import os

import numpy as np
import pandas as pd

from etl.common.BaseDataTrans import BaseDataTrans
from service.plt_service import update_trans_status_error
from service.trans_service import get_fault_warn_conf
from utils.conf.read_conf import read_conf
from utils.file.trans_methods import read_excel_files, read_file_to_df, create_file_path
from utils.log.trans_log import trans_print


class FaultWarnTrans(BaseDataTrans):

    def __init__(self, data: dict = None, save_db=True, step=0, end=4):
        super(FaultWarnTrans, self).__init__(data, save_db, step, end)

    def get_filed_conf(self):
        return get_fault_warn_conf(self.field_code, self.read_type)

    # 第三步 读取 并 保存到临时文件
    def read_and_save_tmp_file(self):
        trans_print("无需保存临时文件")

    # 第四步 统计 并 保存到正式文件
    def statistics_and_save_to_file(self):
        conf_map = self.get_filed_conf()
        if conf_map is None or type(conf_map) == tuple or len(conf_map.keys()) == 0:
            message = f"未找到{self.batch_no}的{self.read_type}配置"
            trans_print(message)
            update_trans_status_error(self.batch_no, self.read_type, message, self.save_db)
        else:

            for key, v in conf_map.items():
                if v and type(v) == str:
                    v = v.replace("\r\n", "").replace("\n", "")
                    conf_map[key] = v

            read_fields_keys = [i for i in conf_map.keys() if i.startswith('field_')]
            # 需要执行 exec的字段
            # exec_fields = [(k.replace("exec_", ""), v) for k, v in conf_map.items() if k.startswith('exec_')]
            # 读取需要执行 筛选的字段
            select_fields = [(k.replace("select_", ""), v) for k, v in conf_map.items() if
                             k.startswith('select_') and v]
            time_format = read_conf(conf_map, 'time_format')

            trans_map = dict()
            trans_cols = []
            for key in read_fields_keys:
                field_value = read_conf(conf_map, key)
                if field_value:
                    vas = str(field_value).split('|')
                    trans_cols.extend(vas)
                    field_key = key.replace("field_", "")
                    for v in vas:
                        trans_map[v] = field_key

            all_files = read_excel_files(self.pathsAndTable.get_excel_tmp_path())

            df = pd.DataFrame()
            for file in all_files:
                now_df = read_file_to_df(file, trans_cols=trans_cols)
                df = pd.concat([df, now_df], ignore_index=True)

            df.rename(columns=trans_map, inplace=True)

            if time_format:
                df['begin_time'] = pd.to_datetime(df['begin_time'])
                df['end_time'] = pd.to_datetime(df['end_time'])
            else:
                df['begin_time'] = pd.to_datetime(df['begin_time'], format=time_format)
                df['end_time'] = pd.to_datetime(df['end_time'], format=time_format)

            exec_wind_turbine_number = read_conf(conf_map, 'exec_wind_turbine_number')
            if exec_wind_turbine_number:
                exec_str = f"df['wind_turbine_number'].apply(lambda wind_name: {exec_wind_turbine_number} )"
                df['wind_turbine_number'] = eval(exec_str)

            for field, select_str in select_fields:
                use_in = True
                if str(select_str).strip().startswith("!"):
                    use_in = False
                    select_str = select_str[1:]

                select_str = select_str.replace("'", "").replace("[", "").replace("]", "")
                values = select_str.split(',')

                if df[field].dtype == int:
                    values = [int(i) for i in values]

                if use_in:
                    df = df[df[field].isin(values)]
                else:
                    df = df[~df[field].isin(values)]

            df['wind_turbine_name'] = df['wind_turbine_number']
            df['wind_turbine_number'] = df['wind_turbine_number'].map(
                self.wind_col_trans).fillna(df['wind_turbine_number'])

            df['time_diff'] = (df['end_time'] - df['begin_time']).dt.total_seconds()
            df.loc[df['time_diff'] < 0, 'time_diff'] = np.nan

            if self.save_zip:
                save_path = os.path.join(self.pathsAndTable.get_save_path(), str(self.batch_name) + '.csv.gz')
            else:
                save_path = os.path.join(self.pathsAndTable.get_save_path(), str(self.batch_name) + '.csv')

            create_file_path(save_path, is_file_path=True)
            if self.save_zip:
                df.to_csv(save_path, compression='gzip', index=False, encoding='utf-8', date_format='%Y-%m-%d %H:%M:%S')
            else:
                df.to_csv(save_path, index=False, encoding='utf-8', date_format='%Y-%m-%d %H:%M:%S')