rfftfreq

paddle.fft. rfftfreq ( n: int, d: float = 1.0, dtype: DTypeLike | None = None, name: str | None = None, *, out: paddle.Tensor | None = None, device: PlaceLike | None = None, requires_grad: bool = False ) → Tensor [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.

Parameters

n (int) – Dimension inputted.
d (float, optional) – Sample spacing (inverse of the sampling rate). Defaults is 1.
dtype (str, optional) – The data type of returns. Defaults is the data type of returns of paddle.get_default_dtype().
out (Tensor, optional) – The output tensor.
device (PlaceLike|None, optional) – The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.
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 api_guide_Name.

Returns

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

Examples

           >>> import paddle

>>> scalar_temp = 0.3
>>> rfftfreq_xp = paddle.fft.rfftfreq(5, d=scalar_temp)
>>> print(rfftfreq_xp)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0., 0.66666669, 1.33333337])

          