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

Cross-correlate two N-dimensional arrays.

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

Original docstring below.

Cross-correlate 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 cross-correlation 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 correlation.


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


    The Fast Fourier Transform is used to perform the correlation more quickly (only available for numerical arrays.)


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

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


correlate – An N-dimensional array containing a subset of the discrete linear cross-correlation of in1 with in2.

Return type: