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

Convolve :None:None:`in1` and in2 using the overlap-add method, with the output size determined by the :None:None:`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).

Notes

Parameters

in1 : array_like

First input.

in2 : array_like

Second input. Should have the same number of dimensions as :None:None:`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 :None:None:`in1` or in2 must be at least as large as the other in every dimension.

same

The output is the same size as :None:None:`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 :None:None:`in1` with in2 .

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

See Also

convolve

Uses the direct convolution or FFT convolution algorithm depending on which is faster.

fftconvolve

An implementation of convolution using FFT.

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()

See :

Back References

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

scipy.signal._signaltools._freq_domain_conv scipy.signal._signaltools.fftconvolve scipy.signal._signaltools.oaconvolve scipy.signal._signaltools.convolve

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