paddle.fft. rfftfreq ( n, d=1.0, dtype=None, name=None ) [source]

Return the Discrete Fourier Transform sample frequencies.

The returned floating-point array “F” contains the center of the frequency unit, and the unit is the number of cycles of the sampling interval (the starting point is zero).

Given input length n and a sample spacing d:

f = [0, 1, ...,     n/2-1,     n/2] / (d*n)   if n is even
f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n)   if n is odd

the Nyquist frequency component is considered to be positive.

  • n (int) – Dimension inputed.

  • d (scalar, optional) – Sample spacing (inverse of the sampling rate). Defaults is 1.

  • 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.


Tensor. A tensor of length n//2 + 1 containing the sample frequencies.


import numpy as np
import paddle

x = np.array([3, 1, 2, 2, 3], dtype=float)
scalar_temp = 0.3
n = x.size
rfftfreq_xp = paddle.fft.rfftfreq(n, d=scalar_temp)

#  Tensor(shape=[3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
#           [0.        , 0.66666669, 1.33333337])