rf401_importttreethx.C: 'DATA AND CATEGORIES' RooFit tutorial macro #401

//////////////////////////////////////////////////////////////////////////
//
// 'DATA AND CATEGORIES' RooFit tutorial macro #401
// 
// Overview of advanced option for importing data from ROOT TTree and THx histograms
// Basic import options are demonstrated in rf102_dataimport.C
//
//
// 07/2008 - Wouter Verkerke 
// 
/////////////////////////////////////////////////////////////////////////

#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooCategory.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "TH1.h"
#include "TTree.h"
#include "TRandom.h"
#include <map>

using namespace RooFit ;



TH1* makeTH1(const char* name, Double_t mean, Double_t sigma) ;
TTree* makeTTree() ;


void rf401_importttreethx()
{
  // I m p o r t  m u l t i p l e   T H 1   i n t o   a   R o o D a t a H i s t
  // --------------------------------------------------------------------------

  // Create thee ROOT TH1 histograms
  TH1* hh_1 = makeTH1("hh1",0,3) ;
  TH1* hh_2 = makeTH1("hh2",-3,1) ;
  TH1* hh_3 = makeTH1("hh3",+3,4) ;

  // Declare observable x
  RooRealVar x("x","x",-10,10) ;

  // Create category observable c that serves as index for the ROOT histograms
  RooCategory c("c","c") ;
  c.defineType("SampleA") ;
  c.defineType("SampleB") ;
  c.defineType("SampleC") ;

  // Create a binned dataset that imports contents of all TH1 mapped by index category c
  RooDataHist* dh = new RooDataHist("dh","dh",x,Index(c),Import("SampleA",*hh_1),Import("SampleB",*hh_2),Import("SampleC",*hh_3)) ;
  dh->Print() ;

  // Alternative constructor form for importing multiple histograms
  map<string,TH1*> hmap ;
  hmap["SampleA"] = hh_1 ;
  hmap["SampleB"] = hh_2 ;
  hmap["SampleC"] = hh_3 ;
  RooDataHist* dh2 = new RooDataHist("dh","dh",x,c,hmap) ;
  dh2->Print() ;

  

  // I m p o r t i n g   a   T T r e e   i n t o   a   R o o D a t a S e t   w i t h   c u t s 
  // -----------------------------------------------------------------------------------------

  TTree* tree = makeTTree() ;

  // Define observables y,z
  RooRealVar y("y","y",-10,10) ;
  RooRealVar z("z","z",-10,10) ;

  // Import only observables (y,z)
  RooDataSet ds("ds","ds",RooArgSet(x,y),Import(*tree)) ;
  ds.Print() ;

  // Import observables (x,y,z) but only event for which (y+z<0) is true
  RooDataSet ds2("ds2","ds2",RooArgSet(x,y,z),Import(*tree),Cut("y+z<0")) ;
  ds2.Print() ;



  // I m p o r t i n g   i n t e g e r   T T r e e   b r a n c h e s
  // ---------------------------------------------------------------

  // Import integer tree branch as RooRealVar
  RooRealVar i("i","i",0,5) ;
  RooDataSet ds3("ds3","ds3",RooArgSet(i,x),Import(*tree)) ;
  ds3.Print() ;

  // Define category i
  RooCategory icat("i","i") ;
  icat.defineType("State0",0) ;
  icat.defineType("State1",1) ;

  // Import integer tree branch as RooCategory (only events with i==0 and i==1
  // will be imported as those are the only defined states)
  RooDataSet ds4("ds4","ds4",RooArgSet(icat,x),Import(*tree)) ;
  ds4.Print() ;



  // I m p o r t  m u l t i p l e   R o o D a t a S e t s   i n t o   a   R o o D a t a S e t 
  // ----------------------------------------------------------------------------------------

  // Create three RooDataSets in (y,z)
  RooDataSet* dsA = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"z<-5") ;
  RooDataSet* dsB = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"abs(z)<5") ;
  RooDataSet* dsC = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"z>5") ;

  // Create a dataset that imports contents of all the above datasets mapped by index category c
  RooDataSet* dsABC = new RooDataSet("dsABC","dsABC",RooArgSet(x,y),Index(c),Import("SampleA",*dsA),Import("SampleB",*dsB),Import("SampleC",*dsC)) ;

  dsABC->Print() ;

}



TH1* makeTH1(const char* name, Double_t mean, Double_t sigma) 
{
  // Create ROOT TH1 filled with a Gaussian distribution

  TH1D* hh = new TH1D(name,name,100,-10,10) ;
  for (int i=0 ; i<1000 ; i++) {
    hh->Fill(gRandom->Gaus(mean,sigma)) ;
  }
  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 ;
  Double_t* pz = new Double_t ;
  Int_t*    pi = new Int_t ;
  tree->Branch("x",px,"x/D") ;
  tree->Branch("y",py,"y/D") ;
  tree->Branch("z",pz,"z/D") ;
  tree->Branch("i",pi,"i/I") ;
  for (int i=0 ; i<100 ; i++) {
    *px = gRandom->Gaus(0,3) ;
    *py = gRandom->Uniform()*30 - 15 ;
    *pz = gRandom->Gaus(0,5) ;
    *pi = i % 3 ;
    tree->Fill() ;
  }
  return tree ;
}