jax.scipy.signal.convolve2d#

jax.scipy.signal.convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0, precision=None)[source]#

Convolve two 2-dimensional arrays.

LAX-backend implementation of scipy.signal._signaltools.convolve2d().

Original docstring below.

Convolve in1 and in2 with output size determined by mode, and boundary conditions determined by boundary and fillvalue.

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:

    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.

  • boundary (str {'fill', 'wrap', 'symm'}, optional) –

    A flag indicating how to handle boundaries:

    fill

    pad input arrays with fillvalue. (default)

    wrap

    circular boundary conditions.

    symm

    symmetrical boundary conditions.

  • fillvalue (scalar, optional) – Value to fill pad input arrays with. Default is 0.

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

Returns:

out – A 2-dimensional array containing a subset of the discrete linear convolution of in1 with in2.

Return type:

ndarray