void plot2(RooRealVar *mass, RooAbsPdf *pdf, RooAbsPdf *pdf1, RooAbsPdf *pdf2, float frac, RooDataSet *data, string name, int bins, int mhLow, int mhHigh){
RooPlot *plot = mass->frame();
mass->setRange("unblindReg_1",85,95);
mass->setRange("unblindReg_2",110,120);
if (BLIND) {
data->plotOn(plot,Binning(bins),CutRange("unblindReg_1"));
data->plotOn(plot,Binning(bins),CutRange("unblindReg_2"));
data->plotOn(plot,Binning(bins),Invisible());
}
else data->plotOn(plot,Binning(bins));
TCanvas *canv = new TCanvas();
pdf->plotOn(plot);
pdf->plotOn(plot,Components(*pdf1),LineColor(kRed),LineStyle(2)) ;
pdf->plotOn(plot,Components(*pdf2),LineColor(kGreen),LineStyle(2)) ;
//,RooFit::NormRange("fitdata_1,fitdata_2"));
//pdf->paramOn(plot,RooFit::Layout(0.35,0.89,0.89),RooFit::Format("NEA",AutoPrecision(1)));
//plot->getAttText()->SetTextSize(0.01);
//plot->SetMaximum(plot->GetMaximum()*2);
if (BLIND) plot->SetMinimum(0.0001);
plot->SetTitle("");
plot->SetYTitle(Form("Events / %.2fGeV",float(mhHigh-mhLow)/float(bins)));
//plot->GetYaxis()->SetLabelSize(0.05);
plot->SetTitleSize(0.045, "Y");
plot->SetTitleOffset(1.,"Y");
//plot->GetXaxis()->SetLabelSize(0.15);
//plot->GetYaxis()->SetLabelSize(0.15);
plot->SetXTitle("m_{#gamma#gamma}(GeV)");
plot->SetTitleSize(0.15, "X");
plot->Draw();
TLatex *lat = new TLatex();
lat->SetNDC();
lat->SetTextFont(42);
lat->DrawLatex(0.68,0.93,"19.7fb^{-1}(8TeV)");
lat->DrawLatex(0.12,0.85,"CMS Preliminary");
lat->DrawLatex(0.7,0.7,Form("Frac = %.3f",frac));
string nameTemp = name;
int catNum = nameTemp.find("cat");
string catNameOLD = nameTemp.replace(0, catNum, "");
string catName = catNameOLD.replace(catNameOLD.length()-4, 4, "");
lat->DrawLatex(0.1,0.92, Form("%s", catName.c_str()));
TLegend *leg = new TLegend(0.12,0.7,0.32,0.8);
leg->SetFillColor(0);
leg->SetLineColor(1);
leg->AddEntry(data,"Data","lep");
leg->Draw("same");
canv->SaveAs(Form("%s.png",name.c_str()));
canv->SaveAs(Form("%s.pdf",name.c_str()));
delete canv;
delete lat;
}
//-------------------------------------------------------------
//Plot the model fitting function for signal+background
//=============================================================
void MakePlots(RooWorkspace *ws, RooFitResult *fitResult2D) {
RooPlot* framex = 0;
RooPlot* framey = 0;
//Import yield variables
RooRealVar *nsig = ws->var("N (Sig)");
RooRealVar *nres = ws->var("N (ResBkg)");
RooRealVar *nnonres = ws->var("N (NonResBkg)");
//Select and plot only one experiment
if (! (nsig->getVal() >= 15.6 && nsig->getVal() <= 17.0
&& nres->getVal() >= 27.9 && nres->getVal() <= 28.0
&& nnonres->getVal() <= 287.))
return;
if (alreadyPlotted) return;
alreadyPlotted = 1;
//Import the PDF's
RooAbsPdf *model2Dpdf = ws->pdf("model2Dpdf");
RooAbsPdf *sigPDFPho = ws->pdf("sigPDFPho");
RooAbsPdf *resPDFPho = ws->pdf("resPDFPho");
RooAbsPdf *nonresPDFPho = ws->pdf("nonresPDFPho");
RooAbsPdf *sigPDFBjet = ws->pdf("sigPDFBjet");
RooAbsPdf *sigPDFBjetCut = ws->pdf("sigPDFBjetCut");
RooAbsPdf *resPDFBjet = ws->pdf("resPDFBjet");
RooAbsPdf *resPDFBjetExt = ws->pdf("resPDFBjetExt");
RooAbsPdf *nonresPDFBjet = ws->pdf("nonresPDFBjet");
//x-axis variables
RooRealVar *massPho = ws->var("massPho");
RooRealVar *massBjet = ws->var("massBjet");
RooRealVar *massBjetExt = ws->var("massBjetExt");
RooRealVar *massBjetCut = ws->var("massBjetCut");
//sig variables
RooRealVar *sigMeanBjet = ws->var("sigMeanBjet");
RooRealVar *sigSigmaBjet = ws->var("sigSigmaBjet");
RooRealVar *sigAlpha = ws->var("sigAlpha");
RooRealVar *sigPower = ws->var("sigPower");
//res bkg variables
RooRealVar *resMeanBjet = ws->var("resMeanBjet");
RooRealVar *resSigmaBjet = ws->var("resSigmaBjet");
RooRealVar *resAlpha = ws->var("resAlpha");
RooRealVar *resPower = ws->var("resPower");
RooRealVar *resExpo = ws->var("resExpo");
RooRealVar *nbbH = ws->var("nbbH");
RooRealVar *nOthers = ws->var("nOthers");
//simulated data
RooDataHist *sigBjetData = (RooDataHist *)ws->data("sigBjetData");
RooDataHist *resBjetDataExt = (RooDataHist *)ws->data("resBjetDataExt");
