void fitSingleMass( const std::string& basedir, float mass, const std::string& width, TGraphErrors* gr_mean, TGraphErrors* gr_sigma, TGraphErrors* gr_width, TGraphErrors* gr_alpha1, TGraphErrors* gr_n1, TGraphErrors* gr_alpha2, TGraphErrors* gr_n2 ) {
std::string outdir = "genSignalShapes";
system( Form("mkdir -p %s", outdir.c_str()) );
std::string dataset( Form( "GluGluSpin0ToZGamma_ZToLL_W_%s_M_%.0f_TuneCUEP8M1_13TeV_pythia8", width.c_str(), mass ) );
std::cout << "-> Starting: " << dataset << std::endl;
system( Form("ls %s/%s/crab_%s/*/0000/genAna_1.root >> toBeAdded.txt", basedir.c_str(), dataset.c_str(), dataset.c_str() ) );
TChain* tree = new TChain("mt2");
ifstream ifs("toBeAdded.txt");
while( ifs.good() ) {
std::string fileName;
ifs >> fileName;
TString fileName_tstr(fileName);
if( !fileName_tstr.Contains("pnfs") ) continue;
tree->Add(Form("$DCAP/%s/mt2", fileName.c_str()) );
}
system( "rm toBeAdded.txt" );
if( tree->GetEntries()==0 ) return;
int ngenPart;
tree->SetBranchAddress( "ngenPart", &ngenPart );
float genPart_pt[100];
tree->SetBranchAddress( "genPart_pt", genPart_pt );
float genPart_eta[100];
tree->SetBranchAddress( "genPart_eta", genPart_eta );
float genPart_phi[100];
tree->SetBranchAddress( "genPart_phi", genPart_phi );
float genPart_mass[100];
tree->SetBranchAddress( "genPart_mass", genPart_mass );
int genPart_pdgId[100];
tree->SetBranchAddress( "genPart_pdgId", genPart_pdgId );
int genPart_motherId[100];
tree->SetBranchAddress( "genPart_motherId", genPart_motherId );
int genPart_status[100];
tree->SetBranchAddress( "genPart_status", genPart_status );
RooRealVar* x = new RooRealVar("boss_mass", "boss_mass", mass, 0.5*mass, 1.5*mass );
RooDataSet* data = new RooDataSet( "data", "data", RooArgSet(*x) );
int nentries = tree->GetEntries();
for( int iEntry = 0; iEntry<nentries; ++iEntry ) {
if( iEntry % 25000 == 0 ) std::cout << " Entry: " << iEntry << " / " << nentries << std::endl;
tree->GetEntry(iEntry);
TLorentzVector leptPlus;
TLorentzVector leptMinus;
TLorentzVector photon;
bool foundLeptPlus = false;
bool foundLeptMinus = false;
bool foundPhoton = false;
bool tauEvent = false;
for( int iPart=0; iPart<ngenPart; ++iPart ) {
if( genPart_status[iPart]!=1 ) continue;
if( abs(genPart_pdgId[iPart])==15 ) {
tauEvent = true;
break;
}
if( (genPart_pdgId[iPart]==+11 || genPart_pdgId[iPart]==+13) && genPart_motherId[iPart]==23 ) {
leptMinus.SetPtEtaPhiM( genPart_pt[iPart], genPart_eta[iPart], genPart_phi[iPart], genPart_mass[iPart] );
foundLeptMinus = true;
}
if( (genPart_pdgId[iPart]==-11 || genPart_pdgId[iPart]==-13) && genPart_motherId[iPart]==23 ) {
leptPlus.SetPtEtaPhiM( genPart_pt[iPart], genPart_eta[iPart], genPart_phi[iPart], genPart_mass[iPart] );
foundLeptPlus = true;
}
if( genPart_pdgId[iPart]==22 && genPart_motherId[iPart]==25 ) {
photon.SetPtEtaPhiM( genPart_pt[iPart], genPart_eta[iPart], genPart_phi[iPart], genPart_mass[iPart] );
foundPhoton = true;
}
} // for genparts
if( tauEvent ) continue;
if( !foundLeptPlus || !foundLeptMinus || !foundPhoton ) continue;
if( photon.Pt() < 40. ) continue;
float ptMax = TMath::Max( leptPlus.Pt(), leptMinus.Pt() );
float ptMin = TMath::Min( leptPlus.Pt(), leptMinus.Pt() );
if( ptMax<25. ) continue;
if( ptMin<20. ) continue;
if( fabs( photon.Eta() ) > 2.5 ) continue;
if( fabs( photon.Eta())>1.44 && fabs( photon.Eta())<1.57 ) continue;
if( fabs( leptPlus.Eta() ) > 2.4 ) continue;
if( fabs( leptMinus.Eta() ) > 2.4 ) continue;
if( photon.DeltaR( leptPlus ) < 0.4 ) continue;
if( photon.DeltaR( leptMinus ) < 0.4 ) continue;
TLorentzVector zBoson = leptPlus + leptMinus;
if( zBoson.M() < 50. ) continue;
TLorentzVector boss = zBoson + photon;
if( boss.M() < 200. ) continue;
if( photon.Pt()/boss.M()< 40./150. ) continue;
x->setVal(boss.M());
data->add(RooArgSet(*x));
}
//RooRealVar* bw_mean = new RooRealVar( "bw_mean", "Breit-Wigner Mean" , mass, 0.2*mass, 1.8*mass );
//RooRealVar* bw_gamma = new RooRealVar( "bw_gamma", "Breit-Wigner Width", 0.01*mass, 0., 0.3*mass );
//RooBreitWigner* model = new RooBreitWigner( "bw", "Breit-Wigner", *x, *bw_mean, *bw_gamma);
// Crystal-Ball
RooRealVar mean( "mean", "mean", mass, 0.9*mass, 1.1*mass );
RooRealVar sigma( "sigma", "sigma", 0.015*mass, 0., 0.07*mass );
RooRealVar alpha1( "alpha1", "alpha1", 1.2, 0., 2.5 );
RooRealVar n1( "n1", "n1", 3., 0., 5. );
RooRealVar alpha2( "alpha2", "alpha2", 1.2, 0., 2.5 );
RooRealVar n2( "n2", "n2", 3., 0., 10. );
RooDoubleCBShape* model = new RooDoubleCBShape( "cb", "cb", *x, mean, sigma, alpha1, n1, alpha2, n2 );
model->fitTo( *data );
int npoints = gr_mean->GetN();
gr_mean ->SetPoint( npoints, mass, mean.getVal() );
gr_sigma ->SetPoint( npoints, mass, sigma.getVal() );
gr_width ->SetPoint( npoints, mass, sigma.getVal()/mean.getVal() );
gr_alpha1->SetPoint( npoints, mass, alpha1.getVal() );
gr_alpha2->SetPoint( npoints, mass, alpha2.getVal() );
gr_n1 ->SetPoint( npoints, mass, n1.getVal() );
gr_n2 ->SetPoint( npoints, mass, n2.getVal() );
gr_mean ->SetPointError( npoints, 0., mean.getError() );
gr_sigma ->SetPointError( npoints, 0., sigma.getError() );
gr_width ->SetPointError( npoints, 0., sigma.getError()/mean.getVal() );
gr_alpha1->SetPointError( npoints, 0., alpha1.getError() );
gr_alpha2->SetPointError( npoints, 0., alpha2.getError() );
gr_n1 ->SetPointError( npoints, 0., n1.getError() );
gr_n2 ->SetPointError( npoints, 0., n2.getError() );
//gr_mean ->SetPoint ( npoints, mass, bw_mean->getVal() );
//gr_gamma->SetPoint ( npoints, mass, bw_gamma->getVal() );
//gr_width->SetPoint ( npoints, mass, bw_gamma->getVal()/bw_mean->getVal() );
//gr_mean ->SetPointError( npoints, 0., bw_mean->getError() );
//gr_gamma->SetPointError( npoints, 0., bw_gamma->getError()/bw_mean->getVal() );
//gr_width->SetPointError( npoints, 0., bw_gamma->getError()/bw_mean->getVal() );
RooPlot* plot = x->frame();
data->plotOn(plot);
model->plotOn(plot);
TCanvas* c1 = new TCanvas( "c1", "", 600, 600 );
c1->cd();
plot->Draw();
c1->SaveAs( Form("%s/fit_m%.0f_%s.eps", outdir.c_str(), mass, width.c_str()) );
c1->SaveAs( Form("%s/fit_m%.0f_%s.pdf", outdir.c_str(), mass, width.c_str()) );
c1->SetLogy();
c1->SaveAs( Form("%s/fit_m%.0f_%s_log.eps", outdir.c_str(), mass, width.c_str()) );
c1->SaveAs( Form("%s/fit_m%.0f_%s_log.pdf", outdir.c_str(), mass, width.c_str()) );
//delete bw_mean;
//delete bw_gamma;
delete c1;
delete data;
delete model;
delete plot;
delete x;
}
void roo_fitWH()
{
gSystem->Load("libRooFit");
gSystem->Load("libRooFitCore");
gSystem->Load("libMatrix");
gSystem->Load("libGpad");
using namespace RooFit;
TString poscharge = "Plus";
TString negcharge = "Minus";
std::vector<TString> charge(2);
charge.at(0) = poscharge;
charge.at(1) = negcharge;
TString bin1 = "50to75";
TString bin2 = "75to100";
TString bin3 = "100toinf";
std::vector<TString> bins(3);
bins.at(0) = bin1;
bins.at(1) = bin2;
bins.at(2) = bin3;
//we only set up the machinery for the plus charge
//TFile *dataplus = new TFile("results/GenW_CS_WJets-lite_madgraph_50_75_100.root");
TFile *dataplus = new TFile("results/RecoW_WJets-lite_madgraph_50_75_100.root");
TFile *refTemplates = new TFile("results/GenW_CS_WJets-lite_madgraph_50_75_100.root");
bool makePlots = false;
TString canvas_name_plus = "MC_WHelicityFramePlots_PlusICVar";
TCanvas *c0 = new TCanvas(canvas_name_plus,"",900,320);
c0->Divide(3,1);
TString canvas_name_minus = "MC_WHelicityFramePlots_MinusICVar";
TCanvas *c1 = new TCanvas(canvas_name_minus,"",900,320);
c1->Divide(3,1);
for(unsigned int i=0; i<bins.size(); i++) {
for(unsigned int j=0; j<charge.size(); j++) {
//unsigned int index = i*charge.size() + j;
//cout << "INDEX= " << index << endl;
//these histograms define the number of events in the complete templates so we can apply correction factors for acceptance etc.
//do not change these histograms!
TString refHist1 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j) + "_LH";
TString refHist2 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j) + "_RH";
TString refHist3 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j) + "_LO";
TH1D *refTempHist1 = (TH1D*)refTemplates->Get(refHist1);
TH1D *refTempHist2 = (TH1D*)refTemplates->Get(refHist2);
TH1D *refTempHist3 = (TH1D*)refTemplates->Get(refHist3);
//these histograms are the ones we want to fit
//TString Hist1 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j) + "_LH";
//TString Hist2 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j) + "_RH";
//TString Hist3 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j) + "_LO";
//TString Hist_data1 = "MC_WPlots_" + bins.at(i) + "/MC_ICVar" + charge.at(j);
TString Hist1 = "RECO_PolPlots_" + bins.at(i) + "/RECO_ICVar"+ charge.at(j) + "_LH";
TString Hist2 = "RECO_PolPlots_" + bins.at(i) + "/RECO_ICVar"+ charge.at(j) + "_RH";
TString Hist3 = "RECO_PolPlots_" + bins.at(i) + "/RECO_ICVar"+ charge.at(j) + "_LO";
TString Hist_data1 = "RECO_PolPlots_" + bins.at(i) + "/RECO_ICVar"+ charge.at(j);
Int_t rbin=10;
TH1D *mc1 = (TH1D*)dataplus->Get(Hist1);
TH1D *mc2 = (TH1D*)dataplus->Get(Hist2);
TH1D *mc3 = (TH1D*)dataplus->Get(Hist3);
TH1D *datahist = (TH1D*)dataplus->Get(Hist_data1);
//we are only fitting for fL and fR
double accFactor1 = refTempHist1->Integral() / mc1->Integral();
double accFactor2 = refTempHist2->Integral() / mc2->Integral();
double accFactor3 = refTempHist3->Integral() / mc3->Integral();
double normFactor = (mc1->Integral() + mc2->Integral() + mc3->Integral()) / (refTempHist1->Integral() + refTempHist2->Integral() + refTempHist3->Integral());
mc1->Rebin(rbin);
mc2->Rebin(rbin);
mc3->Rebin(rbin);
datahist->Rebin(rbin);
//datahist->Scale(10);
//datahist->Scale(invWeightW);//to get MC errors - otherwise comment out
Double_t istat=datahist->Integral();
// Start RooFit session
RooRealVar x("x","ICVar",-1.9,1.9);
// Import binned Data
RooDataHist data1("data1","dataset with WHICVarPlus",x,mc1);
RooDataHist data2("data2","dataset with WHICVarPlus",x,mc2);
RooDataHist data3("data3","dataset with WHICVarPlus",x,mc3);
RooDataHist test("test_data","dataset with WHICVarPlus",x,datahist);
//Float_t fr = (1./3.)*istat;
Float_t fr = (1.0/3.0);
// Relative fractions - allow them to float to negative values too if needs be
RooRealVar f1("fL","fL fraction",fr,0.0,1.0);
RooRealVar f2("fR","fR fraction", fr,0.0,1.0);
RooRealVar f3("f0","f0 fraction",fr,0.0,1.0);
// Templates
RooHistPdf h1("h1","h1",x,data1);
RooHistPdf h2("h2","h2",x,data2);
RooHistPdf h3("h3","h3",x,data3);
// composite PDF
RooAddPdf model("model","model",RooArgList(h1,h2,h3),RooArgList(f1,f2)) ;
// Generate data
Int_t nevt=static_cast<int>(istat);
RooDataSet *gtest = model.generate(x,nevt);
// Fitting
RooFitResult * res1 = model.fitTo(test,Minos(kTRUE), Save());
res1->Print();
if(makePlots) {
// Plotting
gROOT->SetStyle("Plain");
gStyle->SetOptFit(111);
gStyle->SetOptTitle(0);
gStyle->SetOptStat(0);
//gStyle->SetCanvasDefH(600); //Height of canvas
//gStyle->SetCanvasDefW(600); //Width of canvas
//TString canvas_name = "MC_CSFramePlots_" + bins.at(i) + charge.at(j) + "Phi";
//TCanvas *c0 = new TCanvas(canvas_name,"",300,320);
if(charge.at(j) == "Plus") c0->cd(i+1);
if(charge.at(j) == "Minus") c1->cd(i+1);
RooPlot* frame = x.frame();
test.plotOn(frame);
model.plotOn(frame);
//model.paramOn(frame);
model.plotOn(frame, Components(h1),LineColor(kRed),LineStyle(kDashed));
model.plotOn(frame, Components(h2),LineColor(kGreen),LineStyle(kDashed));
model.plotOn(frame, Components(h3),LineColor(kYellow),LineStyle(kDashed));
frame->GetXaxis()->SetTitle("LP Variable");
frame->Draw();
//c0->cd(i)->Update();
//c0->Update();
//c0->Print(canvas_name+".png");
}
const TMatrixDSym& cor = res1->correlationMatrix();
const TMatrixDSym& cov = res1->covarianceMatrix();
//Print correlation, covariance matrix
//cout << "correlation matrix" << endl;
//cor.Print();
//cout << "covariance matrix" << endl;
//cout << "(0,0) = " << cov[0][0] << endl;
//cout << f1.getVal() << endl;
//cov.Print();
double F1 = f1.getVal() * accFactor1 * normFactor;
double F2 = f2.getVal() * accFactor2 * normFactor;
double F3 = (1.0 - F1 - F2);
double F1_err = sqrt(cov[0][0]) * accFactor1 * normFactor;
double F2_err = sqrt(cov[1][1]) * accFactor2 * normFactor;
cout << "Fitting bin " << bins.at(i) << " for " << charge.at(j) << " charge: " << endl;
cout << "fL = " << F1 << " +/- " << F1_err << endl;
cout << "fR = " << F2 << " +/- " << F2_err << endl;
cout << "f0 = " << F3 << " +/- " << "N/A" << endl;
}
}
if(makePlots) {
c0->Print(canvas_name_plus+".png");
c1->Print(canvas_name_minus+".png");
}
return;
}
void fitSignalShapeW(int massBin,int id, int channels,int categ, int sample,
/* float lumi, bool doSfLepton, */double rangeLow, double rangeHigh,
double bwSigma,
double fitValues[9], double fitErrors[9], double covQual[1]){
// ------ root settings ---------
gROOT->Reset();
gROOT->SetStyle("Plain");
gStyle->SetPadGridX(kFALSE);
gStyle->SetPadGridY(kFALSE);
//gStyle->SetOptStat("kKsSiourRmMen");
gStyle->SetOptStat("iourme");
//gStyle->SetOptStat("rme");
//gStyle->SetOptStat("");
gStyle->SetOptFit(11);
gStyle->SetPadLeftMargin(0.14);
gStyle->SetPadRightMargin(0.06);
// ------------------------------
ROOT::Math::MinimizerOptions::SetDefaultTolerance( 1.E-7);
stringstream FileName;
//Insert the file here
if(sample==1) FileName <<"root://lxcms03//data3/Higgs/150915/ZH125/ZZ4lAnalysis.root" ;
else if(sample==2) FileName << "root://lxcms03//data3/Higgs/150915/WplusH125/ZZ4lAnalysis.root";
else if(sample==3) FileName << "root://lxcms03//data3/Higgs/150915/WminusH125/ZZ4lAnalysis.root";
else if(sample==4) FileName << "root://lxcms03//data3/Higgs/150915/ttH125/ZZ4lAnalysis.root";
else {
cout << "Wrong sample." << endl;
return;
}
cout << "Using " << FileName.str() << endl;
TFile* ggFile = TFile::Open(FileName.str().c_str());
TTree* ggTree = (TTree*) ggFile->Get("ZZTree/candTree");
float m4l;
Short_t z1flav, z2flav;
float weight;
Short_t nExtraLeptons;
float ZZPt;
Short_t nJets;
Short_t nBTaggedJets;
std::vector<float> * jetpt = 0;
std::vector<float> * jeteta = 0;
std::vector<float> * jetphi = 0;
std::vector<float> * jetmass = 0;
float jet30pt[10];
float jet30eta[10];
float jet30phi[10];
float jet30mass[10];
float Fisher;
int nentries = ggTree->GetEntries();
//--- ggTree part
ggTree->SetBranchAddress("ZZMass",&m4l);
ggTree->SetBranchAddress("Z1Flav",&z1flav);
ggTree->SetBranchAddress("Z2Flav",&z2flav);
ggTree->SetBranchAddress("genHEPMCweight",&weight);
ggTree->SetBranchAddress("nExtraLep",&nExtraLeptons);
ggTree->SetBranchAddress("nCleanedJets",&nJets);
ggTree->SetBranchAddress("nCleanedJetsPt30BTagged",&nBTaggedJets);
ggTree->SetBranchAddress("DiJetFisher",&Fisher);
ggTree->SetBranchAddress("JetPt",&jetpt);
ggTree->SetBranchAddress("JetEta",&jeteta);
ggTree->SetBranchAddress("JetPhi",&jetphi);
ggTree->SetBranchAddress("JetMass",&jetmass);
ggTree->SetBranchAddress("ZZPt",&ZZPt);
//--- rooFit part
double xMin,xMax,xInit;
xInit = (double) massBin;
xMin = rangeLow;
xMax = rangeHigh ;
cout << "Fit range: [" << xMin << " , " << xMax << "]. Init value = " << xInit << endl;
TH1F *hmass = new TH1F("hmass","hmass",200,xMin,xMax);
//---------
RooRealVar x("mass","m_{4l}",xInit,xMin,xMax,"GeV");
RooRealVar w("myW","myW",1.0,0.,1000.);
RooArgSet ntupleVarSet(x,w);
RooDataSet dataset("mass4l","mass4l",ntupleVarSet,WeightVar("myW"));
for(int k=0; k<nentries; k++){
ggTree->GetEvent(k);
int njet30 = 0;
for (unsigned int ijet = 0; ijet < jetpt->size(); ijet++) {
if ( (*jetpt)[ijet] > 30. ) {
jet30pt[njet30] = (*jetpt)[ijet];
jet30eta[njet30] = (*jeteta)[ijet];
jet30phi[njet30] = (*jetphi)[ijet];
jet30mass[njet30] = (*jetmass)[ijet];
njet30++;
}
}
int Cat = category(nExtraLeptons, ZZPt, m4l, njet30, nBTaggedJets, jet30pt, jet30eta, jet30phi,jet30mass, Fisher);
if (categ >= 0 && categ != Cat ) continue;
if(channels==0 && z1flav*z2flav != 28561) continue;
if(channels==1 && z1flav*z2flav != 14641) continue;
if (weight <= 0 ) cout << "Warning! Negative weight events" << endl;
if(channels==2 && z1flav*z2flav != 20449) continue;
ntupleVarSet.setRealValue("mass",m4l);
ntupleVarSet.setRealValue("myW",weight);
if(x.getVal()>xMin && x.getVal()<xMax)
dataset.add(ntupleVarSet, weight);
hmass->Fill(m4l);
}
//---------
cout << "dataset n entries: " << dataset.sumEntries() << endl;
TCanvas *c1 = new TCanvas("c1","c1",725,725);
c1->cd();
TPad *pad1 = new TPad("pad1","This is pad1",0.05,0.35,0.95,0.97);
pad1->Draw();
TPad *pad2 = new TPad("pad2","This is pad2",0.05,0.02,0.95,0.35);
pad2->Draw();
//--- double CrystalBall
RooRealVar mean("bias","mean of gaussian",0,-5.,5.) ;
RooRealVar sigma("sigma","width of gaussian",1.5,0.,30.);
RooRealVar a1("a1","a1",1.46,0.5,5.);
RooRealVar n1("n1","n1",1.92,0.,10.);
RooRealVar a2("a2","a2",1.46,1.,10.);
RooRealVar n2("n2","n2",20,1.,50.);
RooDoubleCB DCBall("DCBall","Double Crystal ball",x,mean,sigma,a1,n1,a2,n2);
if (channels== 1) mean.setVal(-1.);
//--- Breit-Wigner
float bwSigmaMax,bwSigmaMin;
if(massBin<400) bwSigmaMin=bwSigmaMax=bwSigma;
else {
bwSigmaMin=bwSigma-20.;
bwSigmaMax=bwSigma+20.;
}
RooRealVar mean3("mean3","mean3",xInit) ;
RooRealVar sigma3("sigma3","width3",bwSigma,bwSigmaMin,bwSigmaMax);
RooRealVar scale3("scale3","scale3 ",1.);
RooRelBWUFParam bw("bw","bw",x,mean3,scale3);
//Chebyshev-Polynomial
RooRealVar A1("A1","A1",-1,-3,3.);
RooRealVar A2("A2","A2",0.5,-3.,3.);
RooChebychev BkgPDF("BkgPDF","BkgPDF",x ,RooArgList(A1,A2));
//Fraction
RooRealVar frac("frac","Fraction for PDF",0.5,0.,1.);
x.setBins(10000,"fft");
RooFFTConvPdf model("model","model",x,bw,DCBall);
RooAddPdf totPDF("totPDF","Total PDF ",RooArgList(model,BkgPDF),RooArgList(frac));
RooArgSet* params = totPDF.getParameters(x);
if(sample!=1 && categ!=0 && id!=125){
if(channels==0 ){params->readFromFile("Ch0_Cat0_paraT.txt");}// Read the Parameter for the Resonance + Bkg(ChebyChev)
if(channels==1 ){params->readFromFile("Ch1_Cat0_paraT.txt");}// Read the Parameter for the Resonance + Bkg(ChebyChev)
if(channels==2 ){params->readFromFile("Ch2_Cat0_paraT.txt");}// Read the Parameter for the Resonance + Bkg(ChebyChev)
}
RooFitResult *fitres = (RooFitResult*)totPDF.fitTo(dataset,SumW2Error(1),Range(xMin,xMax),Strategy(2),NumCPU(8),Save(true));
if (sample==1 && categ==0 && id==125){
mean.setConstant(kTRUE);
sigma.setConstant(kTRUE);
a1.setConstant(kTRUE);
n1.setConstant(kTRUE);
a2.setConstant(kTRUE);
n2.setConstant(kTRUE);
mean3.setConstant(kTRUE);
sigma3.setConstant(kTRUE);
scale3.setConstant(kTRUE);
A1.setConstant(kTRUE);
A2.setConstant(kTRUE);
frac.setConstant(kTRUE);
if(channels==0 ){
params->readFromFile("Ch0_Cat0_para.txt"); // Read the Parameter for the Resonance as ggH sample
params->writeToFile("Ch0_Cat0_paraT.txt");} // Writing the Parameter for Full PDF including the Chebyshev-Polynomial
if(channels==1 )
{params->readFromFile("Ch1_Cat0_para.txt"); // Read the Parameter for the Resonance as in ggH sample
params->writeToFile("Ch1_Cat0_paraT.txt");}// Writing the Parameter for Full PDF including the Chebyshev-Polynomial
if(channels==2 ){
params->readFromFile("Ch2_Cat0_para.txt"); // Read the Parameter for the Resonance as ggH sample
params->writeToFile("Ch2_Cat0_paraT.txt");}// Writing the Parameter for Full PDF including the Chebyshev-Polynomial
}
stringstream frameTitle;
if(channels==0){frameTitle << "4#mu, m_{H} = "; }
if(channels==1){frameTitle << "4e, m_{H} = ";}
if(channels==2){frameTitle << "2e2#mu, m_{H} = ";}
frameTitle << massBin << " GeV";
stringstream nameFileRoot;
nameFileRoot << "fitM" << massBin << ".root";
TFile *fileplot = TFile::Open(nameFileRoot.str().c_str(), "recreate");
RooPlot* xframe = x.frame() ;
xframe->SetTitle("");
xframe->SetName("m4lplot");
dataset.plotOn(xframe,DataError(RooAbsData::SumW2), MarkerStyle(kOpenCircle), MarkerSize(1.1) );
int col;
if(channels==0) col=kOrange+7;
if(channels==1) col=kAzure+2;
if(channels==2) col=kGreen+3;
totPDF.plotOn(xframe,LineColor(col));
RooHist* hpull = xframe->pullHist();
RooPlot* frame3 = x.frame(Title("Pull Distribution")) ;
frame3->addPlotable(hpull,"P");
// cosmetics
TLegend *legend = new TLegend(0.20,0.45,0.45,0.60,NULL,"brNDC");
legend->SetBorderSize(0);
legend->SetFillColor(0);
legend->SetTextAlign(12);
legend->SetTextFont (42);
legend->SetTextSize (0.03);
TH1F *dummyPoints = new TH1F("dummyP","dummyP",1,0,1);
TH1F *dummyLine = new TH1F("dummyL","dummyL",1,0,1);
dummyPoints->SetMarkerStyle(kOpenCircle);
dummyPoints->SetMarkerSize(1.1);
dummyLine->SetLineColor(col);
legend->AddEntry(dummyPoints, "Simulation", "pe");
legend->AddEntry(dummyLine, "Parametric Model", "l");
TPaveText *text = new TPaveText(0.15,0.90,0.77,0.98,"brNDC");
text->AddText("CMS Simulation");
text->SetBorderSize(0);
text->SetFillStyle(0);
text->SetTextAlign(12);
text->SetTextFont(42);
text->SetTextSize(0.03);
TPaveText *titlet = new TPaveText(0.15,0.80,0.60,0.85,"brNDC");
titlet->AddText(frameTitle.str().c_str());
titlet->SetBorderSize(0);
titlet->SetFillStyle(0);
titlet->SetTextAlign(12);
titlet->SetTextFont(132);
titlet->SetTextSize(0.045);
TPaveText *sigmat = new TPaveText(0.15,0.65,0.77,0.78,"brNDC");
stringstream sigmaval0, sigmaval1, sigmaval2;
sigmaval0 << fixed;
sigmaval0 << setprecision(1);
sigmaval0 << "m_{dCB} = " << mean.getVal() + massBin << " GeV";
sigmaval1 << fixed;
sigmaval1 << setprecision(1);
sigmaval1 << "#sigma_{dCB} = " << sigma.getVal() << " GeV";
sigmaval2 << fixed;
sigmaval2 << setprecision(1);
sigmaval2 << "RMS_{eff} = " << effSigma(hmass) << " GeV";
sigmat->AddText(sigmaval1.str().c_str());
sigmat->AddText(sigmaval2.str().c_str());
sigmat->SetBorderSize(0);
sigmat->SetFillStyle(0);
sigmat->SetTextAlign(12);
sigmat->SetTextFont(132);
sigmat->SetTextSize(0.04);
xframe->GetYaxis()->SetTitleOffset(1.5);
cout << "EFF RMS = " << effSigma(hmass) << " RMS = " << hmass->GetRMS() << endl;
pad1->cd();
stringstream nameFile, nameFileC, nameFilePng;
nameFile << "fitM" << massBin << "_channel" << channels<< "_category"<< categ << ".pdf";
nameFileC << "fitM" << massBin << "_channel" << channels << "_category"<< categ << ".C";
nameFilePng << "fitM" << massBin << "_channel" << channels << "_category"<< categ << ".png";
xframe->Draw();
gPad->Update(); legend->Draw(); text->Draw(); sigmat->Draw(); titlet->Draw();
pad2->cd() ;
frame3->Draw() ;
frame3->SetMinimum(-3);
frame3->SetMaximum(3);
TLine *line1 = new TLine(105,0,140,0);
line1->SetLineColor(kRed);
line1->Draw();
c1->Print(nameFile.str().c_str());
c1->SaveAs(nameFileC.str().c_str());
c1->SaveAs(nameFilePng.str().c_str());
fileplot->cd();
xframe->Write();
sigmat->Write();
hmass->Write();
fileplot->Close();
if(fitValues!=0){
fitValues[0] = a1.getVal();
fitValues[1] = a2.getVal();
fitValues[2] = mean.getVal();
fitValues[3] = mean3.getVal();
