scipy.signal.oaconvolve¶

scipy.signal.
oaconvolve
(in1, in2, mode='full', axes=None)[source]¶ Convolve two Ndimensional arrays using the overlapadd method.
Convolve in1 and in2 using the overlapadd 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 thanfftconvolve
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
 in1array_like
First input.
 in2array_like
Second input. Should have the same number of dimensions as in1.
 modestr {‘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 zeropadding. 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.
 axesint or array_like of ints or None, optional
Axes over which to compute the convolution. The default is over all axes.
 Returns
 outarray
An Ndimensional 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
New in version 1.4.0.
References
 1
Wikipedia, “Overlapadd_method”. https://en.wikipedia.org/wiki/Overlapadd_method
 2
Richard G. Lyons. Understanding Digital Signal Processing, Third Edition, 2011. Chapter 13.10. ISBN 13: 9780137027415
Examples
Convolve a 100,000 sample signal with a 512sample filter.
>>> from scipy import signal >>> sig = np.random.randn(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()