# -*- coding: utf-8 -*-
"""
Created on Mon Nov 19 15:02:11 2018

Reference : https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-timeseries-data/43512887#43512887
# Implementation of algorithm from https://stackoverflow.com/a/22640362/6029703

@author: tianye
###
A、名称：一阶滞后滤波法
B、方法：
    取a=0-1，本次滤波结果=(1-a)*本次采样值+a*上次滤波结果。
C、优点：
    对周期性干扰具有良好的抑制作用；
    适用于波动频率较高的场合。
D、缺点：
    相位滞后，灵敏度低；
    滞后程度取决于a值大小；
    不能消除滤波频率高于采样频率1/2的干扰信号。

###
A、名称：递推平均滤波法（又称滑动平均滤波法）
B、方法：
    把连续取得的N个采样值看成一个队列，队列的长度固定为N，
    每次采样到一个新数据放入队尾，并扔掉原来队首的一次数据（先进先出原则），
    把队列中的N个数据进行算术平均运算，获得新的滤波结果。
    N值的选取：流量，N=12；压力，N=4；液面，N=4-12；温度，N=1-4。
C、优点：
    对周期性干扰有良好的抑制作用，平滑度高；
    适用于高频振荡的系统。
D、缺点：
    灵敏度低，对偶然出现的脉冲性干扰的抑制作用较差；
    不易消除由于脉冲干扰所引起的采样值偏差；
    不适用于脉冲干扰比较严重的场合；
    比较浪费RAM。


{ // 定义全局变量
    //---Mean & STD
    // Settings: lag = 30, threshold = 5, influence = 0
    // lag for the smoothing, 计算平均值的间隔点数.
    unsigned short nLag = 128;
    unsigned short nThreshold = 3.5;
    // when signal: how much influence for new data? (between 0 and 1)
    // 对当前节点做平滑，平滑系数是(0,1)，值越大越受当前值的影响。when 1 is normal influence, 0.5 is half。
    unsigned short nInfluence = 0;
    section("sdram0_bank3") float filteredY[Lubo_Sample_Rate] = { 0.0 };
    section("sdram0_bank3") float avgFilter[Lubo_Sample_Rate] = { 0.0 };
    section("sdram0_bank3") float stdFilter[Lubo_Sample_Rate] = { 0.0 };
    float nSudPoint = 0.0;

    static float std_algo(const short* fArray, unsigned short len, const float fMean);
    static unsigned short thresholding_algo(const short* AC_Input, unsigned short nLenInput);
}

{ // wave.c 中调用。
			if(Flag_s_UI0_PRO_NegCap == 0)	//出现电容为负超过定值标志￥￥
			{
#define _NEW_CODE_

#ifdef _NEW_CODE_                
				//--Find the START Point				
				unsigned short nSudIndex = 0;
				unsigned short i = 0;				
				unsigned short nSudIndexCnt = 0; // if >=2 means occur Earth Fault.

				nSudIndex = thresholding_algo(&U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL][0], Lubo_Sample_Rate);

				if(0 != nSudIndex)
				{
					for(i=0; i<3; i++, nSudIndex++)
					{
						if( U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL][nSudIndex]*U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][nSudIndex] < 0)
						{
							nSudIndexCnt++;
						}
					}
				}

				if( nSudIndexCnt >= 2 )
				{
					Flag_s_UI0_PRO_NegCap = 1;
					Write_Low_To_High_PRO_SOE(BF533PARA.Line[0].SOE_Start_index+28,&PRO_State[28][0],(cRealClock_625us*5)/8);				
				}
				else
				{
					Flag_s_UI0_PRO_NegCap = 0;
					Flag_s_UI0_PRO_TimeRecord = 0;						
				}
				
				// # Dir.1 对 I0 滤波 --- Test 下面的 3 行代码本应在找到 U0 起点之后对 I0 滤波，这里仅用做代码测试。
				//memset(gfLocalSamp, 0, Lubo_Sample_Rate*sizeof(float));
				//unit_ckf_process(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][0], 1500/*Lubo_Sample_Rate*/, gfLocalSamp);

//#if 0 //#if 0___Counter + 3U0 SuddenChange
				if(1)// (asOverCnt >= OVER_LIMIT_COUNT) && (nSudIndex[2] != 0xEFFF))
				{
					// # 已经找到了 [ U0 的突变点 ] ,接下来找 [ 方向极性 ]
					// # Dir.1 对 I0 滤波
					float *pfLocalSamp = (float *)malloc(Lubo_Sample_Rate*sizeof(float));
					//memset(pfLocalSamp, 0, Lubo_Sample_Rate*sizeof(shortfloat);
					//unit_ckf_process(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][0], Lubo_Sample_Rate, pfLocalSamp);
					
					// # Dir.2 拿滤波后的 I0 点处理 [ 方向极性 ]
					//nIndex = NUM_SIMPLES_PERCYCLE6 +  nSudIndex[1];
					if(1)//if( U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL][nIndex]*U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][nIndex] < 0)
					{
						Flag_s_UI0_PRO_NegCap = 1;
						Write_Low_To_High_PRO_SOE(BF533PARA.Line[0].SOE_Start_index+28,&PRO_State[28][0],(cRealClock_625us*5)/8);
						/** Clear Wave Data , for the next Fault */
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL]));
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL]));						
					}
					else
					{
						Flag_s_UI0_PRO_NegCap = 0;
						Flag_s_UI0_PRO_TimeRecord = 0;						
						/** Clear Wave Data , for the next Fault */
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL]));
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL]));						
					}										
				}
//#endif //#if 0___Counter + 3U0 SuddenChange

}

section("sdram0_bank3") static float std_algo(const short* fArray, unsigned short len, const float fMean)
{
	unsigned short i = 0;
	float fSum = 0.0;	
	//float fAvg = 0.0;
	float fRet = 0.0;
	
	for(i; i<len; i++){
		fSum += (fArray[i]-fMean)*(fArray[i]-fMean);
	}
	//fAvg = fSum/len;
	return fRet = sqrtf(fSum/len);
}

section("sdram0_bank3") static unsigned short thresholding_algo(const short* AC_Input, unsigned short nLenInput)
{
	unsigned short i = 0;
	short aSudMatrix[3][2];	
	unsigned short nSudIndexCnt = 0; // Must be <= 2
	
	if(NULL == AC_Input){
		return 0; //error
	}
	// Fetch MIN
	nLenInput = (Lubo_Sample_Rate>nLenInput ? nLenInput : Lubo_Sample_Rate);
	memset(filteredY, 0, sizeof(filteredY));
	memset(avgFilter, 0, sizeof(avgFilter));
	memset(stdFilter, 0, sizeof(stdFilter));
	memset(aSudMatrix, 0, sizeof(aSudMatrix));

	for(i=0; i<nLenInput; i++){
		filteredY[i] = AC_Input[i];
	}
	
	avgFilter[nLag-1] = meanf(&filteredY[0], nLag);
	stdFilter[nLag-1] = std_algo(&AC_Input[0], nLag, avgFilter[nLag-1]);
	for(i=nLag; i<nLenInput; i++){
		if(abs(filteredY[i] - avgFilter[i-1]) > nThreshold*stdFilter[i-1]){
			filteredY[i] = nInfluence*AC_Input[i] + (1 - nInfluence)*filteredY[i-1];			
			avgFilter[i] = meanf(&filteredY[i-nLag+1], nLag);
			stdFilter[i] = std_algo(&AC_Input[i-nLag+1], nLag, avgFilter[i-1]);
			
			//If Sequence 3 point Over Limited, then Record.
			//nSudPoint = AC_Input[i];
			aSudMatrix[nSudIndexCnt][0] = i;
			aSudMatrix[nSudIndexCnt][1] = AC_Input[i];
			//nSudIndexCnt = (++nSudIndexCnt)>2? 0 : nSudIndexCnt;
			nSudIndexCnt++;
			if(nSudIndexCnt > 2){
				return aSudMatrix[0][0];
			}
		} else {
			filteredY[i] = AC_Input[i];
			avgFilter[i] = meanf(&filteredY[i-nLag+1], nLag);
			stdFilter[i] = std_algo(&AC_Input[i-nLag+1], nLag, avgFilter[i-1]);
			if(nSudIndexCnt <=2 ){
				memset(aSudMatrix, 0, sizeof(aSudMatrix));
			}
		}
	}	
	return 0; //error : not find
}

"""
import numpy as np
import pylab

def thresholding_algo(y, lag, threshold, influence):
    nFlag = 0x00
    nCount = 0
    signals = np.zeros(len(y)) # 返回长度为 len(y) 的数组。
    filteredY = np.array(y)
    avgFilter = [0]*len(y)
    stdFilter = [0]*len(y)
    avgFilter[lag - 1] = np.mean(y[0:lag])
    stdFilter[lag - 1] = np.std(y[0:lag], ddof=1) # 计算全局标准差，默认情况下，numpy 计算的是总体标准偏差，ddof = 0
    for i in range(lag, len(y)):
        if abs(y[i] - avgFilter[i-1]) > threshold * stdFilter [i-1]:
            if y[i] > avgFilter[i-1]:                
                signals[i] = 1
                nCount = nCount + 1
                if nFlag == 0x00:
                    nFlag = i # 打印出来的 nFlag+1 索引就对应波形工具内的点号。
                              # 打印出来的 nFlag+2 索引就对应原始点数据内的点号。
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            else:
                signals[i] = -1
                nCount = nCount + 1
                if nFlag == 0x00:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))

            """
            @2018-11-20 对异常点的数值进行平滑，以便评估下下个点是否为异常点。
             因为不做平滑，由于当前是个异常点，对平均值、方差影响较大，若是下一个点仍是异常点，可能不会识别。
            """
            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i-1] ### 一阶滞后滤波法
            avgFilter[i] = np.mean(filteredY[(i-lag+1):i+1])
            stdFilter[i] = np.std(filteredY[(i-lag+1):i+1], ddof=1)
        else:
            signals[i] = 0
            filteredY[i] = y[i]
            avgFilter[i] = np.mean(filteredY[(i-lag+1):i+1])
            stdFilter[i] = np.std(filteredY[(i-lag+1):i+1], ddof=1)
    print("Total Outer is : %d" % (nCount))

    return dict(signals = np.asarray(signals),
                avgFilter = np.asarray(avgFilter),
                stdFilter = np.asarray(stdFilter))
    
