See the documentation for HogbomCleanModel for an example of how this class can be used to perform deconvolution.
This class also contains specialised functions (like the version of evaluate() for a point source model) that speed up the calculation of the convolution. This specialised version of evaluate() does not need to actually perform the convolution and instead returns a suitable part of the psf (zero padded if necessary). When this function is called this class will get the psf from the convolver and cache it, on the assumption that many evaluations of this function will be requested (as occurs in Clean algorithms).
The size and shape of the psf and the supplied model may be different. The only restriction is that the dimension of the psf must be less than or equal to the dimension of the model. If the dimension of the model is larger than the dimension of the psf then the convolution will be repeated along the slowest moving (last) axis. The dirty image and the supplied model must be the same size and shape.
PagedArray<Float> psf(2,4,4), dirty(2,20,20), model(2,20,20);
.... put some meaningful values into these Lattices....
// create a convolution equation, and a PagedArray model
LatConvEquation convEqn(psf, dirty);
LinearModel< Lattice<Float> > myModel(model);
// now calculate the convolution of the model and the psf
PagedArray<Float> prediction;
convEqn.evaluate(myModel, prediction);
// and calculate the difference between the predicted and actual convolution
PagedArray<Float> residual;
convEqn.residual(mymodel, residual)
Somewhere I read that a destructor should alway be defined even if it does nothing (as this one does).
Calculate the convolution of the model (supplied by the LinearModel class) and the psf and the difference between this and the supplied (presumably measured) convolution.
Calculate the convolution of the model (supplied by the LinearModel class) and the psf and the difference between this and the supplied (presumably measured) convolution. Also return chisq.