Update app.py

app.py

@@ -33,7 +33,7 @@ DATA_PATH = args.data_path
 
 ONI_DATA_PATH = os.path.join(DATA_PATH, 'oni_data.csv')
 TYPHOON_DATA_PATH = os.path.join(DATA_PATH, 'processed_typhoon_data.csv')
-LOCAL_iBtrace_PATH = os.path.join(DATA_PATH, 'ibtracs.ALL.list.v04r01.csv')
+LOCAL_iBtrace_PATH = os.path.join(DATA_PATH, 'ibtracs.WP.list.v04r01.csv')
 iBtrace_uri = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv'
 CACHE_FILE = 'ibtracs_cache.pkl'
 CACHE_EXPIRY_DAYS = 1
@@ -325,6 +325,83 @@ 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
 
+# Get full tracks function for Track Visualization tab
+def get_full_tracks(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
+    start_date = datetime(start_year, start_month, 1)
+    end_date = datetime(end_year, end_month, 28)
+    filtered_data = merged_data[
+        (merged_data['ISO_TIME'] >= start_date) & 
+        (merged_data['ISO_TIME'] <= end_date)
+    ]
+    filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
+    if enso_phase != 'all':
+        filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
+    unique_storms = filtered_data['SID'].unique()
+    count = len(unique_storms)
+    fig = go.Figure()
+    for sid in unique_storms:
+        storm_data = typhoon_data[typhoon_data['SID'] == sid]
+        name = storm_data['NAME'].iloc[0] if not pd.isna(storm_data['NAME'].iloc[0]) else "Unnamed"
+        storm_oni = filtered_data[filtered_data['SID'] == sid]['ONI'].iloc[0]
+        color = 'red' if storm_oni >= 0.5 else ('blue' if storm_oni <= -0.5 else 'green')
+        fig.add_trace(go.Scattergeo(
+            lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines',
+            name=f"{name} ({storm_data['SEASON'].iloc[0]})",
+            line=dict(width=1.5, color=color),
+            hoverinfo="name"
+        ))
+    if typhoon_search:
+        search_mask = typhoon_data['NAME'].str.contains(typhoon_search, case=False, na=False)
+        if search_mask.any():
+            for sid in typhoon_data[search_mask]['SID'].unique():
+                storm_data = typhoon_data[typhoon_data['SID'] == sid]
+                fig.add_trace(go.Scattergeo(
+                    lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines+markers',
+                    name=f"MATCHED: {storm_data['NAME'].iloc[0]} ({storm_data['SEASON'].iloc[0]})",
+                    line=dict(width=3, color='yellow'),
+                    marker=dict(size=5),
+                    hoverinfo="name"
+                ))
+    fig.update_layout(
+        title=f"Typhoon Tracks ({start_year}-{start_month} to {end_year}-{end_month})",
+        geo=dict(
+            projection_type='natural earth',
+            showland=True,
+            showcoastlines=True,
+            landcolor='rgb(243, 243, 243)',
+            countrycolor='rgb(204, 204, 204)',
+            coastlinecolor='rgb(204, 204, 204)',
+            center=dict(lon=140, lat=20),
+            projection_scale=3
+        ),
+        legend_title="Typhoons by ENSO Phase",
+        showlegend=True,
+        height=700
+    )
+    fig.add_annotation(
+        x=0.02, y=0.98, xref="paper", yref="paper",
+        text="Red: El Niño, Blue: La Niña, Green: Neutral",
+        showarrow=False, align="left",
+        bgcolor="rgba(255,255,255,0.8)"
+    )
+    return fig, f"Total typhoons displayed: {count}"
+
+# Analysis functions for Wind, Pressure, and Longitude tabs
+def get_wind_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
+    results = generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search)
+    regression = perform_wind_regression(start_year, start_month, end_year, end_month)
+    return results[1], regression
+
+def get_pressure_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
+    results = generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search)
+    regression = perform_pressure_regression(start_year, start_month, end_year, end_month)
+    return results[2], regression
+
+def get_longitude_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
+    results = generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search)
+    regression = perform_longitude_regression(start_year, start_month, end_year, end_month)
+    return results[3], results[4], regression
+
 # Video animation function with fixed sidebar and wind radius visualization
 def categorize_typhoon_by_standard(wind_speed, standard):
     if standard == 'taiwan':
@@ -523,6 +600,21 @@ def generate_track_video(year, basin, typhoon, standard):
 
