Module « scipy.signal »

Fonction sosfilt - module scipy.signal

Signature de la fonction sosfilt

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

Description

sosfilt.__doc__

    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, sosfreqz

    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()