# Data
"""
y = np.array([1,1,1.1,1,0.9,1,1,1.1,1,0.9,1,1.1,1,1,0.9,1,1,1.1,1,1,1,1,1.1,0.9,1,1.1,1,1,0.9,
       1,1.1,1,1,1.1,1,0.8,0.9,1,1.2,0.9,1,1,1.1,1.2,1,1.5,1,3,2,5,3,2,1,1,1,0.9,1,1,3,
       2.6,4,3,3.2,2,1,1,0.8,4,4,2,2.5,1,1,1])
"""
#y = np.array([2, 2, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 1, 1, 0, 2, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, -3, -11, -18, -27, -35, -43, -53, -62, -70, -78, -87, -97, -106, -114, -122, -130, -139, -147, -154, -161, -168, -175, -182, -189, -195, -201, -206, -210, -216, -220, 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191, 193, 195, 195, 194, 194, 193, 192, 190, 187, 183, 179, 175, 171, 165, 159, 154, 146, 139, 131, 124, 116, 107, 98, 90, 81, 71, 60, 50, 41, 31, 19, 8, -1, -10, -21, -31, -42, -52, -61, -71, -81, -91, -99, -107, -116, -125, -131, -138, -145, -152, -157, -162, -167, -172, -176, -179, -182, -184, -186, -187, -188, -187, -187, -187, -184, -183, -180, -176, -173, -170, -165, -160, -154, -148, -143, -137, -129, -121, -114, -106, -98, -88, -80, -71, -62, -52, -42, -33, -24, -14, -2, 7, 15, 24, 36, 45, 55, 64, 73, 84, 93, 101, 109, 118, 126, 134, 140, 147, 154, 161, 166, 172, 176, 182, 186, 189, 192, 196, 198, 199, 201, 202, 201, 202, 201, 200, 198, 196, 193, 191, 186, 183, 178, 173, 168, 162, 155, 149, 143, 135, 126, 119, 111, 103, 94, 84, 76, 67, 58, 47, 37, 27, 18, 8, -1, -11, -20, -30, -39, -50, -59, -68, -76, -87, -95, -103, -110, -118, -126, -132, -139, -145, -152, -157, -161, -165, -169, -174, -176, -179, -181, -183, -183, -184, -185, -184, -183, -182, -180, -178, -175, -171, -168, -164, -160, -154, -149, -144, -138, -130, -123, -116, -108, -102, -92, -84, -76, -68, -58, -48, -38, -30, -21, -10, 0, 9, 17, 27, 37, 46, 55, 64, 74, 84, 92, 99, 108, 117, 125, 132, 139, 147, 154, 160, 165, 171, 176, 181, 185, 189, 191, 194, 197, 199, 202, 202, 203, 203, 203, 201, 200, 198, 197, 194, 191, 187, 183, 180, 174, 168, 162, 157, 151, 144, 136, 129, 122, 114, 105, 96, 88, 80, 70, 60, 51, 42, 32, 22, 12, 3, -6, -15, -25, -35, -44, -53, -62, -71, -80, -87, -96, -105, -113, -119, -126, -133, -139, -145, -151, -156, -161, -166, -170, -173, -176, -179, -182, -183, -184, -185, -185, -185, -184, -183, -181, -179, -176, -174, -169, -166, -161, -157, -152, -146, -140, -135, -128, -120, -113, -105, -98, -90, -81, -71, -63, -55, -46, -36, -26, -17, -8, 1, 12, 21, 29, 39, 49, 58, 67, 76, 85, 94, 102, 110, 117, 127, 134, 140, 147, 154, 160, 166, 170, 176, 180, 185, 188, 191, 194, 197, 199, 200, 201, 202, 202, 202, 201, 200, 198, 195, 193, 190, 187, 182, 178, 173, 168, 162, 157, 151, 144, 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#---New__Index580__L3 区外 L5 1000欧 B相__BAY01_0046_20181001_044812_313 + I0
#y = np.array([-4, -2, 5, -8, 4, 6, 6, -8, 5, -2, 0, 6, 7, -4, 2, 6, 8, -6, 4, 1, -10, -17, -18, -19, -8, 4, 7, -3, -13, -17, -17, 1, 5, 8, 8, -7, 4, -1, -4, 5, 7, 8, 8, 8, -7, 4, 0, -12, -15, 2, -4, 0, 6, 7, -4, 2, 3, -8, 4, 7, 14, 26, 0, 8, 8, 4, -10, -15, 1, 6, 8, 8, 8, 8, 8, 8, -6, -14, -2, 4, 7, 8, 8, 8, 8, 8, -4, -13, -6, 4, 8, 8, 8, 8, 9, 8, 8, 8, 9, 24, 9, 7, 7, 8, 8, 8, 4, -6, 5, 19, 17, 3, 8, -6, 4, 7, 6, -8, 4, -1, 0, 23, 17, 23, 27, 14, -4, 6, 8, 9, 8, 9, -3, -13, -8, 22, 19, 12, 10, 9, 9, 8, 0, 0, 23, 17, 13, 4, -5, 6, 8, -1, -2, 6, 8, 8, 8, 8, 8, 8, 0, 0, 5, -8, -15, -3, 2, -10, 4, 8, 2, -11, -15, 0, -2, -13, -17, -19, -2, 4, 6, 8, -4, 2, 2, -8, 4, -4, 0, 6, 8, 8, 0, 0, 6, -7, -16, -17, -11, 2, 6, -2, 3, 26, 16, 0, -12, -17, -18, 2, 6, 2, -6, 6, -6, 2, 6, 7, 8, 8, 8, 8, 8, 8, -1, -1, 6, 7, 19, 25, 13, -5, 6, 8, 7, 7, -7, 3, 7, 8, 8, 23, 22, 8, -4, 6, 7, 0, -2, 6, -7, 3, 1, -6, 4, -6, 2, 6, 6, -7, -15, -17, -17, -19, -19, -19, -8, 3, 6, 8, 0, -11, -16, -18, -1, 4, 6, -8, 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#
#--SAR-device.sdb.await__研发中心波形_高阻接地_00025_20171025_201648_049_F__U0.csv
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#
#--SAR-device.sdb.await__2017-07-20 第四项检测L3 区外 L5 1000欧 A相__U0
#y = np.array([0                          , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.034556867414899294       , 0.0                        , 0.0                        , 0.030133588385792182       , 0.024106870708633744       , 0.019285496566906993       , 0.019128470161373697       , 0.01530277612909896        , 0.022314646511620122       , 0.017851717209296097       , 0.020275493647462416       , 0.016220394917969932       , 0.012976315934375945       , 0.010381052747500757       , 0.026252025216898685       , 0.021001620173518948       , 0.016801296138815158       , 0.013441036911052126       , 0.023804037886057592       , 0.019043230308846075       , 0.01523458424707686        , 0.012187667397661488       , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 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0.5359294938960824         , 0.6360847996062611         , 0.7507659115893046         , 0.9807382708353369         , 1.0956024234023636         , 1.2566074802857832         , 1.4199683932074194         , 1.585213990959626          , 1.7174104691613927         , 1.8577245191377052         , 1.9354188917038542         , 2.0666881245865723         , 2.206260378307647          , 2.283361313869607          , 2.3795989297340747         , 2.5257027572554476         , 2.5389152170278475         , 2.618598919675566          , 2.71690274920864           , 1.6897260555583222         , 0.7643140983933702         , 0.058541400076307495       , 0.5406336259922419         , 1.0199736468470815         , 1.3688887961160536         , 1.613464048116332          , 1.8436811171314544         , 2.0278547723435514         , 2.036966226853633          , 2.113369125291497          , 2.1399345766268874         , 2.092073202865402          , 2.1228978386860113         , 2.043886945097802          , 1.9806782302272348         , 1.8955543909158803         , 1.7583415846369987         , 1.6485713396138935         , 1.5261982761805106         , 1.393742958018905          , 1.218664968659821          , 1.1131594445874533         , 0.9596412904997605         , 0.6985992975700103         , 0.5588794380560077         , 0.3434329482001082         , 0.24018949114518728        , 0.019367255841653698       , 0.15729053240117508        , 2.6830406146234886         , 2.5533014767275133         , 0.9638062837142285         , 0.8401587618011824         , 1.01769568358994           , 1.1597252210209454         , 1.3079057183806488         , 1.4610069836833122         , 1.6180448633403408         , 1.7091182996510643         , 3.1988086127105144         , 0.24373677337016034        , 2.3029583310049846         , 2.257049073782779          , 2.323992270249713          , 2.4466605622530575         , 2.855807002589828          , 2.1464181324122653         , 2.7192836609618922         , 2.6937799399930036         , 1.7748984104305106         , 0.7978951148762216         , 0.08540621326258875        , 0.5536986428581162         , 0.9613119255099822         , 1.3565162864612734         , 1.638122907807407          , 1.7942944700545154         , 1.988345454682             , 2.07447250755419           , 2.143374149851941          , 2.1293817288603454         , 2.187301526896866          , 2.0608530282515853         , 2.0633648315800595         , 2.0308174068279397         , 1.831995129951747          , 1.811164778110391          , 1.6908298943926068         , 1.4908913851736822         , 1.4346111800432404         , 1.25135954627929           , 1.1047582392681294         , 10.352388263096909         , 1.1175573263750787         , 0.9977164633447614         , 0.7981731706758106         , 0.8113228736151465         , 0.6836151663070185         , 0.8233470723648098         , 0.8660188623812447         , 0.9001562943943927         , 0.9274662400049105         , 1.0529847987380212         , 1.1533996457245117         , 1.3028452581435026         , 1.3532880132488958         , 1.5664265544077078         , 1.6678236525049586         , 1.748941330982758          , 1.917506076009697          , 2.0178010046163473         , 2.132593814916567          , 2.224428063156744          , 2.3670091965786835         , 2.3774035010715373         , 2.558503281740317          , 2.565155636615743          , 2.7087049901756814         , 2.6507601359491346         , 1.740482567195416          , 0.804919307703045          , 0.09102556752404733        , 0.5492031594489492         , 0.9922724061975476         , 1.3467278035964263         , 1.664848988930429          , 1.746561600123134          , 2.0192728935666944         , 2.099214458661946          , 2.094053975908349          , 2.159039324535269          , 2.176470736021906          , 2.0867452629665166         , 2.0840786193520047         , 1.978274702215697          , 1.89363156850665           , 1.791360194124514          , 1.6749862272039053         , 1.4782164514227216         , 1.4590281004573704         , 1.2363362151956943         , 1.127296441816152          , 0.8672802860380208         , 0.8320516984900129         , 0.5274138891324136         , 0.35281737647613026        , 1.0654639280001685         , 1.6702801788875117         , 0.32250549280515656        , 0.32711812907392296        , 0.5381494425783324         , 0.6724176259669622         , 0.7452753052629654         , 1.0454595206040636         , 1.1473794232173458         , 1.2634722127228695         , 1.4254601791570873         , 1.62416428713426           , 1.7140138386861996         , 1.8550072147575507         , 2.00235878302953           , 2.1202400376471147         , 2.2491019087560806         , 2.2485208033985558         , 2.489953991016831          , 2.6485436736965515         , 2.360733010861537          , 2.6833943592319134         , 2.69962536602392           , 1.7450178838403447         , 0.8431044284338872         , 0.052459929278922825       , 0.5109419352152501         , 0.9962202942254877         , 1.3498861140187783         , 1.6328187698534111         , 1.72093742486152           , 2.1370010230169996         , 2.0551694925625927         , 2.162488605273563          , 2.1101164257827425         , 2.2064461518496836         , 2.1107255956287387         , 2.034149150651984          , 2.0074448620854795         , 1.9169676964024753         , 1.810029096441175          , 1.6208076142274361         , 1.5731010307011435         , 1.3967082942205105         , 1.221037237621106          , 1.1496141271713813         , 0.9196913017371046         , 0.8048667762194828         , 0.5056659513159889         , 0.36997589363789185        , 0.22686698008051387        , 0.043266114404814766       , 0.17272831296554314        , 0.3800807222767312         , 0.476848914895883          , 0.6233772038210021         , 0.8097135697908956         , 0.9587826625668115         , 1.1125948042024438         , 1.3047582523407464         , 1.4239321434364902         , 1.588384991142884          , 1.7199472693079985         , 1.8251970918400906         , 2.0130675521104613         , 2.0596933180820596         , 2.269778267933835          , 2.2996187581556584         , 2.427161752577814          , 2.494639280700641          , 2.583178170613801          , 2.6540092825443287         , 2.7106741720887513         , 1.7538569286922099         , 0.8847325317302784         , 0.05120554450113641        , 0.5465023104523786         , 0.9901117270002915         , 1.3449992602386218         , 1.628909286829286          , 1.7869235732720188         , 2.0170056046709024         , 2.097400627545313          , 2.12715977842994           , 2.150967099137643          , 2.1700129557038053         , 2.150692773541835          , 2.06612289298246           , 1.963910121120061          , 1.8475830362152421         , 1.8581919705360854         , 1.6938947809182634         , 1.527900161809106          , 1.3951044665217809         , 1.2543110428770212         , 1.141676303961214          , 0.9133410431689702         , 0.7306728345351743         , 0.584538267628139          , 0.36396001185781224        , 0.1874974072415518         , 0.08088419096344282        , 0.10807698430374378        , 0.3283596593472903         , 0.47002893196722956        , 0.6524780848929786         , 0.8329942746484764         , 0.9428503590379773         , 1.1344058287942753         , 1.2530933371844128         , 1.4517139461412223         , 1.57605356589177           , 1.710082129107107          , 1.9209755819240757         , 1.951462874518053          , 2.079523310837933          , 2.251085394723635          , 2.3537781944172966         , 2.4013755667573267         , 2.5085671994591494         , 2.5943205056206082         , 2.662923150549775          , 2.6832483990782086         , 1.766473177698675          , 0.8948255309354507         , 0.05927994386527414        , 0.5054859235461693         , 0.9572986174753241         , 1.3878625074484463         , 1.6631998845971454         , 1.8143560514863057         , 1.969837852412533          , 2.0942232931535165         , 2.159174778331403          , 2.1420222316439133         , 2.0937433268790233         , 0.6403154552950342         , 3.634571725551887          , 2.147406297313725          , 2.098050579414871          , 1.9203385354361915         , 1.7781689002532486         , 1.6989900595217933         , 1.462862649862132          , 1.3430744569642017         , 1.1781301678160596         , 1.0461747364975447         , 0.8369397891980357         , 0.600438096528629          , 0.4112367423931031         , 0.2944325264995822         , 0.06276168412516964        , 0.08801812235946109        , 0.2777557023769648         , 0.49865950122076685        , 0.6062688054660099         , 0.7614699836920015         , 0.9201877936876955         , 1.0471620416842513         , 1.2524120423261933         , 1.416612042839747          , 1.5479720432505895         , 1.6876169109941617         , 1.8684465400188197         , 1.9439965084087472         , 2.0735502179504883         , 2.1771931855838806         , 2.294664427105493          , 2.4577551551525816         , 2.450000267930656          , 2.6165806952276114         , 2.6461744348204777         , 2.70440629390967           , 2.0598544328830384         , 0.9913030654233441         , 0.3092463085300854         , 0.37462656664411875        , 0.921724866783482          , 1.255732904650275          , 1.5920530697735078         , 1.7919954670422957         , 1.986506252272225          , 2.038444278211471          , 2.0799946989628673         , 2.32057624005338           , 2.0638021965321            , 2.203951635864068          , 2.0396162480104487         , 2.0118185399722512         , 1.9550235061267924         , 1.8059168768057288         , 1.6866315733488775         , 1.6257601979982956         , 0.8073607539102211         , 2.9134687406064477         , 2.2962181250702582         , 0.9039390798539264         , 0.7231512638831411         , 0.5094072762767152         , 0.3729689536064709         , 0.1947045606404778         , 0.05209304626768333        , 0.13110990006035136        , 0.34678599195257825        , 0.4156562632216599         , 0.6089799498965239         , 0.7981957666513123         , 0.9150115526402451         , 1.1466916510909884         , 1.2629219950217825         , 1.4250200049962187         , 1.5546984129757666         , 1.7275548741892035         , 1.7967263083301561         , 1.955734057887614          , 2.1520539923633795         , 2.1708824702843947         , 2.3241727222808035         , 2.412248056463032          , 2.482708323808814          , 2.5736334051003396         , 2.6809303375484594         , 2.697654148677156          , 1.9853389818672282         , 1.035361306855394          , 0.20626543201612807        , 0.387897533025486          , 0.8977847724736765         , 1.3056945640322295         , 1.597465529864172          , 1.7963254351148263         , 1.95541335931535           , 2.117240566090669          , 2.1430317292662258         , 2.129107792391771          , 2.187082377722007          , 2.1989051785712954         , 2.0701359495911302         , 1.9671205664069973         , 1.9883788621043887         , 1.8326011615878055         , 1.7079790011745388         , 1.5737244054290276         , 1.4317638614177182         , 1.3181954262086697         , 1.158226943211633          , 0.9611384219842057         , 0.8380244724171627         , 0.5667489756890323         , 0.38428544572142714        , 0.20375775433244184        , 0.09389246863615339        , 0.16678409699537397        , 0.34076848208569605        , 0.5145128575728526         , 0.6189514905476794         , 0.7370592643424395         , 1.0043298204527442         , 1.07991879568139           ])
#--1
