import datetime
import multiprocessing
import os
import sys

sys.path.insert(0, os.path.abspath(__file__).split("tmp_file")[0])

from utils.file.trans_methods import read_excel_files, copy_to_new, read_file_to_df
from utils.zip.unzip import unzip, get_desc_path, unrar
import pandas as pd

read_cols = ['Time', '设备主要状态', '功率曲线风速', '湍流强度', '实际风速', '有功功率', '桨叶角度A', '桨叶角度B',
             '桨叶角度C', '机舱内温度', '机舱外温度', '绝对风向', '机舱绝对位置', '叶轮转速', '发电机转速', '瞬时风速',
             '有功设定反馈', '当前理论可发最大功率', '空气密度', '偏航误差', '发电机扭矩', '瞬时功率', '风向1s',
             '偏航压力', '桨叶1速度', '桨叶2速度', '桨叶3速度', '桨叶1角度给定', '桨叶2角度给定', '桨叶3角度给定',
             '轴1电机电流', '轴2电机电流', '轴3电机电流', '轴1电机温度', '轴2电机温度', '轴3电机温度', '待机', '启动',
             '偏航', '并网', '限功率', '正常发电', '故障', '计入功率曲线', '运行发电机冷却风扇1', '运行发电机冷却风扇2',
             '激活偏航解缆阀', '激活偏航刹车阀', '激活风轮刹车阀', '激活顺时针偏航', '激活逆时针偏航', '电缆扭角']

read_path = r'/data/download/collection_data/1进行中/张崾先风电场-陕西-华电/收资数据/sec'
save_dir = r'/data/download/collection_data/1进行中/张崾先风电场-陕西-华电/清理数据/点检表以外测点儿-20241210'
tmp_dir = r'/data/download/collection_data/1进行中/张崾先风电场-陕西-华电/tmp/second/excel_tmp'

# read_path = r'D:\data\张崾先风电场\6'
# save_dir = r'D:\data\张崾先风电场\点检表以外测点儿-20241209'
# tmp_dir = r'D:\data\张崾先风电场\tmp'

os.makedirs(tmp_dir, exist_ok=True)
os.makedirs(save_dir, exist_ok=True)


def get_and_remove(file):
    to_path = tmp_dir
    if str(file).endswith("zip"):
        if str(file).endswith("csv.zip"):
            copy_to_new(file, file.replace(read_path, to_path).replace("csv.zip", 'csv.gz'))
        else:
            desc_path = file.replace(read_path, to_path)
            is_success, e = unzip(file, get_desc_path(desc_path))
            if not is_success:
                # raise e
                pass
    elif str(file).endswith("rar"):
        desc_path = file.replace(read_path, to_path)
        unrar(file, get_desc_path(desc_path))
    else:
        copy_to_new(file, file.replace(read_path, to_path))


def get_resolve(file_path, exist_wind_names, map_lock):
    begin = datetime.datetime.now()
    df = read_file_to_df(file_path, read_cols=read_cols)
    wind_name = str(os.path.basename(file_path)[0:2])
    date = os.path.basename(file_path)[14:24]
    df['Time'] = df['Time'].apply(lambda x: date + ' ' + x)
    df = df[read_cols]
    with map_lock[str(wind_name)]:
        if wind_name in exist_wind_names:
            df.to_csv(save_dir + '/' + wind_name + '.csv', mode='a', index=False, header=False, encoding='utf8')
        else:
            df.to_csv(save_dir + '/' + wind_name + '.csv', index=False, encoding='utf8')
            exist_wind_names.append(wind_name)

    print(os.path.basename(file_path), '执行完成,耗时:', get_haoshi(begin))


def sort_data(file_path):
    df = pd.read_csv(file_path, encoding='utf8')
    df['Time'] = pd.to_datetime(df['Time'], error='coerce')
    df.sort_values(by=['Time'], inplace=True)
    df.to_csv(file_path, index=False, encoding='utf8')


def get_haoshi(begin):
    return datetime.datetime.now() - begin


if __name__ == '__main__':
    begin = datetime.datetime.now()
    # all_files = read_files(read_path)
    # split_count = get_available_cpu_count_with_percent(1 / 2)
    # all_arrays = split_array(all_files, split_count)
    #
    # for index, arr in enumerate(all_arrays):
    #     with multiprocessing.Pool(10) as pool:
    #         pool.starmap(get_and_remove, [(i,) for i in arr])
    #
    # print("移动完成,耗时:", get_haoshi(begin))

    # exist_wind_names = multiprocessing.Manager().list()
    #
    # map_lock = dict()
    # for i in range(26, 42):
    #     map_lock[str(i)] = multiprocessing.Manager().Lock()
    #
    # all_files = read_excel_files(tmp_dir)
    # with multiprocessing.Pool(16) as pool:
    #     pool.starmap(get_resolve, [(i, exist_wind_names, map_lock) for i in all_files])
    #
    # print("整理完成,耗时:", get_haoshi(begin))

    all_files = read_excel_files(save_dir)
    with multiprocessing.Pool(4) as pool:
        pool.map(sort_data, all_files)
    print("排序完成,耗时:", get_haoshi(begin))

    # shutil.rmtree(tmp_dir)
    # print("移除临时文件完成,耗时:", get_haoshi(begin))