model-lab/aannmtcltest_recon.py

#!/usr/bin/python3.6.5
# -*- coding: utf-8 -*-
# Time     : 2021/5/2423:03
# Author  : LiFan,HeKang,RenShaojun
# Github : https://github.com/JustKeepSilence

import time
import numpy as np
import pandas as pd
from scipy.stats import norm
from scipy.stats.distributions import chi2
import json
from json import JSONDecodeError
import sys
import traceback
import pymssql
import requests
import datetime
from scipy.stats import norm
from scipy.stats import f
from scipy.stats import chi2
import jenkspy
# import pca_train_k
import pcamtcltest_k
# import model_performance
import xlrd
import random
from pylab import mpl
import matplotlib.pyplot as plt
from collections import Counter
from operator import itemgetter
import PCA_Test
import config
from recon import Lars, recon_fault_diagnosis_r, recon_fault_diagnosis_r_l, recon_fault_diagnosis_r_c
from AANN_SF_SBSRB import AANN_SF_SBSRB
import scipy.io as scio


def aann(model, LockVariable,Data_origin,number_sample, number_fault_variable,
        low_f,high_f,Test_type,Limit_value, time_data, spe_recon,expand=True, sort_data=False):

    low_f=np.array(low_f)
    high_f=np.array(high_f)
    yy = list(range(0, Data_origin.shape[0]))
    time_sample = []
    if expand:
        if len(yy) < 1000:
            random_row = np.array(list(range(len(yy))))
        else:
            random_row = np.array(random.sample(yy, number_sample))
        time_original = time_data[random_row, :]
        Data_origin = Data_origin[random_row, :]  # 扩充选择1000个样�?
    else:#这个是什么意思，问一下何康
        random_row = np.array(random.sample(yy, number_sample))
        time_original = time_data[random_row, :]
        Data_origin = Data_origin[random_row, :]  # 扩充选择random_row个样�?

    v1 = np.array(model["v1"])
    v2 = np.array(model["v2"])
    w1 = np.array(model["w1"])
    w2 = np.array(model["w2"])
    sigma = model["sigma"]
    mindata = model["mindata"]
    maxdata = model["maxdata"]
    mm, nn = Data_origin.shape
    fault_magnitude = []
    for i in range(mm):
        magnitude=[]
        for j in range(nn):
            magnitude.append(np.random.uniform(low_f[j], high_f[j]))  # 均匀分布
        fault_magnitude.append(magnitude)
    fault_magnitude=np.array(fault_magnitude)
    fault_sample = Data_origin+ fault_magnitude
    # scio.savemat('fault_sample.mat', {'A': fault_sample})
    delt_ff,fdr,far,fdr_variable,far_variable,Reconstruction_precision,spe,ReconRes = AANN_SF_SBSRB(fault_sample, fault_magnitude, v1, v2, w1, w2, sigma, mindata, maxdata)

    max_recon = ""
    time_stamp = time_original.T.tolist()[0]
    train_mean = model["Train_X_mean"]
    train_std = model["Train_X_std"]

    items = [
             ('fdr', np.around(fdr, decimals=3).tolist()),
             ('far', np.around(far, decimals=3).tolist()),
             ('fdr_variable', np.around(fdr_variable, decimals=3).tolist()),
             ('far_variable', np.around(far_variable, decimals=3).tolist()),
             ('Reconstruction_precision', np.around(Reconstruction_precision, decimals=3).tolist()),
             ("error_data", delt_ff),
             ("test_data", fault_sample),
             ("ReconRes", ReconRes),
        ("original_line", spe),
        ("time_stamp", time_stamp),
        ("max_recon", max_recon),
        ("mean", train_mean),
        ("std", train_std)
    ]
    result = dict(items)
    return result

def isnumber(limits):
    flag = True
    for item in limits:
        item = item.replace("-","")
        if not item.isdigit():
            flag = False
            break
    return flag

