浅谈Python小波分析库Pywavelets的一点使用心得_Python

本文介绍了Python小波分析库Pywavelets，分享给大家，具体如下：

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

									import numpy as np

									import math

									import matplotlib.pyplot as plt

									import pandas as pd

									import datetime 

									from scipy import interpolate

									from pandas import DataFrame,Series

									import numpy as np 

									import pywt 

									data = np.linspace(1, 4, 7) 

									# pywt.threshold方法讲解： 

									#        pywt.threshold（data，value，mode ='soft'，substitute = 0 ） 

									#        data：数据集，value：阈值，mode：比较模式默认soft，substitute：替代值，默认0，float类型 

									#data:  [ 1.  1.5 2.  2.5 3.  3.5 4. ] 

									#output：[ 6.  6.  0.  0.5 1.  1.5 2. ] 

									#soft 因为data中1小于2，所以使用6替换，因为data中第二个1.5小于2也被替换，2不小于2所以使用当前值减去2，，2.5大于2，所以2.5-2=0.5..... 

									print(pywt.threshold(data, 2, 'soft',6))  

									#data:  [ 1.  1.5 2.  2.5 3.  3.5 4. ] 

									#hard data中绝对值小于阈值2的替换为6，大于2的不替换 

									print (pywt.threshold(data, 2, 'hard',6)) 

									#data:  [ 1.  1.5 2.  2.5 3.  3.5 4. ] 

									#data中数值小于阈值的替换为6，大于等于的不替换 

									print (pywt.threshold(data, 2, 'greater',6) )

									print (data )

									#data:  [ 1.  1.5 2.  2.5 3.  3.5 4. ] 

									#data中数值大于阈值的，替换为6 

									print (pywt.threshold(data, 2, 'less',6) )

[6. 6. 0. 0.5 1. 1.5 2. ]
[6. 6. 2. 2.5 3. 3.5 4. ]
[6. 6. 2. 2.5 3. 3.5 4. ]
[1. 1.5 2. 2.5 3. 3.5 4. ]
[1. 1.5 2. 6. 6. 6. 6. ]

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									#!/usr/bin/env python

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

									import numpy as np

									import matplotlib.pyplot as plt

									import pywt

									import pywt.data

									ecg = pywt.data.ecg()

									data1 = np.concatenate((np.arange(1, 400),

									            np.arange(398, 600),

									            np.arange(601, 1024)))

									x = np.linspace(0.082, 2.128, num=1024)[::-1]

									data2 = np.sin(40 * np.log(x)) * np.sign((np.log(x)))

									mode = pywt.Modes.smooth

									def plot_signal_decomp(data, w, title):

									  """Decompose and plot a signal S.

									  S = An + Dn + Dn-1 + ... + D1

									  """

									  w = pywt.Wavelet(w)#选取小波函数

									  a = data

									  ca = []#近似分量

									  cd = []#细节分量

									  for i in range(5):

									    (a, d) = pywt.dwt(a, w, mode)#进行5阶离散小波变换

									    ca.append(a)

									    cd.append(d)

									  rec_a = []

									  rec_d = []

									  for i, coeff in enumerate(ca):

									    coeff_list = [coeff, None] + [None] * i

									    rec_a.append(pywt.waverec(coeff_list, w))#重构

									  for i, coeff in enumerate(cd):

									    coeff_list = [None, coeff] + [None] * i

									    if i ==3:

									      print(len(coeff))

									      print(len(coeff_list))

									    rec_d.append(pywt.waverec(coeff_list, w))

									  fig = plt.figure()

									  ax_main = fig.add_subplot(len(rec_a) + 1, 1, 1)

									  ax_main.set_title(title)

									  ax_main.plot(data)

									  ax_main.set_xlim(0, len(data) - 1)

									  for i, y in enumerate(rec_a):

									    ax = fig.add_subplot(len(rec_a) + 1, 2, 3 + i * 2)

									    ax.plot(y, 'r')

									    ax.set_xlim(0, len(y) - 1)

									    ax.set_ylabel("A%d" % (i + 1))

									  for i, y in enumerate(rec_d):

									    ax = fig.add_subplot(len(rec_d) + 1, 2, 4 + i * 2)

									    ax.plot(y, 'g')

									    ax.set_xlim(0, len(y) - 1)

									    ax.set_ylabel("D%d" % (i + 1))

									#plot_signal_decomp(data1, 'coif5', "DWT: Signal irregularity")

									#plot_signal_decomp(data2, 'sym5',

									#          "DWT: Frequency and phase change - Symmlets5")

									plot_signal_decomp(ecg, 'sym5', "DWT: Ecg sample - Symmlets5")

									plt.show()