fitValues[4] = n1.getVal();
fitValues[5] = n2.getVal();
fitValues[6] = sigma.getVal();
fitValues[7] = A1.getVal();
fitValues[8] = A2.getVal();
}
if(fitErrors!=0){
fitErrors[0] = a1.getError();
fitErrors[1] = a2.getError();
fitErrors[2] = mean.getError();
fitErrors[3] = mean3.getError();
fitErrors[4] = n1.getError();
fitErrors[5] = n2.getError();
fitErrors[6] = sigma.getError();
fitErrors[7] = A1.getError();
fitErrors[8] = A2.getError();
}
covQual[0] = fitres->covQual();
}
void FitBias(TString CAT,TString CUT,float SIG,float BKG,int NTOYS)
{
gROOT->ForceStyle();
RooMsgService::instance().setSilentMode(kTRUE);
RooMsgService::instance().setStreamStatus(0,kFALSE);
RooMsgService::instance().setStreamStatus(1,kFALSE);
// -----------------------------------------
TFile *fTemplates = TFile::Open("templates_"+CUT+"_"+CAT+"_workspace.root");
RooWorkspace *wTemplates = (RooWorkspace*)fTemplates->Get("w");
RooRealVar *x = (RooRealVar*)wTemplates->var("mTop");
RooAbsPdf *pdf_signal = (RooAbsPdf*)wTemplates->pdf("ttbar_pdf_Nominal");
RooAbsPdf *pdf_bkg = (RooAbsPdf*)wTemplates->pdf("qcdCor_pdf");
TRandom *rnd = new TRandom();
rnd->SetSeed(0);
x->setBins(250);
RooPlot *frame;
TFile *outf;
if (NTOYS > 1) {
outf = TFile::Open("FitBiasToys_"+CUT+"_"+CAT+".root","RECREATE");
}
float nSigInj,nBkgInj,nSigFit,nBkgFit,eSigFit,eBkgFit,nll;
TTree *tr = new TTree("toys","toys");
tr->Branch("nSigInj",&nSigInj,"nSigInj/F");
tr->Branch("nSigFit",&nSigFit,"nSigFit/F");
tr->Branch("nBkgInj",&nBkgInj,"nBkgInj/F");
tr->Branch("nBkgFit",&nBkgFit,"nBkgFit/F");
tr->Branch("eSigFit",&eSigFit,"eSigFit/F");
tr->Branch("eBkgFit",&eBkgFit,"eBkgFit/F");
tr->Branch("nll" ,&nll ,"nll/F");
for(int itoy=0;itoy<NTOYS;itoy++) {
// generate pseudodataset
nSigInj = rnd->Poisson(SIG);
nBkgInj = rnd->Poisson(BKG);
RooRealVar *nSig = new RooRealVar("nSig","nSig",nSigInj);
RooRealVar *nBkg = new RooRealVar("nBkg","nBkg",nBkgInj);
RooAddPdf *model = new RooAddPdf("model","model",RooArgList(*pdf_signal,*pdf_bkg),RooArgList(*nSig,*nBkg));
RooDataSet *data = model->generate(*x,nSigInj+nBkgInj);
RooDataHist *roohist = new RooDataHist("roohist","roohist",RooArgList(*x),*data);
// build fit model
RooRealVar *nFitSig = new RooRealVar("nFitSig","nFitSig",SIG,0,10*SIG);
RooRealVar *nFitBkg = new RooRealVar("nFitBkg","nFitBkg",BKG,0,10*BKG);
RooAddPdf *modelFit = new RooAddPdf("modelFit","modelFit",RooArgList(*pdf_signal,*pdf_bkg),RooArgList(*nFitSig,*nFitBkg));
// fit the pseudo dataset
RooFitResult *res = modelFit->fitTo(*roohist,RooFit::Save(),RooFit::Extended(kTRUE));
//res->Print();
nSigFit = nFitSig->getVal();
nBkgFit = nFitBkg->getVal();
eSigFit = nFitSig->getError();
eBkgFit = nFitBkg->getError();
nll = res->minNll();
tr->Fill();
if (itoy % 100 == 0) {
cout<<"Toy #"<<itoy<<": injected = "<<nSigInj<<", fitted = "<<nSigFit<<", error = "<<eSigFit<<endl;
}
if (NTOYS == 1) {
frame = x->frame();
roohist->plotOn(frame);
model->plotOn(frame);
}
}
if (NTOYS == 1) {
TCanvas *can = new TCanvas("Toy","Toy",900,600);
frame->Draw();
}
else {
outf->cd();
tr->Write();
outf->Close();
fTemplates->Close();
}
}
vector<Double_t*> simFit(bool makeSoupFit_ = false,
const string tnp_ = "etoTauMargLooseNoCracks70",
const string category_ = "tauAntiEMVA",
const string bin_ = "abseta<1.5",
const float binCenter_ = 0.75,
const float binWidth_ = 0.75,
const float xLow_=60,
const float xHigh_=120,
bool SumW2_ = false,
bool verbose_ = true){
vector<Double_t*> out;
//return out;
//TFile *test = new TFile( outFile->GetName(),"UPDATE");
// output file
TFile *test = new TFile( Form("EtoTauPlotsFit_%s_%s_%f.root",tnp_.c_str(),category_.c_str(),binCenter_),"RECREATE");
test->mkdir(Form("bin%f",binCenter_));
TCanvas *c = new TCanvas("fitCanvas",Form("fitCanvas_%s_%s",tnp_.c_str(),bin_.c_str()),10,30,650,600);
c->SetGrid(0,0);
c->SetFillStyle(4000);
c->SetFillColor(10);
c->SetTicky();
c->SetObjectStat(0);
TCanvas *c2 = new TCanvas("fitCanvasTemplate",Form("fitCanvasTemplate_%s_%s",tnp_.c_str(),bin_.c_str()),10,30,650,600);
c2->SetGrid(0,0);
c2->SetFillStyle(4000);
c2->SetFillColor(10);
c2->SetTicky();
c2->SetObjectStat(0);
// input files
TFile fsup("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup.root");
TFile fbkg("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup_bkg.root");
TFile fsgn("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup_sgn.root");
TFile fdat("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_Data.root");
// data from 2iter:
//TFile fdat("/data_CMS/cms/lbianchini/35pb/testNewWriteFromPAT_Data.root");
//********************** signal only tree *************************/
TTree *fullTreeSgn = (TTree*)fsgn.Get((tnp_+"/fitter_tree").c_str());
TH1F* hSall = new TH1F("hSall","",1,0,150);
TH1F* hSPall = new TH1F("hSPall","",1,0,150);
TH1F* hS = new TH1F("hS","",1,0,150);
TH1F* hSP = new TH1F("hSP","",1,0,150);
fullTreeSgn->Draw("mass>>hS",Form("weight*(%s && mass>%f && mass<%f && mcTrue && signalPFChargedHadrCands<1.5)",bin_.c_str(),xLow_,xHigh_));
fullTreeSgn->Draw("mass>>hSall",Form("weight*(%s && mass>%f && mass<%f)",bin_.c_str(),xLow_,xHigh_));
float SGNtrue = hS->Integral();
float SGNall = hSall->Integral();
fullTreeSgn->Draw("mass>>hSP",Form("weight*(%s && %s>0 && mass>%f && mass<%f && mcTrue && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_));
fullTreeSgn->Draw("mass>>hSPall",Form("weight*(%s && %s>0 && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_));
float SGNtruePass = hSP->Integral();
float SGNallPass = hSPall->Integral();
//********************** background only tree *************************//
TTree *fullTreeBkg = (TTree*)fbkg.Get((tnp_+"/fitter_tree").c_str());
TH1F* hB = new TH1F("hB","",1,0,150);
TH1F* hBP = new TH1F("hBP","",1,0,150);
fullTreeBkg->Draw("mass>>hB",Form("weight*(%s && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),xLow_,xHigh_));
float BKG = hB->Integral();
float BKGUnWeighted = hB->GetEntries();
fullTreeBkg->Draw("mass>>hBP",Form("weight*(%s && %s>0 && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_));
float BKGPass = hBP->Integral();
float BKGUnWeightedPass = hBP->GetEntries();
float BKGFail = BKG-BKGPass;
cout << "*********** BKGFail " << BKGFail << endl;
//********************** soup tree *************************//
TTree *fullTreeSoup = (TTree*)fsup.Get((tnp_+"/fitter_tree").c_str());
//********************** data tree *************************//
TTree *fullTreeData = (TTree*)fdat.Get((tnp_+"/fitter_tree").c_str());
//********************** workspace ***********************//
RooWorkspace *w = new RooWorkspace("w","w");
// tree variables to be imported
w->factory("mass[30,120]");
w->factory("weight[0,10000]");
w->factory("abseta[0,2.5]");
w->factory("pt[0,200]");
w->factory("mcTrue[0,1]");
w->factory("signalPFChargedHadrCands[0,10]");
w->factory((category_+"[0,1]").c_str());
// background pass pdf for MC
w->factory("RooExponential::McBackgroundPdfP(mass,McCP[0,-10,10])");
// background fail pdf for MC
w->factory("RooExponential::McBackgroundPdfF(mass,McCF[0,-10,10])");
// background pass pdf for Data
w->factory("RooExponential::DataBackgroundPdfP(mass,DataCP[0,-10,10])");
// background fail pdf for Data
w->factory("RooExponential::DataBackgroundPdfF(mass,DataCF[0,-10,10])");
// fit parameters for background
w->factory("McEfficiency[0.04,0,1]");
w->factory("McNumSgn[0,1000000]");
w->factory("McNumBkgP[0,100000]");
w->factory("McNumBkgF[0,100000]");
w->factory("expr::McNumSgnP('McEfficiency*McNumSgn',McEfficiency,McNumSgn)");
w->factory("expr::McNumSgnF('(1-McEfficiency)*McNumSgn',McEfficiency,McNumSgn)");
w->factory("McPassing[pass=1,fail=0]");
// fit parameters for data
w->factory("DataEfficiency[0.1,0,1]");
w->factory("DataNumSgn[0,1000000]");
w->factory("DataNumBkgP[0,1000000]");
w->factory("DataNumBkgF[0,10000]");
w->factory("expr::DataNumSgnP('DataEfficiency*DataNumSgn',DataEfficiency,DataNumSgn)");
w->factory("expr::DataNumSgnF('(1-DataEfficiency)*DataNumSgn',DataEfficiency,DataNumSgn)");
w->factory("DataPassing[pass=1,fail=0]");
RooRealVar *weight = w->var("weight");
RooRealVar *abseta = w->var("abseta");
RooRealVar *pt = w->var("pt");
RooRealVar *mass = w->var("mass");
mass->setRange(xLow_,xHigh_);
RooRealVar *mcTrue = w->var("mcTrue");
RooRealVar *cut = w->var( category_.c_str() );
RooRealVar *signalPFChargedHadrCands = w->var("signalPFChargedHadrCands");
// build the template for the signal pass sample:
RooDataSet templateP("templateP","dataset for signal-pass template", RooArgSet(*mass,*weight,*abseta,*pt,*cut,*mcTrue,*signalPFChargedHadrCands), Import( *fullTreeSgn ), /*WeightVar( *weight ),*/ Cut( Form("(mcTrue && %s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()) ) );
// build the template for the signal fail sample:
RooDataSet templateF("templateF","dataset for signal-fail template", RooArgSet(*mass,*weight,*abseta,*pt,*cut,*mcTrue,*signalPFChargedHadrCands), Import( *fullTreeSgn ), /*WeightVar( *weight ),*/ Cut( Form("(mcTrue && %s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()) ) );
mass->setBins(24);
RooDataHist templateHistP("templateHistP","",RooArgSet(*mass), templateP, 1.0);
RooHistPdf TemplateSignalPdfP("TemplateSignalPdfP","",RooArgSet(*mass),templateHistP);
w->import(TemplateSignalPdfP);
mass->setBins(24);
RooDataHist templateHistF("templateHistF","",RooArgSet(*mass),templateF,1.0);
RooHistPdf TemplateSignalPdfF("TemplateSignalPdfF","",RooArgSet(*mass),templateHistF);
w->import(TemplateSignalPdfF);
mass->setBins(10000,"fft");
RooPlot* TemplateFrameP = mass->frame(Bins(24),Title("Template passing"));
templateP.plotOn(TemplateFrameP);
w->pdf("TemplateSignalPdfP")->plotOn(TemplateFrameP);
RooPlot* TemplateFrameF = mass->frame(Bins(24),Title("Template failing"));
templateF.plotOn(TemplateFrameF);
w->pdf("TemplateSignalPdfF")->plotOn(TemplateFrameF);
//w->factory("RooFFTConvPdf::McSignalPdfP(mass,TemplateSignalPdfP,RooTruthModel::McResolModP(mass))");
//w->factory("RooFFTConvPdf::McSignalPdfF(mass,TemplateSignalPdfF,RooTruthModel::McResolModF(mass))");
// FOR GREGORY: PROBLEM WHEN TRY TO USE THE PURE TEMPLATE =>
RooHistPdf McSignalPdfP("McSignalPdfP","McSignalPdfP",RooArgSet(*mass),templateHistP);
RooHistPdf McSignalPdfF("McSignalPdfF","McSignalPdfF",RooArgSet(*mass),templateHistF);
w->import(McSignalPdfP);
w->import(McSignalPdfF);
// FOR GREGORY: FOR DATA, CONVOLUTION IS OK =>
w->factory("RooFFTConvPdf::DataSignalPdfP(mass,TemplateSignalPdfP,RooGaussian::DataResolModP(mass,DataMeanResP[0.0,-5.,5.],DataSigmaResP[0.5,0.,10]))");
w->factory("RooFFTConvPdf::DataSignalPdfF(mass,TemplateSignalPdfF,RooGaussian::DataResolModF(mass,DataMeanResF[-5.,-10.,10.],DataSigmaResF[0.5,0.,10]))");
//w->factory("RooCBShape::DataSignalPdfF(mass,DataMeanF[91.2,88,95.],DataSigmaF[3,0.5,8],DataAlfaF[1.8,0.,10],DataNF[1.0,1e-06,10])");
//w->factory("RooFFTConvPdf::DataSignalPdfF(mass,RooVoigtian::DataVoigF(mass,DataMeanF[85,80,95],DataWidthF[2.49],DataSigmaF[3,0.5,10]),RooCBShape::DataResolModF(mass,DataMeanResF[0.5,0.,10.],DataSigmaResF[0.5,0.,10],DataAlphaResF[0.5,0.,10],DataNResF[1.0,1e-06,10]))");
//w->factory("SUM::DataSignalPdfF(fVBP[0.5,0,1]*RooBifurGauss::bifF(mass,DataMeanResF[91.2,80,95],sigmaLF[10,0.5,40],sigmaRF[0.]), RooVoigtian::voigF(mass, DataMeanResF, widthF[2.49], sigmaVoigF[5,0.1,10]) )" );
// composite model pass for MC
w->factory("SUM::McModelP(McNumSgnP*McSignalPdfP,McNumBkgP*McBackgroundPdfP)");
w->factory("SUM::McModelF(McNumSgnF*McSignalPdfF,McNumBkgF*McBackgroundPdfF)");
// composite model pass for data
w->factory("SUM::DataModelP(DataNumSgnP*DataSignalPdfP,DataNumBkgP*DataBackgroundPdfP)");
w->factory("SUM::DataModelF(DataNumSgnF*DataSignalPdfF,DataNumBkgF*DataBackgroundPdfF)");
// simultaneous fir for MC
w->factory("SIMUL::McModel(McPassing,pass=McModelP,fail=McModelF)");
// simultaneous fir for data
w->factory("SIMUL::DataModel(DataPassing,pass=DataModelP,fail=DataModelF)");
w->Print("V");
w->saveSnapshot("clean", w->allVars());
w->loadSnapshot("clean");
/****************** sim fit to soup **************************/
///////////////////////////////////////////////////////////////
TFile *f = new TFile("dummySoup.root","RECREATE");
TTree* cutTreeSoupP = fullTreeSoup->CopyTree(Form("(%s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
TTree* cutTreeSoupF = fullTreeSoup->CopyTree(Form("(%s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
RooDataSet McDataP("McDataP","dataset pass for the soup", RooArgSet(*mass), Import( *cutTreeSoupP ) );
RooDataSet McDataF("McDataF","dataset fail for the soup", RooArgSet(*mass), Import( *cutTreeSoupF ) );
RooDataHist McCombData("McCombData","combined data for the soup", RooArgSet(*mass), Index(*(w->cat("McPassing"))), Import("pass", *(McDataP.createHistogram("histoP",*mass)) ), Import("fail",*(McDataF.createHistogram("histoF",*mass)) ) ) ;
RooPlot* McFrameP = 0;
RooPlot* McFrameF = 0;
RooRealVar* McEffFit = 0;
if(makeSoupFit_){
cout << "**************** N bins in mass " << w->var("mass")->getBins() << endl;
RooFitResult* ResMcCombinedFit = w->pdf("McModel")->fitTo(McCombData, Extended(1), Minos(1), Save(1), SumW2Error( SumW2_ ), Range(xLow_,xHigh_), NumCPU(4) /*, ExternalConstraints( *(w->pdf("ConstrainMcNumBkgF")) )*/ );
test->cd(Form("bin%f",binCenter_));
ResMcCombinedFit->Write("McFitResults_Combined");
RooArgSet McFitParam(ResMcCombinedFit->floatParsFinal());
McEffFit = (RooRealVar*)(&McFitParam["McEfficiency"]);
RooRealVar* McNumSigFit = (RooRealVar*)(&McFitParam["McNumSgn"]);
RooRealVar* McNumBkgPFit = (RooRealVar*)(&McFitParam["McNumBkgP"]);
RooRealVar* McNumBkgFFit = (RooRealVar*)(&McFitParam["McNumBkgF"]);
McFrameP = mass->frame(Bins(24),Title("MC: passing sample"));
McCombData.plotOn(McFrameP,Cut("McPassing==McPassing::pass"));
w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McSignalPdfP"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McBackgroundPdfP"), LineColor(kGreen),Range(xLow_,xHigh_));
McFrameF = mass->frame(Bins(24),Title("MC: failing sample"));
McCombData.plotOn(McFrameF,Cut("McPassing==McPassing::fail"));
w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McSignalPdfF"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McBackgroundPdfF"), LineColor(kGreen),Range(xLow_,xHigh_));
}
///////////////////////////////////////////////////////////////
/****************** sim fit to data **************************/
///////////////////////////////////////////////////////////////
TFile *f2 = new TFile("dummyData.root","RECREATE");
TTree* cutTreeDataP = fullTreeData->CopyTree(Form("(%s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
TTree* cutTreeDataF = fullTreeData->CopyTree(Form("(%s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()));
RooDataSet DataDataP("DataDataP","dataset pass for the soup", RooArgSet(*mass), Import( *cutTreeDataP ) );
RooDataSet DataDataF("DataDataF","dataset fail for the soup", RooArgSet(*mass), Import( *cutTreeDataF ) );
RooDataHist DataCombData("DataCombData","combined data for the soup", RooArgSet(*mass), Index(*(w->cat("DataPassing"))), Import("pass",*(DataDataP.createHistogram("histoDataP",*mass))),Import("fail",*(DataDataF.createHistogram("histoDataF",*mass)))) ;
RooFitResult* ResDataCombinedFit = w->pdf("DataModel")->fitTo(DataCombData, Extended(1), Minos(1), Save(1), SumW2Error( SumW2_ ), Range(xLow_,xHigh_), NumCPU(4));
test->cd(Form("bin%f",binCenter_));
ResDataCombinedFit->Write("DataFitResults_Combined");
RooArgSet DataFitParam(ResDataCombinedFit->floatParsFinal());
RooRealVar* DataEffFit = (RooRealVar*)(&DataFitParam["DataEfficiency"]);
RooRealVar* DataNumSigFit = (RooRealVar*)(&DataFitParam["DataNumSgn"]);
RooRealVar* DataNumBkgPFit = (RooRealVar*)(&DataFitParam["DataNumBkgP"]);
RooRealVar* DataNumBkgFFit = (RooRealVar*)(&DataFitParam["DataNumBkgF"]);
RooPlot* DataFrameP = mass->frame(Bins(24),Title("Data: passing sample"));
DataCombData.plotOn(DataFrameP,Cut("DataPassing==DataPassing::pass"));
w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataSignalPdfP"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataBackgroundPdfP"), LineColor(kGreen),LineStyle(kDashed),Range(xLow_,xHigh_));
RooPlot* DataFrameF = mass->frame(Bins(24),Title("Data: failing sample"));
DataCombData.plotOn(DataFrameF,Cut("DataPassing==DataPassing::fail"));
w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), LineColor(kBlue),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataSignalPdfF"), LineColor(kRed),Range(xLow_,xHigh_));
w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataBackgroundPdfF"), LineColor(kGreen),LineStyle(kDashed),Range(xLow_,xHigh_));
///////////////////////////////////////////////////////////////
if(makeSoupFit_) c->Divide(2,2);
else c->Divide(2,1);
c->cd(1);
DataFrameP->Draw();
c->cd(2);
DataFrameF->Draw();
if(makeSoupFit_){
c->cd(3);
McFrameP->Draw();
c->cd(4);
McFrameF->Draw();
}
c->Draw();
test->cd(Form("bin%f",binCenter_));
c->Write();
c2->Divide(2,1);
c2->cd(1);
TemplateFrameP->Draw();
c2->cd(2);
TemplateFrameF->Draw();
c2->Draw();
test->cd(Form("bin%f",binCenter_));
c2->Write();
// MINOS errors, otherwise HESSE quadratic errors
float McErrorLo = 0;
float McErrorHi = 0;
if(makeSoupFit_){
McErrorLo = McEffFit->getErrorLo()<0 ? McEffFit->getErrorLo() : (-1)*McEffFit->getError();
McErrorHi = McEffFit->getErrorHi()>0 ? McEffFit->getErrorHi() : McEffFit->getError();
}
float DataErrorLo = DataEffFit->getErrorLo()<0 ? DataEffFit->getErrorLo() : (-1)*DataEffFit->getError();
float DataErrorHi = DataEffFit->getErrorHi()>0 ? DataEffFit->getErrorHi() : DataEffFit->getError();
float BinomialError = TMath::Sqrt(SGNtruePass/SGNtrue*(1-SGNtruePass/SGNtrue)/SGNtrue);
Double_t* truthMC = new Double_t[6];
Double_t* tnpMC = new Double_t[6];
Double_t* tnpData = new Double_t[6];
truthMC[0] = binCenter_;
truthMC[1] = binWidth_;
truthMC[2] = binWidth_;
truthMC[3] = SGNtruePass/SGNtrue;
truthMC[4] = BinomialError;
truthMC[5] = BinomialError;
if(makeSoupFit_){
tnpMC[0] = binCenter_;
tnpMC[1] = binWidth_;
tnpMC[2] = binWidth_;
tnpMC[3] = McEffFit->getVal();
tnpMC[4] = (-1)*McErrorLo;
tnpMC[5] = McErrorHi;
}
tnpData[0] = binCenter_;
tnpData[1] = binWidth_;
tnpData[2] = binWidth_;
tnpData[3] = DataEffFit->getVal();
tnpData[4] = (-1)*DataErrorLo;
tnpData[5] = DataErrorHi;
out.push_back(truthMC);
out.push_back(tnpData);
if(makeSoupFit_) out.push_back(tnpMC);
test->Close();
//delete c; delete c2;
if(verbose_) cout << "returning from bin " << bin_ << endl;
return out;
}
void rf103_interprfuncs()
{
/////////////////////////////////////////////////////////
// G e n e r i c i n t e r p r e t e d p . d . f . //
/////////////////////////////////////////////////////////
// Declare observable x
RooRealVar x("x","x",-20,20) ;
// C o n s t r u c t g e n e r i c p d f f r o m i n t e r p r e t e d e x p r e s s i o n
// -------------------------------------------------------------------------------------------------
// To construct a proper p.d.f, the formula expression is explicitly normalized internally by dividing
// it by a numeric integral of the expresssion over x in the range [-20,20]
//
RooRealVar alpha("alpha","alpha",5,0.1,10) ;
RooGenericPdf genpdf("genpdf","genpdf","(1+0.1*abs(x)+sin(sqrt(abs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
// S a m p l e , f i t a n d p l o t g e n e r i c p d f
// ---------------------------------------------------------------
// Generate a toy dataset from the interpreted p.d.f
RooDataSet* data = genpdf.generate(x,10000) ;
// Fit the interpreted p.d.f to the generated data
genpdf.fitTo(*data) ;
// Make a plot of the data and the p.d.f overlaid
RooPlot* xframe = x.frame(Title("Interpreted expression pdf")) ;
data->plotOn(xframe) ;
genpdf.plotOn(xframe) ;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// S t a n d a r d p . d . f a d j u s t w i t h i n t e r p r e t e d h e l p e r f u n c t i o n //
// //
// Make a gauss(x,sqrt(mean2),sigma) from a standard RooGaussian //
// //
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// C o n s t r u c t s t a n d a r d p d f w i t h f o r m u l a r e p l a c i n g p a r a m e t e r
// ------------------------------------------------------------------------------------------------------------
// Construct parameter mean2 and sigma
RooRealVar mean2("mean2","mean^2",10,0,200) ;
RooRealVar sigma("sigma","sigma",3,0.1,10) ;
// Construct interpreted function mean = sqrt(mean^2)
RooFormulaVar mean("mean","mean","sqrt(mean2)",mean2) ;
// Construct a gaussian g2(x,sqrt(mean2),sigma) ;
RooGaussian g2("g2","h2",x,mean,sigma) ;
// G e n e r a t e t o y d a t a
// ---------------------------------
// Construct a separate gaussian g1(x,10,3) to generate a toy Gaussian dataset with mean 10 and width 3
RooGaussian g1("g1","g1",x,RooConst(10),RooConst(3)) ;
RooDataSet* data2 = g1.generate(x,1000) ;
// F i t a n d p l o t t a i l o r e d s t a n d a r d p d f
// -------------------------------------------------------------------
// Fit g2 to data from g1
RooFitResult* r = g2.fitTo(*data2,Save()) ;
r->Print() ;
// Plot data on frame and overlay projection of g2
RooPlot* xframe2 = x.frame(Title("Tailored Gaussian pdf")) ;
data2->plotOn(xframe2) ;
g2.plotOn(xframe2) ;
// Draw all frames on a canvas
TCanvas* c = new TCanvas("rf103_interprfuncs","rf103_interprfuncs",800,400) ;
c->Divide(2) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.4) ; xframe2->Draw() ;
}
void Purity_1d_fit(int type = 0){
TChain* tree = new TChain("TEvent");
if(!type) tree->Add("/home/vitaly/B0toDh0/TMVA/FIL_b2dh_gen_0-1_full.root");
else tree->Add("/home/vitaly/B0toDh0/TMVA/FIL_b2dh_data.root");
RooCategory b0f("b0f","b0f");
b0f.defineType("signal",1);
b0f.defineType("fsr",10);
b0f.defineType("bad_pi0",5);
b0f.defineType("rho",3);
b0f.defineType("comb",-1);
RooArgSet argset;
const double deMin = -0.15;
const double deMax = 0.3;
RooRealVar mbc("mbc","M_{bc}",mbc_min,mbc_max,"GeV"); argset.add(mbc);
RooRealVar de("de","#DeltaE",deMin,deMax,"GeV"); argset.add(de);
de.setRange("Signal",de_min,de_max);
RooRealVar md("md","md",DMass-md_cut,DMass+md_cut,"GeV"); argset.add(md);
RooRealVar mk("mk","mk",KMass-mk_cut,KMass+mk_cut,"GeV"); argset.add(mk);
RooRealVar mpi0("mpi0","mpi0",Pi0Mass-mpi0_cut,Pi0Mass+mpi0_cut,"GeV"); argset.add(mpi0);
RooRealVar bdtgs("bdtgs","bdtgs",bdtgs_cut,1.); argset.add(bdtgs);
RooRealVar atckpi_max("atckpi_max","atckpi_max",0.,atckpi_cut); argset.add(atckpi_max);
if(!type) argset.add(b0f);
RooDataSet ds("ds","ds",tree,argset,"mbc>0||mbc<=0");
// RooDataSet* ds0 = ds.reduce(RooArgSet(de));
stringstream out;
if(!type){
out.str("");
out << "de<" << de_max << " && de>" << de_min;
Roo1DTable* sigtable = ds.table(b0f,out.str().c_str());
sigtable->Print();
sigtable->Print("v");
