int main(int argc, char **argv) { bool printeff = true; string fc = "none"; gROOT->ProcessLine(".x lhcbStyle.C"); if(argc > 1) { for(int a = 1; a < argc; a++) { string arg = argv[a]; string str = arg.substr(2,arg.length()-2); if(arg.find("-E")!=string::npos) fc = str; if(arg=="-peff") printeff = true; } } int nexp = 100; int nbins = 6; double q2min[] = {8.,15.,11.0,15,16,18}; double q2max[] = {11.,20.,12.5,16,18,20}; TString datafilename = "/afs/cern.ch/work/p/pluca/weighted/Lmumu/candLb.root"; TreeReader * data = new TreeReader("candLb2Lmumu"); data->AddFile(datafilename); TreeReader * datajpsi = new TreeReader("candLb2JpsiL"); datajpsi->AddFile(datafilename); TFile * histFile = new TFile("Afb_bkgSys.root","recreate"); string options = "-quiet-noPlot-lin-stdAxis-XM(#Lambda#mu#mu) (MeV/c^{2})-noCost-noParams"; Analysis::SetPrintLevel("s"); RooRealVar * cosThetaL = new RooRealVar("cosThetaL","cosThetaL",0.,-1.,1.); RooRealVar * cosThetaB = new RooRealVar("cosThetaB","cosThetaB",0.,-1.,1.); RooRealVar * MM = new RooRealVar("Lb_MassConsLambda","Lb_MassConsLambda",5621.,5400.,6000.); MM->setRange("Signal",5600,5640); RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR); //TGraphAsymmErrors * fL_vs_q2 = new TGraphAsymmErrors(); //TCanvas * ceff = new TCanvas(); RooCategory * samples = new RooCategory("samples","samples"); samples->defineType("DD"); samples->defineType("LL"); RooRealVar * afb = new RooRealVar("afb","afb",0.,-0.75,0.75); RooRealVar * fL = new RooRealVar("fL","fL",0.6,0.,1.); TString afbLpdf = "((3./8.)*(1.-fL)*(1 + TMath::Power(cosThetaL,2)) + afb*cosThetaL + (3./4.)*fL*(1 - TMath::Power(cosThetaL,2)))"; RooRealVar * afbB = new RooRealVar("afbB","afbB",0.,-0.5,0.5); TString afbBpdf = "(1 + 2*afbB*cosThetaB)"; RooAbsPdf * teoPdf = new RooGenericPdf("teoPdf",afbLpdf,RooArgSet(*cosThetaL,*afb,*fL)); RooAbsPdf * teoPdfB = new RooGenericPdf("teoPdfB",afbBpdf,RooArgSet(*cosThetaB,*afbB)); TreeReader * mydata = datajpsi; Str2VarMap jpsiParsLL = getJpsiPars("LL", CutsDef::LLcut, histFile); Str2VarMap jpsiParsDD = getJpsiPars("DD", CutsDef::DDcut, histFile); vector<TH1 *> fLsysh, afbsysh, afbBsysh, fLsysh_frac, afbsysh_frac, afbBsysh_frac; for(int i = 0; i < nbins; i++) { TString q2name = ((TString)Form("q2_%4.2f_%4.2f",q2min[i],q2max[i])).ReplaceAll(".",""); if(i>0) { mydata = data; MM->setRange(5400,6000); } else { q2name = "jpsi"; MM->setRange(5500,5850); } TString curq2cut = Form("TMath::Power(J_psi_1S_MM/1000,2) >= %e && TMath::Power(J_psi_1S_MM/1000,2) < %e",q2min[i],q2max[i]); cout << "------------------- q2 bin: " << q2min[i] << " - " << q2max[i] << " -----------------------" << endl; /** GET AND FIT EFFICIENCIES **/ RooAbsPdf * effDDpdf = NULL, * effLLpdf = NULL, * effLLBpdf = NULL, * effDDBpdf = NULL; getEfficiencies(q2min[i],q2max[i],&effLLpdf,&effDDpdf,&effLLBpdf,&effDDBpdf,printeff); cout << "Efficiencies extracted" << endl; histFile->cd(); /** FIT AFB **/ afb->setVal(0); afbB->setVal(-0.37); fL->setVal(0.6); RooAbsPdf * corrPdfLL = new RooProdPdf("sigPdfLL"+q2name,"corrPdfLL",*teoPdf,*effLLpdf); RooAbsPdf * corrPdfDD = new RooProdPdf("sigPdfDD"+q2name,"corrPdfDD",*teoPdf,*effDDpdf); RooAbsPdf * corrPdfLLB = new RooProdPdf("sigPdfLLB"+q2name,"corrPdfLLB",*teoPdfB,*effLLBpdf); RooAbsPdf * corrPdfDDB = new RooProdPdf("sigPdfDDB"+q2name,"corrPdfDDB",*teoPdfB,*effDDBpdf); TCut baseCut = ""; TCut cutLL = CutsDef::LLcut + (TCut)curq2cut + baseCut; TCut cutDD = CutsDef::DDcut + (TCut)curq2cut + baseCut; histFile->cd(); double fracDDv[2], fracLLv[2]; double nsigDD, nsigLL; RooDataSet * dataLL = getDataAndFrac("LL",q2name,mydata,cutLL,MM,&fracLLv[0],jpsiParsLL,&nsigLL); RooDataSet * dataDD = getDataAndFrac("DD",q2name,mydata,cutDD,MM,&fracDDv[0],jpsiParsDD,&nsigDD); double