Update app.py

app.py

@@ -4,6 +4,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.animation as animation
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
 import plotly.graph_objects as go
 import plotly.express as px
 import tropycal.tracks as tracks
@@ -300,7 +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 (using Matplotlib)
+# Video animation function with world map (using Matplotlib and Cartopy)
 def categorize_typhoon_by_standard(wind_speed, standard):
     if standard == 'taiwan':
         wind_speed_ms = wind_speed * 0.514444
@@ -339,17 +341,19 @@ def generate_track_video(year, typhoon, standard):
     lat_padding = max((max_lat - min_lat) * 0.3, 5)
     lon_padding = max((max_lon - min_lon) * 0.3, 5)
 
-    # Set up the figure
-    fig, ax = plt.subplots(figsize=(10, 7))
-    ax.set_xlim(min_lon - lon_padding, max_lon + lon_padding)
-    ax.set_ylim(min_lat - lat_padding, max_lat + lat_padding)
-    ax.set_xlabel('Longitude')
-    ax.set_ylabel('Latitude')
-    ax.set_title(f"{year} {storm.name} Typhoon Path")
-    ax.grid(True)
+    # Set up the figure with Cartopy
+    fig = plt.figure(figsize=(10, 7))
+    ax = plt.axes(projection=ccrs.PlateCarree())
+    ax.set_extent([min_lon - lon_padding, max_lon + lon_padding, min_lat - lat_padding, max_lat + lat_padding], crs=ccrs.PlateCarree())
+
+    # Add world map features
+    ax.add_feature(cfeature.LAND, facecolor='lightgray')
+    ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
+    ax.add_feature(cfeature.COASTLINE, edgecolor='black')
+    ax.add_feature(cfeature.BORDERS, linestyle=':', edgecolor='gray')
+    ax.gridlines(draw_labels=True, linestyle='--', color='gray', alpha=0.5)
 
-    # Plot background (simple map-like grid)
-    ax.plot([], [], 'b-', label='Track')  # Placeholder for legend
+    ax.set_title(f"{year} {storm.name} Typhoon Path")
 
     # Legend for categories
     standard_dict = atlantic_standard if standard == 'atlantic' else taiwan_standard
@@ -358,9 +362,9 @@ def generate_track_video(year, typhoon, standard):
     ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
 
     # Initialize the line and point
-    line, = ax.plot([], [], 'b-', linewidth=2)
-    point, = ax.plot([], [], 'o', markersize=8)
-    date_text = ax.text(0.02, 0.02, '', transform=ax.transAxes, fontsize=12)
+    line, = ax.plot([], [], 'b-', linewidth=2, transform=ccrs.PlateCarree())
+    point, = ax.plot([], [], 'o', markersize=8, transform=ccrs.PlateCarree())
+    date_text = ax.text(0.02, 0.02, '', transform=ax.transAxes, fontsize=12, bbox=dict(facecolor='white', alpha=0.8))
 
     def init():
         line.set_data([], [])
@@ -435,7 +439,7 @@ 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
+        - **Path Animation**: Watch an animated typhoon path with video controls and world map background
         - **Regression Analysis**: Perform statistical regression on typhoon data
         
         Select a tab above to begin your analysis.
@@ -603,7 +607,7 @@ 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 date is displayed on each frame, and the track grows with intensity markers
+        5. The animation shows the typhoon track growing over a world map, with the date displayed and intensity markers
         """)
         
         def update_typhoon_options(year):