Module « scipy.signal »

Fonction oaconvolve - module scipy.signal

Signature de la fonction oaconvolve

def oaconvolve(in1, in2, mode='full', axes=None) 

Description

oaconvolve.__doc__

Convolve two N-dimensional arrays using the overlap-add method.

    Convolve `in1` and `in2` using the overlap-add method, with
    the output size determined by the `mode` argument.

    This is generally much faster than `convolve` for large arrays (n > ~500),
    and generally much faster than `fftconvolve` when one array is much
    larger than the other, but can be slower when only a few output values are
    needed or when the arrays are very similar in shape, and can only
    output float arrays (int or object array inputs will be cast to float).

    Parameters
    ----------
    in1 : array_like
        First input.
    in2 : array_like
        Second input. Should have the same number of dimensions as `in1`.
    mode : str {'full', 'valid', 'same'}, optional
        A string indicating the size of the output:

        ``full``
           The output is the full discrete linear convolution
           of the inputs. (Default)
        ``valid``
           The output consists only of those elements that do not
           rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
           must be at least as large as the other in every dimension.
        ``same``
           The output is the same size as `in1`, centered
           with respect to the 'full' output.
    axes : int or array_like of ints or None, optional
        Axes over which to compute the convolution.
        The default is over all axes.

    Returns
    -------
    out : array
        An N-dimensional array containing a subset of the discrete linear
        convolution of `in1` with `in2`.

    See Also
    --------
    convolve : Uses the direct convolution or FFT convolution algorithm
               depending on which is faster.
    fftconvolve : An implementation of convolution using FFT.

    Notes
    -----
    .. versionadded:: 1.4.0

    Examples
    --------
    Convolve a 100,000 sample signal with a 512-sample filter.

    >>> from scipy import signal
    >>> rng = np.random.default_rng()
    >>> sig = rng.standard_normal(100000)
    >>> filt = signal.firwin(512, 0.01)
    >>> fsig = signal.oaconvolve(sig, filt)

    >>> import matplotlib.pyplot as plt
    >>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
    >>> ax_orig.plot(sig)
    >>> ax_orig.set_title('White noise')
    >>> ax_mag.plot(fsig)
    >>> ax_mag.set_title('Filtered noise')
    >>> fig.tight_layout()
    >>> fig.show()

    References
    ----------
    .. [1] Wikipedia, "Overlap-add_method".
           https://en.wikipedia.org/wiki/Overlap-add_method
    .. [2] Richard G. Lyons. Understanding Digital Signal Processing,
           Third Edition, 2011. Chapter 13.10.
           ISBN 13: 978-0137-02741-5