scipy.signal.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.

New in version 0.19.0.

Returns

convolvearray: An N-dimensional 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
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 and correlate use method='auto', which calls choose_conv_method to choose the fastest method using pre-computed values (choose_conv_method can also measure real-world timing with a keyword argument). Because fftconvolve relies on floating point numbers, there are certain constraints that may force method=direct (more detail in choose_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.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()

../../_images/scipy-signal-convolve-1.png

Signal processing (scipy.signal)

scipy.signal.correlate