RooDataSet *pseudoData2D = (RooDataSet *)ws->data("pseudoData2D");
//Plot of 2D generated data and fits
TH1 *data2d = pseudoData2D->createHistogram("2D Data", *massBjet,Binning(25), YVar(*massPho,Binning(25)));
data2d->SetStats(0);
TH1 *fit2d = model2Dpdf->createHistogram("2D Fit", *massBjet, YVar(*massPho));
fit2d->SetStats(0);
cv = new TCanvas("cv","cv",1600,600);
cv->Divide(2);
cv->cd(1); gPad->SetLeftMargin(0.15); data2d->Draw("LEGO2");
data2d->SetTitle("");
data2d->GetXaxis()->SetTitleOffset(2); data2d->GetXaxis()->SetTitle("M_{bb} [GeV/c^{2}]");
data2d->GetYaxis()->SetTitleOffset(2.2); data2d->GetYaxis()->SetTitle("M_{#gamma#gamma} [GeV/c^{2}]");
data2d->GetZaxis()->SetTitleOffset(1.75);
cv->cd(2); gPad->SetLeftMargin(0.15); fit2d->Draw("SURF1");
fit2d->SetTitle("");
fit2d->GetXaxis()->SetTitleOffset(2); data2d->GetXaxis()->SetTitle("M_{bb} [GeV/c^{2}]");
fit2d->GetYaxis()->SetTitleOffset(2.2); data2d->GetYaxis()->SetTitle("M_{#gamma#gamma} [GeV/c^{2}]");
fit2d->GetZaxis()->SetTitleOffset(1.75);
cv->SaveAs(Form("Plots/AllSignalBkgd/Fits/Toys/twoDimensionalFits_%d.gif",1));
//Plot of massBjet and massPho projections from 2D fit
massPho->setRange("PhoWindow",120,130);
framex = massBjet->frame(Bins(50));
framex->SetTitle(""); framex->SetXTitle("M_{bb} [GeV/c^{2}]"); framex->SetYTitle("Number of Events");
pseudoData2D->plotOn(framex, CutRange("PhoWindow"));
model2Dpdf->plotOn(framex, ProjectionRange("PhoWindow"), VisualizeError(*fitResult2D), FillStyle(3001));
model2Dpdf->plotOn(framex, ProjectionRange("PhoWindow"));
model2Dpdf->plotOn(framex, ProjectionRange("PhoWindow"), Components("sigPDFBjet"), LineStyle(kDashed), LineColor(kRed));
model2Dpdf->plotOn(framex, ProjectionRange("PhoWindow"), Components("resPDFBjet"), LineStyle(kDashed), LineColor(kOrange));
model2Dpdf->plotOn(framex, ProjectionRange("PhoWindow"), Components("nonresPDFBjet"), LineStyle(kDashed), LineColor(kGreen));
framey = massPho->frame(Bins(50));
framey->SetTitle(""); framey->SetXTitle("M_{#gamma#gamma} [GeV/c^{2}]"); framey->SetYTitle("Number of Events");
pseudoData2D->plotOn(framey);
model2Dpdf->plotOn(framey, VisualizeError(*fitResult2D), FillStyle(3001));
model2Dpdf->plotOn(framey);
model2Dpdf->plotOn(framey,Components("sigPDFPho"), LineStyle(kDashed), LineColor(kRed));
model2Dpdf->plotOn(framey,Components("resPDFPho"), LineStyle(kDashed), LineColor(kOrange));
model2Dpdf->plotOn(framey,Components("nonresPDFPho"), LineStyle(kDashed), LineColor(kGreen));
cv = new TCanvas("cv","cv",1600,600);
cv->Divide(2);
cv->cd(1); framex->Draw();
tex = new TLatex();
tex->SetNDC();
tex->SetTextSize(0.042);
tex->SetTextFont(42);
tex->DrawLatex(0.52, 0.84, Form("N_{Sig} = %.2f +/- %.2f", nsig->getVal(), nsig->getPropagatedError(*fitResult2D)));
tex->DrawLatex(0.52, 0.79, Form("N_{ResBkg} = %.2f +/- %.2f", nres->getVal(), nres->getPropagatedError(*fitResult2D)));
tex->DrawLatex(0.52, 0.74, Form("N_{NonResBkg} = %.2f +/- %.2f", nnonres->getVal(), nnonres->getPropagatedError(*fitResult2D)));
tex->Draw();
cv->Update();
cv->cd(2); framey->Draw();
tex->DrawLatex(0.52, 0.84, Form("N_{Sig} = %.2f +/- %.2f", nsig->getVal(), nsig->getPropagatedError(*fitResult2D)));
tex->DrawLatex(0.52, 0.79, Form("N_{ResBkg} = %.2f +/- %.2f", nres->getVal(), nres->getPropagatedError(*fitResult2D)));
tex->DrawLatex(0.52, 0.74, Form("N_{NonResBkg} = %.2f +/- %.2f", nnonres->getVal(), nnonres->getPropagatedError(*fitResult2D)));
tex->Draw();
cv->Update();
cv->SaveAs(Form("Plots/AllSignalBkgd/Fits/Toys/projectionFits_%d.gif",1));
}
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;
}
void AddModels(RooWorkspace *ws, const string inputfilePho, Int_t plotOption, Int_t constbkg) {
gStyle->SetOptFit(0111);
//Import the sample histograms
TFile *filePho = new TFile(inputfilePho.c_str(), "READ");
TH1F *sigPhoMass = (TH1F*)filePho->Get("sigPhoMassBJetWin");
TH1F *nonresPhoMass = (TH1F*)filePho->Get("nonresPhoMassBJetWin");
//-------------------------------------------------------------
//Make Variables for Fit
