paddle.fft. ihfft ( x, n=None, axis=- 1, norm='backward', name=None ) [source]

The inverse FFT of a signal that has Hermitian symmetry.

This function computes the one dimensional n-point inverse FFT of a signal that has Hermitian symmetry by means of an efficient algorithm called the Fast Fourier Transform (FFT).

When the DFT is computed for purely real input, the output is Hermitian-symmetric. This function does not compute the negative frequency terms, and the length of the transformed axis of the output is therefore n//2 + 1.

  • x (Tensor) – Input tensor.

  • n (int, optional) – The number of points along transformation axis in the input to use. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zeros. If n is not given, the length of the input along the axis specified by axis is used.

  • axis (int, optional) – Axis over which to compute the inverse FFT. If not given, the last axis is used.

  • norm (str, optional) – Normalization mode, indicates which direction of the forward/backward pair of transforms is scaled and with what normalization factor. Include {“backward”, “ortho”, “forward”}, default value is “backward”.

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name .


complex tensor.

Return type


Examples: .. code-block:: python

System Message: ERROR/3 (/usr/local/lib/python3.8/site-packages/paddle/fft.py:docstring of paddle.fft.ihfft, line 38)

Unexpected indentation.

import paddle

spectrum = paddle.to_tensor([10.0, -5.0, 0.0, -1.0, 0.0, -5.0]) print(paddle.fft.ifft(spectrum)) # Tensor(shape=[6], dtype=complex64, place=CUDAPlace(0), stop_gradient=True, # [(-0.1666666716337204+0j), (1-1.9868215517249155e-08j), (2.3333334922790527-1.9868215517249155e-08j), (3.5+0j), (2.3333334922790527+1.9868215517249155e-08j), (1+1.9868215517249155e-08j)]) print(paddle.fft.ihfft(spectrum)) # Tensor(shape = [4], dtype = complex64, place = CUDAPlace(0), stop_gradient = True, # [(-0.1666666716337204+0j), (1-1.9868215517249155e-08j), (2.3333334922790527-1.9868215517249155e-08j), (3.5+0j)])