testUnfold5d.C File Reference

Detailed Description

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 auxiliary measurement to constrain some background

The example comprises 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

 
#include <iostream>
#include <cmath>
#include <TMath.h>
#include <TCanvas.h>
#include <TStyle.h>
#include <TGraph.h>
#include <TFile.h>
#include <TH1.h>
#include "TUnfoldDensity.h"
 
using std::cout;
 
// #define PRINT_MATRIX_L
 
#define TEST_INPUT_COVARIANCE
 
void testUnfold5d()
{
  // switch on histogram errors
  TH1::SetDefaultSumw2();
 
  //==============================================
  // step 1 : open output file
  TFile *outputFile=new TFile("testUnfold5_results.root","recreate");
 
  //==============================================
  // step 2 : read binning schemes and input histograms
  TFile *inputFile=new TFile("testUnfold5_histograms.root");
 
  outputFile->cd();
 
  TUnfoldBinning *detectorBinning,*generatorBinning;
 
  inputFile->GetObject("detector",detectorBinning);
  inputFile->GetObject("generator",generatorBinning);
 
  if((!detectorBinning)||(!generatorBinning)) {
     cout<<"problem to read binning schemes\n";
  }
 
  // save binning schemes to output file
  detectorBinning->Write();
  generatorBinning->Write();
 
  // read histograms
  TH1 *histDataReco,*histDataTruth;
  TH2 *histMCGenRec;
 
  inputFile->GetObject("histDataReco",histDataReco);
  inputFile->GetObject("histDataTruth",histDataTruth);
  inputFile->GetObject("histMCGenRec",histMCGenRec);
 
#ifdef TEST_ZERO_UNCORR_ERROR
  // special test (bug in version 17.2 and below)
  // set all errors in hisMCGenRec to zero
  // -> program will crash
  for(int i=0;i<=histMCGenRec->GetNbinsX()+1;i++) {
     for(int j=0;j<=histMCGenRec->GetNbinsY()+1;j++) {
        histMCGenRec->SetBinError(i,j,0.0);
     }
  }
#endif
 
  histDataReco->Write();
  histDataTruth->Write();
  histMCGenRec->Write();
 
  if((!histDataReco)||(!histDataTruth)||(!histMCGenRec)) {
     cout<<"problem to read input histograms\n";
  }
 
  //========================
  // Step 3: unfolding
 
 // preserve the area
  TUnfold::EConstraint constraintMode= TUnfold::kEConstraintArea;
 
  // basic choice of regularisation scheme:
  //    curvature (second derivative)
  TUnfold::ERegMode regMode = TUnfold::kRegModeCurvature;
 
  // density flags
  TUnfoldDensity::EDensityMode densityFlags=
    TUnfoldDensity::kDensityModeBinWidth;
 
  // detailed steering for regularisation
  const char *REGULARISATION_DISTRIBUTION=nullptr;
  const char *REGULARISATION_AXISSTEERING="*[B]";
 
  // set up matrix of migrations
  TUnfoldDensity unfold(histMCGenRec,TUnfold::kHistMapOutputHoriz,
                        regMode,constraintMode,densityFlags,
                        generatorBinning,detectorBinning,
            REGULARISATION_DISTRIBUTION,
            REGULARISATION_AXISSTEERING);
 
  // define the input vector (the measured data distribution)
 
#ifdef TEST_INPUT_COVARIANCE
  // special test to use input covariance matrix
  TH2D *inputEmatrix=
     detectorBinning->CreateErrorMatrixHistogram("input_covar",true);
  for(int i=1;i<=inputEmatrix->GetNbinsX();i++) {
     Double_t e=histDataReco->GetBinError(i);
     inputEmatrix->SetBinContent(i,i,e*e);
     // test: non-zero covariance where variance is zero
     //   if(e<=0.) inputEmatrix->SetBinContent(i,i+1,1.0);
  }
  unfold.SetInput(histDataReco,0.0,0.0,inputEmatrix);
#else
  unfold.SetInput(histDataReco /* ,0.0,1.0 */);
#endif
  // print matrix of regularisation conditions
#ifdef PRINT_MATRIX_L
  TH2 *histL= unfold.GetL("L");
  for(Int_t j=1;j<=histL->GetNbinsY();j++) {
     cout<<"L["<<unfold.GetLBinning()->GetBinName(j)<<"]";
     for(Int_t i=1;i<=histL->GetNbinsX();i++) {
        Double_t c=histL->GetBinContent(i,j);
        if(c!=0.0) cout<<" ["<<i<<"]="<<c;
     }
     cout<<"\n";
  }
#endif
  // run the unfolding
  //
  // here, tau is determined by scanning the global correlation coefficients
 
  Int_t nScan=30;
  TSpline *rhoLogTau=nullptr;
  TGraph *lCurve=nullptr;
 
  // for determining tau, scan the correlation coefficients
  // correlation coefficients may be probed for all distributions
  // or only for selected distributions
  // underflow/overflow bins may be included/excluded
  //
  const char *SCAN_DISTRIBUTION="signal";
  const char *SCAN_AXISSTEERING=nullptr;
 
  Int_t iBest=unfold.ScanTau(nScan,0.,0.,&rhoLogTau,
                             TUnfoldDensity::kEScanTauRhoMax,
                             SCAN_DISTRIBUTION,SCAN_AXISSTEERING,
                             &lCurve);
 
  // create graphs with one point to visualize best choice of tau
  Double_t t[1],rho[1],x[1],y[1];
  rhoLogTau->GetKnot(iBest,t[0],rho[0]);
  lCurve->GetPoint(iBest,x[0],y[0]);
  TGraph *bestRhoLogTau=new TGraph(1,t,rho);
  TGraph *bestLCurve=new TGraph(1,x,y);
  Double_t *tAll=new Double_t[nScan],*rhoAll=new Double_t[nScan];
  for(Int_t i=0;i<nScan;i++) {
     rhoLogTau->GetKnot(i,tAll[i],rhoAll[i]);
  }
  TGraph *knots=new TGraph(nScan,tAll,rhoAll);
 
  cout<<"chi**2="<<unfold.GetChi2A()<<"+"<<unfold.GetChi2L()
      <<" / "<<unfold.GetNdf()<<"\n";
 
 
  //===========================
  // Step 4: retrieve and plot unfolding results
 
  // get unfolding output
  TH1 *histDataUnfold=unfold.GetOutput("unfolded signal",nullptr,nullptr,nullptr,kFALSE);
  // get Monte Carlo reconstructed data
  TH1 *histMCReco=histMCGenRec->ProjectionY("histMCReco",0,-1,"e");
  TH1 *histMCTruth=histMCGenRec->ProjectionX("histMCTruth",0,-1,"e");
  Double_t scaleFactor=histDataTruth->GetSumOfWeights()/
     histMCTruth->GetSumOfWeights();
  histMCReco->Scale(scaleFactor);
  histMCTruth->Scale(scaleFactor);
  // get matrix of probabilities
  TH2 *histProbability=unfold.GetProbabilityMatrix("histProbability");
  // get global correlation coefficients
  /* TH1 *histGlobalCorr=*/ unfold.GetRhoItotal("histGlobalCorr",nullptr,nullptr,nullptr,kFALSE);
  TH1 *histGlobalCorrScan=unfold.GetRhoItotal
     ("histGlobalCorrScan",nullptr,SCAN_DISTRIBUTION,SCAN_AXISSTEERING,kFALSE);
  /* TH2 *histCorrCoeff=*/ unfold.GetRhoIJtotal("histCorrCoeff",nullptr,nullptr,nullptr,kFALSE);
 
  TCanvas canvas;
  canvas.Print("testUnfold5.ps[");
 
  //========== page 1 ============
  // unfolding control plots
  // input, matrix, output
  // tau-scan, global correlations, correlation coefficients
  canvas.Clear();
  canvas.Divide(3,2);
 
  // (1) all bins, compare to original MC distribution
  canvas.cd(1);
  histDataReco->SetMinimum(0.0);
  histDataReco->Draw("E");
  histMCReco->SetLineColor(kBlue);
  histMCReco->Draw("SAME HIST");
  // (2) matrix of probabilities
  canvas.cd(2);
  histProbability->Draw("BOX");
  // (3) unfolded data, data truth, MC truth
  canvas.cd(3);
  gPad->SetLogy();
  histDataUnfold->Draw("E");
  histDataTruth->SetLineColor(kBlue);
  histDataTruth->Draw("SAME HIST");
  histMCTruth->SetLineColor(kRed);
  histMCTruth->Draw("SAME HIST");
  // (4) scan of correlation vs tau
  canvas.cd(4);
  rhoLogTau->Draw();
  knots->Draw("*");
  bestRhoLogTau->SetMarkerColor(kRed);
  bestRhoLogTau->Draw("*");
  // (5) global correlation coefficients for the distributions
  //     used during the scan
  canvas.cd(5);
  //histCorrCoeff->Draw("BOX");
  histGlobalCorrScan->Draw("HIST");
  // (6) L-curve
  canvas.cd(6);
  lCurve->Draw("AL");
  bestLCurve->SetMarkerColor(kRed);
  bestLCurve->Draw("*");
 
 
  canvas.Print("testUnfold5.ps");
 
  canvas.Print("testUnfold5.ps]");
 
}

Version 17.6, in parallel to changes in TUnfold

History:

• Version 17.5, in parallel to changes in TUnfold
• Version 17.4, in parallel to changes in TUnfold
• Version 17.3, in parallel to changes in TUnfold
• Version 17.2, in parallel to changes in TUnfold
• Version 17.1, in parallel to changes in TUnfold
• Version 17.0 example for multi-dimensional unfolding

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/.

Author

Stefan Schmitt DESY, 14.10.2008

Definition in file testUnfold5d.C.