The cleaning works like this. The user constructs a HogbomCleanModel by specifying an initial model of the sky. This can by be one,two,three... dimensional depending on the dimension of the psf (see below). The user then constructs a class which implements the forward equation between the model and the dirty image. Typically this will be the ConvolutionEquation class, although any class which has a ResidualEquation interface will be work (but perhaps very slowly, as the ConvolutionEquation class has member functions optimised for cleaning)
The user then calls the solve() function (with the appropriate equation class as an arguement), and this class will perform the Hogbom clean. The various clean parameters are set (prior to calling solve) using the functions derived from the Iterate class, in particular setGain(), setNumberIterations() & setThreshold() (to set a flux limit).
The solve() function does not return either the deconvolved model or the residuals. The solved model can be obtained using the getModel() function (derived from ArrayModel()) and the residual can be obtained using the residual() member function of the Convolution/Residual Equation Class.
The size and shape of the model used in this class MUST be the same as the convolved data (Dirty Image), stored in the companion ResidualEquation Class. However the model (and convolved data) can have more dimensions than the psf, as well as a different size (either larger or smaller). When the dimensionality is different the cleaning is done independendtly in each "plane" of the model. (Note this has not been implemented yet but is relatively simple to do if necessary).
This multi-dimensionalty is exploited when cleaning arrays of StokesVectors. Here the Array of StokesVectors is decomposed into a stack of 4 Floating point arrays and the cleaning is done on all the the arrays simultaneosly. The criterion for choosing the brightest pixel has been generalised by using the "length" of the Stokesvector in 4 dimensional space.
A companion class to this one is MaskedHogbomCleanModel. This provides the same functionality but is used with MaskedArrays which indicate which regions of the model to search for clean components.
Matrix<Float> psf(12,12), dirty(10,10), initialModel(10,10);
...put appropriate values into psf, dirty, & initialModel....
HogbomCleanModel<Float> deconvolvedModel(initialModel);
ConvolutionEquation convEqn(psf, dirty);
deconvolvedModel.setGain(0.2);
deconvolvedModel.setNumberIterations(1000);
Bool convWorked = deconvolvedModel.solve(convEqn);
Array<Float> finalModel, residuals;
if (convWorked){
finalModel = deconvolvedModel.getModel();
ConvEqn.residual(deconvolvedModel, finalResidual);
}