#coding=gbk
#使用小波分析进行阈值去噪声,使用pywt.threshold
 
import pywt
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt 
import math 
 
data = np.linspace(1, 10, 10)
PRint(data)
# [ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10.]
# pywt.threshold(data, value, mode, substITute) mode 模式有4种，soft, hard, greater, less; substitute是替换值可以点进函数里看,data/np.abs(data) * np.maximum(np.abs(data) - value, 0)
 
data_soft = pywt.threshold(data=data, value=6, mode='soft', substitute=12)
print(data_soft)
# [12. 12. 12. 12. 12.  0.  1.  2.  3.  4.] 将小于6 的值设置为12， 大于等于6 的值全部减去6
 
data_hard = pywt.threshold(data=data, value=6, mode='hard', substitute=12)
print(data_hard)
# [12. 12. 12. 12. 12.  6.  7.  8.  9. 10.] 将小于6 的值设置为12， 其余的值不变
 
data_greater = pywt.threshold(data, 6, 'greater', 12)
print(data_greater)
# [12. 12. 12. 12. 12.  6.  7.  8.  9. 10.] 将小于6 的值设置为12，大于等于阈值的值不变化
 
data_less = pywt.threshold(data, 6, 'less', 12)
print(data_less)
# [ 1.  2.  3.  4.  5.  6. 12. 12. 12. 12.] 将大于6 的值设置为12， 小于等于阈值的值不变

#-*-coding:utf-8-*-

import matplotlib.pyplot as plt
import pywt
import math
import numpy as np

#get Data
ecg=pywt.data.ecg()  #生成心电信号
index=[]
data=[]
coffs=[]

for i in range(len(ecg)-1):
    X=float(i)
    Y=float(ecg[i])
    index.apPEnd(X)
    data.append(Y)
#create wavelet object and define parameters
w=pywt.Wavelet('db8')#选用Daubechies8小波
maxlev=pywt.dwt_max_level(len(data),w.dec_len)
print("maximum level is"+str(maxlev))
threshold=0  #Threshold for filtering

#Decompose into wavelet components,to the level selected:
coffs=pywt.wavedec(data,'db8',level=maxlev) #将信号进行小波分解

for i in range(1,len(coffs)):
    coffs[i]=pywt.threshold(coffs[i],threshold*max(coeffs[i]))

datarec=pywt.waverec(coffs,'db8')#将信号进行小波重构

mintime=0
maxtime=mintime+len(data) 
print(mintime,maxtime)

plt.figure()
plt.subplot(3,1,1)
plt.plot(index[mintime:maxtime], data[mintime:maxtime])
plt.xlabel('time (s)')
plt.ylabel('microvolts (uV)')
plt.title("Raw signal")
plt.subplot(3, 1, 2)
plt.plot(index[mintime:maxtime], datarec[mintime:maxtime])
plt.xlabel('time (s)')
plt.ylabel('microvolts (uV)')
plt.title("De-noised signal using wavelet techniques")
plt.subplot(3, 1, 3)
plt.plot(index[mintime:maxtime],data[mintime:maxtime]-datarec[mintime:maxtime])
plt.xlabel('time (s)')
plt.ylabel('error (uV)')
plt.tight_layout()
plt.show()