Create visualize_data.py

visualize_data.py

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+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.colors as mcolors
+import os
+
+# Set the style for better visualization
+plt.style.use('seaborn-v0_8-darkgrid')
+
+# Read the data
+df = pd.read_csv('محاسبات 2.csv')
+
+# Function to clean and prepare the data
+def prepare_data(df):
+    # Split the data into two dataframes (area and production)
+    area_df = df.iloc[:33].copy()
+    production_df = df.iloc[34:].copy()
+    
+    # Clean the data
+    area_df = area_df.dropna(subset=['اداره'])
+    production_df = production_df.dropna(subset=['تولید'])
+    
+    # Rename columns for consistency
+    production_df = production_df.rename(columns={'تولید': 'اداره'})
+    
+    # Melt the dataframes to get long format
+    area_melted = pd.melt(area_df, 
+                          id_vars=['اداره', 'سن'], 
+                          value_vars=['CP69', 'CP73', 'CP48', 'CP57', 'CP65', 'CP70', 'IR01-412', 'IRC99-07', 'IRC00-14'],
+                          var_name='واریته', 
+                          value_name='مساحت')
+    
+    production_melted = pd.melt(production_df, 
+                               id_vars=['اداره', 'سن'], 
+                               value_vars=['CP69', 'CP73', 'CP48', 'CP57', 'CP65', 'CP70', 'IR01-412', 'IRC99-07', 'IRC00-14'],
+                               var_name='واریته', 
+                               value_name='تولید')
+    
+    # Merge the dataframes
+    merged_df = pd.merge(area_melted, production_melted, on=['اداره', 'سن', 'واریته'])
+    
+    # Convert numeric columns to float
+    merged_df['مساحت'] = pd.to_numeric(merged_df['مساحت'], errors='coerce')
+    merged_df['تولید'] = pd.to_numeric(merged_df['تولید'], errors='coerce')
+    
+    # Fill NaN values with 0
+    merged_df = merged_df.fillna(0)
+    
+    return merged_df
+
+# Prepare the data
+data = prepare_data(df)
+
+# Create output directory if it doesn't exist
+os.makedirs('visualizations', exist_ok=True)
+
+# 1. 3D Surface Histogram for Area by Department, Age, and Variety
+def create_3d_surface_area():
+    # Create a pivot table for the 3D surface
+    pivot_data = data.pivot_table(
+        values='مساحت', 
+        index='سن', 
+        columns='واریته', 
+        aggfunc='sum'
+    ).fillna(0)
+    
+    # Create the 3D surface plot
+    fig = go.Figure(data=[go.Surface(
+        x=pivot_data.columns,
+        y=pivot_data.index,
+        z=pivot_data.values,
+        colorscale='Viridis',
+        showscale=True,
+        colorbar=dict(title='مساحت (هکتار)')
+    )])
+    
+    # Update layout
+    fig.update_layout(
+        title='توزیع مساحت به تفکیک سن و واریته',
+        scene=dict(
+            xaxis_title='واریته',
+            yaxis_title='سن',
+            zaxis_title='مساحت (هکتار)',
+            camera=dict(
+                eye=dict(x=1.5, y=1.5, z=1.5)
+            )
+        ),
+        width=1000,
+        height=800,
+        font=dict(family="Vazirmatn, Arial", size=14)
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/3d_surface_area.html')
+    fig.write_image('visualizations/3d_surface_area.png', scale=2)
+    
+    return fig
+
+# 2. 3D Surface Histogram for Production by Department, Age, and Variety
+def create_3d_surface_production():
+    # Create a pivot table for the 3D surface
+    pivot_data = data.pivot_table(
+        values='تولید', 
+        index='سن', 
+        columns='واریته', 
+        aggfunc='sum'
+    ).fillna(0)
+    
+    # Create the 3D surface plot
+    fig = go.Figure(data=[go.Surface(
+        x=pivot_data.columns,
+        y=pivot_data.index,
+        z=pivot_data.values,
+        colorscale='Plasma',
+        showscale=True,
+        colorbar=dict(title='تولید (تن)')
+    )])
+    
+    # Update layout
+    fig.update_layout(
+        title='توزیع تولید به تفکیک سن و واریته',
+        scene=dict(
+            xaxis_title='واریته',
+            yaxis_title='سن',
+            zaxis_title='تولید (تن)',
+            camera=dict(
+                eye=dict(x=1.5, y=1.5, z=1.5)
+            )
+        ),
+        width=1000,
+        height=800,
+        font=dict(family="Vazirmatn, Arial", size=14)
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/3d_surface_production.html')
+    fig.write_image('visualizations/3d_surface_production.png', scale=2)
+    
+    return fig
+
+# 3. Area Chart by Department and Age
+def create_area_chart_by_dept_age():
+    # Group by department and age
+    dept_age_data = data.groupby(['اداره', 'سن'])['مساحت'].sum().reset_index()
+    
+    # Create the area chart