//=============================================================
//Variables for diPhoton
RooRealVar massPho("massPho","M_{#gamma#gamma}",100.,150.,"GeV/c^{2}");
RooRealVar massPhoCut("massPhoCut","M_{#gamma#gamma} (Cut)",115.,130.,"GeV/c^{2}");
//Signal diPhoton
RooRealVar sigMeanPho("sigMeanPho", "#bar{M}_{#gamma#gamma}", 125., 120., 130., "GeV/c^{2}");
RooRealVar sigSigmaPho("sigSigmaPho", "#sigma_{#gamma#gamma}", 1.5, 1., 2., "GeV/c^{2}");
RooRealVar sigAlphaPho("sigAlphaPho","#alpha_{#gamma#gamma} (cut)", 1., 0.1, 2.);
RooRealVar sigPowerPho("sigPowerPho", "n_{#gamma#gamma} (power)", 3., 1., 5.);
//Non-Resonant Background diPhoton
RooRealVar expRatePho("expRatePho", "#lambda_{#gamma#gamma}", -.027, -.1, 0.);
//Weights for the fit
RooRealVar nsig("N (Sig)", "# signal events", 18.5, -50,100); //(12.7 + 5.8)
RooRealVar nnonres("N (NonResBkg)", "# non-resonant background events", 99.7, 0,1000);
//-------------------------------------------------------------
//Make PDFs for Signal + Background
//=============================================================
//PDFs for diPhoton
//Preliminary Fit for Cut Signal
RooCBShape sigPDFPhoCut("sigPDFPhoCut", "Signal PDF #gamma#gamma", massPhoCut, sigMeanPho, sigSigmaPho, sigAlphaPho, sigPowerPho);
RooDataHist sigPhoData("sigPhoData", "sigPhoData", massPhoCut, sigPhoMass);
RooFitResult *sigPhoCBResult = sigPDFPhoCut.fitTo(sigPhoData, RooFit::Strategy(2));
//Signal
RooCBShape sigPDFPho("sigPDFPho", "Signal PDF #gamma#gamma", massPho, sigMeanPho, sigSigmaPho, sigAlphaPho, sigPowerPho);
//Non-Resonant Background
RooDataHist nonresPhoData("nonresPhoData", "nonresPhoData", massPho, nonresPhoMass);
RooExponential nonresPDFPho("nonresPDFPho", "Non-Resonant Background PDF #gamma#gamma", massPho, expRatePho);
RooFitResult *nonresPhoResult = nonresPDFPho.fitTo(nonresPhoData, RooFit::Strategy(2));
RooAddPdf modelpdf("modelpdf", "Signal+Background PDF", RooArgList(sigPDFPho, nonresPDFPho), RooArgList(nsig, nnonres));
//Variables to set constant
sigMeanPho.setConstant();
sigSigmaPho.setConstant();
sigAlphaPho.setConstant();
sigPowerPho.setConstant();
expRatePho.setConstant();
//Add all stuff to workspace
modelpdf.graphVizTree("fullModel.dot");
ws->import(sigPhoData, Rename("sigPhoData"));
ws->import(nonresPhoData, Rename("nonresPhoData"));
ws->import(sigPDFPhoCut);
ws->import(modelpdf);
//-------------------------------------------------------------
//Plot the fitting functions for signal+background
//=============================================================
if (plotOption == 1) {
RooPlot *framex = 0;
//Plot the Crystal Ball Fit for Signal Photon
cv = new TCanvas("cv","cv",800,600);
framex = massPhoCut.frame(Bins(50));
sigPhoData.plotOn(framex, DrawOption("B"), DataError(RooAbsData::None), FillColor(kRed));
sigPDFPhoCut.plotOn(framex);
framex->SetTitle(""); framex->SetXTitle("M_{#gamma#gamma} [GeV/c^{2}]"); framex->SetYTitle("Number of Events");
framex->Draw();
tex = new TLatex();
tex->SetNDC();
tex->SetTextSize(0.048);
tex->SetTextFont(42);
tex->DrawLatex(0.64, 0.84, Form("#bar{M}_{#gamma#gamma} = %.2f", sigMeanPho.getVal()));
tex->DrawLatex(0.64, 0.79, Form("#sigma_{#gamma#gamma} = %.2f", sigSigmaPho.getVal()));
//tex->DrawLatex(0.64, 0.74, Form("#alpha_{#gamma#gamma} = %.2f", sigAlpha.getVal()));
//tex->DrawLatex(0.64, 0.69, Form("n_{#gamma#gamma} = %.2f", sigPower.getVal()));
tex->Draw();
cv->Update();
cv->SaveAs("Plots/AllSignalBkgd/Fits/InitialFits/sigPhoCutCB.gif");
//Plot Exponential Fit to Non-Resonant Background Bjet
cv = new TCanvas("cv","cv",800,600);
framex = massPho.frame(Bins(50));
nonresPhoData.plotOn(framex, DrawOption("B"), DataError(RooAbsData::None), FillColor(kGreen));
nonresPDFPho.plotOn(framex);
framex->SetTitle(""); framex->SetXTitle("M_{#gamma#gamma} [GeV/c^{2}]"); framex->SetYTitle("Number of Events");
framex->Draw();
tex = new TLatex();
tex->SetNDC();
tex->SetTextSize(0.048);
tex->SetTextFont(42);
tex->DrawLatex(0.64, 0.84, Form("#lambda_{#gamma#gamma} = %.3f", expRatePho.getVal()));
