jax.scipy.signal.correlate

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

Cross-correlation of two N-dimensional arrays.

JAX implementation of scipy.signal.correlate().

Parameters:

• in1 (Array) – left-hand input to the cross-correlation.

• in2 (Array) – right-hand input to the cross-correlation. Must have in1.ndim == in2.ndim.

• mode (str) –

  controls the size of the output. Available operations are:

  • "full": (default) output the full cross-correlation of the inputs.

  • "same": return a centered portion of the "full" output which is the same size as in1.

  • "valid": return the portion of the "full" output which do not depend on padding at the array edges.

• method (str) –

  controls the computation method. Options are

  • "auto": (default) always uses the "direct" method.

  • "direct": lower to jax.lax.conv_general_dilated().

  • "fft": compute the result via a fast Fourier transform.

• precision (PrecisionLike) – Specify the precision of the computation. Refer to jax.lax.Precision for a description of available values.

Returns:

Array containing the cross-correlation result.

Return type:

Array

See also

• jax.numpy.correlate(): 1D cross-correlation

• jax.scipy.signal.correlate2d(): 2D cross-correlation

• jax.scipy.signal.convolve(): ND convolution

Examples

A few 1D correlation examples:

>>> x = jnp.array([1, 2, 3, 2, 1])
>>> y = jnp.array([1, 3, 2])

Full 1D correlation uses implicit zero-padding at the edges:

>>> jax.scipy.signal.correlate(x, y, mode='full')
Array([ 2.,  7., 13., 15., 11.,  5.,  1.], dtype=float32)

Specifying mode = 'same' returns a centered 1D correlation of the same size as the first input:

>>> jax.scipy.signal.correlate(x, y, mode='same')
Array([ 7., 13., 15., 11.,  5.], dtype=float32)

Specifying mode = 'valid' returns only the portion of 1D correlation where the two arrays fully overlap:

>>> jax.scipy.signal.correlate(x, y, mode='valid')
Array([13., 15., 11.], dtype=float32)