{
using namespace RooFit;


gROOT->ProcessLine(".L ../../RooTH1Var.cxx+");

//Construct a TH1 histogram of weights
const int nBins = 60;
TH1F *histo = new TH1F("histo","histo",nBins,0,3);

Double_t dat[nBins] = { 6, 1,10,12, 6,13,23,22,15,21,
                        23,26,36,25,27,35,40,44,66,81,
                        75,57,48,45,46,41,35,36,53,32,
                        40,37,38,31,36,44,42,37,32,32,
                        43,44,35,33,33,39,29,41,32,44,
                        26,39,29,35,32,21,21,15,25,15};

for(int i=0; i < nBins;  i++) histo->SetBinContent(i+1,dat[i]);


// Declare observable
RooRealVar p("p","p",0,3) ;
p.setBins(40) ;

// Construction a uniform pdf
RooPolynomial p0("pp","pp",p) ;
RooRealVar sig("sig","sig",0,10);
RooRealVar mean("mean","mean",0,10);


// Sample 1000 events from pdf
RooDataSet* data = p0.generate(p,10000) ;
TH1 *histo_Cloned=histo.Clone();
//Construct the RooFit variable from histogram of weights along p
RooTH1Var weightVar("weightVar","weightVar",histo_Cloned,p);


// C a l c u l a t e   w e i g h t   a n d   m a k e   d a t a s e t   w e i g h t e d 
// Add column with variable w to previously generated dataset
RooRealVar* w = (RooRealVar*) data->addColumn(weightVar) ;

// Dataset d is now a dataset with two observable (x,w) with 1000 entries
data->Print() ;

// Instruct dataset wdata in interpret w as event weight rather than as observable
RooDataSet wdata(data->GetName(),data->GetTitle(),data,*data->get(),0,w->GetName()) ;


// Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K
wdata.Print();

TCanvas *c1 = new TCanvas("c1","Fitting Demo",10,10,700,500);
c1->SetFillColor(33);
c1->SetFrameFillColor(41);
c1->SetGrid();

//Make plot
histo.Scale(200);
histo.Draw();
RooPlot *pframe = p.frame();
data.plotOn(pframe);
wdata.plotOn(pframe);
pframe.Draw("SAME");

}