Update part2_visualization.py

part2_visualization.py

@@ -4,6 +4,7 @@ import folium
 from folium import plugins
 import numpy as np
 import branca.colormap as cm
+from datetime import datetime
 
 class VisualizationHandler:
     def __init__(self, optimal_conditions):
@@ -21,13 +22,16 @@ class VisualizationHandler:
 
     def create_interactive_plots(self, df):
         """Create enhanced interactive Plotly visualizations"""
+        if df is None or df.empty:
+            return go.Figure()  # Return empty figure if no data
+            
         fig = make_subplots(
             rows=4, cols=1,
             subplot_titles=(
                 '<b>Temperature Pattern (°C)</b>',
                 '<b>Humidity Pattern (%)</b>',
                 '<b>Rainfall Pattern (mm/day)</b>',
-                '<b>Vegetation & Suitability Patterns</b>'
+                '<b>Vegetation & Suitability Indices</b>'
             ),
             vertical_spacing=0.08,
             row_heights=[0.28, 0.24, 0.24, 0.24]
@@ -42,15 +46,15 @@ class VisualizationHandler:
         # Add rainfall visualization
         self.add_rainfall_plot(fig, df)
         
-        # Add combined NDVI and suitability visualization
-        self.add_combined_patterns_plot(fig, df)
+        # Add vegetation and suitability visualization
+        self.add_combined_indices_plot(fig, df)
 
         # Update layout
         fig.update_layout(
             height=1000,
             showlegend=True,
             title={
-                'text': "Agricultural Conditions Analysis with Pattern Recognition",
+                'text': "Agricultural Conditions Analysis",
                 'y':0.95,
                 'x':0.5,
                 'xanchor': 'center',
@@ -73,38 +77,40 @@ class VisualizationHandler:
         # Add season shading
         self.add_season_shading(fig, df)
         
+        # Update axes for all subplots
         fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
         fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
         
         return fig
 
     def add_temperature_plot(self, fig, df):
-        """Add temperature visualization with range and patterns"""
+        """Add temperature visualization with range"""
         # Temperature range area
-        fig.add_trace(
-            go.Scatter(
-                x=df['date'],
-                y=df['temp_max'],
-                name='Temperature Range',
-                line=dict(color='rgba(255,0,0,0.0)'),
-                showlegend=False
-            ),
-            row=1, col=1
-        )
-        
-        fig.add_trace(
-            go.Scatter(
-                x=df['date'],
-                y=df['temp_min'],
-                name='Daily Range',
-                fill='tonexty',
-                fillcolor='rgba(255,0,0,0.1)',
-                line=dict(color='rgba(255,0,0,0.0)')
-            ),
-            row=1, col=1
-        )
+        if 'temp_max' in df.columns and 'temp_min' in df.columns:
+            fig.add_trace(
+                go.Scatter(
+                    x=df['date'],
+                    y=df['temp_max'],
+                    name='Max Temperature',
+                    line=dict(color='rgba(255,0,0,0.0)'),
+                    showlegend=False
+                ),
+                row=1, col=1
+            )
+            
+            fig.add_trace(
+                go.Scatter(
+                    x=df['date'],
+                    y=df['temp_min'],
+                    name='Temperature Range',
+                    fill='tonexty',
+                    fillcolor='rgba(255,0,0,0.1)',
+                    line=dict(color='rgba(255,0,0,0.0)')
+                ),
+                row=1, col=1
+            )
 
-        # Add main temperature line
+        # Main temperature line
         fig.add_trace(
             go.Scatter(
                 x=df['date'],
@@ -116,30 +122,31 @@ class VisualizationHandler:
             row=1, col=1
         )
 
-        # Add 7-day average
-        fig.add_trace(
-            go.Scatter(
-                x=df['date'],
-                y=df['temp_7day_avg'],
-                name='7-Day Trend',
-                line=dict(color='darkred', width=1, dash='dot'),
-                mode='lines'
-            ),
-            row=1, col=1
-        )
+        # Add rolling average
+        if 'temp_7day_avg' in df.columns:
+            fig.add_trace(
+                go.Scatter(
+                    x=df['date'],
+                    y=df['temp_7day_avg'],
+                    name='7-Day Average',
+                    line=dict(color='darkred', width=1, dash='dot'),
+                    mode='lines'
+                ),
+                row=1, col=1
+            )
 
         # Add optimal range
-        fig.add_hline(y=self.optimal_conditions['temperature']['min'],
-                     line_dash="dash", line_color="green",
-                     annotation_text="Min Optimal",
-                     row=1, col=1)
-        fig.add_hline(y=self.optimal_conditions['temperature']['max'],
-                     line_dash="dash", line_color="green",
-                     annotation_text="Max Optimal",
-                     row=1, col=1)
+        for limit_type, value in self.optimal_conditions['temperature'].items():
+            fig.add_hline(
+                y=value,
+                line_dash="dash",
+                line_color="green",
+                annotation_text=f"Optimal {limit_type}",
+                row=1, col=1
+            )
 
     def add_humidity_plot(self, fig, df):
-        """Add humidity visualization with patterns"""
+        """Add humidity visualization"""
         fig.add_trace(
             go.Scatter(
                 x=df['date'],
@@ -150,31 +157,31 @@ class VisualizationHandler:
             ),
             row=2, col=1
         )
-        
-        fig.add_trace(
-            go.Scatter(
-                x=df['date'],
-                y=df['humidity_7day_avg'],
-                name='Humidity Trend',
-                line=dict(color='darkblue', width=1, dash='dot'),
-                mode='lines'
-            ),
-            row=2, col=1
-        )
+
+        if 'humidity_7day_avg' in df.columns:
+            fig.add_trace(
+                go.Scatter(
+                    x=df['date'],
+                    y=df['humidity_7day_avg'],
+                    name='7-Day Average',
+                    line=dict(color='darkblue', width=1, dash='dot'),
+                    mode='lines'
+                ),
+                row=2, col=1
+            )
 
         # Add optimal range
-        fig.add_hline(y=self.optimal_conditions['humidity']['min'],
-                     line_dash="dash", line_color="green",
-                     annotation_text="Min Optimal",
-                     row=2, col=1)
-        fig.add_hline(y=self.optimal_conditions['humidity']['max'],
-                     line_dash="dash", line_color="green",
-                     annotation_text="Max Optimal",
-                     row=2, col=1)
+        for limit_type, value in self.optimal_conditions['humidity'].items():
+            fig.add_hline(
+                y=value,
+                line_dash="dash",
+                line_color="green",
+                annotation_text=f"Optimal {limit_type}",
+                row=2, col=1
+            )
 
     def add_rainfall_plot(self, fig, df):
-        """Add rainfall visualization with patterns"""
-        # Daily rainfall bars
+        """Add rainfall visualization"""
         fig.add_trace(
             go.Bar(
                 x=df['date'],
@@ -186,71 +193,72 @@ class VisualizationHandler:
             row=3, col=1
         )
 
-        # Rainfall trend
-        fig.add_trace(
-            go.Scatter(
-                x=df['date'],
-                y=df['rainfall_7day_avg'],
-                name='Rainfall Trend',
-                line=dict(color='blue', width=2),
-                mode='lines'
-            ),
-            row=3, col=1
-        )
+        if 'rainfall_7day_avg' in df.columns:
+            fig.add_trace(
+                go.Scatter(
+                    x=df['date'],
+                    y=df['rainfall_7day_avg'],
+                    name='7-Day Average',
+                    line=dict(color='blue', width=2),
+                    mode='lines'
+                ),
+                row=3, col=1
+            )
 
-    def add_combined_patterns_plot(self, fig, df):
-        """Add combined NDVI and suitability visualization"""
-        # NDVI line
-        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
-        )
+    def add_combined_indices_plot(self, fig, df):
+        """Add vegetation and suitability indices visualization"""
+        if 'estimated_ndvi' in df.columns:
+            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
+            )
 
-        # Suitability score
-        fig.add_trace(
-            go.Scatter(
-                x=df['date'],
-                y=df['daily_suitability'],
-                name='Growing Suitability',
-                line=dict(color='purple', width=2),
-                mode='lines'
-            ),
-            row=4, col=1
-        )
+        if 'daily_suitability' in df.columns:
+            fig.add_trace(
+                go.Scatter(
+                    x=df['date'],
+                    y=df['daily_suitability'],
+                    name='Growing Suitability',
+                    line=dict(color='purple', width=2),
+                    mode='lines'
+                ),
+                row=4, col=1
+            )
 
     def add_season_shading(self, fig, df):
-        """Add season indicators to all plots"""
-        seasons = df['season'].unique()
-        season_colors = {
-            'Main': 'rgba(0,255,0,0.1)',    # Green
-            'Early': 'rgba(255,255,0,0.1)',  # Yellow
-            'Late': 'rgba(255,165,0,0.1)',   # Orange
-            'Dry': 'rgba(255,0,0,0.1)'       # Red
-        }
-        
-        for season in seasons:
-            season_data = df[df['season'] == season]
-            if not season_data.empty:
-                for row in range(1, 5):
-                    fig.add_vrect(
-                        x0=season_data['date'].iloc[0],
-                        x1=season_data['date'].iloc[-1],
-                        fillcolor=season_colors[season],
-                        layer="below",
-                        line_width=0,
-                        annotation_text=season if row == 1 else None,
-                        annotation_position="top left",
-                        row=row, col=1
-                    )
+        """Add season shading to all plots"""
+        if 'season' in df.columns:
+            seasons = df['season'].unique()
+            season_colors = {
+                'Main': 'rgba(0,255,0,0.1)',    # Green
+                'Early': 'rgba(255,255,0,0.1)',  # Yellow
+                'Late': 'rgba(255,165,0,0.1)',   # Orange
+                'Dry': 'rgba(255,0,0,0.1)'       # Red
+            }
+            
+            for season in seasons:
+                season_data = df[df['season'] == season]
+                if not season_data.empty:
+                    for row in range(1, 5):
+                        fig.add_vrect(
+                            x0=season_data['date'].iloc[0],
+                            x1=season_data['date'].iloc[-1],
+                            fillcolor=season_colors.get(season, 'rgba(128,128,128,0.1)'),
+                            layer="below",
+                            line_width=0,
+                            annotation_text=season if row == 1 else None,
+                            annotation_position="top left",
+                            row=row, col=1
+                        )
 
     def create_enhanced_map(self, lat, lon, score, ndvi_value):
-        """Create an interactive map with both weather and vegetation analysis"""
+        """Create an interactive map with analysis overlays"""
         m = folium.Map(location=[lat, lon], zoom_start=13)
         
         # Add measurement tools