lib.ddes_utils

Helper functions to apply the non-uniform Fourier transform (NUFFT).

lib.ddes_utils.bisect(E_threshold, tolerance, abschirp, L_norm)

calculates the threshold for keeping E_hreshold= percentile of total energy of the chirp kernel, where abschirp is the absolute value of the row of the convoution kernel L_norm =1;

lib.ddes_utils.get_details_wkernel(W, FoV, gImDims, paddFourierFactor, uvHalfBW)

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lib.ddes_utils.get_wkernel(W, nTerm, PHASE, levelC)

————————————————————————-% Code: Arwa Dabbech. Last revised: [21/01/2022] ————————————————————————-%

w-term in the image domain, aka chirp

lib.ddes_utils.op_p_nufft_wproj_dde(nufft, p, w, nW, wproj, ddes, kernel)

Create the nonuniform gridding matrix and fft operators to be used for parallel processing

in: p{:}[2] - nonuniformly distributed frequency location points for each

cell member which will be treated in parallel

N[2] - size of the reconstruction image Nn[2] - size of the kernels (number of neighbors considered on each direction) No[2] - oversampled fft from which to recover the non uniform fft via

kernel convolution

Ns[2] - fft shift

out: A[@] - function handle for direct operator At[@] - function handle for adjoint operator G{:}[:][:] - convolution kernel matrix (small) associated with each

patch in the fourier plane

W{:} - mask of the values that contribute to the convolution ktype : string

Selected interpolator (among 'minmax:kb', 'minmax:tuned' and 'kaiser', see nufft.compute_interp_coeffs_extended() for associated kernel documentation). Recommended default is 'minmax:kb'.

lib.ddes_utils.sconv2_modified(A, B, shape)

C = sconv2(A, B, shape) Like conv2 but suitable for convolution of sparse matrices Author: Bruno Luong <brunoluong@yahoo.com> Date creation: 15/April/2013 See also: conv2

lib.ddes_utils.shift_ind(ind, Nx, Ny)

lib.ddes_utils.update_G_ddes(GNUFFT, W, ddes, param)

input GNUFFT: transpose of the de-gridding matrix W: w-coordinates ddes: available antenna gains for each row