Update app.py

app.py

@@ -354,159 +354,159 @@ class TyphoonAnalyzer:
         }
 
     def create_tracks_plot(self, data):
-       """Create typhoon tracks visualization"""
-       fig = go.Figure()
-       
-       fig.update_layout(
-           title={
-               'text': 'Typhoon Tracks',
-               'y':0.95,
-               'x':0.5,
-               'xanchor': 'center',
-               'yanchor': 'top'
-           },
-           showlegend=True,
-           legend=dict(
-               yanchor="top",
-               y=0.99,
-               xanchor="left",
-               x=0.01,
-               bgcolor='rgba(255, 255, 255, 0.8)'
-           ),
-           geo=dict(
-               projection_type='mercator',
-               showland=True,
-               showcoastlines=True,
-               landcolor='rgb(243, 243, 243)',
-               countrycolor='rgb(204, 204, 204)',
-               coastlinecolor='rgb(214, 214, 214)',
-               showocean=True,
-               oceancolor='rgb(230, 250, 255)',
-               showlakes=True,
-               lakecolor='rgb(230, 250, 255)',
-               lataxis=dict(range=[0, 50]),
-               lonaxis=dict(range=[100, 180]),
-               center=dict(lat=20, lon=140),
-               bgcolor='rgba(255, 255, 255, 0.5)'
-           ),
-           paper_bgcolor='rgba(255, 255, 255, 0.5)',
-           plot_bgcolor='rgba(255, 255, 255, 0.5)'
-       )
-
-       for category in COLOR_MAP.keys():
-           category_data = data[data['Category'] == category]
-           for _, storm in category_data.groupby('SID'):
-               track_data = self.typhoon_data[self.typhoon_data['SID'] == storm['SID'].iloc[0]]
-               track_data = track_data.sort_values('ISO_TIME')
-               
-               fig.add_trace(go.Scattergeo(
-                   lon=track_data['LON'],
-                   lat=track_data['LAT'],
-                   mode='lines',
-                   line=dict(
-                       width=2,
-                       color=COLOR_MAP[category]
-                   ),
-                   name=category,
-                   legendgroup=category,
-                   showlegend=True if storm.iloc[0]['SID'] == category_data.iloc[0]['SID'] else False,
-                   hovertemplate=(
-                       f"Name: {storm['NAME'].iloc[0]}<br>" +
-                       f"Category: {category}<br>" +
-                       f"Wind Speed: {storm['USA_WIND'].iloc[0]:.1f} kt<br>" +
-                       f"Pressure: {storm['WMO_PRES'].iloc[0]:.1f} hPa<br>" +
-                       f"Date: {track_data['ISO_TIME'].dt.strftime('%Y-%m-%d %H:%M').iloc[0]}<br>" +
-                       f"Lat: {track_data['LAT'].iloc[0]:.2f}°N<br>" +
-                       f"Lon: {track_data['LON'].iloc[0]:.2f}°E<br>" +
-                       "<extra></extra>"
-                   )
-               ))
-
-       return fig
-
-   def analyze_clusters(self, year, n_clusters):
-       """Analyze typhoon clusters for a specific year"""
-       year_data = self.typhoon_data[self.typhoon_data['SEASON'] == year]
-       if year_data.empty:
-           return go.Figure(), "No data available for selected year"
-
-       # Prepare data for clustering
-       routes = []
-       for _, storm in year_data.groupby('SID'):
-           if len(storm) > 1:
-               # Standardize route length
-               t = np.linspace(0, 1, len(storm))
-               t_new = np.linspace(0, 1, 100)
-               lon_interp = interp1d(t, storm['LON'], kind='linear')(t_new)
-               lat_interp = interp1d(t, storm['LAT'], kind='linear')(t_new)
-               routes.append(np.column_stack((lon_interp, lat_interp)))
-       
-       if len(routes) < n_clusters:
-           return go.Figure(), f"Not enough typhoons ({len(routes)}) for {n_clusters} clusters"
-
-       # Perform clustering
-       routes_array = np.array(routes)
-       routes_reshaped = routes_array.reshape(routes_array.shape[0], -1)
-       kmeans = KMeans(n_clusters=n_clusters, random_state=42)
-       clusters = kmeans.fit_predict(routes_reshaped)
-       
-       # Create visualization
