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 ofnumpy.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 ofa along the specifiedaxes.

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

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

Returns:

An array containing the multidimensional inverse discrete Fourier transformofa.

Return type:

Array

See also

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

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

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

Examples

jnp.fft.ifftn computes the transform along all the axes by default whenaxes argument isNone.

>>>x=jnp.array([[1,2,5,3],...[4,1,2,6],...[5,3,2,1]])>>>withjnp.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]]

Whens=[3], dimension of the transform alongaxis-1 will be3and dimension along other axes will be the same as that of input.

>>>withjnp.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]]

Whens=[2] andaxes=[0], dimension of the transform alongaxis0will be2 and dimension along other axes will be same as that of input.

>>>withjnp.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]]

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

>>>withjnp.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]]