Roo1DTable* fulltable = ds.table(b0f);
fulltable->Print();
fulltable->Print("v");
}
// RooDataHist* dh = ds0->binnedClone();
// ds0->Print();
////////////////
// Signal PDF //
////////////////
////////////
// de pdf //
////////////
RooRealVar de0("de0","de0",m_de0,-0.1,0.1); if(cSig) de0.setConstant(kTRUE);
RooRealVar s1("s1","s1",m_s1,0.,0.5); if(cSig) s1.setConstant(kTRUE);
RooGaussian g1("g1","g1",de,de0,s1);
RooRealVar deCBl("deCBl","deCBl",m_deCBl,-0.1,0.1); if(cSig) deCBl.setConstant(kTRUE);
RooRealVar sCBl("sCBl","sCBl",m_sCBl,0.,0.5); if(cSig) sCBl.setConstant(kTRUE);
RooRealVar nl("nl","nl",m_nl,0.,100.); if(cSig) nl.setConstant(kTRUE);
RooRealVar alphal("alphal","alphal",m_alphal,-10.,10.); if(cSig) alphal.setConstant(kTRUE);
RooRealVar deCBr("deCBr","deCBr",m_deCBr,-0.1,0.1); if(cSig) deCBr.setConstant(kTRUE);
RooRealVar sCBr("sCBr","sCBr",m_sCBr,0.,0.5); if(cSig) sCBr.setConstant(kTRUE);
RooRealVar nr("nr","nr",m_nr,0.,100.); if(cSig) nr.setConstant(kTRUE);
RooRealVar alphar("alphar","alphar",m_alphar,-10.,10.); if(cSig) alphar.setConstant(kTRUE);
RooCBShape CBl("CBl","CBl",de,deCBl,sCBl,alphal,nl);
RooCBShape CBr("CBr","CBr",de,deCBr,sCBr,alphar,nr);
RooRealVar fCBl("fCBl","fCBl",m_fCBl,0.,1.); if(cSig) fCBl.setConstant(kTRUE);
RooRealVar fCBr("fCBr","fCBr",m_fCBr,0.,1.); if(cSig) fCBr.setConstant(kTRUE);
RooAddPdf pdf_sig("pdf_sig","pdf_sig",RooArgList(CBl,CBr,g1),RooArgSet(fCBl,fCBr));
//////////////
// Comb PDF //
//////////////
////////////
// de pdf //
////////////
RooRealVar c1("c1","c1",mc_c1_1d,-10.,10.); if(cComb) c1.setConstant(kTRUE);
RooRealVar c2("c2","c2",mc_c2_1d,-10.,10.); if(cComb) c2.setConstant(kTRUE);
RooChebychev pdf_comb("pdf_comb","pdf_comb",de,RooArgSet(c1,c2));
/////////////
// Rho PDF //
/////////////
////////////
// de pdf //
////////////
if(de_rho_param == 0){
RooRealVar exppar("exppar","exppar",mr_exppar,-40.,-25.);// if(cRho) exppar.setConstant(kTRUE);
RooExponential pdf_rho("pdf_rho","pdf_rho",de,exppar);
}
RooRealVar de0r("de0r","de0r",mr_de0r,-0.2,0.12); if(cRho) de0r.setConstant(kTRUE);
if(de_rho_param == 1){
RooRealVar slopel("slopel","slopel",mr_slopel,-1000,-500.); if(cRho) slopel.setConstant(kTRUE);
RooRealVar sloper("sloper","sloper",mr_sloper,-10000,0.); if(cRho) sloper.setConstant(kTRUE);
RooRealVar steep("steep","steep",mr_steep,7.,9.); if(cRho) steep.setConstant(kTRUE);
RooRealVar p5("p5","p5",mr_p5,0.01,1000.); if(cRho) p5.setConstant(kTRUE);
RooRhoDeltaEPdf pdf_rho("pdf_rho","pdf_rho",de,de0r,slopel,sloper,steep,p5);
}
if(de_rho_param == -1){
RooRealVar x0("x0","x0",mr_x0_1d,-0.2,0.12); if(cRho) x0.setConstant(kTRUE);
RooRealVar p1("p1","p1",mr_p1_1d,-1000.,100.); if(cRho) p1.setConstant(kTRUE);
RooRealVar p2("p2","p2",mr_p2_1d,0.,100.); if(cRho) p2.setConstant(kTRUE);
RooGenericPdf pdf_rho("pdf_rho","1+@0*@1-@2*TMath::Log(1+TMath::Exp(@2*(@0-@1)/@3))",RooArgSet(de,x0,p1,p2));
}
//////////////////
// Complete PDF //
//////////////////
RooRealVar Nsig("Nsig","Nsig",700,100.,1500.);// fsig.setConstant(kTRUE);
RooRealVar Nrho("Nrho","Nrho",400,100,1500.);// frho.setConstant(kTRUE);
RooRealVar Ncmb("Ncmb","Ncmb",1000,100,100000);// frho.setConstant(kTRUE);
RooAddPdf pdf("pdf","pdf",RooArgList(pdf_sig,pdf_rho,pdf_comb),RooArgList(Nsig,Nrho,Ncmb));
pdf.fitTo(ds,Verbose(),Timer(true));
RooAbsReal* intSig = pdf_sig.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
RooAbsReal* intRho = pdf_rho.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
RooAbsReal* intCmb = pdf_comb.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
const double nsig = intSig->getVal()*Nsig.getVal();
const double nsig_err = intSig->getVal()*Nsig.getError();
const double nsig_err_npq = TMath::Sqrt(nsig*(Nsig.getVal()-nsig)/Nsig.getVal());
const double nsig_err_total = TMath::Sqrt(nsig_err*nsig_err+nsig_err_npq*nsig_err_npq);
const double nrho = intRho->getVal()*Nrho.getVal();
const double nrho_err = intRho->getVal()*Nrho.getError();
const double nrho_err_npq = TMath::Sqrt(nrho*(Nrho.getVal()-nrho)/Nrho.getVal());
const double nrho_err_total = TMath::Sqrt(nrho_err*nrho_err+nrho_err_npq*nrho_err_npq);
const double ncmb = intCmb->getVal()*Ncmb.getVal();
const double ncmb_err = intCmb->getVal()*Ncmb.getError();
const double ncmb_err_npq = TMath::Sqrt(ncmb*(Ncmb.getVal()-ncmb)/Ncmb.getVal());
const double ncmb_err_total = TMath::Sqrt(ncmb_err*ncmb_err+ncmb_err_npq*ncmb_err_npq);
const double purity = nsig/(nsig+nrho+ncmb);
const double purity_err = nsig_err_total/(nsig+nrho+ncmb);
cout << "Nsig = " << nsig <<" +- " << nsig_err << endl;
cout << "Nrho = " << nrho <<" +- " << nrho_err << endl;
cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << endl;
/////////////
// Plots //
/////////////
// de //
RooPlot* deFrame = de.frame();
ds.plotOn(deFrame,DataError(RooAbsData::SumW2),MarkerSize(1));
pdf.plotOn(deFrame,Components(pdf_sig),LineStyle(kDashed));
pdf.plotOn(deFrame,Components(pdf_rho),LineStyle(kDashed));
pdf.plotOn(deFrame,Components(pdf_comb),LineStyle(kDashed));
pdf.plotOn(deFrame,LineWidth(2));
RooHist* hdepull = deFrame->pullHist();
RooPlot* dePull = de.frame(Title("#Delta E pull distribution"));
dePull->addPlotable(hdepull,"P");
dePull->GetYaxis()->SetRangeUser(-5,5);
TCanvas* cm = new TCanvas("Delta E","Delta E",600,700);
cm->cd();
TPad *pad3 = new TPad("pad3","pad3",0.01,0.20,0.99,0.99);
TPad *pad4 = new TPad("pad4","pad4",0.01,0.01,0.99,0.20);
pad3->Draw();
pad4->Draw();
pad3->cd();
pad3->SetLeftMargin(0.15);
pad3->SetFillColor(0);
deFrame->GetXaxis()->SetTitleSize(0.05);
deFrame->GetXaxis()->SetTitleOffset(0.85);
deFrame->GetXaxis()->SetLabelSize(0.04);
deFrame->GetYaxis()->SetTitleOffset(1.6);
deFrame->Draw();
stringstream out1;
TPaveText *pt = new TPaveText(0.6,0.75,0.98,0.9,"brNDC");
pt->SetFillColor(0);
pt->SetTextAlign(12);
out1.str("");
out1 << "#chi^{2}/n.d.f = " << deFrame->chiSquare();
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "S: " << (int)(nsig+0.5) << " #pm " << (int)(nsig_err_total+0.5);
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "Purity: " << std::fixed << std::setprecision(2) << purity*100. << " #pm " << purity_err*100;
pt->AddText(out1.str().c_str());
pt->Draw();
TLine *de_line_RIGHT = new TLine(de_max,0,de_max,50);
de_line_RIGHT->SetLineColor(kRed);
de_line_RIGHT->SetLineStyle(1);
de_line_RIGHT->SetLineWidth((Width_t)2.);
de_line_RIGHT->Draw();
TLine *de_line_LEFT = new TLine(de_min,0,de_min,50);
de_line_LEFT->SetLineColor(kRed);
de_line_LEFT->SetLineStyle(1);
de_line_LEFT->SetLineWidth((Width_t)2.);
de_line_LEFT->Draw();
pad4->cd(); pad4->SetLeftMargin(0.15); pad4->SetFillColor(0);
dePull->SetMarkerSize(0.05); dePull->Draw();
TLine *de_lineUP = new TLine(deMin,3,deMax,3);
de_lineUP->SetLineColor(kBlue);
de_lineUP->SetLineStyle(2);
de_lineUP->Draw();
TLine *de_line = new TLine(deMin,0,deMax,0);
de_line->SetLineColor(kBlue);
de_line->SetLineStyle(1);
de_line->SetLineWidth((Width_t)2.);
de_line->Draw();
TLine *de_lineDOWN = new TLine(deMin,-3,deMax,-3);
de_lineDOWN->SetLineColor(kBlue);
de_lineDOWN->SetLineStyle(2);
de_lineDOWN->Draw();
cm->Update();
if(!type){
out.str("");
out << "de<" << de_max << " && de>" << de_min;
Roo1DTable* sigtable = ds.table(b0f,out.str().c_str());
sigtable->Print();
sigtable->Print("v");
Roo1DTable* fulltable = ds.table(b0f);
fulltable->Print();
fulltable->Print("v");
}
cout << "Nsig = " << nsig <<" +- " << nsig_err << " +- " << nsig_err_npq << " (" << nsig_err_total << ")" << endl;
cout << "Nrho = " << nrho <<" +- " << nrho_err << " +- " << nrho_err_npq << " (" << nrho_err_total << ")" << endl;
cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << " +- " << ncmb_err_npq << " (" << ncmb_err_total << ")" << endl;
cout << "Pury = " << purity << " +- " << purity_err << endl;
}
TCanvas* comparePlots2(RooPlot *plot_bC, RooPlot *plot_bS, TH1F *data, TH1F *qcd, std::string title)
{
RooRealVar x("x", "m_{X} (GeV)", SR_lo, SR_hi);
TCanvas *c=new TCanvas(("c_RooFit_"+title).c_str(), "c", 700, 700);
TPad *p_1=new TPad("p_1", "p_1", 0, 0.35, 1, 1);
gStyle->SetPadGridX(0);
gStyle->SetPadGridY(0);
gROOT->SetStyle("Plain");
p_1->SetFrameFillColor(0);
TPad *p_2 = new TPad("p_2", "p_2",0,0.003740648,0.9975278,0.3391022);
p_2->Range(160.1237,-0.8717948,1008.284,2.051282);
p_2->SetFillColor(0);
p_2->SetBorderMode(0);
p_2->SetBorderSize(2);
p_2->SetTopMargin(0.02);
p_2->SetBottomMargin(0.3);
p_2->SetFrameBorderMode(0);
p_2->SetFrameBorderMode(0);
p_1->Draw();
p_2->Draw();
p_1->cd();
double maxdata=data->GetMaximum();
double maxqcd=qcd->GetMaximum();
double maxy=(maxdata>maxqcd) ? maxdata : maxqcd;
title=";m_{X} (GeV); Events / "+itoa(data->GetBinWidth(1))+" GeV";
p_1->DrawFrame(SR_lo, 0, SR_hi, maxy*1., title.c_str());
plot_bS->SetMarkerStyle(20);
plot_bS->Draw("same");
// plot_bS->Draw("same");
CMS_lumi( p_1, iPeriod, iPos );
p_2->cd();
/* TH1F *h_ratio=(TH1F*)data->Clone("h_ratio");
h_ratio->GetYaxis()->SetTitle("VR/VSB Ratio");
h_ratio->GetXaxis()->SetTitle("m_{X} (GeV)");
h_ratio->SetTitle("");//("VR/VR-SB Ratio "+title+" ; VR/VR-SB Ratio").c_str());
h_ratio->GetYaxis()->SetTitleSize(0.07);
h_ratio->GetYaxis()->SetTitleOffset(0.5);
h_ratio->GetXaxis()->SetTitleSize(0.09);
h_ratio->GetXaxis()->SetTitleOffset(1.0);
h_ratio->GetXaxis()->SetLabelSize(0.07);
h_ratio->GetYaxis()->SetLabelSize(0.06);
h_ratio->Divide(qcd);
h_ratio->SetLineColor(1);
h_ratio->SetMarkerStyle(20);
h_ratio->GetXaxis()->SetRangeUser(SR_lo, SR_hi-10);
h_ratio->GetYaxis()->SetRangeUser(0.,2.);
*/
RooHist* hpull;
hpull = plot_bS->pullHist();
hpull->GetXaxis()->SetRangeUser(SR_lo, SR_hi);
RooPlot* frameP = x.frame() ;
frameP->SetTitle("");
frameP->GetYaxis()->SetTitle("Pull");
frameP->GetXaxis()->SetRangeUser(SR_lo, SR_hi);
frameP->addPlotable(hpull,"P");
frameP->GetYaxis()->SetTitle("Pull");
frameP->GetYaxis()->SetTitleSize(0.07);
frameP->GetYaxis()->SetTitleOffset(0.5);
frameP->GetXaxis()->SetTitleSize(0.09);
frameP->GetXaxis()->SetTitleOffset(1.0);
frameP->GetXaxis()->SetLabelSize(0.07);
frameP->GetYaxis()->SetLabelSize(0.06);
frameP->Draw();
// TLine *m_one_line = new TLine(SR_lo,1,SR_hi,1);
// h_ratio->Draw("");
// m_one_line->Draw("same");
p_1->cd();
return c;
}
int FitInvMassBkg(TH1D* histo, TH1D* histo_bkg, TString signal,TString _bkg){
//Set Style
setTDRStyle();
cout<<"21"<<endl;
//Path for input and output file. Written in FitDataPath.txt
ifstream file("FitDataPath.txt");
string str;
getline(file,str);
TString _path = str;
//Rebin(histo);
//Getting info about the histogram to fit
int n = histo->GetEntries();
double w = histo->GetXaxis()->GetBinWidth(1);
int ndf;
double hmin0 = histo->GetXaxis()->GetXmin();
double hmax0 = histo->GetXaxis()->GetXmax();
histo->GetXaxis()->SetRangeUser(hmin0,hmax0);
//Declare observable x
//Try to rebin using this. Doesn't work for now
//RooBinning xbins = Rebin2(histo);
RooRealVar x("x","x",hmin0,hmax0) ;
RooDataHist dh("dh","dh",x,Import(*histo)) ;
//Define the frame
RooPlot* frame;
frame = x.frame();
dh.plotOn(frame,DataError(RooAbsData::SumW2), MarkerColor(1),MarkerSize(0.9),MarkerStyle(7)); //this will show histogram data points on canvas
//x.setRange("R0",0,200) ;
x.setRange("R1",55,200) ;
/////////////////////
//Define fit function
/////////////////////
//fsig for adding two funciton i.e. F(x) = fsig*sig(x) + (1-fsig)*bkg(x)
RooRealVar fsig("fsig","sigal fraction",0.5, 0., 1.);//before 0.9
//Various parameters
//True mean
RooRealVar mean("mean","PDG mean of Z",91.186);//, 70.0, 120.0);
//For the BW
RooRealVar width("width","PDG width of Z",2.4952);//, 0., 5.);
//For the Gauss and the CB alone
RooRealVar sigma("sigma","sigma",1, 0., 10.);
RooRealVar alpha("alpha","alpha",0.7, 0., 7);
RooRealVar ncb("ncb","ncb",7, 0, 150);
//For the CB used for convolution, i.e. CBxBW
RooRealVar cb_bias("cb_bias","bias",0, -3.,3.);
RooRealVar cb_sigma("cb_sigma","response",1, 0.,5);
RooRealVar cb_alpha("cb_alpha","alpha",1.,0.,7);
RooRealVar cb_ncb("cb_ncb","ncb",2, 0, 10);
mean.setRange(88,94);
width.setRange(0,20);
sigma.setRange(0.5,10);
//fsig.setConstant(kTRUE);
//alpha.setConstant(kTRUE);
RooVoigtian sig_bwgau("sig_bwgau","BWxgauss",x,mean,width,sigma);
RooBreitWigner sig_bw("sig_bw","BW",x,mean,width);
RooGaussian sig_gau("sig_gau","gauss",x,mean,sigma);
RooCBShape sig_cb("sig_cb", "Crystal Ball",x,mean,sigma,alpha,ncb);
RooCBShape sig_cb_resp("sig_cb_resp", "Crystal Ball for conv.",x,cb_bias,cb_sigma,cb_alpha,cb_ncb);
x.setBins(10000,"cache");
RooFFTConvPdf sig_cbbw("sig_cbbw","CBxBW",x,sig_cb_resp,sig_bw);
//NB: The CrystalBall shape is Gaussian that is 'connected' to an exponential taill at 'alpha' sigma of the Gaussian. The sign determines if it happens on the left or right side. The 'n' parameter control the slope of the exponential part.
RooAbsPdf* sig;
if(signal == "BWxGau"){sig = &sig_bwgau;}
else if(signal == "BW"){sig = &sig_bw;}
else if(signal == "Gau"){sig = &sig_gau;}
else if(signal == "CB"){sig = &sig_cb;}
else if(signal == "CBxBW"){ sig = &sig_cbbw;}
else{ cout<<"Wrong signal function name"<<endl;
return 1;
}
/////////////////////////////
//Background fitting function
/////////////////////////////
//Get the initial parameter of the background
//
vector<double> vec = FitBkg(histo_bkg,_bkg);
//Chebychev
RooRealVar a0("a0","a0",vec[0],-5.,0.) ;
RooRealVar a1("a1","a1",vec[1],-2,1.2) ;
RooRealVar a2("a2","a2",vec[2],-1.,1.) ;
RooRealVar a3("a3","a3",vec[3],-2.5,0.) ;
RooRealVar a4("a4","a4",vec[4],-1.,1.) ;
RooRealVar a5("a5","a5",vec[5],-1.,1.) ;
RooRealVar a6("a6","a6",vec[6],-1.,1.) ;
RooChebychev bkg_cheb("bkg","Background",x,RooArgSet(a0,a1,a2,a3,a4,a5,a6));
//Novo
RooRealVar peak_bkg("peak_bkg","peak",vec[0],0,250);
RooRealVar width_bkg("width_bkg","width",vec[1],0,1) ;
RooRealVar tail_bkg("tail_bkg","tail",vec[2],0,10) ;
RooNovosibirsk bkg_nov("bkg","Background",x,peak_bkg,width_bkg,tail_bkg);
RooAbsPdf* bkg;
if(_bkg == "Cheb"){bkg = &bkg_cheb;}
if(_bkg == "Novo"){bkg = &bkg_nov;}
//////////////////////////
//Adding the two functions
//////////////////////////
RooAddPdf model("model","Signal+Background", RooArgList(*sig,*bkg),fsig);
//Perform the fit
RooAbsPdf* fit_func;
fit_func = &model;
RooFitResult* filters = fit_func->fitTo(dh,Range("R1"),"qr");
fit_func->plotOn(frame);
fit_func->plotOn(frame,Components(*sig),LineStyle(kDashed),LineColor(kRed));
fit_func->plotOn(frame,Components(*bkg),LineStyle(kDashed),LineColor(kGreen));
frame->Draw();
fit_func->paramOn(frame);
dh.statOn(frame); //this will display hist stat on canvas
frame->SetTitle(histo->GetTitle());
frame->GetXaxis()->SetTitle("m (in GeV/c^{2})");
frame->GetXaxis()->SetTitleOffset(1.2);
float binsize = histo->GetBinWidth(1);
//Store result in .root file
frame->SetName(histo->GetName());
frame->Write();
return 0;
}
void fitGraphs( const ZGConfig& cfg, const std::vector<float> masses, const std::string& width, const std::string& outdir, TFile* outfile, const std::string& name, const std::string& sel ) {
std::cout << "+++ STARTING: " << name << std::endl;
TString name_tstr(name);
std::string plotdir = outdir + "/plots";
system( Form("mkdir -p %s", plotdir.c_str() ) );
TGraphErrors* gr_mean = new TGraphErrors(0);
TGraphErrors* gr_sigma = new TGraphErrors(0);
TGraphErrors* gr_width = new TGraphErrors(0);
TGraphErrors* gr_alpha1 = new TGraphErrors(0);
TGraphErrors* gr_n1 = new TGraphErrors(0);
TGraphErrors* gr_alpha2 = new TGraphErrors(0);
TGraphErrors* gr_n2 = new TGraphErrors(0);
gr_mean ->SetName(Form("mean_w%s_%s" , width.c_str(), name.c_str()));
gr_sigma ->SetName(Form("sigma_w%s_%s" , width.c_str(), name.c_str()));
gr_width ->SetName(Form("width_w%s_%s" , width.c_str(), name.c_str()));
gr_alpha1->SetName(Form("alpha1_w%s_%s", width.c_str(), name.c_str()));
gr_n1 ->SetName(Form("n1_w%s_%s" , width.c_str(), name.c_str()));
gr_alpha2->SetName(Form("alpha2_w%s_%s", width.c_str(), name.c_str()));
gr_n2 ->SetName(Form("n2_w%s_%s" , width.c_str(), name.c_str()));
TFile* file = TFile::Open( Form("%s/trees.root", cfg.getEventYieldDir().c_str()) );
outfile->cd();
int iPoint = 0;
for( unsigned i=0; i<masses.size(); ++i ) {
float thisMass = masses[i];
if( thisMass==1250. && name=="mm" && width=="0p014" ) continue;
std::cout << "-> Starting mass: " << thisMass << std::endl;
std::string signalName;
if( thisMass <= 500. ) {
signalName = std::string(Form("XZg_Spin0ToZG_ZToLL_W_%s_M_%.0f", width.c_str(), thisMass ));
} else {
signalName = std::string(Form("GluGluSpin0ToZG_ZToLL_W%s_M%.0f", width.c_str(), thisMass ));
}
TTree* tree0 = (TTree*)file->Get( signalName.c_str() );
if( tree0 == 0 ) continue;
TTree* tree;
if( sel=="" ) {
tree = tree0;
} else {
tree = tree0->CopyTree(sel.c_str());
}
float maxMassFact = (thisMass>1000. || width=="5p6" ) ? 1.5 : 1.3;
RooRealVar x("boss_mass", "boss_mass", thisMass, 0.5*thisMass, maxMassFact*thisMass );
// Crystal-Ball
RooRealVar mean( "mean", "mean", thisMass, 0.9*thisMass, 1.1*thisMass );
float sigmaResolution = ( width=="5p6" ) ? 0.03 : 0.015;
RooRealVar sigma( "sigma", "sigma", sigmaResolution*thisMass, 0., 0.07*thisMass );
float alpha1_init = (width=="5p6" && thisMass<1000. ) ? 0.8 : 1.2;
if( width=="5p6" && thisMass==1500.) alpha1_init=1.4;
RooRealVar alpha1( "alpha1", "alpha1", alpha1_init, 0., 2.5 );
RooRealVar n1( "n1", "n1", 3., 0., 5. );
RooRealVar alpha2( "alpha2", "alpha2", 1.2, 0., 2.5 );
RooRealVar n2( "n2", "n2", 3.5, 0., 10. );
RooDoubleCBShape cb( "cb", "cb", x, mean, sigma, alpha1, n1, alpha2, n2 );
RooDataSet* data = new RooDataSet( "data", "data", RooArgSet(x), RooFit::Import(*tree) );
cb.fitTo( *data, RooFit::Strategy(2) );
RooPlot* frame = x.frame();
data->plotOn(frame);
cb.plotOn(frame);
TCanvas* c1 = new TCanvas("c1", "", 600, 600);
c1->cd();
frame->Draw();
c1->SaveAs( Form("%s/fit_%s_m%.0f_w%s.eps", plotdir.c_str(), name.c_str(), thisMass, width.c_str()) );
c1->SaveAs( Form("%s/fit_%s_m%.0f_w%s.pdf", plotdir.c_str(), name.c_str(), thisMass, width.c_str()) );
c1->SetLogy();
c1->SaveAs( Form("%s/fit_%s_m%.0f_w%s_log.eps", plotdir.c_str(), name.c_str(), thisMass, width.c_str()) );
c1->SaveAs( Form("%s/fit_%s_m%.0f_w%s_log.pdf", plotdir.c_str(), name.c_str(), thisMass, width.c_str()) );
delete c1;
gr_mean ->SetPoint( iPoint, thisMass, mean.getVal() );
gr_sigma ->SetPoint( iPoint, thisMass, sigma.getVal() );
gr_width ->SetPoint( iPoint, thisMass, sigma.getVal()/mean.getVal() );
gr_alpha1->SetPoint( iPoint, thisMass, alpha1.getVal() );
gr_n1 ->SetPoint( iPoint, thisMass, n1.getVal() );
gr_alpha2->SetPoint( iPoint, thisMass, alpha2.getVal() );
gr_n2 ->SetPoint( iPoint, thisMass, n2.getVal() );
gr_mean ->SetPointError( iPoint, 0., mean.getError() );
gr_sigma ->SetPointError( iPoint, 0., sigma.getError() );
gr_width ->SetPointError( iPoint, 0., sigma.getError()/mean.getVal() ); // random
gr_alpha1->SetPointError( iPoint, 0., alpha1.getError() );
gr_n1 ->SetPointError( iPoint, 0., n1.getError() );
gr_alpha2->SetPointError( iPoint, 0., alpha2.getError() );
gr_n2 ->SetPointError( iPoint, 0., n2.getError() );
//gr_width->SetPointError( iPoint, 0., sqrt( sigma.getError()*sigma.getError()/(mean.getVal()*mean.getVal()) + sigma.getVal()*sigma.getVal()*mean.getError()*mean.getError()/(mean.getError()*mean.getError()*mean.getError()*mean.getError()) ) );
iPoint++;
delete tree;
delete data;
} // for i
TF1* f1_mean = fitGraph(plotdir, gr_mean , "Gaussian Mean [GeV]");
TF1* f1_sigma = fitGraph(plotdir, gr_sigma , "Gaussian Sigma [GeV]");
TF1* f1_width = fitGraph(plotdir, gr_width , "Gaussian #sigma/#mu");
TF1* f1_alpha1 = fitGraph(plotdir, gr_alpha1, "CB left #alpha");
TF1* f1_n1 = fitGraph(plotdir, gr_n1 , "CB left N");
TF1* f1_alpha2 = fitGraph(plotdir, gr_alpha2, "CB right #alpha");
TF1* f1_n2 = fitGraph(plotdir, gr_n2 , "CB right N");
f1_mean ->Write();
f1_sigma ->Write();
f1_width ->Write();
f1_alpha1->Write();
f1_n1 ->Write();
f1_alpha2->Write();
f1_n2 ->Write();
gr_mean ->Write();
gr_sigma ->Write();
gr_width ->Write();
gr_alpha1->Write();
gr_n1 ->Write();
gr_alpha2->Write();
gr_n2 ->Write();
}
void BackgroundPrediction(std::string pname,int rebin_factor,int model_number = 0,int imass=750, bool plotBands = false)
{
rebin = rebin_factor;
std::string fname = std::string("../fitFilesMETPT34/") + pname + std::string("/histos_bkg.root");
stringstream iimass ;
iimass << imass;
std::string dirName = "info_"+iimass.str()+"_"+pname;
gStyle->SetOptStat(000000000);
gStyle->SetPadGridX(0);
gStyle->SetPadGridY(0);
setTDRStyle();
gStyle->SetPadGridX(0);
gStyle->SetPadGridY(0);
gStyle->SetOptStat(0000);
writeExtraText = true; // if extra text
extraText = "Preliminary"; // default extra text is "Preliminary"
lumi_13TeV = "2.7 fb^{-1}"; // default is "19.7 fb^{-1}"
lumi_7TeV = "4.9 fb^{-1}"; // default is "5.1 fb^{-1}"
double ratio_tau=-1;
TFile *f=new TFile(fname.c_str());
TH1F *h_mX_CR_tau=(TH1F*)f->Get("distribs_18_10_1")->Clone("CR_tau");
TH1F *h_mX_SR=(TH1F*)f->Get("distribs_18_10_0")->Clone("The_SR");
double maxdata = h_mX_SR->GetMaximum();
double nEventsSR = h_mX_SR->Integral(600,4000);
ratio_tau=(h_mX_SR->GetSumOfWeights()/(h_mX_CR_tau->GetSumOfWeights()));
//double nEventsSR = h_mX_SR->Integral(600,4000);
std::cout<<"ratio tau "<<ratio_tau<<std::endl;
TH1F *h_SR_Prediction;
TH1F *h_SR_Prediction2;
if(blind) {
h_SR_Prediction2 = (TH1F*)h_mX_CR_tau->Clone("h_SR_Prediction2");
h_mX_CR_tau->Rebin(rebin);
h_mX_CR_tau->SetLineColor(kBlack);
h_SR_Prediction=(TH1F*)h_mX_CR_tau->Clone("h_SR_Prediction");
} else {
h_SR_Prediction2=(TH1F*)h_mX_SR->Clone("h_SR_Prediction2");
h_mX_SR->Rebin(rebin);
h_mX_SR->SetLineColor(kBlack);
h_SR_Prediction=(TH1F*)h_mX_SR->Clone("h_SR_Prediction");
}
h_SR_Prediction->SetMarkerSize(0.7);
h_SR_Prediction->GetYaxis()->SetTitleOffset(1.2);
h_SR_Prediction->Sumw2();
/*TFile *f_sig = new TFile((dirName+"/w_signal_"+iimass.str()+".root").c_str());
RooWorkspace* xf_sig = (RooWorkspace*)f_sig->Get("Vg");
RooAbsPdf *xf_sig_pdf = (RooAbsPdf *)xf_sig->pdf((std::string("signal_fixed_")+pname).c_str());
RooWorkspace w_sig("w");
w_sig.import(*xf_sig_pdf,RooFit::RenameVariable((std::string("signal_fixed_")+pname).c_str(),(std::string("signal_fixed_")+pname+std::string("low")).c_str()),RooFit::RenameAllVariablesExcept("low","x"));
xf_sig_pdf = w_sig.pdf((std::string("signal_fixed_")+pname+std::string("low")).c_str());
RooArgSet* biasVars = xf_sig_pdf->getVariables();
TIterator *it = biasVars->createIterator();
RooRealVar* var = (RooRealVar*)it->Next();
while (var) {
var->setConstant(kTRUE);
var = (RooRealVar*)it->Next();
}
*/
RooRealVar x("x", "m_{X} (GeV)", SR_lo, SR_hi);
RooRealVar nBackground((std::string("bg_")+pname+std::string("_norm")).c_str(),"nbkg",h_mX_SR->GetSumOfWeights());
RooRealVar nBackground2((std::string("alt_bg_")+pname+std::string("_norm")).c_str(),"nbkg",h_mX_SR->GetSumOfWeights());
