import multiprocessing
import os

import pandas as pd

from etl.common.PathsAndTable import PathsAndTable
from utils.file.trans_methods import read_excel_files, read_file_to_df, copy_to_new
from utils.log.trans_log import trans_print
from utils.systeminfo.sysinfo import get_available_cpu_count_with_percent


class CombineAndSaveFormalFile(object):

    def __init__(self, pathsAndTable: PathsAndTable):
        self.pathsAndTable = pathsAndTable
        self.update_files = multiprocessing.Manager().list()

    def combine_and_save(self, file_path, key, exists_file_path):
        exists_same = False
        if exists_file_path:
            exists_same = True
            exists_df = read_file_to_df(exists_file_path)
            now_df = read_file_to_df(file_path)
            # 合并两个 DataFrame
            combined_df = pd.concat([exists_df, now_df])
            # 去重，保留 now_df 的值
            combined_df = combined_df.drop_duplicates(subset='time_stamp', keep='last')
            # 按 time_stamp 排序
            combined_df = combined_df.sort_values(by='time_stamp').reset_index(drop=True)
            combined_df.to_csv(exists_file_path, encoding='utf-8', index=False)
            self.update_files.append(exists_file_path)
        else:
            save_path = str(os.path.join(self.pathsAndTable.get_save_path(), key[0], key[1]))
            copy_to_new(file_path, save_path)
            self.update_files.append(save_path)
        trans_print(f"{key[0]}/{key[1]} {'包含' if exists_same else '不包含'} 相同文件,保存成功")

    def combine_and_save_formal_file(self):
        exists_files = read_excel_files(self.pathsAndTable.get_save_path())
        exists_file_maps = dict()
        for file_path in exists_files:
            name = (os.path.basename(os.path.dirname(file_path)), os.path.basename(file_path))
            exists_file_maps[name] = file_path

        new_files = read_excel_files(self.pathsAndTable.get_tmp_formal_path())
        new_file_maps = dict()
        for file_path in new_files:
            name = (os.path.basename(os.path.dirname(file_path)), os.path.basename(file_path))
            new_file_maps[name] = file_path

        same_keys = list(set(exists_file_maps.keys()).intersection(new_file_maps.keys()))
        split_count = get_available_cpu_count_with_percent(2 / 3)
        with multiprocessing.Pool(split_count) as pool:
            pool.starmap(self.combine_and_save,
                         [(file_path, key, exists_file_maps[key] if key in same_keys else None) for key, file_path in
                          new_file_maps.items()])

    def run(self):
        self.combine_and_save_formal_file()
        print(self.update_files)
        return list(self.update_files)