jax.numpy.fft.ifftn

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

Compute a multidimensional inverse discrete Fourier transform.

JAX implementation of numpy.fft.ifftn().

Parameters:

• a (ArrayLike) – input array

• s (Shape | None | 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 | None) – sequence of integers, default=None. Specifies the axes along which the transform is computed. If None, computes the transform along all the axes.

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

Returns:

An array containing the multidimensional inverse discrete Fourier transform of a.

Return type:

Array

See also

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

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

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

Examples

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

>>> x = jnp.array([[1, 2, 5, 3],
...                [4, 1, 2, 6],
...                [5, 3, 2, 1]])
>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jnp.fft.ifftn(x))
[[ 2.92+0.j    0.08-0.33j  0.25+0.j    0.08+0.33j]
 [-0.08+0.14j -0.04-0.03j  0.  -0.29j -1.05-0.11j]
 [-0.08-0.14j -1.05+0.11j  0.  +0.29j -0.04+0.03j]]

When s=[3], dimension of the transform along axis -1 will be 3 and dimension along other axes will be the same as that of input.

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jnp.fft.ifftn(x, s=[3]))
[[ 2.67+0.j   -0.83-0.87j -0.83+0.87j]
 [ 2.33+0.j    0.83-0.29j  0.83+0.29j]
 [ 3.33+0.j    0.83+0.29j  0.83-0.29j]]

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(jnp.fft.ifftn(x, s=[2], axes=[0]))
[[ 2.5+0.j  1.5+0.j  3.5+0.j  4.5+0.j]
 [-1.5+0.j  0.5+0.j  1.5+0.j -1.5+0.j]]

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

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jnp.fft.ifftn(x, s=[2, 3]))
[[ 2.5 +0.j    0.  -0.58j  0.  +0.58j]
 [ 0.17+0.j   -0.83-0.29j -0.83+0.29j]]