def main(info):
    try:
        ItemsInfo, SamplingTimePeriods = [], []
        model_id = info["Model_id"]
        version = info["version"]
        if version == "v-test":
            res = PCA_Test.get_model_by_ID(model_id)
        else:
            res = PCA_Test.get_model_by_id_and_version(model_id, version)
        lock = []
        point_info = res["pointInfo"]
        interval = info["Test_Data"]["interval"]
        for i in range(len(point_info)):
            try:
                if point_info[i]["lock"]:
                    lock.append(i)
            except:
                continue
        Test_Data = info["Test_Data"]
        points = Test_Data["points"].split(",")
        try:
            times = Test_Data["time"].split(";")
            for i in range(len(times)):
                Eachsampletime = {}
                timess = times[i].split(',')
                Eachsampletime["StartingTime"] = timess[0]
                Eachsampletime["TerminalTime"] = timess[1]
                SamplingTimePeriods.append(Eachsampletime)
        except KeyError:
            SamplingTimePeriods = [{"StartingTime": item["st"],
                                    "TerminalTime": item["et"]} for item in res["trainTime"]]
        model = res["para"]["Model_info"]
        condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<")
        dead = info["dead"].split(',')
        limit = info["limit"].split(',')
        uplower = info["uplow"].split(';')
        count = 0
        Constraint = ""
        for i in range(len(points)):
            iteminfo = {}
            iteminfo["ItemName"] = points[i]  # 加点
            if dead[i] == "1":  # 判断是否参与死区清洗
                iteminfo["ClearDeadZone"] = "true"
            else:
                iteminfo["ClearDeadZone"] = "false"
            if limit[i] == "1":  # 参与上下限清�?
                limits = uplower[i].split(',')
                if isnumber(limits):  # 输入上下限正�?
                    count += 1
                    Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
                        1] + " and "
            ItemsInfo.append(iteminfo)
        if count != 0:
            Constraint = Constraint[:len(Constraint) - 4:]
        else:
            Constraint = "1==1"  # 没有上下限清�?
        Constraint += " and (" + condition + ")"
        Constraint = Constraint.replace("\n", " ")
        url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % (
            ItemsInfo, SamplingTimePeriods, Constraint, interval)
        response = requests.get(url)
        content = json.loads(response.text)
        data = np.array([item for item in content["ClearData"]]).T  # test data
        time_data = np.array([item for item in content["TimeData"]]).reshape(data.shape[0], -1)
        test_type = info["Test_Type"]
        limit_value = info["Limit_Value"]
        number_sample = info["number_sample"]
        try:
            expand = info["expand"]
            # sort_data = info["sort_data"]
        except KeyError:
            expand = True
            # sort_data = True
        spe_recon = False
        far, fdr, amplitude = [], [], []
        recon = []
        result = {}
        for _, ass in enumerate(info["assess"]):
            number_fault_variable = ass["number_fault_variable"]
            low_f = [float(item) for item in ass["low_f"].split(',')]
            high_f = [float(item) for item in ass["high_f"].split(',')]

            result_temp = aann(model, lock, data, number_sample, number_fault_variable, low_f, high_f, test_type,
                         limit_value, time_data, spe_recon, expand)
            far.append(result_temp["far"])
            fdr.append(result_temp["fdr"])
            recon.append(result_temp["Reconstruction_precision"])
            t_index = np.nonzero(high_f)[0][0]#索引high_f中第一个非0数位置的行数；在矩阵的第3行，t_index == 2
            amplitude.append(high_f[t_index])
        try:
            index = Test_Data["time"].index(",")
            result["time"] = Test_Data["time"][:index:]
        except KeyError:
            result["time"] = sorted(res["trainTime"], key=lambda item: item["st"])[0]["st"]
        result["BeforeCleanSamNum"] = content["BeforeCleanSamNum"]
        result["AfterCleanSamNum"] = content["AfterCleanSamNum"]
        result["amplitude"] = amplitude
        result["CleanOrNot"] = True
        result["far_variable"] = far
        result["fdr_variable"] = fdr
        result["Reconstruction_precision"] = recon
        return result
    except Exception as e:
        msg = traceback.format_exc()
        result = [{"CleanOrNot": False, 'msg': msg}]
    return result  #


if __name__ == "__main__":
    #info_str = {"expand":True,"Test_Data":{"time":"2021-03-17 18:01:14,2021-05-21 17:28:52","points":"DH4_DEA-LVL,DH4_40LAA10CL101,DH4_40LAA10CL102","interval":300000},"dead":"1,1,1","limit":"0,0,0","Model_id":"1161","number_sample":1000,"assess":[{"number_fault_variable":1,"low_f":"-31.16,0,0","high_f":"-31.16,0,0"},{"number_fault_variable":1,"low_f":"0,-31.11,0","high_f":"0,-31.11,0"},{"number_fault_variable":1,"low_f":"0,0,-33.22","high_f":"0,0,-33.22"}],"condition":"1=1","Test_Type":"FAI","Limit_Value":1.353,"uplow":"null,null;null,null;null,null","number":0,"version":"v-test"}
    info_str = {"expand":True,"Test_Data":{"time":"2020-08-30 19:03:10,2020-09-04 12:02:16;2020-09-05 22:00:33,2020-09-08 15:35:07","points":"DH4_40MAG20CT362,DH4_40MAG20AN002GT,DH4_40MAG20CE102,DH4_40MAG20CT312,DH4_40MAG20CT322,DH4_40MAG20CT332","interval":300000},"dead":"1,1,1,1,1,1","limit":"0,0,0,0,0,0","Model_id":"1274","Model_alg":"AANN","number_sample":1000,"assess":[{"number_fault_variable":1,"low_f":"-3.96,0,0,0,0,0","high_f":"-3.96,0,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,-6.00,0,0,0,0","high_f":"0,-6.00,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,-21.12,0,0,0","high_f":"0,0,-21.12,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,-8.38,0,0","high_f":"0,0,0,-8.38,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,-8.43,0","high_f":"0,0,0,0,-8.43,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,0,-8.31","high_f":"0,0,0,0,0,-8.31"}],"condition":"[DH4_40MAG20CE102]>20","Test_Type":"FAI","Limit_Value":0.0,"uplow":"null,null;null,null;null,null;null,null;null,null;null,null","number":0,"version":"v-test"}
    res = main(info_str)
    print("aaa")