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sosfilt(sos, x, axis=-1, zi=None)

Filter a data sequence, x, using a digital IIR filter defined by :None:None:`sos`.

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.

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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.
x : array_like: An N-dimensional input array.
axis : int, optional: The axis of the input data array along which to apply the linear filter. The filter is applied to each subarray along this axis. Default is -1.
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 :None:None:`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 :None:None:`zi` is None, this is not returned, otherwise, :None:None:`zf` holds the final filter delay values.

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

Examples

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):

>>> 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()

See :

Back References

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

scipy.signal._filter_design.butter scipy.signal._signaltools.sosfiltfilt scipy.signal._filter_design.ellip scipy.signal._filter_design.cheby1 scipy.signal._signaltools.lfilter scipy.signal._filter_design.zpk2sos scipy.signal._signaltools.filtfilt scipy.signal._filter_design.sos2zpk scipy.signal._filter_design.cheby2 scipy.signal._filter_design.sos2tf scipy.signal._filter_design.sosfreqz scipy.signal._signaltools.sosfilt_zi scipy.signal._signaltools.sosfilt scipy.signal._filter_design.tf2sos

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