%% generate light data_file for efficient processing

Training=2; %1 for Overtraining, 2 for acquisition

%% OT dataSet

if Training==1
    load('Rextendedtraining.mat');
    Erefnames=R.Erefnames(1:7);
    Tm=R.Param.Tm;
    Ninfo=R.Ninfo;
    Coord=R.Coord;

    %Class=R.Class(:,1);
    
    for i=1:length(Erefnames)
        Ev(i).PSTHz(:,:)=R.Ev(i).PSTHz(:,:);
        Ev(i).PSTHraw(:,:)=R.Ev(i).PSTHraw(:,:);
        Ev(i).PSTHrawBL(:,:)=R.Ev(i).PSTHrawBL(:,:);
        Ev(i).Meanz(:,1)=R.Ev(i).Meanz(:,1);
        %Ev(i).BW(:,1)=R.Ev(i).BW(:,1);
        Ev(i).MeanzPRE(:,1)=R.Ev(i).MeanzPRE(:,1);
        A(:,i)=R.Ev(i).RespDirPost;
        B(:,i)=R.Ev(i).RespDirPre;
        if ~isempty(R.Ev(i).rawMeanz)
                Ev(i).rawMeanz(:,1)=R.Ev(i).rawMeanz(:,1);
                Ev(i).rawMeanzPre(:,1)=R.Ev(i).rawMeanzPre(:,1);
            else
                Ev(i).rawMeanz(length(Ev(i).Meanz(:,1)),1)=nan;
                Ev(i).rawMeanzPre(:,1)=R.Ev(i).rawMeanzPre(:,1);
        end    
    end
    TRNevent=[B(:,1:6) A(:,1:5) A(:,7)]; 
    TRN(:,1)=nansum(abs(TRNevent),2);
    
    save('Rextendedtraining_light.mat','Coord','Erefnames','Ev','Ninfo','Tm','TRN')


%% Acquisition data set

else
    load('Rearlytraining.mat');
    path='C:\Users\yvandae1\Documents\MATLAB\DT Nex Files\RESULTdt.xls';
    [~,Session] = xlsread(path,'Windows','c18:c30');
    Erefnames=R.Erefnames(1:7);
    Tm=R.Param.Tm;
    
    
    for i=1:size(R.Ses,2) %include sessions DT and DT5
        A=[];B=[];
        A(1:length(R.Ses(i).Coord),1:length(Erefnames))=NaN(length(R.Ses(i).Coord),length(Erefnames));
        Ses(i).Coord=R.Ses(i).Coord;
        Ses(i).Ninfo=R.Ses(i).Ninfo;
        for j=1:length(Erefnames)
            Ses(i).Ev(j).PSTHz(:,:)=R.Ses(i).Ev(j).PSTHz(:,:);
            Ses(i).Ev(j).PSTHraw(:,:)=R.Ses(i).Ev(j).PSTHraw(:,:);
            Ses(i).Ev(j).PSTHrawBL(:,:)=R.Ses(i).Ev(j).PSTHrawBL(:,:);
            Ses(i).Ev(j).Meanz(:,1)=R.Ses(i).Ev(j).Meanz(:,1);
            if ~isempty(R.Ses(i).Ev(j).rawMeanz)
                Ses(i).Ev(j).rawMeanz(:,1)=R.Ses(i).Ev(j).rawMeanz(:,1);
                Ses(i).Ev(j).MeanzPRE(:,1)=R.Ses(i).Ev(j).MeanzPRE(:,1);
            else
                Ses(i).Ev(j).rawMeanz(length(Ses(i).Ev(j).Meanz(:,1)),1)=nan;
                Ses(i).Ev(j).rawMeanz(length(Ses(i).Ev(j).Meanz(:,1)),1)=nan;
            end
            A(:,j)=R.Ses(i).Ev(j).RespDirPost;
            B(:,j)=R.Ses(i).Ev(j).RespDirPre;
        end
        TRNevent=[B(:,1:6) A(:,1:5) A(:,7)]; 
        Ses(i).TRN(:,1)=nansum(abs(TRNevent),2);
    end

save('Rearlytraining_light.mat','Erefnames','Ses','Tm')    
end