oaconvolve#
- scipy.signal.oaconvolve(in1, in2, mode='full', axes=None)[source]#
- 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 - convolvefor large arrays (n > ~500), and generally much faster than- fftconvolvewhen 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 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. 
 
- 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 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 - Added in version 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() 