tex->Draw();
cv->Update();
cv->SaveAs("Plots/AllSignalBkgd/Fits/InitialFits/nonresPhoExpo.gif");
}//end of plotting
}
void fitDilMass(){
using namespace RooFit;
gROOT->SetStyle("Plain");
//declare variables
RooRealVar mll("mll", "dilepton mass", 0,100 ,"GeV");
RooRealVar edge("edge", "dilepton mass edge", 0,100 ,"GeV");
RooRealVar min("min", "min dilepton mass", 0,100 ,"GeV");
RooRealVar k("k", "constant", 0,100 ,"GeV");
RooRealVar mean("mean", "mean of mll resolution", 0,100 ,"GeV");
RooRealVar sigma("sigma", "sigma of mll resolution", 0,10 ,"GeV");
//set variables
mll.setBins(100);
edge.setVal(50.);
mean.setVal(0.);
sigma.setVal(2.);
min.setVal(10.);
min.setConstant();
k.setVal(1.);
mean.setConstant();
sigma.setConstant();
mll.setMin(0);
mll.setMax(100);
//get data
TFile* file = TFile::Open("LM0_dilmass.root");
TH1D* hist = static_cast<TH1D *>((file)->Get("dilmass"));
RooDataHist data("data","data histogram",RooArgSet(mll),hist);
//declare and make PDFs
RooGenericPdf tri("tri","( (mll < min && mll > 0) * k + (mll > min && mll < edge ) * (mll - min) + (mll > edge && mll < 200) * k)", RooArgList(mll , edge, min, k) );
RooGaussian gaus("gaus","Detector Gaussian",mll,mean,sigma);
RooNumConvPdf trigaus("trigaus","triangle + gaussian function",mll,tri,gaus);
trigaus.setConvolutionWindow(mean,sigma,25);
//do fit
RooFitResult *result = trigaus.fitTo( data , Save() , Minos(kFALSE) , SumW2Error(kFALSE));
//make plot
TCanvas *c1=new TCanvas("c1","",800,600);
c1->cd();
RooPlot* frame = mll.frame();
frame->SetXTitle("dilepton mass (GeV)");
frame->SetYTitle("");
frame->SetTitle("");
data.plotOn(frame);
tri.plotOn(frame , LineStyle(kDashed));
trigaus.plotOn( frame );
trigaus.paramOn( frame );
frame->Draw();
//frame->SetMinimum(-10);
//frame->SetMaximum(100);
int nfloatpars = result->floatParsFinal().getSize();
int ndf = 20 - nfloatpars;
float chi2 = frame->chiSquare(nfloatpars)*ndf;
float prob = TMath::Prob(chi2,ndf);
cout << endl << endl;
cout << "Fit Results-------------------------------------------" << endl;
cout << "chi2/ndf = " << chi2 << " / " << ndf << endl;
cout << "prob = " << prob << endl;
cout << "edge = " << edge.getVal() << " +/- " << edge.getError() << endl;
cout << "k = " << k.getVal() << " +/- " << k.getError() << endl;
cout << "------------------------------------------------------" << endl;
cout << endl << endl;
}
void StandardHistFactoryPlotsWithCategories(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double nSigmaToVary=5.;
double muVal=0;
bool doFit=false;
// -------------------------------------------------------
// 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;
}
// -------------------------------------------------------
// now use the profile inspector
RooRealVar* obs = (RooRealVar*)mc->GetObservables()->first();
TList* list = new TList();
RooRealVar * firstPOI = dynamic_cast<RooRealVar*>(mc->GetParametersOfInterest()->first());
firstPOI->setVal(muVal);
// firstPOI->setConstant();
if(doFit){
mc->GetPdf()->fitTo(*data);
}
// -------------------------------------------------------
mc->GetNuisanceParameters()->Print("v");
int nPlotsMax = 1000;
cout <<" check expectedData by category"<<endl;
RooDataSet* simData=NULL;
RooSimultaneous* simPdf = NULL;
if(strcmp(mc->GetPdf()->ClassName(),"RooSimultaneous")==0){
cout <<"Is a simultaneous PDF"<<endl;
simPdf = (RooSimultaneous *)(mc->GetPdf());
} else {
cout <<"Is not a simultaneous PDF"<<endl;
}
if(doFit) {
RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
TIterator* iter = channelCat->typeIterator() ;
RooCatType* tt = NULL;
tt=(RooCatType*) iter->Next();
RooAbsPdf* pdftmp = ((RooSimultaneous*)mc->GetPdf())->getPdf(tt->GetName()) ;
RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
obs = ((RooRealVar*)obstmp->first());
RooPlot* frame = obs->frame();
cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));
Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;
pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
frame->Draw();
