xorbits.numpy.fft.rfftfreq#

xorbits.numpy.fft.rfftfreq(n, d=1.0, gpu=None, chunk_size=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 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.

Returns

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

Return type

ndarray

Examples

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

gpubool, optional: Allocate the tensor on GPU if True, False as default
chunk_sizeint or tuple of int or tuple of ints, optional: Desired chunk size on each dimension

This docstring was copied from numpy.fft.