175 lines
6.6 KiB
Python
175 lines
6.6 KiB
Python
# -*- coding: utf-8 -*-
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import pandas as pd
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import json
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import random
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import os
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import glob
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def generate_pm_json(excel_file_path, output_file_path):
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"""
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从 Excel 文件中提取英文名称和空间粒度，生成 PM JSON 文件
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"""
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try:
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# 检查文件是否存在
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if not os.path.exists(excel_file_path):
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print(f"错误：文件 {excel_file_path} 不存在")
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return False
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print(f"正在读取 Excel 文件: {excel_file_path}")
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# 读取 Excel 文件的所有工作表
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excel_data = pd.read_excel(excel_file_path, sheet_name=None)
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# 使用字典按 granularity 分组
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granularity_groups = {}
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processed_sheets = 0
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# 遍历所有工作表，查找包含"英文名称"列的表格
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for sheet_name, df in excel_data.items():
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print(f" 处理工作表: {sheet_name}")
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# 检查是否包含"英文名称"列
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if '英文名称' in df.columns:
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processed_sheets += 1
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print(f" - 找到英文名称列，共 {len(df)} 行数据")
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# 检查是否有空间粒度列
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granularity_col = None
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for col in df.columns:
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if '空间粒度' in str(col) or '粒度' in str(col):
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granularity_col = col
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break
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# 用于记录当前sheet中最后一个不为空的空间粒度
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last_valid_granularity = "DefaultFunction"
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for index, row in df.iterrows():
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english_name = row['英文名称']
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# 跳过空值和非字符串值
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if pd.notna(english_name) and str(english_name).strip() and str(english_name) != 'nan':
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english_name = str(english_name).strip()
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# 获取空间粒度信息
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current_granularity = None
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if granularity_col and pd.notna(row[granularity_col]):
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current_granularity = str(row[granularity_col]).strip()
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if current_granularity: # 确保不是空字符串
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last_valid_granularity = current_granularity
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# 如果当前行的空间粒度为空，使用最后一个有效的空间粒度
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granularity = last_valid_granularity
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# 生成随机值
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random_value = random.randint(0, 16)
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kpi = {
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"KPIID": english_name,
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"KPIValues": [
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{
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"Name": "Total",
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"Value": random_value
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}
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]
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}
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# 按 granularity 分组
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if granularity not in granularity_groups:
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granularity_groups[granularity] = []
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granularity_groups[granularity].append(kpi)
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# 显示使用的粒度信息
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granularity_info = ""
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if current_granularity:
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granularity_info = f" (当前: {granularity})"
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else:
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granularity_info = f" (继承: {granularity})"
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print(f" 添加 KPI: {english_name} = {random_value}{granularity_info}")
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else:
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print(f" - 跳过（无英文名称列）")
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# 生成最终的 JSON 结构
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result = []
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for granularity, kpis in granularity_groups.items():
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result.append({
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"ObjectType": granularity,
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"KPIs": kpis
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})
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# 写入文件
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with open(output_file_path, 'w', encoding='utf-8') as f:
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json.dump(result, f, indent=4, ensure_ascii=False)
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print(f" 成功生成文件: {output_file_path}")
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print(f" 处理了 {processed_sheets} 个工作表")
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print(f" 生成了 {len(granularity_groups)} 个对象类型")
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for granularity, kpis in granularity_groups.items():
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print(f" - {granularity}: {len(kpis)} 个 KPI")
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return True
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except Exception as e:
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print(f"错误: {str(e)}")
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import traceback
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traceback.print_exc()
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return False
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def main():
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print("PM JSON 批量生成器")
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print("=" * 60)
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# 定义要处理的文件列表
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pm_files = [
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"AMF-PM(V1.1.5)-Company-Version00.xlsx",
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"PCF-PM(V1.1.4)-Company-Version00.xlsx",
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"SMF-PM(V1.2.1)-Company-Version00.xlsx",
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"UDM-PM(V1.2.2)-Company-Version00.xlsx",
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"UPF-PM(V1.2.1)-Company-Version00.xlsx"
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]
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# 也可以自动发现当前目录下的所有 PM Excel 文件
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# pm_files = glob.glob("*-PM(*)-Company-Version*.xlsx")
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successful_files = 0
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failed_files = 0
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for excel_file in pm_files:
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print(f"\n处理文件: {excel_file}")
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print("-" * 50)
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# 检查文件是否存在
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if not os.path.exists(excel_file):
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print(f" 警告：文件 {excel_file} 不存在，跳过")
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failed_files += 1
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continue
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# 生成输出文件名
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base_name = os.path.splitext(excel_file)[0]
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output_file = f"{base_name}-generated.json"
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# 处理文件
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if generate_pm_json(excel_file, output_file):
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successful_files += 1
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else:
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failed_files += 1
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# 汇总结果
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print("\n" + "=" * 60)
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print("批量处理完成！")
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print(f"成功处理: {successful_files} 个文件")
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print(f"失败/跳过: {failed_files} 个文件")
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print(f"总计: {successful_files + failed_files} 个文件")
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if successful_files > 0:
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print("\n生成的文件列表:")
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for excel_file in pm_files:
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if os.path.exists(excel_file):
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base_name = os.path.splitext(excel_file)[0]
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output_file = f"{base_name}-generated.json"
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if os.path.exists(output_file):
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print(f" - {output_file}")
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if __name__ == "__main__":
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main() |