cout <<"expected events = " << mc->GetPdf()->expectedEvents(*data->get()) <<endl;
return;
}
int nPlots=0;
if(!simPdf){
TIterator* it = mc->GetNuisanceParameters()->createIterator();
RooRealVar* var = NULL;
while( (var = (RooRealVar*) it->Next()) != NULL){
RooPlot* frame = obs->frame();
frame->SetYTitle(var->GetName());
data->plotOn(frame,MarkerSize(1));
var->setVal(0);
mc->GetPdf()->plotOn(frame,LineWidth(1.));
var->setVal(1);
mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(1));
var->setVal(-1);
mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(1));
list->Add(frame);
var->setVal(0);
}
} else {
RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
// TIterator* iter = simPdf->indexCat().typeIterator() ;
TIterator* iter = channelCat->typeIterator() ;
RooCatType* tt = NULL;
while(nPlots<nPlotsMax && (tt=(RooCatType*) iter->Next())) {
cout << "on type " << tt->GetName() << " " << endl;
// Get pdf associated with state from simpdf
RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;
// Generate observables defined by the pdf associated with this state
RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
// obstmp->Print();
obs = ((RooRealVar*)obstmp->first());
TIterator* it = mc->GetNuisanceParameters()->createIterator();
RooRealVar* var = NULL;
while(nPlots<nPlotsMax && (var = (RooRealVar*) it->Next())){
TCanvas* c2 = new TCanvas("c2");
RooPlot* frame = obs->frame();
frame->SetName(Form("frame%d",nPlots));
frame->SetYTitle(var->GetName());
cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));
Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal());
var->Print();
} else{
var->setVal(0);
}
// w->allVars().Print("v");
// mc->GetNuisanceParameters()->Print("v");
// pdftmp->plotOn(frame,LineWidth(2.));
// mc->GetPdf()->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal()+0.05);
var->Print();
} else{
var->setVal(nSigmaToVary);
}
// pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2));
// mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),LineColor(kRed),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal()-0.05);
var->Print();
} else{
var->setVal(-nSigmaToVary);
}
// pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2));
// mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),LineColor(kGreen),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;
// set them back to normal
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal());
var->Print();
} else{
var->setVal(0);
}
list->Add(frame);
// quit making plots
++nPlots;
frame->Draw();
c2->SaveAs(Form("%s_%s_%s.pdf",tt->GetName(),obs->GetName(),var->GetName()));
delete c2;
}
}
}
// -------------------------------------------------------
// now make plots
TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200);
if(list->GetSize()>4){
double n = list->GetSize();
int nx = (int)sqrt(n) ;
int ny = TMath::CeilNint(n/nx);
nx = TMath::CeilNint( sqrt(n) );
c1->Divide(ny,nx);
} else
c1->Divide(list->GetSize());
for(int i=0; i<list->GetSize(); ++i){
c1->cd(i+1);
list->At(i)->Draw();
}
}
void MakePlots(RooWorkspace* ws){
std::cout << "make plots" << std::endl;
RooAbsPdf* model = ws->pdf("model");
RooAbsPdf* model_dY = ws->pdf("model_dY");
RooRealVar* nsig = ws->var("nsig");
RooRealVar* nsigDPS = ws->var("nsigDPS");
RooRealVar* nsigSPS = ws->var("nsigSPS");
RooRealVar* nBbkg = ws->var("nBbkg");
RooRealVar* nbkg = ws->var("nbkg");
RooRealVar* nbkg2 = ws->var("nbkg2");
RooRealVar* FourMu_Mass = ws->var("FourMu_Mass");
RooRealVar* Psi1_Mass = ws->var("Psi1_Mass");
RooRealVar* Psi2_Mass = ws->var("Psi2_Mass");
RooRealVar* Psi1_CTxy = ws->var("Psi1_CTxy");
RooRealVar* Psi2_CTxy = ws->var("Psi2_CTxy");
RooRealVar* Psi1To2_dY = ws->var("Psi1To2_dY");
RooRealVar* Psi1To2Significance = ws->var("Psi1To2Significance");
// note, we get the dataset with sWeights
RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");
model->fitTo(*data, Extended() );
// make TTree with efficiency variation info
//TFile *fFile = new TFile("Fit_Results_Eff_Cut.root","recreate");
setTDRStyle();
