CRE_RB_and_CCRE/TestCREClass.m

%% Test CCRE and CRE class
% show some default behaviour
% and test the error handling. 
%% set some constants
A=randn(100);
%% create instance of CRE class
S=CREClass;
%% check histogram vs "new" technique 
% NB I come to the conclusion that the new method is not always working.
% Why? 
S.Data=A;% set gaussian data
S.nBin=(numel(S.Data));      % set nbin to npoints; This is fine as we use the cummulative distribution and the formulas as defined in Zografos
% use histogram
S.UseHistProxy=true;
S.Calc;
S
% Use my aproximation for the histogram
% only do this if the number of data points is not too big. 
S.UseHistProxy=false;
S.Calc;
S

%% Show effect of scaling or ofsett the data
% Scale
S.Data=100*A;
S.UseHistProxy=true;
S.Calc;
S
% Offset
S.Data=A+10;
S.UseHistProxy=true;
S.Calc;
S
%% 

%% Test CCRE class
figure(1);clf;hold on
figure(2);clf;
figure(3);clf;hold on;
A=randn(100);
B=A;
B(:)=A(randperm(numel(A)))
for k=-1:0.1:1
    CS=CCREClass;
    CS.Data=1*(1-abs(k))*A+k*B;
    CS.nBin=50;
    CS.DataRef=B;
    CS.nBinRef=500;
    CS.Calc;
    figure(1);
    plot(k,CS.CCRE,'o')
    plot(k,CS.CRE,'x');
    figure(2);
    scatter(CS.DataRef(:),CS.Data(:));
    figure(3);
    plot(k,CS.CCRE./CS.CRE,'d');
end
CS
snapnow