nevts = nsigDD+nsigLL; cout << fixed << setprecision(3) << fracDDv[0] << " " << fracDDv[1] << endl; RooRealVar * fracLL = new RooRealVar("fracLL","fracLL",fracLLv[0]); RooRealVar * fracDD = new RooRealVar("fracDD","fracDD",fracDDv[0]); RooAbsPdf * bkgLL = NULL, * bkgLLB = NULL, * bkgDD = NULL, * bkgDDB = NULL; buildBkgPdfs(q2min[i],q2max[i],"LL",CutsDef::LLcut,&bkgLL,&bkgLLB); buildBkgPdfs(q2min[i],q2max[i],"DD",CutsDef::DDcut,&bkgDD,&bkgDDB); cout << "Backgrounds extracted" << endl; RooAbsPdf * modelLL = new RooAddPdf("modelLL","modelLL",RooArgSet(*corrPdfLL,*bkgLL),*fracLL); RooAbsPdf * modelDD = new RooAddPdf("modelDD","modelDD",RooArgSet(*corrPdfDD,*bkgDD),*fracDD); RooAbsPdf * modelLLB = new RooAddPdf("modelLLB","modelLLB",RooArgSet(*corrPdfLLB,*bkgLLB),*fracLL); RooAbsPdf * modelDDB = new RooAddPdf("modelDDB","modelDDB",RooArgSet(*corrPdfDDB,*bkgDDB),*fracDD); // CREATE COMBINED DATASET RooDataSet * combData = new RooDataSet(Form("combData_%i",i),"combined data",RooArgSet(*MM,*cosThetaL,*cosThetaB),Index(*samples),Import("DD",*dataDD),Import("LL",*dataLL)); Str2VarMap params; params["fL"] = fL; params["afb"] = afb; Str2VarMap paramsB; paramsB["afbB"] = afbB; // FIT COS LEPTON RooSimultaneous * combModel = new RooSimultaneous(Form("combModel_%i",i),"",*samples); combModel->addPdf(*modelLL,"LL"); combModel->addPdf(*modelDD,"DD"); RooFitResult * res = safeFit(combModel,combData,params,&isInAllowedArea); // FIT COS HADRON RooSimultaneous * combModelB = new RooSimultaneous(Form("combModelB_%i",i),"",*samples); combModelB->addPdf(*modelLLB,"LL"); combModelB->addPdf(*modelDDB,"DD"); RooFitResult * resB = safeFit(combModelB,combData,paramsB,&isInAllowedAreaB); cout << endl << fixed << setprecision(6) << "AfbB = " << afbB->getVal() << " +/- " << afbB->getError() << endl; cout << "Afb = " << afb->getVal() << " +/- " << afb->getError() << endl; cout << "fL = " << fL->getVal() << " +/- " << fL->getError() << endl; cout << endl; cout << "lepton: " << res->edm() << " " << res->covQual() << endl; cout << "baryon: " << resB->edm() << " " << resB->covQual() << endl; cout << endl; TH1F * fLsys = new TH1F(Form("fLsys_%i",i),"fLsys",40,-1,1); TH1F * afbsys = new TH1F(Form("afbsys_%i",i),"afbsys",40,-1,1); TH1F * afbBsys = new TH1F(Form("afbBsys_%i",i),"afbBsys",40,-1,1); TH1F * fLsys_frac = new TH1F(Form("fLsys_frac%i",i),"fLsys",40,-1,1); TH1F * afbsys_frac = new TH1F(Form("afbsys_frac%i",i),"afbsys",40,-1,1); TH1F * afbBsys_frac = new TH1F(Form("afbBsys_frac%i",i),"afbBsys",40,-1,1); RooAbsPdf * mybkgDD_2 = NULL, * mybkgDDB_2 = NULL; buildBkgPdfs(q2min[i],q2max[i],"DD",CutsDef::DDcut,&mybkgDD_2,&mybkgDDB_2,"RooKeyPdf"); //cout << nevts << endl; //TRandom3 r(0); for(int e = 0; e < nexp; e++) { histFile->cd(); RooAbsPdf * toypdf = (RooAbsPdf *)modelDD->Clone(); Analysis * toy = new Analysis("toy",cosThetaL,modelDD,nevts); RooAbsPdf * toypdfB = (RooAbsPdf *)modelDDB->Clone(); Analysis * toyB = new Analysis("toyB",cosThetaB,modelDDB,nevts); afb->setVal(0); afbB->setVal(-0.37); fL->setVal(0.6); safeFit(toypdf,toy->GetDataSet("-recalc"),params,&isInAllowedArea); safeFit(toypdfB,toyB->GetDataSet("-recalc"),paramsB,&isInAllowedAreaB); double def_afb = afb->getVal(); double def_fL = fL->getVal(); double def_afbB = afbB->getVal(); afb->setVal(0); afbB->setVal(-0.37); fL->setVal(0.6); RooAbsPdf * modelDD_2 = new RooAddPdf("modelDD_2","modelDD",RooArgSet(*corrPdfDD,*mybkgDD_2),*fracDD); RooAbsPdf * modelDDB_2 = new RooAddPdf("modelDDB_2","modelDDB",RooArgSet(*corrPdfDDB,*mybkgDDB_2),*fracDD); safeFit(modelDD_2,toy->GetDataSet("-recalc"),params,&isInAllowedArea); safeFit(modelDDB_2,toyB->GetDataSet("-recalc"),paramsB,&isInAllowedAreaB); double