This package provides a Julia implementation of the Non-equidistant Fast Fourier Transform (NFFT). This algorithm is also referred as Gridding in the literature (e.g. in MRI literature). For a detailed introduction into the NFFT and its application please have a look at www.nfft.org.
The NFFT is a fast implementation of the Non-equidistant Discrete Fourier Transform (NDFT) that is
basically a DFT with non-equidistant sampling nodes in either Fourier or time/space domain.
In contrast to the FFT, the NFFT is an approximative algorithm whereas the accuracy can be controlled
by two parameters:
the window width
m and the oversampling factor
In Julia, run
Basic usage of NFFT.jl is shown in the following example for 1D:
using NFFT M, N = 1024, 512 x = linspace(-0.4, 0.4, M) # nodes at which the NFFT is evaluated fHat = randn(M) + randn(M)*im # data to be transformed p = NFFTPlan(x, N) # create plan. m and sigma are optional parameters f = nfft_adjoint(p, fHat) # calculate adjoint NFFT g = nfft(p, f) # calculate forward NFFT
M, N = 1024, 16 x = rand(2, M) - 0.5 fHat = randn(M) + randn(M)*im p = NFFTPlan(x, (N,N)) f = nfft_adjoint(p, fHat) g = nfft(p, f)
There are special methods for computing 1D NFFT's for each 1D slice along a particular dimension of a higher dimensional array.
M = 11 y = rand(M) - 0.5 N = (16,20) P1 = NFFTPlan(y, 1, N) f = randn(N) + randn(N)*im fHat = nfft(P1, f)
size(f) = (16,20) and
size(fHat) = (11,20) since we compute an NFFT along the first dimension.
To compute the NFFT along the second dimension
P2 = NFFTPlan(y, 2, N) fHat = nfft(P2, f)
size(fHat) = (16,11).
18 days ago