jax.numpy.fft.irfft

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jax.numpy.fft.irfft#

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

Compute a real-valued one-dimensional inverse discrete Fourier transform.

JAX implementation of numpy.fft.irfft().

Parameters:

  • a (ArrayLike) – input array.

  • n (int | None | None) – int. Specifies the dimension of the result along axis. If not specified, n = 2*(m-1), where m is 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:

A real-valued array containing the one-dimensional inverse discrete Fourier transform of a, with a dimension of n along axis.

Return type:

Array

See also

Examples

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

>>> x = jnp.array([[1, 3, 5],
...                [2, 4, 6]])
>>> jnp.fft.irfft(x)
Array([[ 3., -1.,  0., -1.],
       [ 4., -1.,  0., -1.]], dtype=float32)

When n=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):
...   jnp.fft.irfft(x, n=3)
Array([[ 2.33, -0.67, -0.67],
       [ 3.33, -0.67, -0.67]], dtype=float32)

When n=4 and axis=0, dimension of the transform along axis 0 will be 4 and dimension along other axes will be same as that of input.

>>> with jnp.printoptions(precision=2, suppress=True):
...   jnp.fft.irfft(x, n=4, axis=0)
Array([[ 1.25,  2.75,  4.25],
       [ 0.25,  0.75,  1.25],
       [-0.75, -1.25, -1.75],
       [ 0.25,  0.75,  1.25]], dtype=float32)
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