`scipy.fft._helper:rfftfreq`

source: /scipy/fft/_helper.py :202

Signature

def rfftfreq ( n , d = 1.0 , * , xp = None , device = None )

Summary

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

Extended Summary

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 dev-arrayapi 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
freq = scipy.fft.rfftfreq(n, d=1./sample_rate)

✓

freq

✗

Aliases

scipy.fft.rfftfreq