std::string blah = pname;
//pname=""; //Antibtag=tag to constrain b-tag to the anti-btag shape
/* RooRealVar bg_p0((std::string("bg_p0_")+pname).c_str(), "bg_p0", 4.2, 0, 200.);
RooRealVar bg_p1((std::string("bg_p1_")+pname).c_str(), "bg_p1", 4.5, 0, 300.);
RooRealVar bg_p2((std::string("bg_p2_")+pname).c_str(), "bg_p2", 0.000047, 0, 10.1);
RooGenericPdf bg_pure = RooGenericPdf((std::string("bg_pure_")+blah).c_str(),"(pow(1-@0/13000,@1)/pow(@0/13000,@2+@3*log(@0/13000)))",RooArgList(x,bg_p0,bg_p1,bg_p2));
*/
RooRealVar bg_p0((std::string("bg_p0_")+pname).c_str(), "bg_p0", 0., -1000, 200.);
RooRealVar bg_p1((std::string("bg_p1_")+pname).c_str(), "bg_p1", -13, -1000, 1000.);
RooRealVar bg_p2((std::string("bg_p2_")+pname).c_str(), "bg_p2", -1.4, -1000, 1000.);
bg_p0.setConstant(kTRUE);
//RooGenericPdf bg_pure = RooGenericPdf((std::string("bg_pure_")+blah).c_str(),"(pow(@0/13000,@1+@2*log(@0/13000)))",RooArgList(x,bg_p1,bg_p2));
RooGenericPdf bg = RooGenericPdf((std::string("bg_")+blah).c_str(),"(pow(@0/13000,@1+@2*log(@0/13000)))",RooArgList(x,bg_p1,bg_p2));
/*TF1* biasFunc = new TF1("biasFunc","(0.63*x/1000-1.45)",1350,3600);
TF1* biasFunc2 = new TF1("biasFunc2","TMath::Min(2.,2.3*x/1000-3.8)",1350,3600);
double bias_term_s = 0;
if ((imass > 2450 && blah == "antibtag") || (imass > 1640 && blah == "btag")) {
if (blah == "antibtag") {
bias_term_s = 2.7*biasFunc->Eval(imass);
} else {
bias_term_s = 2.7*biasFunc2->Eval(imass);
}
bias_term_s/=nEventsSR;
}
RooRealVar bias_term((std::string("bias_term_")+blah).c_str(), "bias_term", 0., -bias_term_s, bias_term_s);
//bias_term.setConstant(kTRUE);
RooAddPdf bg((std::string("bg_")+blah).c_str(), "bg_all", RooArgList(*xf_sig_pdf, bg_pure), bias_term);
*/
string name_output = "CR_RooFit_Exp";
std::cout<<"Nevents "<<nEventsSR<<std::endl;
RooDataHist pred("pred", "Prediction from SB", RooArgList(x), h_SR_Prediction);
RooFitResult *r_bg=bg.fitTo(pred, RooFit::Minimizer("Minuit2"), RooFit::Range(SR_lo, SR_hi), RooFit::SumW2Error(kTRUE), RooFit::Save());
//RooFitResult *r_bg=bg.fitTo(pred, RooFit::Range(SR_lo, SR_hi), RooFit::Save());
//RooFitResult *r_bg=bg.fitTo(pred, RooFit::Range(SR_lo, SR_hi), RooFit::Save(),RooFit::SumW2Error(kTRUE));
std::cout<<" --------------------- Building Envelope --------------------- "<<std::endl;
//std::cout<< "bg_p0_"<< pname << " param "<<bg_p0.getVal() << " "<<bg_p0.getError()<<std::endl;
std::cout<< "bg_p1_"<< pname << " param "<<bg_p1.getVal() << " "<<100*bg_p1.getError()<<std::endl;
std::cout<< "bg_p2_"<< pname << " param "<<bg_p2.getVal() << " "<<100*bg_p2.getError()<<std::endl;
//std::cout<< "bias_term_"<< blah << " param 0 "<<bias_term_s<<std::endl;
RooPlot *aC_plot=x.frame();
pred.plotOn(aC_plot, RooFit::MarkerColor(kPink+2));
if (!plotBands) {
bg.plotOn(aC_plot, RooFit::VisualizeError(*r_bg, 2), RooFit::FillColor(kYellow));
bg.plotOn(aC_plot, RooFit::VisualizeError(*r_bg, 1), RooFit::FillColor(kGreen));
}
bg.plotOn(aC_plot, RooFit::LineColor(kBlue));
//pred.plotOn(aC_plot, RooFit::LineColor(kBlack), RooFit::MarkerColor(kBlack));
TGraph* error_curve[5]; //correct error bands
TGraphAsymmErrors* dataGr = new TGraphAsymmErrors(h_SR_Prediction->GetNbinsX()); //data w/o 0 entries
for (int i=2; i!=5; ++i) {
error_curve[i] = new TGraph();
}
error_curve[2] = (TGraph*)aC_plot->getObject(1)->Clone("errs");
int nPoints = error_curve[2]->GetN();
error_curve[0] = new TGraph(2*nPoints);
error_curve[1] = new TGraph(2*nPoints);
error_curve[0]->SetFillStyle(1001);
error_curve[1]->SetFillStyle(1001);
error_curve[0]->SetFillColor(kGreen);
error_curve[1]->SetFillColor(kYellow);
error_curve[0]->SetLineColor(kGreen);
error_curve[1]->SetLineColor(kYellow);
if (plotBands) {
RooDataHist pred2("pred2", "Prediction from SB", RooArgList(x), h_SR_Prediction2);
error_curve[3]->SetFillStyle(1001);
error_curve[4]->SetFillStyle(1001);
error_curve[3]->SetFillColor(kGreen);
error_curve[4]->SetFillColor(kYellow);
error_curve[3]->SetLineColor(kGreen);
error_curve[4]->SetLineColor(kYellow);
error_curve[2]->SetLineColor(kBlue);
error_curve[2]->SetLineWidth(3);
double binSize = rebin;
for (int i=0; i!=nPoints; ++i) {
double x0,y0, x1,y1;
error_curve[2]->GetPoint(i,x0,y0);
RooAbsReal* nlim = new RooRealVar("nlim","y0",y0,-100000,100000);
//double lowedge = x0 - (SR_hi - SR_lo)/double(2*nPoints);
//double upedge = x0 + (SR_hi - SR_lo)/double(2*nPoints);
double lowedge = x0 - binSize/2.;
double upedge = x0 + binSize/2.;
x.setRange("errRange",lowedge,upedge);
RooExtendPdf* epdf = new RooExtendPdf("epdf","extpdf",bg, *nlim,"errRange");
// Construct unbinned likelihood
RooAbsReal* nll = epdf->createNLL(pred2,NumCPU(2));
// Minimize likelihood w.r.t all parameters before making plots
RooMinimizer* minim = new RooMinimizer(*nll);
minim->setMinimizerType("Minuit2");
minim->setStrategy(2);
minim->setPrintLevel(-1);
minim->migrad();
minim->hesse();
RooFitResult* result = minim->lastMinuitFit();
double errm = nlim->getPropagatedError(*result);
//std::cout<<x0<<" "<<lowedge<<" "<<upedge<<" "<<y0<<" "<<nlim->getVal()<<" "<<errm<<std::endl;
error_curve[0]->SetPoint(i,x0,(y0-errm));
error_curve[0]->SetPoint(2*nPoints-i-1,x0,y0+errm);
error_curve[1]->SetPoint(i,x0,(y0-2*errm));
error_curve[1]->SetPoint(2*nPoints-i-1,x0,(y0+2*errm));
error_curve[3]->SetPoint(i,x0,-errm/sqrt(y0));
error_curve[3]->SetPoint(2*nPoints-i-1,x0,errm/sqrt(y0));
error_curve[4]->SetPoint(i,x0,-2*errm/sqrt(y0));
error_curve[4]->SetPoint(2*nPoints-i-1,x0,2*errm/sqrt(y0));
}
int npois = 0;
dataGr->SetMarkerSize(1.0);
dataGr->SetMarkerStyle (20);
const double alpha = 1 - 0.6827;
for (int i=0; i!=h_SR_Prediction->GetNbinsX(); ++i){
if (h_SR_Prediction->GetBinContent(i+1) > 0) {
int N = h_SR_Prediction->GetBinContent(i+1);
double L = (N==0) ? 0 : (ROOT::Math::gamma_quantile(alpha/2,N,1.));
double U = ROOT::Math::gamma_quantile_c(alpha/2,N+1,1) ;
dataGr->SetPoint(npois,h_SR_Prediction->GetBinCenter(i+1),h_SR_Prediction->GetBinContent(i+1));
dataGr->SetPointEYlow(npois, N-L);
dataGr->SetPointEYhigh(npois, U-N);
npois++;
}
}
}
double xG[2] = {-10,4000};
double yG[2] = {0.0,0.0};
TGraph* unityG = new TGraph(2, xG, yG);
unityG->SetLineColor(kBlue);
unityG->SetLineWidth(1);
double xPad = 0.3;
TCanvas *c_rooFit=new TCanvas("c_rooFit", "c_rooFit", 800*(1.-xPad), 600);
c_rooFit->SetFillStyle(4000);
c_rooFit->SetFrameFillColor(0);
TPad *p_1=new TPad("p_1", "p_1", 0, xPad, 1, 1);
p_1->SetFillStyle(4000);
p_1->SetFrameFillColor(0);
p_1->SetBottomMargin(0.02);
TPad* p_2 = new TPad("p_2", "p_2",0,0,1,xPad);
p_2->SetBottomMargin((1.-xPad)/xPad*0.13);
p_2->SetTopMargin(0.03);
p_2->SetFillColor(0);
p_2->SetBorderMode(0);
p_2->SetBorderSize(2);
p_2->SetFrameBorderMode(0);
p_2->SetFrameBorderMode(0);
p_1->Draw();
p_2->Draw();
p_1->cd();
int nbins = (int) (SR_hi- SR_lo)/rebin;
x.setBins(nbins);
std::cout << "chi2(data) " << aC_plot->chiSquare()<<std::endl;
//std::cout << "p-value: data under hypothesis H0: " << TMath::Prob(chi2_data->getVal(), nbins - 1) << std::endl;
aC_plot->GetXaxis()->SetRangeUser(SR_lo, SR_hi);
aC_plot->GetXaxis()->SetLabelOffset(0.02);
aC_plot->GetYaxis()->SetRangeUser(0.1, 1000.);
h_SR_Prediction->GetXaxis()->SetRangeUser(SR_lo, SR_hi);
string rebin_ = itoa(rebin);
aC_plot->GetXaxis()->SetTitle("M_{Z#gamma} [GeV] ");
aC_plot->GetYaxis()->SetTitle(("Events / "+rebin_+" GeV ").c_str());
aC_plot->SetMarkerSize(0.7);
aC_plot->GetYaxis()->SetTitleOffset(1.2);
aC_plot->Draw();
if (plotBands) {
error_curve[1]->Draw("Fsame");
error_curve[0]->Draw("Fsame");
error_curve[2]->Draw("Lsame");
dataGr->Draw("p e1 same");
}
aC_plot->SetTitle("");
TPaveText *pave = new TPaveText(0.85,0.4,0.67,0.5,"NDC");
pave->SetBorderSize(0);
pave->SetTextSize(0.05);
pave->SetTextFont(42);
pave->SetLineColor(1);
pave->SetLineStyle(1);
pave->SetLineWidth(2);
pave->SetFillColor(0);
pave->SetFillStyle(0);
char name[1000];
sprintf(name,"#chi^{2}/n = %.2f",aC_plot->chiSquare());
pave->AddText(name);
//pave->Draw();
TLegend *leg = new TLegend(0.88,0.65,0.55,0.90,NULL,"brNDC");
leg->SetBorderSize(0);
leg->SetTextSize(0.05);
leg->SetTextFont(42);
leg->SetLineColor(1);
leg->SetLineStyle(1);
leg->SetLineWidth(2);
leg->SetFillColor(0);
leg->SetFillStyle(0);
h_SR_Prediction->SetMarkerColor(kBlack);
h_SR_Prediction->SetLineColor(kBlack);
h_SR_Prediction->SetMarkerStyle(20);
h_SR_Prediction->SetMarkerSize(1.0);
//h_mMMMMa_3Tag_SR->GetXaxis()->SetTitleSize(0.09);
if (blind)
leg->AddEntry(h_SR_Prediction, "Data: sideband", "ep");
else {
if (blah == "antibtag" )
leg->AddEntry(h_SR_Prediction, "Data: anti-b-tag SR", "ep");
else
leg->AddEntry(h_SR_Prediction, "Data: b-tag SR", "ep");
}
leg->AddEntry(error_curve[2], "Fit model", "l");
leg->AddEntry(error_curve[0], "Fit #pm1#sigma", "f");
leg->AddEntry(error_curve[1], "Fit #pm2#sigma", "f");
leg->Draw();
aC_plot->Draw("axis same");
CMS_lumi( p_1, iPeriod, iPos );
p_2->cd();
RooHist* hpull;
hpull = aC_plot->pullHist();
RooPlot* frameP = x.frame() ;
frameP->SetTitle("");
frameP->GetXaxis()->SetRangeUser(SR_lo, SR_hi);
frameP->addPlotable(hpull,"P");
frameP->GetYaxis()->SetRangeUser(-7,7);
frameP->GetYaxis()->SetNdivisions(505);
frameP->GetYaxis()->SetTitle("#frac{(data-fit)}{#sigma_{stat}}");
frameP->GetYaxis()->SetTitleSize((1.-xPad)/xPad*0.06);
frameP->GetYaxis()->SetTitleOffset(1.0/((1.-xPad)/xPad));
frameP->GetXaxis()->SetTitleSize((1.-xPad)/xPad*0.06);
//frameP->GetXaxis()->SetTitleOffset(1.0);
frameP->GetXaxis()->SetLabelSize((1.-xPad)/xPad*0.05);
frameP->GetYaxis()->SetLabelSize((1.-xPad)/xPad*0.05);
frameP->Draw();
if (plotBands) {
error_curve[4]->Draw("Fsame");
error_curve[3]->Draw("Fsame");
unityG->Draw("same");
hpull->Draw("psame");
frameP->Draw("axis same");
}
c_rooFit->SaveAs((dirName+"/"+name_output+".pdf").c_str());
const int nModels = 9;
TString models[nModels] = {
"env_pdf_0_13TeV_dijet2", //0
"env_pdf_0_13TeV_exp1", //1
"env_pdf_0_13TeV_expow1", //2
"env_pdf_0_13TeV_expow2", //3 => skip
"env_pdf_0_13TeV_pow1", //4
"env_pdf_0_13TeV_lau1", //5
"env_pdf_0_13TeV_atlas1", //6
"env_pdf_0_13TeV_atlas2", //7 => skip
"env_pdf_0_13TeV_vvdijet1" //8
};
int nPars[nModels] = {
2, 1, 2, 3, 1, 1, 2, 3, 2
};
TString parNames[nModels][3] = {
"env_pdf_0_13TeV_dijet2_log1","env_pdf_0_13TeV_dijet2_log2","",
"env_pdf_0_13TeV_exp1_p1","","",
"env_pdf_0_13TeV_expow1_exp1","env_pdf_0_13TeV_expow1_pow1","",
"env_pdf_0_13TeV_expow2_exp1","env_pdf_0_13TeV_expow2_pow1","env_pdf_0_13TeV_expow2_exp2",
"env_pdf_0_13TeV_pow1_p1","","",
"env_pdf_0_13TeV_lau1_l1","","",
"env_pdf_0_13TeV_atlas1_coeff1","env_pdf_0_13TeV_atlas1_log1","",
"env_pdf_0_13TeV_atlas2_coeff1","env_pdf_0_13TeV_atlas2_log1","env_pdf_0_13TeV_atlas2_log2",
"env_pdf_0_13TeV_vvdijet1_coeff1","env_pdf_0_13TeV_vvdijet1_log1",""
}
if(bias){
//alternative model
gSystem->Load("libHiggsAnalysisCombinedLimit");
gSystem->Load("libdiphotonsUtils");
TFile *f = new TFile("antibtag_multipdf.root");
RooWorkspace* xf = (RooWorkspace*)f->Get("wtemplates");
RooWorkspace *w_alt=new RooWorkspace("Vg");
for(int i=model_number; i<=model_number; i++){
RooMultiPdf *alternative = (RooMultiPdf *)xf->pdf("model_bkg_AntiBtag");
std::cout<<"Number of pdfs "<<alternative->getNumPdfs()<<std::endl;
for (int j=0; j!=alternative->getNumPdfs(); ++j){
std::cout<<alternative->getPdf(j)->GetName()<<std::endl;
}
RooAbsPdf *alt_bg = alternative->getPdf(alternative->getCurrentIndex()+i);//->clone();
w_alt->import(*alt_bg, RooFit::RenameVariable(alt_bg->GetName(),("alt_bg_"+blah).c_str()));
w_alt->Print("V");
std::cerr<<w_alt->var("x")<<std::endl;
RooRealVar * range_ = w_alt->var("x");
range_->setRange(SR_lo,SR_hi);
char* asd = ("alt_bg_"+blah).c_str() ;
w_alt->import(nBackground2);
std::cout<<alt_bg->getVal() <<std::endl;
w_alt->pdf(asd)->fitTo(pred, RooFit::Minimizer("Minuit2"), RooFit::Range(SR_lo, SR_hi), RooFit::SumW2Error(kTRUE), RooFit::Save());
RooArgSet* altVars = w_alt->pdf(asd)->getVariables();
TIterator *it2 = altVars->createIterator();
RooRealVar* varAlt = (RooRealVar*)it2->Next();
while (varAlt) {
varAlt->setConstant(kTRUE);
varAlt = (RooRealVar*)it2->Next();
}
alt_bg->plotOn(aC_plot, RooFit::LineColor(i+1), RooFit::LineStyle(i+2));
p_1->cd();
aC_plot->GetYaxis()->SetRangeUser(0.01, maxdata*50.);
aC_plot->Draw("same");
TH1F *h=new TH1F();
h->SetLineColor(1+i);
h->SetLineStyle(i+2);
leg->AddEntry(h, alt_bg->GetName(), "l");
w_alt->SaveAs((dirName+"/w_background_alternative.root").c_str());
}
leg->Draw();
p_1->SetLogy();
c_rooFit->Update();
c_rooFit->SaveAs((dirName+"/"+name_output+blah+"_multipdf.pdf").c_str());
for (int i=0; i!=nPars[model_number]; ++i) {
std::cout<<parNames[model_number][i]<<" param "<< w_alt->var(parNames[model_number][i])->getVal()<<" "<<w_alt->var(parNames[model_number][i])->getError()<<std::endl;
}
} else {
p_1->SetLogy();
c_rooFit->Update();
c_rooFit->SaveAs((dirName+"/"+name_output+"_log.pdf").c_str());
}
RooWorkspace *w=new RooWorkspace("Vg");
w->import(bg);
w->import(nBackground);
w->SaveAs((dirName+"/w_background_GaussExp.root").c_str());
TH1F *h_mX_SR_fakeData=(TH1F*)h_mX_SR->Clone("h_mX_SR_fakeData");
h_mX_SR_fakeData->Scale(nEventsSR/h_mX_SR_fakeData->GetSumOfWeights());
RooDataHist data_obs("data_obs", "Data", RooArgList(x), h_mX_SR_fakeData);
std::cout<<" Background number of events = "<<nEventsSR<<std::endl;
RooWorkspace *w_data=new RooWorkspace("Vg");
w_data->import(data_obs);
w_data->SaveAs((dirName+"/w_data.root").c_str());
}
exampleScript()
{
gSystem->CompileMacro("betaHelperFunctions.h" ,"kO") ;
gSystem->CompileMacro("RooNormalFromFlatPdf.cxx" ,"kO") ;
gSystem->CompileMacro("RooBetaInverseCDF.cxx" ,"kO") ;
gSystem->CompileMacro("RooBetaPrimeInverseCDF.cxx" ,"kO") ;
gSystem->CompileMacro("RooCorrelatedBetaGeneratorHelper.cxx" ,"kO") ;
gSystem->CompileMacro("RooCorrelatedBetaPrimeGeneratorHelper.cxx" ,"kO") ;
gSystem->CompileMacro("rooFitBetaHelperFunctions.h","kO") ;
TFile betaTest("betaTest.root","RECREATE");
betaTest.cd();
RooWorkspace workspace("workspace");
TString correlatedName("testVariable");
TString observables("observables");
TString nuisances("nuisances");
RooAbsArg* betaOne = getCorrelatedBetaConstraint(workspace,"betaOne","",
0.5 , 0.1 ,
observables, nuisances,
correlatedName );
printf("\n\n *** constraint name is %s from betaOne and %s\n\n", betaOne->GetName(), correlatedName.Data() ) ;
RooAbsArg* betaTwo = getCorrelatedBetaConstraint(workspace,"betaTwo","",
0 , 0 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaThree = getCorrelatedBetaConstraint(workspace,"betaThree","",
0.2 , 0.01 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaFour = getCorrelatedBetaConstraint(workspace,"betaFour","",
0.7 , 0.1 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaFourC = getCorrelatedBetaConstraint(workspace,"betaFourC","",
0.7 , 0.1 ,
observables, nuisances,
correlatedName, kTRUE );
RooAbsArg* betaPrimeOne = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeOne","",
1.0 , 0.5 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaPrimeOneC = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeOneC","",
1.0 , 0.5 ,
observables, nuisances,
correlatedName, kTRUE );
RooAbsArg* betaPrimeTwo = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeTwo","",
0.7 , 0.5 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaPrimeThree = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeThree","",
0.1 , 0.05 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaPrimeFour = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeFour","",
7 , 1 ,
observables, nuisances,
correlatedName );
RooRealVar* correlatedParameter = workspace.var(correlatedName);
RooAbsPdf* normalFromFlat = workspace.pdf(correlatedName+"_Constraint");
RooDataSet* data = normalFromFlat->generate(RooArgSet(*correlatedParameter),1e5);
data->addColumn(*normalFromFlat);
data->addColumn(*betaOne);
data->addColumn(*betaTwo);
data->addColumn(*betaThree);
data->addColumn(*betaFour);
data->addColumn(*betaFourC);
data->addColumn(*betaPrimeOne);
data->addColumn(*betaPrimeTwo);
data->addColumn(*betaPrimeThree);
data->addColumn(*betaPrimeFour);
data->addColumn(*betaPrimeOneC);
data->Print("v");
workspace.Print() ;
//Setup Plotting Kluges:
RooRealVar normalPlotter (correlatedName+"_Constraint" , correlatedName+"_Constraint" ,0,1);
RooPlot* normalPlot = normalPlotter.frame();
data->plotOn(normalPlot);
RooRealVar betaOnePlotter ("betaOne_BetaInverseCDF" ,"betaOne_BetaInverseCDF" ,0,1);
RooRealVar betaTwoPlotter ("betaTwo_BetaInverseCDF" ,"betaTwo_BetaInverseCDF" ,0,1);
RooRealVar betaThreePlotter("betaThree_BetaInverseCDF","betaThree_BetaInverseCDF",0,1);
RooRealVar betaFourPlotter ("betaFour_BetaInverseCDF" ,"betaFour_BetaInverseCDF" ,0,1);
RooRealVar betaFourCPlotter ("betaFourC_BetaInverseCDF" ,"betaFourC_BetaInverseCDF" ,0,1);
RooRealVar betaPrimeOnePlotter ("betaPrimeOne_BetaPrimeInverseCDF" ,"betaPrimeOne_BetaPrimeInverseCDF" ,0,4);
RooRealVar betaPrimeOneCPlotter ("betaPrimeOneC_BetaPrimeInverseCDF" ,"betaPrimeOneC_BetaPrimeInverseCDF" ,0,4);
RooRealVar betaPrimeTwoPlotter ("betaPrimeTwo_BetaPrimeInverseCDF" ,"betaPrimeTwo_BetaPrimeInverseCDF" ,0,4);
RooRealVar betaPrimeThreePlotter("betaPrimeThree_BetaPrimeInverseCDF","betaPrimeThree_BetaPrimeInverseCDF",0,0.3);
RooRealVar betaPrimeFourPlotter ("betaPrimeFour_BetaPrimeInverseCDF" ,"betaPrimeFour_BetaPrimeInverseCDF" ,4,12);
RooPlot* betaOnePlot = betaOnePlotter .frame();
RooPlot* betaTwoPlot = betaTwoPlotter .frame();
RooPlot* betaThreePlot = betaThreePlotter.frame();
RooPlot* betaFourPlot = betaFourPlotter .frame();
RooPlot* betaFourCPlot = betaFourCPlotter .frame();
data->plotOn(betaOnePlot );
data->plotOn(betaTwoPlot );
data->plotOn(betaThreePlot);
data->plotOn(betaFourPlot );
data->plotOn(betaFourCPlot );
RooPlot* betaPrimeOnePlot = betaPrimeOnePlotter .frame();
RooPlot* betaPrimeOneCPlot = betaPrimeOneCPlotter .frame();
RooPlot* betaPrimeTwoPlot = betaPrimeTwoPlotter .frame();
RooPlot* betaPrimeThreePlot = betaPrimeThreePlotter.frame();
RooPlot* betaPrimeFourPlot = betaPrimeFourPlotter .frame();
data->plotOn(betaPrimeOnePlot );
data->plotOn(betaPrimeOneCPlot );
data->plotOn(betaPrimeTwoPlot );
data->plotOn(betaPrimeThreePlot);
data->plotOn(betaPrimeFourPlot );
TCanvas* underlyingVariable = new TCanvas("underlyingVariable","underlyingVariable",800,800);
underlyingVariable->Divide(2,2);
underlyingVariable->cd(1);
RooPlot* underlyingPlot = correlatedParameter->frame();
data->plotOn(underlyingPlot);
underlyingPlot->Draw();
underlyingVariable->cd(2);
normalPlot->Draw();
underlyingVariable->cd(3);
TH2F* underlying = data->createHistogram(*correlatedParameter,normalPlotter,50,50);
underlying->Draw("col");
TH2F* legoUnderlying = (TH2F*)underlying->Clone();
underlyingVariable->cd(4);
legoUnderlying->Draw("lego");
underlyingVariable->SaveAs("underlyingVariable.pdf");
TCanvas* betaCanvas = new TCanvas("betaCanvas","betaCanvas",800,800);
betaCanvas->Divide(3,2);
betaCanvas->cd(1);
betaOnePlot->Draw();
betaCanvas->cd(2);
betaTwoPlot->Draw();
betaCanvas->cd(3);
betaThreePlot->Draw();
betaCanvas->cd(4);
betaFourPlot->Draw();
betaCanvas->cd(5);
betaFourCPlot->Draw();
betaCanvas->SaveAs("betaVariables.pdf");
TCanvas* betaPrimeCanvas = new TCanvas("betaPrimeCanvas","betaPrimeCanvas",1200,800);
betaPrimeCanvas->Divide(3,2);
betaPrimeCanvas->cd(1);
betaPrimeOnePlot->Draw();
betaPrimeCanvas->cd(2);
betaPrimeTwoPlot->Draw();
betaPrimeCanvas->cd(3);
betaPrimeThreePlot->Draw();
betaPrimeCanvas->cd(4);
betaPrimeFourPlot->Draw();
betaPrimeCanvas->cd(5);
betaPrimeOneCPlot->Draw();
betaPrimeCanvas->SaveAs("betaPrimeVariables.pdf");
TCanvas* betaCorrelationsCanvas = new TCanvas("betaCorrelationsCanvas","betaCorrelationsCanvas",1600,800);
betaCorrelationsCanvas->Divide(4,2);
TH2F* oneTwo = data->createHistogram(betaOnePlotter,betaTwoPlotter,30,30);
TH2F* oneThree = data->createHistogram(betaOnePlotter,betaThreePlotter,30,30);
TH2F* oneFour = data->createHistogram(betaOnePlotter,betaFourPlotter,30,30);
TH2F* twoThree = data->createHistogram(betaTwoPlotter,betaThreePlotter,30,30);
TH2F* twoFour = data->createHistogram(betaTwoPlotter,betaFourPlotter,30,30);
TH2F* threeFour = data->createHistogram(betaThreePlotter,betaFourPlotter,30,30);
TH2F* twoFourC = data->createHistogram(betaTwoPlotter,betaFourCPlotter,30,30);
TH2F* fourFourC = data->createHistogram(betaFourPlotter,betaFourCPlotter,30,30);
betaCorrelationsCanvas->cd(1);
oneTwo->DrawCopy("lego");
betaCorrelationsCanvas->cd(2);
oneThree->DrawCopy("lego");
betaCorrelationsCanvas->cd(3);
oneFour->DrawCopy("lego");
betaCorrelationsCanvas->cd(4);
twoThree->DrawCopy("lego");
betaCorrelationsCanvas->cd(5);
twoFour->DrawCopy("lego");
betaCorrelationsCanvas->cd(6);
threeFour->DrawCopy("lego");
betaCorrelationsCanvas->cd(7);
twoFourC->DrawCopy("lego");
betaCorrelationsCanvas->cd(8);
fourFourC->DrawCopy("lego");
betaCorrelationsCanvas->SaveAs("betaCorrelations.pdf");
TCanvas* betaPrimeCorrelationsCanvas = new TCanvas("betaPrimeCorrelationsCanvas","betaPrimeCorrelationsCanvas",1600,800);
betaPrimeCorrelationsCanvas->Divide(4,2);
TH2F* oneTwo = data->createHistogram(betaPrimeOnePlotter,betaPrimeTwoPlotter,30,30);
TH2F* oneThree = data->createHistogram(betaPrimeOnePlotter,betaPrimeThreePlotter,30,30);
TH2F* oneFour = data->createHistogram(betaPrimeOnePlotter,betaPrimeFourPlotter,30,30);
TH2F* twoThree = data->createHistogram(betaPrimeTwoPlotter,betaPrimeThreePlotter,30,30);
TH2F* twoFour = data->createHistogram(betaPrimeTwoPlotter,betaPrimeFourPlotter,30,30);
TH2F* threeFour = data->createHistogram(betaPrimeThreePlotter,betaPrimeFourPlotter,30,30);
TH2F* oneOneC = data->createHistogram(betaPrimeOnePlotter,betaPrimeOneCPlotter,30,30);
betaPrimeCorrelationsCanvas->cd(1);
oneTwo->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(2);
oneThree->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(3);
oneFour->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(4);
twoThree->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(5);
twoFour->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(6);
threeFour->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(7);
oneOneC->DrawCopy("lego");
betaPrimeCorrelationsCanvas->SaveAs("betaPrimeCorrelations.pdf");
RooProdPdf totalPdf("totalPdf","totalPdf",workspace.allPdfs());
totalPdf.Print("v");
RooArgSet* observableSet = workspace.set("observables");
observableSet->Print();
RooDataSet* allDataOne = totalPdf.generate(*observableSet,1);
allDataOne->Print("v");
correlatedParameter->setVal(0.25);
RooDataSet* allDataTwo = totalPdf.generate(*observableSet,1);
allDataTwo->Print("v");
correlatedParameter->setVal(0.75);
RooDataSet* allDataThree = totalPdf.generate(*observableSet,1);
allDataThree->Print("v");
//Testing for extreme values!