#y = np.array([-0.036313812       , 0.007668578        , 0.120373704        , 0.112618798        , 0.0248157          , 0.085144514        , 0.080345394        , 0.019396771        , 0.015517417        , 0.028733768        , -0.009652655       , 0                  , -2.273818704       , -1.557937611       , -0.246760225       , -2.000749894       , -0.848939585       , -1.418004704       , -0.430714476       , -0.556729429       , -0.608678798       , -0.779553647       , -0.050101873       , -0.431757527       , -0.867640726       , -0.122918373       , -0.065695029       , -0.546231017       , -0.485163071       , -0.378963553       , -0.359827636       , -0.471830349       , -0.355136421       , -0.300428971       , -0.355008783       , -0.147178016       , -0.070871993       , -0.189664962       , -0.410781299       , -0.043888797       , -0.079990582       , -0.280230273       , -0.154824922       , -0.060865326       , -0.180687123       , -0.160869533       , 0.039554031        , -0.049994857       , -1.035505791       , -0.089932121       , -0.333063049       , -0.76828535        , 0.042362255        , -0.31199693        , -0.251053142       , -0.140932816       , -0.462527914       , -0.284986588       , -0.146390098       , -0.346398207       , -0.166959683       , -0.017581731       , -0.070950561       , -0.170999291       , -0.267358109       , -0.181246818       , -0.161317289       , -0.009539406       , 0.019730892        , -0.028136996       , -0.071559026       , -0.110286683       , -0.088229346       , 0.025935135        , -0.059730749       , -0.033535922       , 2.021533415        , 1.703400273        , -0.012674376       , 1.983687114        , 1.023915401        , 1.238714092        , 0.346242078        , 0.542196503        , 0.498930187        , 0.860179475        , 0.067989868        , 0.380788585        , 0.945184372        , 0.131913828        , 0.105531062        , 0.423104608        , 0.504095453        , 0.336066287        , 0.291820679        , 0.434584198        , 0.350779572        , 0.264303823        , 0.39304938         , 0.069641986        , 0.052581996        , 0.156361738        , 0.349487115        , 0.071511802        , 0.053129483        , 0.291381062        , 0.194691958        , 0.046949295        , 0.196410125        , 0.14428639         , -0.015129564       , 0.185792335        , 1.031304561        , 0.109266877        , 0.53274021         , 0.053024294        , -0.30097014        , 0.108863004        , -0.12381003        , -0.148007528       , 0.102086188        , 0.042166053        , 0.013333049        , 0.258550836        , -0.156275649       , -0.043546149       , -0.056768854       , 0.0647438          , 0.235393178        ])
#--2
#y = np.array([0.184234584             , 0.04946866              , -0.130213312            , -0.038891312            , -0.045336929            , -0.01586975             , 0.085223207             , 0.068178565             , -0.02705632             , 0.006161267             , 0.02151714              , -0.021274647            , 0.032654888             , 0.009804076             , 0.024163095             , 0.026542451             , -0.039965419            , -0.006333166            , 0.024197627             , 0.003038267             , 0.017604035             , -0.004583443            , -0.003666754            , -0.036817822            , 0.004997203             , -0.044961741            , -0.009711618            , -0.009576097            , 0.044371599             , 0.013052305             , -0.017297714            , 0.02467331              , 0.012901021             , 0.026076811             , 0.030910949             , 0.013425192             , 0.029574908             , 0.009352028             , -0.020217227            , 0.032785722             , -0.006411091            , -0.023022716            , 0.069172908             , -0.004942638            , 0.019307875             , -0.017193369            , 0.015747584             , 0.018371946             , -0.007195796            , 0.019101971             , 0.721114419             , 0.160735755             , 0.079629101             , 0.394179929             , 0.026956563             , 0.27627173              , 0.138662364             , 0.094610057             , 0.169109472             , 0.18832704              , 0.064982501             , 0.186624635             , 0.149299708             , 0.033760635             , -0.001551202            , 0.161957383             , 0.124290323             , 0.022053553             , 0.099129608             , 0.042673984             , 0.021899311             , 0.033839284             , 0.059711096             , 0.015129208             , 0.036577486             , -2.012344803            , -1.760834311            , 0.01568665              , -2.047502654            , -1.058647998            , -1.202642277            , -0.308211631            , -0.585205871            , -0.500698013            , -0.894233404            , -0.078913177            , -0.356887563            , -0.954623265            , -0.094585397            , -0.108307987            , -0.478162454            , -0.500160743            , -0.318529422            , -0.304130795            , -0.462528207            , -0.37895228             , -0.275176155            , -0.404092746            , -0.105975981            , -0.066177684            , -0.130655991            , -0.399858865            , -0.073011942            , -0.03800976             , -0.350463861            , -0.173652732            , -0.061914518            , -0.147041385            , -0.133952942            , -0.011463638            , -0.15633116             , -1.03897566             , -0.135769666            , -0.544177647            , -0.676059677            , -0.079139366            , -0.193870169            , -0.286738357            , -0.03585747             , -0.497628687           ])
#--3
#y = np.array([-0.19206438             , -0.153651504            , -0.002092466            , -0.001673973            , -0.019754967            , -0.068843436            , -0.083634459            , -0.070987526            , -0.055330586            , 0.0116248               , 0.015225064             , 1.816415642             , 1.929772571             , 0.153932553             , 1.935991338             , 1.165276959             , 1.102067091             , 0.215855198             , 0.596295406             , 0.383271386             , 0.979810282             , 0.098415176             , 0.323529659             , 1.043420533             , 0.180131255             , 0.046185997             , 0.422766843             , 0.486117541             , 0.213455812             , 0.198886727             , 0.395281097             , 0.372901015             , 0.190919597             , 0.471497993             , 0.083441373             , 0.018428018             , 0.158665411             , 0.335010219             , 0.059930285             , 0.04386427              , 0.300288726             , 0.123486189             , 0.029802432             , 0.167158268             , 0.094144795             , -0.006283337            , 0.286543815             , 0.941533892             , 0.355028074             , 0.6213495               , -0.114914194            , 0.028451858             , -0.058837686            , -0.089680554            , -0.014808351            , 0.325040841             , 0.045194101             , 0.081034826             , 0.225172315             , -0.150338797            , -0.058462459            , -0.123144557            , 0.081002534             , 0.199440662             , 0.171792405             , -0.05840409             , -0.124290323            , -0.030927223            , -0.063358555            , 0.018672453             , 0.096537135             , 0.028270204             , -0.058983009            , -0.014546738            , -0.021002278            , 0.033121657             , -0.022462178            , 0.030989761             , 0.074697333             , 0.010798363             , -0.035776956            , 0.021919967             , 0.028959858             , 0.028063837             , 0.022451069             , 0.017960856             , -0.018270985            , -0.001970678            , -0.040253141            , 0.000437157             , 0.040609825             , -0.012927896            , -0.026662152            , 0.067580894             , 0.006881293             , 3.879123314             , 6.396399306             , 0.529543296             , 1.087607485             , 0.185501265             , 2.658610524             , 4.18068466              , 2.550958122             , 1.126855765             , 1.458992154             , 3.194293729             , 2.360108515             , 1.102285539             , 0.155050269             , 1.411883896             , 1.837643685             , 1.384435816             , 0.118229087             , 0.170614041             , 0.230496789             , 0.496789056             , 1.433740736             , 0.836915733             , 0.422959909             , 0.079827832             , 2.666847758             , 1.627563336             , 4.764077094             , 5.285903947             , 4.632639062             , 3.730591001             , 4.509336839             , 4.048937394             ])
#--4
#y = np.array([1.199170601           , 0.836937722           , 2.986966678           , 2.667010529           , 0.33026671            , -0.013223819          , 0.768693043           , -0.009296264          , 0.562297762           , 0.300874178           , 0.656846404           , 0.737634972           , -0.050445527          , 3.744464797           , -1.453347476          , 2.51941328            , 3.257350786           , 2.740125064           , 1.861623402           , 1.19954755            , 2.183625628           , 3.550242216           , 3.040111746           , 1.705856761           , 2.205156886           , 0.939973866           , -0.329209944          , -0.263367955          , 2.94534E-05           , 1.122449818           , 0.657708181           , 0.306292062           , 1.150784465           , 1.275300271           , 2.954140606           , 2.268839014           , 0.304362074           , 0.259077679           , 0.154222681           , 1.608135455           , 2.889932104           , 2.49962378            , 1.355065561           , 0.60669729            , 0.024322507           , 0.095265856           , 0.576580696           , 0.730541826           , 0.596529991           , -0.63226242           , -0.481330185          , -0.109691374          , 0.369202267           , -0.13938294           , 0.062546849           , 0.044764224           , 0.900762608           , 0.598211328           , 0.274571131           , 1.325325693           , 1.524766638           , 1.112589762           , 1.155269121           , 1.630048139           , 1.532516194           , 1.621768942           , 2.276653062           , 2.144493443           , 1.705252027           , 2.02107496            , 1.567900465           , 0.907523888           , 0.607700311           , 0.172931481           , 0.747748275           , 0.774077421           , 1.32101482            , 1.73000503            , 1.469683155           , 1.436863876           , 2.015399876           , 2.084013073           , 1.659866533           , 1.649393966           , 1.960068677           , 1.784292749           , 1.553912917           , 1.785358597           , 1.670961407           , 1.01485262            , 0.257387245           , 3.388685564           , 4.285812481           , 2.346089321           , 0.057209909           , -2.475588589          , -4.909881166          , -0.370212816          , 0.017075637           , 0.780692732           , -2.693349804          , -3.57919612           , -1.431291418          , 0.32783193            , -0.342382221          , -2.664761533          , -1.55653506           , 0.258236706           , 1.480081984           , 0.453316571           , 0.228014622           , 0.545528016           , 1.533931283           , 1.79242743            , 2.233447328           , 1.878282142           , 0.825352581           , 1.74555106            , 1.645318324           , 1.173930962           , -2.337033896          , -1.231934942          , -4.102636345          , -4.314338609          , -3.080194652          , -2.015360272          , -2.905635103          , -2.275548579          ])
#---DCU1923ZeroOrder(4_8)2017-11-10 11_51_02_831016---U0
#y = np.array([241, 229, 218, 205, 187, 185, 134, 143, 140, 126, 103, 113, 61, 56, 63, 21, 0, -4, -34, -46, -61, -75, -137, -116, -175, -153, -155, -194, -223, -131, -241, -232, -250, -227, -277, -299, -302, -317, -315, -294, -350, -397, -377, -377, -393, -407, -411, -417, -433, -425, -447, -455, -433, -471, -503, -465, -487, -496, -502, -501, -517, -529, -520, -535, -552, -582, -559, -568, -539, -580, -596, -591, -600, -603, -620, -609, -621, -634, -626, -633, -639, -616, -641, -638, -646, -642, -636, -629, -630, -638, -624, -630, -617, -609, -591, -643, -587, -567, -560, -552, -548, -535, -523, -511, -489, -514, -477, -457, -468, -440, -416, -427, -408, -384, -393, -381, -369, -349, -363, -339, -305, -315, -307, -359, -274, -266, -241, -247, -244, -217, -213, -209, -183, -164, -181, -146, -150, -166, -102, -69, -68, -50, -40, -31, -2, 2, 31, 54, 80, 86, 91, 149, 131, 153, 162, 172, 194, 204, 222, 238, 249, 281, 280, 297, 284, 314, 323, 356, 357, 354, 368, 392, 388, 398, 414, 420, 417, 461, 447, 413, 430, 463, 455, 484, 488, 486, 505, 514, 518, 519, 537, 541, 549, 566, 562, 557, 577, 580, 595, 598, 600, 635, 610, 619, 612, 620, 622, 615, 642, 670, 651, 640, 690, 648, 639, 644, 638, 624, 624, 626, 606, 609, 602, 556, 586, 596, 565, 558, 550, 541, 527, 512, 515, 497, 478, 489, 486, 457, 458, 417, 408, 395, 398, 375, 369, 355, 353, 339, 343, 302, 308, 325, 292, 276, 256, 253, 245, 216, 218, 216, 187, 180, 155, 144, 136, 112, 97, 83, 34, 49, 26, 22, 4, -15, -32, -50, -53, -83, -93, -108, -129, -168, -164, -166, -195, -215, -243, -236, -256, -266, -279, -300, -284, -321, -374, -342, -357, -369, -379, -371, -391, -409, -395, -419, -434, -424, -446, -464, -465, -467, -486, -480, -482, -495, -506, -499, -512, -530, -539, -538, -540, -551, -562, -563, -597, -583, -590, -591, -594, -608, -610, -607, -647, -626, -621, -529, -629, -630, -637, -649, -646, -636, -624, -637, -622, -620, -624, -613, -616, -600, -573, -592, -574, -571, -565, -569, -544, -517, -536, -516, -518, -472, -479, -452, -453, -437, -409, -436, -410, -387, -381, -402, -363, -344, -345, -336, -333, -310, -308, -291, -274, -273, -263, -252, -224, -223, -211, -196, -174, -164, -159, -117, -127, -105, -95, -81, -63, -52, -32, -15, -17, 11, 32, 61, 95, 78, 88, 136, 135, 148, 165, 179, 195, 204, 235, 245, 256, 294, 283, 281, 330, 317, 318, 349, 350, 391, 373, 388, 398, 392, 430, 423, 425, 454, 443, 452, 478, 481, 460, 483, 479, 492, 507, 524, 516, 525, 543, 509, 548, 560, 563, 563, 571, 585, 577, 596, 609, 627, 617, 628, 613, 632, 626, 628, 641, 647, 647, 648, 641, 654, 609, 631, 644, 597, 625, 614, 587, 609, 611, 594, 581, 586, 570, 552, 550, 537, 496, 511, 512, 488, 481, 454, 413, 443, 439, 420, 416, 398, 390, 368, 377, 361, 330, 446, 333, 325, 307, 311, 284, 269, 274, 249, 233, 246, 210, 206, 193, 167, 162, 162, 125, 112, 95, 85, 68, 35, 44, 18, -9, -6, -29, -69, -49, -86, -148, -116, -130, -145, -166, -191, -201, -215, -217, -237, -251, -251, -283, -281, -310, -320, -334, -329, -352, -367, -377, -366, -422, -407, -427, -433, -431, -414, -440, -453, -448, -468, -481, -483, -489, -503, -506, -510, -510, -523, -516, -561, -543, -544, -556, -577, -573, -573, -579, -598, -588, -623, -615, -652, -643, -625, -614, -639, -642, -636, -645, -649, -642, -630, -652, -635, -624, -606, -618, -641, -601, -608, -580, -564, -566, -577, -565, -556, -517, -529, -523, -492, -495, -491, -476, -457, -452, -435, -429, -418, -403, -392, -387, -371, -361, -356, -347, -308, -331, -326, -310, -288, -280, -270, -248, -246, -258, -231, -208, -161, -123, -163, -158, -132, -125, -112, -88, -68, -61, -48, -26, -9, -8, 46, 37, 79, 70, 84, 90, 121, 134, 143, 167, 171, 201, 212, 243, 227, 259, 294, 294, 296, 343, 322, 320, 344, 355, 366, 372, 379, 367, 403, 444, 405, 433, 475, 448, 453, 460, 483, 464, 482, 488, 461, 502, 535, 514, 530, 545, 536, 545, 552, 559, 557, 576, 591, 578, 593, 589, 610, 614, 618, 625, 630, 659, 619, 640, 653, 646, 657, 606, 639, 575, 640, 639, 638, 624, 615, 547, 599, 607, 589, 583, 581, 562, 559, 526, 535, 544, 524, 503, 518, 487, 463, 455, 440, 433, 422, 384, 389, 389, 402, 387, 351, 347, 335, 325, 316, 303, 309, 283, 271, 274, 248, 251, 223, 210, 192, 202, 166, 147, 147, 125, 108, 95, 80, 43, 47, 27, 14, 3, -14, -41, -50, -52, -86, -110, -122, -133, -160, -170, -186, -192, -211, -229, -275, -261, -221, -282, -308, -313, -319, -335, -345, -352, -364, -379, -384, -416, -403, -382, -422, -430, -441, -452, -466, -462, -459, -496, -489, -476, -545, -508, -513, -525, -521, -515, -540, -556, -546, -556, -563, -566, -573, -591, -596, -598, -610, -619, -613, -622, -628, -625, -639, -646, -632, -643, -656, -635, -639, -630, -638, -629, -622, -622, -611, -609, -601, -589, -586, -583, -563, -556, -534, -538, -530, -546, -501, -495, -489, -474, -458, -444, -451, -422, -416, -427, -401, -387, -395, -372, -360, -375, -331, -319, -321, -298, -283, -265, -265, -269, -243, -233, -200, -208, -213, -182, -158, -164, -137, -112, -108, -90, -65, -44, -40, -39, -12, 