import pandas as pd
import random
import numpy as np


# 生成170条测试数据的函数
def generate_test_data(num_rows=170):
    data = {
        'Code': [i for i in range(0, num_rows)],  # 生成0到170的公司ID
        '原材料': [round(random.uniform(100, 1000), 2) for _ in range(num_rows)],  # 原材料
        '库存商品': [round(random.uniform(100, 1000), 2) for _ in range(num_rows)],  # 库存商品
        '设备数量': [round(random.uniform(100, 1000), 2) for _ in range(num_rows)],  # 固定资产原值
        'Revenue': [round(random.uniform(10000, 100000), 2) for _ in range(num_rows)],  # Revenue
        'Total Employees (People)': [random.randint(50, 1000) for _ in range(num_rows)],  # 员工总数
        'Type_Region': [random.choice(['Urban', 'Rural', 'Suburban']) for _ in range(num_rows)],  # 区域类型
        'Self-supply Business (Yes/No)': [random.choice(['Yes', 'No']) for _ in range(num_rows)]  # 自营业务
    }

    df = pd.DataFrame(data)

    # 添加Revenue_Log列
    df['Revenue_Log'] = np.log(df['Revenue'])
    df['production_output'] = df['设备数量'] / 10+np.random.randint(100, 500, size=len(df))
    df['demand_quantity'] = df['原材料'] / 10 +np.random.randint(100, 500, size=len(df))
    return df


# 生成数据
df_test_data = generate_test_data()
# 显示前几行
print(df_test_data.head())

# 保存数据到CSV文件
df_test_data.to_csv('input_data/测试 Firm_amended 170.csv', index=False)