#!/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")