PYTHON

wavelet transform in machine learning

import pywt
import matplotlib.pyplot as plt
 
db_wavelets = pywt.wavelist('db')[:5]
print(db_wavelets)
*** ['db1', 'db2', 'db3', 'db4', 'db5']
 
fig, axarr = plt.subplots(ncols=5, nrows=5, figsize=(20,16))
fig.suptitle('Daubechies family of wavelets', fontsize=16)
for col_no, waveletname in enumerate(db_wavelets):
    wavelet = pywt.Wavelet(waveletname)
    no_moments = wavelet.vanishing_moments_psi
    family_name = wavelet.family_name
    for row_no, level in enumerate(range(1,6)):
        wavelet_function, scaling_function, x_values = wavelet.wavefun(level = level)
        axarr[row_no, col_no].set_title("{} - level {}
{} vanishing moments
{} samples".format(
            waveletname, level, no_moments, len(x_values)), loc='left')
        axarr[row_no, col_no].plot(x_values, wavelet_function, 'bD--')
        axarr[row_no, col_no].set_yticks([])
        axarr[row_no, col_no].set_yticklabels([])
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.show()