6, 46, 44, 53, 78, 94, 107, 121, 146, 134, 165, 381, 362, 146, 73, 38, 25, -16, -43, -87, -115, -142, -177, -201, -155, -215, -201, -160, -203, -173, -137, -157, -151, -105, -146, -158, -158, -171, -181, -177, -162, -194, -164, -137, -147, -155, -140, -146, -64, -73, -70, -50, -8, 34, 84, 143, 161, 232, 252, 304, 353, 398, 477, 518, 574, 646, 682, 744, 840, 847, 947, 1007, 1027, 1086, 1144, 1208, 1258, 1314, 1373, 1437, 1493, 1559, 1614, 1611, 1759, 1777, 1822, 1896, 1946, 1989, 2053, 2133, 2171, 2234, 2318, 2359, 2416, 2476, 2542, 2577, 2684, 2742, 2796, 2780, 2937, 2972, 3070, 3128, 3216, 3266, 3357, 3383, 3459, 3502, 3584, 3640, 3719, 3766, 3818, 3867, 3923, 3970, 4017, 4078, 4087, 4151, 4206, 4201, 4263, 4316, 4327, 4351, 4380, 4406, 4407, 4439, 4451, 4492, 4488, 4531, 4518, 4524, 4506, 4536, 4546, 4526, 4553, 4557, 4559, 4565, 4561, 4563, 4565, 4518, 4554, 4566, 4537, 4527, 4522, 4469, 4497, 4478, 4480, 4433, 4414, 4400, 4361, 4369, 4312, 4309, 4249, 4206, 4162, 4125, 4087, 4032, 3982, 3929, 3872, 3816, 3761, 3684, 3623, 3536, 3475, 3396, 3334, 3266, 3181, 3094, 3034, 2939, 2833, 2726, 2689, 2599, 2562, 2438, 2309, 2276, 2181, 2127, 2007, 1936, 1846, 1760, 1678, 1594, 1506, 1428, 1353, 1285, 1192, 1113, 1019, 963, 874, 788, 708, 629, 557, 479, 395, 293, 246, 161, 86, -6, -55, -157, -221, -323, -406, -464, -573, -667, -792, -816, -895, -991, -1054, -1136, -1213, -1287, -1359, -1425, -1500, -1568, -1621, -1685, -1762, -1822, -1968, -1939, -1952, -2046, -2089, -2120, -2177, -2221, -2262, -2301, -2343, -2365, -2390, -2422, -2450, -2478, -2496, -2529, -2535, -2590, -2581, -2605, -2618, -2632, -2663, -2662, -2681, -2690, -2705, -2719, -2723, -2721, -2735, -2736, -2734, -2738, -2740, -2740, -2729, -2726, -2714, -2706, -2744, -2674, -2660, -2676, -2620, -2585, -2562, -2534, -2492, -2463, -2428, -2389, -2341, -2316, -2218, -2203, -2137, -2087, -2030, -1979, -1910, -1840, -1786, -1724, -1655, -1578, -1516, -1465, -1372, -1316, -1216, -1167, -1091, -1003, -953, -867, -810, -725, -663, -593, -515, -460, -372, -291, -251, -170, -113, -35, 25, 105, 164, 241, 313, 388, 433, 521, 626, 653, 747, 898, 892, 961, 1037, 1137, 1185, 1272, 1352, 1409, 1502, 1582, 1661, 1738, 1854, 1875, 1961, 2033, 2105, 2164, 2233, 2313, 2373, 2426, 2489, 2559, 2605, 2677, 2709, 2767, 2827, 2853, 2903, 2937, 2973, 3015, 3048, 3083, 3098, 3133, 3158, 3166, 3208, 3222, 3242, 3260, 3287, 3295, 3308, 3309, 3331, 3350, 3343, 3365, 3325, 3438, 3389, 3319, 3390, 3392, 3391, 3379, 3391, 3377, 3349, 3366, 3343, 3316, 3300, 3291, 3262, 3275, 3225, 3191, 3168, 3140, 3095, 3049, 3033, 2976, 2912, 2860, 2818, 2773, 2688, 2634, 2552, 2509, 2440, 2366, 2295, 2227, 2160, 2078, 2019, 1921, 1837, 1752, 1677, 1599, 1519, 1425, 1344, 1264, 1183, 1103, 1020, 936, 821, 774, 711, 633, 536, 427, 382, 307, 233, 162, 96, 15, -48, -118, -191, -277, -335, -388, -413, -559, -607, -656, -739, -812, -877, -943, -1029, -1100, -1177, -1265, -1326, -1389, -1472, -1541, -1592, -1700, -1762, -1851, -1898, -1977, -2039, -2106, -2169, -2234, -2293, -2363, -2413, -2448, -2521, -2567, -2608, -2671, -2703, -2747, -2786, -2818, -2859, -2882, -2957, -2964, -2975, -2985, -3020, -3039, -3063, -3074, -3092, -3109, -3116, -3137, -3154, -3193, -3148, -3190, -3228, -3183, -3211, -3224, -3220, -3220, -3226, -3242, -3219, -3224, -3237, -3217, -3201, -3194, -3205, -3173, -3136, -3126, -3113, -3083, -3067, -3042, -2991, -2981, -2950, -2895, -2847, -2811, -2764, -2724, -2666, -2601, -2551, -2470, -2423, -2373, -2304, -2228, -2173, -2102, -2005, -1956, -1889, -1839, -1744, -1673, -1566, -1519, -1456, -1377, -1302, -1218, -1160, -1078, -1019, -943, -869, -821, -725, -626, -582, -510, -437, -355, -293, -226, -145, -108, -10, 42, 129, 212, 306, 358, 441, 498, 594, 669, 746, 832, 919, 981, 1065, 1154, 1227, 1319, 1377, 1452, 1550, 1606, 1683, 1754, 1830, 1905, 1970, 2038, 2104, 2150, 2229, 2283, 2346, 2406, 2449, 2506, 2552, 2605, 2645, 2696, 2726, 2768, 2821, 2835, 2854, 2919, 2921, 2954, 2971, 3002, 3017, 3030, 3065, 3073, 3065, 3100, 3121, 3134, 3157, 3152, 3125, 3176, 3175, 3182, 3179, 3192, 3187, 3174, 3183, 3178, 3163, 3169, 3147, 3156, 3122, 3111, 3082, 3080, 3040, 3019, 2985, 2945, 2916, 2878, 2853, 2788, 2746, 2713, 2650, 2592, 2566, 2475, 2413, 2347, 2285, 2215, 2143, 2079, 1974, 1923, 1850, 1783, 1692, 1629, 1533, 1448, 1367, 1292, 1201, 1123, 1052, 985, 883, 825, 710, 644, 527, 485, 416, 343, 264, 184, 115, 48, -31, -103, -151, -235, -302, -378, -438, -506, -577, -655, -725, -773, -855, -927, -988, -1064, -1147, -1200, -1285, -1427, -1443, -1514, -1586, -1660, -1737, -1805])
#---DCU1923ZeroOrder(4_8)2017-11-07 11_56_54_491016---U0
#y = np.array([-19, -10, 5, -20, -6, -9, -23, -5, -18, -19, -16, -26, -9, -16, -48, -2, -19, -17, -12, -16, -20, -1, -12, -7, -11, -1, -4, -18, 5, -2, -14, 21, 1, 4, 21, 0, -2, 14, 10, 3, 8, 12, 16, 19, 17, 19, -17, 29, 19, 12, 23, 27, 26, 59, 18, 43, 28, 35, 23, 31, 36, -42, 27, 64, 35, 32, 33, 39, 39, 31, 36, 35, 32, 37, 36, 38, 72, 20, 37, 33, 31, 26, 30, 34, 34, 25, 34, 25, 28, 33, 4, 38, 82, 29, 29, 6, 26, 18, 26, 30, 49, 24, 24, 7, 21, 45, 17, 17, 22, 33, 26, 18, 18, 29, 37, 21, 17, -17, 19, 2, 14, 21, 23, 19, 23, 24, 26, 34, 18, -2, 14, 18, 13, 46, 23, 10, 15, 31, 20, 13, 27, 25, 10, 39, 19, 28, 55, 12, 28, 19, 17, 5, 9, 14, 8, 1, 5, 9, 2, 7, 0, -16, 5, -2, -8, 12, -9, -17, -15, -7, 15, -14, -1, -17, -19, 0, -15, -25, 18, -18, -23, -26, -18, -65, -24, -26, -19, -26, 6, -38, -30, 5, -30, -37, -35, -26, -36, -37, -21, -88, -38, 16, -123, -32, 9, -32, -34, -35, -32, -31, -33, -20, -45, -33, -29, -50, -33, -17, -32, -36, -6, -31, -28, -35, -16, -17, -10, -22, -14, -21, -27, 24, -22, -29, -15, -24, 39, -21, -34, -54, -28, 36, 11, -20, -15, -40, -18, -24, -19, -15, -18, -28, -17, -17, -36, -12, -17, -47, -15, -24, -31, -17, -19, -19, -17, -13, -17, -22, -7, -14, -24, -10, -16, 3, -15, -17, -19, -6, 0, 3, -10, -1, -5, -8, -5, -4, 5, 12, 1, 2, 2, 6, 2, 11, 9, 5, 9, 17, 9, 18, 12, -14, 14, 100, 28, 21, 27, 30, 12, 27, 33, 22, 27, 37, 18, 35, 11, 21, 34, 32, 36, 25, 34, 42, 22, 36, 45, 4, 35, 55, 42, 37, 51, 5, 31, 27, 34, 6, 20, 33, 35, 30, 39, 31, 29, 46, 20, 23, 31, 27, 34, 50, 25, 26, 68, 22, -5, 51, 26, 36, 25, 17, 25, 20, 19, 21, 16, 17, 20, 14, 28, 19, -6, 48, 19, 19, 24, 19, 21, 17, 11, 22, 9, 26, 22, -2, 41, 19, 6, -4, 22, 9, 21, 30, 22, 23, 34, 19, 5, 42, 14, 21, 37, 24, 16, 15, 4, 7, 8, 13, 15, 7, 31, -10, 0, 37, 7, -5, -5, -1, -15, -8, -1, -13, -8, -4, -33, -12, -61, -29, -16, -21, -17, -24, -25, -14, -26, -28, -19, -35, -28, -66, -41, -25, -20, -27, -40, -10, -31, -33, -31, -32, -39, -34, -23, -47, -35, -31, -33, -35, -27, -32, -34, -34, -31, -34, -1, -25, -31, -9, -30, 36, -31, -25, -27, -34, -26, -27, -4, -23, -29, -33, -20, -25, -54, 5, -23, -22, -24, -17, -24, -35, -6, -18, -21, -35, -19, -10, -16, -25, -32, -16, -18, -22, -11, -10, -18, -15, -16, -21, -5, -4, -21, -29, -14, -18, -35, -18, -8, -16, -25, -24, -43, -22, 7, -25, -12, 0, -12, -18, 2, -15, -12, -12, -1, 5, -9, -19, -17, -7, -3, -9, -1, 24, 2, -5, 5, 21, -8, 7, 29, 13, 13, 7, -18, 19, 19, 15, 22, 23, 19, 20, 22, 33, 44, 26, 32, 18, 27, 72, 23, 28, 38, 15, 34, 32, 38, 40, 35, 36, 27, 43, 32, -21, 31, 24, 18, 32, 40, 36, 31, 16, 32, 48, 15, 29, 35, 89, 34, 20, 28, 49, 25, 22, 60, 35, 26, 29, 29, 38, 45, 24, 24, 27, 26, 13, 26, 32, 23, 8, 9, 23, 23, 34, 20, 39, 23, 19, 20, 39, 14, 13, 23, 25, 19, 19, 40, 9, 19, 37, 23, 16, 33, 21, 23, 19, 23, 17, 21, 19, 11, 21, -6, -9, 21, 48, 20, 14, 18, 26, -1, 10, 16, -13, 8, 8, 0, 13, 50, 1, 7, 0, 1, -11, -5, 10, 1, -7, 17, -5, -10, 10, -15, -10, 4, -10, -26, -28, -18, -12, -20, -24, -45, -26, -26, -35, -33, -28, -16, -30, -23, -30, -32, -26, -33, -39, -24, -37, -34, 2, -32, -84, -33, -26, -31, -38, -30, -29, -34, -30, -32, -44, -14, -32, -36, -23, -33, -29, -28, -20, -31, -35, -34, -23, -19, -24, -27, -33, 1, -26, -29, -19, -28, -19, -28, -11, -14, -23, -3, -16, -22, -18, -24, -16, -21, -23, -14, -18, -27, -30, -20, -11, -18, -19, -12, -24, -22, -29, -14, -25, -20, -13, -12, -16, -3, -12, -14, -31, -75, -14, -5, -36, -18, -16, -12, -23, -14, -6, -44, -14, 2, 21, -4, 29, -11, -1, -14, -8, -4, 4, 3, -10, 5, 7, 10, 8, 20, -3, 14, 0, 13, 14, 21, 14, 20, 42, 25, 21, 2, 26, 23, 1, 26, 39, 26, 34, 31, 28, 44, 32, 32, 28, 32, 34, 62, 34, 36, 53, 34, 35, 37, 33, 32, 22, 26, 30, 22, 6, 32, 36, 40, 28, 11, 34, 36, 28, 25, 30, 21, 34, 47, 29, 34, 63, 28, 40, 31, 33, 19, 46, 29, 27, 24, 35, 42, 22, 24, 4, 19, 52, 21, 22, 21, 20, 8, 20, 22, 22, 25, 25, 9, 21, 50, 22, 22, 39, 24, 23, 20, 30, 23, 20, 35, 38, 20, 35, -14, 22, 35, 26, 15, 19, 25, 14, 13, 25, -13, 9, 25, 5, 12, 4, 1, 4, 24, -9, 7, -1, -15, 2, -11, -3, -13, -25, -9, -57, -16, -11, -22, -21, -3, -19, -25, -24, -23, -9, 4, -25, -73, -2, -27, -40, -29, -26, -31, -25, -23, -27, -29, -24, -27, -55, -28, -37, -56, -30, -36, -33, -30, -29, -35, -27, -53, -36, -57, -7, -32, -42, -39, -28, -28, -13, -35, -57, -35, -20, -33, -30, -18, -54, -29, -12, -22, -22, -7, -23, -46, -24, -8, -25, -21, -8, -9, -17, -26, -36, -19, 2, -22, -24, -17, -11, -23, -22, -8, -21, -23, 6, -3, -19, -81, -28, -20, -1, -25, -23, -21, -17, -43, -20, -8, 3, -19, -26, -14, -18, -2, -38, -24, -18, -15, -6, -19, -51, -2, -10, -10, -8, -10, -9, -80, -14, -7, 0, -9, 8, 2, 7, 9, 6, -33, 42, 13, 54, 15, 27, 17, 13, 20, 23, 18, 68, 24, 26, 17, 18, 56, 32, 23, 15, 23, 34, 33, 22, 30, 34, 8, 50, 30, -12, 32, 35, 29, 55, 38, 38, 34, 43, 39, 33, 45, 26, 36, 30, 42, 33, 96, 324, 809, 102, 77, 53, 94, 144, 125, 153, 174, 187, 205, 214, 231, 241, 267, 285, 286, 304, 341, 341, 351, 368, 403, 390, 439, 408, 427, 436, 442, 444, 460, 466, 476, 482, 499, 486, 499, 514, 510, 519, 482, 508, 518, 513, 522, 515, 523, 528, 525, 527, 526, 513, 514, 521, 511, 505, 503, 493, 579, 584, 553, 460, 442, 434, 423, 421, 392, 313, 367, 358, 340, 323, 314, 295, 274, 264, 242, 184, 220, 178, 127, 174, 122, 104, 86, 67, 43, 12, 9, -25, -73, -69, -93, -107, -149, -164, -190, -197, -221, -235, -276, -288, -320, -340, -338, -378, -402, -409, -444, -481, -488, -503, -529, -545, -567, -580, -603, -621, -636, -652, -642, -686, -705, -722, -735, -759, -770, -778, -791, -814, -827, -793, -850, -852, -769, -880, -873, -936, -919, -922, -938, -952, -953, -952, -963, -974, -972, -984, -987, -965, -1006, -989, -975, -996, -997, -1001, -985, -987, -974, -968, -981, -969, -954, -1016, -948, -919, -937, -917, -889, -904, -886, -872, -862, -852, -821, -820, -795, -797, -775, -812, -747, -718, -708, -691, -657, -649, -650, -615, -586, -575, -536, -522, -456, -444, -450, -429, -395, -365, -342, -319, -279, -256, -211, -200, -171, -157, -94, -91, -68, -18, 4, 38, 69, 96, 136, 168, 213, 228, 252, 312, 318, 310, 369, 405, 426, 485, 493, 518, 554, 599, 590, 633, 665, 659, 708, 743, 765, 786, 826, 837, 852, 881, 906, 927, 938, 951, 1000, 1003, 1049, 1010, 1049, 1072, 1079, 1088, 1112, 1133, 1134, 1148, 1172, 1172, 1190, 1239, 1216, 1223, 1254, 1238, 1236, 1222, 1241, 1265, 1266, 1272, 1261, 1270, 1280, 1252, 1271, 1296, 1272, 1270, 1268, 1253, 1255, 1240, 1238, 1215, 1222, 1214, 1173, 1182, 1180, 1159, 1151, 1147, 1105, 1104, 1103, 1080, 1048, 1073, 1023, 1001, 981, 959, 927, 979, 893, 857, 839, 820, 793, 759, 736, 710, 681, 661, 617, 582, 572, 510, 437, 449, 409, 364, 334, 300, 249, 198, 178, 132, 86, 55, 3, -38, -85, -129, -193, -213, -256, -303, -363, -383, -459, -495, -521, -539, -624, -669, -710, -760, -791, -839, -901, -936, -980, -1007, -1060, -1096, -1143, -1180, -1223, -1263, -1299, -1317, -1369, -1413, -1442, -1467, -1506, -1539, -1575, -1550, -1627, -1682, -1629, -1702, -1699, -1763, -1782, -1801, -1826, -1855, -1866, -1884, -1902, -1916, -1921, -1956, -1957, -1947, -1975, -1985, -1985, -2001, -2009, -2016, -2014, -2058, -2035, -2012, -2022, -2017, -2009, -2000, -1996, -1987, -1979, -1962, -1959, -1940, -1922, -1923, -1895, -1881, -1855, -1836, -1807, -1746, -1764, -1833, -1721, -1682, -1658, -1624, -1588, -1558, -1525, -1491, -1457, -1404, -1368, -1342, -1271, -1241, -1213, -1167, -1127, -1067, -1028, -987, -921, -881, -832, -757, -719, -679, -596, -561, -547, -442, -382, -327, -269, -199, -144, -91, -26, 34, 105, 192, 223, 285, 356, 421, 484, 545, 619, 655, 739, 806, 844, 930, 1005, 1039, 1117, 1248, 1246, 1299, 1363, 1421, 1473, 1533, 1606, 1642, 1703, 1773, 1809, 1863, 1906, 1959, 2012, 2054, 2101, 2140, 2184, 2240, 2270, 2303, 2335, 2371, 2413, 2452, 2466, 2508, 2536, 2556, 2589, 2613, 2640, 2660, 2696, 2707, 2699, 2728, 2740, 2770, 2769, 2788, 2783, 2784, 2782, 2794, 2795, 2790, 2788, 2775, 2797, 2773, 2762, 2719, 2736, 2719, 2712, 2696, 2676, 2652, 2631, 2605, 2574, 2536, 2522, 2481, 2462, 2417, 2388, 2376, 2300, 2266, 2214, 2173, 2122, 2070, 2024, 1974, 1927, 1878])
#---DCU1923ZeroOrder(4_8)2017-11-07 11_56_54_491016---I0--第25列
#y = np.array([ 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 3, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 3, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 3, 2, 2, 1, 3, 1, 2, 2, 2, 2, 2, 1, 1, 2, 3, 2, 3, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 3, 1, 1, 1, 2, 1, 3, 2, 3, 2, 2, 2, 1, 2, 1, 2, 2, 3, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 3, 3, 2, 2, 3, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 3, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 3, 2, 2, 2, 2, 1, 1, 1, 3, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 0, 3, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 0, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 0, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 0, 1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 3, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 1, 1, 0, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 3, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 2, 3, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 3, 1, 0, 1, 2, 2, 2, 3, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 0, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 0, 1, 1, 1, 0, 1, 2, 2, 1, 1, 0, 1, 1, 2, 2, 0, 2, 1, 1, 2, 1, 0, 2, 1, 1, 2, 1, 2, 0, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 3, 2, 1, 1, 3, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 3, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 1, 2, 2, 3, 1, 2, 1, 1, 2, 2, 1, 2, 3, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 0, 1, 1, 2, 2, 3, 3, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 3, 2, 1, 2, 3, 2, 2, 1, 1, 1, 3, 1, 2, 2, 2, 0, 1, 1, 1, 1, 2, 2, 1, 3, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 3, 2, 1, 1, 1, 1, 0, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 0, 2, 3, 1, 1, 2, 2, 2, 3, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 0, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 3, 1, 3, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 3, 2, 3, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 1, 2, 2, 1, 2, 1, 1, 3, 2, 1, 1, 2, 2, 1, 2, 3, 1, 2, 2, 2, 1, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 3, 2, 1, 0, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 0, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 3, 1, 2, 1, 2, 1, 1, 2, 0, 1, 1, 3, 1, 3, 2, 2, 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#---SAR-device.sdb.await__DCU1923ZeroOrder(4_8)2017-11-07 11_56_54_491016.csv
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#---SAR-device.sdb.await__DCU1923ZeroOrder(4_8)2017-11-10 11_51_02_831016.csv
y = np.array([241, 229, 218, 205, 187, 185, 134, 143, 140, 126, 103, 113, 61, 56, 63, 21, 0, -4, -34, -46, -61, -75, -137, -116, -175, -153, -155, -194, -223, -131, -241, -232, -250, -227, -277, -299, -302, -317, -315, -294, -350, -397, -377, -377, -393, -407, -411, -417, -433, -425, -447, -455, -433, -471, -503, -465, -487, -496, -502, -501, -517, -529, -520, -535, -552, -582, -559, -568, -539, -580, -596, -591, -600, -603, -620, -609, -621, -634, -626, -633, -639, -616, -641, -638, -646, -642, -636, -629, -630, -638, -624, -630, -617, -609, -591, -643, -587, -567, -560, -552, -548, -535, -523, -511, -489, -514, -477, -457, -468, -440, -416, -427, -408, -384, -393, -381, -369, -349, -363, -339, -305, -315, -307, -359, -274, -266, -241, -247, -244, -217, -213, -209, -183, -164, -181, -146, -150, -166, -102, -69, -68, -50, -40, -31, -2, 2, 31, 54, 80, 86, 91, 149, 131, 153, 162, 172, 194, 204, 222, 238, 249, 281, 280, 297, 284, 314, 323, 356, 357, 354, 368, 392, 388, 398, 414, 420, 417, 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# Settings: lag = 30, threshold = 5, influence = 0
# lag for the smoothing, 计算平均值的间隔点数.
lag = 128#128
threshold = 5
# when signal: how much influence for new data? (between 0 and 1)
# 对当前节点做平滑，平滑系数是(0,1)，值越大越受当前值的影响。when 1 is normal influence, 0.5 is half。
influence = 0

