scipy.signal.convolve¶

scipy.signal.
convolve
(in1, in2, mode='full', method='auto')[source]¶ Convolve two Ndimensional arrays.
Convolve in1 and in2, with the output size determined by the mode argument.
 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.
 methodstr {‘auto’, ‘direct’, ‘fft’}, optional
A string indicating which method to use to calculate the convolution.
direct
The convolution is determined directly from sums, the definition of convolution.
fft
The Fourier Transform is used to perform the convolution by calling
fftconvolve
.auto
Automatically chooses direct or Fourier method based on an estimate of which is faster (default). See Notes for more detail.
New in version 0.19.0.
 Returns
 convolvearray
An Ndimensional array containing a subset of the discrete linear convolution of in1 with in2.
See also
numpy.polymul
performs polynomial multiplication (same operation, but also accepts poly1d objects)
choose_conv_method
chooses the fastest appropriate convolution method
Notes
By default,
convolve
andcorrelate
usemethod='auto'
, which callschoose_conv_method
to choose the fastest method using precomputed values (choose_conv_method
can also measure realworld timing with a keyword argument). Becausefftconvolve
relies on floating point numbers, there are certain constraints that may force method=direct (more detail inchoose_conv_method
docstring).Examples
Smooth a square pulse using a Hann window:
>>> from scipy import signal >>> sig = np.repeat([0., 1., 0.], 100) >>> win = signal.hann(50) >>> filtered = signal.convolve(sig, win, mode='same') / sum(win)
>>> import matplotlib.pyplot as plt >>> fig, (ax_orig, ax_win, ax_filt) = plt.subplots(3, 1, sharex=True) >>> ax_orig.plot(sig) >>> ax_orig.set_title('Original pulse') >>> ax_orig.margins(0, 0.1) >>> ax_win.plot(win) >>> ax_win.set_title('Filter impulse response') >>> ax_win.margins(0, 0.1) >>> ax_filt.plot(filtered) >>> ax_filt.set_title('Filtered signal') >>> ax_filt.margins(0, 0.1) >>> fig.tight_layout() >>> fig.show()