// make our canvas
TCanvas* cmass1 = new TCanvas("sPlotMass1","sPlotMass1", 600, 600);
cmass1->cd();
cmass1->SetFillColor(kWhite);
RooPlot* Mass1Plot = Psi1_Mass->frame(20);
data->plotOn(Mass1Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(Mass1Plot,Name("all"));
model->plotOn(Mass1Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(Mass1Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(Mass1Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(Mass1Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
Mass1Plot->SetTitle("");
Mass1Plot->SetXTitle("#mu^{+}#mu^{-} 1 Invariant Mass (GeV/c^{2})");
Mass1Plot->SetYTitle("Events / 0.025 GeV/c^{2}");
Mass1Plot->SetLabelOffset(0.012);
//Mass1Plot->SetTitleOffset(0.95);
Mass1Plot->Draw();
//cmass1->SaveAs("pic/Psi1_mass.pdf");
//cmass1->Close();
TCanvas* cmass2 = new TCanvas("sPlotMass2","sPlotMass2", 600, 600);
cmass2->cd();
cmass2->SetFillColor(kWhite);
RooPlot* Mass2Plot = Psi2_Mass->frame(20);
data->plotOn(Mass2Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(Mass2Plot,Name("all"));
model->plotOn(Mass2Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(Mass2Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(Mass2Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(Mass2Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
Mass2Plot->SetTitle("");
Mass2Plot->SetXTitle("#mu^{+}#mu^{-} 2 Invariant Mass (GeV/c^{2})");
Mass2Plot->SetYTitle("Events / 0.025 GeV/c^{2}");
Mass2Plot->SetLabelOffset(0.012);
//Mass2Plot->SetTitleOffset(0.95);
Mass2Plot->Draw();
//cmass2->SaveAs("pic/Psi2_mass.pdf");
//cmass2->Close();
TCanvas* cctxy1 = new TCanvas("sPlotCTxy1","sPlotCTxy1", 600, 600);
cctxy1->cd();
cctxy1->SetFillColor(kWhite);
RooPlot* CTxy1Plot = Psi1_CTxy->frame(30);
data->plotOn(CTxy1Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(CTxy1Plot,Name("all"));
model->plotOn(CTxy1Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(CTxy1Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(CTxy1Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(CTxy1Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
CTxy1Plot->SetTitle("");
CTxy1Plot->SetXTitle("J/#psi^{1} ct_{xy} (cm)");
CTxy1Plot->SetYTitle("Events / 0.005 cm");
CTxy1Plot->SetMaximum(2000);
CTxy1Plot->SetMinimum(0.1);
CTxy1Plot->Draw();
cctxy1->SetLogy();
//cctxy1->SaveAs("pic/Psi1_CTxy.pdf");
//cctxy1->Close();
TCanvas* csig = new TCanvas("sPlotSig","sPlotSig", 600, 600);
csig->cd();
RooPlot* SigPlot = Psi1To2Significance->frame(20);
data->plotOn(SigPlot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(SigPlot,Name("all"));
model->plotOn(SigPlot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(SigPlot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(SigPlot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(SigPlot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
SigPlot->SetTitle("");
SigPlot->SetYTitle("Events / 0.4");
SigPlot->SetXTitle("J/#psi Distance Significance");
SigPlot->Draw();
//csig->SaveAs("pic/Psi1To2Significance.pdf");
//csig->Close();
// create weighted data set (signal-weighted)
//RooDataSet * dataw_sig = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"nsig_sw");
// model_dY->fitTo(*data);
//TCanvas* cdata2 = new TCanvas("sPlot2","sPlots2", 700, 500);
//cdata2->cd();
//RooPlot* frame2 = Psi1To2_dY->frame(20);
//dataw_sig->plotOn(frame2, DataError(RooAbsData::SumW2) );
/*
model_dY->fitTo(*dataw_sig, SumW2Error(kTRUE), Extended());
model_dY->plotOn(frame2);
model_dY->plotOn(frame2,RooFit::Components("*DPS"),RooFit::LineColor(kRed), RooFit::LineStyle(kDashed));
model_dY->plotOn(frame2,RooFit::Components("*SPS"),RooFit::LineColor(kGreen), RooFit::LineStyle(kDashed));
*/
//frame2->SetTitle("DeltaY distribution for sig");
//frame2->SetMinimum(1e-03);
//frame2->Draw();
//TCanvas* cdata3 = new TCanvas("sPlot3","sPlots3", 700, 500);
//cdata3->cd();
//RooPlot* frame3 = FourMu_Mass->frame(Bins(14),Range(6.,20.));
//dataw_sig->plotOn(frame3, DataError(RooAbsData::SumW2));