# Run algo with settings from above
result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence)

# Plot result
pylab.figure(figsize=(16, 8))
#
pylab.subplot(211)
pylab.plot(np.arange(1, len(y)+1), y)

pylab.plot(np.arange(1, len(y)+1),
           result["avgFilter"], color="cyan", lw=2)

pylab.plot(np.arange(1, len(y)+1),
           result["avgFilter"] + threshold * result["stdFilter"], color="green", lw=2)

pylab.plot(np.arange(1, len(y)+1),
           result["avgFilter"] - threshold * result["stdFilter"], color="green", lw=2)

pylab.subplot(212)
pylab.step(np.arange(1, len(y)+1), result["signals"], color="red", lw=2)
pylab.ylim(-1.5, 1.5)

d5526ba5411f79208072af0c57361c1b9f79fe2d
<mask token>


def thresholding_algo(y, lag, threshold, influence):
    nFlag = 0
    nCount = 0
    signals = np.zeros(len(y))
    filteredY = np.array(y)
    avgFilter = [0] * len(y)
    stdFilter = [0] * len(y)
    avgFilter[lag - 1] = np.mean(y[0:lag])
    stdFilter[lag - 1] = np.std(y[0:lag], ddof=1)
    for i in range(lag, len(y)):
        if abs(y[i] - avgFilter[i - 1]) > threshold * stdFilter[i - 1]:
            if y[i] > avgFilter[i - 1]:
                signals[i] = 1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            else:
                signals[i] = -1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            """
            @2018-11-20 对异常点的数值进行平滑，以便评估下下个点是否为异常点。
             因为不做平滑，由于当前是个异常点，对平均值、方差影响较大，若是下一个点仍是异常点，可能不会识别。
            """
            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i - 1
                ]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
        else:
            signals[i] = 0
            filteredY[i] = y[i]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
    print('Total Outer is : %d' % nCount)
    return dict(signals=np.asarray(signals), avgFilter=np.asarray(avgFilter
        ), stdFilter=np.asarray(stdFilter))


<mask token>

<mask token>


def thresholding_algo(y, lag, threshold, influence):
    nFlag = 0
    nCount = 0
    signals = np.zeros(len(y))
    filteredY = np.array(y)
    avgFilter = [0] * len(y)
    stdFilter = [0] * len(y)
    avgFilter[lag - 1] = np.mean(y[0:lag])
    stdFilter[lag - 1] = np.std(y[0:lag], ddof=1)
    for i in range(lag, len(y)):
        if abs(y[i] - avgFilter[i - 1]) > threshold * stdFilter[i - 1]:
            if y[i] > avgFilter[i - 1]:
                signals[i] = 1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            else:
                signals[i] = -1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            """
            @2018-11-20 对异常点的数值进行平滑，以便评估下下个点是否为异常点。
             因为不做平滑，由于当前是个异常点，对平均值、方差影响较大，若是下一个点仍是异常点，可能不会识别。
            """
            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i - 1
                ]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
        else:
            signals[i] = 0
            filteredY[i] = y[i]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
    print('Total Outer is : %d' % nCount)
    return dict(signals=np.asarray(signals), avgFilter=np.asarray(avgFilter
        ), stdFilter=np.asarray(stdFilter))


<mask token>
pylab.figure(figsize=(16, 8))
pylab.subplot(211)
pylab.plot(np.arange(1, len(y) + 1), y)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'], color='cyan', lw=2)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'] + threshold *
    result['stdFilter'], color='green', lw=2)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'] - threshold *
    result['stdFilter'], color='green', lw=2)
pylab.subplot(212)
pylab.step(np.arange(1, len(y) + 1), result['signals'], color='red', lw=2)
pylab.ylim(-1.5, 1.5)

<mask token>


def thresholding_algo(y, lag, threshold, influence):
    nFlag = 0
    nCount = 0
    signals = np.zeros(len(y))
    filteredY = np.array(y)
    avgFilter = [0] * len(y)
    stdFilter = [0] * len(y)
    avgFilter[lag - 1] = np.mean(y[0:lag])
    stdFilter[lag - 1] = np.std(y[0:lag], ddof=1)
    for i in range(lag, len(y)):
        if abs(y[i] - avgFilter[i - 1]) > threshold * stdFilter[i - 1]:
            if y[i] > avgFilter[i - 1]:
                signals[i] = 1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            else:
                signals[i] = -1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            """
            @2018-11-20 对异常点的数值进行平滑，以便评估下下个点是否为异常点。
             因为不做平滑，由于当前是个异常点，对平均值、方差影响较大，若是下一个点仍是异常点，可能不会识别。
            """
            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i - 1
                ]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
        else:
            signals[i] = 0
            filteredY[i] = y[i]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
    print('Total Outer is : %d' % nCount)
    return dict(signals=np.asarray(signals), avgFilter=np.asarray(avgFilter
        ), stdFilter=np.asarray(stdFilter))