oth_afb = afb->getVal(); double oth_fL = fL->getVal(); double oth_afbB = afbB->getVal(); fLsys->Fill(oth_fL-def_fL); afbsys->Fill(oth_afb-def_afb); afbBsys->Fill(oth_afbB-def_afbB); afb->setVal(0.); afbB->setVal(-0.37); fL->setVal(0.6); //double rdm_frac = r.Gaus(fracDDv[0],fracDDv[1]); double rdm_frac = fracDDv[0] + fracDDv[1]; RooRealVar * fracDD_2 = new RooRealVar("fracDD_2","fracDD_2",rdm_frac); RooAbsPdf * modelDD_3 = new RooAddPdf("modelDD_3","modelDD",RooArgSet(*corrPdfDD,*bkgDD),*fracDD_2); RooAbsPdf * modelDDB_3 = new RooAddPdf("modelDDB_3","modelDDB",RooArgSet(*corrPdfDDB,*bkgDDB),*fracDD_2); safeFit(modelDD_3,toy->GetDataSet("-recalc"),params,&isInAllowedArea); safeFit(modelDDB_3,toyB->GetDataSet("-recalc"),paramsB,&isInAllowedAreaB); double frc_afb = afb->getVal(); double frc_fL = fL->getVal(); double frc_afbB = afbB->getVal(); fLsys_frac->Fill(frc_fL-def_fL); afbsys_frac->Fill(frc_afb-def_afb); afbBsys_frac->Fill(frc_afbB-def_afbB); } afbsysh.push_back(afbsys); afbBsysh.push_back(afbBsys); fLsysh.push_back(fLsys); afbsysh_frac.push_back(afbsys_frac); afbBsysh_frac.push_back(afbBsys_frac); fLsysh_frac.push_back(fLsys_frac); } for(int q = 0; q < nbins; q++) { cout << fixed << setprecision(2) << "-------- Bin " << q2min[q] << "-" << q2max[q] << endl; cout << fixed << setprecision(5) << "fL sys = " << fLsysh[q]->GetMean() << " +/- " << fLsysh[q]->GetMeanError() << endl; cout << "Afb sys = " << afbsysh[q]->GetMean() << " +/- " << afbsysh[q]->GetMeanError() << endl; cout << "AfbB sys = " << afbBsysh[q]->GetMean() << " +/- " << afbBsysh[q]->GetMeanError() << endl; } cout << "#################################################################" << endl; for(int q = 0; q < nbins; q++) { cout << fixed << setprecision(2) << "-------- Bin " << q2min[q] << "-" << q2max[q] << endl; cout << fixed << setprecision(5) << "fL sys = " << fLsysh_frac[q]->GetMean() << " +/- " << fLsysh_frac[q]->GetMeanError() << endl; cout << "Afb sys = " << afbsysh_frac[q]->GetMean() << " +/- " << afbsysh_frac[q]->GetMeanError() << endl; cout << "AfbB sys = " << afbBsysh_frac[q]->GetMean() << " +/- " << afbBsysh_frac[q]->GetMeanError() << endl; } cout << "#################################################################" << endl; for(int q = 0; q < nbins; q++) { cout << fixed << setprecision(2) << "-------- Bin " << q2min[q] << "-" << q2max[q] << endl; cout << fixed << setprecision(5) << "fL sys = " << TMath::Sqrt(TMath::Power(fLsysh_frac[q]->GetMean(),2) + TMath::Power(fLsysh[q]->GetMean(),2) ) << endl; cout << "Afb sys = " << TMath::Sqrt(TMath::Power(afbsysh_frac[q]->GetMean(),2) + TMath::Power(afbsysh[q]->GetMean(),2) ) << endl; cout << "AfbB sys = " << TMath::Sqrt(TMath::Power(afbBsysh_frac[q]->GetMean(),2) + TMath::Power(afbBsysh[q]->GetMean(),2) ) << endl; } }
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(); }
/// /// Find the global minimum in a more thorough way. /// First fit with external start parameters, then /// for each parameter that starts with "d" or "r" (typically angles and ratios): /// - at upper scan range, rest at start parameters /// - at lower scan range, rest at start parameters /// This amounts to a maximum of 1+2^n fits, where n is the number /// of parameters to be varied. /// /// \param w Workspace holding the pdf. /// \param name Name of the pdf without leading "pdf_". /// \param forceVariables Apply the force method for these variables only. Format /// "var1,var2,var3," (list must end with comma). Default is to apply for all angles, /// all ratios except rD_k3pi and rD_kpi, and the k3pi coherence factor. /// RooFitResult* Utils::fitToMinForce(RooWorkspace *w, TString name, TString forceVariables) { bool debug = true; TString