jax.numpy.fft.ifftn — JAX documentation (original) (raw)

jax.numpy.fft.ifftn#

jax.numpy.fft.ifftn(a, s=None, axes=None, norm=None)[source]#

Compute a multidimensional inverse discrete Fourier transform.

JAX implementation of numpy.fft.ifftn().

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. If None, computes the transform along all the axes.
• norm (str | 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 whenaxes 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 3and 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 0will 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]]