for(int i = 0; i< 101; i++)
{
correlatedParameter->setVal((double)i/100.);
cout << "Correlation parameter has value of " << correlatedParameter->getVal();
cout << " and the pdf has an unnormalized value of " << normalFromFlat->getVal() << endl;
}
}
FitStruct getFitResults( const std::string& outputdir, TTree* tree, const std::string& name, float xMin, float xMax, std::string& cut, std::string& whatToProject ) {
DrawTools::setStyle();
std::string histoName(Form("h1_%s", name.c_str() ));
std::string histoName2(Form("h2_%s", name.c_str() ));
TH1D* h1;
TF1* f1;
gStyle->SetOptFit(1);
h1 = new TH1D(histoName.c_str(), "", 5000, xMin, xMax);
tree->Project( histoName.c_str(), whatToProject.c_str(), cut.c_str() );
f1 = new TF1( Form("gaus_%s", name.c_str()), "gaus", xMin, xMax);
h1->Fit( f1, "RQN" );
f1->SetLineColor(kRed);
double peakpos = f1->GetParameter(1);
double sigma = f1->GetParameter(2);
double fitmin;
double fitmax;
if( (peakpos-5*sigma) < 1000 ){
fitmin = 1000;
} else{
fitmin = peakpos-5*sigma;
}
if(((peakpos>0) && (sigma > 2*peakpos)) ) {
fitmax = peakpos*2;
} else{
fitmax = peakpos+5*sigma;
}
if (sigma < 100) sigma = 100;
TH1D* h2 = new TH1D("h2", "", 200, fitmin,fitmax);
tree->Project( "h2", whatToProject.c_str(), cut.c_str() );
RooRealVar x("x","ADC Channel", fitmin, fitmax);
RooDataHist data("data","dataset with x",x,Import(*h2) );
RooPlot* frame;
RooPlot* xframe = x.frame();
frame = x.frame("Title");
data.plotOn(frame); //this will show histogram data points on canvas
// data.statOn(frame); //this will display hist stat on canvas
RooRealVar meanr("meanr","Mean",peakpos,peakpos-2*sigma, peakpos+2*sigma);
RooRealVar width("width","#sigma",sigma, 100.0, 5.*sigma);
RooRealVar A("A","Dist",1., 0.0, 5.0);
RooRealVar N("N","Deg",3, 0.0, 15);
meanr.setRange(1000. , 3000000.);
width.setRange(100, 1000000);
RooCBShape fit_fct("fit_fct","fit_fct",x,meanr,width,A,N); int ndf = 4;
fit_fct.fitTo(data);
fit_fct.plotOn(frame,LineColor(4));//this will show fit overlay on canvas
// fit_fct.paramOn(frame); //this will display the fit parameters on canvas
double mean = meanr.getVal();
double meanErr = meanr.getError();
double rms = width.getVal();
double rmsErr = width.getError();
double reso = 100.* rms/mean; //in percent
double resoErr = 100.* getRatioError( rms, mean, meanErr, rmsErr );
TCanvas* cans = new TCanvas("cans", "un canvas", 600,600);
cans->cd();
frame->Draw();
TLegend* lego = new TLegend(0.65, 0.7, 0.9, 0.92);
lego->SetTextSize(0.038);
lego->AddEntry( (TObject*)0 ,Form("#mu = %.0f #pm %.0f", meanr.getVal(), meanr.getError() ), "");
lego->AddEntry( (TObject*)0 ,Form("#sigma = %.0f #pm %.0f ", width.getVal(), width.getError() ), "");
lego->AddEntry( (TObject*)0 ,Form("#chi^{2} = %.2f / %d ", frame->chiSquare(ndf) , ndf ), "");
lego->AddEntry( (TObject*)0 ,Form("#sigma/#mu = %.2f #pm %.2f ", reso , resoErr ), "");
lego->SetFillColor(0);
lego->Draw("same");
cans->SaveAs( Form( "%s/CBFit_%s.png", outputdir.c_str(), name.c_str() ) );
FitStruct fs;
fs.mean = mean;
fs.mean_err = meanErr;
fs.sigma = rms;
fs.sigma_err = rmsErr;
fs.reso = reso;
fs.reso_err = resoErr;
delete h1;
delete f1;
delete h2;
delete cans;
return fs;
}
void drawMassFrom2DPlot(RooWorkspace& myws, // Local workspace
string outputDir, // Output directory
struct InputOpt opt, // Variable with run information (kept for legacy purpose)
struct KinCuts cut, // Variable with current kinematic cuts
map<string, string> parIni, // Variable containing all initial parameters
string plotLabel, // The label used to define the output file name
// Select the type of datasets to fit
string DSTAG, // Specifies the type of datasets: i.e, DATA, MCJPSINP, ...
bool isPbPb, // Define if it is PbPb (True) or PP (False)
// Select the type of object to fit
bool incJpsi, // Includes Jpsi model
bool incPsi2S, // Includes Psi(2S) model
bool incBkg, // Includes Background model
// Select the fitting options
// Select the drawing options
bool setLogScale, // Draw plot with log scale
bool incSS, // Include Same Sign data
double binWidth, // Bin width
bool paperStyle=false // if true, print less info
)
{
RooMsgService::instance().getStream(0).removeTopic(Caching);
RooMsgService::instance().getStream(1).removeTopic(Caching);
RooMsgService::instance().getStream(0).removeTopic(Plotting);
RooMsgService::instance().getStream(1).removeTopic(Plotting);
RooMsgService::instance().getStream(0).removeTopic(Integration);
RooMsgService::instance().getStream(1).removeTopic(Integration);
RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING) ;
if (DSTAG.find("_")!=std::string::npos) DSTAG.erase(DSTAG.find("_"));
int nBins = min(int( round((cut.dMuon.M.Max - cut.dMuon.M.Min)/binWidth) ), 1000);
string pdfTotName = Form("pdfCTAUMASS_Tot_%s", (isPbPb?"PbPb":"PP"));
string pdfJpsiPRName = Form("pdfCTAUMASS_JpsiPR_%s", (isPbPb?"PbPb":"PP"));
string pdfJpsiNoPRName = Form("pdfCTAUMASS_JpsiNoPR_%s", (isPbPb?"PbPb":"PP"));
string pdfPsi2SPRName = Form("pdfCTAUMASS_Psi2SPR_%s", (isPbPb?"PbPb":"PP"));
string pdfPsi2SNoPRName = Form("pdfCTAUMASS_Psi2SNoPR_%s", (isPbPb?"PbPb":"PP"));
string dsOSName = Form("dOS_%s_%s", DSTAG.c_str(), (isPbPb?"PbPb":"PP"));
string dsOSNameCut = dsOSName+"_CTAUCUT";
string dsSSName = Form("dSS_%s_%s", DSTAG.c_str(), (isPbPb?"PbPb":"PP"));
bool isWeighted = myws.data(dsOSName.c_str())->isWeighted();
bool isMC = (DSTAG.find("MC")!=std::string::npos);
double normDSTot = 1.0; if (myws.data(dsOSNameCut.c_str())) { normDSTot = myws.data(dsOSName.c_str())->sumEntries()/myws.data(dsOSNameCut.c_str())->sumEntries(); }
// Create the main plot of the fit
RooPlot* frame = myws.var("invMass")->frame(Bins(nBins), Range(cut.dMuon.M.Min, cut.dMuon.M.Max));
myws.data(dsOSName.c_str())->plotOn(frame, Name("dOS"), DataError(RooAbsData::SumW2), XErrorSize(0), MarkerColor(kBlack), LineColor(kBlack), MarkerSize(1.2));
if (paperStyle) TGaxis::SetMaxDigits(3); // to display powers of 10
myws.pdf(pdfTotName.c_str())->plotOn(frame,Name("BKG"),Components(RooArgSet(*myws.pdf(Form("pdfMASS_Bkg_%s", (isPbPb?"PbPb":"PP"))))),
FillStyle(paperStyle ? 0 : 1001), FillColor(kAzure-9), VLines(), DrawOption("LCF"), LineColor(kBlue), LineStyle(kDashed)
);
if (!paperStyle) {
if (incJpsi) {
if ( myws.pdf(Form("pdfCTAUMASS_JpsiPR_%s", (isPbPb?"PbPb":"PP"))) ) {
myws.pdf(pdfTotName.c_str())->plotOn(frame,Name("JPSIPR"),Components(RooArgSet(*myws.pdf(Form("pdfCTAUMASS_JpsiPR_%s", (isPbPb?"PbPb":"PP"))), *myws.pdf(Form("pdfCTAUMASS_Bkg_%s", (isPbPb?"PbPb":"PP"))))),
ProjWData(RooArgSet(*myws.var("ctauErr")), *myws.data(dsOSName.c_str()), kTRUE),
Normalization(normDSTot, RooAbsReal::NumEvent),
LineColor(kRed+3), LineStyle(1), Precision(1e-4), NumCPU(32)
);
}
if ( myws.pdf(Form("pdfCTAUMASS_JpsiNoPR_%s", (isPbPb?"PbPb":"PP"))) ) {
myws.pdf(pdfTotName.c_str())->plotOn(frame,Name("JPSINOPR"),Components(RooArgSet(*myws.pdf(Form("pdfCTAUMASS_JpsiNoPR_%s", (isPbPb?"PbPb":"PP"))), *myws.pdf(Form("pdfCTAUMASS_Bkg_%s", (isPbPb?"PbPb":"PP"))))),
ProjWData(RooArgSet(*myws.var("ctauErr")), *myws.data(dsOSName.c_str()), kTRUE),
Normalization(normDSTot, RooAbsReal::NumEvent),
LineColor(kGreen+3), LineStyle(1), Precision(1e-4), NumCPU(32)
);
}
}
if (incPsi2S) {
if ( myws.pdf(Form("pdfCTAUMASS_Psi2SPR_%s", (isPbPb?"PbPb":"PP"))) ) {
myws.pdf(pdfTotName.c_str())->plotOn(frame,Name("PSI2SPR"),Components(RooArgSet(*myws.pdf(Form("pdfCTAUMASS_Psi2SPR_%s", (isPbPb?"PbPb":"PP"))))),
ProjWData(RooArgSet(*myws.var("ctauErr")), *myws.data(dsOSName.c_str()), kTRUE),
Normalization(normDSTot, RooAbsReal::NumEvent),
LineColor(kRed+3), LineStyle(1), Precision(1e-4), NumCPU(32)
);
}
if ( myws.pdf(Form("pdfCTAUMASS_Psi2SNoPR_%s", (isPbPb?"PbPb":"PP"))) ) {
myws.pdf(pdfTotName.c_str())->plotOn(frame,Name("PSI2SNOPR"),Components(RooArgSet(*myws.pdf(Form("pdfCTAUMASS_Psi2SNoPR_%s", (isPbPb?"PbPb":"PP"))))),
ProjWData(RooArgSet(*myws.var("ctauErr")), *myws.data(dsOSName.c_str()), kTRUE),
Normalization(normDSTot, RooAbsReal::NumEvent),
LineColor(kGreen+3), LineStyle(1), Precision(1e-4), NumCPU(32)
);
}
}
}
if (incSS) {
myws.data(dsSSName.c_str())->plotOn(frame, Name("dSS"), MarkerColor(kRed), LineColor(kRed), MarkerSize(1.2));
}
myws.data(dsOSName.c_str())->plotOn(frame, Name("dOS"), DataError(RooAbsData::SumW2), XErrorSize(0), MarkerColor(kBlack), LineColor(kBlack), MarkerSize(1.2));
myws.pdf(pdfTotName.c_str())->plotOn(frame,Name("PDF"),
ProjWData(RooArgSet(*myws.var("ctauErr")), *myws.data(dsOSName.c_str()), kTRUE),
Normalization(normDSTot, RooAbsReal::NumEvent),
LineColor(kBlack), NumCPU(32)
);
// Create the pull distribution of the fit
RooPlot* frameTMP = (RooPlot*)frame->Clone("TMP");
int nBinsTMP = nBins;
RooHist *hpull = frameTMP->pullHist(0, 0, true);
hpull->SetName("hpull");
RooPlot* frame2 = myws.var("invMass")->frame(Title("Pull Distribution"), Bins(nBins), Range(cut.dMuon.M.Min, cut.dMuon.M.Max));
frame2->addPlotable(hpull, "PX");
// set the CMS style
setTDRStyle();
// Create the main canvas
TCanvas *cFig = new TCanvas(Form("cMassFig_%s", (isPbPb?"PbPb":"PP")), "cMassFig",800,800);
TPad *pad1 = new TPad(Form("pad1_%s", (isPbPb?"PbPb":"PP")),"",0,paperStyle ? 0 : 0.23,1,1);
TPad *pad2 = new TPad(Form("pad2_%s", (isPbPb?"PbPb":"PP")),"",0,0,1,.228);
TLine *pline = new TLine(cut.dMuon.M.Min, 0.0, cut.dMuon.M.Max, 0.0);
// TPad *pad4 = new TPad("pad4","This is pad4",0.55,0.46,0.97,0.87);
TPad *pad4 = new TPad("pad4","This is pad4",0.55,paperStyle ? 0.29 : 0.36,0.97,paperStyle ? 0.70 : 0.77);
pad4->SetFillStyle(0);
pad4->SetLeftMargin(0.28);
pad4->SetRightMargin(0.10);
pad4->SetBottomMargin(0.21);
pad4->SetTopMargin(0.072);
frame->SetTitle("");
frame->GetXaxis()->CenterTitle(kTRUE);
if (!paperStyle) {
frame->GetXaxis()->SetTitle("");
frame->GetXaxis()->SetTitleSize(0.045);
frame->GetXaxis()->SetTitleFont(42);
frame->GetXaxis()->SetTitleOffset(3);
frame->GetXaxis()->SetLabelOffset(3);
frame->GetYaxis()->SetLabelSize(0.04);
frame->GetYaxis()->SetTitleSize(0.04);
frame->GetYaxis()->SetTitleOffset(1.7);
frame->GetYaxis()->SetTitleFont(42);
} else {
frame->GetXaxis()->SetTitle("m_{#mu^{+}#mu^{-}} (GeV/c^{2})");
frame->GetXaxis()->SetTitleOffset(1.1);
frame->GetYaxis()->SetTitleOffset(1.45);
frame->GetXaxis()->SetTitleSize(0.05);
frame->GetYaxis()->SetTitleSize(0.05);
}
setMassFrom2DRange(myws, frame, dsOSName, setLogScale);
if (paperStyle) {
double Ydown = 0.;//frame->GetMinimum();
double Yup = 0.9*frame->GetMaximum();
frame->GetYaxis()->SetRangeUser(Ydown,Yup);
}
cFig->cd();
pad2->SetTopMargin(0.02);
pad2->SetBottomMargin(0.4);
pad2->SetFillStyle(4000);
pad2->SetFrameFillStyle(4000);
if (!paperStyle) pad1->SetBottomMargin(0.015);
//plot fit
pad1->Draw();
pad1->cd();
frame->Draw();
printMassFrom2DParameters(myws, pad1, isPbPb, pdfTotName, isWeighted);
pad1->SetLogy(setLogScale);
// Drawing the text in the plot
TLatex *t = new TLatex(); t->SetNDC(); t->SetTextSize(0.032);
float dy = 0;
t->SetTextSize(0.03);
if (!paperStyle) { // do not print selection details for paper style
t->DrawLatex(0.20, 0.86-dy, "2015 HI Soft Muon ID"); dy+=0.045;
if (isPbPb) {
t->DrawLatex(0.20, 0.86-dy, "HLT_HIL1DoubleMu0_v1"); dy+=2.0*0.045;
} else {
t->DrawLatex(0.20, 0.86-dy, "HLT_HIL1DoubleMu0_v1"); dy+=2.0*0.045;
}
}
if (cut.dMuon.AbsRap.Min>0.1) {t->DrawLatex(0.5175, 0.86-dy, Form("%.1f < |y^{#mu#mu}| < %.1f",cut.dMuon.AbsRap.Min,cut.dMuon.AbsRap.Max)); dy+=0.045;}
else {t->DrawLatex(0.5175, 0.86-dy, Form("|y^{#mu#mu}| < %.1f",cut.dMuon.AbsRap.Max)); dy+=0.045;}
t->DrawLatex(0.5175, 0.86-dy, Form("%g < p_{T}^{#mu#mu} < %g GeV/c",cut.dMuon.Pt.Min,cut.dMuon.Pt.Max)); dy+=0.045;
if (isPbPb) {t->DrawLatex(0.5175, 0.86-dy, Form("Cent. %d-%d%%", (int)(cut.Centrality.Start/2), (int)(cut.Centrality.End/2))); dy+=0.045;}
// Drawing the Legend
double ymin = 0.7602;
if (incPsi2S && incJpsi && incSS) { ymin = 0.7202; }
if (incPsi2S && incJpsi && !incSS) { ymin = 0.7452; }
if (paperStyle) { ymin = 0.72; }
TLegend* leg = new TLegend(0.5175, ymin, 0.7180, 0.8809); leg->SetTextSize(0.03);
if (frame->findObject("dOS")) { leg->AddEntry(frame->findObject("dOS"), (incSS?"Opposite Charge":"Data"),"pe"); }
if (incSS) { leg->AddEntry(frame->findObject("dSS"),"Same Charge","pe"); }
if (frame->findObject("PDF")) { leg->AddEntry(frame->findObject("PDF"),"Total fit","l"); }
if (frame->findObject("JPSIPR")) { leg->AddEntry(frame->findObject("JPSIPR"),"Prompt J/#psi","l"); }
if (frame->findObject("JPSINOPR")) { leg->AddEntry(frame->findObject("JPSINOPR"),"Non-Prompt J/#psi","l"); }
if (incBkg && frame->findObject("BKG")) { leg->AddEntry(frame->findObject("BKG"),"Background",paperStyle ? "l" : "fl"); }
leg->Draw("same");
//Drawing the title
TString label;
if (isPbPb) {
if (opt.PbPb.RunNb.Start==opt.PbPb.RunNb.End){
label = Form("PbPb Run %d", opt.PbPb.RunNb.Start);
} else {
label = Form("%s [%s %d-%d]", "PbPb", "HIOniaL1DoubleMu0", opt.PbPb.RunNb.Start, opt.PbPb.RunNb.End);
}
} else {
if (opt.pp.RunNb.Start==opt.pp.RunNb.End){
label = Form("PP Run %d", opt.pp.RunNb.Start);
} else {
label = Form("%s [%s %d-%d]", "PP", "DoubleMu0", opt.pp.RunNb.Start, opt.pp.RunNb.End);
}
}
// CMS_lumi(pad1, isPbPb ? 105 : 104, 33, label);
CMS_lumi(pad1, isPbPb ? 108 : 107, 33, "");
if (!paperStyle) gStyle->SetTitleFontSize(0.05);
pad1->Update();
cFig->cd();
if (!paperStyle) {
//---plot pull
pad2->Draw();
pad2->cd();
frame2->SetTitle("");
frame2->GetYaxis()->CenterTitle(kTRUE);
frame2->GetYaxis()->SetTitleOffset(0.4);
frame2->GetYaxis()->SetTitleSize(0.1);
frame2->GetYaxis()->SetLabelSize(0.1);
frame2->GetYaxis()->SetTitle("Pull");
frame2->GetXaxis()->CenterTitle(kTRUE);
frame2->GetXaxis()->SetTitleOffset(1);
frame2->GetXaxis()->SetTitleSize(0.12);
frame2->GetXaxis()->SetLabelSize(0.1);
frame2->GetXaxis()->SetTitle("m_{#mu^{+}#mu^{-}} (GeV/c^{2})");
frame2->GetYaxis()->SetRangeUser(-7.0, 7.0);
frame2->Draw();
// *** Print chi2/ndof
printChi2(myws, pad2, frameTMP, "invMass", dsOSName.c_str(), pdfTotName.c_str(), nBinsTMP, false);
pline->Draw("same");
pad2->Update();
}
// Save the plot in different formats
gSystem->mkdir(Form("%sctauMass/%s/plot/root/", outputDir.c_str(), DSTAG.c_str()), kTRUE);
cFig->SaveAs(Form("%sctauMass/%s/plot/root/PLOT_%s_%s_%s%s_pt%.0f%.0f_rap%.0f%.0f_cent%d%d.root", outputDir.c_str(), DSTAG.c_str(), "MASS", DSTAG.c_str(), (isPbPb?"PbPb":"PP"), plotLabel.c_str(), (cut.dMuon.Pt.Min*10.0), (cut.dMuon.Pt.Max*10.0), (cut.dMuon.AbsRap.Min*10.0), (cut.dMuon.AbsRap.Max*10.0), cut.Centrality.Start, cut.Centrality.End));
gSystem->mkdir(Form("%sctauMass/%s/plot/png/", outputDir.c_str(), DSTAG.c_str()), kTRUE);
cFig->SaveAs(Form("%sctauMass/%s/plot/png/PLOT_%s_%s_%s%s_pt%.0f%.0f_rap%.0f%.0f_cent%d%d.png", outputDir.c_str(), DSTAG.c_str(), "MASS", DSTAG.c_str(), (isPbPb?"PbPb":"PP"), plotLabel.c_str(), (cut.dMuon.Pt.Min*10.0), (cut.dMuon.Pt.Max*10.0), (cut.dMuon.AbsRap.Min*10.0), (cut.dMuon.AbsRap.Max*10.0), cut.Centrality.Start, cut.Centrality.End));
gSystem->mkdir(Form("%sctauMass/%s/plot/pdf/", outputDir.c_str(), DSTAG.c_str()), kTRUE);
cFig->SaveAs(Form("%sctauMass/%s/plot/pdf/PLOT_%s_%s_%s%s_pt%.0f%.0f_rap%.0f%.0f_cent%d%d.pdf", outputDir.c_str(), DSTAG.c_str(), "MASS", DSTAG.c_str(), (isPbPb?"PbPb":"PP"), plotLabel.c_str(), (cut.dMuon.Pt.Min*10.0), (cut.dMuon.Pt.Max*10.0), (cut.dMuon.AbsRap.Min*10.0), (cut.dMuon.AbsRap.Max*10.0), cut.Centrality.Start, cut.Centrality.End));
cFig->Clear();
cFig->Close();
};
void StandardBayesianNumericalDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
/////////////////////////////////////////////
// create and use the BayesianCalculator
// to find and plot the 95% credible interval
// on the parameter of interest as specified
// in the model config
// before we do that, we must specify our prior
// it belongs in the model config, but it may not have
// been specified
RooUniform prior("prior","",*mc->GetParametersOfInterest());
w->import(prior);
mc->SetPriorPdf(*w->pdf("prior"));
// do without systematics
//mc->SetNuisanceParameters(RooArgSet() );
BayesianCalculator bayesianCalc(*data,*mc);
bayesianCalc.SetConfidenceLevel(0.95); // 95% interval
// default of the calculator is central interval. here use shortest , central or upper limit depending on input
// doing a shortest interval might require a longer time since it requires a scan of the posterior function
if (intervalType == 0) bayesianCalc.SetShortestInterval(); // for shortest interval
if (intervalType == 1) bayesianCalc.SetLeftSideTailFraction(0.5); // for central interval
if (intervalType == 2) bayesianCalc.SetLeftSideTailFraction(0.); // for upper limit
if (!integrationType.IsNull() ) {
bayesianCalc.SetIntegrationType(integrationType); // set integrationType
bayesianCalc.SetNumIters(nToys); // set number of ietrations (i.e. number of toys for MC integrations)
}
// in case of toyMC make a nnuisance pdf
if (integrationType.Contains("TOYMC") ) {
RooAbsPdf * nuisPdf = RooStats::MakeNuisancePdf(*mc, "nuisance_pdf");
cout << "using TOYMC integration: make nuisance pdf from the model " << std::endl;
nuisPdf->Print();
bayesianCalc.ForceNuisancePdf(*nuisPdf);
scanPosterior = true; // for ToyMC the posterior is scanned anyway so used given points
}
// compute interval by scanning the posterior function
if (scanPosterior)
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooRealVar* poi = (RooRealVar*) mc->GetParametersOfInterest()->first();
if (maxPOI != -999 && maxPOI > poi->getMin())
poi->setMax(maxPOI);
SimpleInterval* interval = bayesianCalc.GetInterval();
// print out the iterval on the first Parameter of Interest
cout << "\n95% interval on " << poi->GetName()<<" is : ["<<
interval->LowerLimit() << ", "<<
interval->UpperLimit() <<"] "<<endl;
// make a plot
// since plotting may take a long time (it requires evaluating
// the posterior in many points) this command will speed up
// by reducing the number of points to plot - do 50
cout << "\nDrawing plot of posterior function....." << endl;
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooPlot * plot = bayesianCalc.GetPosteriorPlot();
plot->Draw();
}
vector<double> FitBkg(TH1D* histo, TString _bkg ){
setTDRStyle();
vector<double> vec;
int n = histo->GetEntries();
double w = histo->GetXaxis()->GetBinWidth(1);
int ndf;
RooPlot* frame;
double hmin0 = histo->GetXaxis()->GetXmin();
double hmax0 = histo->GetXaxis()->GetXmax();
histo->GetXaxis()->SetRangeUser(hmin0,hmax0);
// Declare observable x
RooRealVar x("x","x",hmin0,hmax0) ;
RooDataHist dh("dh","dh",x,Import(*histo,kFALSE)) ;
frame = x.frame(Title(histo->GetName())) ;
dh.plotOn(frame,DataError(RooAbsData::SumW2), MarkerColor(1),MarkerSize(0.9),MarkerStyle(7));
dh.statOn(frame);
x.setRange("R0",0,200) ;
//Defining the fitting functions
if(_bkg == "Novo"){
//Novo
RooRealVar peak("peak","peak",45.,0.,100.);
RooRealVar width("width","width",20.,0.,40.) ;
RooRealVar tail("tail","tail",0.01,0.,1.) ;
RooNovosibirsk bkg("bkg","Background",x,peak,width,tail);
//Fitting
RooFitResult* filters = bkg.fitTo(dh/*,Range("R0"),"qr"*/);
bkg.plotOn(frame,LineColor(2));
bkg.paramOn(frame);
vec.push_back(peak.getVal());
vec.push_back(width.getVal());
vec.push_back(tail.getVal());
}
if(_bkg == "Cheb"){
//Chebychev
RooRealVar a0("a0","a0",-1,-5.,0.) ;
RooRealVar a1("a1","a1",0,-2,1.2) ;
RooRealVar a2("a2","a2",0,-1.,1.) ;
RooRealVar a3("a3","a3",0,-2.5,0.) ;
RooRealVar a4("a4","a4",0,-1.,1.) ;
RooRealVar a5("a5","a5",0,-1.,1.) ;
RooRealVar a6("a6","a6",0,-1.,1.) ;
RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1,a2,a3,a4,a5,a6));
//Fitting
RooFitResult* filters = bkg.fitTo(dh,Range("R0"),"qr");
bkg.plotOn(frame,LineColor(2));
bkg.paramOn(frame);
vec.push_back(a0.getVal());
vec.push_back(a1.getVal());
vec.push_back(a2.getVal());
vec.push_back(a3.getVal());
vec.push_back(a4.getVal());
vec.push_back(a5.getVal());
vec.push_back(a6.getVal());
}
if(_bkg == "Land"){
//Background fitting function
//Landau (X) Gauss
RooRealVar resp_mean("resp_mean","resp_mean",1,0.,20) ;
RooRealVar resp_sigma("resp_sigma","resp_sigma",20,5,30) ;
RooGaussian resp_bkg("resp","gauss",x,resp_mean,resp_sigma) ;
RooRealVar mean_bkg("mean_bkg","mean",40,30,70) ;
RooRealVar sigma_bkg("sigma_bkg","sigma",10,0,20) ;
RooLandau Land_bkg("Land","Background",x,mean_bkg,sigma_bkg);
sigma_bkg.setRange(30,50);
resp_sigma.setRange(5,10);
x.setBins(10000,"cache") ;
RooFFTConvPdf bkg("bkg","Background",x,Land_bkg,resp_bkg);
//Fitting
RooFitResult* filters = bkg.fitTo(dh,Range("R0"),"qr");
frame->Draw();
bkg.plotOn(frame,LineColor(2));
bkg.paramOn(frame);
vec.push_back(0);
vec.push_back(0);
vec.push_back(0);
}
frame->GetXaxis()->SetTitle("Z mass (in GeV/c^{2})");
frame->GetXaxis()->SetTitleOffset(1.2);
float binsize = histo->GetBinWidth(1);
//Store result in .root file
frame->Write(histo->GetName());
return vec;
}
void MakePlots(RooWorkspace* ws){
// Here we make plots of the discriminating variable (invMass) after the fit
// and of the control variable (isolation) after unfolding with sPlot.
std::cout << "make plots" << std::endl;
// make our canvas
TCanvas* cdata = new TCanvas("sPlot","sPlot demo", 400, 600);
cdata->Divide(1,3);
// get what we need out of the workspace
RooAbsPdf* model = ws->pdf("model");
RooAbsPdf* zModel = ws->pdf("zModel");
RooAbsPdf* qcdModel = ws->pdf("qcdModel");
RooRealVar* isolation = ws->var("isolation");
RooRealVar* invMass = ws->var("invMass");
// note, we get the dataset with sWeights
RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");
// this shouldn't be necessary, need to fix something with workspace
// do this to set parameters back to their fitted values.
model->fitTo(*data, Extended() );
//plot invMass for data with full model and individual componenets overlayed
// TCanvas* cdata = new TCanvas();
cdata->cd(1);
RooPlot* frame = invMass->frame() ;
data->plotOn(frame ) ;
model->plotOn(frame) ;
model->plotOn(frame,Components(*zModel),LineStyle(kDashed), LineColor(kRed)) ;
model->plotOn(frame,Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
frame->SetTitle("Fit of model to discriminating variable");
frame->Draw() ;
// Now use the sWeights to show isolation distribution for Z and QCD.
// The SPlot class can make this easier, but here we demonstrait in more
// detail how the sWeights are used. The SPlot class should make this
// very easy and needs some more development.