parsName = "par_"+name; TString obsName = "obs_"+name; TString pdfName = "pdf_"+name; RooFitResult *r = 0; int printlevel = -1; RooMsgService::instance().setGlobalKillBelow(ERROR); // save start parameters if ( !w->set(parsName) ){ cout << "MethodProbScan::scan2d() : ERROR : parsName not found: " << parsName << endl; exit(1); } RooDataSet *startPars = new RooDataSet("startParsForce", "startParsForce", *w->set(parsName)); startPars->add(*w->set(parsName)); // set up parameters and ranges RooArgList *varyPars = new RooArgList(); TIterator* it = w->set(parsName)->createIterator(); while ( RooRealVar* p = (RooRealVar*)it->Next() ) { if ( p->isConstant() ) continue; if ( forceVariables=="" && ( false || TString(p->GetName()).BeginsWith("d") ///< use these variables // || TString(p->GetName()).BeginsWith("r") || TString(p->GetName()).BeginsWith("k") || TString(p->GetName()) == "g" ) && ! ( TString(p->GetName()) == "rD_k3pi" ///< don't use these || TString(p->GetName()) == "rD_kpi" // || TString(p->GetName()) == "dD_kpi" || TString(p->GetName()) == "d_dk" || TString(p->GetName()) == "d_dsk" )) { varyPars->add(*p); } else if ( forceVariables.Contains(TString(p->GetName())+",") ) { varyPars->add(*p); } } delete it; int nPars = varyPars->getSize(); if ( debug ) cout << "Utils::fitToMinForce() : nPars = " << nPars << " => " << pow(2.,nPars) << " fits" << endl; if ( debug ) cout << "Utils::fitToMinForce() : varying "; if ( debug ) varyPars->Print(); ////////// r = fitToMinBringBackAngles(w->pdf(pdfName), false, printlevel); ////////// int nErrors = 0; // We define a binary mask where each bit corresponds // to parameter at max or at min. for ( int i=0; i<pow(2.,nPars); i++ ) { if ( debug ) cout << "Utils::fitToMinForce() : fit " << i << " \r" << flush; setParameters(w, parsName, startPars->get(0)); for ( int ip=0; ip<nPars; ip++ ) { RooRealVar *p = (RooRealVar*)varyPars->at(ip); float oldMin = p->getMin(); float oldMax = p->getMax(); setLimit(w, p->GetName(), "force"); if ( i/(int)pow(2.,ip) % 2==0 ) { p->setVal(p->getMin()); } if ( i/(int)pow(2.,ip) % 2==1 ) { p->setVal(p->getMax()); } p->setRange(oldMin, oldMax); } // check if start parameters are sensible, skip if they're not double startParChi2 = getChi2(w->pdf(pdfName)); if ( startParChi2>2000 ){ nErrors += 1; continue; } // refit RooFitResult *r2 = fitToMinBringBackAngles(w->pdf(pdfName), false, printlevel); // In case the initial fit failed, accept the second one. // If both failed, still select the second one and hope the // next fit succeeds. if ( !(r->edm()<1 && r->covQual()==3) ){ delete r; r = r2; } else if ( r2->edm()<1 && r2->covQual()==3 && r2->minNll()<r->minNll() ){ // better minimum found! delete r; r = r2; } else{ delete r2; } } if ( debug ) cout << endl; if ( debug ) cout << "Utils::fitToMinForce() : nErrors = " << nErrors << endl; RooMsgService::instance().setGlobalKillBelow(INFO); // (re)set to best parameters setParameters(w, parsName, r); delete startPars; return r; }
void crossfeeds_nondiag(TString title, TString bkgfile, TString epsfile, TString txtfile, Double_t alpha_, Double_t mass_, Double_t n_, Double_t sigma_ ) { RooRealVar mbc("mbc", "m_{BC}", 1.83, 1.89, "GeV"); RooRealVar ebeam("ebeam", "Ebeam", 0., 100., "GeV"); RooRealVar chg("chg", "Charge", -2, 2); RooCategory passed("passed", "Event should be used for plot"); passed.defineType("yes", 1); passed.defineType("no", 0); RooRealVar arg_cutoff ("arg_cutoff", "Argus cutoff", 1.8865, 1.885, 1.8875,"GeV"); RooRealVar arg_slope ("arg_slope", "Argus slope", -13, -100, 40); RooRealVar mbc_float ("mbc_float", "Floating D mass", mass_, "GeV"); RooRealVar sigma ("sigma", "CB width", sigma_, "GeV"); RooRealVar alpha("alpha", "CB shape