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47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import matplotlib.pyplot as plt
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from pylab import subplot, plot, title, figure
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from numpy import random, arange, sin
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from sg_filter import SavitzkyGolay, smoothn # calc_coeff, smooth
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def example_reconstruct_noisy_chirp():
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figure(figsize=(7, 12))
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# generate chirp signal
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tvec = arange(0, 6.28, .02)
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true_signal = sin(tvec * (2.0 + tvec))
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# add noise to signal
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noise = 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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subplot(311)
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plot(signal)
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title('signal')
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# smooth and plot signal
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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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plot(s_signal)
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plot(s2)
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plot(true_signal, 'r--')
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title('smoothed signal')
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# smooth derivative of signal and plot it
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subplot(313)
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savgol1 = SavitzkyGolay(n=8, degree=1, diff_order=1)
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d_signal = savgol1.smooth(signal)
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plot(d_signal)
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title('smoothed derivative of signal')
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plt.show('hold') # show plot
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if __name__ == '__main__':
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example_reconstruct_noisy_chirp()
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