
     return fig
-
-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 (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, 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 (seconds)')
-    plt.ylabel('Body Posture Timecode (seconds)')
-    plt.close()
-    return joint_plot.fig