Update visualization.py

visualization.py

@@ -70,7 +70,6 @@ def plot_mse(df, mse_values, title, color='navy', time_threshold=3, anomaly_thre
             ax.plot(segment_df['Seconds'], mean, color=color, linewidth=0.5)
             ax.fill_between(segment_df['Seconds'], mean - std, mean + std, color=color, alpha=0.1)
 
-    # Rest of the function remains the same
     median = np.median(mse_values)
     ax.axhline(y=median, color='black', linestyle='--', label='Median Baseline')
 
@@ -88,32 +87,8 @@ def plot_mse(df, mse_values, title, color='navy', time_threshold=3, anomaly_thre
                             mse_values[anomalies]))
     anomaly_data.sort(key=lambda x: x[1])
 
-    grouped_anomalies = []
-    current_group = []
-    for timecode, sec, mse in anomaly_data:
-        if not current_group or sec - current_group[-1][1] <= time_threshold:
-            current_group.append((timecode, sec, mse))
-        else:
-            grouped_anomalies.append(current_group)
-            current_group = [(timecode, sec, mse)]
-    if current_group:
-        grouped_anomalies.append(current_group)
-
-    for group in grouped_anomalies:
-        start_sec = group[0][1]
-        end_sec = group[-1][1]
-        rect = Rectangle((start_sec, ax.get_ylim()[0]), end_sec - start_sec, ax.get_ylim()[1] - ax.get_ylim()[0],
-                         facecolor='red', alpha=0.2, zorder=1)
-        ax.add_patch(rect)
-
-    for group in grouped_anomalies:
-        highest_mse_anomaly = max(group, key=lambda x: x[2])
-        timecode, sec, mse = highest_mse_anomaly
-        ax.annotate(timecode, (sec, mse), textcoords="offset points", xytext=(0, 10),
-                    ha='center', fontsize=6, color='red')
-
     max_seconds = df['Seconds'].max()
-    num_ticks = 100
+    num_ticks = 80
     tick_locations = np.linspace(0, max_seconds, num_ticks)
     tick_labels = [seconds_to_timecode(int(s)) for s in tick_locations]