torch_fft_rfft: Rfft

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Rfft

Description

Computes the one dimensional Fourier transform of real-valued input.

Usage

torch_fft_rfft(self, n = NULL, dim = -1L, norm = NULL)

Arguments

self	(Tensor) the real input tensor
n	(int) Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the real FFT.
dim	(int, optional) – The dimension along which to take the one dimensional real FFT.
norm	norm (str, optional) – Normalization mode. For the forward transform, these correspond to: "forward" - normalize by 1/n "backward" - no normalization "ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal) Calling the backward transform (torch_fft_irfft()) with the same normalization mode will apply an overall normalization of 1/n between the two transforms. This is required to make irfft() the exact inverse. Default is "backward" (no normalization).

Details

The FFT of a real signal is Hermitian-symmetric, X[i] = conj(X[-i]) so the output contains only the positive frequencies below the Nyquist frequency. To compute the full output, use torch_fft_fft().

Examples

if (torch_is_installed()) {
t <- torch_arange(start = 0, end = 3)
torch_fft_rfft(t)

}

torch documentation built on May 29, 2024, 9:54 a.m.