Double_t getBestFitShift(TTree *dataTree, TTree *mcTree,
TCut dataCandidateCut, TCut sidebandCut,
TCut mcSignalCut,
Double_t backgroundShift, Double_t purityBinVal)
{
dtree_ = dataTree;
mtree_ = mcTree;
dCut_ = dataCandidateCut;
sCut_ = sidebandCut;
mCut_ = mcSignalCut;
bkg_shift_ = backgroundShift;
purityBinVal_ = purityBinVal;
// ROOT::Minuit2::Minuit2Minimizer min ( ROOT::Minuit2::kMigrad );
// min.SetMaxFunctionCalls(1000000);
// min.SetMaxIterations(100000);
// min.SetTolerance(0.00001);
// ROOT::Math::Functor f(&minimizerPurity,1);
// double step = 0.00001;
// double variable = 0;
// min.SetFunction(f);
// // Set the free variables to be minimized!
// //min.SetLimitedVariable(0,"sigShift",variable, step,-0.0005,0.0005);
// min.SetVariable(0,"sigShift",variable, step);
// min.Minimize();
ROOT::Math::Functor1D func(&minimizerPurity);
ROOT::Math::BrentMinimizer1D bm;
bm.SetFunction(func, -0.0005,0.0005);
bm.Minimize(10);
//cout << "f(" << bm.XMinimum() << ") = " <<bm.FValMinimum() << endl;
//return min.X()[0];
return bm.XMinimum();
}
void optimizenorm(size_t i, const std::string& path)
{
if (v_graphs.size()==1) {
double *py = v_graphs[0]->GetY();
int npts = v_graphs[0]->GetN();
double norm = 1./py[0];
// cout << npts << endl;
for (int j=0; j<npts; j++) py[j] = norm*py[j];
} else if (v_graphs.size() > 1) {
ROOT::Math::Functor1D func(& mydistance);
ROOT::Math::BrentMinimizer1D bm;
bm.SetFunction(func, 1,7000); // "x" is the norm (denom) scale factor
bm.Minimize(100,0.01,0.01);
cout << path << ": best norm scale " << bm.XMinimum()
<< ", gives distance " << bm.FValMinimum() << endl;
double *py = v_graphs[i]->GetY();
for (int j=0; j<v_graphs[0]->GetN(); j++) py[j] /= bm.XMinimum();
}
} // optimizenorm