<mask token>
y = np.array([241, 229, 218, 205, 187, 185, 134, 143, 140, 126, 103, 113, 
    61, 56, 63, 21, 0, -4, -34, -46, -61, -75, -137, -116, -175, -153, -155,
    -194, -223, -131, -241, -232, -250, -227, -277, -299, -302, -317, -315,
    -294, -350, -397, -377, -377, -393, -407, -411, -417, -433, -425, -447,
    -455, -433, -471, -503, -465, -487, -496, -502, -501, -517, -529, -520,
    -535, -552, -582, -559, -568, -539, -580, -596, -591, -600, -603, -620,
    -609, -621, -634, -626, -633, -639, -616, -641, -638, -646, -642, -636,
    -629, -630, -638, -624, -630, -617, -609, -591, -643, -587, -567, -560,
    -552, -548, -535, -523, -511, -489, -514, -477, -457, -468, -440, -416,
    -427, -408, -384, -393, -381, -369, -349, -363, -339, -305, -315, -307,
    -359, -274, -266, -241, -247, -244, -217, -213, -209, -183, -164, -181,
    -146, -150, -166, -102, -69, -68, -50, -40, -31, -2, 2, 31, 54, 80, 86,
    91, 149, 131, 153, 162, 172, 194, 204, 222, 238, 249, 281, 280, 297, 
    284, 314, 323, 356, 357, 354, 368, 392, 388, 398, 414, 420, 417, 461, 
    447, 413, 430, 463, 455, 484, 488, 486, 505, 514, 518, 519, 537, 541, 
    549, 566, 562, 557, 577, 580, 595, 598, 600, 635, 610, 619, 612, 620, 
    622, 615, 642, 670, 651, 640, 690, 648, 639, 644, 638, 624, 624, 626, 
    606, 609, 602, 556, 586, 596, 565, 558, 550, 541, 527, 512, 515, 497, 
    478, 489, 486, 457, 458, 417, 408, 395, 398, 375, 369, 355, 353, 339, 
    343, 302, 308, 325, 292, 276, 256, 253, 245, 216, 218, 216, 187, 180, 
    155, 144, 136, 112, 97, 83, 34, 49, 26, 22, 4, -15, -32, -50, -53, -83,
    -93, -108, -129, -168, -164, -166, -195, -215, -243, -236, -256, -266, 
    -279, -300, -284, -321, -374, -342, -357, -369, -379, -371, -391, -409,
    -395, -419, -434, -424, -446, -464, -465, -467, -486, -480, -482, -495,
    -506, -499, -512, -530, -539, -538, -540, -551, -562, -563, -597, -583,
    -590, -591, -594, -608, -610, -607, -647, -626, -621, -529, -629, -630,
    -637, -649, -646, -636, -624, -637, -622, -620, -624, -613, -616, -600,
    -573, -592, -574, -571, -565, -569, -544, -517, -536, -516, -518, -472,
    -479, -452, -453, -437, -409, -436, -410, -387, -381, -402, -363, -344,
    -345, -336, -333, -310, -308, -291, -274, -273, -263, -252, -224, -223,
    -211, -196, -174, -164, -159, -117, -127, -105, -95, -81, -63, -52, -32,
    -15, -17, 11, 32, 61, 95, 78, 88, 136, 135, 148, 165, 179, 195, 204, 
    235, 245, 256, 294, 283, 281, 330, 317, 318, 349, 350, 391, 373, 388, 
    398, 392, 430, 423, 425, 454, 443, 452, 478, 481, 460, 483, 479, 492, 
    507, 524, 516, 525, 543, 509, 548, 560, 563, 563, 571, 585, 577, 596, 
    609, 627, 617, 628, 613, 632, 626, 628, 641, 647, 647, 648, 641, 654, 
    609, 631, 644, 597, 625, 614, 587, 609, 611, 594, 581, 586, 570, 552, 
    550, 537, 496, 511, 512, 488, 481, 454, 413, 443, 439, 420, 416, 398, 
    390, 368, 377, 361, 330, 446, 333, 325, 307, 311, 284, 269, 274, 249, 
    233, 246, 210, 206, 193, 167, 162, 162, 125, 112, 95, 85, 68, 35, 44, 
    18, -9, -6, -29, -69, -49, -86, -148, -116, -130, -145, -166, -191, -
    201, -215, -217, -237, -251, -251, -283, -281, -310, -320, -334, -329, 
    -352, -367, -377, -366, -422, -407, -427, -433, -431, -414, -440, -453,
    -448, -468, -481, -483, -489, -503, -506, -510, -510, -523, -516, -561,
    -543, -544, -556, -577, -573, -573, -579, -598, -588, -623, -615, -652,
    -643, -625, -614, -639, -642, -636, -645, -649, -642, -630, -652, -635,
    -624, -606, -618, -641, -601, -608, -580, -564, -566, -577, -565, -556,
    -517, -529, -523, -492, -495, -491, -476, -457, -452, -435, -429, -418,
    -403, -392, -387, -371, -361, -356, -347, -308, -331, -326, -310, -288,
    -280, -270, -248, -246, -258, -231, -208, -161, -123, -163, -158, -132,
    -125, -112, -88, -68, -61, -48, -26, -9, -8, 46, 37, 79, 70, 84, 90, 
    121, 134, 143, 167, 171, 201, 212, 243, 227, 259, 294, 294, 296, 343, 
    322, 320, 344, 355, 366, 372, 379, 367, 403, 444, 405, 433, 475, 448, 
    453, 460, 483, 464, 482, 488, 461, 502, 535, 514, 530, 545, 536, 545, 
    552, 559, 557, 576, 591, 578, 593, 589, 610, 614, 618, 625, 630, 659, 
    619, 640, 653, 646, 657, 606, 639, 575, 640, 639, 638, 624, 615, 547, 
    599, 607, 589, 583, 581, 562, 559, 526, 535, 544, 524, 503, 518, 487, 
    463, 455, 440, 433, 422, 384, 389, 389, 402, 387, 351, 347, 335, 325, 
    316, 303, 309, 283, 271, 274, 248, 251, 223, 210, 192, 202, 166, 147, 
    147, 125, 108, 95, 80, 43, 47, 27, 14, 3, -14, -41, -50, -52, -86, -110,
    -122, -133, -160, -170, -186, -192, -211, -229, -275, -261, -221, -282,
    -308, -313, -319, -335, -345, -352, -364, -379, -384, -416, -403, -382,
    -422, -430, -441, -452, -466, -462, -459, -496, -489, -476, -545, -508,
    -513, -525, -521, -515, -540, -556, -546, -556, -563, -566, -573, -591,
    -596, -598, -610, -619, -613, -622, -628, -625, -639, -646, -632, -643,
    -656, -635, -639, -630, -638, -629, -622, -622, -611, -609, -601, -589,
    -586, -583, -563, -556, -534, -538, -530, -546, -501, -495, -489, -474,
    -458, -444, -451, -422, -416, -427, -401, -387, -395, -372, -360, -375,
    -331, -319, -321, -298, -283, -265, -265, -269, -243, -233, -200, -208,
    -213, -182, -158, -164, -137, -112, -108, -90, -65, -44, -40, -39, -12,
    6, 46, 44, 53, 78, 94, 107, 121, 146, 134, 165, 381, 362, 146, 73, 38, 
    25, -16, -43, -87, -115, -142, -177, -201, -155, -215, -201, -160, -203,
    -173, -137, -157, -151, -105, -146, -158, -158, -171, -181, -177, -162,
    -194, -164, -137, -147, -155, -140, -146, -64, -73, -70, -50, -8, 34, 
    84, 143, 161, 232, 252, 304, 353, 398, 477, 518, 574, 646, 682, 744, 
    840, 847, 947, 1007, 1027, 1086, 1144, 1208, 1258, 1314, 1373, 1437, 
    1493, 1559, 1614, 1611, 1759, 1777, 1822, 1896, 1946, 1989, 2053, 2133,
    2171, 2234, 2318, 2359, 2416, 2476, 2542, 2577, 2684, 2742, 2796, 2780,
    2937, 2972, 3070, 3128, 3216, 3266, 3357, 3383, 3459, 3502, 3584, 3640,
    3719, 3766, 3818, 3867, 3923, 3970, 4017, 4078, 4087, 4151, 4206, 4201,
    4263, 4316, 4327, 4351, 4380, 4406, 4407, 4439, 4451, 4492, 4488, 4531,
    4518, 4524, 4506, 4536, 4546, 4526, 4553, 4557, 4559, 4565, 4561, 4563,
    4565, 4518, 4554, 4566, 4537, 4527, 4522, 4469, 4497, 4478, 4480, 4433,
    4414, 4400, 4361, 4369, 4312, 4309, 4249, 4206, 4162, 4125, 4087, 4032,
    3982, 3929, 3872, 3816, 3761, 3684, 3623, 3536, 3475, 3396, 3334, 3266,
    3181, 3094, 3034, 2939, 2833, 2726, 2689, 2599, 2562, 2438, 2309, 2276,
    2181, 2127, 2007, 1936, 1846, 1760, 1678, 1594, 1506, 1428, 1353, 1285,
    1192, 1113, 1019, 963, 874, 788, 708, 629, 557, 479, 395, 293, 246, 161,
    86, -6, -55, -157, -221, -323, -406, -464, -573, -667, -792, -816, -895,
    -991, -1054, -1136, -1213, -1287, -1359, -1425, -1500, -1568, -1621, -
    1685, -1762, -1822, -1968, -1939, -1952, -2046, -2089, -2120, -2177, -
    2221, -2262, -2301, -2343, -2365, -2390, -2422, -2450, -2478, -2496, -
    2529, -2535, -2590, -2581, -2605, -2618, -2632, -2663, -2662, -2681, -
    2690, -2705, -2719, -2723, -2721, -2735, -2736, -2734, -2738, -2740, -
    2740, -2729, -2726, -2714, -2706, -2744, -2674, -2660, -2676, -2620, -
    2585, -2562, -2534, -2492, -2463, -2428, -2389, -2341, -2316, -2218, -
    2203, -2137, -2087, -2030, -1979, -1910, -1840, -1786, -1724, -1655, -
    1578, -1516, -1465, -1372, -1316, -1216, -1167, -1091, -1003, -953, -
    867, -810, -725, -663, -593, -515, -460, -372, -291, -251, -170, -113, 
    -35, 25, 105, 164, 241, 313, 388, 433, 521, 626, 653, 747, 898, 892, 
    961, 1037, 1137, 1185, 1272, 1352, 1409, 1502, 1582, 1661, 1738, 1854, 
    1875, 1961, 2033, 2105, 2164, 2233, 2313, 2373, 2426, 2489, 2559, 2605,
    2677, 2709, 2767, 2827, 2853, 2903, 2937, 2973, 3015, 3048, 3083, 3098,
    3133, 3158, 3166, 3208, 3222, 3242, 3260, 3287, 3295, 3308, 3309, 3331,
    3350, 3343, 3365, 3325, 3438, 3389, 3319, 3390, 3392, 3391, 3379, 3391,
    3377, 3349, 3366, 3343, 3316, 3300, 3291, 3262, 3275, 3225, 3191, 3168,
    3140, 3095, 3049, 3033, 2976, 2912, 2860, 2818, 2773, 2688, 2634, 2552,
    2509, 2440, 2366, 2295, 2227, 2160, 2078, 2019, 1921, 1837, 1752, 1677,
    1599, 1519, 1425, 1344, 1264, 1183, 1103, 1020, 936, 821, 774, 711, 633,
    536, 427, 382, 307, 233, 162, 96, 15, -48, -118, -191, -277, -335, -388,
    -413, -559, -607, -656, -739, -812, -877, -943, -1029, -1100, -1177, -
    1265, -1326, -1389, -1472, -1541, -1592, -1700, -1762, -1851, -1898, -
    1977, -2039, -2106, -2169, -2234, -2293, -2363, -2413, -2448, -2521, -
    2567, -2608, -2671, -2703, -2747, -2786, -2818, -2859, -2882, -2957, -
    2964, -2975, -2985, -3020, -3039, -3063, -3074, -3092, -3109, -3116, -
    3137, -3154, -3193, -3148, -3190, -3228, -3183, -3211, -3224, -3220, -
    3220, -3226, -3242, -3219, -3224, -3237, -3217, -3201, -3194, -3205, -
    3173, -3136, -3126, -3113, -3083, -3067, -3042, -2991, -2981, -2950, -
    2895, -2847, -2811, -2764, -2724, -2666, -2601, -2551, -2470, -2423, -
    2373, -2304, -2228, -2173, -2102, -2005, -1956, -1889, -1839, -1744, -
    1673, -1566, -1519, -1456, -1377, -1302, -1218, -1160, -1078, -1019, -
    943, -869, -821, -725, -626, -582, -510, -437, -355, -293, -226, -145, 
    -108, -10, 42, 129, 212, 306, 358, 441, 498, 594, 669, 746, 832, 919, 
    981, 1065, 1154, 1227, 1319, 1377, 1452, 1550, 1606, 1683, 1754, 1830, 
    1905, 1970, 2038, 2104, 2150, 2229, 2283, 2346, 2406, 2449, 2506, 2552,
    2605, 2645, 2696, 2726, 2768, 2821, 2835, 2854, 2919, 2921, 2954, 2971,
    3002, 3017, 3030, 3065, 3073, 3065, 3100, 3121, 3134, 3157, 3152, 3125,
    3176, 3175, 3182, 3179, 3192, 3187, 3174, 3183, 3178, 3163, 3169, 3147,
    3156, 3122, 3111, 3082, 3080, 3040, 3019, 2985, 2945, 2916, 2878, 2853,
    2788, 2746, 2713, 2650, 2592, 2566, 2475, 2413, 2347, 2285, 2215, 2143,
    2079, 1974, 1923, 1850, 1783, 1692, 1629, 1533, 1448, 1367, 1292, 1201,
    1123, 1052, 985, 883, 825, 710, 644, 527, 485, 416, 343, 264, 184, 115,
    48, -31, -103, -151, -235, -302, -378, -438, -506, -577, -655, -725, -
    773, -855, -927, -988, -1064, -1147, -1200, -1285, -1427, -1443, -1514,
    -1586, -1660, -1737, -1805])
lag = 128
threshold = 5
influence = 0
result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence
    )
pylab.figure(figsize=(16, 8))
pylab.subplot(211)
pylab.plot(np.arange(1, len(y) + 1), y)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'], color='cyan', lw=2)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'] + threshold *
    result['stdFilter'], color='green', lw=2)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'] - threshold *
    result['stdFilter'], color='green', lw=2)
pylab.subplot(212)
pylab.step(np.arange(1, len(y) + 1), result['signals'], color='red', lw=2)
pylab.ylim(-1.5, 1.5)

<mask token>
import numpy as np
import pylab


def thresholding_algo(y, lag, threshold, influence):
    nFlag = 0
    nCount = 0
    signals = np.zeros(len(y))
    filteredY = np.array(y)
    avgFilter = [0] * len(y)
    stdFilter = [0] * len(y)
    avgFilter[lag - 1] = np.mean(y[0:lag])
    stdFilter[lag - 1] = np.std(y[0:lag], ddof=1)
    for i in range(lag, len(y)):
        if abs(y[i] - avgFilter[i - 1]) > threshold * stdFilter[i - 1]:
            if y[i] > avgFilter[i - 1]:
                signals[i] = 1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            else:
                signals[i] = -1
                nCount = nCount + 1
                if nFlag == 0:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            """
            @2018-11-20 对异常点的数值进行平滑，以便评估下下个点是否为异常点。
             因为不做平滑，由于当前是个异常点，对平均值、方差影响较大，若是下一个点仍是异常点，可能不会识别。
            """
            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i - 1
                ]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
        else:
            signals[i] = 0
            filteredY[i] = y[i]
            avgFilter[i] = np.mean(filteredY[i - lag + 1:i + 1])
            stdFilter[i] = np.std(filteredY[i - lag + 1:i + 1], ddof=1)
    print('Total Outer is : %d' % nCount)
    return dict(signals=np.asarray(signals), avgFilter=np.asarray(avgFilter
        ), stdFilter=np.asarray(stdFilter))


