Create part2_visualization.py

part2_visualization.py

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+# part2_visualization.py
+
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import folium
+import numpy as np
+
+class VisualizationHandler:
+    def __init__(self, optimal_conditions):
+        self.optimal_conditions = optimal_conditions
+
+    def create_interactive_plots(self, df):
+        """Create enhanced interactive Plotly visualizations"""
+        fig = make_subplots(
+            rows=3, cols=1,
+            subplot_titles=(
+                '<b>Temperature (°C)</b>',
+                '<b>Humidity (%)</b>',
+                '<b>Rainfall (mm/day)</b>'
+            ),
+            vertical_spacing=0.12,
+            row_heights=[0.33, 0.33, 0.33]
+        )
+        
+        # Temperature plot with forecast types
+        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
+        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'
+            ),
+            row=1, col=1
+        )
+
+        # Humidity plot with forecast types
+        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
+        fig.add_trace(
+            go.Scatter(
+                x=df['date'],
+                y=df['humidity_7day_avg'],
+                name='7-day Humidity Average',
+                line=dict(color='purple', width=1, dash='dot'),
+                mode='lines'
+            ),
+            row=2, col=1
+        )
+
+        # Rainfall plot with forecast types
+        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
+                )
+
+        # Add optimal ranges
+        for row, (param, limits) in enumerate([
+            ('temperature', self.optimal_conditions['temperature']),
+            ('humidity', self.optimal_conditions['humidity']),
+            ('rainfall', self.optimal_conditions['rainfall'])
+        ], 1):
+            fig.add_hline(y=limits['min'], line_dash="dash", line_color="green",
+                         annotation_text="Min Optimal", row=row, col=1)
+            fig.add_hline(y=limits['max'], line_dash="dash", line_color="green",
+                         annotation_text="Max Optimal", row=row, col=1)
+
+        # Add seasonal indicators
+        seasons = df['season'].unique()
+        season_colors = {
+            'Spring': 'rgba(0,255,0,0.1)',
+            'Summer': 'rgba(255,255,0,0.1)',
+            'Fall': 'rgba(255,165,0,0.1)',
+            'Winter': 'rgba(0,0,255,0.1)'
+        }
+        
+        for season in seasons:
+            season_data = df[df['season'] == season]
+            if not season_data.empty:
+                fig.add_vrect(
+                    x0=season_data['date'].iloc[0],
+                    x1=season_data['date'].iloc[-1],
+                    fillcolor=season_colors[season],
+                    layer="below",
+                    line_width=0,
+                    row="all"
+                )
+
+        # Update layout
+        fig.update_layout(
+            height=800,
+            showlegend=True,
+            title={
+                'text': "Enhanced Tobacco Growing Conditions Analysis",
+                'y':0.95,
+                'x':0.5,
+                'xanchor': 'center',
+                'yanchor': 'top',
+                'font': dict(size=20)
+            },
+            paper_bgcolor='white',
+            plot_bgcolor='rgba(0,0,0,0.05)',
+            font=dict(size=12),
+            legend=dict(
+                orientation="h",
+                yanchor="bottom",
+                y=1.02,
+                xanchor="right",
+                x=1
+            ),
+            margin=dict(l=60, r=30, t=100, b=60)
+        )
+
+        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 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]:
+            folium.Circle(
+                radius=radius,
+                location=[lat, lon],
+                color=color,
+                fill=False,
+                weight=1
+            ).add_to(m)
+        
+        return m._repr_html_()
+
+    def create_gauge_chart(self, 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",
+                'font': {'size': 24}
+            },
+            delta={
+                'reference': 0.8,
+                'increasing': {'color': "green"},
+                'decreasing': {'color': "red"}
+            },
+            gauge={
+                'axis': {'range': [None, 1], 'tickwidth': 1, 'tickcolor': "darkblue"},
+                'bar': {'color': "darkblue"},
+                'bgcolor': "white",
+                'borderwidth': 2,
+                'bordercolor': "gray",
+                'steps': [
+                    {'range': [0, 0.4], 'color': 'rgba(255, 0, 0, 0.6)'},
+                    {'range': [0.4, 0.6], 'color': 'rgba(255, 255, 0, 0.6)'},
+                    {'range': [0.6, 0.8], 'color': 'rgba(144, 238, 144, 0.6)'},
+                    {'range': [0.8, 1], 'color': 'rgba(0, 128, 0, 0.6)'}
+                ],
+                'threshold': {
+                    'line': {'color': "red", 'width': 4},
+                    'thickness': 0.75,
+                    'value': 0.8
+                }
+            }
+        ))
+        
+        fig.update_layout(
+            height=300,
+            margin=dict(l=20, r=20, t=60, b=20),
+            paper_bgcolor="white",
+            font={'color': "darkblue", 'family': "Arial"}
+        )
+        
+        return fig