# -*- coding: utf-8 -*-
"""
Created on Sun Feb 28 10:04:26 2016
PCA source code
@author: liudiwei
"""

import numpy as np
import json
import pymssql
import requests
import config


"""
参数：
	- XMat：传入的是一个numpy的矩阵格式，行表示样本数，列表示特征    
	- k：表示取前k个特征值对应的特征向量
返回值：
	- finalData：参数一指的是返回的低维矩阵，对应于输入参数二
	- reconData：参数二对应的是移动坐标轴后的矩阵
"""


def min_pos(X):
    X[X <= 0] = np.max(X)
    m = np.min(X)
    re = np.where(X == m)
    min_i = re[0]
    min_j = re[1]
    if m < 0:
        m = 0
    return m, min_i, min_j


def Lars(X, Y, D, DIAG, t, limit_line, lamuta):
    n, m = X.shape
    beta = np.zeros((1, m))
    A = []
    N_Co_added = 0
    i = 0
    mse = []
    for k in range(m):
        i += 1
        ero = np.array(Y - beta[-1, :].T)
        #          c=np.dot(P,DIAG,P.T,ero)
        c = np.dot(D, DIAG).dot(D.T).dot(ero)  # 计算相关性
        C = np.max(np.abs(c))
        mse.append(np.dot(ero.T, D).dot(DIAG).dot(D.T).dot(ero))
        if mse[k] < limit_line:
            break
        elif k == 0:
            addTndex = np.where(abs(c) == C)[-1][0]
        A.append(addTndex)  # 活动集
        # 更新正在添加的相应协方差索引的数量
        N_Co_added = N_Co_added + 1
        A_c = list(set(range(0, m)).difference(set(A)))  # 非活动集
        s_A = np.diag(np.sign(c[A]))

        num_Zero_Coeff = len(A_c)
        ## 计算 X_A， A_A ， u_A  ，the inner product vecto
        X_A = np.dot(X[:, A], s_A).reshape(n, -1)
        G_A = np.dot(X_A.T, X_A)
        One_A = np.ones((len(A), 1))
        s = One_A.copy()
        if G_A.shape == ():
            inv_GA = 1 / G_A
        else:
            inv_GA = np.linalg.pinv(G_A)
        #          G_a_inv_red_cols = np.sum(inv_GA, 1)
        A_A = 1 / np.sqrt(np.dot(s.T, inv_GA).dot(s))
        w_A = (A_A * inv_GA).dot(s)  # w_A: (less then 90%)构成等角的单位向量
        u_A = np.dot(X_A, w_A)  # .reshape(n)
        a = X.T.dot(u_A)  # inner product vector
        gamma_Test = np.zeros((num_Zero_Coeff, 2))
        #          gamma=[]
        if N_Co_added == m - 1:
            gamma = C / A_A
        else:
            for j in range(num_Zero_Coeff):
                j_p = A_c[j]
                first_term = (C - c[j_p]) / (A_A - a[j_p])
                second_term = (C + c[j_p]) / (A_A + a[j_p])
                gamma_Test[j, :] = np.array([first_term, second_term]).reshape(1, -1)
            gamma, min_i, min_j = min_pos(gamma_Test)
            #               gamma.append(m_s)
            addTndex = A_c[np.min(min_i)]
        beta_temp = np.zeros((m, 1))
        beta_temp[A] = beta[k, A].reshape(-1, 1) + np.dot(s_A, gamma * w_A)
        beta = np.vstack((beta, beta_temp.transpose()))  # 更新的系数即故障f
    return beta, mse


# import sklearn
# q=sklearn.linear_model.Lars


class MSSQL:
    def __init__(self,host,user,pwd,database):
        self.host = host
        self.user = user
        self.pwd = pwd
        self.db = database

    def __GetConnect(self):
        """
        得到连接信息
        返回: conn.cursor()
        """
        if not self.db:
            raise(NameError,"没有设置数据库信息")
        self.conn = pymssql.connect(host=self.host,user=self.user,password=self.pwd,database=self.db,port=config._PORT, charset="utf8")
        cur = self.conn.cursor()
        if not cur:
            raise(NameError,"连接数据库失败")
        else:
            return cur

    def ExecQuery(self,sql):
        """
        执行查询语句
        返回的是一个包含tuple的list，list的元素是记录行，tuple的元素是每行记录的字段
        """
        cur = self.__GetConnect()
        cur.execute(sql)
        resList = cur.fetchall()

