rf102_dataimport.cxx

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//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #102
// 
// Importing data from ROOT TTrees and THx histograms
//
//
//
// 07/2008 - Wouter Verkerke 
// 
/////////////////////////////////////////////////////////////////////////


#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TTree.h"
#include "TH1D.h"
#include "TRandom.h"
using namespace RooFit ;


class TestBasic102 : public RooFitTestUnit
{
public: 
  TestBasic102(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooFitTestUnit("Data import methods",refFile,writeRef,verbose) {} ;

  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() ;
    }

    //delete px ;
    //delete py ;

    return tree ;
  }
  
  Bool_t testCode() {
    
    ////////////////////////////////////////////////////////
    // 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 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
    regPlot(frame ,"rf102_plot1") ;
    regPlot(frame2,"rf102_plot2") ;
    regPlot(frame3,"rf102_plot3") ;
    regPlot(frame4,"rf102_plot4") ;
    
    delete hh ;
    delete tree ;

    return kTRUE ;
  }
} ;