import os
from datetime import datetime
from pathlib import Path
from pprint import pprint

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

NOW             = datetime.now().replace(second=0,microsecond=0)
PATH            = os.path.dirname(os.path.realpath(__file__)).replace('\\','/')
MODEL_FUNC_PATH = f'{PATH}/model_func.py'
MODEL_FILE_PATH = f'./model.pkl'

def train(*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:
        
        # 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
        
        # 训练模型 
        try:
            equp_model:DHU_A = MODEL()
            equp_model.fit(
                input_data    = equp_data,
                observed_data = equp_data,
                plot_TVP      = False,
                rw_FA_val     = True, #TODO
                exist_Fa_H    = config_reader.get_equp_info(each_eaup_name,'存在回风口','bool'),
                exist_Fa_B    = config_reader.get_equp_info(each_eaup_name,'存在补风口','bool'),
            )
            Path(f'{config_reader_path}/model').mkdir(parents=True, exist_ok=True)
            equp_model.save(f'{config_reader_path}/model/{each_eaup_name}.pkl')
            save_data(f'{config_reader_path}/data/train/data_TVP',f'{each_eaup_name}.csv',equp_model.TVP_data)
            save_data(f'{config_reader_path}/data/train/data_metric',f'{each_eaup_name}.csv',equp_model.TVP_metric)
        except Exception as E:
            ALL_RESULT['EXCEPTION']['Fit'][each_eaup_name] = E
            continue
        
        # 模型迭代
        if not config_reader.get_app_info(each_eaup_name,'模型训练','保存模型','bool'):
            continue
        try:
            monitor_point = config_reader.point.loc[lambda dt:dt.类型=='B']
            model_update_info = {}
            for i in range(len(monitor_point)):
                name    = monitor_point.loc[:,'编号'].iat[i]
                name_cn = monitor_point.loc[:,'名称'].iat[i]
                MAE     = monitor_point.loc[:,'指标MAE'].iat[i]
                model_update_info[name] = {
                    'point_id'   : name,
                    'point_name' : name_cn,
                    'point_class': name,
                    'thre_mae'   : MAE,
                    'thre_mape'  : 1,
                    'thre_days'  : 7
                }
            equp_model.save_to_platform(
                version_id      = datetime.now().strftime('%Y%m'),
                model_id        = config_reader.get_equp_info(each_eaup_name,'模型编号','str'),
                update_method   = 'update',
                model_info      = model_update_info,
                MODEL_FILE_PATH = MODEL_FILE_PATH,
                MODEL_FUNC_PATH = MODEL_FUNC_PATH,
            )
        except Exception as E:
            ALL_RESULT['EXCEPTION']['Save'][each_eaup_name] = E
            continue
    
    pprint(ALL_RESULT)

def clean_data(data) -> pd.DataFrame:
    data = (
        data
        .resample('60min')
        .mean()
    )
    return data

def save_data(dir,file:str,data:pd.DataFrame):
    Path(dir).mkdir(parents=True,exist_ok=True)
    if file.endswith('.csv'):
        data.to_csv(os.path.join(dir,file),index=True)
    elif file.endswith('.pkl'):
        data.to_pickle(os.path.join(dir,file))
    else:
        raise Exception('file type error')