function m = grid2w(d,k,w,n)

% function m = grid2w(d,k,w,n)
%     d -- k-space data
%     k -- k-trajectory, scaled -0.5 to 0.5
%     w -- k-space weighting
%     n -- image size (m will be 2n X 2n)
%
%     m -- gridded k-space data
%

% convert to single column
d = d(:);
k = k(:);
w = w(:);

% preweight
dw = d.*w;

% convert k-space samples to matrix indices
nx = (n+1) + 2*n*real(k);
ny = (n+1) + 2*n*imag(k);

m = zeros(2*n,2*n);

% loop over samples in kernel
for lx = -2:2,
  for ly = -2:2,

    % find nearest samples
    nxt = round(nx+lx);
    nyt = round(ny+ly);

    % seperable triangular window
    kwx = max(1-0.5*abs(nx-nxt),0);
    kwy = max(1-0.5*abs(ny-nyt),0);

    % if data falls outtside matrix, put it at the edge
    nxt = max(nxt,1); nxt = min(nxt,2*n);
    nyt = max(nyt,1); nyt = min(nyt,2*n);

    % accumulate gridded data
    m = m+sparse(nxt,nyt,dw.*kwx.*kwy,2*n,2*n);
  end;
end;

% zero out data at edges
m(:,1) = 0; m(:,2*n) = 0;
m(1,:) = 0; m(2*n,:) = 0;