fftconvolve(in1, in2, mode='full', axes=None)
Convolve :None:None:`in1` and in2
using the fast Fourier transform method, with the output size determined by the :None:None:`mode` argument.
This is generally much faster than convolve
for large arrays (n > ~500), but can be slower when only a few output values are needed, and can only output float arrays (int or object array inputs will be cast to float).
As of v0.19, convolve
automatically chooses this method or the direct method based on an estimation of which is faster.
First input.
Second input. Should have the same number of dimensions as :None:None:`in1`.
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 over which to compute the convolution. The default is over all axes.
An N-dimensional array containing a subset of the discrete linear convolution of :None:None:`in1` with in2
.
Convolve two N-dimensional arrays using FFT.
convolve
Uses the direct convolution or FFT convolution algorithm depending on which is faster.
oaconvolve
Uses the overlap-add method to do convolution, which is generally faster when the input arrays are large and significantly different in size.
Autocorrelation of white noise is an impulse.
>>> from scipy import signal
... rng = np.random.default_rng()
... sig = rng.standard_normal(1000)
... autocorr = signal.fftconvolve(sig, sig[::-1], mode='full')
>>> 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(np.arange(-len(sig)+1,len(sig)), autocorr)
... ax_mag.set_title('Autocorrelation')
... fig.tight_layout()
... fig.show()
Gaussian blur implemented using FFT convolution. Notice the dark borders around the image, due to the zero-padding beyond its boundaries. The convolve2d
function allows for other types of image boundaries, but is far slower.
>>> from scipy import misc
... face = misc.face(gray=True)
... kernel = np.outer(signal.windows.gaussian(70, 8),
... signal.windows.gaussian(70, 8))
... blurred = signal.fftconvolve(face, kernel, mode='same')
>>> fig, (ax_orig, ax_kernel, ax_blurred) = plt.subplots(3, 1,See :
... figsize=(6, 15))
... ax_orig.imshow(face, cmap='gray')
... ax_orig.set_title('Original')
... ax_orig.set_axis_off()
... ax_kernel.imshow(kernel, cmap='gray')
... ax_kernel.set_title('Gaussian kernel')
... ax_kernel.set_axis_off()
... ax_blurred.imshow(blurred, cmap='gray')
... ax_blurred.set_title('Blurred')
... ax_blurred.set_axis_off()
... fig.show()
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.lib.stride_tricks.sliding_window_view
scipy.fft._pocketfft.helper.set_workers
scipy.signal._signaltools._freq_domain_conv
scipy.signal._signaltools._init_freq_conv_axes
scipy.signal._signaltools.fftconvolve
scipy.signal._signaltools.choose_conv_method
numpy.convolve
scipy.signal._signaltools.convolve
scipy.signal._signaltools.oaconvolve
scipy.signal._signaltools._apply_conv_mode
dask.array.overlap.sliding_window_view
scipy.signal._signaltools.correlate
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