cutoff", alpha_); RooRealVar n("n", "CB tail parameter", n_); RooCBShape cb_float ("cb_float", "Floating Crystal Barrel", mbc, mbc_float, sigma, alpha, n); RooArgusBG argus("argus", "Argus BG", mbc, arg_cutoff, arg_slope); RooRealVar yld("yield", "D yield", 0, -30, 100000); RooRealVar bkg("bkg", "Background", 20, 0, 40000); // Build pdf RooAddPdf sumpdf_float("sumpdf_float", "Generic D sum pdf", RooArgList(cb_float, argus), RooArgList(yld, bkg)); RooDataSet* dset = RooDataSet::read(bkgfile, RooArgList(mbc, ebeam, passed), "", ""); RooPlot* xframe = mbc.frame(); RooDataSet* dset2 = dset->reduce("passed==1"); dset2->plotOn(xframe); // RooFitResult* rv = sumpdf_float.fitTo(*dset2, Extended(kTRUE), Save(kTRUE), // Hesse(kTRUE), Verbose(kTRUE)); RooFitResult* rv = sumpdf_float.fitTo(*dset2, "ermh"); sumpdf_float.paramOn(xframe, dset2); if ((yld.getVal() < 0) && (-yld.getVal()/bkg.getVal() > 0.5)){ yld.setVal(0); bkg.setVal(1); } sumpdf_float.plotOn(xframe); sumpdf_float.plotOn(xframe, Components(RooArgSet(argus)), LineColor(kRed), LineStyle(kDashed)); TCanvas* c1 = new TCanvas("c1","Canvas", 2); xframe->SetTitleOffset(2.2, "Y"); xframe->SetTitleOffset(1.1, "X"); xframe->SetTitle(title); c1->SetLeftMargin(0.17); xframe->Draw(); if ( rv && rv->covQual() != 3){ // fit has failed TText *txt = new TText(); txt->SetTextSize(.08); txt->SetTextAlign(22); txt->SetTextAngle(30); txt->DrawTextNDC(0.5, 0.5, "FAILED"); } c1->Update(); c1->Print(epsfile); c1->Clear(); FILE* table = fopen(txtfile.Data(), "w+"); fprintf(table, "Name\t|| Value\t|| Error\n"); // fprintf(table, "yldsigma\t| %.10f\t| \n", yld.getVal()/yld.getError()); fprintf(table, "entries\t| %.10f\t| \n", dset->numEntries()); fprintf(table, "yld\t| %.10f\t| %.10f\n", yld.getVal(), yld.getError()); // fprintf(table, "ratio\t| %.10f\t| \n", yld.getVal()/dset->numEntries()); // fprintf(table, "ratioerr\t| %.10f\t| \n", yld.getError()/dset->numEntries()); fclose(table); cout << "Saved output as: " << txtfile << endl; rv->Delete(); }
/// /// Perform the 1d Prob scan. /// Saves chi2 values and the prob-Scan p-values in a root tree /// For the datasets stuff, we do not yet have a MethodDatasetsProbScan class, so we do it all in /// MethodDatasetsProbScan /// \param nRun Part of the root tree file name to facilitate parallel production. /// int MethodDatasetsProbScan::scan1d(bool fast, bool reverse) { if (fast) return 0; // tmp if ( arg->debug ) cout << "MethodDatasetsProbScan::scan1d() : starting ... " << endl; // Set limit to all parameters. this->loadParameterLimits(); /// Default is "free", if not changed by cmd-line parameter // Define scan parameter and scan range. RooRealVar *parameterToScan = w->var(scanVar1); float parameterToScan_min = hCL->GetXaxis()->GetXmin(); float parameterToScan_max = hCL->GetXaxis()->GetXmax(); // do a free fit RooFitResult *result = this->loadAndFit(this->pdf); // fit on data assert(result); RooSlimFitResult *slimresult = new RooSlimFitResult(result,true); slimresult->setConfirmed(true); solutions.push_back(slimresult); double freeDataFitValue = w->var(scanVar1)->getVal(); // Define outputfile system("mkdir -p root"); TString probResName = Form("root/scan1dDatasetsProb_" + this->pdf->getName() + "_%ip" + "_" + scanVar1 + ".root", arg->npoints1d); TFile* outputFile = new TFile(probResName, "RECREATE"); // Set up toy root tree this->probScanTree = new ToyTree(this->pdf, arg); this->probScanTree->init(); this->probScanTree->nrun = -999; //\todo: why does this branch even exist in the output tree of the prob scan? // Save parameter values that were active at function // call. We'll reset them at the end to be transparent // to the outside. RooDataSet* parsFunctionCall = new