+    fig = px.area(
+        dept_age_data, 
+        x='سن', 
+        y='مساحت', 
+        color='اداره',
+        title='مس��حت به تفکیک اداره و سن',
+        labels={'مساحت': 'مساحت (هکتار)', 'سن': 'سن', 'اداره': 'اداره'},
+        color_discrete_sequence=px.colors.qualitative.Set3
+    )
+    
+    # Update layout
+    fig.update_layout(
+        width=1000,
+        height=600,
+        font=dict(family="Vazirmatn, Arial", size=14),
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        )
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/area_chart_by_dept_age.html')
+    fig.write_image('visualizations/area_chart_by_dept_age.png', scale=2)
+    
+    return fig
+
+# 4. Area Chart by Department and Variety
+def create_area_chart_by_dept_variety():
+    # Group by department and variety
+    dept_variety_data = data.groupby(['اداره', 'واریته'])['مساحت'].sum().reset_index()
+    
+    # Create the area chart
+    fig = px.area(
+        dept_variety_data, 
+        x='واریته', 
+        y='مساحت', 
+        color='اداره',
+        title='مساحت به تفکیک اداره و واریته',
+        labels={'مساحت': 'مساحت (هکتار)', 'واریته': 'واریته', 'اداره': 'اداره'},
+        color_discrete_sequence=px.colors.qualitative.Set3
+    )
+    
+    # Update layout
+    fig.update_layout(
+        width=1000,
+        height=600,
+        font=dict(family="Vazirmatn, Arial", size=14),
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        )
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/area_chart_by_dept_variety.html')
+    fig.write_image('visualizations/area_chart_by_dept_variety.png', scale=2)
+    
+    return fig
+
+# 5. 3D Bar Chart for Area by Department, Age, and Variety
+def create_3d_bar_chart():
+    # Create a figure
+    fig = plt.figure(figsize=(15, 10))
+    ax = fig.add_subplot(111, projection='3d')
+    
+    # Get unique values
+    departments = data['اداره'].unique()
+    ages = data['سن'].unique()
+    varieties = data['واریته'].unique()
+    
+    # Create a color map
+    colors = plt.cm.viridis(np.linspace(0, 1, len(varieties)))
+    
+    # Create the 3D bar chart
+    for i, dept in enumerate(departments):
+        for j, age in enumerate(ages):
+            for k, variety in enumerate(varieties):
+                # Get the value
+                value = data[(data['اداره'] == dept) & 
+                            (data['سن'] == age) & 
+                            (data['واریته'] == variety)]['مساحت'].values
+                
+                if len(value) > 0 and value[0] > 0:
+                    # Create the bar
+                    ax.bar3d(i, j, k, 0.8, 0.8, value[0], color=colors[k], alpha=0.8)
+    
+    # Set labels
+    ax.set_xlabel('اداره')
+    ax.set_ylabel('سن')
+    ax.set_zlabel('مساحت (هکتار)')
+    
+    # Set ticks
+    ax.set_xticks(np.arange(len(departments)) + 0.4)
+    ax.set_xticklabels(departments)
+    
+    ax.set_yticks(np.arange(len(ages)) + 0.4)
+    ax.set_yticklabels(ages)
+    
+    # Set title
+    plt.title('توزیع مساحت به تفکیک اداره، سن و واریته', fontsize=16)
+    
+    # Add a colorbar
+    sm = plt.cm.ScalarMappable(cmap=plt.cm.viridis, norm=plt.Normalize(vmin=0, vmax=len(varieties)-1))
+    sm.set_array([])
+    cbar = plt.colorbar(sm, ax=ax, pad=0.1)
+    cbar.set_label('واریته')
+    cbar.set_ticks(np.arange(len(varieties)))
+    cbar.set_ticklabels(varieties)
+    
+    # Save the figure
+    plt.tight_layout()
+    plt.savefig('visualizations/3d_bar_chart.png', dpi=300, bbox_inches='tight')
+    
+    return fig
+
+# 6. Heatmap for Area by Department and Age
+def create_heatmap_area():
+    # Create a pivot table for the heatmap
+    pivot_data = data.pivot_table(
+        values='مساحت', 
+        index='اداره', 
+        columns='سن', 
+        aggfunc='sum'
+    ).fillna(0)
+    
+    # Create the heatmap
+    fig = go.Figure(data=go.Heatmap(
+        z=pivot_data.values,
+        x=pivot_data.columns,
+        y=pivot_data.index,
+        colorscale='Viridis',
+        colorbar=dict(title='مساحت (هکتار)')
+    ))
+    
+    # Update layout
+    fig.update_layout(
+        title='مساحت به تفکیک اداره و سن',
+        xaxis_title='سن',
+        yaxis_title='اداره',
+        width=900,
+        height=600,
+        font=dict(family="Vazirmatn, Arial", size=14)
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/heatmap_area.html')
+    fig.write_image('visualizations/heatmap_area.png', scale=2)
+    
+    return fig
+
+# 7. Heatmap for Production by Department and Age
+def create_heatmap_production():
+    # Create a pivot table for the heatmap
+    pivot_data = data.pivot_table(
