tmv::SymMatrix<double, tmv::FortranStyle|tmv::Upper> calculateCovarianceSymMatrix(
const SBProfile& sbp, const Bounds<int>& bounds, double dx)
{
// Calculate the required dimensions
int idim = 1 + bounds.getXMax() - bounds.getXMin();
int jdim = 1 + bounds.getYMax() - bounds.getYMin();
int covdim = idim * jdim;
int k, ell; // k and l are indices that refer to image pixel separation vectors in the
// correlation func.
double x_k, y_ell; // physical vector separations in the correlation func, dx * k etc.
tmv::SymMatrix<double, tmv::FortranStyle|tmv::Upper> cov = tmv::SymMatrix<
double, tmv::FortranStyle|tmv::Upper>(covdim);
for (int i=1; i<=covdim; i++){ // note that the Image indices use the FITS convention and
// start from 1!!
for (int j=i; j<=covdim; j++){
k = ((j - 1) / jdim) - ((i - 1) / idim); // using integer division rules here
ell = ((j - 1) % jdim) - ((i - 1) % idim);
x_k = double(k) * dx;
y_ell = double(ell) * dx;
Position<double> p = Position<double>(x_k, y_ell);
cov(i, j) = sbp.xValue(p); // fill in the upper triangle with the correct value
}
}
return cov;
}
