xrscipy.signal.sosfilt

xrscipy.signal.sosfilt(sos: ndarray, darray: DataArray, dim: str = None, zi: ndarray = None) → DataArray | tuple[DataArray, ndarray]

Filter data along one dimension using cascaded second-order sections.

Filter a data sequence, x, using a digital IIR filter defined by sos.

Parameters:

  • sos (array_like) – Array of second-order filter coefficients, must have shape (n_sections, 6). Each row corresponds to a second-order section, with the first three columns providing the numerator coefficients and the last three providing the denominator coefficients.

  • darray (xarray.DataArray) – The data to be filtered.

  • dim (str, optional) – The dimension of the array darray along which the filter is to be applied. Default is the only dimension if 1D, otherwise must be specified.

  • zi (array_like, optional) – Initial conditions for the cascaded filter delays. It is a (at least 2D) vector of shape (n_sections, ..., 2, ...), where ..., 2, ... denotes the shape of x, but with x.shape[axis] replaced by 2. If zi is None or is not given then initial rest (i.e. all zeros) is assumed. Note that these initial conditions are not the same as the initial conditions given by lfiltic or lfilter_zi.

Returns:

  • y (ndarray) – The output of the digital filter.

  • zf (ndarray, optional) – If zi is None, this is not returned, otherwise, zf holds the final filter delay values.

See also

zpk2sos, sos2zpk, sosfilt_zi, sosfiltfilt, freqz_sos

scipy.signal.sosfilt

Original scipy implementation

Notes

The filter function is implemented as a series of second-order filters with direct-form II transposed structure. It is designed to minimize numerical precision errors for high-order filters.

Added in version 0.16.0.

Examples

None Plot a 13th-order filter’s impulse response using both lfilter and sosfilt, showing the instability that results from trying to do a 13th-order filter in a single stage (the numerical error pushes some poles outside of the unit circle):

Examples

>>> import matplotlib.pyplot as plt
>>> from scipy import signal
>>> b, a = signal.ellip(13, 0.009, 80, 0.05, output='ba')
>>> sos = signal.ellip(13, 0.009, 80, 0.05, output='sos')
>>> x = signal.unit_impulse(700)
>>> y_tf = signal.lfilter(b, a, x)
>>> y_sos = signal.sosfilt(sos, x)
>>> plt.plot(y_tf, 'r', label='TF')
>>> plt.plot(y_sos, 'k', label='SOS')
>>> plt.legend(loc='best')
>>> plt.show()