commit

Generate_Data.py

@@ -0,0 +1,163 @@
+import pandas as pd
+import numpy as np
+from io import StringIO
+import matplotlib.pyplot as plt
+
+def generate_intelligent_sourcing_excel(
+    output_filename,
+    num_of_warehouses,
+    num_of_products,
+    num_of_orders,
+    weightage_Cost,
+    weightage_Priority,
+    weightage_distance,
+    weightage_days,
+    range_priority,
+    range_prod_stock,
+    range_order,
+    range_cost,
+    range_distance,
+    range_days
+):
+    """
+    Generates an Excel file containing warehouse, order, and shipping data.
+    
+    Parameters:
+    output_filename (str): The name of the output Excel file.
+    num_of_warehouses (int): Number of warehouses.
+    num_of_products (int): Number of products.
+    num_of_orders (int): Number of orders.
+    weightage_Cost (float): Weightage for cost.
+    weightage_Priority (float): Weightage for priority.
+    weightage_distance (float): Weightage for distance.
+    weightage_days (float): Weightage for days.
+    range_priority (tuple): Range for priority values.
+    range_prod_stock (tuple): Range for product stock values.
+    range_order (tuple): Range for order quantities.
+    range_cost (tuple): Range for cost values.
+    range_distance (tuple): Range for distance values.
+    range_days (tuple): Range for delivery days values.
+    """
+    rng = np.random.default_rng(seed=42)  # Random number generator
+
+    # Create weightage DataFrame
+    weightage_df = pd.DataFrame({
+        'Variable': ['Cost', 'Priority', 'Distance', 'Days'],
+        'Weightage': [weightage_Cost, weightage_Priority, weightage_distance, weightage_days]
+    })
+
+    # Generate priority data
+    priority_df = pd.DataFrame({
+        'Warehouse': [f'Warehouse#{w}' for w in range(1, num_of_warehouses + 1)],
+        'Priority': rng.integers(*range_priority, size=num_of_warehouses)
+    })
+
+    # Generate warehouse stock data
+    warehouse_df = pd.DataFrame({
+        'Warehouse': [f'Warehouse#{w}' for w in range(1, num_of_warehouses + 1)]
+    })
+    for p in range(1, num_of_products + 1):
+        warehouse_df[f'Product#{p}'] = rng.integers(*range_prod_stock, size=num_of_warehouses)
+
+    # Generate order data
+    order_df = pd.DataFrame({
+        'Order': [f'Order#{o}' for o in range(1, num_of_orders + 1)]
+    })
+    for p in range(1, num_of_products + 1):
+        order_df[f'Product#{p}'] = rng.integers(*range_order, size=num_of_orders)
+
+    # Function to generate shipping data
+    def generate_shipping_data(metric_name, value_range):
+        return pd.DataFrame([
+            (w, o, p, rng.integers(*value_range))
+            for w in warehouse_df["Warehouse"]
+            for o in order_df["Order"]
+            for p in warehouse_df.columns[1:]
+        ], columns=["Warehouse", "Order", "Product", metric_name])
+
+    # Generate cost, distance, and days DataFrames
+    cost_df = generate_shipping_data("Cost", range_cost)
+    distance_df = generate_shipping_data("Distance", range_distance)
+    days_df = generate_shipping_data("Days", range_days)
+
+    # Write data to Excel
+    with pd.ExcelWriter(output_filename, engine='xlsxwriter') as writer:
+        weightage_df.to_excel(writer, sheet_name='Weightage', index=False)
+        priority_df.to_excel(writer, sheet_name='Priority Data', index=False)
+        warehouse_df.to_excel(writer, sheet_name='Stock Data', index=False)
+        order_df.to_excel(writer, sheet_name='Order Data', index=False)
+        cost_df.to_excel(writer, sheet_name='Cost Data', index=False)
+        distance_df.to_excel(writer, sheet_name='Distance Data', index=False)
+        days_df.to_excel(writer, sheet_name='Days Data', index=False)
+
+    print(f"Excel file '{output_filename}' has been successfully created.")
+    return 
+
+
+def plot_histograms(excel_filename):
+    """
+    Reads the generated Excel file and plots separate histograms for relevant data columns.
+    """
+    xls = pd.ExcelFile(excel_filename)
+    
+    data_sheets = {
+        "Priority": pd.read_excel(xls, "Priority Data")["Priority"],
+        "Product Stock": pd.read_excel(xls, "Stock Data").iloc[:, 1:].values.flatten(),
+        "Order Quantity": pd.read_excel(xls, "Order Data").iloc[:, 1:].values.flatten(),
+        "Cost": pd.read_excel(xls, "Cost Data")["Cost"],
+        "Distance": pd.read_excel(xls, "Distance Data")["Distance"],
+        "Days": pd.read_excel(xls, "Days Data")["Days"]
+    }
+
+    fig, axes = plt.subplots(2, 3, figsize=(15, 10))
+    axes = axes.flatten()
+    
+    for i, (label, data) in enumerate(data_sheets.items()):
+        axes[i].hist(data, bins=20, alpha=0.7, color='blue', edgecolor='black')
+        axes[i].set_title(label)
+        axes[i].set_xlabel("Value")
+        axes[i].set_ylabel("Frequency")
+        axes[i].grid(True)
+    
+    plt.tight_layout()
+    plt.savefig("histogram_ranges.png")
+    plt.close()
+    print("Histogram saved as 'histogram_ranges.png'")
+
+
+if __name__ == "__main__":
+    # Define parameters for execution
+    output_filename = "Intelligent_Sourcing.xlsx"
+    num_of_warehouses = 10
+    num_of_products = 100
+    num_of_orders = 200
+    weightage_Cost = 1
+    weightage_Priority = 0.8
+    weightage_distance = 0.6
+    weightage_days = 0.4
+    range_priority = (1, 10)
+    range_prod_stock = (1, 100)
+    range_order = (1, 10)
+    range_cost = (1, 300)
+    range_distance = (1, 200)
+    range_days = (1, 7)
+
+    # Run function
+    generate_intelligent_sourcing_excel(
+        output_filename,
+        num_of_warehouses,
+        num_of_products,
+        num_of_orders,
+        weightage_Cost,
+        weightage_Priority,
+        weightage_distance,
+        weightage_days,
+        range_priority,
+        range_prod_stock,
+        range_order,
+        range_cost,
+        range_distance,
+        range_days
+    )
+
+    plot_histograms(excel_filename=output_filename)