Update app.py

app.py

@@ -302,7 +302,7 @@ def generate_main_analysis(start_year, start_month, end_year, end_month, enso_ph
     
     return tracks_fig, wind_scatter, pressure_scatter, regression_fig, slopes_text
 
-# Video animation function with adjusted scale
+# Video animation function with color legend and typhoon details
 def categorize_typhoon_by_standard(wind_speed, standard):
     if standard == 'taiwan':
         wind_speed_ms = wind_speed * 0.514444
@@ -342,7 +342,7 @@ def generate_track_video(year, typhoon, standard):
     lon_padding = max((max_lon - min_lon) * 0.3, 5)
 
     # Set up the figure with Gradio-friendly scale (1000x700 pixels at 100 DPI)
-    fig = plt.figure(figsize=(20, 10), dpi=300)  # 10x7 inches = 1000x700 pixels
+    fig = plt.figure(figsize=(10, 7), dpi=100)  # 10x7 inches = 1000x700 pixels
     ax = plt.axes([0.05, 0.05, 0.65, 0.9], projection=ccrs.PlateCarree())  # Map on left 65% of figure
     ax.set_extent([min_lon - lon_padding, max_lon + lon_padding, min_lat - lat_padding, max_lat + lat_padding], crs=ccrs.PlateCarree())
 
@@ -355,11 +355,12 @@ def generate_track_video(year, typhoon, standard):
 
     ax.set_title(f"{year} {storm.name} Typhoon Path")
 
-    # Legend for categories
+    # Legend for categories with explicit color meanings
     standard_dict = atlantic_standard if standard == 'atlantic' else taiwan_standard
-    for cat, details in standard_dict.items():
-        ax.plot([], [], 'o', color=details['color'], label=cat)
-    ax.legend(loc='upper left', bbox_to_anchor=(1.7, 1))
+    legend_elements = [plt.Line2D([0], [0], marker='o', color='w', label=f"{cat} ({details['color']})",
+                                  markerfacecolor=details['color'], markersize=10)
+                       for cat, details in standard_dict.items()]
+    ax.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(1.7, 0.5), title="Intensity Categories")
 
     # Initialize the line and point
     line, = ax.plot([], [], 'b-', linewidth=2, transform=ccrs.PlateCarree())
@@ -449,7 +450,7 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         ### Features:
         - **Track Visualization**: View typhoon tracks by time period and ENSO phase
         - **Statistical Analysis**: Examine relationships between ONI values and typhoon characteristics
-        - **Path Animation**: Watch an animated typhoon path with video controls, world map, and details sidebar
+        - **Path Animation**: Watch an animated typhoon path with video controls, world map, color legend, and details sidebar
         - **Regression Analysis**: Perform statistical regression on typhoon data
         
         Select a tab above to begin your analysis.
@@ -617,7 +618,10 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         2. Choose a classification standard (Atlantic or Taiwan)
         3. Click "Generate Animation"
         4. Use the video player's built-in controls to play, pause, or scrub through the animation
-        5. The animation shows the typhoon track growing over a world map, with date on the bottom left and details on the right
+        5. The animation shows the typhoon track growing over a world map, with:
+           - Date on the bottom left
+           - Details (name, date, wind speed, category) on the right
+           - Color legend showing intensity categories on the right
         """)
         
         def update_typhoon_options(year):