mesa/测试数据 产业-原材料消耗-产品生产量.py

import pandas as pd
import numpy as np

# 设置随机种子，确保结果可重复
np.random.seed(42)

# 定义产业数量
num_industries = 107

# 创建产业ID列表
industry_ids = [i for i in range(0, num_industries + 1)]

# 为每个产业生成随机的材料id、消耗量、产品id和制造量
consumed_materials_data = []
produced_products_data = []

for industry in industry_ids:
    # 每个产业消耗的材料（生成1到3个随机材料ID和消耗量）
    num_materials = np.random.randint(1, 4)
    for _ in range(num_materials):
        material_id = np.random.randint(0, 100)
        consumption_quantity = np.random.randint(50, 500)
        consumed_materials_data.append([industry, material_id, consumption_quantity])

    # 每个产业制造的产品（生成1到3个随机产品ID和制造量）
    num_products = np.random.randint(1, 4)
    for _ in range(num_products):
        product_id = np.random.randint(100, 201)
        production_quantity = np.random.randint(100, 1000)
        produced_products_data.append([industry, product_id, production_quantity])

# 创建两个数据框
df_consumed_materials = pd.DataFrame(consumed_materials_data, columns=['产业id', '消耗材料id', '消耗量'])
df_produced_products = pd.DataFrame(produced_products_data, columns=['产业id', '制造产品id', '制造量'])

# 保存两个数据框为CSV文件
file_path_consumed = '测试数据 consumed_materials.csv'
file_path_produced = '测试数据 produced_products.csv'

df_consumed_materials.to_csv(file_path_consumed, index=False)
df_produced_products.to_csv(file_path_produced, index=False)