// Plot isolation for Z component.
// Do this by plotting all events weighted by the sWeight for the Z component.
// The SPlot class adds a new variable that has the name of the corresponding
// yield + "_sw".
cdata->cd(2);
// create weightfed data set
RooDataSet * dataw_z = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"zYield_sw") ;
RooPlot* frame2 = isolation->frame() ;
dataw_z->plotOn(frame2, DataError(RooAbsData::SumW2) ) ;
frame2->SetTitle("isolation distribution for Z");
frame2->Draw() ;
// Plot isolation for QCD component.
// Eg. plot all events weighted by the sWeight for the QCD component.
// The SPlot class adds a new variable that has the name of the corresponding
// yield + "_sw".
cdata->cd(3);
RooDataSet * dataw_qcd = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"qcdYield_sw") ;
RooPlot* frame3 = isolation->frame() ;
dataw_qcd->plotOn(frame3,DataError(RooAbsData::SumW2) ) ;
frame3->SetTitle("isolation distribution for QCD");
frame3->Draw() ;
// cdata->SaveAs("SPlot.gif");
}
void DrawMass(int iSEL)
{
gROOT->ForceStyle();
TString SET[2] = {"A","B"};
RooMsgService::instance().setSilentMode(kTRUE);
for(int i=0;i<2;i++) {
RooMsgService::instance().setStreamStatus(i,kFALSE);
}
TString SELECTION[2] = {"NOM","VBF"};
TString MASS_RAW[2] = {"mbb[1]","mbb[2]"};
TString MASS_REG[2] = {"mbbReg[1]","mbbReg[2]"};
// TFile *inf = TFile::Open("Fit_VBFPowheg125_sel"+SELECTION[iSEL]+".root");
TFile *inf = TFile::Open("/usb/data2/UAData/2015/flatTree_VBFPowheg125.root");
TTree *tr = (TTree*)inf->Get("Hbb/events");
TH1F *hRaw = new TH1F("hRawMass","hRawMass",150,0,300);
TH1F *hReg = new TH1F("hRegMass","hRegMass",150,0,300);
RooRealVar x("mbb","mbb",60,170);
TCanvas *can = new TCanvas("Mbb_sel"+SELECTION[iSEL],"Mbb_sel"+SELECTION[iSEL],900,750);
TCut ct1 = TCut("triggerResult[0]==1||triggerResult[1]==1");
TCut cs1 = TCut("jetBtag[b1[1]]>0.244 && jetBtag[b2[1]]>0.244 && jetPt[3]>40. && jetPt[2]>50. && jetPt[1]>70. && jetPt[0]>80. && dEtaqq[1]>2.5 && mqq[1]>250 && dPhibb[1]<2.0 && nLeptons==0");
tr->Draw(MASS_RAW[iSEL]+">>hRawMass",ct1&&cs1);
tr->Draw(MASS_REG[iSEL]+">>hRegMass",ct1&&cs1);
hRaw->Sumw2();
hReg->Sumw2();
hRaw->Scale(1./(hRaw->Integral()*hRaw->GetBinWidth(1)));
hReg->Scale(1./(hReg->Integral()*hReg->GetBinWidth(1)));
RooDataHist *rRaw = new RooDataHist("rRaw","rRaw",x,hRaw);
RooDataHist *rReg = new RooDataHist("rReg","rReg",x,hReg);
RooRealVar m1("m1","m1",125,110,140);
RooRealVar m2("m2","m2",125,110,140);
RooRealVar sL1("sL1","sL1",12,3,30);
RooRealVar sL2("sL2","sL2",12,3,30);
RooRealVar sR1("sR1","sR1",12,3,30);
RooRealVar sR2("sR2","sR2",12,3,30);
RooRealVar a1("a1","a1",1,-10,10);
RooRealVar a2("a2","a2",1,-10,10);
RooRealVar n1("n1","n1",1,0,100);
RooRealVar n2("n2","n2",1,0,100);
RooRealVar b10("b10","b10",0.5,0.,1.);
RooRealVar b11("b11","b11",0.5,0.,1.);
RooRealVar b12("b12","b12",0.5,0.,1.);
RooRealVar b13("b13","b13",0.5,0.,1.);
RooRealVar b20("b20","b20",0.5,0.,1.);
RooRealVar b21("b21","b21",0.5,0.,1.);
RooRealVar b22("b22","b22",0.5,0.,1.);
RooRealVar b23("b23","b23",0.5,0.,1.);
RooBernstein bkg1("bkg1","bkg1",x,RooArgSet(b10,b11,b12,b13));
RooBernstein bkg2("bkg2","bkg2",x,RooArgSet(b20,b21,b22,b23));
RooRealVar fsig1("fsig1","fsig1",0.7,0.,1.);
RooRealVar fsig2("fsig2","fsig2",0.7,0.,1.);
RooBifurGauss sig1("sig1","sig1",x,m1,sL1,sR1);
RooBifurGauss sig2("sig2","sig2",x,m2,sL2,sR2);
//RooCBShape sig1("sig1","sig1",x,m1,s1,a1,n1);
//RooCBShape sig2("sig2","sig2",x,m2,s2,a2,n2);
RooAddPdf *model1 = new RooAddPdf("model1","model1",RooArgList(sig1,bkg1),fsig1);
RooAddPdf *model2 = new RooAddPdf("model2","model2",RooArgList(sig2,bkg2),fsig2);
model1->fitTo(*rRaw,SumW2Error(kFALSE),"q");
model2->fitTo(*rReg,SumW2Error(kFALSE),"q");
hRaw->SetLineWidth(2);
hReg->SetLineWidth(2);
hRaw->SetLineColor(kBlack);
hReg->SetLineColor(kRed+1);
hReg->SetFillColor(kRed-10);
hRaw->SetMarkerStyle(21);
hReg->SetMarkerStyle(20);
hRaw->SetMarkerSize(1.5);
hReg->SetMarkerSize(1.5);
hRaw->SetMarkerColor(kBlack);
hReg->SetMarkerColor(kRed+1);
RooPlot* frame = x.frame();
rRaw->plotOn(frame,LineColor(kBlack),LineWidth(1),MarkerColor(kBlack),MarkerStyle(21));
model1->plotOn(frame,LineColor(kBlack),LineWidth(2));
rReg->plotOn(frame,LineColor(kRed+1),LineWidth(1),MarkerColor(kRed+1),MarkerStyle(20));
model2->plotOn(frame,LineColor(kRed+1),LineWidth(2));
TF1 *tmp_func1 = model1->asTF(x,fsig1,x);
TF1 *tmp_func2 = model2->asTF(x,fsig2,x);
double y01 = tmp_func1->GetMaximum();
double x01 = tmp_func1->GetMaximumX();
double x11 = tmp_func1->GetX(y01/2,60,x01);
double x21 = tmp_func1->GetX(y01/2,x01,200);
double FWHM1 = x21-x11;
double y02 = tmp_func2->GetMaximum();
double x02 = tmp_func2->GetMaximumX();
double x12 = tmp_func2->GetX(y02/2,60,x02);
double x22 = tmp_func2->GetX(y02/2,x02,200);
double FWHM2 = x22-x12;
hReg->GetXaxis()->SetRangeUser(60,170);
hReg->GetXaxis()->SetTitle("m_{bb} (GeV)");
hReg->GetYaxis()->SetTitle("1/N #times dN/dm_{bb} (GeV^{-1})");
hReg->GetYaxis()->SetNdivisions(505);
hReg->SetMaximum(0.035);
hReg->Draw("HIST");
hRaw->Draw("HIST SAME");
frame->Draw("same");
TLegend *leg = new TLegend(0.18,0.7,0.43,0.91);
leg->SetTextFont(42);
leg->SetTextSize(0.05);
leg->SetBorderSize(0);
leg->SetFillColor(0);
leg->SetHeader("m_{H} = 125 GeV (set "+SET[iSEL]+")");
leg->AddEntry(hReg,"Regressed","LP");
leg->AddEntry(hRaw,"Raw","LP");
leg->Draw();
TPaveText *pave1 = new TPaveText(0.18,0.56,0.43,0.7,"NDC");
pave1->AddText(TString::Format("PEAK = %1.1f GeV",x02));
pave1->AddText(TString::Format("FWHM = %1.1f GeV",FWHM2));
pave1->SetTextFont(42);
pave1->SetTextSize(0.04);
pave1->SetTextColor(kRed+1);
pave1->SetBorderSize(0);
pave1->SetFillColor(0);
pave1->Draw();
TPaveText *pave2 = new TPaveText(0.18,0.42,0.43,0.56,"NDC");
pave2->AddText(TString::Format("PEAK = %1.1f GeV",x01));
pave2->AddText(TString::Format("FWHM = %1.1f GeV",FWHM1));
pave2->SetTextFont(42);
pave2->SetTextSize(0.04);
pave2->SetTextColor(kBlack);
pave2->SetBorderSize(0);
pave2->SetFillColor(0);
pave2->Draw();
can->SaveAs("regressionKost.pdf");
// CMS_lumi(can,0,0);
}
fitYnSMassUnbinned(const char *filename = "YMassOS_186nb.txt",
const char* plotOpt = "NEU",
const int nbins = 40,
const char* filenameB = "YMassSS_186nb.txt")
{
gROOT->ProcessLine(".L tdrstyle.C");
setTDRStyle();
gStyle->SetPadRightMargin(0.05);
RooRealVar mass("mass","M(#mu^{+}#mu^{-})", 8.0, 12.0,"GeV/c^{2}");
// Read data set
RooDataSet *data = RooDataSet::read(filename,RooArgSet(mass));
RooDataSet *dataB = RooDataSet::read(filenameB,RooArgSet(mass));
// Build p.d.f.
////////////////////////////////////////////////
// Parameters //
////////////////////////////////////////////////
// Signal p.d.f. parameters
// Parameters for a Gaussian and a Crystal Ball Lineshape
RooRealVar m0 ("m_{0}", "Bias", 9.46, 9.2, 9.7,"GeV/c^{2}");
RooRealVar sigma("#sigma","Width", 0.04,0.01,0.1,"GeV/c^{2}");
RooRealVar cut ("#alpha","Cut", 0.6,0.6,2.0);
RooRealVar power("power","Power", 10.0, 0.5, 20.0);
// Background p.d.f. parameters
// Parameters for a polynomial lineshape
RooRealVar c0("c_{0}", "c0", 0., -10, 10);
RooRealVar c1("c_{1}", "c1", 0., -100, 0);
RooRealVar c2("c_{2}", "c2", 0., -100, 100);
// c0.setConstant();
// fraction of signal
// RooRealVar frac("frac", "Signal Fraction", 0.1,0.,0.3.);
RooRealVar nsig("N_{S}", "#signal events", 9000, 0.,10000.);
RooRealVar nbkg("N_{B}", "#background events", 1000,2,10000.);
////////////////////////////////////////////////
// P.D.F.s //
////////////////////////////////////////////////
// Di-photon mass signal p.d.f.
RooGaussian signal("signal", "A Gaussian Lineshape", mass, m0, sigma);
// RooCBShape signal("signal", "A Crystal Ball Lineshape", mass, m0,sigma, cut, power);
// Di-photon mass background p.d.f.
RooPolynomial bg("bg", "Backgroung Distribution", mass, RooArgList(c0,c1));
// Di-photon mass model p.d.f.
// RooAddPdf model("model", "Di-photon mass model", signal, bg, frac);
RooAddPdf model("model", "Di-photon mass model", RooArgList(signal, bg), RooArgList(nsig, nbkg));
TStopwatch t ;
t.Start() ;
model->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
t.Print() ;
c = new TCanvas("c","J/psi->mu mu Distributions", 0,0,800,600);
// Plot the fit results
RooPlot* plot = mass.frame(Range(8.0,12.0),Bins(nbins));
// Plot 1
dataB->plotOn(plot, MarkerColor(kRed));
data->plotOn(plot);
model.plotOn(plot);
//model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(nsig, nbkg, m0, sigma)));
model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(m0, sigma)));
/// model.plotOn(plot, Components("signal"), LineStyle(kDashed), LineColor(kRed));
model.plotOn(plot, Components("bg"), LineStyle(kDashed), LineColor(kRed));
plot->Draw();
TLatex * tex = new TLatex(0.2,0.8,"CMS preliminary");
tex->SetTextFont(42);
tex->SetNDC();
tex->SetLineWidth(2);
tex->Draw();
tex->DrawLatex(0.2, 0.725, "#sqrt{s} = 7 TeV");
tex->DrawLatex(0.2, 0.650, "L = 186 nb^{-1}");
float fsig_peak = NormalizedIntegral(signal,
mass,
m0.getVal() - 2.5*sigma.getVal(),
m0.getVal() + 2.5*sigma.getVal()
);
float fbkg_peak = NormalizedIntegral(bg,
mass,
m0.getVal() - 2.5*sigma.getVal(),
m0.getVal() + 2.5*sigma.getVal()
);
double nsigVal = fsig_peak * nsig.getVal();
double nsigErr = fsig_peak * nsig.getError();
double nsigErrRel = nsigErr / nsigVal;
double nbkgVal = fbkg_peak * nbkg.getVal();
double nbkgErr = fbkg_peak * nbkg.getError();
double nbkgErrRel = nbkgErr / nbkgVal;
cout << "nsig " << nsigVal << " +/- " << nsigErr << endl;
cout << "S/B_{#pm2.5#sigma} " << nsigVal/nbkgVal << " +/- "
<< (nsigVal/nbkgVal)*sqrt(nsigErrRel*nsigErrRel + nbkgErrRel*nbkgErrRel)
<< endl;
tex->DrawLatex(0.2, 0.5, Form("N_{S} = %.0f#pm%.0f", nsigVal, nsigErr) );
tex->DrawLatex(0.2, 0.425, Form("S/B_{#pm2.5#sigma} = %.1f", nsigVal/nbkgVal) );
tex->DrawLatex(0.2, 0.35, Form("#frac{S}{#sqrt{B}}_{#pm2.5#sigma} = %.1f", nsigVal/sqrt(nbkgVal)));
leg = new TLegend(0.65,0.6,0.9,0.75);
leg->SetFillColor(kWhite);
leg->SetLineColor(kWhite);
leg->SetShadowColor(kWhite);
leg->SetTextFont(42);
TLegendEntry * ldata = leg->AddEntry(data, "Opposite Sign");
TLegendEntry * ldataB = leg->AddEntry(dataB, "Same Sign");
ldata->SetMarkerStyle(20);
ldataB->SetMarkerStyle(20);
ldataB->SetMarkerColor(kRed);
leg->Draw();
}
void RooToyMCFit1Bin(const int _bin){
if(_bin<1 || _bin>8){
cout << "Bin number should be between 1 and 8" << endl;
return;
}
stringstream out;
out.str("");
out << "toyMC_" << _sigma_over_tau << "_" << _purity << "_" << mistag_rate << ".root";
TFile* file = TFile::Open(out.str().c_str());
TTree* tree = (TTree*)file->Get("ToyTree");
cout << "The tree has been readed from the file " << out.str().c_str() << endl;
RooRealVar tau("tau","tau",_tau,"ps"); tau.setConstant(kTRUE);
RooRealVar dm("dm","dm",_dm,"ps^{-1}"); dm.setConstant(kTRUE);
RooRealVar sin2beta("sin2beta","sin2beta",_sin2beta,-5.,5.); if(constBeta) sin2beta.setConstant(kTRUE);
RooRealVar cos2beta("cos2beta","cos2beta",_cos2beta,-5.,5.); if(constBeta) cos2beta.setConstant(kTRUE);
RooRealVar dt("dt","#Deltat",-5.,5.,"ps");
RooRealVar avgMisgat("avgMisgat","avgMisgat",mistag_rate,0.0,0.5); if(constMistag) avgMisgat.setConstant(kTRUE);
RooRealVar delMisgat("delMisgat","delMisgat",0); delMisgat.setConstant(kTRUE);
RooRealVar mu("mu","mu",0); mu.setConstant(kTRUE);
RooRealVar moment("moment","moment",0.); moment.setConstant(kTRUE);
RooRealVar parity("parity","parity",-1.); parity.setConstant(kTRUE);
cout << "Preparing coefficients..." << endl;
RooRealVar* K = new RooRealVar("K","K",K8[_bin-1],0.,1.); if(constK) K->setConstant(kTRUE);
RooFormulaVar* Kb = new RooFormulaVar("Kb","Kb","1-@0",RooArgList(*K));
RooRealVar* C = new RooRealVar("C","C",_C[_bin-1]); C->setConstant(kTRUE);
RooRealVar* S = new RooRealVar("S","S",_S[_bin-1]); S->setConstant(kTRUE);
RooFormulaVar* a1 = new RooFormulaVar("a1","a1","-(@0-@1)/(@0+@1)",RooArgList(*K,*Kb));
RooFormulaVar* a1b = new RooFormulaVar("a1b","a1b","(@0-@1)/(@0+@1)",RooArgList(*K,*Kb));
RooFormulaVar* a2 = new RooFormulaVar("a2","a2","(@0-@1)/(@0+@1)",RooArgList(*K,*Kb));
RooFormulaVar* a2b = new RooFormulaVar("a2b","a2b","-(@0-@1)/(@0+@1)",RooArgList(*K,*Kb));
RooFormulaVar* b1 = new RooFormulaVar("b1","b1","2.*(@2*@4+@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1);",RooArgList(*K,*Kb,*C,*S,sin2beta,cos2beta));
RooFormulaVar* b1b = new RooFormulaVar("b1b","b1b","2.*(@2*@4-@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1);",RooArgList(*K,*Kb,*C,*S,sin2beta,cos2beta));
RooFormulaVar* b2 = new RooFormulaVar("b2","b2","-2.*(@2*@4+@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1);",RooArgList(*K,*Kb,*C,*S,sin2beta,cos2beta));
RooFormulaVar* b2b = new RooFormulaVar("b2b","b2b","-2.*(@2*@4-@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1);",RooArgList(*K,*Kb,*C,*S,sin2beta,cos2beta));
RooRealVar* dgamma = new RooRealVar("dgamma","dgamma",0.); dgamma->setConstant(kTRUE);
RooRealVar* f0 = new RooRealVar("f0","f0",1.); f0->setConstant(kTRUE);
RooRealVar* f1 = new RooRealVar("f1","f1",0.); f1->setConstant(kTRUE);
RooCategory tag("tag","tag");
tag.defineType("B0",1);
tag.defineType("anti-B0",-1);
RooCategory bin("bin","bin");
bin.defineType("bin",_bin);
bin.defineType("binb",-_bin);
RooSuperCategory bintag("bintag","bintag",RooArgSet(bin,tag));
RooDataSet d("data","data",tree,RooArgSet(dt,bin,tag));
cout << "DataSet is ready." << endl;
d.Print();
RooRealVar mean("mean","mean",0.,"ps"); mean.setConstant(kTRUE);
RooRealVar sigma("sigma","sigma",_sigma_over_tau*_tau,0.,_tau,"ps"); if(constSigma) sigma.setConstant(kTRUE);
RooGaussModel rf("rf","rf",dt,mean,sigma);
// RooTruthModel rf("rf","rf",dt);
RooGaussian rfpdf("rfpdf","rfpdf",dt,mean,sigma);
cout << "Preparing PDFs..." << endl;
RooBDecay* sigpdf1 = new RooBDecay("sigpdf1","sigpdf1",dt,tau,*dgamma,*f0,*f1,*a1,*b1,dm,rf,RooBDecay::DoubleSided);
RooBDecay* sigpdf2 = new RooBDecay("sigpdf2","sigpdf2",dt,tau,*dgamma,*f0,*f1,*a2,*b2,dm,rf,RooBDecay::DoubleSided);
RooBDecay* sigpdf1b = new RooBDecay("sigpdf1b","sigpdf1b",dt,tau,*dgamma,*f0,*f1,*a1b,*b1b,dm,rf,RooBDecay::DoubleSided);
RooBDecay* sigpdf2b = new RooBDecay("sigpdf2b","sigpdf2b",dt,tau,*dgamma,*f0,*f1,*a2b,*b2b,dm,rf,RooBDecay::DoubleSided);
RooRealVar fsig("fsig","fsigs",_purity,0.,1.); if(constFSig) fsig.setConstant(kTRUE);
RooAddPdf* PDF1 = new RooAddPdf("PDF1","PDF1",RooArgList(*sigpdf1,rfpdf),RooArgList(fsig));
RooAddPdf* PDF2 = new RooAddPdf("PDF2","PDF2",RooArgList(*sigpdf2,rfpdf),RooArgList(fsig));
RooAddPdf* PDF1b= new RooAddPdf("PDF1b","PDF1b",RooArgList(*sigpdf1b,rfpdf),RooArgList(fsig));
RooAddPdf* PDF2b= new RooAddPdf("PDF2b","PDF2b",RooArgList(*sigpdf2b,rfpdf),RooArgList(fsig));
//Adding mistaging
RooAddPdf* pdf1 = new RooAddPdf("pdf1","pdf1",RooArgList(*PDF2,*PDF1),RooArgList(avgMisgat));
RooAddPdf* pdf2 = new RooAddPdf("pdf2","pdf2",RooArgList(*PDF1,*PDF2),RooArgList(avgMisgat));
RooAddPdf* pdf1b= new RooAddPdf("pdf1b","pdf1b",RooArgList(*PDF2b,*PDF1b),RooArgList(avgMisgat));
RooAddPdf* pdf2b= new RooAddPdf("pdf2b","pdf2b",RooArgList(*PDF1b,*PDF2b),RooArgList(avgMisgat));
RooSimultaneous pdf("pdf","pdf",bintag);
pdf.addPdf(*pdf1,"{bin;B0}");
pdf.addPdf(*pdf2,"{bin;anti-B0}");
pdf.addPdf(*pdf1b,"{binb;B0}");
pdf.addPdf(*pdf2b,"{binb;anti-B0}");
cout << "Fitting..." << endl;
pdf.fitTo(d,Verbose(),Timer());
cout << "Drawing plots." << endl;
// Plus bin
RooPlot* dtFrame = dt.frame();
// B0
out.str("");
out << "tag == 1 && bin == " << _bin;
cout << out.str() << endl;
RooDataSet* ds = d.reduce(out.str().c_str());
ds->Print();
ds->plotOn(dtFrame,DataError(RooAbsData::SumW2),MarkerSize(1),MarkerColor(kBlue));
bintag = "{bin;B0}";
pdf.plotOn(dtFrame,ProjWData(RooArgSet(),*ds),Slice(bintag),LineColor(kBlue));
double chi2 = dtFrame->chiSquare();
// anti-B0
out.str("");
out << "tag == -1 && bin == " << _bin;
cout << out.str() << endl;
RooDataSet* dsb = d.reduce(out.str().c_str());
dsb->Print();
dsb->plotOn(dtFrame,DataError(RooAbsData::SumW2),MarkerSize(1),MarkerColor(kRed));
bintag = "{bin;anti-B0}";
pdf.plotOn(dtFrame,ProjWData(RooArgSet(),*dsb),Slice(bintag),LineColor(kRed));
double chi2b = dtFrame->chiSquare();
// Canvas
out.str("");
out << "#Delta t, toy MC, bin == " << _bin;
TCanvas* cm = new TCanvas(out.str().c_str(),out.str().c_str(),600,400);
cm->cd();
dtFrame->GetXaxis()->SetTitleSize(0.05);
dtFrame->GetXaxis()->SetTitleOffset(0.85);
dtFrame->GetXaxis()->SetLabelSize(0.05);
dtFrame->GetYaxis()->SetTitleOffset(1.6);
TPaveText *pt = new TPaveText(0.7,0.6,0.98,0.99,"brNDC");
pt->SetFillColor(0);
pt->SetTextAlign(12);
out.str("");
out << "bin = " << _bin;
pt->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(B^{0}) = " << chi2;
pt->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(#barB^{0}) = " << chi2b;
pt->AddText(out.str().c_str());
out.str("");
out << "#sigma/#tau = " << sigma.getVal()/tau.getVal();
pt->AddText(out.str().c_str());
out.str("");
out << "purity = " << _purity;
pt->AddText(out.str().c_str());
out.str("");
out << "mistag = " << mistag_rate;
pt->AddText(out.str().c_str());
dtFrame->Draw();
pt->Draw();
// Minus bin
RooPlot* dtFrameB = dt.frame();
// B0
out.str("");
out << "tag == 1 && bin == " << -_bin;
cout << out.str() << endl;
RooDataSet* ds2 = d.reduce(out.str().c_str());
ds2->Print();
ds2->plotOn(dtFrameB,DataError(RooAbsData::SumW2),MarkerSize(1),MarkerColor(kBlue));
bintag = "{binb;B0}";
pdf.plotOn(dtFrameB,ProjWData(RooArgSet(),*ds2),Slice(bintag),LineColor(kBlue));
chi2 = dtFrameB->chiSquare();
//anti-B0
out.str("");
out << "tag == -1 && bin == " << -_bin;
cout << out.str() << endl;
RooDataSet* dsb2 = d.reduce(out.str().c_str());
dsb2->Print();
dsb2->plotOn(dtFrameB,DataError(RooAbsData::SumW2),MarkerSize(1),MarkerColor(kRed));
bintag = "{binb;anti-B0}";
pdf.plotOn(dtFrameB,ProjWData(RooArgSet(),*dsb2),Slice(bintag),LineColor(kRed));
chi2b = dtFrameB->chiSquare();
//Canvas
out.str("");
out << "#Delta t, toy MC, bin == " << -_bin;
TCanvas* cm2 = new TCanvas(out.str().c_str(),out.str().c_str(),600,400);
cm2->cd();
dtFrameB->GetXaxis()->SetTitleSize(0.05);
dtFrameB->GetXaxis()->SetTitleOffset(0.85);
dtFrameB->GetXaxis()->SetLabelSize(0.05);
dtFrameB->GetYaxis()->SetTitleOffset(1.6);
TPaveText *ptB = new TPaveText(0.7,0.65,0.98,0.99,"brNDC");
ptB->SetFillColor(0);
ptB->SetTextAlign(12);
out.str("");
out << "bin = " << -_bin;
ptB->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(B^{0}) = " << chi2;
ptB->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(#barB^{0}) = " << chi2b;
ptB->AddText(out.str().c_str());
out.str("");
out << "#sigma/#tau = " << sigma.getVal()/tau.getVal();
ptB->AddText(out.str().c_str());
out.str("");
out << "purity = " << _purity;
ptB->AddText(out.str().c_str());
out.str("");
out << "mistag = " << mistag_rate;
ptB->AddText(out.str().c_str());
dtFrameB->Draw();
ptB->Draw();
return;
}
void LL(){
//y0 = 0.000135096401209 sigma_y0 = 0.000103896581837 x0 = 0.000446013873443 sigma_x0 =1.81384394011e-06
//0.014108652249 0.0168368471049 0.0219755396247 0.000120423865262 1.5575931164 1.55759310722 3.41637854038
//0.072569437325 0.084063541977 0.0376693978906 0.000284216132439 0.51908074913 0.519080758095 1.12037749267
// double d = 0.014108652249;
// double sd = 0.0168368471049;
// double mc = 0.0219755396247;
// double smc = 0.000120423865262;
// double r0 = d/mc;
double d = 0.072569437325;
double sd = 0.084063541977;
double mc = 0.0376693978906;
double smc = 0.00028421613243;
double r0 = d/mc;
RooRealVar x("x","x",mc*0.9,mc*1.1);
RooRealVar x0("x0","x0",mc);
RooRealVar sx("sx","sx",smc);
RooRealVar r("r","r",r0,0.,5.);
RooRealVar y0("y0","y0",d);
RooRealVar sy("sy","sy",sd);
RooProduct rx("rx","rx",RooArgList(r,x));
RooGaussian g1("g1","g1",x,x0,sx);
RooGaussian g2("g2","g2",rx,y0,sy);
RooProdPdf LL("LL","LL",g1,g2);
RooArgSet obs(x0,y0); //observables
RooArgSet poi(r); //parameters of interest
RooDataSet data("data", "data", obs);
data.add(obs); //actually add the data
RooFitResult* res = LL.fitTo(data,RooFit::Minos(poi),RooFit::Save(),RooFit::Hesse(false));
if(res->status()==0) {
r.Print();
x.Print();
cout << r.getErrorLo() << " " << r.getErrorHi() << endl;
} else {
cout << "Likelihood maximization failed" << endl;
}
RooAbsReal* nll = LL.createNLL(data);
RooPlot* frame = r.frame();
RooAbsReal* pll = nll->createProfile(poi);
pll->plotOn(frame);//,RooFit::LineColor(ROOT::kRed));
frame->Draw();
r.setVal(0.);
cout << pll->getVal() << endl;
return;
}
void RooKeysEx()
{
cout << "--------------------------"<< endl;
cout << "Start the RooKeys Example "<< endl;
cout << "--------------------------"<< endl;
// This is the variable that we will use ex: it could be the invariant mass Lb- >pK gamma
RooRealVar x("K#pi#mu#mu","K#pi#mu#mu",1800,1940, "MeV/c2") ;
// This is some constant number that we use to generate the toy data
RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;
// These are some PDF that we use to generate the toy day
RooExponential exp("exp", "exp", x, RooConst(-0.01));
RooGaussian signal("signal", "signal", x, RooConst(1866), RooConst(8));
RooGaussian G1("G1", "G1",x,RooConst(1850),RooConst(10) );
RooGaussian G2("G2", "G2",x,RooConst(1840),RooConst(15) );
RooAddPdf SumG1andG2("SumG1andG2", "SumG1andG2",RooArgList(G1,G2), RooArgList(RooConst(0.6) ) );
//RooAddPdf TotalPDF ("TotalPDF", "TotalPDF", RooArgList(signal, SumG1andG2),RooArgList(RooConst(0.6)));
RooAddPdf TotalPDF("TotalPDF", "TotalPDF", RooArgList(signal, SumG1andG2, exp),RooArgList(RooConst(0.4), RooConst(0.2) ));
// Prepare the today data
//RooDataSet* data1 = p.generate(x,10000) ;
//RooDataSet* data1 = G.generate(x,5000) ;
//RooDataSet* data1 = SumG1andG2.generate(x,500) ;
// Here we generate some events in the x variable using the PDFs that we defined before
RooDataSet* data1= SumG1andG2.generate(x,1000);
RooDataSet* data2 = TotalPDF.generate(x,10000) ; // generate signal and background
cout << "--------------------------"<< endl;
cout << " Done generating the data " << endl;
cout << "--------------------------"<< endl;
// is mirrored over the boundaries to minimize edge effects in distribution
// that do not fall to zero towards the edges
RooKeysPdf kest1("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth) ;
// An adaptive kernel estimation pdf on the same data without mirroring option
// for comparison
RooKeysPdf kest2("kest2","kest2",x,*data1,RooKeysPdf::NoMirror) ;
// Adaptive kernel estimation pdf with increased bandwidth scale factor
// (promotes smoothness over detail preservation)
RooKeysPdf kest3("kest3","kest3",x,*data1,RooKeysPdf::MirrorBoth,2) ;
//===========================================================================
// Here are the PDF and variable that we will use in the fit :
//===========================================================================
RooRealVar signal_mean("signal_mean","signal_mean", 1850, 1840, 1900);
RooRealVar signal_width("signal_width", "signal_width", 10, 0, 20);
//----
RooRealVar comb_slope ("comb_slope", "comb_slope", 0, -1., 1);
//----
RooRealVar Nsig("Nsig", "Nsig", 100, 0, 10000);