RooDataSet("parsFunctionCall", "parsFunctionCall", *w->set(pdf->getParName())); parsFunctionCall->add(*w->set(pdf->getParName())); // start scan cout << "MethodDatasetsProbScan::scan1d_prob() : starting ... with " << nPoints1d << " scanpoints..." << endl; ProgressBar progressBar(arg, nPoints1d); for ( int i = 0; i < nPoints1d; i++ ) { progressBar.progress(); // scanpoint is calculated using min, max, which are the hCL x-Axis limits set in this->initScan() // this uses the "scan" range, as expected // don't add half the bin size. try to solve this within plotting method float scanpoint = parameterToScan_min + (parameterToScan_max - parameterToScan_min) * (double)i / ((double)nPoints1d - 1); if (arg->debug) cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() " << scanpoint << " " << parameterToScan_min << " " << parameterToScan_max << endl; this->probScanTree->scanpoint = scanpoint; if (arg->debug) cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() - scanpoint in step " << i << " : " << scanpoint << endl; // don't scan in unphysical region // by default this means checking against "free" range if ( scanpoint < parameterToScan->getMin() || scanpoint > parameterToScan->getMax() + 2e-13 ) { cout << "it seems we are scanning in an unphysical region: " << scanpoint << " < " << parameterToScan->getMin() << " or " << scanpoint << " > " << parameterToScan->getMax() + 2e-13 << endl; exit(EXIT_FAILURE); } // FIT TO REAL DATA WITH FIXED HYPOTHESIS(=SCANPOINT). // THIS GIVES THE NUMERATOR FOR THE PROFILE LIKELIHOOD AT THE GIVEN HYPOTHESIS // THE RESULTING NUISANCE PARAMETERS TOGETHER WITH THE GIVEN HYPOTHESIS ARE ALSO // USED WHEN SIMULATING THE TOY DATA FOR THE FELDMAN-COUSINS METHOD FOR THIS HYPOTHESIS(=SCANPOINT) // Here the scanvar has to be fixed -> this is done once per scanpoint // and provides the scanner with the DeltaChi2 for the data as reference // additionally the nuisances are set to the resulting fit values parameterToScan->setVal(scanpoint); parameterToScan->setConstant(true); RooFitResult *result = this->loadAndFit(this->pdf); // fit on data assert(result); if (arg->debug) { cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() - minNll data scan at scan point " << scanpoint << " : " << 2 * result->minNll() << ": "<< 2 * pdf->getMinNll() << endl; } this->probScanTree->statusScanData = result->status(); // set chi2 of fixed fit: scan fit on data // CAVEAT: chi2min from fitresult gives incompatible results to chi2min from pdf // this->probScanTree->chi2min = 2 * result->minNll(); this->probScanTree->chi2min = 2 * pdf->getMinNll(); this->probScanTree->covQualScanData = result->covQual(); this->probScanTree->scanbest = freeDataFitValue; // After doing the fit with the parameter of interest constrained to the scanpoint, // we are now saving the fit values of the nuisance parameters. These values will be // used to generate toys according to the PLUGIN method. this->probScanTree->storeParsScan(); // \todo : figure out which one of these is semantically the right one this->pdf->deleteNLL(); // also save the chi2 of the free data fit to the tree: this->probScanTree->chi2minGlobal = this->getChi2minGlobal(); this->probScanTree->chi2minBkg = this->getChi2minBkg(); this->probScanTree->genericProbPValue = this->getPValueTTestStatistic(this->probScanTree->chi2min - this->probScanTree->chi2minGlobal); this->probScanTree->fill(); if(arg->debug && pdf->getBkgPdf()) { float pval_cls = this->getPValueTTestStatistic(this->probScanTree->chi2min - this->probScanTree->chi2minBkg, true); cout << "DEBUG in MethodDatasetsProbScan::scan1d() - p value CLs: " << pval_cls << endl; } // reset setParameters(w, pdf->getParName(), parsFunctionCall->get(0)); //setParameters(w, pdf->getObsName(), obsDataset->get(0)); } // End