//frame3->SetTitle("FourMu_Mass distribution for sig");
//frame3->SetMinimum(1e-03);
//frame3->Draw();
//fFile->Write();
//fFile->Close();
//delete fFile;
}
double final4_D0::doFit(bool usePixel, bool isMC)
{
gROOT->SetBatch(kTRUE);
gROOT->SetStyle("Plain");
//setTDRStyle();
RooRealVar x("","",1.7,2.05);
x.SetTitle("M(K#pi) [GeV/c^{ 2}]");
RooRealVar mean("mean", "mean", 1.86484,1.5, 2.2);
//RooRealVar mean("mean", "mean", 1.865116);
RooRealVar sigma("sigma", "sigma", 0.017, 0.0002, 0.02);
//RooRealVar sigma("sigma", "sigma", 0.015332);
RooGaussian gauss("gauss","gaussian PDF", x, mean, sigma);
RooRealVar alpha("alpha", "alpha", -1.0, -10.0, 10.0);
RooRealVar power("power", "power", 3.0, 0.0, 50.0);
RooCBShape cball("cball", "crystal ball PDF", x, mean, sigma, alpha, power);
RooRealVar dm0("dm0", "dm0", 0.13957);
dm0.setConstant(kTRUE);
RooRealVar shape("shape","shape",0.,-100.,100.);
RooRealVar dstp1("p1","p1",0.,-500.,500.);
RooRealVar dstp2("p2","p2",0.,-500.,500.);
shape.setRange(0.000001,10.0);//was 0.02
shape.setVal(0.0017);
dstp1.setVal(0.45);
dstp2.setVal(13.0);
RooDstD0BG bkg("bkg","bkg",x,dm0,shape,dstp1,dstp2);
RooRealVar c0("c0","c0",10.0,-10.0,11.0);
RooRealVar c1("c1","c1",10.0,-10.0,11.0);
RooRealVar c2("c2","c2",10.0,-10.0,11.0);
RooRealVar c3("c3","c3",10.0,-10.0,11.0);
RooRealVar c4("c4","c4",10.0,-10.0,11.0);
RooRealVar c5("c5","c5",10.0,-10.0,11.0);
RooRealVar c6("c6","c6",10.0,-10.0,11.0);
RooRealVar c7("c7","c7",10.0,-10.0,11.0);
RooRealVar c8("c8","c8",10.0,-10.0,11.0);
RooGenericPdf cutoff("cutoff","cutoff","(@0 > @1)*(@2*abs(@0-@1) + @3*pow(abs(@0-@1),2) + @4*pow(abs(@0-@1),3) + @5*pow(abs(@0-@1),4) + @6*pow(abs(@0-@1),5) + @7*pow(abs(@0-@1),6) + @8*pow(abs(@0-@1),7))",RooArgSet(x,dm0,c0,c1,c2,c3,c4,c5,c6));
RooRealVar poly1("poly1","poly1",0.,-5000.0,5000.0);
RooRealVar poly2("poly2","poly2",1.0,-5000.0,5000.0);
RooRealVar poly3("poly3","poly3",1.0,-5000.0,5000.0);
RooRealVar poly4("poly4","poly4",1.0,-5000.0,5000.0);
RooPolynomial polybkg("polybkg","polybkg",x,RooArgSet(poly1));
RooRealVar cheby0("cheby0","cheby0",1.0,-500.0,500.0);
RooRealVar cheby1("cheby1","cheby1",1.0,-500.0,500.0);
RooRealVar cheby2("cheby2","cheby2",1.0,-500.0,500.0);
RooRealVar cheby3("cheby3","cheby3",1.0,-500.0,500.0);
RooChebychev chebybkg("chebybkg","chebybkg",x,RooArgSet(cheby0,cheby1,cheby2,cheby3));
RooRealVar mean2("mean2", "mean2", 0.14548,0.144, 0.147);
RooRealVar sigma2("sigma2", "sigma2", 0.00065, 0.0002, 0.005);
RooGaussian gauss2("gauss2","gaussian PDF 2", x, mean2, sigma2);
RooRealVar S("S", "Signal Yield", 1100, 0, 300000);
//RooRealVar S("S", "Signal Yield", 0, 0, 300000);
RooRealVar SS("SS", "Signal Yield #2", 100, 0, 100000);
RooRealVar S2("S2", "Signal2 Yield (MC only)", 0, 0, 200);
RooRealVar B("B", "Background Yield", 4000, 0, 30000000);
//RooRealVar B("B", "Background Yield", 0, 0, 30000000);
//RooAddPdf sum("sum", "gaussian plus threshold PDF",RooArgList(gauss, bkg), RooArgList(S, B));
RooAddPdf sum("sum", "gaussian plus linear PDF", RooArgList(gauss, polybkg), RooArgList(S,B));
//RooAddPdf sum("sum", "background PDF",RooArgList(polybkg), RooArgList(B));
//RooAddPdf sum("sum", "background PDF",RooArgList(chebybkg), RooArgList(B));
//RooAddPdf sum("sum", "background PDF",RooArgList(cutoff), RooArgList(B));
// RooAddPdf sum("sum", "gaussians plus threshold PDF",RooArgList(gauss, gauss2, bkg), RooArgList(S, SS, B));
//RooAddPdf sum("sum", "crystal ball plus threshold PDF",RooArgList(cball, bkg), RooArgList(S, B));
RooAddPdf sumMC("sumMC","double gaussian",RooArgList(gauss, gauss2), RooArgList(S, S2));
fstream file;
char filename[50];
double cut=5.5;
sprintf(filename,"D0Mass.dat");
RooDataSet* data = RooDataSet::read(filename,RooArgList(x));
RooFitResult* fit = 0;
if (isMC == 0)
{
fit = sum.fitTo(*data,RooFit::Extended(),PrintLevel(1),Save(true),RooFit::NumCPU(8),RooFit::Strategy(2));
file << "cut: " << cut << "GeV" << endl;
file << "status: " << fit->status() << endl;
file << "covQual: " << fit->covQual() << endl;
