// Author: Stefan Schmitt
// DESY, 14.10.2008
// Version 17.0 example for multi-dimensional unfolding
//
#include <iostream>
#include <cmath>
#include <map>
#include <TMath.h>
#include <TCanvas.h>
#include <TStyle.h>
#include <TGraph.h>
#include <TFile.h>
#include <TH1.h>
#include "TUnfoldDensity.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
//
///////////////////////////////////////////////////////////////////////
// #define PRINT_MATRIX_L
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);
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=0;
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)
unfold.SetInput(histDataReco /* ,0.0,1.0 */);
// 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=0;
TGraph *lCurve=0;
// 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=0;
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: retreive and plot unfolding results
// get unfolding output
TH1 *histDataUnfold=unfold.GetOutput("unfolded signal",0,0,0,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",0,0,0,kFALSE);
TH1 *histGlobalCorrScan=unfold.GetRhoItotal
("histGlobalCorrScan",0,SCAN_DISTRIBUTION,SCAN_AXISSTEERING,kFALSE);
TH2 *histCorrCoeff=unfold.GetRhoIJtotal("histCorrCoeff",0,0,0,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]");
}
