`scipy.fft._basic:hfftn`

source: /scipy/fft/_basic.py :1357

Signature

   def     hfftn (    x    ,    s    =  None   ,    axes    =  None   ,    norm    =  None   ,    overwrite_x    =  False   ,    workers    =  None   ,  * ,     plan    =  None     )

Summary

Compute the N-D FFT of Hermitian symmetric complex input, i.e., a signal with a real spectrum.

Extended Summary

This function computes the N-D discrete Fourier Transform for a Hermitian symmetric complex input over any number of axes in an M-D array by means of the Fast Fourier Transform (FFT). In other words, ihfftn(hfftn(x, s)) == x to within numerical accuracy. (s here is x.shape with s[-1] = x.shape[-1] * 2 - 1, this is necessary for the same reason x.shape would be necessary for irfft.)

Parameters

x : array_like: Input array.
s : sequence of ints, optional: Shape (length of each transformed axis) of the output (s[0] refers to axis 0, s[1] to axis 1, etc.). s is also the number of input points used along this axis, except for the last axis, where s[-1]//2+1 points of the input are used. Along any axis, if the shape indicated by s is smaller than that of the input, the input is cropped. If it is larger, the input is padded with zeros. If s is not given, the shape of the input along the axes specified by axes is used. Except for the last axis which is taken to be 2*(m-1) where m is the length of the input along that axis.
axes : sequence of ints, optional: Axes over which to compute the inverse FFT. If not given, the last len(s) axes are used, or all axes if s is also not specified.
norm : {"backward", "ortho", "forward"}, optional: Normalization mode (see fft). Default is "backward".
overwrite_x : bool, optional: If True, the contents of x can be destroyed; the default is False. See fft for more details.
workers : int, optional: Maximum number of workers to use for parallel computation. If negative, the value wraps around from os.cpu_count(). See fft for more details.
plan : object, optional: This argument is reserved for passing in a precomputed plan provided by downstream FFT vendors. It is currently not used in SciPy.
versionadded 1.5.0

Returns

out : ndarray: The truncated or zero-padded input, transformed along the axes indicated by axes, or by a combination of s or x, as explained in the parameters section above. The length of each transformed axis is as given by the corresponding element of s, or the length of the input in every axis except for the last one if s is not given. In the final transformed axis the length of the output when s is not given is 2*(m-1) where m is the length of the final transformed axis of the input. To get an odd number of output points in the final axis, s must be specified.

Raises

: ValueError: If s and axes have different length.
: IndexError: If an element of axes is larger than the number of axes of x.

Notes

For a 1-D signal x to have a real spectrum, it must satisfy the Hermitian property

x[i] == np.conj(x[-i]) for all i

This generalizes into higher dimensions by reflecting over each axis in turn

x[i, j, k, ...] == np.conj(x[-i, -j, -k, ...]) for all i, j, k, ...

This should not be confused with a Hermitian matrix, for which the transpose is its own conjugate

x[i, j] == np.conj(x[j, i]) for all i, j

The default value of s assumes an even output length in the final transformation axis. When performing the final complex to real transformation, the Hermitian symmetry requires that the last imaginary component along that axis must be 0 and so it is ignored. To avoid losing information, the correct length of the real input must be given.

Array API Standard Support

hfftn has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ✅                   
JAX                   ✅                     ✅                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

import scipy.fft
import numpy as np
x = np.ones((3, 2, 2))

✓

scipy.fft.hfftn(x)

✗

Aliases

scipy.fft.hfftn