python小波分解wavelet去噪重构_python 小波重构

前言

采用小波分解方式对数据进行分解，然后重构，演示整体代码

代码

import pywt
import matplotlib.pyplot as plt
import pandas as pd

sampleData = [ 6. ,  7.2,  5.4,  6. ,  4.8,  4.6,  6.7,  7.1,  6.8,  0. ,  5.2,
        6.4,  4.8,  5. ,  7.8,  6.4,  3.1,  3.6,  4.7,  5.5,  7.1,  6. ,
        8.2,  7.2,  6.5,  9.3,  6.8,  8.4,  7.4,  6.7,  8.6,  5.4,  4.1,
        3.8,  5.6,  4.1,  7.3,  6.7,  6.5,  7.6,  8.6,  4.3,  6.2,  7.7,
        7.2,  6.9,  5.1,  4.6,  6.3,  5.2,  6. ,  6.1,  6.2,  7.7,  8.1,
        6.9,  6.4,  6.4,  6.5,  6.8,  5.6,  5.2,  8.9,  5.4,  3.8,  5.9,
        5.8,  4.9,  4.3,  8.5,  6.2,  3.1,  3.9,  2.4,  7.3,  5.8,  7.5,
        7.3,  6. ,  7.1,  7. ,  7.3,  7.5,  4.6,  5.7,  4. ,  7.6,  5.5,
        3.8,  7. ,  4.2,  4.3,  5.7,  6.2,  6.9,  6.1,  5.5,  9.5,  8.9,
        6.1,  7. ,  8.8,  4.8,  7.6,  9.5,  7.6,  7. ,  4.6,  8.4,  5.4,
        7.9,  9.2,  5.8, 10.2,  8.9,  5.8,  7.2,  8.9,  6.8,  8.8,  7.7,
        6.4,  8. , 10.6,  7.9,  9.4,  5.8,  7.2,  7.1,  9.2,  7.3, 10.3,
        3.1,  9.8,  6.8, 11.4,  8.1,  7.7,  7.5,  7.0] 

w = pywt.Wavelet('db8')  # 选用Daubechies8小波
maxlev = pywt.dwt_max_level(len(sampleData), w.dec_len)
print("maxlev: ", maxlev)
threshold = 0.05  # Threshold for filtering
 # 将信号进行小波分解

for i in range(1, len(coeffs)):
     # 将噪声滤波
 
sampleData_rec = pywt.waverec(coeffs, 'db8')  # 将信号进行小波重构
# 保存
dataframe = pd.DataFrame(sampleData_rec)
dataframe.to_csv(r"imf-1_rec.csv",index=False,sep=',') 
plt.plot(sampleData,"b",label='true_data')
plt.plot(sampleData_rec,"r",label='rec_data')
print(len(sampleData_rec))

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结果展示

maxlev:  3
(30,)
77
[array([14.89060208, 17.48774106, 14.62732431, 14.16111973, 17.39674364,
       15.23687513, 13.66198872, 16.98478131, 16.04582462, 13.46882215,
       16.5251981 , 16.84176687, 13.05983399, 18.43398009, 18.69129045,
       18.13412361, 17.22175699, 18.33764118, 18.91146961, 13.79601851,
       18.22412343, 16.31726674, 16.19902123, 20.36499062, 20.34979924,
       22.27118849, 22.1946934 , 22.49473075, 22.026928  , 21.37043685]), array([ 0.02597377,  0.04627224,  0.83377698, -0.4094727 , -0.66537233,
        0.9654267 ,  2.37728835,  1.73186496, -0.13460124,  4.2582881 ,
       -1.6431639 ,  3.08247987,  0.06009821,  1.84186106,  1.99219538,
       -0.86047581, -0.26259531, -2.15543405,  0.12983753, -2.04923737,
        0.45461431,  0.26755979, -1.97147193, -1.14392232,  2.75158527,
        0.496431  , -0.76652795,  0.97028847,  1.45129   ,  0.42297384]), array([-1.53165283,  3.90167297,  1.0713417 , -0.88960218, -0.93377672,
       -4.37583791, -0.28086216, -2.60909404, -0.02110962,  0.34448458,
       -0.49989578, -1.12639543, -0.15444765, -0.4400528 ,  0.82299644,
        1.48393952, -0.32030378, -0.76129654, -1.01577166, -1.11048871,
        0.96183349, -0.99455448, -1.64585353,  2.25273347, -1.06781376,
       -0.76168693,  1.67411943,  0.47655085,  0.43817838,  1.63985866,
        1.33641088, -0.74595449,  0.96958856, -2.15826138,  0.03824809,
        2.0088925 , -2.82540503, -1.50755191,  1.44302522, -0.7894779 ,
       -0.10165996,  2.97673399,  0.17404927, -2.01625666,  1.99990472,
        0.66159429]), array([ 0.34482481, -1.29156053, -0.47973075,  0.52203713,  0.51414751,
        3.92438116, -1.26310503,  0.85073165,  0.07343796, -0.97518959,
        0.9308112 ,  1.19327555,  0.06607814, -2.22254163, -0.62430618,
        0.99976817,  1.20568031, -0.24606229,  2.19671547,  0.0838348 ,
       -0.21327042,  2.68511782, -1.43872316,  0.19455079,  0.09690823,
        1.17238324, -0.38030448, -0.36054123,  0.6819401 , -0.47211671,
       -0.15042381,  1.07032598,  1.58819977, -2.1150514 ,  0.9529688 ,
       -2.83481343,  2.23710703,  1.26627991,  1.99366416, -0.81516264,
       -0.65308376,  0.41792825,  1.53076635,  1.39865169,  0.74781752,
       -3.02966721,  0.85346178,  0.10372268,  0.09628719, -2.0333159 ,
        2.13431488, -2.70836621, -0.55006196,  1.67096465,  1.23637851,
        2.42158039, -2.04923712, -2.270118  ,  2.33425608, -2.02102422,
       -0.78420639,  0.89368156, -1.85879552, -1.61098924, -1.07499289,
       -1.01434951, -3.61867498, -4.44962534, -1.56543207, -0.0356764 ,
        0.12405043, -0.15293619,  1.03965714,  3.61517855,  4.13996129,
        2.10191932,  0.57358088])]
140
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备注:

需要源代码和数据集,或者想要沟通交流,请私聊,谢谢.