jax.numpy.fft.ifft

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jax.numpy.fft.ifft#

jax.numpy.fft.ifft(a, n=None, axis=-1, norm=None)[source]#

Compute a one-dimensional inverse discrete Fourier transform.

JAX implementation of numpy.fft.ifft().

Parameters:

  • a (ArrayLike) – input array

  • n (int | None | None) – int. Specifies the dimension of the result along axis. If not specified, it will default to the dimension of a along axis.

  • axis (int) – int, default=-1. Specifies the axis along which the transform is computed. If not specified, the transform is computed along axis -1.

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

Returns:

An array containing the one-dimensional discrete Fourier transform of a.

Return type:

Array

See also

Examples

jnp.fft.ifft computes the transform along axis -1 by default.

>>> x = jnp.array([[3, 1, 4, 6],
...                [2, 5, 7, 1]])
>>> jnp.fft.ifft(x)
Array([[ 3.5 +0.j  , -0.25-1.25j,  0.  +0.j  , -0.25+1.25j],
      [ 3.75+0.j  , -1.25+1.j  ,  0.75+0.j  , -1.25-1.j  ]],      dtype=complex64)

When n=5, dimension of the transform along axis -1 will be 5 and dimension along other axes will be the same as that of input.

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jnp.fft.ifft(x, n=5))
[[ 2.8 +0.j   -0.96-0.04j  1.06+0.5j   1.06-0.5j  -0.96+0.04j]
 [ 3.  +0.j   -0.59+1.66j  0.09-0.55j  0.09+0.55j -0.59-1.66j]]

When n=3 and axis=0, dimension of the transform along axis 0 will be 3 and dimension along other axes will be same as that of input.

>>> with jnp.printoptions(precision=2, suppress=True):
...   print(jnp.fft.ifft(x, n=3, axis=0))
[[ 1.67+0.j    2.  +0.j    3.67+0.j    2.33+0.j  ]
 [ 0.67+0.58j -0.5 +1.44j  0.17+2.02j  1.83+0.29j]
 [ 0.67-0.58j -0.5 -1.44j  0.17-2.02j  1.83-0.29j]]
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