file << "edm: " << fit->edm() << endl;
file << "Yield: " << S.getVal() << " " << S.getError() << endl;
file << "Bkg: " << B.getVal() << " " << B.getError() << endl;
file << "sigma: " << sigma.getVal() << " " << sigma.getError() << endl;
file << "mean: " << mean.getVal() << " " << mean.getError() << endl;
file << "shape: " << shape.getVal() << " " << shape.getError() << endl;
file << "dstp1: " << dstp1.getVal() << " " << dstp1.getError() << endl;
file << "dstp2: " << dstp2.getVal() << " " << dstp2.getError() << endl;
file << endl;
}
else
{
fit = sumMC.fitTo(*data,RooFit::Extended(),PrintLevel(1),Save(true),RooFit::NumCPU(8),RooFit::Strategy(2),Range(0.142,0.15));
file << "cut: " << cut << "GeV" << endl;
file << "status: " << fit->status() << endl;
file << "covQual: " << fit->covQual() << endl;
file << "edm: " << fit->edm() << endl;
file << "Yield: " << S.getVal() << " " << S.getError() << endl;
file << "Yield2: " << S2.getVal() << " " << S2.getError() << endl;
file << "sigma: " << sigma.getVal() << " " << sigma.getError() << endl;
file << "mean: " << mean.getVal() << " " << mean.getError() << endl;
file << "sigma2: " << sigma2.getVal() << " " << sigma2.getError() << endl;
file << "mean2: " << mean2.getVal() << " " << mean2.getError() << endl;
file << endl;
}
RooPlot* xFrame = x.frame(Bins(35));
xFrame->SetTitle("D* #rightarrow D^{0}(K#pi)#pi");
data->plotOn(xFrame);
if(isMC == 0)
{
sum.plotOn(xFrame);
sum.plotOn(xFrame,RooFit::Components(bkg),RooFit::LineStyle(kDashed));
}
else
{
sumMC.plotOn(xFrame, Range(0.139,0.159));
// sumMC.plotOn(xFrame,RooFit::Components(gauss2),RooFit::LineStyle(kDashed),Range(0.139,0.159));
}
data->plotOn(xFrame);
file << xFrame->chiSquare() << endl;
TCanvas c;
TPaveText* ptext = 0;
TPaveText* ptex = 0;
if(usePixel == 0)
{
ptext = new TPaveText(0.47,0.33,0.9,0.43,"TRNDC");
ptex = new TPaveText(0.47,0.18,0.9,0.28,"NDC");
}
else
{
if(isMC == 0)
{
ptext = new TPaveText(0.47,0.8,0.9,0.9,"TRNDC");
ptex = new TPaveText(0.47,0.8,0.9,0.9,"NDC");
}
else
{
ptext = new TPaveText(0.47,0.78,0.9,0.88,"TRNDC");
ptex = new TPaveText(0.47,0.63,0.9,0.73,"NDC");
}
}
ptext->SetFillColor(0);
ptext->SetTextSize(0.04);
ptext->SetTextAlign(13);
ptext->AddText("CMS Preliminary");
//ptext->AddText("#sqrt{s} = 13 TeV, 40.0 pb^{-1}");
ptext->AddText("#sqrt{s} = 13 TeV, Spring15 MinBias MC");
xFrame->SetYTitle("Events / 10 MeV/c^{ 2}");
xFrame->GetYaxis()->SetTitleOffset(1.3);
xFrame->GetYaxis()->SetLabelSize(0.03);
xFrame->GetXaxis()->SetLabelSize(0.03);
ptex->SetFillColor(0);
ptex->SetTextSize(0.033);
ptex->SetTextAlign(13);
char theyield[50];
char themean[50];
char thesigma[50];
if(isMC == 0)
{
sprintf(theyield,"Yield = %u #pm %u",(unsigned)S.getVal(),(unsigned)S.getError());
sprintf(themean,"Mean = (%.3f #pm %.3f) MeV/c^{2}",mean.getVal()*1000.0,mean.getError()*1000.0);
sprintf(thesigma,"Sigma = (%.3f #pm %.3f) MeV/c^{2}",sigma.getVal()*1000.0,sigma.getError()*1000.0);
}
else
{
sprintf(theyield,"Yield = %u #pm %u",(unsigned)(S.getVal()+S2.getVal()),(unsigned)(sqrt(pow(S.getError(),2)+pow(S2.getError(),2))));
sprintf(themean,"Mean = (%.3f #pm %.3f) MeV/c^{2}",mean.getVal()*1000.0,mean.getError()*1000.0);
sprintf(thesigma,"Sigma = (%.3f #pm %.3f) MeV/c^{2}",sigma.getVal()*1000.0,sigma.getError()*1000.0);
}
ptex->AddText(theyield);
ptex->AddText(themean);
ptex->AddText(thesigma);
xFrame->Draw();
ptext->Draw("same");
ptex->Draw("same");
c.SaveAs("D0Mass.png");
c.SaveAs("D0Mass.pdf");
S.Print();
// compute integrals
double sfactor;
if(isMC)
sfactor = 0.0;
else
{
double sigbkg, bkg1, bkg2;
double base = 0.145421;
x.setRange("signal",base-0.0013,base+0.0013);
x.setRange("background1",base-0.0051,base-0.0025);
x.setRange("background2",base+0.0025,base+0.0051);
RooAbsReal* iSB = B.createIntegral(x,Range("signal"));
RooAbsReal* iB1 = B.createIntegral(x,Range("background1"));
RooAbsReal* iB2 = B.createIntegral(x,Range("background2"));
sigbkg = iSB->getVal();
bkg1 = iB1->getVal();
bkg2 = iB2->getVal();
sfactor = -1.0 * (sigbkg / (bkg1 + bkg2));
}
return sfactor;
}