-       fig = go.Figure()
-       
-       # Set layout
-       fig.update_layout(
-           title=f'Typhoon Route Clusters ({year})',
-           showlegend=True,
-           geo=dict(
-               projection_type='mercator',
-               showland=True,
-               showcoastlines=True,
-               landcolor='rgb(243, 243, 243)',
-               countrycolor='rgb(204, 204, 204)',
-               coastlinecolor='rgb(214, 214, 214)',
-               showocean=True,
-               oceancolor='rgb(230, 250, 255)',
-               lataxis=dict(range=[0, 50]),
-               lonaxis=dict(range=[100, 180]),
-               center=dict(lat=20, lon=140)
-           )
-       )
-       
-       # Plot routes colored by cluster
-       for route, cluster_id in zip(routes, clusters):
-           fig.add_trace(go.Scattergeo(
-               lon=route[:, 0],
-               lat=route[:, 1],
-               mode='lines',
-               line=dict(
-                   width=1, 
-                   color=f'hsl({cluster_id * 360/n_clusters}, 50%, 50%)'
-               ),
-               name=f'Cluster {cluster_id + 1}',
-               showlegend=False
-           ))
-       
-       # Plot cluster centers
-       for i in range(n_clusters):
-           center = kmeans.cluster_centers_[i].reshape(-1, 2)
-           fig.add_trace(go.Scattergeo(
-               lon=center[:, 0],
-               lat=center[:, 1],
-               mode='lines',
-               name=f'Cluster {i+1} Center',
-               line=dict(
-                   width=3,
-                   color=f'hsl({i * 360/n_clusters}, 100%, 50%)'
-               )
-           ))
-       
-       # Generate statistics text
-       stats_text = "### Clustering Results\n\n"
-       cluster_counts = np.bincount(clusters)
-       for i in range(n_clusters):
-           stats_text += f"- Cluster {i+1}: {cluster_counts[i]} typhoons\n"
-       
-       return fig, stats_text
+        """Create typhoon tracks visualization"""
+        fig = go.Figure()
+        
+        fig.update_layout(
+            title={
+                'text': 'Typhoon Tracks',
+                'y':0.95,
+                'x':0.5,
+                'xanchor': 'center',
+                'yanchor': 'top'
+            },
+            showlegend=True,
+            legend=dict(
+                yanchor="top",
+                y=0.99,
+                xanchor="left",
+                x=0.01,
+                bgcolor='rgba(255, 255, 255, 0.8)'
+            ),
+            geo=dict(
+                projection_type='mercator',
+                showland=True,
+                showcoastlines=True,
+                landcolor='rgb(243, 243, 243)',
+                countrycolor='rgb(204, 204, 204)',
+                coastlinecolor='rgb(214, 214, 214)',
+                showocean=True,
+                oceancolor='rgb(230, 250, 255)',
+                showlakes=True,
+                lakecolor='rgb(230, 250, 255)',
+                lataxis=dict(range=[0, 50]),
+                lonaxis=dict(range=[100, 180]),
+                center=dict(lat=20, lon=140),
+                bgcolor='rgba(255, 255, 255, 0.5)'
+            ),
+            paper_bgcolor='rgba(255, 255, 255, 0.5)',
+            plot_bgcolor='rgba(255, 255, 255, 0.5)'
+        )
+
+        for category in COLOR_MAP.keys():
+            category_data = data[data['Category'] == category]
+            for _, storm in category_data.groupby('SID'):
+                track_data = self.typhoon_data[self.typhoon_data['SID'] == storm['SID'].iloc[0]]
+                track_data = track_data.sort_values('ISO_TIME')
+                
+                fig.add_trace(go.Scattergeo(
+                    lon=track_data['LON'],
+                    lat=track_data['LAT'],
+                    mode='lines',
+                    line=dict(
+                        width=2,
+                        color=COLOR_MAP[category]
+                    ),
+                    name=category,
+                    legendgroup=category,
+                    showlegend=True if storm.iloc[0]['SID'] == category_data.iloc[0]['SID'] else False,
