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//////////////////////////////////////////////////////////////////////////
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//Geng-yuan Jeng/UC Riverside
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//2009/04/20 14:41:01
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/////////////////////////////////////////////////////////////////////////
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#ifndef __CINT__
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#include "RooGlobalFunc.h"
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#endif
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#include "RooRealVar.h"
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#include "RooDataSet.h"
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#include "RooGaussian.h"
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#include "TCanvas.h"
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#include "RooPlot.h"
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#include "TH1D.h"
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#include "TTree.h"
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using namespace RooFit;
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void fitNewRelIso()
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{
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TString fileName = "skimmed/QCDbin2_005.root";
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// TString fileName = "skimmed/QCDbinall_NewRelIso.root";
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// TString fileName = "skimmed/QCDbin4_NewRelIso_CD30_rebin2.root";
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// TString fileName = "skimmed/QCDbin3_NewRelIso_CD.root";
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//TString fileName = "skimmed/QCDbin4_rebin.root";
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//TString fileName = "skimmed/QCDbin4_NewRelIso_FastSim.root";
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// Dynamic(true) or static(false) range
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bool dynamic = true;
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int np = 100;
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double epslon = 1.e-5;
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double epslon1 = 1.e-6;
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// Dynamic fit region weight
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double wxmin = 1.7; // 1.81 1.7
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double wxmax = .94; // 0.9 .95
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Double_t ax0=0.;
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Double_t axf=0.05;
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Double_t axF=0.11;
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Double_t bx0=0.2;
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Double_t bxf=2.;
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int niter = 1;
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int nsiter = 0;
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Double_t temp = 0.;
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Double_t tempb = 0.;
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Double_t pass[3] = {0.,0.,0.};
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Double_t bound[3] = {10e6.,0.,0.};
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gStyle->SetOptStat(1);
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gStyle->SetPalette(1);
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// Import histos
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TFile *file = TFile::Open(fileName);
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TTree *tree = (TTree*)file->Get("myTree");
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TH1D *h0 = (TH1D*)file->Get("histos/h_RelIso_all");
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TH1D *h1 = (TH1D*)file->Get("histos/h_RelIso_qcd");
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Int_t jbin;
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Double_t na;
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tree->SetBranchAddress("jbin",&jbin);
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tree->SetBranchAddress("na",&na);
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tree->GetEntry(0);
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TH1D *h_all = (TH1D*)h0->Clone();
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TH1D *h_qcd = (TH1D*)h1->Clone();
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Int_t nbxMax = h_all->GetXaxis()->FindBin(2.0);
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h_all->GetXaxis()->SetRange(2,h_all->GetXaxis()->FindBin(bxf));
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Int_t nbMax = h_all->GetMaximumBin();
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cout << "=========> Bin number at peak= " << nbMax << endl;
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h_all->GetXaxis()->SetRange(1,nbxMax);
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TAxis *axis0 = h_all->GetXaxis();
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double bwidth = axis0->GetBinWidth(nbMax);
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cout << "=========> bwidth = " << bwidth << endl;
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cout << "\n>>>>> Iteration step: "<< niter << endl;
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RooRealVar x("x","x",0.,2.);
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RooRealVar y("y","y",0.,2.);
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RooDataHist *dh0 = new RooDataHist("dh0","dh0",x,Import(*h0));
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RooDataHist *dh1 = new RooDataHist("dh1","dh1",x,Import(*h1));
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RooDataHist *dhy0 = new RooDataHist("dhy0","dhy0",y,Import(*h0));
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RooDataHist *dhy1 = new RooDataHist("dhy1","dhy1",y,Import(*h1));
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// Choose signal region:
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x.setRange("signalregion",ax0,axf);
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// x.setRange("signalregion",ax0,axF);
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x.setRange("ctrlregion",bx0,bxf);
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// S e t u p m o d e l
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// ---------------------
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// Declare variables x,mean,sigma with associated name, title, initial value and allowed range
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// Gaussian
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RooRealVar mean("mean","mean of gaussian",1.5,0.,2.);
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RooRealVar sigma("sigma","width of gaussian",1,0.,100.);
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// Landau
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RooRealVar MPV("MPV","mean of landau",1,0,2);
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RooRealVar Sig("Sig","sigma of landau",1,0,2);
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// Build gaussian p.d.f in terms of x,mean and sigma
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RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma);
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RooLandau landau("landau","landau PDF",x,MPV,Sig);
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// Construct plot frame in 'x'
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RooPlot* xframe = x.frame(Title("Landau p.d.f. with data"));
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RooPlot* yframe = y.frame(Title("Landau p.d.f. with data"));
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// P l o t m o d e l a n d c h a n g e p a r a m e t e r v a l u e s
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// ---------------------------------------------------------------------------
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// Change the value of Parameters
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// G e n e r a t e e v e n t s
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// -----------------------------
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// Make a second plot frame in x and draw both the
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// data and the p.d.f in the frame
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dh0->plotOn(xframe);
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dh1->plotOn(xframe,MarkerColor(9));
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dhy0->plotOn(yframe);
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dhy1->plotOn(yframe,MarkerColor(9));
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// F i t m o d e l t o d a t a
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// -----------------------------
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// Fit pdf to data
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// MPV.setRange(0.4,1.);
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// Sig.setRange(0.1,0.5);
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// Sig.setConstant(kFALSE);
