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

Convolve two N-dimensional arrays.

LAX-backend implementation of scipy.signal._signaltools.convolve().

Original docstring below.

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

  • 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:


    The output is the full discrete linear convolution of the inputs. (Default)


    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.


    The output is the same size as in1, centered with respect to the ‘full’ output.

  • method (str {'auto', 'direct', 'fft'}, optional) –

    A string indicating which method to use to calculate the convolution.


    The convolution is determined directly from sums, the definition of convolution.


    The Fourier Transform is used to perform the convolution by calling fftconvolve.


    Automatically chooses direct or Fourier method based on an estimate of which is faster (default). See Notes for more detail.

  • precision (Union[None, str, Precision, Tuple[str, str], Tuple[Precision, Precision]]) –


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

Return type: