doc/v610/rf102__dataimport_8C.html

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TTree.h"
#include "TH1D.h"
#include "TRandom.h"
using namespace RooFit ;
TH1* makeTH1() ;
TTree* makeTTree() ;
void rf102_dataimport()
{
   // ---------------------------------------------------
   // I m p o r t i n g   R O O T   h i s t o g r a m s
   // ===================================================
   // I m p o r t   T H 1   i n t o   a   R o o D a t a H i s t
   // ---------------------------------------------------------
   // Create a ROOT TH1 histogram
   TH1* hh = makeTH1() ;
   // Declare observable x
   RooRealVar x("x","x",-10,10) ;
   // Create a binned dataset that imports contents of TH1 and associates its contents to observable 'x'
   RooDataHist dh("dh","dh",x,Import(*hh)) ;
   // P l o t   a n d   f i t   a   R o o D a t a H i s t
   // ---------------------------------------------------
   // Make plot of binned dataset showing Poisson error bars (RooFit default)
   RooPlot* frame = x.frame(Title("Imported TH1 with Poisson error bars")) ;
   dh.plotOn(frame) ;
   // Fit a Gaussian p.d.f to the data
   RooRealVar mean("mean","mean",0,-10,10) ;
   RooRealVar sigma("sigma","sigma",3,0.1,10) ;
   RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
   gauss.fitTo(dh) ;
   gauss.plotOn(frame) ;
   // P l o t   a n d   f i t   a   R o o D a t a H i s t   w i t h   i n t e r n a l   e r r o r s
   // ---------------------------------------------------------------------------------------------
   // If histogram has custom error (i.e. its contents is does not originate from a Poisson process
   // but e.g. is a sum of weighted events) you can data with symmetric 'sum-of-weights' error instead
   // (same error bars as shown by ROOT)
   RooPlot* frame2 = x.frame(Title("Imported TH1 with internal errors")) ;
   dh.plotOn(frame2,DataError(RooAbsData::SumW2)) ;
   gauss.plotOn(frame2) ;
   // Please note that error bars shown (Poisson or SumW2) are for visualization only, the are NOT used
   // in a maximum likelihood fit
   //
   // A (binned) ML fit will ALWAYS assume the Poisson error interpretation of data (the mathematical definition
   // of likelihood does not take any external definition of errors). Data with non-unit weights can only be correctly
   // fitted with a chi^2 fit (see rf602_chi2fit.C)
   // -----------------------------------------
   // I m p o r t i n g   R O O T  T T r e e s
   // =========================================
   // I m p o r t   T T r e e   i n t o   a   R o o D a t a S e t
   // -----------------------------------------------------------
   TTree* tree = makeTTree() ;
   // Define 2nd observable y
   RooRealVar y("y","y",-10,10) ;
   // Construct unbinned dataset importing tree branches x and y matching between branches and RooRealVars
   // is done by name of the branch/RRV
   //
   // Note that ONLY entries for which x,y have values within their allowed ranges as defined in
   // RooRealVar x and y are imported. Since the y values in the import tree are in the range [-15,15]
   // and RRV y defines a range [-10,10] this means that the RooDataSet below will have less entries than the TTree 'tree'
   RooDataSet ds("ds","ds",RooArgSet(x,y),Import(*tree)) ;
   // P l o t   d a t a s e t   w i t h   m u l t i p l e   b i n n i n g   c h o i c e s
   // ------------------------------------------------------------------------------------
   // Print number of events in dataset
   ds.Print() ;
   // Print unbinned dataset with default frame binning (100 bins)
   RooPlot* frame3 = y.frame(Title("Unbinned data shown in default frame binning")) ;
   ds.plotOn(frame3) ;
   // Print unbinned dataset with custom binning choice (20 bins)
   RooPlot* frame4 = y.frame(Title("Unbinned data shown with custom binning")) ;
   ds.plotOn(frame4,Binning(20)) ;
   // Draw all frames on a canvas
   TCanvas* c = new TCanvas("rf102_dataimport","rf102_dataimport",800,800) ;
   c->Divide(2,2) ;
   c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
   c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
   c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
   c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.4) ; frame4->Draw() ;
}
TH1* makeTH1()
{
   // Create ROOT TH1 filled with a Gaussian distribution
   TH1D* hh = new TH1D("hh","hh",25,-10,10) ;
   for (int i=0 ; i<100 ; i++) {
      hh->Fill(gRandom->Gaus(0,3)) ;
   }
   return hh ;
}
TTree* makeTTree()
{
  // Create ROOT TTree filled with a Gaussian distribution in x and a uniform distribution in y
  TTree* tree = new TTree("tree","tree") ;
  Double_t* px = new Double_t ;
  Double_t* py = new Double_t ;
  tree->Branch("x",px,"x/D") ;
  tree->Branch("y",py,"y/D") ;
  for (int i=0 ; i<100 ; i++) {
    *px = gRandom->Gaus(0,3) ;
    *py = gRandom->Uniform()*30 - 15 ;
    tree->Fill() ;
  }
  return tree ;
}