jax.numpy.fft.fftn

jax.numpy.fft.fftn(a, s=None, axes=None, norm=None)

Compute a multidimensional discrete Fourier transform along given axes.

JAX implementation of numpy.fft.fftn().

Parameters:

• a (ArrayLike) – input array

• s (Shape | None) – sequence of integers. Specifies the shape of the result. If not specified, it will default to the shape of a along the specified axes.

• axes (Sequence[int] | None) – sequence of integers, default=None. Specifies the axes along which the transform is computed.

• norm (str | None) – string. The normalization mode. “backward”, “ortho” and “forward” are supported.

Returns:

An array containing the multidimensional discrete Fourier transform of a.

Return type:

Array

See also

• jax.numpy.fft.fft(): Computes a one-dimensional discrete Fourier transform.

• jax.numpy.fft.ifft(): Computes a one-dimensional inverse discrete Fourier transform.

• jax.numpy.fft.ifftn(): Computes a multidimensional inverse discrete Fourier transform.

Examples

jnp.fft.fftn computes the transform along all the axes by default when axes argument is None.

>>> x = jnp.array([[1, 2, 5, 6],
...                [4, 1, 3, 7],
...                [5, 9, 2, 1]])
>>> with jnp.printoptions(precision=2, suppress=True):
...   jnp.fft.fftn(x)
Array([[ 46.  +0.j  ,   0.  +2.j  ,  -6.  +0.j  ,   0.  -2.j  ],
       [ -2.  +1.73j,   6.12+6.73j,   0.  -1.73j, -18.12-3.27j],
       [ -2.  -1.73j, -18.12+3.27j,   0.  +1.73j,   6.12-6.73j]],      dtype=complex64)

When s=[2], dimension of the transform along axis -1 will be 2 and dimension along other axes will be the same as that of input.

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.numpy.fft.fftn(x, s=[2]))
[[ 3.+0.j -1.+0.j]
 [ 5.+0.j  3.+0.j]
 [14.+0.j -4.+0.j]]

When s=[2] and axes=[0], dimension of the transform along axis 0 will be 2 and dimension along other axes will be same as that of input.

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.numpy.fft.fftn(x, s=[2], axes=[0]))
[[ 5.+0.j  3.+0.j  8.+0.j 13.+0.j]
 [-3.+0.j  1.+0.j  2.+0.j -1.+0.j]]

When s=[2, 3], shape of the transform will be (2, 3).

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jax.numpy.fft.fftn(x, s=[2, 3]))
[[16. +0.j   -0.5+4.33j -0.5-4.33j]
 [ 0. +0.j   -4.5+0.87j -4.5-0.87j]]

jnp.fft.ifftn can be used to reconstruct x from the result of jnp.fft.fftn.

>>> x_fftn = jnp.fft.fftn(x)
>>> jnp.allclose(x, jnp.fft.ifftn(x_fftn))
Array(True, dtype=bool)