Vous êtes un professionnel et vous avez besoin d'une formation ? Calcul scientifique
avec Python Voir le programme détaillé

Module « scipy.signal »

Fonction sosfilt - module scipy.signal

Signature de la fonction sosfilt

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

Description

help(scipy.signal.sosfilt)

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.
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 `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

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.

.. versionadded:: 0.16.0

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()

Vous êtes un professionnel et vous avez besoin d'une formation ? Calcul scientifique
avec Python Voir le programme détaillé