import os
from datetime import datetime
from pathlib import Path

import pandas as pd

from ...model.DHU.DHU_A import DHU_A
from ...model.DHU.DHU_B import DHU_B
from ...tools.config_reader import ConfigReader
from ...tools.data_loader import DataLoader


def optimize(*inputs,config=None):
    config = {} if config is None else config
    
    if '__LOCAL' in config.keys():
        config_reader_path = config['__LOCAL']
        data_URL           = config['__URL']
    else:
        config_reader_path = '/mnt/workflow_data'
        data_URL           = 'http://basedataportal-svc:8080/data/getpointsdata'
        
    config_reader = ConfigReader(path=f'{config_reader_path}/DHU_A配置.xlsx')
    if config_reader.meta['设备类型'] == 'DHU_A':
        MODEL = DHU_A
    elif config_reader.meta['设备类型'] == 'DHU_B':
        MODEL = DHU_B
    else:
        raise NotImplementedError(config_reader.meta['设备类型'])
    
    ALL_RESULT = {
        'EXCEPTION':{
            'Data': {},
            'Fit' : {},
            'Save': {}
        }
    }
    
    for each_eaup_name in config_reader.all_equp_names:
        # 加载数据
        # 运行判断
        #   稳态判断：房间露点设定值与反馈值是否接近
        #   模型判断：模型精度是否满足要求
        #   模式判断：
        #       1. 基于当前露点优化模式：基于房间露点设定值减偏差
        #       2. 快速提升送风露点模式：约束送风露点保持不变
        
        
        
        
        # 获取数据
        try:
            data_loader = DataLoader(
                path       = f'{config_reader_path}/data/train/data_his/',
                start_time = config_reader.get_app_info(each_eaup_name,app_type='模型训练',key='开始时间',info_type='datetime'),
                end_time   = config_reader.get_app_info(each_eaup_name,app_type='模型训练',key='结束时间',info_type='datetime')
            )
            data_loader.dowload_equp_data(
                equp_name   = each_eaup_name,
                point       = config_reader.get_equp_point(each_eaup_name,equp_class=['A','B']),
                url         = data_URL,
                clean_cache = False
            )
            equp_data = data_loader.get_equp_data(each_eaup_name)
            equp_data = clean_data(equp_data)
            save_data(f'{config_reader_path}/data/train/data_his_clean',f'{each_eaup_name}.pkl',equp_data)
        except Exception as E:
            ALL_RESULT['EXCEPTION']['Data'][each_eaup_name] = E
            continue