RooRealVar Nbkg_comb("Nbkg_comb", "Nbkg_comb", 100, 0, 10000);
RooRealVar Nbkg_misID("Nbkg_misID", "Nbkg_misID", 100, 0, 10000);
//----
RooGaussian signal_fitted ("signal_fitted", "signal_fitted", x, signal_mean, signal_width);
// RooKeysPdf background_fitted ("background_fitted", "background_fitted", x, *data1, RooKeysPdf::MirrorBoth );
//----
RooExponential comb_PDF ("comb_PDF", "comb_PDF", x, comb_slope);
//---
//RooAddPdf TotalPDF_fitted ("TotalPDF_fitted", "TotalPDF_fitted", RooArgList(signal_fitted, background_fitted), RooArgList(Nsig,Nbkg));
RooAddPdf TotalPDF_fitted ("TotalPDF_fitted", "TotalPDF_fitted", RooArgList(signal_fitted, kest1, comb_PDF ), RooArgList(Nsig,Nbkg_misID, Nbkg_comb));
TotalPDF_fitted -> fitTo(*data2);
cout << "----------------------------"<< endl;
cout << " Done doing the fit to data " << endl;
cout << "----------------------------"<< endl;
// Here we are going to plot stuff
RooPlot* frame0 = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ;
// G1.plotOn(frame0, LineColor(1)) ;
//G2.plotOn(frame0, LineColor(2)) ;
SumG1andG2.plotOn(frame0) ;
// Plot kernel estimation pdfs with and without mirroring over data
RooPlot* frame = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ;
data1->plotOn(frame) ;
kest1.plotOn(frame, LineColor(875)) ;
//kest2.plotOn(frame, LineColor(429)) ;
//kest3.plotOn(frame, LineColor(625)) ;
// Plot kernel estimation pdfs with regular and increased bandwidth
RooPlot* frame2 = x.frame(Title("Adaptive kernel estimation pdf with regular, increased bandwidth")) ;
kest1.plotOn(frame2, LineColor(875)) ;
kest3.plotOn(frame2, LineColor(807)) ;
RooPlot* frame3 = x.frame(Title(""));
data2->plotOn(frame3);
TotalPDF_fitted.plotOn(frame3, Components(kest1), LineColor(875));
TotalPDF_fitted.plotOn(frame3, Components(signal_fitted), LineColor(429));
TotalPDF_fitted.plotOn(frame3, Components(comb_PDF), LineColor(801));
TotalPDF_fitted.plotOn(frame3);
TCanvas* c = new TCanvas("c","c",600,600) ;
c->Divide(2,2) ;
c->cd(1) ;
frame0->GetYaxis()->SetTitleOffset(1.4) ; frame0->Draw() ;
c->cd(2) ;
frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
c->cd(3) ;
frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;
c->cd(4) ;
frame3->GetYaxis()->SetTitleOffset(1.8) ; frame3->Draw() ;
c->SaveAs("Keys.pdf");
TCanvas * c1 = new TCanvas("c1","c1", 400,400);
//c1->cd();
frame3->GetYaxis()->SetTitleOffset(1.8) ; frame3->Draw() ;
frame3->SetTitle("");
c1->SaveAs("KeysResult.pdf");
cout<< "Fin"<< endl;
}
void CreateBkgTemplates(float XMIN, float XMAX, TString OUTPATH, bool MERGE)
{
gROOT->ProcessLineSync(".x ../common/styleCMSTDR.C");
gROOT->ForceStyle();
RooMsgService::instance().setSilentMode(kTRUE);
for(int i=0;i<2;i++) {
RooMsgService::instance().setStreamStatus(i,kFALSE);
}
const int NSEL(2);
if (!MERGE) {const int NCAT[NSEL] = {4,3};}
else {const int NCAT[NSEL] = {4,2};}
if (!MERGE) {const double MVA_BND[NSEL][NCAT[0]+1] = {{-0.6,0.0,0.7,0.84,1},{-0.1,0.4,0.8,1}};}
else {const double MVA_BND[NSEL][NCAT[0]+1] = {{-0.6,0.0,0.7,0.84,1},{-0.1,0.4,1}};}
float LUMI[2] = {19784,18281};
TString SELECTION[2] = {"NOM","VBF"};
TString SELNAME[2] = {"NOM","PRK"};
TString MASS_VAR[2] = {"mbbReg[1]","mbbReg[2]"};
TString TRIG_WT[2] = {"trigWtNOM[1]","trigWtVBF"};
TString PATH("flat/");
TFile *inf[9];
TTree *tr;
TH1F *hMbb[9],*hMbbYield[9],*hPass;
TH1F *hZ,*hW,*hTT,*hST,*hTop;
TH1F *hZYield,*hWYield,*hTTYield,*hSTYield,*hTopYield;
char name[1000];
float LUMI;
float XSEC[9] = {56.4,11.1,3.79,30.7,11.1,1.76,245.8,650,1.2*1205};
RooDataHist *roohist_Z[5],*roohist_T[5];
RooRealVar *kJES[10],*kJER[10];
RooWorkspace *w = new RooWorkspace("w","workspace");
TString tMERGE = MERGE ? "_CATmerge56" : "";
//RooRealVar x("mbbReg","mbbReg",XMIN,XMAX);
int counter(0);
for(int isel=0;isel<NSEL;isel++) {
inf[0] = TFile::Open(PATH+"Fit_T_t-channel_sel"+SELECTION[isel]+".root");
inf[1] = TFile::Open(PATH+"Fit_T_tW-channel_sel"+SELECTION[isel]+".root");
inf[2] = TFile::Open(PATH+"Fit_T_s-channel_sel"+SELECTION[isel]+".root");
inf[3] = TFile::Open(PATH+"Fit_Tbar_t-channel_sel"+SELECTION[isel]+".root");
inf[4] = TFile::Open(PATH+"Fit_Tbar_tW-channel_sel"+SELECTION[isel]+".root");
inf[5] = TFile::Open(PATH+"Fit_Tbar_s-channel_sel"+SELECTION[isel]+".root");
inf[6] = TFile::Open(PATH+"Fit_TTJets_sel"+SELECTION[isel]+".root");
inf[7] = TFile::Open(PATH+"Fit_ZJets_sel"+SELECTION[isel]+".root");
inf[8] = TFile::Open(PATH+"Fit_WJets_sel"+SELECTION[isel]+".root");
TCanvas *canZ = new TCanvas("canZ_"+SELECTION[isel],"canZ_"+SELECTION[isel],900,600);
TCanvas *canT = new TCanvas("canT_"+SELECTION[isel],"canT_"+SELECTION[isel],900,600);
canZ->Divide(2,2);
canT->Divide(2,2);
TCanvas *can = new TCanvas();
sprintf(name,"CMS_vbfbb_scale_mbb_sel%s",SELECTION[isel].Data());
kJES[isel] = new RooRealVar(name,name,1.0);
sprintf(name,"CMS_vbfbb_res_mbb_sel%s",SELECTION[isel].Data());
kJER[isel] = new RooRealVar(name,name,1.0);
kJES[isel]->setConstant(kTRUE);
kJER[isel]->setConstant(kTRUE);
for(int icat=0;icat<NCAT[isel];icat++) {
if (MERGE && SELECTION[isel]=="VBF" && icat==1) counter = 56;
/*
sprintf(name,"CMS_vbfbb_scale_mbb_CAT%d",counter);
kJES[counter] = new RooRealVar(name,name,1.0);
sprintf(name,"CMS_vbbb_res_mbb_CAT%d",counter);
kJER[counter] = new RooRealVar(name,name,1.0);
kJES[counter]->setConstant(kTRUE);
kJER[counter]->setConstant(kTRUE);
*/
for(int i=0;i<9;i++) {
hPass = (TH1F*)inf[i]->Get("TriggerPass");
sprintf(name,"Hbb/events",icat);
tr = (TTree*)inf[i]->Get(name);
sprintf(name,"puWt[0]*%s*(mva%s>%1.2f && mva%s<=%1.2f)",TRIG_WT[isel].Data(),SELECTION[isel].Data(),MVA_BND[isel][icat],SELECTION[isel].Data(),MVA_BND[isel][icat+1]);
TCut cut(name);
int NBINS(20);
//if (icat > 1 && icat<=2) NBINS = 20;
if (icat > 2) NBINS = 12;
sprintf(name,"hMbb%d_sel%s_CAT%d",i,SELECTION[isel].Data(),icat);
hMbb[i] = new TH1F(name,name,NBINS,XMIN,XMAX);
hMbb[i]->Sumw2();
can->cd();
tr->Draw(MASS_VAR[isel]+">>"+hMbb[i]->GetName(),cut);
sprintf(name,"hMbbYield%d_sel%s_CAT%d",i,SELECTION[isel].Data(),icat);
hMbbYield[i] = new TH1F(name,name,NBINS,XMIN,XMAX);
hMbbYield[i]->Sumw2();
tr->Draw(MASS_VAR[isel]+">>"+hMbbYield[i]->GetName(),cut);
hMbbYield[i]->Scale(LUMI[isel]*XSEC[i]/hPass->GetBinContent(1));
}
hZ = (TH1F*)hMbb[7]->Clone("Z");
hW = (TH1F*)hMbb[8]->Clone("W");
hTT = (TH1F*)hMbb[6]->Clone("TT");
hST = (TH1F*)hMbb[0]->Clone("ST");
hST->Add(hMbb[1]);
hST->Add(hMbb[2]);
hST->Add(hMbb[3]);
hST->Add(hMbb[4]);
hST->Add(hMbb[5]);
hTop = (TH1F*)hTT->Clone("Top");
hTop->Add(hST);
//hZ->Add(hW);
hZYield = (TH1F*)hMbbYield[7]->Clone("ZYield");
hWYield = (TH1F*)hMbbYield[8]->Clone("WYield");
hTTYield = (TH1F*)hMbbYield[6]->Clone("TTYield");
hSTYield = (TH1F*)hMbbYield[0]->Clone("STYield");
hSTYield->Add(hMbbYield[1]);
hSTYield->Add(hMbbYield[2]);
hSTYield->Add(hMbbYield[3]);
hSTYield->Add(hMbbYield[4]);
hSTYield->Add(hMbbYield[5]);
hTopYield = (TH1F*)hTTYield->Clone("TopYield");
hTopYield->Add(hSTYield);
hZYield->Add(hWYield);
RooRealVar x("mbbReg_"+TString::Format("CAT%d",counter),"mbbReg_"+TString::Format("CAT%d",counter),XMIN,XMAX);
sprintf(name,"yield_ZJets_CAT%d",counter);
RooRealVar *YieldZ = new RooRealVar(name,name,hZYield->Integral());
sprintf(name,"yield_WJets_CAT%d",counter);
RooRealVar *YieldW = new RooRealVar(name,name,hWYield->Integral());
sprintf(name,"yield_Top_CAT%d",counter);
RooRealVar *YieldT = new RooRealVar(name,name,hTopYield->Integral());
sprintf(name,"yield_TT_CAT%d",counter);
RooRealVar *YieldTT = new RooRealVar(name,name,hTTYield->Integral());
sprintf(name,"yield_ST_CAT%d",counter);
RooRealVar *YieldST = new RooRealVar(name,name,hSTYield->Integral());
sprintf(name,"roohist_Z_CAT%d",counter);
roohist_Z[icat] = new RooDataHist(name,name,x,hZ);
sprintf(name,"Z_mean_CAT%d",counter);
RooRealVar mZ(name,name,95,80,110);
sprintf(name,"Z_sigma_CAT%d",counter);
RooRealVar sZ(name,name,12,9,20);
sprintf(name,"Z_mean_shifted_CAT%d",counter);
RooFormulaVar mZShift(name,"@0*@1",RooArgList(mZ,*(kJES[isel])));
sprintf(name,"Z_sigma_shifted_CAT%d",counter);
RooFormulaVar sZShift(name,"@0*@1",RooArgList(sZ,*(kJER[isel])));
sprintf(name,"Z_a_CAT%d",counter);
RooRealVar aZ(name,name,-1,-10,10);
sprintf(name,"Z_n_CAT%d",counter);
RooRealVar nZ(name,name,1,0,10);
RooRealVar Zb0("Z_b0_CAT"+TString::Format("%d",counter),"Z_b0_CAT"+TString::Format("%d",counter),0.5,0,1.);
RooRealVar Zb1("Z_b1_CAT"+TString::Format("%d",counter),"Z_b1_CAT"+TString::Format("%d",counter),0.5,0,1.);
RooRealVar Zb2("Z_b2_CAT"+TString::Format("%d",counter),"Z_b2_CAT"+TString::Format("%d",counter),0.5,0,1.);
RooBernstein Zbkg("Z_bkg_CAT"+TString::Format("%d",counter),"Z_bkg_CAT"+TString::Format("%d",counter),x,RooArgSet(Zb0,Zb1,Zb2));
RooRealVar fZsig("fZsig_CAT"+TString::Format("%d",counter),"fZsig_CAT"+TString::Format("%d",counter),0.7,0.,1.);
RooCBShape Zcore("Zcore_CAT"+TString::Format("%d",counter),"Zcore_CAT"+TString::Format("%d",counter),x,mZShift,sZShift,aZ,nZ);
RooAddPdf modelZ("Z_model_CAT"+TString::Format("%d",counter),"Z_model_CAT"+TString::Format("%d",counter),RooArgList(Zcore,Zbkg),fZsig);
RooFitResult *resZ = modelZ.fitTo(*roohist_Z[icat],RooFit::Save(),RooFit::SumW2Error(kFALSE),"q");
canZ->cd(icat+1);
RooPlot* frame = x.frame();
roohist_Z[icat]->plotOn(frame);
modelZ.plotOn(frame,RooFit::LineWidth(2));
frame->GetXaxis()->SetTitle("M_{bb} (GeV)");
frame->Draw();
TPaveText *pave = new TPaveText(0.7,0.76,0.9,0.9,"NDC");
pave->SetTextAlign(11);
pave->SetFillColor(0);
pave->SetBorderSize(0);
pave->SetTextFont(62);
pave->SetTextSize(0.045);
pave->AddText(TString::Format("%s selection",SELNAME[isel].Data()));
pave->AddText(TString::Format("CAT%d",counter));
TText *lastline = pave->AddText("Z template");
pave->SetY1NDC(pave->GetY2NDC()-0.055*3);
TPaveText *paveorig = (TPaveText*)pave->Clone();
paveorig->Draw();
sprintf(name,"roohist_T_CAT%d",counter);
if (icat < 3) {
roohist_T[icat] = new RooDataHist(name,name,x,hTopYield);
}
else {
roohist_T[icat] = new RooDataHist(name,name,x,hSTYield);
}
sprintf(name,"Top_mean_CAT%d",counter);
RooRealVar mT(name,name,130,0,200);
sprintf(name,"Top_sigma_CAT%d",counter);
RooRealVar sT(name,name,50,0,200);
sprintf(name,"Top_mean_shifted_CAT%d",counter);
RooFormulaVar mTShift(name,"@0*@1",RooArgList(mT,*(kJES[isel])));
sprintf(name,"Top_sigma_shifted_CAT%d",counter);
RooFormulaVar sTShift(name,"@0*@1",RooArgList(sT,*(kJER[isel])));
sprintf(name,"Top_model_CAT%d",counter);
RooGaussian *modelT = new RooGaussian(name,name,x,mTShift,sTShift);
RooFitResult *resT = modelT->fitTo(*roohist_T[icat],Save(),SumW2Error(kTRUE),"q");
/*
TF1 *tmp_func = new TF1("tmpFunc","gaus",XMIN,XMAX);
tmp_func->SetParameters(1,a0.getVal(),a1.getVal());
if (icat < 3) {
float norm = tmp_func->Integral(XMIN,XMAX)/hTopYield->GetBinWidth(1);
tmp_func->SetParameter(0,hTopYield->Integral()/norm);
}
else {
float norm = tmp_func->Integral(XMIN,XMAX)/hSTYield->GetBinWidth(1);
tmp_func->SetParameter(0,hSTYield->Integral()/norm);
}
*/
canT->cd(icat+1);
RooPlot* frame = x.frame();
roohist_T[icat]->plotOn(frame);
modelT->plotOn(frame,RooFit::LineWidth(2));
//modelT->plotOn(frame,VisualizeError(*resT,1,kTRUE),FillColor(kGray),MoveToBack());
frame->GetXaxis()->SetTitle("M_{bb} (GeV)");
frame->Draw();
//tmp_func->Draw("sameL");
lastline->SetTitle("Top template");
pave->Draw();
mZ.setConstant(kTRUE);
sZ.setConstant(kTRUE);
aZ.setConstant(kTRUE);
nZ.setConstant(kTRUE);
Zb0.setConstant(kTRUE);
Zb1.setConstant(kTRUE);
Zb2.setConstant(kTRUE);
fZsig.setConstant(kTRUE);
mT.setConstant(kTRUE);
sT.setConstant(kTRUE);
w->import(modelZ);
w->import(*modelT);
w->import(*YieldZ);
w->import(*YieldT);
w->import(*YieldTT);
w->import(*YieldST);
YieldZ->Print();
YieldW->Print();
YieldT->Print();
YieldTT->Print();
YieldST->Print();
counter++;
}// category loop
system(TString::Format("[ ! -d %s/ ] && mkdir %s/",OUTPATH.Data(),OUTPATH.Data()).Data());
system(TString::Format("[ ! -d %s/plots ] && mkdir %s/plots",OUTPATH.Data(),OUTPATH.Data()).Data());
system(TString::Format("[ ! -d %s/plots/bkgTemplates ] && mkdir %s/plots/bkgTemplates",OUTPATH.Data(),OUTPATH.Data()).Data());
TString FULLPATH(OUTPATH+"/plots/bkgTemplates");
canT->SaveAs(TString::Format("%s/%s.png",FULLPATH.Data(),canT->GetName()));
canZ->SaveAs(TString::Format("%s/%s.png",FULLPATH.Data(),canZ->GetName()));
canT->SaveAs(TString::Format("%s/%s.pdf",FULLPATH.Data(),canT->GetName()));
canZ->SaveAs(TString::Format("%s/%s.pdf",FULLPATH.Data(),canZ->GetName()));
delete can;
}// selection loop
system(TString::Format("[ ! -d %s/ ] && mkdir %s/",OUTPATH.Data(),OUTPATH.Data()).Data());
system(TString::Format("[ ! -d %s/output ] && mkdir %s/output",OUTPATH.Data(),OUTPATH.Data()).Data());
w->Print();
w->writeToFile(TString::Format("%s/output/bkg_shapes_workspace%s.root",OUTPATH.Data(),tMERGE.Data()).Data());
}
int main(){
RooRealVar zero("zero","",0,0,150);
//input parameters for the distribution
double alpha_double = 2.38;
double beta_double = 0.092;
double lambda_double = 2.21;
double theta_double = 1.12;
double c_frac = 0.5;
//RooRealVar fsig("fsig","signal fraction",0.3) ;
string epsname = "plots/gamma_pion20";
hit_info multi_shower("fullsim/example_pi20Gev.root");
//hit_info multi_shower("fullsim/example_ele_20GeV.root");
double beta_gflash_factor = 1.49;
double theta_gflash_factor = 16.42;
beta_double = beta_double/beta_gflash_factor;
theta_double = theta_double/theta_gflash_factor;
// --- Observable ---
RooRealVar depth("depth","depth [cm]",0,800);
depth.removeMax(); // set infinite range
// --- Weight ---
RooRealVar* weight = new RooRealVar("weight","1/E dE/dr", 0.0, 1.0);
// --- Gamma pdf ---
//if one of the Values is set to const you can not ask for it in RooFitModel, otherwise the program will crash
//RooRealVar alpha("alpha","alpha",alpha_double,0.0,4);
RooRealVar alpha("alpha","alpha",0.0,20.);
//alpha.removeMax(); // set infinite range
//RooRealVar beta("beta","beta",1./beta_double,0.0,100.);
RooRealVar beta("beta","beta",0.1,.1,3.5);
//beta.removeMax(); // set infinite range
RooRealVar mu("mu","mu",0); // no ranges means variable is fixed in fit
RooRealVar nu("nu","nu",0); // no ranges means variable is fixed in fit
//RooRealVar lambda("lambda","lambda",lambda_double,0.0,6.);
RooRealVar lambda("lambda","lambda",0.0,40.);
//lambda.removeMax(); // set infinite range
//RooRealVar theta("theta","theta",1./theta_double,0.0,100.) ;
RooRealVar theta("theta","theta",1.,47) ;
//theta.removeMax(); // set infinite range
RooGamma gamma_one("gamma_one","gamma_one pdf",depth,alpha,beta,mu) ;
RooGamma gamma_two("gamma_two","gamma_two pdf",depth,lambda,theta,nu) ;
RooRealVar fsig("fsig","signal fraction",c_frac,0.0,1.);
//RooRealVar fsig("fsig","signal fraction",c_frac);
RooAddPdf model("model","model",RooArgList(gamma_two,gamma_one),fsig) ;
RooDataSet mean("mean","mean",RooArgSet(depth,weight),"weight");
TH1F* mean_fithist = new TH1F("mean_fithist"," mean fit hist",50,0,150);
TH1F* log_alpha1 = new TH1F("log_alpha1"," log(#alpha_{1})",20,-1,3);
TH1F* log_beta1 = new TH1F("log_beta1"," log(#beta_{1})",20,-1,3);
TH2F* log_corr1 = new TH2F("log_corr1"," correlation #alpha_{1} #beta_{1}",20,-1,3,20,-1,3);
TH1F* log_alpha2 = new TH1F("log_alpha2"," log(#alpha_{2})",20,-1,3);
TH1F* log_beta2 = new TH1F("log_beta2"," log(#beta_{2})",20,-1,3);
TH2F* log_corr2 = new TH2F("log_corr2"," correlation #alpha_{2} #beta_{2}",20,-1,3,20,-1,3);
TH2F* mean_hist = new TH2F("mean_hist"," mean hist",100,0,150,100.,0.,.0015);
TCanvas * can = new TCanvas("can", "can", 600, 500);
can->cd();
set_style();
//can->Print(epsname+"[");
double c_tot =0;
unsigned int n_part =100;
for(unsigned int p = 0; p<n_part ; ++p){
TLegend * legend = new TLegend(0.5,0.65,.88,0.88);
legend->SetTextFont(72);
legend->SetTextSize(0.04);
legend->SetFillColor(kWhite);
legend->SetBorderSize(0);
one_shower shower = multi_shower.all_shower.at(p);
//DataSet I am looking at
RooDataSet full_sim_data("full_sim_data","full_sim_data",RooArgSet(depth,weight),"weight");
TH1F* datahisto = new TH1F("datahist","datahist",15,0,120);
double sumHCAL=0;
double deltar =-1;
for(unsigned int i = 0; i < shower.mytype.size(); ++i){
sumHCAL += shower.myenergy.at(i);
double x_val = shower.myz.at(i);
double y_val = shower.myy.at(i);
double z_val = shower.myz.at(i);
z_val = z_val - shower.interactPoint;
double distance=-1;
if(sqrt(x_val*x_val+y_val*y_val+z_val*z_val)/10 < 200) distance = sqrt(x_val*x_val+y_val*y_val+z_val*z_val)/10;
if(deltar < distance) deltar = z_val;
}
deltar=deltar/shower.mytype.size();
c_tot += shower.ePi0first_energy/n_part;
fsig.setVal(shower.ePi0first_energy);
fsig.setRange(shower.ePi0first_energy-0.05 > 0 ? shower.ePi0first_energy-0.05: 0,shower.ePi0first_energy+0.05<1?shower.ePi0first_energy+0.05:1);
//fsig.setRange(1-shower.ePi0first_energy-0.05 > 0 ? 1-shower.ePi0first_energy-0.05: 0,1-shower.ePi0first_energy+0.05<1 ? 1-shower.ePi0first_energy+0.05:1);
for(unsigned int i = 0; i < shower.mytype.size(); ++i){
double x_val = shower.myx.at(i);
double y_val = shower.myy.at(i);
double z_val = shower.myz.at(i);
// if(shower.mytype.at(i) !=2)continue;
z_val = z_val - shower.interactPoint;
//if(z_val<=0) continue;
depth.setVal(sqrt(x_val*x_val+y_val*y_val+z_val*z_val)/10);
//depth.setVal(z_val);
//cout <<depth.getVal()<<endl;
datahisto->Fill(depth.getVal(),shower.myenergy.at(i)/sumHCAL/deltar);
mean_fithist->Fill(depth.getVal(),shower.myenergy.at(i)/sumHCAL/deltar/n_part);
mean_hist->Fill(depth.getVal(),shower.myenergy.at(i)/sumHCAL/deltar);
full_sim_data.add(depth,shower.myenergy.at(i)/sumHCAL/deltar,0);
mean.add(depth,shower.myenergy.at(i)/sumHCAL/deltar,0);
}
RooDataHist roodatahist("roodatahist","roodatahist",depth,Import(*datahisto)) ;
//model.fitTo(roodatahist, Strategy(2) );
model.fitTo(full_sim_data);
RooDataHist* full_sim_binned = full_sim_data.binnedClone("full_sim_binned","full sim binned");
//prepare to plot fits
RooPlot * depthFrame = depth.frame(0,120,100);
depthFrame->SetTitle("");
depthFrame->SetTitleOffset(1.2,"Y");
depthFrame->SetLabelSize(0.02,"Y");
depthFrame->SetYTitle("1/E dE/dr");
//zero.plotOn(depthFrame,LineStyle(kDashed),LineColor(kBlack));
//full_sim_data.plotOn(depthFrame);
roodatahist.plotOn(depthFrame);
//full_sim_binned->plotOn(depthFrame);
model.plotOn(depthFrame,Components(gamma_one),LineStyle(kDashed),LineColor(kGreen),Name("gamma_one"));
model.plotOn(depthFrame,Components(gamma_two),LineStyle(kDashed),LineColor(kRed),Name("gamma_two"));
model.plotOn(depthFrame,Name("model"));
stringstream gamma_one_text;
gamma_one_text.precision(3);
gamma_one_text << "#Gamma_{1} ("<<alpha.getVal()<<","<<(1/beta.getVal()*beta_gflash_factor)<<")"<<endl;
stringstream gamma_two_text;
gamma_two_text.precision(3);
gamma_two_text << "#Gamma_{2} (" <<lambda.getVal()<<","<<(1/theta.getVal()*theta_gflash_factor)<< ")"<<endl;
stringstream gamma_text;
gamma_text.precision(2);
gamma_text << "#Gamma_{1/2} c=" <<fsig.getVal() <<"("<<shower.ePi0first_energy<<")"<<endl;
stringstream chi2;
chi2.precision(2);
chi2 << "#chi^{2} prob. = " << TMath::Prob(depthFrame->chiSquare(),14) <<endl;
legend->AddEntry(depthFrame->findObject("gamma_one"),gamma_one_text.str().c_str(),"L");
legend->AddEntry(depthFrame->findObject("gamma_two"),gamma_two_text.str().c_str(),"L");
legend->AddEntry(depthFrame->findObject("model"),gamma_text.str().c_str(),"L");
legend->AddEntry((TObject*)0, chi2.str().c_str(), "");
cout << "alpha, beta "<< alpha.getVal() << " " << 1/beta.getVal()*beta_gflash_factor << std::endl;
cout << "lambda, theta "<<lambda.getVal() << " " << 1/theta.getVal()*theta_gflash_factor << std::endl;
cout <<"c, chi2 "<< fsig.getVal() << " "<<depthFrame->chiSquare()<< std::endl;
cout <<"starting , interacting Points "<<shower.startingPoint<<" "<<shower.interactPoint <<endl;
cout<< "pi0first, pi0tot "<<shower.ePi0first_energy <<" "<<shower.ePi0tot_energy<<endl;
/*
log_alpha1->Fill(log10(alpha.getVal()));
log_alpha2->Fill(log10(lambda.getVal()));
//cout<<log(1/beta.getVal()*beta_gflash_factor)<<endl;
log_beta1 ->Fill(log10(1/beta.getVal()*beta_gflash_factor));
log_beta2 ->Fill(log10(1/theta.getVal()*theta_gflash_factor));
log_corr1 ->Fill(log10(alpha.getVal()),log10(1/beta.getVal()*beta_gflash_factor));
log_corr2 ->Fill(log10(lambda.getVal()),log10(1/theta.getVal()*theta_gflash_factor));
*/
if(fsig.getVal()>0.1)log_alpha1->Fill(log(alpha.getVal()));
if(fsig.getVal()<0.9)log_alpha2->Fill(log(lambda.getVal()));
//cout<<log(1/beta.getVal()*beta_gflash_factor)<<endl;
if(fsig.getVal()>0.1)log_beta1 ->Fill(log(1/beta.getVal()*beta_gflash_factor));
if(fsig.getVal()<0.9)log_beta2 ->Fill(log(1/theta.getVal()*theta_gflash_factor));
if(fsig.getVal()>0.1)log_corr1 ->Fill(log(alpha.getVal()),log(1/beta.getVal()*beta_gflash_factor));
if(fsig.getVal()<0.9)log_corr2 ->Fill(log(lambda.getVal()),log(1/theta.getVal()*theta_gflash_factor));
//prepare to dump all the plots in the ps file
// one data sample and its fit is drawn
/*
datahisto->Draw();
datahisto0->SetLineColor(kRed);
datahisto0->Draw("same");
datahisto1->SetLineColor(kGreen);
datahisto1->Draw("same");
datahisto2->SetLineColor(kBlue);
datahisto2->Draw("same");
can->Print(epsname);
*/
//if(depthFrame->chiSquare()>5) continue;
depthFrame->Draw("HIST p");
legend->Draw();
stringstream particle_number;
particle_number.precision(1);
particle_number << p <<endl;
can->Print((epsname+"_"+particle_number.str()+".pdf").c_str());
}
RooDataHist roomeandatahist("roomeandatahist","roomeandatahist",depth,Import(*mean_fithist)) ;
fsig.setVal(c_tot);
alpha.removeMax(); // set infinite range
beta.removeMax(); // set infinite range
lambda.removeMax(); // set infinite range
theta.removeMax(); // set infinite range
//model.fitTo(roomeandatahist);
//model.fitTo(mean,SumW2Error(kTRUE));
TLegend * mean_legend = new TLegend(0.5,0.65,.9,0.9);
mean_legend->SetTextFont(72);
mean_legend->SetTextSize(0.04);
mean_legend->SetFillColor(kWhite);
mean_legend->SetBorderSize(0);
stringstream mean_gamma_one_text;
mean_gamma_one_text.precision(3);
mean_gamma_one_text << "#Gamma_{1} ("<<alpha.getVal()<<","<<(1/beta.getVal()*beta_gflash_factor)<<")"<<endl;
stringstream mean_gamma_two_text;
mean_gamma_two_text.precision(3);
mean_gamma_two_text << "#Gamma_{2} (" <<lambda.getVal()<<","<<(1/theta.getVal()*theta_gflash_factor)<< ")"<<endl;
stringstream mean_gamma_text;
mean_gamma_text.precision(2);
mean_gamma_text << "#Gamma_{1/2} c=" <<fsig.getVal() <<"("<< c_tot<<")"<<endl;
stringstream mean_chi2;
mean_chi2.precision(2);
//mean_chi2 << "#chi^{2} " <<depthFrame->chiSquare() <<endl;
RooPlot * meanFrame = depth.frame(0,120,200);
//meanFrame->SetMaximum(1);
meanFrame->SetTitle("Mean Fit");
meanFrame->SetYTitle("1/E dE/dr");
model.plotOn(meanFrame,Components(gamma_one),LineStyle(kDashed),LineColor(kGreen),Name("gamma_one"));
model.plotOn(meanFrame,Components(gamma_two),LineStyle(kDashed),LineColor(kRed),Name("gamma_two"));
model.plotOn(meanFrame,Name("model"));
//mean.plotOn(meanFrame);
//roomeandatahist.plotOn(meanFrame);
mean_legend->AddEntry(meanFrame->findObject("gamma_one"),mean_gamma_one_text.str().c_str(),"L");