        #查询完毕后必须关闭连接
        self.conn.close()
        return resList

    def ExecNonQuery(self,sql):
        """
        执行非查询语句

        调用示例：
            cur = self.__GetConnect()
            cur.execute(sql)
            self.conn.commit()
            self.conn.close()
        """
        cur = self.__GetConnect()
        cur.execute(sql)
        self.conn.commit()
        self.conn.close()

def get_model_by_ID(model_id, version="v-test"):
    ms = MSSQL(host=config._SQL_IP, user="root", pwd="123456", database="alert")
    resList = ms.ExecQuery(f"SELECT Model_info FROM model_cfg where model_id={model_id}")
    return json.loads(resList[0][0])


def get_model_by_id_and_version(model_id, version):
    ms = MSSQL(host=config._SQL_IP, user="root", pwd="123456", database="alert")
    resList = ms.ExecQuery(f"SELECT Model_info FROM model_version where model_id={model_id} and version='{version}'")
    return json.loads(resList[0][0])

def pca(model, Data_origin):
    Data = (Data_origin - model["Train_X_mean"]) / model["Train_X_std"]
    featValue = np.array(model["featValue"])  # 训练数据的特征值
    k=(model["K"])  # 主元个数
    featVec = np.array(model["featVec"])  # 训练数据的特征向量
    selectVec1 = np.array(model["selectVec"])
    selectVec=featVec[:, 0:k]
    index = np.argsort(-np.array(featValue))  # 按照featValue进行从大到小排序
    featValue_sort = featValue[index]  # 排序后的特征值

    ############----------*********-SPE-**************----------########################
    numbel_variable = featValue.shape[0]
    C_ = np.eye(numbel_variable) - np.dot(selectVec, selectVec.T)

    SPE_list = []
    for i in range(Data.shape[0]):
        Y = Data[i, :]  # 测试数据的每一行
        #########*********************计算SPE******************************
        SPE_line =np.dot(Y, C_).dot(Y.T)###SPE根号
        SPE_list.append(SPE_line)

    paraState = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]])
    finalData = np.dot(Data, selectVec).dot(selectVec.T)
    reconData = np.add(np.multiply(finalData, model["Train_X_std"]), model["Train_X_mean"])  # 重构值
    errorData =Data_origin - reconData   # 偏差值
    # cos检验值
    R = 0
    res={}
    for index in range(0, reconData.shape[1]):
        vector1 = Data_origin[:, index]
        vector2 = np.array(reconData)[:, index]
        R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2)))
    R /= reconData.shape[1]
    #items = [('reconData', np.around(reconData, decimals=3).tolist())
     #   , ('errorData', np.around(errorData, decimals=3).tolist()), ('R', R.tolist()), ('SPE', np.array(SPE_list).tolist()),
      #       ('paraState', paraState.tolist())]
    #res["sampleData"]=np.transpose(Data_origin.tolist())
    res["sampleData"]=np.transpose(np.array(Data_origin)).tolist()
    res["reconData"]=np.around(np.transpose(np.array(reconData)), decimals=3).tolist()
    res["errorData"]=np.around(np.transpose(np.array(errorData)), decimals=3).tolist()
    res["R"]=np.around(R, decimals=3).tolist()
    res["SPE"]=np.around(np.transpose(np.array(SPE_list)), decimals=3).tolist()
    res["paraState"]=np.transpose(np.array(paraState)).tolist()
    
