Update part2_visualization.py

part2_visualization.py

@@ -1,5 +1,3 @@
-# part2_visualization.py
-
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import folium
@@ -24,26 +22,36 @@ class VisualizationHandler:
     def create_interactive_plots(self, df):
         """Create enhanced interactive Plotly visualizations"""
         fig = make_subplots(
-            rows=4, cols=1,  # Added one more row for NDVI
+            rows=5, cols=1,  # Added rows for NDVI and Daily Suitability
             subplot_titles=(
                 '<b>Temperature (°C)</b>',
                 '<b>Humidity (%)</b>',
                 '<b>Rainfall (mm/day)</b>',
-                '<b>Vegetation Index (NDVI)</b>'
+                '<b>Vegetation Index (NDVI)</b>',
+                '<b>Daily Growing Suitability</b>'
             ),
-            vertical_spacing=0.08,
-            row_heights=[0.25, 0.25, 0.25, 0.25]
+            vertical_spacing=0.07,
+            row_heights=[0.2, 0.2, 0.2, 0.2, 0.2]
         )
         
-        # Add standard weather plots
-        self.add_weather_plots(fig, df)
+        # Temperature plot
+        self.add_temperature_plot(fig, df)
         
-        # Add NDVI plot
+        # Humidity plot
+        self.add_humidity_plot(fig, df)
+        
+        # Rainfall plot
+        self.add_rainfall_plot(fig, df)
+        
+        # NDVI plot
         self.add_ndvi_plot(fig, df)
         
+        # Daily Suitability plot
+        self.add_suitability_plot(fig, df)
+        
         # Update layout
         fig.update_layout(
-            height=1000,  # Increased height for additional plot
+            height=1200,
             showlegend=True,
             title={
                 'text': "Enhanced Tobacco Growing Conditions Analysis",
@@ -71,55 +79,58 @@ class VisualizationHandler:
         
         return fig
 
-    def add_weather_plots(self, fig, df):
-        """Add weather-related plots"""
-        # Temperature plot
-        for data_type, color in [('historical', 'royalblue'), 
-                               ('forecast_5day', 'firebrick'),
-                               ('forecast_extended', 'rgba(255, 165, 0, 0.5)')]:
-            mask = df['type'] == data_type
-            if any(mask):
-                fig.add_trace(
-                    go.Scatter(
-                        x=df[mask]['date'],
-                        y=df[mask]['temperature'],
-                        name=f'{data_type.replace("_", " ").title()} Temperature',
-                        line=dict(color=color, width=2),
-                        mode='lines'
-                    ),
-                    row=1, col=1
-                )
-
-        # Add temperature rolling average
+    def add_temperature_plot(self, fig, df):
+        """Add temperature visualization"""
+        # Add detailed temperature data
         fig.add_trace(
             go.Scatter(
                 x=df['date'],
-                y=df['temp_7day_avg'],
-                name='7-day Temperature Average',
-                line=dict(color='purple', width=1, dash='dot'),
-                mode='lines'
+                y=df['temp_max'],
+                name='Max Temperature',
+                line=dict(color='red', width=1),
+                mode='lines',
+                fillcolor='rgba(255,0,0,0.1)',
+                fill='tonexty'
             ),
             row=1, col=1
         )
+        
+        fig.add_trace(
+            go.Scatter(
+                x=df['date'],
+                y=df['temp_min'],
+                name='Min Temperature',
+                line=dict(color='blue', width=1),
+                mode='lines',
+                fillcolor='rgba(0,0,255,0.1)',
+                fill='tonexty'
+            ),
+            row=1, col=1
+        )
+        
+        # Add optimal range
+        for limit in ['min', 'max']:
+            fig.add_hline(
+                y=self.optimal_conditions['temperature'][limit],
+                line_dash="dash",
+                line_color="green",
+                annotation_text=f"Optimal {limit}",
+                row=1, col=1
+            )
 
-        # Humidity plot
-        for data_type, color in [('historical', 'royalblue'), 
-                               ('forecast_5day', 'firebrick'),
-                               ('forecast_extended', 'rgba(255, 165, 0, 0.5)')]:
-            mask = df['type'] == data_type
-            if any(mask):
-                fig.add_trace(
-                    go.Scatter(
-                        x=df[mask]['date'],
-                        y=df[mask]['humidity'],
-                        name=f'{data_type.replace("_", " ").title()} Humidity',
-                        line=dict(color=color, width=2),
-                        mode='lines'
-                    ),
-                    row=2, col=1
-                )
-
-        # Add humidity rolling average
+    def add_humidity_plot(self, fig, df):
+        """Add humidity visualization"""
+        fig.add_trace(
+            go.Scatter(
+                x=df['date'],
+                y=df['humidity'],
+                name='Humidity',
+                line=dict(color='blue', width=2),
+                mode='lines'
+            ),
+            row=2, col=1
+        )
+        
         fig.add_trace(
             go.Scatter(
                 x=df['date'],
@@ -131,67 +142,75 @@ class VisualizationHandler:
             row=2, col=1
         )
 
