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Module « scipy.signal »

Fonction oaconvolve - module scipy.signal

Signature de la fonction oaconvolve

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

Description

help(scipy.signal.oaconvolve)

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

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

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

>>> import numpy as np
>>> 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()

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