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RooFitResult*rest = landau.fitTo(*dh0,Range("ctrlregion"),PrintLevel(-1),Save());
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rest->Print("v");
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niter++;
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// Print values of mean and sigma (that now reflect fitted values and errors)
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Double_t xb0 = MPV.getVal()-wxmin*Sig.getVal();
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Double_t xbf = MPV.getVal()+wxmax*Sig.getVal();
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RooAbsReal* nQCDsigw = landau.createIntegral(x,Range("signalregion"));
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RooAbsReal* nQCDbkgw = landau.createIntegral(x,Range("ctrlregion"));
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// Draw all frames on a canvas
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TCanvas* c = new TCanvas("fitNewRelIso","fitNewRelIso",1000,600);
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c->Divide(2,1);
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c->cd(1);
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for (;niter <= np && nsiter < 2; niter++){
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xb0 = MPV.getVal()-wxmin*Sig.getVal();
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xbf = MPV.getVal()+wxmax*Sig.getVal();
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cout << "\n>>>>> Iteration step: "<< niter << endl;
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cout << "=========> xb0, xbf = " << xb0 << ", " << xbf << endl;
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x.setRange("ctrlregion",xb0,xbf);
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// Construct a chi^2 of the data and the model,
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// which is the input probability density scaled
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// by the number of events in the dataset
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RooChi2Var chi2("chi2","chi2",landau,*dh0,Range("ctrlregion"));
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// Use RooMinuit interface to minimize chi^2
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RooMinuit m(chi2);
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m.migrad();
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m.hesse();
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if ((fabs(chi2.getVal() - bound[0]) <= epslon) ||
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(fabs(chi2.getVal() - temp) <= epslon) ||
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(fabs(xb0 - tempb) < = epslon1 )
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){
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nsiter++;
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cout << "=========> Delta chi2 converges for " << nsiter << "time(s)" << endl;
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}
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if (chi2.getVal() < bound[0]){
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bound[0] = chi2.getVal();
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bound[1] = xb0;
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bound[2] = xbf;
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pass[1] = MPV.getVal();
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pass[2] = Sig.getVal();
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nQCDsigw = landau.createIntegral(x,NormSet(x),Range("signalregion"));
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nQCDbkgw = landau.createIntegral(x,NormSet(x),Range("ctrlregion"));
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cout << "=========> New Min chi2 = " << chi2.getVal() <<endl;
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cout << "=========> Fitting range = " << bound[1] << " to " << bound[2] << endl;
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landau.plotOn(xframe,NormRange("ctrlregion"),Range("ctrlregion"),LineColor(niter));
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landau.paramOn(xframe,Layout(0.28,0.75,0.42));
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landau.plotOn(xframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(niter));
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xframe->Draw();
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nQCDsigw->Print();
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nQCDbkgw->Print();
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}
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temp = chi2.getVal();
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tempb = xb0;
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}
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// plot
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Double_t mpv1 = pass[1];
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Double_t sig1 = pass[2];
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RooRealVar MPV1("MPV1","MPV1",mpv1);
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RooRealVar Sig1("Sig1","Sig1",sig1);
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RooLandau landau1("landau1","landau1",y,MPV1,Sig1);
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y.setRange("signalregion",ax0,axf);
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y.setRange("centralregion",axf,bound[1]);
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y.setRange("ctrlregion",bound[1],bound[2]);
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landau1.plotOn(yframe,NormRange("ctrlregion"),Range("ctrlregion"));
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landau1.plotOn(yframe,NormRange("ctrlregion"),Range("centralregion"),
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LineColor(3),LineStyle(kDashed));
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landau1.plotOn(yframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(2));
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c->cd(2);
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yframe->Draw();
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cout << "=========> chi2 of the last iteration = " << chi2.getVal() << endl;
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cout << "=========> Fit range of the last iteration = " << xb0 << " to " << xbf << endl;
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cout << "================================================================" << endl;
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cout << "=========> Minimum chi2 = " << bound[0] << endl;
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cout << "=========> Final fit range = " << bound[1] << " to " << bound[2] << endl;
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cout << "=========> MPV = " << pass[1] << "; Sig = " << pass[2] << endl;
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// Print values of mean and sigma (that now reflect fitted values and errors)
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// mean.Print();
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// sigma.Print();
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// MPV.Print();
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// Sig.Print();
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cout << "=========> Final MPV = pass[1] = " << pass[1] << endl;
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cout << "=========> Final MPV = pass[2] = " << pass[2] << endl;
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// Get number of events within fitted region ==> cross check
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TAxis *axis = h0->GetXaxis();
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int bmin = axis->FindBin(xb0);
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int bmax = axis->FindBin(xbf);
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double nfac = h0->Integral(bmin,bmax);
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nfac -= (h0->GetBinContent(bmin))*(xb0-axis->GetBinLowEdge(bmin))/axis->GetBinWidth(bmin);
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nfac -= (h0->GetBinContent(bmax))*(axis->GetBinUpEdge(bmax)-xbf)/axis->GetBinWidth(bmax);
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cout << "=========> Final Nfac = " << nfac << endl;
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nQCDsigw->Print();
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nQCDbkgw->Print();
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Double_t nQCDsig = nfac * nQCDsigw->getVal()/nQCDbkgw->getVal();
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cout << "=========> Number of observed QCD evts = " << nQCDsig << endl;
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// Print results
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cout << "\n";
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cout << ">>>>> Jet bin " << jbin << ":" << endl;
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cout << ">>>>> Observed Ns = " << nQCDsig << endl;
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cout << ">>>>> Expected Na = " << na << endl;
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cout << "Deviation = " << (nQCDsig-na)/na*100.0 << " %" <<endl;
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}
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