flopscope.numpy.fft.rfftfreq

fnp.fft.rfftfreq(n, d=1.0, device=None)[flopscope source][numpy source]

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

Adapted from NumPy docs np.fft.rfftfreq

Areafft

Typefree

NumPy Refnp.fft.rfftfreq

Cost

Flopscope Context

Real FFT sample frequencies. No arithmetic; returns index array.

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.
device:str, optional: The device on which to place the created array. Default: None. For Array-API interoperability only, so must be "cpu" if passed.
Added in version 2.0.0.

Returns

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

Examples

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