rfftfreq#
- scipy.fft.rfftfreq(n, d=1.0, *, xp=None, device=None)[source]#
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 likescipy.fftpack.rfftfreq
) the Nyquist frequency component is considered to be positive.- Parameters:
- nint
Window length.
- dscalar, optional
Sample spacing (inverse of the sampling rate). Defaults to 1.
- xparray_namespace, optional
The namespace for the return array. Default is None, where NumPy is used.
- devicedevice, optional
The device for the return array. Only valid when xp.fft.rfftfreq implements the device parameter.
- Returns:
- fndarray
Array of length
n//2 + 1
containing the sample frequencies.
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.])