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scipy.fft.

# rfftfreq

scipy.fft.rfftfreq(n, d=1.0, *, xp=None, device=None)

  Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft).

  The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second.

  Given a window 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

  Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`) the Nyquist frequency component is considered to be positive.

  Parameters:
    n int

      Window length.

    d scalar, optional

      Sample spacing (inverse of the sampling rate). Defaults to 1.

    xp array_namespace, optional

      The namespace for the return array. Default is None, where NumPy is used.

    device device, optional

      The device for the return array. Only valid when xp.fft.rfftfreq implements the device parameter.

  Returns:
    f ndarray

      Array of length `n//2 + 1` containing the sample frequencies.

  Notes

  Array API Standard Support

  `rfftfreq` 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 | ✅ | n/a

  > See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> import scipy.fft
      >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float)
      >>> fourier = scipy.fft.rfft(signal)
      >>> n = signal.size
      >>> sample_rate = 100
      >>> freq = scipy.fft.fftfreq(n, d=1./sample_rate)
      >>> freq
      array([  0.,  10.,  20., ..., -30., -20., -10.])
      >>> freq = scipy.fft.rfftfreq(n, d=1./sample_rate)
      >>> freq
      array([  0.,  10.,  20.,  30.,  40.,  50.])