     return temp_file.name
 
+def simplified_track_video(year, basin, typhoon, standard):
+    if not typhoon:
+        return None
+    
+    # Extract storm ID from the dropdown selection
+    typhoon_id = typhoon.split('(')[-1].strip(')')
+    
+    # Extract basin code from the basin selection
+    basin_code = "All"
+    if basin != "All Basins":
+        basin_code = basin.split(' - ')[0]
+    
+    # Generate the animation
+    return generate_track_video(year, basin_code, typhoon, standard)
+
 # Logistic regression functions
 def perform_wind_regression(start_year, start_month, end_year, end_month):
     start_date = datetime(start_year, start_month, 1)
@@ -916,6 +1008,45 @@ def update_route_clusters(start_year, start_month, end_year, end_month, enso_val
     cluster_info_text = "\n".join(cluster_info_lines)
     return fig_tsne, fig_routes, fig_stats, cluster_info_text
 
+# Define the basin to prefix mapping
+basin_to_prefix = {
+    "All Basins": None,
+    "NA - North Atlantic": "AL",
+    "EP - Eastern North Pacific": "EP",
+    "WP - Western North Pacific": "WP",
+    "NI - North Indian": ["IO", "BB", "AS"],  # Multiple prefixes for North Indian
+    "SI - South Indian": "SI",
+    "SP - Southern Pacific": "SP",
+    "SA - South Atlantic": "SL"
+}
+
+# Update typhoon options function for animation tab
+def update_typhoon_options(year, basin):
+    try:
+        season = ibtracs.get_season(int(year))
+        storm_summary = season.summary()
+        
+        # Get the prefix for filtering
+        prefix = basin_to_prefix.get(basin)
+        
+        # Get all storms for the year
+        options = []
+        for i in range(len(storm_summary)):
+            try:
+                name = storm_summary['name'][i] if not pd.isna(storm_summary['name'][i]) else "Unnamed"
+                storm_id = storm_summary['id'][i]
+                
+                # Filter by basin if a specific basin is selected
+                if prefix is None or (isinstance(prefix, list) and any(storm_id.startswith(p) for p in prefix)) or (not isinstance(prefix, list) and storm_id.startswith(prefix)):
+                    options.append(f"{name} ({storm_id})")
+            except Exception:
+                continue
+        
+        return gr.update(choices=options, value=options[0] if options else None)
+    except Exception as e:
+        print(f"Error updating typhoon options: {e}")
+        return gr.update(choices=[], value=None)
+
 # Gradio Interface
 with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
     gr.Markdown("# Typhoon Analysis Dashboard")
@@ -949,7 +1080,6 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         tracks_plot = gr.Plot(label="Typhoon Tracks", elem_id="tracks_plot")
         typhoon_count = gr.Textbox(label="Number of Typhoons Displayed")
         
-        # Function definitions remain the same
         analyze_btn.click(
             fn=get_full_tracks,
             inputs=[start_year, start_month, end_year, end_month, enso_phase, typhoon_search],
@@ -968,7 +1098,6 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         wind_scatter = gr.Plot(label="Wind Speed vs ONI")
         wind_regression_results = gr.Textbox(label="Wind Regression Results")
         
-        # Function definitions remain the same
         wind_analyze_btn.click(
             fn=get_wind_analysis,
             inputs=[wind_start_year, wind_start_month, wind_end_year, wind_end_month, wind_enso_phase, wind_typhoon_search],
@@ -987,7 +1116,6 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         pressure_scatter = gr.Plot(label="Pressure vs ONI")
         pressure_regression_results = gr.Textbox(label="Pressure Regression Results")
         
-        # Function definitions remain the same
         pressure_analyze_btn.click(
             fn=get_pressure_analysis,
             inputs=[pressure_start_year, pressure_start_month, pressure_end_year, pressure_end_month, pressure_enso_phase, pressure_typhoon_search],
@@ -1007,7 +1135,6 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         slopes_text = gr.Textbox(label="Regression Slopes")
         lon_regression_results = gr.Textbox(label="Longitude Regression Results")
         
-        # Function definitions remain the same
         lon_analyze_btn.click(
             fn=get_longitude_analysis,
             inputs=[lon_start_year, lon_start_month, lon_end_year, lon_end_month, lon_enso_phase, lon_typhoon_search],
@@ -1058,8 +1185,6 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
         Different agencies use different wind speed averaging periods: USA (1-min), JTWC (1-min), JMA (10-min), IMD (3-min).
         """)
         
-        # Basin to prefix mapping and functions remain the same
-        
         year_dropdown.change(fn=update_typhoon_options, inputs=[year_dropdown, basin_dropdown], outputs=typhoon_dropdown)
         basin_dropdown.change(fn=update_typhoon_options, inputs=[year_dropdown, basin_dropdown], outputs=typhoon_dropdown)