mean_legend->AddEntry(meanFrame->findObject("gamma_two"),mean_gamma_two_text.str().c_str(),"L");
mean_legend->AddEntry(meanFrame->findObject("model"),mean_gamma_text.str().c_str(),"L");
meanFrame->Draw();
mean_legend->Draw();
//datahisto->Draw();
//can->Print(epsname);
//mean_hist->Draw("box");
//can->Print(epsname);
can->Print((epsname+"_mean.pdf").c_str());
log_alpha1->Draw();
can->Print((epsname+"_alpha1_log.pdf").c_str());
log_beta1->Draw();
can->Print((epsname+"_beta1_log.pdf").c_str());
log_corr1->Draw("colz");
can->Print((epsname+"corr1_log.pdf").c_str());
log_alpha2->Draw();
can->Print((epsname+"apha2_log.pdf").c_str());
log_beta2->Draw();
can->Print((epsname+"beta2_log.pdf").c_str());
log_corr2->Draw("colz");
can->Print((epsname+"corr2_log.pdf").c_str());
///can->Print(epsname+"]");
return 0;
}
double getSignalContribution(TH1D * h, double &syserr, TString someoptions=""){ //options: (CaC: cut and count without fitting; only stat error)
using namespace std;
using namespace RooFit;
double nSignalOS;
if(someoptions == "CaC"){
nSignalOS = h->Integral();
syserr = sqrt(h->getIntegral);
}
else{
double rangemin=h->GetXaxis()->GetXmin();
double rangemax=h->GetXaxis()->GetXmax();
RooRealVar mass("mass","mass",rangemin,rangemax);
mass.setRange("cutcut",rangemin,rangemax);
RooDataHist roohist("roohist","roohist",mass,Import(*h));
TString fitname=h->GetName();
RooPlot * frame = mass.frame(Title("Fit " + fitname));
roohist.plotOn(frame,MarkerColor(1),MarkerSize(0.9),MarkerStyle(21));
roohist.statOn(frame);
//signal parameter
RooRealVar nsig("nsig","nsig",1000.,0.,10000000.);
// VOIGTIAN FUNCTION
RooRealVar mean("mean","mean",91.2, 20.0,120.0);
RooRealVar width("width","width",5.0, 0.0, 100.0);
RooRealVar sigmaV("sigmaV","sigmaV",5.0, 0.0, 100.0);
RooVoigtian voigt("voigt","voigt",mass,mean,width,sigmaV);
//======// Parameters for CrystalBall
RooRealVar m0("M_{ll}", "Bias", 114., 80, 120,"GeV");
RooRealVar sigma("#sigma_{CB}","Width", 9.2,3.0,10.0);//,"GeV/c^{2}");
RooRealVar cut("#alpha","Cut", 6., 5., 7.);
RooRealVar power("#gamma","Power", 10., 1., 20.);
RooCBShape CrystalBall("CrystalBall", "A Crystal Ball Lineshape", mass, m0,sigma, cut, power);
//======//Parameters for Breit-Wigner Distribution
RooRealVar mRes("M_{ll}", "Z Resonance Mass", 91.2, 89,94);//,"GeV/c^{2}");
RooRealVar Gamma("#Gamma", "#Gamma", 4.0, 1.0,20.0);//,"GeV/c^{2}");
RooBreitWigner BreitWigner("BreitWigner","A Breit-Wigner Distribution",mass,mRes,Gamma);
// SIGNAL MODEL
RooFFTConvPdf ResolutionModel("Convolution","Convolution", mass, BreitWigner, CrystalBall);
//BG model
RooRealVar nbkg("nbkg","nbkg",10.,0.,200000.,"GeV");
RooRealVar bkg_slope("bkg_slope","slope of the background exponential mass PDF",-0.1,0.1);
RooExponential bkgModel("bkgModel","background mass PDF",mass,bkg_slope);
//add sig + bg
RooAddPdf pdfFinal("pdfFinal","pdfFinal",RooArgList(voigt,bkgModel),RooArgList(nsig,nbkg));
RooFitResult *fitResult = pdfFinal.fitTo(roohist, RooFit::Save(true),
RooFit::Extended(true), RooFit::PrintLevel(-1));
fitResult->Print();//"v"); //verbose
pdfFinal.plotOn(frame,LineColor(4));
pdfFinal.plotOn(frame,Components("bkgModel"),LineColor(kRed),LineStyle(kDashed));
pdfFinal.paramOn(frame);
nSignalOS = nsig.getVal();
double signError = nsig.getError();
cout << "\n\n\nsignal contribution: " << nSignalOS << " +- " << signError << " background contribution: " << nbkg.getVal() << endl;
TCanvas c = TCanvas(fitname+" Zmass",fitname + " Zmass",800,400) ;
c.cd() ; gPad->SetLeftMargin(0.15);
frame->Draw();
c.Write();
syserr=signError;
}
return nSignalOS;
}
void rf316_llratioplot()
{
// C r e a t e 3 D p d f a n d d a t a
// -------------------------------------------
// Create observables
RooRealVar x("x","x",-5,5) ;
RooRealVar y("y","y",-5,5) ;
RooRealVar z("z","z",-5,5) ;
// Create signal pdf gauss(x)*gauss(y)*gauss(z)
RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
// Create background pdf poly(x)*poly(y)*poly(z)
RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
RooPolynomial pz("pz","pz",z) ;
RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
// Create composite pdf sig+bkg
RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
RooDataSet* data = model.generate(RooArgSet(x,y,z),20000) ;
// P r o j e c t p d f a n d d a t a o n x
// -------------------------------------------------
// Make plain projection of data and pdf on x observable
RooPlot* frame = x.frame(Title("Projection of 3D data and pdf on X"),Bins(40)) ;
data->plotOn(frame) ;
model.plotOn(frame) ;
// D e f i n e p r o j e c t e d s i g n a l l i k e l i h o o d r a t i o
// ----------------------------------------------------------------------------------
// Calculate projection of signal and total likelihood on (y,z) observables
// i.e. integrate signal and composite model over x
RooAbsPdf* sigyz = sig.createProjection(x) ;
RooAbsPdf* totyz = model.createProjection(x) ;
// Construct the log of the signal / signal+background probability
RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
// P l o t d a t a w i t h a L L r a t i o c u t
// -------------------------------------------------------
// Calculate the llratio value for each event in the dataset
data->addColumn(llratio_func) ;
// Extract the subset of data with large signal likelihood
RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
// Make plot frame
RooPlot* frame2 = x.frame(Title("Same projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
// Plot select data on frame
dataSel->plotOn(frame2) ;
// M a k e M C p r o j e c t i o n o f p d f w i t h s a m e L L r a t i o c u t
// ---------------------------------------------------------------------------------------------
// Generate large number of events for MC integration of pdf projection
RooDataSet* mcprojData = model.generate(RooArgSet(x,y,z),10000) ;
// Calculate LL ratio for each generated event and select MC events with llratio)0.7
mcprojData->addColumn(llratio_func) ;
RooDataSet* mcprojDataSel = (RooDataSet*) mcprojData->reduce(Cut("llratio>0.7")) ;
// Project model on x, integrating projected observables (y,z) with Monte Carlo technique
// on set of events with the same llratio cut as was applied to data
model.plotOn(frame2,ProjWData(*mcprojDataSel)) ;
TCanvas* c = new TCanvas("rf316_llratioplot","rf316_llratioplot",800,400) ;
c->Divide(2) ;
c->cd(1) ;
gPad->SetLeftMargin(0.15) ;
frame->GetYaxis()->SetTitleOffset(1.4) ;
frame->Draw() ;
c->cd(2) ;
gPad->SetLeftMargin(0.15) ;
frame2->GetYaxis()->SetTitleOffset(1.4) ;
frame2->Draw() ;
}
void rf403_weightedevts()
{
// C r e a t e o b s e r v a b l e a n d u n w e i g h t e d d a t a s e t
// -------------------------------------------------------------------------------
// Declare observable
RooRealVar x("x","x",-10,10) ;
x.setBins(40) ;
// Construction a uniform pdf
RooPolynomial p0("px","px",x) ;
// Sample 1000 events from pdf
RooDataSet* data = p0.generate(x,1000) ;
// C a l c u l a t e w e i g h t a n d m a k e d a t a s e t w e i g h t e d
// -----------------------------------------------------------------------------------
// Construct formula to calculate (fake) weight for events
RooFormulaVar wFunc("w","event weight","(x*x+10)",x) ;
// Add column with variable w to previously generated dataset
RooRealVar* w = (RooRealVar*) data->addColumn(wFunc) ;
// Dataset d is now a dataset with two observable (x,w) with 1000 entries
data->Print() ;
// Instruct dataset wdata in interpret w as event weight rather than as observable
RooDataSet wdata(data->GetName(),data->GetTitle(),data,*data->get(),0,w->GetName()) ;
// Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K
wdata.Print() ;
// U n b i n n e d M L f i t t o w e i g h t e d d a t a
// ---------------------------------------------------------------
// Construction quadratic polynomial pdf for fitting
RooRealVar a0("a0","a0",1) ;
RooRealVar a1("a1","a1",0,-1,1) ;
RooRealVar a2("a2","a2",1,0,10) ;
RooPolynomial p2("p2","p2",x,RooArgList(a0,a1,a2),0) ;
// Fit quadratic polynomial to weighted data
// NOTE: A plain Maximum likelihood fit to weighted data does in general
// NOT result in correct error estimates, unless individual
// event weights represent Poisson statistics themselves.
//
// Fit with 'wrong' errors
RooFitResult* r_ml_wgt = p2.fitTo(wdata,Save()) ;
// A first order correction to estimated parameter errors in an
// (unbinned) ML fit can be obtained by calculating the
// covariance matrix as
//
// V' = V C-1 V
//
// where V is the covariance matrix calculated from a fit
// to -logL = - sum [ w_i log f(x_i) ] and C is the covariance
// matrix calculated from -logL' = -sum [ w_i^2 log f(x_i) ]
// (i.e. the weights are applied squared)
//
// A fit in this mode can be performed as follows:
RooFitResult* r_ml_wgt_corr = p2.fitTo(wdata,Save(),SumW2Error(kTRUE)) ;
// P l o t w e i g h e d d a t a a n d f i t r e s u l t
// ---------------------------------------------------------------
// Construct plot frame
RooPlot* frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data")) ;
// Plot data using sum-of-weights-squared error rather than Poisson errors
wdata.plotOn(frame,DataError(RooAbsData::SumW2)) ;
// Overlay result of 2nd order polynomial fit to weighted data
p2.plotOn(frame) ;
// M L F i t o f p d f t o e q u i v a l e n t u n w e i g h t e d d a t a s e t
// -----------------------------------------------------------------------------------------
// Construct a pdf with the same shape as p0 after weighting
RooGenericPdf genPdf("genPdf","x*x+10",x) ;
// Sample a dataset with the same number of events as data
RooDataSet* data2 = genPdf.generate(x,1000) ;
// Sample a dataset with the same number of weights as data
RooDataSet* data3 = genPdf.generate(x,43000) ;
// Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison
RooFitResult* r_ml_unw10 = p2.fitTo(*data2,Save()) ;
RooFitResult* r_ml_unw43 = p2.fitTo(*data3,Save()) ;
// C h i 2 f i t o f p d f t o b i n n e d w e i g h t e d d a t a s e t
// ------------------------------------------------------------------------------------
// Construct binned clone of unbinned weighted dataset
RooDataHist* binnedData = wdata.binnedClone() ;
binnedData->Print("v") ;
// Perform chi2 fit to binned weighted dataset using sum-of-weights errors
//
// NB: Within the usual approximations of a chi2 fit, a chi2 fit to weighted
// data using sum-of-weights-squared errors does give correct error
// estimates
RooChi2Var chi2("chi2","chi2",p2,*binnedData,DataError(RooAbsData::SumW2)) ;
RooMinuit m(chi2) ;
m.migrad() ;
m.hesse() ;
// Plot chi^2 fit result on frame as well
RooFitResult* r_chi2_wgt = m.save() ;
p2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
// C o m p a r e f i t r e s u l t s o f c h i 2 , M L f i t s t o ( u n ) w e i g h t e d d a t a
// ---------------------------------------------------------------------------------------------------------------
// Note that ML fit on 1Kevt of weighted data is closer to result of ML fit on 43Kevt of unweighted data
// than to 1Kevt of unweighted data, whereas the reference chi^2 fit with SumW2 error gives a result closer to
// that of an unbinned ML fit to 1Kevt of unweighted data.
cout << "==> ML Fit results on 1K unweighted events" << endl ;
r_ml_unw10->Print() ;
cout << "==> ML Fit results on 43K unweighted events" << endl ;
r_ml_unw43->Print() ;
cout << "==> ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
r_ml_wgt->Print() ;
cout << "==> Corrected ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
r_ml_wgt_corr->Print() ;
cout << "==> Chi2 Fit results on 1K weighted events with a summed weight of 43K" << endl ;
r_chi2_wgt->Print() ;
new TCanvas("rf403_weightedevts","rf403_weightedevts",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.8) ; frame->Draw() ;
}
void draw_data_mgg(TString folderName,bool blind=true,float min=103,float max=160)
{
TFile inputFile(folderName+"/data.root");
const int nCat = 5;
TString cats[5] = {"HighPt","Hbb","Zbb","HighRes","LowRes"};
TCanvas cv;
for(int iCat=0; iCat < nCat; iCat++) {
RooWorkspace *ws = (RooWorkspace*)inputFile.Get(cats[iCat]+"_mgg_workspace");
RooFitResult* res = (RooFitResult*)ws->obj("fitresult_pdf_data");
RooRealVar * mass = ws->var("mgg");
mass->setRange("all",min,max);
mass->setRange("blind",121,130);
mass->setRange("low",106,121);
mass->setRange("high",130,160);
mass->setUnit("GeV");
mass->SetTitle("m_{#gamma#gamma}");
RooAbsPdf * pdf = ws->pdf("pdf");
RooPlot *plot = mass->frame(min,max,max-min);
plot->SetTitle("");
RooAbsData* data = ws->data("data")->reduce(Form("mgg > %f && mgg < %f",min,max));
double nTot = data->sumEntries();
if(blind) data = data->reduce("mgg < 121 || mgg>130");
double nBlind = data->sumEntries();
double norm = nTot/nBlind; //normalization for the plot
data->plotOn(plot);
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"),RooFit::LineWidth(0.1) );
plot->Print();
//add the fix error band
RooCurve* c = plot->getCurve("pdf_Norm[mgg]_Range[Full]_NormRange[Full]");
const int Nc = c->GetN();
//TGraphErrors errfix(Nc);
//TGraphErrors errfix2(Nc);
TGraphAsymmErrors errfix(Nc);
TGraphAsymmErrors errfix2(Nc);
Double_t *x = c->GetX();
Double_t *y = c->GetY();
double NtotalFit = ws->var("Nbkg1")->getVal()*ws->var("Nbkg1")->getVal() + ws->var("Nbkg2")->getVal()*ws->var("Nbkg2")->getVal();
for( int i = 0; i < Nc; i++ )
{
errfix.SetPoint(i,x[i],y[i]);
errfix2.SetPoint(i,x[i],y[i]);
mass->setVal(x[i]);
double shapeErr = pdf->getPropagatedError(*res)*NtotalFit;
//double totalErr = TMath::Sqrt( shapeErr*shapeErr + y[i] );
//total normalization error
double totalErr = TMath::Sqrt( shapeErr*shapeErr + y[i]*y[i]/NtotalFit );
if ( y[i] - totalErr > .0 )
{
errfix.SetPointError(i, 0, 0, totalErr, totalErr );
}
else
{
errfix.SetPointError(i, 0, 0, y[i] - 0.01, totalErr );
}
//2sigma
if ( y[i] - 2.*totalErr > .0 )
{
errfix2.SetPointError(i, 0, 0, 2.*totalErr, 2.*totalErr );
}
else
{
errfix2.SetPointError(i, 0, 0, y[i] - 0.01, 2.*totalErr );
}
/*
std::cout << x[i] << " " << y[i] << " "
<< " ,pdf get Val: " << pdf->getVal()
<< " ,pdf get Prop Err: " << pdf->getPropagatedError(*res)*NtotalFit
<< " stat uncertainty: " << TMath::Sqrt(y[i]) << " Ntot: " << NtotalFit << std::endl;
*/
}
errfix.SetFillColor(kYellow);
errfix2.SetFillColor(kGreen);
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kFALSE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kFALSE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kTRUE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kTRUE));
plot->addObject(&errfix,"4");
plot->addObject(&errfix2,"4");
plot->addObject(&errfix,"4");
data->plotOn(plot);
TBox blindBox(121,plot->GetMinimum()-(plot->GetMaximum()-plot->GetMinimum())*0.015,130,plot->GetMaximum());
blindBox.SetFillColor(kGray);
if(blind) {
plot->addObject(&blindBox);
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kTRUE));
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kTRUE));
}
//plot->addObject(&errfix,"4");
//data->plotOn(plot);
//pdf->plotOn(plot,RooFit::Normalization( norm ) );
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"),RooFit::LineWidth(1.5) );
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"), RooFit::LineWidth(1));
data->plotOn(plot);
/*
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::Range("all"),RooFit::LineWidth(0.8) );
//pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kGreen),RooFit::Range("all"), RooFit::VisualizeError(*res,2.0,kFALSE));
//pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kYellow),RooFit::Range("all"), RooFit::VisualizeError(*res,1.0,kFALSE));
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kGreen),RooFit::Range("all"), RooFit::VisualizeError(*res,2.0,kTRUE));
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kYellow),RooFit::Range("all"), RooFit::VisualizeError(*res,1.0,kTRUE));
data->plotOn(plot);
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::Range("all"),RooFit::LineWidth(0.8) );
*/
TLatex lbl0(0.1,0.96,"CMS Preliminary");
lbl0.SetNDC();
lbl0.SetTextSize(0.042);
plot->addObject(&lbl0);
TLatex lbl(0.4,0.96,Form("%s Box",cats[iCat].Data()));
lbl.SetNDC();
lbl.SetTextSize(0.042);
plot->addObject(&lbl);
TLatex lbl2(0.6,0.96,"#sqrt{s}=8 TeV L = 19.78 fb^{-1}");
lbl2.SetNDC();
lbl2.SetTextSize(0.042);
plot->addObject(&lbl2);
int iObj=-1;
TNamed *obj;
while( (obj = (TNamed*)plot->getObject(++iObj)) ) {
obj->SetName(Form("Object_%d",iObj));
}
plot->Draw();
TString tag = (blind ? "_BLIND" : "");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".png");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".pdf");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".C");
}
}
void plotRegrVsNoRegr(int channel, int massBin) {
stringstream filenom, filenoregr;
filenom << "m4lplots/nominal/fitM" << massBin << "_channel" << channel << ".root";
filenoregr << "m4lplots/noregr/fitM" << massBin << "_channel" << channel << ".root";
int col;
if(channel==0) col=kOrange+7;
if(channel==1) col=kAzure+2;
if(channel==2) col=kGreen+3;
TCanvas *c1 = new TCanvas("c1","c1",750,750);
TFile *tfilenom = TFile::Open(filenom.str().c_str());
RooPlot *plotnom = (RooPlot*)tfilenom->Get("m4lplot");
plotnom->SetMarkerStyle(kOpenSquare);
plotnom->Draw();
TPaveText *pavenom = (TPaveText*)tfilenom->Get("TPave");
pavenom->SetTextColor(col);
pavenom->Draw("same");
TFile *tfilenoregr = TFile::Open(filenoregr.str().c_str());
RooPlot *plotnoregr = (RooPlot*)tfilenoregr->Get("m4lplot");
plotnoregr->Draw("same");
TPaveText *pavenoregr = (TPaveText*)tfilenoregr->Get("TPave");
pavenoregr->Draw("same");
// cosmetics
TLegend *legend = new TLegend(0.20,0.45,0.45,0.60,NULL,"brNDC");
legend->SetBorderSize( 0);
legend->SetFillColor ( 0);
legend->SetTextAlign ( 12);
legend->SetTextFont ( 42);
legend->SetTextSize (0.03);
TH1F *dummyPointsNom = new TH1F("dummyPNom","dummyPNom",1,0,1);
TH1F *dummyPointsNoRegr = new TH1F("dummyPNoregr","dummyPNoregr",1,0,1);
TH1F *dummyLine = new TH1F("dummyL","dummyL",1,0,1);
dummyPointsNoRegr->SetMarkerStyle(kFullCircle);
dummyPointsNoRegr->SetMarkerSize(1.1);
dummyPointsNom->SetMarkerStyle(kFullSquare);
dummyPointsNom->SetMarkerColor(col);
dummyPointsNom->SetLineColor(col);
dummyPointsNom->SetMarkerSize(1.1);
dummyLine->SetLineColor(col);
legend->AddEntry(dummyPointsNoRegr, "Simulation (E_{std}-p comb.)", "pel");
legend->AddEntry(dummyPointsNom, "Simulation (E_{regr}-p comb.)", "pel");
legend->Draw();
TPaveText *text = new TPaveText(0.15,0.90,0.77,0.98,"brNDC");
text->AddText("CMS Simulation");
text->SetBorderSize(0);
text->SetFillStyle(0);
text->SetTextAlign(12);
text->SetTextFont(42);
text->SetTextSize(0.03);
text->Draw();
stringstream frameTitle;
if(channel==0){frameTitle << "4#mu, m_{H} = ";}
if(channel==1){frameTitle << "4e, m_{H} = ";}
if(channel==2){frameTitle << "2e2#mu, m_{H} = ";}
frameTitle << massBin << " GeV";
TPaveText *titlet = new TPaveText(0.15,0.80,0.60,0.85,"brNDC");
titlet->AddText(frameTitle.str().c_str());
titlet->SetBorderSize(0);
titlet->SetFillStyle(0);
titlet->SetTextAlign(12);
titlet->SetTextFont(132);
titlet->SetTextSize(0.045);
titlet->Draw();
c1->SaveAs("comp.pdf");
}
void fit_MC_norm(std::string input_file = "/afs/cern.ch/work/a/apmorris/private/cern/ntuples/new_tuples/normalisation_samples/reduced_Lb2JpsipK_MC_2011_2012_norm.root", std::string out_file_mass = "~/cern/plots/fitting/Lb2JpsipK_MC_2011_2012_cut_mass_fit.png"){
//
gROOT->ProcessLine(".L ~/cern/scripts/lhcbStyle.C");
//lhcbStyle();
const std::string filename(input_file.c_str());
const std::string treename = "DecayTree";
TFile* file = TFile::Open( filename.c_str() );
if( !file ) std::cout << "file " << filename << " does not exist" << std::endl;
TTree* tree = (TTree*)file->Get( treename.c_str() );
if( !tree ) std::cout << "tree " << treename << " does not exist" << std::endl;
// -- signal, mass shape
RooRealVar Lambda_b0_DTF_MASS_constr1("Lambda_b0_DTF_MASS_constr1","m(J/#psi pK^{-})", 5450., 5850., "MeV/c^{2}");
RooRealVar Jpsi_M("Jpsi_M","m(#mu#mu)", 3000., 3200., "MeV/c^{2}");
//RooRealVar chi_c_M("chi_c_M","m(J/#psi#gamma)", 3350., 3750., "MeV/c^{2}");
RooRealVar mean("mean","mean", 5620., 5595., 5650.);
RooRealVar sigma1("sigma1","sigma1", 10., 1., 100.);
RooRealVar sigma2("sigma2","sigma2", 100., 1., 1000.);
RooRealVar alpha1("alpha1","alpha1", 1.0, 0.5, 5.0);
RooRealVar n1("n1","n1", 1.8, 0.2, 15.0);
RooRealVar alpha2("alpha2","alpha2", -0.5, -5.5, 0.0);
RooRealVar n2("n2","n2", 0.7, 0.2, 10.0);
//RooRealVar bkgcat_chic("bkgcat_chic","bkgcat_chic", 0, 100);
RooGaussian gauss1("gauss1","gauss1", Lambda_b0_DTF_MASS_constr1, mean, sigma1);
RooGaussian gauss2("gauss2","gauss2", Lambda_b0_DTF_MASS_constr1, mean, sigma2);
RooCBShape cb1("cb1","cb1", Lambda_b0_DTF_MASS_constr1, mean, sigma1, alpha1, n1);
RooCBShape cb2("cb2","cb2", Lambda_b0_DTF_MASS_constr1, mean, sigma2, alpha2, n2);
/*
// the chi_c2 component
RooRealVar mean3("mean3","mean3", 5570., 5520., 5580.);
RooRealVar sigma3("sigma3","sigma3", 10., 1., 20.);
RooGaussian gauss3("gauss3","gauss3", Lambda_b0_DTF_MASS_constr1, mean3, sigma3);
*/
RooRealVar cbRatio("cbRatio","cbRatio", 0.8, 0.1, 1.0);
RooRealVar frac2("frac2","frac2", 0.3, 0., 1.);
/*
alpha1.setVal( 2.1 );
alpha2.setVal( -4.9 );
n1.setVal( 3.2 );
n2.setVal( 7.9 );
cbRatio.setVal( 0.6808 );
alpha1.setConstant( true );
alpha2.setConstant( true );
cbRatio.setConstant( true );
n1.setConstant( true );
n2.setConstant( true );
*/
// -- add signal & bg
//RooAddPdf pdf("pdf", "pdf", RooArgList(gauss1, gauss2), RooArgList( frac2 ));
RooAddPdf pdf("pdf", "pdf", RooArgList(cb1, cb2), RooArgList( cbRatio ));
RooArgSet obs;
obs.add(Lambda_b0_DTF_MASS_constr1);
obs.add(Jpsi_M);
//obs.add(chi_c_M);
//obs.add(bkgcat_chic);
RooDataSet ds("ds","ds", obs, RooFit::Import(*tree));
//RooFit::Cut("Lambda_b0_DTF_MASS_constr1 > 5580")
RooPlot* plot = Lambda_b0_DTF_MASS_constr1.frame();
plot->SetAxisRange(5500., 5750.);
pdf.fitTo( ds );
ds.plotOn( plot, RooFit::Binning(200) );
pdf.plotOn( plot );
//gauss3.plotOn( plot );
RooPlot* plotPullMass = Lambda_b0_DTF_MASS_constr1.frame();
plotPullMass->addPlotable( plot->pullHist() );
//plotPullMass->SetMinimum();
//plotPullMass->SetMaximum();
plotPullMass->SetAxisRange(5500., 5750.);
TCanvas* c = new TCanvas();
c->cd();
TPad* pad1 = new TPad("pad1","pad1", 0, 0.3, 1, 1.0);
pad1->SetBottomMargin(0.1);
pad1->SetTopMargin(0.1);
pad1->Draw();
//TPad* pad2 = new TPad("pad2","pad2", 0, 0.05, 1, 0.4);
TPad* pad2 = new TPad("pad2","pad2", 0, 0, 1, 0.3);
pad2->SetBottomMargin(0.1);
pad2->SetTopMargin(0.0);
pad2->Draw();
pdf.plotOn( plot, RooFit::Components( cb1 ), RooFit::LineColor( kRed ), RooFit::LineStyle(kDashed) );
pdf.plotOn( plot, RooFit::Components( cb2 ), RooFit::LineColor( kOrange ), RooFit::LineStyle(kDotted) );
//pdf.plotOn( plot, RooFit::Components( bgPdf ), RooFit::LineColor( kBlue ), RooFit::LineStyle(kDashDotted) );
pad1->cd();
//pad1->SetLogy();
plot->Draw();
pad2->cd();
plotPullMass->Draw("AP");
c->SaveAs(out_file_mass.c_str());
}
