scipy.signal.savgol_coeffs¶
- scipy.signal.savgol_coeffs(window_length, polyorder, deriv=0, delta=1.0, pos=None, use='conv')[source]¶
Compute the coefficients for a 1-d Savitzky-Golay FIR filter.
Parameters: window_length : int
The length of the filter window (i.e. the number of coefficients). window_length must be an odd positive integer.
polyorder : int
The order of the polynomial used to fit the samples. polyorder must be less than window_length.
deriv : int, optional
The order of the derivative to compute. This must be a nonnegative integer. The default is 0, which means to filter the data without differentiating.
delta : float, optional
The spacing of the samples to which the filter will be applied. This is only used if deriv > 0.
pos : int or None, optional
If pos is not None, it specifies evaluation position within the window. The default is the middle of the window.
use : str, optional
Either ‘conv’ or ‘dot’. This argument chooses the order of the coefficients. The default is ‘conv’, which means that the coefficients are ordered to be used in a convolution. With use=’dot’, the order is reversed, so the filter is applied by dotting the coefficients with the data set.
Returns: coeffs : 1-d ndarray
The filter coefficients.
See also
References
A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.
Examples
>>> savgol_coeffs(5, 2) array([-0.08571429, 0.34285714, 0.48571429, 0.34285714, -0.08571429]) >>> savgol_coeffs(5, 2, deriv=1) array([ 2.00000000e-01, 1.00000000e-01, 2.00607895e-16, -1.00000000e-01, -2.00000000e-01])
Note that use=’dot’ simply reverses the coefficients.
>>> savgol_coeffs(5, 2, pos=3) array([ 0.25714286, 0.37142857, 0.34285714, 0.17142857, -0.14285714]) >>> savgol_coeffs(5, 2, pos=3, use='dot') array([-0.14285714, 0.17142857, 0.34285714, 0.37142857, 0.25714286])
x contains data from the parabola x = t**2, sampled at t = -1, 0, 1, 2, 3. c holds the coefficients that will compute the derivative at the last position. When dotted with x the result should be 6.
>>> x = array([1, 0, 1, 4, 9]) >>> c = savgol_coeffs(5, 2, pos=4, deriv=1, use='dot') >>> c.dot(x) 6.0000000000000018