#!/usr/bin/env python
# -*- coding: utf-8 -*-
# time: 2023/3/2 10:28
# file: config.py
# author: David
# company: shenyang JY

"""
模型调参及系统功能配置
"""
import argparse
import pandas as pd
from pathlib import Path
from tf_lstm_train import model_training
from app.common.logs import args, logger

""""
调用思路
   xxxx 1. 从入口参数中获取IN OUT文件位置 xxxx
        2. 按照训练和预测加载和解析数据
        3. 对数据进行预处理
        4. 执行训练，保存模型，输出状态
        5. 执行预测，输出结果，输出状态
"""

def material(input_file, isDq=True):
    basi, station_info_w, station_info_d_w, station_info_s, station_info_d_s, nwp_w, nwp_s, nwp_w_h, nwp_s_h, power = (
        'DQYC_IN_BASIC.txt', 'DQYC_IN_PLANT_WIND.txt', 'DQYC_IN_PLANT_DETAIL_WIND.txt', 'DQYC_IN_PLANT_SOLAR.txt',
        'DQYC_IN_PLANT_DETAIL_SOLAR.txt', 'DQYC_IN_FORECAST_WEATHER_WIND.txt', 'DQYC_IN_FORECAST_WEATHER_SOLAR.txt',
        'DQYC_IN_FORECAST_WEATHER_WIND_H.txt', 'DQYC_IN_FORECAST_WEATHER_SOLAR_H.txt', 'DQYC_IN_HISTORY_POWER_LONG.txt')
    basi_area = 'DQYC_AREA_IN_BASIC'
    nwp_v, nwp_v_h = 'DQYC_IN_FORECAST_WEATHER.txt', 'DQYC_IN_FORECAST_WEATHER_H.txt' # 多版本气象
    nwp_own, nwp_own_h = 'DQYC_IN_FORECAST_WEATHER_OWNER.txt', 'DQYC_IN_FORECAST_WEATHER_OWNER_H.txt',
    input_file = Path(input_file)
    basic = pd.read_csv(input_file / basi, sep='\s+', header=0)
    power = pd.read_csv(input_file / power, sep='\s+', header=0)
    plant_type = int(basic.loc[basic['PropertyID'].to_list().index(('PlantType')), 'Value'])
    if isDq:
        nwp_v = pd.read_csv(input_file / '0' / nwp_v, sep='\s+', header=0)
        nwp_v_h = pd.read_csv(input_file / '0' / nwp_v_h, sep='\s+', header=0)
        nwp_own = pd.read_csv(input_file / '1' / nwp_own, sep='\s+', header=0)
        nwp_own_h = pd.read_csv(input_file / '1' / nwp_own_h, sep='\s+', header=0)
        if args['switch_nwp_owner']:
            nwp_v, nwp_v_h = nwp_own, nwp_own_h
        # 如果是风电
        if plant_type == 0:
            station_info = pd.read_csv(input_file / station_info_w, sep='\s+', header=0)
            station_info_d = pd.read_csv(input_file / station_info_d_w, sep='\s+', header=0)
            nwp = pd.read_csv(input_file / nwp_w, sep='\s+', header=0)
            nwp_h = pd.read_csv(input_file / nwp_w_h, sep='\s+', header=0)
            return station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h
        # 如果是光伏
        elif plant_type == 1:
            station_info = pd.read_csv(input_file / station_info_s, sep='\s+', header=0)
            station_info_d = pd.read_csv(input_file / station_info_d_s, sep='\s+', header=0)
            nwp = pd.read_csv(input_file / nwp_s, sep='\s+', header=0)
            nwp_h = pd.read_csv(input_file / nwp_s_h, sep='\s+', header=0)
            return station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h
    else:
        # 区域级预测待定，可能需要遍历获取场站数据
        basic_area = pd.read_csv(input_file / basi_area, sep='\s+', header=0)
        return basic_area

def input_file_handler(input_file: str):
    # DQYC：短期预测，qy：区域级
    if 'dqyc' in input_file.lower():
        station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h = material(input_file, True)
        cap = round(station_info['PlantCap'][0], 2)
        # 含有model，训练
        if 'model' in input_file.lower():
            train_data = pd.merge(nwp_v_h, power, on='Datetime')
            model_training(train_data, input_file, cap)
        # 含有predict，预测
        else:
            logger.info("训练路径错误！")
    else:
        # 区域级预测：未完
        basic_area = material(input_file, False)

def main():
    # 创建解析器对象
    parser = argparse.ArgumentParser(description="程序描述")
    # 创建
    # 添加参数
    parser.add_argument("input_file", help="输入文件路径")

    # 解析参数
    args = parser.parse_args()

    # 使用参数
    print(f"文件: {args.input_file}")
    input_file_handler(args.input_file)


if __name__ == "__main__":
    main()