<mask token>
y = np.array([241, 229, 218, 205, 187, 185, 134, 143, 140, 126, 103, 113, 
    61, 56, 63, 21, 0, -4, -34, -46, -61, -75, -137, -116, -175, -153, -155,
    -194, -223, -131, -241, -232, -250, -227, -277, -299, -302, -317, -315,
    -294, -350, -397, -377, -377, -393, -407, -411, -417, -433, -425, -447,
    -455, -433, -471, -503, -465, -487, -496, -502, -501, -517, -529, -520,
    -535, -552, -582, -559, -568, -539, -580, -596, -591, -600, -603, -620,
    -609, -621, -634, -626, -633, -639, -616, -641, -638, -646, -642, -636,
    -629, -630, -638, -624, -630, -617, -609, -591, -643, -587, -567, -560,
    -552, -548, -535, -523, -511, -489, -514, -477, -457, -468, -440, -416,
    -427, -408, -384, -393, -381, -369, -349, -363, -339, -305, -315, -307,
    -359, -274, -266, -241, -247, -244, -217, -213, -209, -183, -164, -181,
    -146, -150, -166, -102, -69, -68, -50, -40, -31, -2, 2, 31, 54, 80, 86,
    91, 149, 131, 153, 162, 172, 194, 204, 222, 238, 249, 281, 280, 297, 
    284, 314, 323, 356, 357, 354, 368, 392, 388, 398, 414, 420, 417, 461, 
    447, 413, 430, 463, 455, 484, 488, 486, 505, 514, 518, 519, 537, 541, 
    549, 566, 562, 557, 577, 580, 595, 598, 600, 635, 610, 619, 612, 620, 
    622, 615, 642, 670, 651, 640, 690, 648, 639, 644, 638, 624, 624, 626, 
    606, 609, 602, 556, 586, 596, 565, 558, 550, 541, 527, 512, 515, 497, 
    478, 489, 486, 457, 458, 417, 408, 395, 398, 375, 369, 355, 353, 339, 
    343, 302, 308, 325, 292, 276, 256, 253, 245, 216, 218, 216, 187, 180, 
    155, 144, 136, 112, 97, 83, 34, 49, 26, 22, 4, -15, -32, -50, -53, -83,
    -93, -108, -129, -168, -164, -166, -195, -215, -243, -236, -256, -266, 
    -279, -300, -284, -321, -374, -342, -357, -369, -379, -371, -391, -409,
    -395, -419, -434, -424, -446, -464, -465, -467, -486, -480, -482, -495,
    -506, -499, -512, -530, -539, -538, -540, -551, -562, -563, -597, -583,
    -590, -591, -594, -608, -610, -607, -647, -626, -621, -529, -629, -630,
    -637, -649, -646, -636, -624, -637, -622, -620, -624, -613, -616, -600,
    -573, -592, -574, -571, -565, -569, -544, -517, -536, -516, -518, -472,
    -479, -452, -453, -437, -409, -436, -410, -387, -381, -402, -363, -344,
    -345, -336, -333, -310, -308, -291, -274, -273, -263, -252, -224, -223,
    -211, -196, -174, -164, -159, -117, -127, -105, -95, -81, -63, -52, -32,
    -15, -17, 11, 32, 61, 95, 78, 88, 136, 135, 148, 165, 179, 195, 204, 
    235, 245, 256, 294, 283, 281, 330, 317, 318, 349, 350, 391, 373, 388, 
    398, 392, 430, 423, 425, 454, 443, 452, 478, 481, 460, 483, 479, 492, 
    507, 524, 516, 525, 543, 509, 548, 560, 563, 563, 571, 585, 577, 596, 
    609, 627, 617, 628, 613, 632, 626, 628, 641, 647, 647, 648, 641, 654, 
    609, 631, 644, 597, 625, 614, 587, 609, 611, 594, 581, 586, 570, 552, 
    550, 537, 496, 511, 512, 488, 481, 454, 413, 443, 439, 420, 416, 398, 
    390, 368, 377, 361, 330, 446, 333, 325, 307, 311, 284, 269, 274, 249, 
    233, 246, 210, 206, 193, 167, 162, 162, 125, 112, 95, 85, 68, 35, 44, 
    18, -9, -6, -29, -69, -49, -86, -148, -116, -130, -145, -166, -191, -
    201, -215, -217, -237, -251, -251, -283, -281, -310, -320, -334, -329, 
    -352, -367, -377, -366, -422, -407, -427, -433, -431, -414, -440, -453,
    -448, -468, -481, -483, -489, -503, -506, -510, -510, -523, -516, -561,
    -543, -544, -556, -577, -573, -573, -579, -598, -588, -623, -615, -652,
    -643, -625, -614, -639, -642, -636, -645, -649, -642, -630, -652, -635,
    -624, -606, -618, -641, -601, -608, -580, -564, -566, -577, -565, -556,
    -517, -529, -523, -492, -495, -491, -476, -457, -452, -435, -429, -418,
    -403, -392, -387, -371, -361, -356, -347, -308, -331, -326, -310, -288,
    -280, -270, -248, -246, -258, -231, -208, -161, -123, -163, -158, -132,
    -125, -112, -88, -68, -61, -48, -26, -9, -8, 46, 37, 79, 70, 84, 90, 
    121, 134, 143, 167, 171, 201, 212, 243, 227, 259, 294, 294, 296, 343, 
    322, 320, 344, 355, 366, 372, 379, 367, 403, 444, 405, 433, 475, 448, 
    453, 460, 483, 464, 482, 488, 461, 502, 535, 514, 530, 545, 536, 545, 
    552, 559, 557, 576, 591, 578, 593, 589, 610, 614, 618, 625, 630, 659, 
    619, 640, 653, 646, 657, 606, 639, 575, 640, 639, 638, 624, 615, 547, 
    599, 607, 589, 583, 581, 562, 559, 526, 535, 544, 524, 503, 518, 487, 
    463, 455, 440, 433, 422, 384, 389, 389, 402, 387, 351, 347, 335, 325, 
    316, 303, 309, 283, 271, 274, 248, 251, 223, 210, 192, 202, 166, 147, 
    147, 125, 108, 95, 80, 43, 47, 27, 14, 3, -14, -41, -50, -52, -86, -110,
    -122, -133, -160, -170, -186, -192, -211, -229, -275, -261, -221, -282,
    -308, -313, -319, -335, -345, -352, -364, -379, -384, -416, -403, -382,
    -422, -430, -441, -452, -466, -462, -459, -496, -489, -476, -545, -508,
    -513, -525, -521, -515, -540, -556, -546, -556, -563, -566, -573, -591,
    -596, -598, -610, -619, -613, -622, -628, -625, -639, -646, -632, -643,
    -656, -635, -639, -630, -638, -629, -622, -622, -611, -609, -601, -589,
    -586, -583, -563, -556, -534, -538, -530, -546, -501, -495, -489, -474,
    -458, -444, -451, -422, -416, -427, -401, -387, -395, -372, -360, -375,
    -331, -319, -321, -298, -283, -265, -265, -269, -243, -233, -200, -208,
    -213, -182, -158, -164, -137, -112, -108, -90, -65, -44, -40, -39, -12,
    6, 46, 44, 53, 78, 94, 107, 121, 146, 134, 165, 381, 362, 146, 73, 38, 
    25, -16, -43, -87, -115, -142, -177, -201, -155, -215, -201, -160, -203,
    -173, -137, -157, -151, -105, -146, -158, -158, -171, -181, -177, -162,
    -194, -164, -137, -147, -155, -140, -146, -64, -73, -70, -50, -8, 34, 
    84, 143, 161, 232, 252, 304, 353, 398, 477, 518, 574, 646, 682, 744, 
    840, 847, 947, 1007, 1027, 1086, 1144, 1208, 1258, 1314, 1373, 1437, 
    1493, 1559, 1614, 1611, 1759, 1777, 1822, 1896, 1946, 1989, 2053, 2133,
    2171, 2234, 2318, 2359, 2416, 2476, 2542, 2577, 2684, 2742, 2796, 2780,
    2937, 2972, 3070, 3128, 3216, 3266, 3357, 3383, 3459, 3502, 3584, 3640,
    3719, 3766, 3818, 3867, 3923, 3970, 4017, 4078, 4087, 4151, 4206, 4201,
    4263, 4316, 4327, 4351, 4380, 4406, 4407, 4439, 4451, 4492, 4488, 4531,
    4518, 4524, 4506, 4536, 4546, 4526, 4553, 4557, 4559, 4565, 4561, 4563,
    4565, 4518, 4554, 4566, 4537, 4527, 4522, 4469, 4497, 4478, 4480, 4433,
    4414, 4400, 4361, 4369, 4312, 4309, 4249, 4206, 4162, 4125, 4087, 4032,
    3982, 3929, 3872, 3816, 3761, 3684, 3623, 3536, 3475, 3396, 3334, 3266,
    3181, 3094, 3034, 2939, 2833, 2726, 2689, 2599, 2562, 2438, 2309, 2276,
    2181, 2127, 2007, 1936, 1846, 1760, 1678, 1594, 1506, 1428, 1353, 1285,
    1192, 1113, 1019, 963, 874, 788, 708, 629, 557, 479, 395, 293, 246, 161,
    86, -6, -55, -157, -221, -323, -406, -464, -573, -667, -792, -816, -895,
    -991, -1054, -1136, -1213, -1287, -1359, -1425, -1500, -1568, -1621, -
    1685, -1762, -1822, -1968, -1939, -1952, -2046, -2089, -2120, -2177, -
    2221, -2262, -2301, -2343, -2365, -2390, -2422, -2450, -2478, -2496, -
    2529, -2535, -2590, -2581, -2605, -2618, -2632, -2663, -2662, -2681, -
    2690, -2705, -2719, -2723, -2721, -2735, -2736, -2734, -2738, -2740, -
    2740, -2729, -2726, -2714, -2706, -2744, -2674, -2660, -2676, -2620, -
    2585, -2562, -2534, -2492, -2463, -2428, -2389, -2341, -2316, -2218, -
    2203, -2137, -2087, -2030, -1979, -1910, -1840, -1786, -1724, -1655, -
    1578, -1516, -1465, -1372, -1316, -1216, -1167, -1091, -1003, -953, -
    867, -810, -725, -663, -593, -515, -460, -372, -291, -251, -170, -113, 
    -35, 25, 105, 164, 241, 313, 388, 433, 521, 626, 653, 747, 898, 892, 
    961, 1037, 1137, 1185, 1272, 1352, 1409, 1502, 1582, 1661, 1738, 1854, 
    1875, 1961, 2033, 2105, 2164, 2233, 2313, 2373, 2426, 2489, 2559, 2605,
    2677, 2709, 2767, 2827, 2853, 2903, 2937, 2973, 3015, 3048, 3083, 3098,
    3133, 3158, 3166, 3208, 3222, 3242, 3260, 3287, 3295, 3308, 3309, 3331,
    3350, 3343, 3365, 3325, 3438, 3389, 3319, 3390, 3392, 3391, 3379, 3391,
    3377, 3349, 3366, 3343, 3316, 3300, 3291, 3262, 3275, 3225, 3191, 3168,
    3140, 3095, 3049, 3033, 2976, 2912, 2860, 2818, 2773, 2688, 2634, 2552,
    2509, 2440, 2366, 2295, 2227, 2160, 2078, 2019, 1921, 1837, 1752, 1677,
    1599, 1519, 1425, 1344, 1264, 1183, 1103, 1020, 936, 821, 774, 711, 633,
    536, 427, 382, 307, 233, 162, 96, 15, -48, -118, -191, -277, -335, -388,
    -413, -559, -607, -656, -739, -812, -877, -943, -1029, -1100, -1177, -
    1265, -1326, -1389, -1472, -1541, -1592, -1700, -1762, -1851, -1898, -
    1977, -2039, -2106, -2169, -2234, -2293, -2363, -2413, -2448, -2521, -
    2567, -2608, -2671, -2703, -2747, -2786, -2818, -2859, -2882, -2957, -
    2964, -2975, -2985, -3020, -3039, -3063, -3074, -3092, -3109, -3116, -
    3137, -3154, -3193, -3148, -3190, -3228, -3183, -3211, -3224, -3220, -
    3220, -3226, -3242, -3219, -3224, -3237, -3217, -3201, -3194, -3205, -
    3173, -3136, -3126, -3113, -3083, -3067, -3042, -2991, -2981, -2950, -
    2895, -2847, -2811, -2764, -2724, -2666, -2601, -2551, -2470, -2423, -
    2373, -2304, -2228, -2173, -2102, -2005, -1956, -1889, -1839, -1744, -
    1673, -1566, -1519, -1456, -1377, -1302, -1218, -1160, -1078, -1019, -
    943, -869, -821, -725, -626, -582, -510, -437, -355, -293, -226, -145, 
    -108, -10, 42, 129, 212, 306, 358, 441, 498, 594, 669, 746, 832, 919, 
    981, 1065, 1154, 1227, 1319, 1377, 1452, 1550, 1606, 1683, 1754, 1830, 
    1905, 1970, 2038, 2104, 2150, 2229, 2283, 2346, 2406, 2449, 2506, 2552,
    2605, 2645, 2696, 2726, 2768, 2821, 2835, 2854, 2919, 2921, 2954, 2971,
    3002, 3017, 3030, 3065, 3073, 3065, 3100, 3121, 3134, 3157, 3152, 3125,
    3176, 3175, 3182, 3179, 3192, 3187, 3174, 3183, 3178, 3163, 3169, 3147,
    3156, 3122, 3111, 3082, 3080, 3040, 3019, 2985, 2945, 2916, 2878, 2853,
    2788, 2746, 2713, 2650, 2592, 2566, 2475, 2413, 2347, 2285, 2215, 2143,
    2079, 1974, 1923, 1850, 1783, 1692, 1629, 1533, 1448, 1367, 1292, 1201,
    1123, 1052, 985, 883, 825, 710, 644, 527, 485, 416, 343, 264, 184, 115,
    48, -31, -103, -151, -235, -302, -378, -438, -506, -577, -655, -725, -
    773, -855, -927, -988, -1064, -1147, -1200, -1285, -1427, -1443, -1514,
    -1586, -1660, -1737, -1805])
lag = 128
threshold = 5
influence = 0
result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence
    )
pylab.figure(figsize=(16, 8))
pylab.subplot(211)
pylab.plot(np.arange(1, len(y) + 1), y)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'], color='cyan', lw=2)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'] + threshold *
    result['stdFilter'], color='green', lw=2)
pylab.plot(np.arange(1, len(y) + 1), result['avgFilter'] - threshold *
    result['stdFilter'], color='green', lw=2)
pylab.subplot(212)
pylab.step(np.arange(1, len(y) + 1), result['signals'], color='red', lw=2)
pylab.ylim(-1.5, 1.5)

# -*- coding: utf-8 -*-
"""
Created on Mon Nov 19 15:02:11 2018

Reference : https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-timeseries-data/43512887#43512887
# Implementation of algorithm from https://stackoverflow.com/a/22640362/6029703

@author: tianye
###
A、名称：一阶滞后滤波法
B、方法：
    取a=0-1，本次滤波结果=(1-a)*本次采样值+a*上次滤波结果。
C、优点：
    对周期性干扰具有良好的抑制作用；
    适用于波动频率较高的场合。
D、缺点：
    相位滞后，灵敏度低；
    滞后程度取决于a值大小；
    不能消除滤波频率高于采样频率1/2的干扰信号。

###
A、名称：递推平均滤波法（又称滑动平均滤波法）
B、方法：
    把连续取得的N个采样值看成一个队列，队列的长度固定为N，
    每次采样到一个新数据放入队尾，并扔掉原来队首的一次数据（先进先出原则），
    把队列中的N个数据进行算术平均运算，获得新的滤波结果。
    N值的选取：流量，N=12；压力，N=4；液面，N=4-12；温度，N=1-4。
C、优点：
    对周期性干扰有良好的抑制作用，平滑度高；
    适用于高频振荡的系统。
D、缺点：
    灵敏度低，对偶然出现的脉冲性干扰的抑制作用较差；
    不易消除由于脉冲干扰所引起的采样值偏差；
    不适用于脉冲干扰比较严重的场合；
    比较浪费RAM。


{ // 定义全局变量
    //---Mean & STD
    // Settings: lag = 30, threshold = 5, influence = 0
    // lag for the smoothing, 计算平均值的间隔点数.
    unsigned short nLag = 128;
    unsigned short nThreshold = 3.5;
    // when signal: how much influence for new data? (between 0 and 1)
    // 对当前节点做平滑，平滑系数是(0,1)，值越大越受当前值的影响。when 1 is normal influence, 0.5 is half。
    unsigned short nInfluence = 0;
    section("sdram0_bank3") float filteredY[Lubo_Sample_Rate] = { 0.0 };
    section("sdram0_bank3") float avgFilter[Lubo_Sample_Rate] = { 0.0 };
    section("sdram0_bank3") float stdFilter[Lubo_Sample_Rate] = { 0.0 };
    float nSudPoint = 0.0;

    static float std_algo(const short* fArray, unsigned short len, const float fMean);
    static unsigned short thresholding_algo(const short* AC_Input, unsigned short nLenInput);
}

{ // wave.c 中调用。
			if(Flag_s_UI0_PRO_NegCap == 0)	//出现电容为负超过定值标志￥￥
			{
#define _NEW_CODE_

#ifdef _NEW_CODE_                
				//--Find the START Point				
				unsigned short nSudIndex = 0;
				unsigned short i = 0;				
				unsigned short nSudIndexCnt = 0; // if >=2 means occur Earth Fault.

				nSudIndex = thresholding_algo(&U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL][0], Lubo_Sample_Rate);

				if(0 != nSudIndex)
				{
					for(i=0; i<3; i++, nSudIndex++)
					{
						if( U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL][nSudIndex]*U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][nSudIndex] < 0)
						{
							nSudIndexCnt++;
						}
					}
				}

				if( nSudIndexCnt >= 2 )
				{
					Flag_s_UI0_PRO_NegCap = 1;
					Write_Low_To_High_PRO_SOE(BF533PARA.Line[0].SOE_Start_index+28,&PRO_State[28][0],(cRealClock_625us*5)/8);				
				}
				else
				{
					Flag_s_UI0_PRO_NegCap = 0;
					Flag_s_UI0_PRO_TimeRecord = 0;						
				}
				
				// # Dir.1 对 I0 滤波 --- Test 下面的 3 行代码本应在找到 U0 起点之后对 I0 滤波，这里仅用做代码测试。
				//memset(gfLocalSamp, 0, Lubo_Sample_Rate*sizeof(float));
				//unit_ckf_process(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][0], 1500/*Lubo_Sample_Rate*/, gfLocalSamp);

//#if 0 //#if 0___Counter + 3U0 SuddenChange
				if(1)// (asOverCnt >= OVER_LIMIT_COUNT) && (nSudIndex[2] != 0xEFFF))
				{
					// # 已经找到了 [ U0 的突变点 ] ,接下来找 [ 方向极性 ]
					// # Dir.1 对 I0 滤波
					float *pfLocalSamp = (float *)malloc(Lubo_Sample_Rate*sizeof(float));
					//memset(pfLocalSamp, 0, Lubo_Sample_Rate*sizeof(shortfloat);
					//unit_ckf_process(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][0], Lubo_Sample_Rate, pfLocalSamp);
					
					// # Dir.2 拿滤波后的 I0 点处理 [ 方向极性 ]
					//nIndex = NUM_SIMPLES_PERCYCLE6 +  nSudIndex[1];
					if(1)//if( U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL][nIndex]*U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL][nIndex] < 0)
					{
						Flag_s_UI0_PRO_NegCap = 1;
						Write_Low_To_High_PRO_SOE(BF533PARA.Line[0].SOE_Start_index+28,&PRO_State[28][0],(cRealClock_625us*5)/8);
						/** Clear Wave Data , for the next Fault */
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL]));
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL]));						
					}
					else
					{
						Flag_s_UI0_PRO_NegCap = 0;
						Flag_s_UI0_PRO_TimeRecord = 0;						
						/** Clear Wave Data , for the next Fault */
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[U0_FAULTWAVE_CHANNEL]));
						//memset(&U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL], 0x00, sizeof(U0_Lubo_Sample_Send_Ch_Calu[I0_FAULTWAVE_CHANNEL]));						
					}										
				}
//#endif //#if 0___Counter + 3U0 SuddenChange

}

section("sdram0_bank3") static float std_algo(const short* fArray, unsigned short len, const float fMean)
{
	unsigned short i = 0;
	float fSum = 0.0;	
	//float fAvg = 0.0;
	float fRet = 0.0;
	
	for(i; i<len; i++){
		fSum += (fArray[i]-fMean)*(fArray[i]-fMean);
	}
	//fAvg = fSum/len;
	return fRet = sqrtf(fSum/len);
}

section("sdram0_bank3") static unsigned short thresholding_algo(const short* AC_Input, unsigned short nLenInput)
{
	unsigned short i = 0;
	short aSudMatrix[3][2];	
	unsigned short nSudIndexCnt = 0; // Must be <= 2
	
	if(NULL == AC_Input){
		return 0; //error
	}
	// Fetch MIN
	nLenInput = (Lubo_Sample_Rate>nLenInput ? nLenInput : Lubo_Sample_Rate);
	memset(filteredY, 0, sizeof(filteredY));
	memset(avgFilter, 0, sizeof(avgFilter));
	memset(stdFilter, 0, sizeof(stdFilter));
	memset(aSudMatrix, 0, sizeof(aSudMatrix));

	for(i=0; i<nLenInput; i++){
		filteredY[i] = AC_Input[i];
	}
	
	avgFilter[nLag-1] = meanf(&filteredY[0], nLag);
	stdFilter[nLag-1] = std_algo(&AC_Input[0], nLag, avgFilter[nLag-1]);
	for(i=nLag; i<nLenInput; i++){
		if(abs(filteredY[i] - avgFilter[i-1]) > nThreshold*stdFilter[i-1]){
			filteredY[i] = nInfluence*AC_Input[i] + (1 - nInfluence)*filteredY[i-1];			
			avgFilter[i] = meanf(&filteredY[i-nLag+1], nLag);
			stdFilter[i] = std_algo(&AC_Input[i-nLag+1], nLag, avgFilter[i-1]);
			
			//If Sequence 3 point Over Limited, then Record.
			//nSudPoint = AC_Input[i];
			aSudMatrix[nSudIndexCnt][0] = i;
			aSudMatrix[nSudIndexCnt][1] = AC_Input[i];
			//nSudIndexCnt = (++nSudIndexCnt)>2? 0 : nSudIndexCnt;
			nSudIndexCnt++;
			if(nSudIndexCnt > 2){
				return aSudMatrix[0][0];
			}
		} else {
			filteredY[i] = AC_Input[i];
			avgFilter[i] = meanf(&filteredY[i-nLag+1], nLag);
			stdFilter[i] = std_algo(&AC_Input[i-nLag+1], nLag, avgFilter[i-1]);
			if(nSudIndexCnt <=2 ){
				memset(aSudMatrix, 0, sizeof(aSudMatrix));
			}
		}
	}	
	return 0; //error : not find
}

"""
import numpy as np
import pylab

def thresholding_algo(y, lag, threshold, influence):
    nFlag = 0x00
    nCount = 0
    signals = np.zeros(len(y)) # 返回长度为 len(y) 的数组。
    filteredY = np.array(y)
    avgFilter = [0]*len(y)
    stdFilter = [0]*len(y)
    avgFilter[lag - 1] = np.mean(y[0:lag])
    stdFilter[lag - 1] = np.std(y[0:lag], ddof=1) # 计算全局标准差，默认情况下，numpy 计算的是总体标准偏差，ddof = 0
    for i in range(lag, len(y)):
        if abs(y[i] - avgFilter[i-1]) > threshold * stdFilter [i-1]:
            if y[i] > avgFilter[i-1]:                
                signals[i] = 1
                nCount = nCount + 1
                if nFlag == 0x00:
                    nFlag = i # 打印出来的 nFlag+1 索引就对应波形工具内的点号。
                              # 打印出来的 nFlag+2 索引就对应原始点数据内的点号。
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))
            else:
                signals[i] = -1
                nCount = nCount + 1
                if nFlag == 0x00:
                    nFlag = i
                    print('nFlag = %d,  y[i] = %f' % (nFlag, y[i]))

            """
            @2018-11-20 对异常点的数值进行平滑，以便评估下下个点是否为异常点。
             因为不做平滑，由于当前是个异常点，对平均值、方差影响较大，若是下一个点仍是异常点，可能不会识别。
            """
            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i-1] ### 一阶滞后滤波法
            avgFilter[i] = np.mean(filteredY[(i-lag+1):i+1])
            stdFilter[i] = np.std(filteredY[(i-lag+1):i+1], ddof=1)
        else:
            signals[i] = 0
            filteredY[i] = y[i]
            avgFilter[i] = np.mean(filteredY[(i-lag+1):i+1])
            stdFilter[i] = np.std(filteredY[(i-lag+1):i+1], ddof=1)
    print("Total Outer is : %d" % (nCount))

    return dict(signals = np.asarray(signals),
                avgFilter = np.asarray(avgFilter),
                stdFilter = np.asarray(stdFilter))
    
