# cupyx.scipy.signal.correlate¶

cupyx.scipy.signal.correlate(in1, in2, mode='full', method='auto')[source]

Cross-correlate two N-dimensional arrays.

Cross-correlate in1 and in2, with the output size determined by the mode argument.

Parameters
• in1 (cupy.ndarray) – First input.

• in2 (cupy.ndarray) – Second input. Should have the same number of dimensions as in1.

• mode (str) –

Indicates the size of the output:

• 'full': output is the full discrete linear convolution (default)

• 'valid': output consists only of those elements that do not rely on the zero-padding. Either in1 or in2 must be at least as large as the other in every dimension.

• 'same': - output is the same size as in1, centered with respect to the 'full' output

• method (str) –

Indicates which method to use for the computations:

• '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 choose direct of FFT based on an estimate of which is faster for the arguments (default).

Returns

the result of correlation.

Return type

cupy.ndarray

cupyx.scipy.signal.fftconvolve()

cupyx.scipy.signal.oaconvolve()

cupyx.scipy.ndimage.correlation()
scipy.signal.correlation()
By default, convolve and correlate use method='auto', which calls choose_conv_method to choose the fastest method using pre-computed values. CuPy may not choose the same method to compute the convolution as SciPy does given the same inputs.