Update part2_visualization.py

part2_visualization.py

@@ -4,10 +4,23 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import folium
 import numpy as np
+import ee
+import geemap
+from branca.colormap import LinearColormap
 
 class VisualizationHandler:
     def __init__(self, optimal_conditions):
         self.optimal_conditions = optimal_conditions
+        self.ndvi_colors = [
+            '#d73027',  # Very low vegetation
+            '#f46d43',  # Low vegetation
+            '#fdae61',  # Sparse vegetation
+            '#fee08b',  # Moderate vegetation
+            '#d9ef8b',  # Good vegetation
+            '#a6d96a',  # High vegetation
+            '#66bd63',  # Very high vegetation
+            '#1a9850'   # Dense vegetation
+        ]
 
     def create_interactive_plots(self, df):
         """Create enhanced interactive Plotly visualizations"""
@@ -150,57 +163,97 @@ class VisualizationHandler:
         
         return fig
 
-    def create_map(self, lat, lon, score):
-        """Create an interactive map with growing suitability overlay"""
-        m = folium.Map(location=[lat, lon], zoom_start=10)
-        
-        # Add location marker
-        folium.Marker(
-            [lat, lon],
-            popup='Analysis Location',
-            icon=folium.Icon(color='red', icon='info-sign')
-        ).add_to(m)
-        
-        # Color based on score
-        if score >= 0.8:
-            color = 'green'
-        elif score >= 0.6:
-            color = 'yellow'
-        elif score >= 0.4:
-            color = 'orange'
-        else:
-            color = 'red'
-        
-        # Add analysis zones
-        folium.Circle(
-            radius=5000,
-            location=[lat, lon],
-            popup=f'Growing Suitability Score: {score:.2f}',
-            color=color,
-            fill=True,
-            fillOpacity=0.4
-        ).add_to(m)
-        
-        # Add smaller analysis zones
-        for radius in [2000, 3500]:
+    def create_enhanced_map(self, lat, lon, weather_score, ndvi_data=None):
+        """Create an interactive map with both weather and NDVI data"""
+        try:
+            # Create base map
+            m = folium.Map(location=[lat, lon], zoom_start=15)
+            
+            # If NDVI data is available, add it to the map
+            if ndvi_data and 'image' in ndvi_data:
+                try:
+                    # Get NDVI visualization parameters
+                    vis_params = {
+                        'min': -0.2,
+                        'max': 0.8,
+                        'palette': self.ndvi_colors
+                    }
+                    
+                    # Add NDVI layer
+                    map_id_dict = ndvi_data['image'].getMapId(vis_params)
+                    folium.TileLayer(
+                        tiles=map_id_dict['tile_fetcher'].url_format,
+                        attr='NDVI Data',
+                        overlay=True,
+                        name='NDVI Layer'
+                    ).add_to(m)
+
+                    # Add NDVI legend
+                    colormap = LinearColormap(
+                        colors=self.ndvi_colors,
+                        vmin=-0.2,
+                        vmax=0.8,
+                        caption='NDVI Values'
+                    )
+                    colormap.add_to(m)
+
+                except Exception as e:
+                    print(f"Error adding NDVI layer: {e}")
+
+            # Calculate combined score if NDVI data is available
+            if ndvi_data and 'stats' in ndvi_data:
+                ndvi_score = (ndvi_data['stats'].get('NDVI_mean', 0) + 1) / 2
+                combined_score = (weather_score * 0.6 + ndvi_score * 0.4)
+            else:
+                combined_score = weather_score
+
+            # Color based on combined score
+            if combined_score >= 0.8:
+                color = 'green'
+            elif combined_score >= 0.6:
+                color = 'yellow'
+            elif combined_score >= 0.4:
+                color = 'orange'
+            else:
+                color = 'red'
+
+            # Add analysis circles
             folium.Circle(
-                radius=radius,
+                radius=2000,
                 location=[lat, lon],
+                popup=f'Combined Score: {combined_score:.2f}<br>Weather Score: {weather_score:.2f}',
                 color=color,
-                fill=False,
-                weight=1
+                fill=True,
+                fillOpacity=0.4
             ).add_to(m)
-        
-        return m._repr_html_()
 
-    def create_gauge_chart(self, score):
+            # Add smaller analysis zones
+            for radius in [1000, 1500]:
+                folium.Circle(
+                    radius=radius,
+                    location=[lat, lon],
+                    color=color,
+                    fill=False,
+                    weight=1
+                ).add_to(m)
+
+            # Add layer control
+            folium.LayerControl().add_to(m)
+
+            return m._repr_html_()
+
+        except Exception as e:
+            print(f"Error creating enhanced map: {e}")
+            return None
+
+    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={
@@ -235,4 +288,35 @@ class VisualizationHandler:
             font={'color': "darkblue", 'family': "Arial"}
         )
         
-        return fig
+        return fig
+
+    def create_ndvi_report(self, ndvi_data):
+        """Create a detailed NDVI analysis report"""
+        if not ndvi_data or 'stats' not in ndvi_data:
+            return None
+
+        stats = ndvi_data['stats']
+        mean_ndvi = stats.get('NDVI_mean', 0)
+        
+        # Create analysis text
+        if mean_ndvi < 0:
+            vegetation_status = "Very low vegetation - likely bare soil or water"
+        elif mean_ndvi < 0.2:
+            vegetation_status = "Low vegetation - sparse cover or stressed vegetation"
+        elif mean_ndvi < 0.4:
+            vegetation_status = "Moderate vegetation - typical for agricultural areas"
+        elif mean_ndvi < 0.6:
+            vegetation_status = "High vegetation - healthy crops or natural vegetation"
+        else:
+            vegetation_status = "Very high vegetation - dense, healthy vegetation"
+
+        report = {
+            'mean_ndvi': mean_ndvi,
+            'std_dev': stats.get('NDVI_stdDev', 0),
+            'min_ndvi': stats.get('NDVI_min', 0),
+            'max_ndvi': stats.get('NDVI_max', 0),
+            'vegetation_status': vegetation_status,
+            'optimal_range': self.optimal_conditions['ndvi']
+        }
+        
+        return report