#!/users/dsc/bin/Python3
#-*-coding: UTF-8 -*-
from typing_extensions import Self
from ml_model import models
from Serialization import Serialization,Deserialization
import ihyper_db
import numpy as np
import datetime
import copy
def train(ruleid,model_type,x_train,y_train):
    """_train_训练模型
    
    Args:
        ruleid (_str_): _保存模型的文件名称_
        model_type (_str_): _模型类型_:  1.MLP-多层感知机;2.LR-逻辑斯蒂回归;3.RFC-随机森林;4.DTC-决策树
        x_data (_<class 'numpy.ndarray'>_): _特征数据_
        y_data (_<class 'numpy.ndarray'>_): _目标数据_
    return:
        训练的准确率
    """
    now_model=models.model(model_type)
    now_model.fit(x_train, y_train)
    key=ruleid+"_"+model_type
    Serialization(key,now_model)
    return now_model.score(x_train, y_train)

def predict_score(ruleid,model_type,x_test,y_test):
    """_predict_score_运用模型

    Args:
        ruleid (_str_): _保存模型的文件名称_
        model_type (_str_): _模型类型_:  1.MLP-多层感知机;2.LR-逻辑斯蒂回归;3.RFC-随机森林;4.DTC-决策树
        x_test (_<class 'numpy.ndarray'>_): _特征数据_
        y_test (_<class 'numpy.ndarray'>_): _目标数据_
    return
        测试数据的准确率
    """
    key=ruleid+"_"+model_type
    now_model=Deserialization(key)
    if(now_model):
        return now_model.score(x_test, y_test)
    else:
        return train(ruleid,model_type,x_test,y_test)
    
def read_mode(ruleid,model_type):
    key=ruleid+"_"+model_type
    now_model=Deserialization(key)
    return now_model
    
class ihyperDB(object):
    """_ihyperDB_
    查询ihd数据
    """
    def __init__(self):
        self.flag_=False
        self.value_=[]
        self.flags_=[]
        self.values_=[]
    def select_raw_data(self,tag_name,stime,etime,interval=50):
        """_select_raw_data_
        查询原始记录 
        Args:
            tag_name (_type_): _items_
            stime (_type_): _开始时间_
            etime (_type_): _结束时间_
            interval (int, optional): _数据时间间隔_. Defaults to 50ms.

        Returns:
            _type_: _查询结果_ [是否正确查询，数据结果]
        """
        time_points,data_nums=time_delta_split(stime,etime,interval=50)
        self.flags_=[]
        self.values_=[]
        for t,n in zip(time_points,data_nums):
            print("t:{},n:{}".format(t,n))
            self.__select_raw_data(tag_name,t,n)
            if(self.flag_):
                (self.value_).reverse()
                # print(self.value_)
                # self.values_.append(copy.deepcopy(self.value_))
                for k in self.value_:
                    self.values_.append(k)
            self.flags_.append(self.flag_)
        # print(self.values_)
        return self.flags_,self.values_
        
    def __select_raw_data(self,tag_name,etime,data_num,interval=50):
        """_select_raw_data_
        查询原始记录 最多65535条记录，从etime往前查
        Args:
            tag_name (_type_): _items__
            etime (_type_): _结束时间_
            data_num (_type_): _查询数据量_最大为65535
            interval (int, optional): _数据时间间隔_. Defaults to 50ms.

        Returns:
            _type_: _查询结果_ [是否正确查询，数据结果]
        """
        if(isinstance(etime,int)):
            if(data_num>65535):
                data_num=65535
            timepoint=datetime.datetime.fromtimestamp(etime/1000)
        elif(isinstance(etime,datetime.datetime)):
            if(data_num>65535):
                data_num=65535
            timepoint=etime
        [flag,value]=ihyper_db.get_tag_small_time_data(tag_name,timepoint,int(data_num))
        if(flag==0):
            self.flag_=True
            self.value_=value
        else:
            self.flag_=False
        return self.flag_,self.value_
        

def time_delta_split(stime,etime,interval=50,start_mode=False):
    timepoints=[]
    data_nums=[]
    if(isinstance(stime,int) and isinstance(etime,int)):
        data_num=(etime-stime)/interval
        nmax=int(data_num/65535)
        nmin=int(data_num%65535)

        if(nmax>0):
            for k in range(nmax+1):
                # print(k)
                eetime=int((etime-(nmax-k)*interval*65535)/1000)
                # sstime=int((stime+(nmax-k)*interval*65535)/1000)
                # print("nmax:{},k:{},sstime:{}:".format(nmax,k,sstime))
                timepoints.append(datetime.datetime.fromtimestamp(eetime))
                data_nums.append(65530 if k>0 else nmin)
    elif(isinstance(stime,datetime.datetime) and isinstance(etime,datetime.datetime)):
        data_num=(etime-stime).total_seconds()*1000/interval
        nmax=int(data_num/65535)
        nmin=int(data_num%65535)
        if(nmax>0):
            for k in range(nmax+1):
                timepoints.append(etime-datetime.timedelta(seconds=(nmax-k)*interval*65535)/1000)
                data_nums.append(65530 if k>0 else nmin)
    return timepoints,data_nums