+                    hovertemplate=(
+                        f"Name: {storm['NAME'].iloc[0]}<br>" +
+                        f"Category: {category}<br>" +
+                        f"Wind Speed: {storm['USA_WIND'].iloc[0]:.1f} kt<br>" +
+                        f"Pressure: {storm['WMO_PRES'].iloc[0]:.1f} hPa<br>" +
+                        f"Date: {track_data['ISO_TIME'].dt.strftime('%Y-%m-%d %H:%M').iloc[0]}<br>" +
+                        f"Lat: {track_data['LAT'].iloc[0]:.2f}°N<br>" +
+                        f"Lon: {track_data['LON'].iloc[0]:.2f}°E<br>" +
+                        "<extra></extra>"
+                    )
+                ))
+
+        return fig
+
+    def analyze_clusters(self, year, n_clusters):
+        """Analyze typhoon clusters for a specific year"""
+        year_data = self.typhoon_data[self.typhoon_data['SEASON'] == year]
+        if year_data.empty:
+            return go.Figure(), "No data available for selected year"
+
+        # Prepare data for clustering
+        routes = []
+        for _, storm in year_data.groupby('SID'):
+            if len(storm) > 1:
+                # Standardize route length
+                t = np.linspace(0, 1, len(storm))
+                t_new = np.linspace(0, 1, 100)
+                lon_interp = interp1d(t, storm['LON'], kind='linear')(t_new)
+                lat_interp = interp1d(t, storm['LAT'], kind='linear')(t_new)
+                routes.append(np.column_stack((lon_interp, lat_interp)))
+        
+        if len(routes) < n_clusters:
+            return go.Figure(), f"Not enough typhoons ({len(routes)}) for {n_clusters} clusters"
+
+        # Perform clustering
+        routes_array = np.array(routes)
+        routes_reshaped = routes_array.reshape(routes_array.shape[0], -1)
+        kmeans = KMeans(n_clusters=n_clusters, random_state=42)
+        clusters = kmeans.fit_predict(routes_reshaped)
+        
+        # Create visualization
+        fig = go.Figure()
+        
+        # Set layout
+        fig.update_layout(
+            title=f'Typhoon Route Clusters ({year})',
+            showlegend=True,
+            geo=dict(
+                projection_type='mercator',
+                showland=True,
+                showcoastlines=True,
+                landcolor='rgb(243, 243, 243)',
+                countrycolor='rgb(204, 204, 204)',
+                coastlinecolor='rgb(214, 214, 214)',
+                showocean=True,
+                oceancolor='rgb(230, 250, 255)',
+                lataxis=dict(range=[0, 50]),
+                lonaxis=dict(range=[100, 180]),
+                center=dict(lat=20, lon=140)
+            )
+        )
+        
+        # Plot routes colored by cluster
+        for route, cluster_id in zip(routes, clusters):
+            fig.add_trace(go.Scattergeo(
+                lon=route[:, 0],
+                lat=route[:, 1],
+                mode='lines',
+                line=dict(
+                    width=1, 
+                    color=f'hsl({cluster_id * 360/n_clusters}, 50%, 50%)'
+                ),
+                name=f'Cluster {cluster_id + 1}',
+                showlegend=False
+            ))
+        
+        # Plot cluster centers
+        for i in range(n_clusters):
+            center = kmeans.cluster_centers_[i].reshape(-1, 2)
+            fig.add_trace(go.Scattergeo(
+                lon=center[:, 0],
+                lat=center[:, 1],
+                mode='lines',
+                name=f'Cluster {i+1} Center',
+                line=dict(
+                    width=3,
+                    color=f'hsl({i * 360/n_clusters}, 100%, 50%)'
+                )
+            ))
+        
+        # Generate statistics text
+        stats_text = "### Clustering Results\n\n"
+        cluster_counts = np.bincount(clusters)
+        for i in range(n_clusters):
+            stats_text += f"- Cluster {i+1}: {cluster_counts[i]} typhoons\n"
+        
+        return fig, stats_text
     def get_typhoons_for_year(self, year):
         """Get list of typhoons for a specific year"""
         year_data = self.typhoon_data[self.typhoon_data['ISO_TIME'].dt.year == year]