# Data
"""
y = np.array([1,1,1.1,1,0.9,1,1,1.1,1,0.9,1,1.1,1,1,0.9,1,1,1.1,1,1,1,1,1.1,0.9,1,1.1,1,1,0.9,
       1,1.1,1,1,1.1,1,0.8,0.9,1,1.2,0.9,1,1,1.1,1.2,1,1.5,1,3,2,5,3,2,1,1,1,0.9,1,1,3,
       2.6,4,3,3.2,2,1,1,0.8,4,4,2,2.5,1,1,1])
"""
#y = np.array([2, 2, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 1, 1, 0, 2, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, -3, -11, -18, -27, -35, -43, -53, -62, -70, -78, -87, -97, -106, -114, -122, -130, -139, -147, -154, -161, -168, -175, -182, -189, -195, -201, -206, -210, -216, -220, -224, -227, -230, -232, -235, -236, -237, -238, -238, -239, -238, -237, -236, -233, -230, -228, -225, -221, -216, -212, -207, -202, -195, -189, -183, -176, -168, -160, -153, -145, -137, -127, -118, -109, -100, -91, -80, -70, -61, -53, -42, -30, -21, -12, -1, 8, 17, 27, 36, 45, 53, 63, 71, 79, 89, 97, 104, 111, 118, 124, 130, 136, 141, 146, 152, 156, 159, 163, 165, 168, 169, 171, 171, 172, 172, 172, 170, 169, 168, 165, 163, 160, 155, 151, 147, 143, 137, 131, 125, 119, 113, 105, 98, 91, 84, 75, 66, 58, 49, 41, 30, 21, 13, 4, -5, -15, -23, -32, -41, -52, -61, -69, -77, -86, -96, -104, -111, -118, -126, -134, -140, -146, -153, -160, -166, -170, -176, -180, -184, -187, -190, -193, -196, -198, -198, -200, -200, -200, -200, -199, -198, -195, -192, -188, -186, -181, -176, -171, -166, -160, -152, -145, -138, -130, -122, -113, -104, -95, -86, -76, -66, -56, -46, -37, -24, -14, -3, 7, 17, 28, 38, 47, 58, 68, 78, 88, 96, 106, 115, 123, 130, 138, 146, 152, 159, 164, 170, 175, 179, 183, 186, 190, 191, 193, 195, 195, 194, 194, 193, 192, 190, 187, 183, 179, 175, 171, 165, 159, 154, 146, 139, 131, 124, 116, 107, 98, 90, 81, 71, 60, 50, 41, 31, 19, 8, -1, -10, -21, -31, -42, -52, -61, -71, -81, -91, -99, -107, -116, -125, -131, -138, -145, -152, -157, -162, -167, -172, -176, -179, -182, -184, -186, -187, -188, -187, -187, -187, -184, -183, -180, -176, -173, -170, -165, -160, -154, -148, -143, -137, -129, -121, -114, -106, -98, -88, -80, -71, -62, -52, -42, -33, -24, -14, -2, 7, 15, 24, 36, 45, 55, 64, 73, 84, 93, 101, 109, 118, 126, 134, 140, 147, 154, 161, 166, 172, 176, 182, 186, 189, 192, 196, 198, 199, 201, 202, 201, 202, 201, 200, 198, 196, 193, 191, 186, 183, 178, 173, 168, 162, 155, 149, 143, 135, 126, 119, 111, 103, 94, 84, 76, 67, 58, 47, 37, 27, 18, 8, -1, -11, -20, -30, -39, -50, -59, -68, -76, -87, -95, -103, -110, -118, -126, -132, -139, -145, -152, -157, -161, -165, -169, -174, -176, -179, -181, -183, -183, -184, -185, -184, -183, -182, -180, -178, -175, -171, -168, -164, -160, -154, -149, -144, -138, -130, -123, -116, -108, -102, -92, -84, -76, -68, -58, -48, -38, -30, -21, -10, 0, 9, 17, 27, 37, 46, 55, 64, 74, 84, 92, 99, 108, 117, 125, 132, 139, 147, 154, 160, 165, 171, 176, 181, 185, 189, 191, 194, 197, 199, 202, 202, 203, 203, 203, 201, 200, 198, 197, 194, 191, 187, 183, 180, 174, 168, 162, 157, 151, 144, 136, 129, 122, 114, 105, 96, 88, 80, 70, 60, 51, 42, 32, 22, 12, 3, -6, -15, -25, -35, -44, -53, -62, -71, -80, -87, -96, -105, -113, -119, -126, -133, -139, -145, -151, -156, -161, -166, -170, -173, -176, -179, -182, -183, -184, -185, -185, -185, -184, -183, -181, -179, -176, -174, -169, -166, -161, -157, -152, -146, -140, -135, -128, -120, -113, -105, -98, -90, -81, -71, -63, -55, -46, -36, -26, -17, -8, 1, 12, 21, 29, 39, 49, 58, 67, 76, 85, 94, 102, 110, 117, 127, 134, 140, 147, 154, 160, 166, 170, 176, 180, 185, 188, 191, 194, 197, 199, 200, 201, 202, 202, 202, 201, 200, 198, 195, 193, 190, 187, 182, 178, 173, 168, 162, 157, 151, 144, 136, 129, 121, 113, 106, 96, 87, 79, 70, 61, 51, 42, 32, 24, 14, 4, -4, -14, -23, -34, -43, -52, -61, -70, -79, -87, -94, -103, -111, -119, -125, -132, -138, -145, -150, -155, -160, -165, -169, -173, -175, -179, -182, -183, -184, -185, -186, -186, -185, -184, -183, -181, -179, -176, -173, -168, -164, -160, -156, -151, -144, -138, -132, -125, -118, -110, -103, -95, -86, -78, -69, -61, -52, -42, -31, -22, -14, -4, 5, 14, 23, 32, 42, 52, 61, 70, 78, 88, 97, 105, 113, 120, 128, 135, 142, 149, 155, 161, 167, 172, 176, 181, 185, 189, 191, 195, 197, 198, 200, 201, 200, 201, 201, 200, 198, 196, 194, 191, 188, 184, 180, 175, 171, 166, 160, 152, 147, 141, 133, 125, 117, 110, 102, 93, 84, 75, 67, 58, 47, 38, 30, 19, 10, 0, -8, -17, -27, -37, -46, -55, -63, -72, -83, -90, -98, -105, -114, -121, -128, -134, -141, -147, -152, -158, -163, -168, -172, -174, -178, -181, -183, -184, -186, -187, -188, -187, -187, -186, -184, -182, -180, -177, -173, -170, -165, -160, -156, -151, -145, -138, -132, -126, -118, -110, -103, -95, -87, -78, -69, -61, -52, -42, -32, -24, -15, -4, 5, 14, 23, 32, 42, 52, 61, 69, 78, 88, 96, 104, 112, 120, 128, 135, 142, 149, 154, 161, 166, 171, 175, 181, 184, 188, 191, 194, 197, 198, 199, 200, 200, 200, 199, 198, 197, 195, 192, 190, 186, 183, 178, 174, 169, 165, 158, 151, 145, 139, 131, 124, 117, 109, 100, 91, 82, 74, 66, 56, 46, 37, 28, 19, 8, -1, -10, -18, -29, -39, -48, -56, -66, -75, -84, -91, -99, -107, -116, -123, -129, -135, -142, -148, -153, -158, -163, -169, -172, -176, -179, -182, -184, -185, -187, -188, -188, -188, -187, -187, -185, -183, -180, -177, -175, -171, -167, -161, -157, -151, -146, -140, -134, -127, -119, -111, -103, -96, -88, -79, -70, -62, -53, -43, -33, -23, -15, -6, 4, 14, 22, 31, 41, 50, 61, 68, 77, 87, 95, 103, 111, 118, 127, 135, 141, 146, 153, 160, 165, 170, 175, 179, 183, 187, 190, 193, 195, 197, 198, 199, 199, 198, 198, 198, 196, 194, 191, 189, 186, 182, 178, 173, 169, 163, 157, 151, 145, 138, 131, 123, 115, 108, 100, 90, 81, 74, 65, 56, 45, 36, 28, 18, 7, -1, -10, -19, -29, -39, -49, -56, -65, -75, -83, -91, -99, -107, -115, -123, -129, -136, -143, -148, -154, -159, -164, -168, -173, -176, -179, -182, -185, -187, -188, -189, -189, -189, -189, -188, -186, -184, -182, -179, -175, -172, -167, -163, -159, -152, -146])
#y = np.array([4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 3, 3, 4, 3, 3, 3, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 3, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 1, 2, 3, 2, 2, 3, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 3, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 3, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 0, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 0, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 2, 6, 11, 17, 24, 30, 36, 42, 47, 51, 56, 60, 63, 65, 67, 68, 68, 68, 68, 67, 67, 63, 61, 57, 53, 49, 44, 38, 32, 27, 19, 12, 3, -2, -9, -18, -27, -36, -45, -54, -63, -74, -82, -91, -100, -109, -118, -126, -135, -142, -150, -158, -165, -171, -178, -183, -188, -192, -197, -200, -203, -205, -206, -206, -206, -207, -207, -206, -205, -202, -200, -196, -191, -186, -180, -173, -166, -157, -149, -140, -129, -118, -106, -94, -83, -68, -54, -43, -34, -22, -8, 6, 21, 37, 52, 68, 85, 101, 117, 133, 151, 167, 183, 198, 214, 226, 235, 243, 254, 268, 279, 287, 294, 305, 316, 327, 336, 347, 356, 365, 373, 381, 387, 392, 396, 398, 401, 402, 403, 402, 401, 398, 395, 391, 385, 381, 375, 367, 359, 349, 336, 324, 312, 297, 283, 266, 248, 231, 214, 193, 173, 152, 130, 109, 86, 61, 38, 15, -7, -33, -57, -79, -99, -123, -148, -171, -192, -217, -242, -265, -282, -293, -307, -325, -345, -364, -384, -405, -422, -439, -455, -470, -485, -498, -510, -520, -531, -540, -547, -553, -558, -562, -564, -565, -565, -562, -559, -555, -550, -543, -534, -524, -513, -502, -489, -476, -468, -462, -453, -437, -423, -406, -388, -367, -344, -321, -298, -274, -248, -221, -197, -176, -156, -129, -99, -69, -39, -8, 22, 52, 81, 110, 141, 172, 201, 229, 258, 287, 316, 343, 368, 395, 421, 446, 469, 491, 513, 535, 555, 574, 591, 607, 623, 636, 648, 660, 670, 678, 685, 690, 693, 695, 696, 696, 695, 694, 691, 687, 682, 674, 666, 656, 645, 634, 621, 609, 598, 587, 569, 549, 528, 506, 489, 471, 452, 429, 404, 377, 358, 341, 318, 289, 259, 228, 196, 163, 128, 91, 57, 23, -12, -50, -85, -119, -154, -190, -226, -259, -290, -325, -358, -389, -420, -448, -468, -490, -514, -540, -567, -594, -618, -639, -659, -680, -699, -715, -731, -745, -756, -766, -776, -784, -789, -792, -794, -794, -793, -792, -787, -781, -775, -765, -753, -739, -723, -705, -687, -669, -648, -623, -596, -565, -535, -504, -472, -437, -400, -362, -326, -286, -244, -202, -160, -120, -80, -46, -8, 30, 72, 116, 159, 202, 243, 283, 325, 365, 403, 440, 478, 514, 549, 582, 614, 644, 674, 701, 726, 749, 772, 792, 809, 825, 839, 849, 859, 865, 870, 872, 873, 871, 867, 860, 851, 839, 826, 811, 793, 772, 751, 728, 704, 674, 642, 610, 576, 540, 502, 465, 426, 384, 340, 297, 255, 210, 162, 116, 80, 56, 36, 2, -36, -79, -124, -162, -201, -240, -281, -324, -366, -407, -449, -489, -530, -568, -603, -638, -671, -704, -734, -762, -787, -811, -834, -854, -871, -887, -901, 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#---New__Index580__L3 区外 L5 1000欧 B相__BAY01_0046_20181001_044812_313 + I0
#y = np.array([-4, -2, 5, -8, 4, 6, 6, -8, 5, -2, 0, 6, 7, -4, 2, 6, 8, -6, 4, 1, -10, -17, -18, -19, -8, 4, 7, -3, -13, -17, -17, 1, 5, 8, 8, -7, 4, -1, -4, 5, 7, 8, 8, 8, -7, 4, 0, -12, -15, 2, -4, 0, 6, 7, -4, 2, 3, -8, 4, 7, 14, 26, 0, 8, 8, 4, -10, -15, 1, 6, 8, 8, 8, 8, 8, 8, -6, -14, -2, 4, 7, 8, 8, 8, 8, 8, -4, -13, -6, 4, 8, 8, 8, 8, 9, 8, 8, 8, 9, 24, 9, 7, 7, 8, 8, 8, 4, -6, 5, 19, 17, 3, 8, -6, 4, 7, 6, -8, 4, -1, 0, 23, 17, 23, 27, 14, -4, 6, 8, 9, 8, 9, -3, -13, -8, 22, 19, 12, 10, 9, 9, 8, 0, 0, 23, 17, 13, 4, -5, 6, 8, -1, -2, 6, 8, 8, 8, 8, 8, 8, 0, 0, 5, -8, -15, -3, 2, -10, 4, 8, 2, -11, -15, 0, -2, -13, -17, -19, -2, 4, 6, 8, -4, 2, 2, -8, 4, -4, 0, 6, 8, 8, 0, 0, 6, -7, -16, -17, -11, 2, 6, -2, 3, 26, 16, 0, -12, -17, -18, 2, 6, 2, -6, 6, -6, 2, 6, 7, 8, 8, 8, 8, 8, 8, -1, -1, 6, 7, 19, 25, 13, -5, 6, 8, 7, 7, -7, 3, 7, 8, 8, 23, 22, 8, -4, 6, 7, 0, -2, 6, -7, 3, 1, -6, 4, -6, 2, 6, 6, -7, -15, -17, -17, -19, -19, -19, -8, 3, 6, 8, 0, -11, -16, -18, -1, 4, 6, -8, 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88, 88, 74, 52, 59, 75, 80, 56, 44, 40, 30, 17, 12, 8, 8, 8, 6, -8, -16, -28, -39, -28, -41, -35, -34, -47, -68, -77, -81, -86, -103, -93, -101, -116, -133, -141, -143, -144, -145, -145, -145, -144, -145, -145, -145, -145, -145, -145, -144, -145, -145, -145, -145, -144, -129, -132, -133, -123, -118, -115, -114, -110, -94, -104, -97, -88, -84, -83, -68, -58, -54, -51, -34, -23, -22, -10, 3, 7, 19, 32, 36, 37, 39, 39, 39, 39, 39, 51, 70, 87, 78, 88, 86, 86, 112, 108, 104, 101, 101, 100, 101, 112, 123, 128, 113, 132, 143, 139, 153, 143, 147, 150, 141, 135, 134, 133, 132, 132, 132, 132, 132, 130, 114, 121, 128, 125, 110, 104, 101, 101, 95, 62, 62, 67, 69, 69, 52, 43, 39, 37, 19, 13, 9, 9, 8, 7, -19, -31, -23, -22, -38, -58, -73, -80, -82, -82, -93, -102, -91, -104, -97, -96, -108, -112, -113, -123, -133, -123, -134, -141, -151, -167, -172, -159, -149, -153, -166, -154, -149, -146, -145, -145, -145, -133, -128, -138, -141, -144, -135, -122, -117, -116, -115, -108, -93, -86, -69, -67, -72, -59, -54, -52, -43, -28, -23, -21, -2, 0, -6, 5, 21, 22, 21, 50, 46, 55, 70, 87, 78, 74, 81, 93, 98, 116, 126, 130, 131, 132, 132, 132, 132, 133, 149, 144, 145, 155, 160, 146, 148, 151, 141, 152, 158, 152, 141, 136, 149, 158, 154, 140, 117, 106, 103, 101, 101, 100, 99, 93, 79, 74, 71, 60, 51, 60, 32, 30, 34, 36, 38, 23, 14, 4, -10, -16, -19, -28, -43, -49, -67, -67, -57, -56, -72, -93, -96, -93, -106, -111, -126, -137, -142, -144, -144, -145, -145, -145, -145, -145, -145, -145, -145, -145, -145, -151, -167, -173, -162, -151, -147, -145, -145, -145, -145, -145, -145, -115, -105, -111, -113, -114, -101, -96, -105, -92, -86, -94, -96, -69, -59, -54, -53, -49, -32, -24, -21, -6, 5, 23, 33, 28, 19, 32, 36, 38, 39, 41, 58, 65, 78, 91, 97, 84, 101, 113, 108, 123, 128, 131, 137, 151, 158, 161, 154, 141, 136, 148, 147, 143, 153, 144, 149, 149, 139, 136, 149, 146, 138, 135, 134, 145, 147, 138, 134, 132, 121, 108, 103, 101, 100, 100, 99, 99, 83, 87, 88, 77, 73, 71, 59, 45, 40, 21, 26, 25, 10, -8, 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#
#--SAR-device.sdb.await__研发中心波形_高阻接地_00025_20171025_201648_049_F__U0.csv
#y = np.array([0                 , 0                 , 0.016319835       , 0.003263967       , 0.001468785       , -0.021052587      , 0.062433159       , -0.045126301      , -0.017306858      , 0.899691378       , 1.807001576       , -2.211914292      , 1.210577102       , 0.034285041       , -1.413690866      , -0.032550319      , 0.385791417       , -0.614630031      , 1.313074353       , -1.160878094      , -0.027031618      , 0.602608375       , -0.419584156      , -0.307107615      , 0.296948406       , 0.33955769        , -0.368907352      , -0.040879457      , -0.27737329       , 0.127561027       , 0.286045805       , -0.395795871      , 0.581514931       , -0.550891076      , -0.052962133      , 0.312064628       , -0.064505112      , -0.1984826        , 0.114571148       , 0.181416008       , -0.238383304      , -0.003028547      , 0.107736046       , -0.125969012      , -0.068135541      , 0.101018108       , 0.937117685       , -1.049553602      , 0.492846398       , 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#
#--SAR-device.sdb.await__2017-07-20 第四项检测L3 区外 L5 1000欧 A相__U0
#y = np.array([0                          , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.034556867414899294       , 0.0                        , 0.0                        , 0.030133588385792182       , 0.024106870708633744       , 0.019285496566906993       , 0.019128470161373697       , 0.01530277612909896        , 0.022314646511620122       , 0.017851717209296097       , 0.020275493647462416       , 0.016220394917969932       , 0.012976315934375945       , 0.010381052747500757       , 0.026252025216898685       , 0.021001620173518948       , 0.016801296138815158       , 0.013441036911052126       , 0.023804037886057592       , 0.019043230308846075       , 0.01523458424707686        , 0.012187667397661488       , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 0.0                        , 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1.527900161809106          , 1.3951044665217809         , 1.2543110428770212         , 1.141676303961214          , 0.9133410431689702         , 0.7306728345351743         , 0.584538267628139          , 0.36396001185781224        , 0.1874974072415518         , 0.08088419096344282        , 0.10807698430374378        , 0.3283596593472903         , 0.47002893196722956        , 0.6524780848929786         , 0.8329942746484764         , 0.9428503590379773         , 1.1344058287942753         , 1.2530933371844128         , 1.4517139461412223         , 1.57605356589177           , 1.710082129107107          , 1.9209755819240757         , 1.951462874518053          , 2.079523310837933          , 2.251085394723635          , 2.3537781944172966         , 2.4013755667573267         , 2.5085671994591494         , 2.5943205056206082         , 2.662923150549775          , 2.6832483990782086         , 1.766473177698675          , 0.8948255309354507         , 0.05927994386527414        , 0.5054859235461693         , 0.9572986174753241         , 1.3878625074484463         , 1.6631998845971454         , 1.8143560514863057         , 1.969837852412533          , 2.0942232931535165         , 2.159174778331403          , 2.1420222316439133         , 2.0937433268790233         , 0.6403154552950342         , 3.634571725551887          , 2.147406297313725          , 2.098050579414871          , 1.9203385354361915         , 1.7781689002532486         , 1.6989900595217933         , 1.462862649862132          , 1.3430744569642017         , 1.1781301678160596         , 1.0461747364975447         , 0.8369397891980357         , 0.600438096528629          , 0.4112367423931031         , 0.2944325264995822         , 0.06276168412516964        , 0.08801812235946109        , 0.2777557023769648         , 0.49865950122076685        , 0.6062688054660099         , 0.7614699836920015         , 0.9201877936876955         , 1.0471620416842513         , 1.2524120423261933         , 1.416612042839747          , 1.5479720432505895         , 1.6876169109941617         , 1.8684465400188197         , 1.9439965084087472         , 2.0735502179504883         , 2.1771931855838806         , 2.294664427105493          , 2.4577551551525816         , 2.450000267930656          , 2.6165806952276114         , 2.6461744348204777         , 2.70440629390967           , 2.0598544328830384         , 0.9913030654233441         , 0.3092463085300854         , 0.37462656664411875        , 0.921724866783482          , 1.255732904650275          , 1.5920530697735078         , 1.7919954670422957         , 1.986506252272225          , 2.038444278211471          , 2.0799946989628673         , 2.32057624005338           , 2.0638021965321            , 2.203951635864068          , 2.0396162480104487         , 2.0118185399722512         , 1.9550235061267924         , 1.8059168768057288         , 1.6866315733488775         , 1.6257601979982956         , 0.8073607539102211         , 2.9134687406064477         , 2.2962181250702582         , 0.9039390798539264         , 0.7231512638831411         , 0.5094072762767152         , 0.3729689536064709         , 0.1947045606404778         , 0.05209304626768333        , 0.13110990006035136        , 0.34678599195257825        , 0.4156562632216599         , 0.6089799498965239         , 0.7981957666513123         , 0.9150115526402451         , 1.1466916510909884         , 1.2629219950217825         , 1.4250200049962187         , 1.5546984129757666         , 1.7275548741892035         , 1.7967263083301561         , 1.955734057887614          , 2.1520539923633795         , 2.1708824702843947         , 2.3241727222808035         , 2.412248056463032          , 2.482708323808814          , 2.5736334051003396         , 2.6809303375484594         , 2.697654148677156          , 1.9853389818672282         , 1.035361306855394          , 0.20626543201612807        , 0.387897533025486          , 0.8977847724736765         , 1.3056945640322295         , 1.597465529864172          , 1.7963254351148263         , 1.95541335931535           , 2.117240566090669          , 2.1430317292662258         , 2.129107792391771          , 2.187082377722007          , 2.1989051785712954         , 2.0701359495911302         , 1.9671205664069973         , 1.9883788621043887         , 1.8326011615878055         , 1.7079790011745388         , 1.5737244054290276         , 1.4317638614177182         , 1.3181954262086697         , 1.158226943211633          , 0.9611384219842057         , 0.8380244724171627         , 0.5667489756890323         , 0.38428544572142714        , 0.20375775433244184        , 0.09389246863615339        , 0.16678409699537397        , 0.34076848208569605        , 0.5145128575728526         , 0.6189514905476794         , 0.7370592643424395         , 1.0043298204527442         , 1.07991879568139           ])
#--1
#y = np.array([-0.036313812       , 0.007668578        , 0.120373704        , 0.112618798        , 0.0248157          , 0.085144514        , 0.080345394        , 0.019396771        , 0.015517417        , 0.028733768        , -0.009652655       , 0                  , -2.273818704       , -1.557937611       , -0.246760225       , -2.000749894       , -0.848939585       , -1.418004704       , -0.430714476       , -0.556729429       , -0.608678798       , -0.779553647       , -0.050101873       , -0.431757527       , -0.867640726       , -0.122918373       , -0.065695029       , -0.546231017       , -0.485163071       , -0.378963553       , -0.359827636       , -0.471830349       , -0.355136421       , -0.300428971       , -0.355008783       , -0.147178016       , -0.070871993       , -0.189664962       , -0.410781299       , -0.043888797       , -0.079990582       , -0.280230273       , -0.154824922       , -0.060865326       , -0.180687123       , -0.160869533       , 0.039554031        , -0.049994857       , -1.035505791       , -0.089932121       , -0.333063049       , -0.76828535        , 0.042362255        , -0.31199693        , -0.251053142       , -0.140932816       , -0.462527914       , -0.284986588       , -0.146390098       , -0.346398207       , -0.166959683       , -0.017581731       , -0.070950561       , -0.170999291       , -0.267358109       , -0.181246818       , -0.161317289       , -0.009539406       , 0.019730892        , -0.028136996       , -0.071559026       , -0.110286683       , -0.088229346       , 0.025935135        , -0.059730749       , -0.033535922       , 2.021533415        , 1.703400273        , -0.012674376       , 1.983687114        , 1.023915401        , 1.238714092        , 0.346242078        , 0.542196503        , 0.498930187        , 0.860179475        , 0.067989868        , 0.380788585        , 0.945184372        , 0.131913828        , 0.105531062        , 0.423104608        , 0.504095453        , 0.336066287        , 0.291820679        , 0.434584198        , 0.350779572        , 0.264303823        , 0.39304938         , 0.069641986        , 0.052581996        , 0.156361738        , 0.349487115        , 0.071511802        , 0.053129483        , 0.291381062        , 0.194691958        , 0.046949295        , 0.196410125        , 0.14428639         , -0.015129564       , 0.185792335        , 1.031304561        , 0.109266877        , 0.53274021         , 0.053024294        , -0.30097014        , 0.108863004        , -0.12381003        , -0.148007528       , 0.102086188        , 0.042166053        , 0.013333049        , 0.258550836        , -0.156275649       , -0.043546149       , -0.056768854       , 0.0647438          , 0.235393178        ])
#--2
#y = np.array([0.184234584             , 0.04946866              , -0.130213312            , -0.038891312            , -0.045336929            , -0.01586975             , 0.085223207             , 0.068178565             , -0.02705632             , 0.006161267             , 0.02151714              , -0.021274647            , 0.032654888             , 0.009804076             , 0.024163095             , 0.026542451             , -0.039965419            , -0.006333166            , 0.024197627             , 0.003038267             , 0.017604035             , -0.004583443            , -0.003666754            , -0.036817822            , 0.004997203             , -0.044961741            , -0.009711618            , -0.009576097            , 0.044371599             , 0.013052305             , -0.017297714            , 0.02467331              , 0.012901021             , 0.026076811             , 0.030910949             , 0.013425192             , 0.029574908             , 0.009352028             , -0.020217227            , 0.032785722             , -0.006411091            , -0.023022716            , 0.069172908             , -0.004942638            , 0.019307875             , -0.017193369            , 0.015747584             , 0.018371946             , -0.007195796            , 0.019101971             , 0.721114419             , 0.160735755             , 0.079629101             , 0.394179929             , 0.026956563             , 0.27627173              , 0.138662364             , 0.094610057             , 0.169109472             , 0.18832704              , 0.064982501             , 0.186624635             , 0.149299708             , 0.033760635             , -0.001551202            , 0.161957383             , 0.124290323             , 0.022053553             , 0.099129608             , 0.042673984             , 0.021899311             , 0.033839284             , 0.059711096             , 0.015129208             , 0.036577486             , -2.012344803            , -1.760834311            , 0.01568665              , -2.047502654            , -1.058647998            , -1.202642277            , -0.308211631            , -0.585205871            , -0.500698013            , -0.894233404            , -0.078913177            , -0.356887563            , -0.954623265            , -0.094585397            , -0.108307987            , -0.478162454            , -0.500160743            , -0.318529422            , -0.304130795            , -0.462528207            , -0.37895228             , -0.275176155            , -0.404092746            , -0.105975981            , -0.066177684            , -0.130655991            , -0.399858865            , -0.073011942            , -0.03800976             , -0.350463861            , -0.173652732            , -0.061914518            , -0.147041385            , -0.133952942            , -0.011463638            , -0.15633116             , -1.03897566             , -0.135769666            , -0.544177647            , -0.676059677            , -0.079139366            , -0.193870169            , -0.286738357            , -0.03585747             , -0.497628687           ])
#--3
#y = np.array([-0.19206438             , -0.153651504            , -0.002092466            , -0.001673973            , -0.019754967            , -0.068843436            , -0.083634459            , -0.070987526            , -0.055330586            , 0.0116248               , 0.015225064             , 1.816415642             , 1.929772571             , 0.153932553             , 1.935991338             , 1.165276959             , 1.102067091             , 0.215855198             , 0.596295406             , 0.383271386             , 0.979810282             , 0.098415176             , 0.323529659             , 1.043420533             , 0.180131255             , 0.046185997             , 0.422766843             , 0.486117541             , 0.213455812             , 0.198886727             , 0.395281097             , 0.372901015             , 0.190919597             , 0.471497993             , 0.083441373             , 0.018428018             , 0.158665411             , 0.335010219             , 0.059930285             , 0.04386427              , 0.300288726             , 0.123486189             , 0.029802432             , 0.167158268             , 0.094144795             , -0.006283337            , 0.286543815             , 0.941533892             , 0.355028074             , 0.6213495               , -0.114914194            , 0.028451858             , -0.058837686            , -0.089680554            , -0.014808351            , 0.325040841             , 0.045194101             , 0.081034826             , 0.225172315             , -0.150338797            , -0.058462459            , -0.123144557            , 0.081002534             , 0.199440662             , 0.171792405             , -0.05840409             , -0.124290323            , -0.030927223            , -0.063358555            , 0.018672453             , 0.096537135             , 0.028270204             , -0.058983009            , -0.014546738            , -0.021002278            , 0.033121657             , -0.022462178            , 0.030989761             , 0.074697333             , 0.010798363             , -0.035776956            , 0.021919967             , 0.028959858             , 0.028063837             , 0.022451069             , 0.017960856             , -0.018270985            , -0.001970678            , -0.040253141            , 0.000437157             , 0.040609825             , -0.012927896            , -0.026662152            , 0.067580894             , 0.006881293             , 3.879123314             , 6.396399306             , 0.529543296             , 1.087607485             , 0.185501265             , 2.658610524             , 4.18068466              , 2.550958122             , 1.126855765             , 1.458992154             , 3.194293729             , 2.360108515             , 1.102285539             , 0.155050269             , 1.411883896             , 1.837643685             , 1.384435816             , 0.118229087             , 0.170614041             , 0.230496789             , 0.496789056             , 1.433740736             , 0.836915733             , 0.422959909             , 0.079827832             , 2.666847758             , 1.627563336             , 4.764077094             , 5.285903947             , 4.632639062             , 3.730591001             , 4.509336839             , 4.048937394             ])
#--4
#y = np.array([1.199170601           , 0.836937722           , 2.986966678           , 2.667010529           , 0.33026671            , -0.013223819          , 0.768693043           , -0.009296264          , 0.562297762           , 0.300874178           , 0.656846404           , 0.737634972           , -0.050445527          , 3.744464797           , -1.453347476          , 2.51941328            , 3.257350786           , 2.740125064           , 1.861623402           , 1.19954755            , 2.183625628           , 3.550242216           , 3.040111746           , 1.705856761           , 2.205156886           , 0.939973866           , -0.329209944          , -0.263367955          , 2.94534E-05           , 1.122449818           , 0.657708181           , 0.306292062           , 1.150784465           , 1.275300271           , 2.954140606           , 2.268839014           , 0.304362074           , 0.259077679           , 0.154222681           , 1.608135455           , 2.889932104           , 2.49962378            , 1.355065561           , 0.60669729            , 0.024322507           , 0.095265856           , 0.576580696           , 0.730541826           , 0.596529991           , -0.63226242           , -0.481330185          , -0.109691374          , 0.369202267           , -0.13938294           , 0.062546849           , 0.044764224           , 0.900762608           , 0.598211328           , 0.274571131           , 1.325325693           , 1.524766638           , 1.112589762           , 1.155269121           , 1.630048139           , 1.532516194           , 1.621768942           , 2.276653062           , 2.144493443           , 1.705252027           , 2.02107496            , 1.567900465           , 0.907523888           , 0.607700311           , 0.172931481           , 0.747748275           , 0.774077421           , 1.32101482            , 1.73000503            , 1.469683155           , 1.436863876           , 2.015399876           , 2.084013073           , 1.659866533           , 1.649393966           , 1.960068677           , 1.784292749           , 1.553912917           , 1.785358597           , 1.670961407           , 1.01485262            , 0.257387245           , 3.388685564           , 4.285812481           , 2.346089321           , 0.057209909           , -2.475588589          , -4.909881166          , -0.370212816          , 0.017075637           , 0.780692732           , -2.693349804          , -3.57919612           , -1.431291418          , 0.32783193            , -0.342382221          , -2.664761533          , -1.55653506           , 0.258236706           , 1.480081984           , 0.453316571           , 0.228014622           , 0.545528016           , 1.533931283           , 1.79242743            , 2.233447328           , 1.878282142           , 0.825352581           , 1.74555106            , 1.645318324           , 1.173930962           , -2.337033896          , -1.231934942          , -4.102636345          , -4.314338609          , -3.080194652          , -2.015360272          , -2.905635103          , -2.275548579          ])
#---DCU1923ZeroOrder(4_8)2017-11-10 11_51_02_831016---U0
#y = np.array([241, 229, 218, 205, 187, 185, 134, 143, 140, 126, 103, 113, 61, 56, 63, 21, 0, -4, -34, -46, -61, -75, -137, -116, -175, -153, -155, -194, -223, -131, -241, -232, -250, -227, -277, -299, -302, -317, -315, -294, -350, -397, -377, -377, -393, -407, -411, -417, -433, -425, -447, -455, -433, -471, -503, -465, -487, -496, -502, -501, -517, -529, -520, -535, -552, -582, -559, -568, -539, -580, -596, -591, -600, -603, -620, -609, -621, -634, -626, -633, -639, -616, -641, -638, -646, -642, -636, -629, -630, -638, -624, -630, -617, -609, -591, -643, -587, -567, -560, -552, -548, -535, -523, -511, -489, -514, -477, -457, -468, -440, -416, -427, -408, -384, -393, -381, -369, -349, -363, -339, -305, -315, -307, -359, -274, -266, -241, -247, -244, -217, -213, -209, -183, -164, -181, -146, -150, -166, -102, -69, -68, -50, -40, -31, -2, 2, 31, 54, 80, 86, 91, 149, 131, 153, 162, 172, 194, 204, 222, 238, 249, 281, 280, 297, 284, 314, 323, 356, 357, 354, 368, 392, 388, 398, 414, 420, 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#---DCU1923ZeroOrder(4_8)2017-11-07 11_56_54_491016---U0
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#---DCU1923ZeroOrder(4_8)2017-11-07 11_56_54_491016---I0--第25列
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#---SAR-device.sdb.await__DCU1923ZeroOrder(4_8)2017-11-07 11_56_54_491016.csv
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#---SAR-device.sdb.await__DCU1923ZeroOrder(4_8)2017-11-10 11_51_02_831016.csv
y = np.array([241, 229, 218, 205, 187, 185, 134, 143, 140, 126, 103, 113, 61, 56, 63, 21, 0, -4, -34, -46, -61, -75, -137, -116, -175, -153, -155, -194, -223, -131, -241, -232, -250, -227, -277, -299, -302, -317, -315, -294, -350, -397, -377, -377, -393, -407, -411, -417, -433, -425, -447, -455, -433, -471, -503, -465, -487, -496, -502, -501, -517, -529, -520, -535, -552, -582, -559, -568, -539, -580, -596, -591, -600, -603, -620, -609, -621, -634, -626, -633, -639, -616, -641, -638, -646, -642, -636, -629, -630, -638, -624, -630, -617, -609, -591, -643, -587, -567, -560, -552, -548, -535, -523, -511, -489, -514, -477, -457, -468, -440, -416, -427, -408, -384, -393, -381, -369, -349, -363, -339, -305, -315, -307, -359, -274, -266, -241, -247, -244, -217, -213, -209, -183, -164, -181, -146, -150, -166, -102, -69, -68, -50, -40, -31, -2, 2, 31, 54, 80, 86, 91, 149, 131, 153, 162, 172, 194, 204, 222, 238, 249, 281, 280, 297, 284, 314, 323, 356, 357, 354, 368, 392, 388, 398, 414, 420, 417, 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# Settings: lag = 30, threshold = 5, influence = 0
# lag for the smoothing, 计算平均值的间隔点数.
lag = 128#128
threshold = 5
# when signal: how much influence for new data? (between 0 and 1)
# 对当前节点做平滑，平滑系数是(0,1)，值越大越受当前值的影响。when 1 is normal influence, 0.5 is half。
influence = 0

# Run algo with settings from above
result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence)

# Plot result
pylab.figure(figsize=(16, 8))
#
pylab.subplot(211)
pylab.plot(np.arange(1, len(y)+1), y)

pylab.plot(np.arange(1, len(y)+1),
           result["avgFilter"], color="cyan", lw=2)

pylab.plot(np.arange(1, len(y)+1),
           result["avgFilter"] + threshold * result["stdFilter"], color="green", lw=2)

pylab.plot(np.arange(1, len(y)+1),
           result["avgFilter"] - threshold * result["stdFilter"], color="green", lw=2)

pylab.subplot(212)
pylab.step(np.arange(1, len(y)+1), result["signals"], color="red", lw=2)
pylab.ylim(-1.5, 1.5)
