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55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from wafo.sg_filter import SavitzkyGolay, smoothn # calc_coeff, smooth
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def example_reconstruct_noisy_chirp():
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"""
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Example
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-------
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>>> example_reconstruct_noisy_chirp()
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"""
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plt.figure(figsize=(7, 12))
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# generate chirp signal
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tvec = np.arange(0, 6.28, .02)
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true_signal = np.sin(tvec * (2.0 + tvec))
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true_d_signal = (2+tvec) * np.cos(tvec * (2.0 + tvec))
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# add noise to signal
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noise = np.random.normal(size=true_signal.shape)
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signal = true_signal + .15 * noise
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# plot signal
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plt.subplot(311)
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plt.plot(signal)
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plt.title('signal')
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# smooth and plot signal
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plt.subplot(312)
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savgol = SavitzkyGolay(n=8, degree=4)
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s_signal = savgol.smooth(signal)
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s2 = smoothn(signal, robust=True)
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plt.plot(s_signal)
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plt.plot(s2)
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plt.plot(true_signal, 'r--')
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plt.title('smoothed signal')
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# smooth derivative of signal and plot it
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plt.subplot(313)
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savgol1 = SavitzkyGolay(n=8, degree=1, diff_order=1)
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dt = tvec[1]-tvec[0]
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d_signal = savgol1.smooth(signal) / dt
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plt.plot(d_signal)
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plt.plot(true_d_signal, 'r--')
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plt.title('smoothed derivative of signal')
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if __name__ == '__main__':
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from wafo.testing import test_docstrings
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test_docstrings(__file__)
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# example_reconstruct_noisy_chirp()
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# plt.show('hold') # show plot
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