jax.scipy.signal.convolve¶

jax.scipy.signal.convolve(in1, in2, mode='full', method='auto', precision=None)[source]¶

Convolve two N-dimensional arrays.

LAX-backend implementation of convolve(). Original docstring below.

Convolve in1 and in2, with the output size determined by the mode argument.

Parameters

in1 (array_like) – First input.
in2 (array_like) – Second input. Should have the same number of dimensions as in1.
mode (str {'full', 'valid', 'same'}, optional) – A string indicating the size of the output:
method (str {'auto', 'direct', 'fft'}, optional) – A string indicating which method to use to calculate the convolution.

Returns

convolve – An N-dimensional array containing a subset of the discrete linear convolution of in1 with in2.

Return type

array

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.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()