savgol_coeffs(window_length, polyorder, deriv=0, delta=1.0, pos=None, use='conv')

Notes

Parameters

window_length : int

The length of the filter window (i.e., the number of coefficients).

polyorder : int

The order of the polynomial used to fit the samples. :None:None:`polyorder` must be less than :None:None:`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.

Compute the coefficients for a 1-D Savitzky-Golay FIR filter.

See Also

savgol_filter

Examples

>>> from scipy.signal import savgol_coeffs
... 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,  2.07548111e-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])

>>> savgol_coeffs(4, 2, pos=3, deriv=1, use='dot')
array([0.45,  -0.85,  -0.65,  1.05])

:None:None:`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 :None:None:`x` the result should be 6.

>>> x = np.array([1, 0, 1, 4, 9])
... c = savgol_coeffs(5, 2, pos=4, deriv=1, use='dot')
... c.dot(x)
6.0

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.signal._savitzky_golay.savgol_filter scipy.signal._savitzky_golay.savgol_coeffs

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