    #result = json.dumps(dict(items))  # json.dumps(result)
    #return result
    return res


def get_history_value(points,time,interval):
    #url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys"
    url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
    headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token()
    point_array = points.split(",")
    time_span = time.split(";")
    value_array = []
    for item in point_array:
        value_group = []
        for time_piece in time_span:
            st = time_piece.split(",")[0]
            et = time_piece.split(",")[1]
            para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval}
            response = requests.get(url, headers=headers, params=para)
            content = response.text.replace('"[','[').replace(']"',']')
            value = json.loads(content)
            if not isinstance(value, list):
                print("aaa")
            for row in value:
                value_group.append(row[1])
        value_array.append(value_group)
    return np.transpose(np.array(value_array))
    #return valres


def main(info):
    model_id = info["Model_id"]
    try:
        version = info["version"]
    except KeyError:
        version = "v-test"
    if version == "v-test":
        res = get_model_by_ID(model_id)
    else:
        res = get_model_by_id_and_version(model_id, version)
    Test_Data = info["Test_Data"]
    points = Test_Data["points"]
    time1 = Test_Data["time"]
    interval = Test_Data["interval"]
    model = res["para"]["Model_info"]
    Data = get_history_value(points, time1, interval)
    result = pca(model, Data)
    index = time1.index(",")
    result["time"] = time1[:index:]
    return result

# 根据数据集data.txt


if __name__ == "__main__":
    info_str='{"Test_Data":{"time":"2021-01-13 12:52:40,2021-01-14 12:52:40","points":"JL_D2_20SCS02A:MAG10CT311.PNT,JL_D2_20DAS01B:MAG10CE101.PNT,JL_D2_20MCS01A:MAG10AN001ZT.PNT,JL_D2_20SCS02A:MAG10CT312.PNT,JL_D2_20DAS01B:MAG10CE102.PNT,JL_D2_20MCS01A:MAG10AN002ZT.PNT,JL_D2_20SCS02A:MAG10CT313.PNT,JL_D2_20DAS01B:MAG10CE103.PNT,JL_D2_20MCS01A:MAG10AN003ZT.PNT,JL_D2_20SCS02A:MAG10CT314.PNT,JL_D2_20DAS01B:MAG10CE104.PNT,JL_D2_20MCS01A:MAG10AN004ZT.PNT,JL_D2_20SCS02A:MAG10CT315.PNT,JL_D2_20DAS01B:MAG10CE105.PNT,JL_D2_20MCS01A:MAG10AN005ZT.PNT,JL_D2_20SCS02A:MAG10CT316.PNT,JL_D2_20DAS01B:MAG10CE106.PNT,JL_D2_20MCS01A:MAG10AN006ZT.PNT,JL_D2_20SCS02A:MAG10CT317.PNT,JL_D2_20DAS01B:MAG10CE107.PNT,JL_D2_20MCS01A:MAG10AN007ZT.PNT,JL_D2_20DAS01B:MAJ10CT101.PNT,JL_D2_20DAS01B:MAJ10CT102.PNT,JL_D2_20SCS02A:MAG10CT101.PNT,JL_D2_20SCS02A:MAG10CT102.PNT,JL_D2_20DAS01B:MAG03CG101.PNT,JL_D2_20DAS01B:MAG03CS101.PNT","interval":300000},"Model_id":528,"version":"v-test"}'
    info = json.loads(info_str)
    print(main(info))
    # model_id=info["Model_id"]
    # Test_Data = info["Test_Data"]
    # points = Test_Data["points"]
    # time = Test_Data["time"]
    # interval = Test_Data["interval"]
    # Data = get_history_value(points, time, interval)
    # # workbook = xw.Workbook("pca_test.xlsx")
    # # worksheet = workbook.add_worksheet()
    # # for row, item in enumerate(Data.tolist()):
    # #     for col, cell in enumerate(item):
    # #         worksheet.write(row, col, cell)
    # # workbook.close()
    # model = PCA_Test_offline.get_model_by_ID(model_id)["para"]["Model_info"]
    # result = pca(model,Data)#模型参数，训练数据
    # aaa=json.dumps(result)
    # print (result)