% Linear least squares optimization for Jake the Dogg

% set up the optimizer setup
optSettings = [];
optSettings.optInfo.Niterations = 100;
optSettings.optInfo.Nperbatch = 50;
optSettings.optInfo.optRun = 'no';
optSettings.optInfo.waitForResults = 'no';
optSettings.optInfo.saveOptInfo = 'yes';
optSettings.beamletInfo.saveBeamlets = 'no';
optSettings.beamletInfo.saveBeamletHeader = 'yes';
optSettings.prescInfo.savePresc = 'yes';

%   Prescription table
%   (code name)               (file name)         (plot flag)    (color)      (alpha)  (Bplus) (dplus)  (Bminus)  (dminus) (BVplus)   (dVplus) (Vplus) (BVminus)  (dVminus)  (Vminus)        
prescTable = {
    'Tumor'                'tumor.short.am'              1       'blue'            1      100       100      100       100        0         0       0         0         0         0        
    'Normal'               'normal.short.am'             0       'magenta'         1       10         0        0         0        0         0       0         0         0         0                
    'Eyes'                 'eyes.short.am'               2       'green'           1        1         0        0         0        0         0       0         0         0         0
    };

% Create the prescription structure
presc = [];   % erase the old prescription structure, if it exists

% assign the parameters to the prescription structure
for j=1:size(prescTable,1)
    
    % pull out the prescription indices for each tissue
    tissName = prescTable{j,1};  % current tissue name

    amiraFileName = prescTable{
    
    %convert amira files to matlab data structures
    ROI = {'Tumor' 'Normal' 'Eyes'};
    ROI{j} = [];
    ROI{j} = am2geom(prescTable{j,2});
       
    if strcmp(tissName,'Tumor')
        ind = find(ROI{j}.data)-1;
    elseif strcmp(tissName,'Normal')
        ind = find(ROI{j}.data)-1;
    elseif strcmp(tissName,'Eyes')
        ind = find(ROI{j}.data)-1;
    else
        error('Unknown tissue type');
    end
    
    presc.tissue(j).name = prescTable{j,1};
    presc.tissue(j).filename = prescTable(j,2);
    presc.tissue(j).plotFlag = prescTable{j,3};
    presc.tissue(j).colorName = prescTable{j,4};
    
    % look up the color code (an error will be thrown if it doesn't exist)
    presc.tissue(j).colorCode = plotColors(presc.tissue(j).colorName);
    
    presc.tissue(j).ind = ind;
    presc.tissue(j).alpha = prescTable{j,5};
    presc.tissue(j).betaPlus = prescTable{j,6};
    presc.tissue(j).dPlus = single(zeros(size(ind)) + prescTable{j,7});
    presc.tissue(j).betaMinus = prescTable{j,8};
    presc.tissue(j).dMinus = single(zeros(size(ind)) + prescTable{j,9});
    presc.tissue(j).betaVPlus = prescTable{j,10};
    presc.tissue(j).dVPlus = prescTable{j,11};
    presc.tissue(j).vPlus = prescTable{j,12};
    presc.tissue(j).betaVMinus = prescTable{j,13};
    presc.tissue(j).dVMinus = prescTable{j,14};
    presc.tissue(j).vMinus = prescTable{j,15};
end

optSettings.prescInfo.presc = presc;  % set prescription

% calculate initial guess
w0 = calcNormalizedInitialGuess(B360,presc);
optSettings.initialGuessInfo.w0 = w0;
optSettings.beamletInfo.beamlets = B360;
[w,dose] = linlsqOptimization(optSettings);