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#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import pywt
import pywt.data
ecg = pywt.data.ecg()
wavelet = pywt.Wavelet('db8') #'db16')
level = 4
coeffs = pywt.mra(ecg, wavelet=wavelet, level=level)
ca = coeffs[0]
details = coeffs[1:]
# Create a plot using the same axis limits for all coefficient arrays to
# illustrate the preservation of alignment across decomposition levels.
ylim = [ecg.min(), ecg.max()]
def mark_peaks(ax):
# add dashed lines at the locations of the ECG peaks for reference
ylim = ax.get_ylim()
ax.plot([190, 190], ylim, 'k--')
ax.plot([518, 518], ylim, 'k--')
ax.plot([848, 848], ylim, 'k--')
fig, axes = plt.subplots(len(coeffs) + 1, 2, figsize=[12, 4])
axes[0][0].set_title("MRA decomposition")
axes[0][0].plot(ecg)
axes[0][0].set_ylabel('ECG Signal')
axes[0][0].set_xlim(0, len(ecg) - 1)
axes[0][0].set_ylim(ylim[0], ylim[1])
mark_peaks(axes[0][0])
for i, x in enumerate(coeffs):
ax = axes[-i - 1][0]
ax.plot(coeffs[i], 'g')
if i == 0:
ax.set_ylabel("A%d" % (len(coeffs) - 1))
else:
ax.set_ylabel("D%d" % (len(coeffs) - i))
# Scale axes
ax.set_xlim(0, len(ecg) - 1)
ax.set_ylim(ylim[0], ylim[1])
mark_peaks(ax)
"""
repeat using the SWT instead of MRA as the decomposition
"""
coeffs = pywt.swt(ecg, wavelet=wavelet, level=level, norm=True, trim_approx=True)
ca = coeffs[0]
details = coeffs[1:]
axes[0][1].set_title("normalized SWT decomposition")
axes[0][1].plot(ecg)
axes[0][1].set_ylabel('ECG Signal')
axes[0][1].set_xlim(0, len(ecg) - 1)
axes[0][1].set_ylim(ylim[0], ylim[1])
mark_peaks(axes[0][1])
for i, x in enumerate(coeffs):
ax = axes[-i - 1][1]
ax.plot(coeffs[i], 'g')
if i == 0:
ax.set_ylabel("A%d" % (len(coeffs) - 1))
else:
ax.set_ylabel("D%d" % (len(coeffs) - i))
# Scale axes
ax.set_xlim(0, len(ecg) - 1)
ax.set_ylim(ylim[0], ylim[1])
mark_peaks(ax)
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