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jax.scipy.signal.fftconvolve

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jax.scipy.signal.fftconvolve#

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

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

JAX implementation of scipy.signal.fftconvolve().

Parameters:

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

  • in2 (jax.typing.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) – optional sequence of axes along which to apply the convolution.

Returns:

Array containing the convolved result.

Return type:

Array

See also

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.]
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