pywt

1
"""A visual illustration of the various signal extension modes supported in
2
PyWavelets. For efficiency, in the C routines the array is not actually
3
extended as is done here. This is just a demo for easier visual explanation of
4
the behavior of the various boundary modes.
5

6
In practice, which signal extension mode is beneficial will depend on the
7
signal characteristics.  For this particular signal, some modes such as
8
"periodic",  "antisymmetric" and "zero" result in large discontinuities that
9
would lead to large amplitude boundary coefficients in the detail coefficients
10
of a discrete wavelet transform.
11
"""
12
import numpy as np
13
from matplotlib import pyplot as plt
14

15
from pywt._doc_utils import boundary_mode_subplot
16

17
# synthetic test signal
18
x = 5 - np.linspace(-1.9, 1.1, 9)**2
19

20
# Create a figure with one subplots per boundary mode
21
fig, axes = plt.subplots(3, 3, figsize=(10, 6))
22
plt.subplots_adjust(hspace=0.5)
23
axes = axes.ravel()
24
boundary_mode_subplot(x, 'symmetric', axes[0], symw=False)
25
boundary_mode_subplot(x, 'reflect', axes[1], symw=True)
26
boundary_mode_subplot(x, 'periodic', axes[2], symw=False)
27
boundary_mode_subplot(x, 'antisymmetric', axes[3], symw=False)
28
boundary_mode_subplot(x, 'antireflect', axes[4], symw=True)
29
boundary_mode_subplot(x, 'periodization', axes[5], symw=False)
30
boundary_mode_subplot(x, 'smooth', axes[6], symw=False)
31
boundary_mode_subplot(x, 'constant', axes[7], symw=False)
32
boundary_mode_subplot(x, 'zero', axes[8], symw=False)
33
plt.show()
34