savitzky_golay module

Created on Mon Jan 10 13:57:54 2022

@author: user

savitzky_golay.savitzky_golay(y, window_size, order, deriv=0, rate=1)

Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques. :param y: the values of the time history of the signal. :type y: array_like, shape (N,) :param window_size: the length of the window. Must be an odd integer number. :type window_size: int :param order: the order of the polynomial used in the filtering.

Must be less then window_size - 1.

Parameters

deriv (int) – the order of the derivative to compute (default = 0 means only smoothing)

Returns

ys (ndarray, shape (N)) – the smoothed signal (or it’s n-th derivative).

Notes

The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.

Examples

t = np.linspace(-4, 4, 500) y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape) ysg = savitzky_golay(y, window_size=31, order=4) import matplotlib.pyplot as plt plt.plot(t, y, label=’Noisy signal’) plt.plot(t, np.exp(-t**2), ‘k’, lw=1.5, label=’Original signal’) plt.plot(t, ysg, ‘r’, label=’Filtered signal’) plt.legend() plt.show()

References

1

A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.

2

Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688