-        # Rainfall plot
-        for data_type, color in [('historical', 'royalblue'), 
-                               ('forecast_5day', 'firebrick'),
-                               ('forecast_extended', 'rgba(255, 165, 0, 0.5)')]:
-            mask = df['type'] == data_type
-            if any(mask):
-                fig.add_trace(
-                    go.Bar(
-                        x=df[mask]['date'],
-                        y=df[mask]['rainfall'],
-                        name=f'{data_type.replace("_", " ").title()} Rainfall',
-                        marker_color=color
-                    ),
-                    row=3, col=1
-                )
+    def add_rainfall_plot(self, fig, df):
+        """Add rainfall visualization"""
+        fig.add_trace(
+            go.Bar(
+                x=df['date'],
+                y=df['rainfall'],
+                name='Rainfall',
+                marker_color='blue'
+            ),
+            row=3, col=1
+        )
+        
+        fig.add_trace(
+            go.Scatter(
+                x=df['date'],
+                y=df['rainfall_7day_avg'],
+                name='7-day Rainfall Average',
+                line=dict(color='purple', width=1, dash='dot'),
+                mode='lines'
+            ),
+            row=3, col=1
+        )
 
     def add_ndvi_plot(self, fig, df):
-        """Add NDVI plot"""
-        # Historical NDVI
-        mask = df['type'] == 'historical'
-        if any(mask):
-            fig.add_trace(
-                go.Scatter(
-                    x=df[mask]['date'],
-                    y=df[mask]['estimated_ndvi'],
-                    name='Historical NDVI',
-                    line=dict(color='green', width=2),
-                    mode='lines'
-                ),
-                row=4, col=1
-            )
-
-        # Forecast NDVI
-        mask = df['type'].isin(['forecast_5day', 'forecast_extended'])
-        if any(mask):
-            fig.add_trace(
-                go.Scatter(
-                    x=df[mask]['date'],
-                    y=df[mask]['estimated_ndvi'],
-                    name='Forecast NDVI',
-                    line=dict(color='orange', width=2, dash='dot'),
-                    mode='lines'
-                ),
+        """Add NDVI visualization"""
+        fig.add_trace(
+            go.Scatter(
+                x=df['date'],
+                y=df['estimated_ndvi'],
+                name='Vegetation Index',
+                line=dict(color='green', width=2),
+                mode='lines'
+            ),
+            row=4, col=1
+        )
+        
+        # Add optimal NDVI range
+        for limit in ['min', 'max']:
+            fig.add_hline(
+                y=self.optimal_conditions['ndvi'][limit],
+                line_dash="dash",
+                line_color="green",
+                annotation_text=f"Optimal {limit}",
                 row=4, col=1
             )
 
-        # Add optimal NDVI range
-        fig.add_hline(y=self.optimal_conditions['ndvi']['min'], 
-                     line_dash="dash", line_color="green",
-                     annotation_text="Min Optimal NDVI", 
-                     row=4, col=1)
-        fig.add_hline(y=self.optimal_conditions['ndvi']['max'], 
-                     line_dash="dash", line_color="green",
-                     annotation_text="Max Optimal NDVI", 
-                     row=4, col=1)
+    def add_suitability_plot(self, fig, df):
+        """Add daily suitability visualization"""
+        fig.add_trace(
+            go.Scatter(
+                x=df['date'],
+                y=df['daily_suitability'],
+                name='Growing Suitability',
+                line=dict(color='darkblue', width=2),
+                mode='lines',
+                fill='tozeroy'
+            ),
+            row=5, col=1
+        )
 
     def create_enhanced_map(self, lat, lon, score, ndvi_value):
         """Create an interactive map with both weather and vegetation analysis"""
         m = folium.Map(location=[lat, lon], zoom_start=13)
         
-        # Add base marker
+        # Add measurement tools
+        plugins.MeasureControl(position='topright').add_to(m)
+        plugins.Fullscreen().add_to(m)
+        
+        # Add location marker
         folium.Marker(
             [lat, lon],
             popup='Analysis Location',
@@ -206,43 +225,34 @@ class VisualizationHandler:
             caption='NDVI Values'
         )
         
-        # Add NDVI-based circle
-        ndvi_color = ndvi_colormap(ndvi_value)
+        # Add NDVI circle
         folium.Circle(
             radius=2000,
             location=[lat, lon],
             popup=f'NDVI: {ndvi_value:.2f}',
-            color=ndvi_color,
+            color=ndvi_colormap(ndvi_value),
             fill=True,
             fillOpacity=0.4
         ).add_to(m)
         
-        # Add score-based circles
+        # Add growing suitability circles
         score_color = self.get_score_color(score)
         for radius in [500, 1000, 1500]:
             folium.Circle(
                 radius=radius,
                 location=[lat, lon],
+                popup=f'Suitability Score: {score:.2f}',
                 color=score_color,
-                popup=f'Growing Score: {score:.2f}',
                 fill=False,
                 weight=2
             ).add_to(m)
         
-        # Add measurement tools
-        plugins.MeasureControl(position='topright').add_to(m)
-        
-        # Add fullscreen option
-        plugins.Fullscreen().add_to(m)
-        
         # Add mini map
         minimap = plugins.MiniMap()
         m.add_child(minimap)
         
-        # Add layer control
+        # Add layer control and colormap
         folium.LayerControl().add_to(m)
-        
-        # Add colormap to map
         m.add_child(ndvi_colormap)
         
         return m._repr_html_()
@@ -257,14 +267,14 @@ class VisualizationHandler:
             return 'orange'
         return 'red'
 
-    def create_gauge_chart(self, score):
+    def create_gauge_chart(self, score, title="Growing Conditions Score"):
         """Create an enhanced gauge chart for the overall score"""
         fig = go.Figure(go.Indicator(
             mode="gauge+number+delta",
             value=score,
             domain={'x': [0, 1], 'y': [0, 1]},
             title={
-                'text': "Growing Conditions Score",
+                'text': title,
                 'font': {'size': 24}
             },
             delta={