#!/users/dsc/bin/Python3
#-*-coding: UTF-8 -*-
# from Serialization import Serialization,Deserialization
# a={"a":1,"b":2}
# file_name="test"
# c=Serialization(file_name,a)
# print(c)
# b={"c":3,"d":4}
# e=Deserialization(file_name)
# if(e):
#     print("e---")
# print(e)    
# print(a)
# print(b)
# from mntest import mnTest,mnTrain
# print(mnTrain())
from ml_utiliy import ihyperDB,time_delta_split
import datetime
# tag_name="TCM1750_ENT-40-183-FH-2"
# stime=1704441868000
# etime=1704445468000

# etime2=datetime.datetime.now()
# stime2=etime2-datetime.timedelta(minutes=105)
# ihd=ihyperDB()
# print(type(ihd.value_))
# ihd.select_raw_data(tag_name,stime,etime)

# if(ihd.flag_):
#     print(len(ihd.value_))
# else:
#     print("ERROR")
# ihd.select_raw_data(tag_name,stime2,etime2)
# if(ihd.flag_):
#     print(len(ihd.values_))
#     print(type(ihd.values_))
# else:
#     print("ERROR")

# [a,b]=time_delta_split(stime2,etime2)
# print(a)
# print(b)


import json
import numpy as np
import ml_train
sampleJson = """{"key1": "value1", "key2": "value2"}"""
json_path="/users/dsc/code/eqpalg/alg_json/ml_param.json"
with open(json_path,'r',encoding='utf8')as fp:
    json_data = json.load(fp)
print(json_data)
tm1=ml_train.TrainModle()
# tm1.Train(json_data)
tm1.Predict(json_data)