# from datetime import date
# from sqlalchemy.orm import Session
# from openpyxl import load_workbook
# from pathlib import Path
#
# import pandas as pd
# import os
# import logging
#
# from app.entity.database.session import get_db
# from app.entity.models.PowerOutageStats import PowerOutageStats
#
# # 获取日志记录器
# logger = logging.getLogger(__name__)
#
# # 三份累计表
# def accumulated_statistics(start_time, end_time, save_path=None):
#
#     try:
#         logger.info('对数据库的查询结果进行处理，完成三个累计表数据的组装')
#
#         db: Session = next(get_db())
#         # 查询某个时间段的数据
#         results = db.query(PowerOutageStats.province_company,PowerOutageStats.outage_users,PowerOutageStats.short_outage_users,
#                            PowerOutageStats.repair_count,PowerOutageStats.complaint_count,PowerOutageStats.public_opinion_count,
#                            PowerOutageStats.major_event_count, PowerOutageStats.statistics_time)
#
#         # Excel 模板路径
#         # 获取当前文件夹路径
#         current_path = Path(__file__).parent
#         templates_path = str(os.path.join(current_path.parent, 'templates')).replace('\\', '/')
#
#         # 加载 Excel 模板
#         book = load_workbook(f'{templates_path}/累计数据模板.xlsx')
#
#         # 选择要写入的 Sheet 页
#         sheet_name = 'Sheet1'  # 替换为你的 Sheet 页名称
#         sheet = book[sheet_name]
#
#         # 查询结果用pandas进行处理
#         if results:
#
#             # 将数据转成pandas数据结构
#             df = pd.read_sql(results.statement, results.session.bind)
#             # 插入序号列作为第一列
#             df.insert(0, 'num', df.index + 1)
#
#             # 组成表1数据
#             df_temp = df[(df['statistics_time'] >= start_time) & (df['statistics_time'] <= end_time)]
#             df_table1 = df_temp[['statistics_time', 'outage_users', 'complaint_count','public_opinion_count']]
#             df_table1 = df_table1.groupby('statistics_time').sum()
#             df_table1 = df_table1.reset_index()
#
#             # 表1写入excel的位置
#             start_row1 = 3
#             start_col1 = 1
#             print(df_table1)
#             write_to_excel(df_table1,sheet,start_row1,start_col1)
#
#
#             # 组成表2数据
#             df_table2 = df_temp[['statistics_time', 'outage_users', 'short_outage_users', 'repair_count','complaint_count','public_opinion_count']]
#             df_table2 = df_table2.groupby('statistics_time').sum()
#             df_table2 = df_table2.reset_index()
#
#             # 表2写入excel的位置
#             start_row2 = 3
#             start_col2 = 6
#             print(df_table2)
#             write_to_excel(df_table2,sheet,start_row2,start_col2)
#
#             # 表3写入excel的位置
#             start_row3 = 3
#             start_col3 = 13
#             df_table3 = df.drop('statistics_time', axis=1)
#             write_to_excel(df_table3,sheet,start_row3,start_col3)
#
#             # 最终结果生成
#             book.save(f'{save_path}/累积统计表.xlsx')
#     except Exception as e:
#         logger.error(f'写入excel失败: {e}')
#         raise e
#
# #对三张表进行组装
# def write_to_excel(df, sheet, start_row, start_col):
#
#     try:
#         logger.info('开始写入excel')
#         # 将 DataFrame 写入指定位置
#         for i, row in enumerate(df.itertuples(index=False), start=start_row):
#             for j, value in enumerate(row, start=start_col):
#                 sheet.cell(row=i, column=j, value=value)
#     except Exception as e:
#         logger.error(f'写入excel失败: {e}')
#         raise e
#
#
# if __name__ == '__main__':
#
#
#     start_time = date(2025,3,9)
#     end_time = date(2025,3,10)
#     print(end_time)
#     accumulated_statistics(start_time, end_time)
#
#