jax.scipy.signal.fftconvolve

jax.scipy.signal.fftconvolve(in1, in2, mode='full', axes=None)

Convolve two N-dimensional arrays using Fast Fourier Transform (FFT).

JAX implementation of scipy.signal.fftconvolve().

Parameters:

• in1 (ArrayLike) – left-hand input to the convolution.

• in2 (ArrayLike) – right-hand input to the convolution. Must have in1.ndim == in2.ndim.

• mode (str) –

  controls the size of the output. Available operations are:

  • "full": (default) output the full convolution 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.

• axes (Sequence[int] | None | None) – optional sequence of axes along which to apply the convolution.

Returns:

Array containing the convolved result.

Return type:

Array

See also

• jax.numpy.convolve(): 1D convolution

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

Examples

A few 1D convolution examples. Because FFT-based convolution is approximate, We use jax.numpy.printoptions() below to adjust the printing precision:

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

Full convolution uses implicit zero-padding at the edges:

>>> with jax.numpy.printoptions(precision=3):
...   print(jax.scipy.signal.fftconvolve(x, y, mode='full'))
[1. 3. 6. 7. 6. 3. 1.]

Specifying mode = 'same' returns a centered convolution the same size as the first input:

>>> with jax.numpy.printoptions(precision=3):
...   print(jax.scipy.signal.fftconvolve(x, y, mode='same'))
[3. 6. 7. 6. 3.]

Specifying mode = 'valid' returns only the portion where the two arrays fully overlap:

>>> with jax.numpy.printoptions(precision=3):
...   print(jax.scipy.signal.fftconvolve(x, y, mode='valid'))
[6. 7. 6.]