testUnfold5b.C: DESY, 14.10.2008

// Author: Stefan Schmitt
// DESY, 14.10.2008

//  Version 17.0 example for multi-dimensional unfolding
//

#include <iostream>
#include <fstream>
#include <TFile.h>
#include "TUnfoldBinning.h"

using namespace std;

/*
  This file is part of TUnfold.

  TUnfold is free software: you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation, either version 3 of the License, or
  (at your option) any later version.

  TUnfold is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with TUnfold.  If not, see <http://www.gnu.org/licenses/>.
*/

///////////////////////////////////////////////////////////////////////
//
// Test program for the classes TUnfoldDensity and TUnfoldBinning
//
// A toy test of the TUnfold package
//
// This is an example of unfolding a two-dimensional distribution
// also using an auxillary measurement to constrain some background
//
// The example comprizes several macros
//   testUnfold5a.C   create root files with TTree objects for
//                      signal, background and data
//            -> write files  testUnfold5_signal.root
//                            testUnfold5_background.root
//                            testUnfold5_data.root
//
//   testUnfold5b.C   create a root file with the TUnfoldBinning objects
//            -> write file  testUnfold5_binning.root
//
//   testUnfold5c.C   loop over trees and fill histograms based on the
//                      TUnfoldBinning objects
//            -> read  testUnfold5_binning.root
//                     testUnfold5_signal.root
//                     testUnfold5_background.root
//                     testUnfold5_data.root
//
//            -> write testUnfold5_histograms.root
//
//   testUnfold5d.C   run the unfolding
//            -> read  testUnfold5_histograms.root
//            -> write testUnfold5_result.root
//                     testUnfold5_result.ps
//
///////////////////////////////////////////////////////////////////////

void testUnfold5b()
{

  // write binning schemes to root file
  TFile *binningSchemes=new TFile("testUnfold5_binning.root","recreate");

  // reconstructed pt, eta, discriminator
#define NBIN_PT_FINE 8
#define NBIN_ETA_FINE 10
#define NBIN_DISCR 4

  // generated pt, eta
#define NBIN_PT_COARSE 3
#define NBIN_ETA_COARSE 3

  // pt binning
  Double_t ptBinsFine[NBIN_PT_FINE+1]=
     {3.5,4.0,4.5,5.0,6.0,7.0,8.0,10.0,13.0};
  Double_t ptBinsCoarse[NBIN_PT_COARSE+1]=
     {    4.0,    5.0,    7.0,    10.0};
  // eta binning
  Double_t etaBinsFine[NBIN_ETA_FINE+1]=
     {-3.,-2.5,-2.0,-1.,-0.5,0.0,0.5,1.,2.,2.5,3.};
  Double_t etaBinsCoarse[NBIN_ETA_COARSE+1]=
     {         -2.0,    -0.5,    0.5,   2. };

  // discriminator bins
  Double_t discrBins[NBIN_DISCR+1]={0.,0.15,0.5,0.85,1.0};

  //=======================================================================
  // detector level binning scheme

  TUnfoldBinning *detectorBinning=new TUnfoldBinning("detector");
  // highest discriminator bin has fine binning
  TUnfoldBinning *detectorDistribution=
     detectorBinning->AddBinning("detectordistribution");
  detectorDistribution->AddAxis("pt",NBIN_PT_FINE,ptBinsFine,
                                false, // no underflow bin (not reconstructed)
                                true // overflow bin
                                );
  detectorDistribution->AddAxis("eta",NBIN_ETA_FINE,etaBinsFine,
                                false, // no underflow bin (not reconstructed)
                                false // no overflow bin (not reconstructed)
                                );
  detectorDistribution->AddAxis("discriminator",NBIN_DISCR,discrBins,
                                false, // no underflow bin (empty)
                                false // no overflow bin (empty)
                                );
  /* TUnfoldBinning *detectorExtra=
     detectorBinning->AddBinning("detectorextra",7,"one;zwei;three"); */
  detectorBinning->PrintStream(cout);

  //=======================================================================
  // generator level binning
  TUnfoldBinning *generatorBinning=new TUnfoldBinning("generator");

  // signal distribution is measured with coarse binning
  // underflow and overflow bins are needed ot take care of
  // what happens outside the phase-space
  TUnfoldBinning *signalBinning = generatorBinning->AddBinning("signal");
  signalBinning->AddAxis("ptgen",NBIN_PT_COARSE,ptBinsCoarse,
                         true, // underflow bin
                         true // overflow bin
                         );
  signalBinning->AddAxis("etagen",NBIN_ETA_COARSE,etaBinsCoarse,
                         true, // underflow bin
                         true // overflow bin
                         );
  // background distribution is unfolded with fine binning
  // !!! in the reconstructed variable !!!
  //
  // This has the effect of "normalizing" the background in each
  // pt,eta bin to the low discriminator values
  // Only the shape of the discriminator in each (pt,eta) bin
  // is taken from Monte Carlo
  //
  // This method has been applied e.g. in
  //   H1 Collaboration, "Prompt photons in Photoproduction"
  //   Eur.Phys.J. C66 (2010) 17
  //
  TUnfoldBinning *bgrBinning = generatorBinning->AddBinning("background");
  bgrBinning->AddAxis("ptrec",NBIN_PT_FINE,ptBinsFine,
                      false, // no underflow bin (not reconstructed)
                      true // overflow bin
                      );
  bgrBinning->AddAxis("etarec",NBIN_ETA_FINE,etaBinsFine,
                      false, // no underflow bin (not reconstructed)
                      false // no overflow bin (not reconstructed)
                      );
  generatorBinning->PrintStream(cout);

  detectorBinning->Write();
  generatorBinning->Write();

  delete binningSchemes;
}