+        values='تولید', 
+        index='اداره', 
+        columns='سن', 
+        aggfunc='sum'
+    ).fillna(0)
+    
+    # Create the heatmap
+    fig = go.Figure(data=go.Heatmap(
+        z=pivot_data.values,
+        x=pivot_data.columns,
+        y=pivot_data.index,
+        colorscale='Plasma',
+        colorbar=dict(title='تولید (تن)')
+    ))
+    
+    # Update layout
+    fig.update_layout(
+        title='تولید به تفکیک اداره و سن',
+        xaxis_title='سن',
+        yaxis_title='اداره',
+        width=900,
+        height=600,
+        font=dict(family="Vazirmatn, Arial", size=14)
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/heatmap_production.html')
+    fig.write_image('visualizations/heatmap_production.png', scale=2)
+    
+    return fig
+
+# 8. Stacked Bar Chart for Area by Department and Variety
+def create_stacked_bar_chart():
+    # Create a pivot table for the stacked bar chart
+    pivot_data = data.pivot_table(
+        values='مساحت', 
+        index='اداره', 
+        columns='واریته', 
+        aggfunc='sum'
+    ).fillna(0)
+    
+    # Create the stacked bar chart
+    fig = go.Figure()
+    
+    for variety in pivot_data.columns:
+        fig.add_trace(go.Bar(
+            name=variety,
+            x=pivot_data.index,
+            y=pivot_data[variety],
+            text=pivot_data[variety].round(1),
+            textposition='auto',
+        ))
+    
+    # Update layout
+    fig.update_layout(
+        title='مساحت به تفکیک اداره و واریته',
+        xaxis_title='اداره',
+        yaxis_title='مساحت (هکتار)',
+        barmode='stack',
+        width=1000,
+        height=600,
+        font=dict(family="Vazirmatn, Arial", size=14),
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        )
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/stacked_bar_chart.html')
+    fig.write_image('visualizations/stacked_bar_chart.png', scale=2)
+    
+    return fig
+
+# 9. 3D Scatter Plot for Area, Production, and Age
+def create_3d_scatter_plot():
+    # Create the 3D scatter plot
+    fig = go.Figure(data=[go.Scatter3d(
+        x=data['مساحت'],
+        y=data['تولید'],
+        z=data['سن'].map(lambda x: {'P': 0, 'R1': 1, 'R2': 2, 'R3': 3, 'R4': 4, 'R5': 5, 'R6': 6, 'R7': 7, 'R8': 8, 'R9': 9}.get(x, 0)),
+        mode='markers',
+        marker=dict(
+            size=8,
+            color=data['اداره'].map({'1': 0, '2': 1, '3': 2, '4': 3, 'دهخدا': 4}),
+            colorscale='Viridis',
+            opacity=0.8
+        ),
+        text=data['واریته'],
+        hovertemplate="<b>واریته:</b> %{text}<br>" +
+                      "<b>مساحت:</b> %{x:.2f} هکتار<br>" +
+                      "<b>تولید:</b> %{y:.2f} تن<br>" +
+                      "<b>سن:</b> %{z}<br>" +
+                      "<extra></extra>"
+    )])
+    
+    # Update layout
+    fig.update_layout(
+        title='رابطه بین مساحت، تولید و سن',
+        scene=dict(
+            xaxis_title='مساحت (هکتار)',
+            yaxis_title='تولید (تن)',
+            zaxis_title='سن',
+            zaxis=dict(
+                ticktext=['P', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9'],
+                tickvals=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+            )
+        ),
+        width=1000,
+        height=800,
+        font=dict(family="Vazirmatn, Arial", size=14)
+    )
+    
+    # Save the figure
+    fig.write_html('visualizations/3d_scatter_plot.html')
+    fig.write_image('visualizations/3d_scatter_plot.png', scale=2)
+    
+    return fig
+
+# Generate all visualizations
+def generate_all_visualizations():
+    print("Generating 3D Surface Histogram for Area...")
+    create_3d_surface_area()
+    
+    print("Generating 3D Surface Histogram for Production...")
+    create_3d_surface_production()
+    
+    print("Generating Area Chart by Department and Age...")
+    create_area_chart_by_dept_age()
+    
+    print("Generating Area Chart by Department and Variety...")
+    create_area_chart_by_dept_variety()
+    
+    print("Generating 3D Bar Chart...")
+    create_3d_bar_chart()
+    
+    print("Generating Heatmap for Area...")
+    create_heatmap_area()
+    
+    print("Generating Heatmap for Production...")
+    create_heatmap_production()
+    
+    print("Generating Stacked Bar Chart...")
+    create_stacked_bar_chart()
+    
+    print("Generating 3D Scatter Plot...")
+    create_3d_scatter_plot()
+    
+    print("All visualizations have been generated and saved to the 'visualizations' directory.")
+
+# Run the function to generate all visualizations
+if __name__ == "__main__":
+    generate_all_visualizations()