convolve#
- scipy.signal.convolve(in1, in2, mode='full', method='auto')[source]#
Convolve two N-dimensional 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 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.
- 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.
Added in version 0.19.0.
- Returns:
- convolvearray
An N-dimensional array containing a subset of the discrete linear convolution of in1 with in2.
- Warns:
- RuntimeWarning
Use of the FFT convolution on input containing NAN or INF will lead to the entire output being NAN or INF. Use method=’direct’ when your input contains NAN or INF values.
See also
numpy.polymul
performs polynomial multiplication (same operation, but also accepts poly1d objects)
choose_conv_method
chooses the fastest appropriate convolution method
fftconvolve
Always uses the FFT method.
oaconvolve
Uses the overlap-add method to do convolution, which is generally faster when the input arrays are large and significantly different in size.
Notes
By default,
convolve
andcorrelate
usemethod='auto'
, which callschoose_conv_method
to choose the fastest method using pre-computed values (choose_conv_method
can also measure real-world timing with a keyword argument). Becausefftconvolve
relies on floating point numbers, there are certain constraints that may forcemethod='direct'
(more detail inchoose_conv_method
docstring).Examples
Smooth a square pulse using a Hann window:
>>> import numpy as np >>> from scipy import signal >>> sig = np.repeat([0., 1., 0.], 100) >>> win = signal.windows.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()