of npoints loop probScanTree->writeToFile(); if (bkgOnlyFitResult) bkgOnlyFitResult->Write(); if (dataFreeFitResult) dataFreeFitResult->Write(); outputFile->Close(); std::cout << "Wrote ToyTree to file" << std::endl; delete parsFunctionCall; // This is kind of a hack. The effect is supposed to be the same as callincg // this->sethCLFromProbScanTree(); here, but the latter gives a segfault somehow.... // \todo: use this->sethCLFromProbScanTree() directly after figuring out the cause of the segfault. this->loadScanFromFile(); return 0; }
void ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root", const char* new_poi_name = "n_M234_H4_3b", int npoiPoints = 20, double poiMinVal = 0., double poiMaxVal = 20., double constraintWidth = 1.5, double ymax = 10., int verbLevel=0 ) { gStyle->SetOptStat(0) ; //--- make output directory. char command[10000] ; sprintf( command, "basename %s", wsfile ) ; TString wsfilenopath = gSystem->GetFromPipe( command ) ; wsfilenopath.ReplaceAll(".root","") ; char outputdirstr[1000] ; sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ; TString outputdir( outputdirstr ) ; printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ; sprintf(command, "mkdir -p %s", outputdir.Data() ) ; gSystem->Exec( command ) ; //--- Tell RooFit to shut up about anything less important than an ERROR. RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ; if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; } TFile* wstf = new TFile( wsfile ) ; RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") ); if ( verbLevel > 0 ) { ws->Print() ; } RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ; if ( verbLevel > 0 ) { printf("\n\n\n ===== RooDataSet ====================\n\n") ; rds->Print() ; rds->printMultiline(cout, 1, kTRUE, "") ; } ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ; RooAbsPdf* likelihood = modelConfig->GetPdf() ; RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ; if ( rrv_mu_susy_all0lep == 0x0 ) { printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ; return ; } //-- do BG only. rrv_mu_susy_all0lep->setVal(0.) ; rrv_mu_susy_all0lep->setConstant( kTRUE ) ; //-- do a prefit. printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ; RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel)); int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ; if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; } double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ; if ( verbLevel > 0 ) { dataFitResultSusyFixed->Print("v") ; } printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ; delete dataFitResultSusyFixed ; //-- Construct the new POI parameter. RooAbsReal* new_poi_rar(0x0) ; new_poi_rar = ws->var( new_poi_name ) ; if ( new_poi_rar == 0x0 ) { printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ; new_poi_rar = ws->function( new_poi_name ) ; if ( new_poi_rar == 0x0 ) { printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ; return ; } } else { printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ; } if ( npoiPoints <=0 ) { printf("\n\n Quitting now.\n\n" ) ; return ; } double startPoiVal = new_poi_rar->getVal() ; //--- The RooNLLVar is NOT equivalent to what minuit uses. // RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ; // printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ; //--- output of createNLL IS what minuit uses, so use that. RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ; RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ; /// rrv_poiValue->setVal( poiMinVal ) ; /// rrv_poiValue->setConstant(kTRUE) ; RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ; rrv_constraintWidth -> setVal( constraintWidth ) ; rrv_constraintWidth -> setConstant(kTRUE) ; if ( verbLevel > 0 ) { printf("\n\n ======= debug likelihood print\n\n") ; likelihood->Print("v") ; printf("\n\n ======= debug nll print\n\n") ; nll->Print("v") ; } //---------------------------------------------------------------------------------------------- RooMinuit* rminuit( 0x0 ) ; RooMinuit* rminuit_uc = new RooMinuit( *nll ) ; rminuit_uc->setPrintLevel(verbLevel-1) ; rminuit_uc->setNoWarn() ; rminuit_uc->migrad() ; rminuit_uc->hesse() ; RooFitResult* rfr_uc = rminuit_uc->fit("mr") ; double floatParInitVal[10000] ; char floatParName[10000][100] ; int nFloatParInitVal(0) ; RooArgList ral_floats = rfr_uc->floatParsFinal() ; TIterator* floatParIter = ral_floats.createIterator() ; { RooRealVar* par ; while ( (par = (RooRealVar*) floatParIter->Next()) ) { sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ; floatParInitVal[nFloatParInitVal] = par->getVal() ; nFloatParInitVal++ ; } } //------- printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ; double best_poi_val = new_poi_rar->getVal() ; char minuit_formula[10000] ; sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)", nll->GetName(), rrv_constraintWidth->GetName(), new_poi_rar->GetName(), rrv_poiValue->GetName(), new_poi_rar->GetName(), rrv_poiValue->GetName() ) ; printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ; RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula, RooArgList( *nll, *rrv_constraintWidth, *new_poi_rar, *rrv_poiValue, *new_poi_rar, *rrv_poiValue ) ) ; printf("\n\n Current value is %.2f\n\n", new_minuit_var->getVal() ) ; rminuit = new RooMinuit( *new_minuit_var ) ; RooAbsReal* plot_var = nll ; printf("\n\n Current value is %.2f\n\n", plot_var->getVal() ) ; rminuit->setPrintLevel(verbLevel-1) ; if ( verbLevel <=0 ) { rminuit->setNoWarn() ; } //---------------------------------------------------------------------------------------------- //-- If POI range is -1 to -1, automatically determine the range using the set value. if ( poiMinVal < 0. && poiMaxVal < 0. ) { printf("\n\n Automatic determination of scan range.\n\n") ; if ( startPoiVal <= 0. ) { printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ; return ; } poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ; poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ; if ( poiMinVal < 0. ) { poiMinVal = 0. ; } printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ; } //---------------------------------------------------------------------------------------------- double poiVals_scanDown[1000] ; double nllVals_scanDown[1000] ; //-- Do scan down from best value. printf("\n\n +++++ Starting scan down from best value.\n\n") ; double minNllVal(1.e9) ; for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) { ////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ; double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ; rrv_poiValue -> setVal( poiValue ) ; rrv_poiValue -> setConstant( kTRUE ) ; //+++++++++++++++++++++++++++++++++++ rminuit->migrad() ; rminuit->hesse() ; RooFitResult* rfr = rminuit->save() ; //+++++++++++++++++++++++++++++++++++ if ( verbLevel > 0 ) { rfr->Print("v") ; } float fit_minuit_var_val = rfr->minNll() ; printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n", poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ; cout << flush ; poiVals_scanDown[poivi] = new_poi_rar->getVal() ; nllVals_scanDown[poivi] = plot_var->getVal() ; if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; } delete rfr ; } // poivi printf("\n\n +++++ Resetting floats to best fit values.\n\n") ; for ( int pi=0; pi<nFloatParInitVal; pi++ ) { RooRealVar* par = ws->var( floatParName[pi] ) ; par->setVal( floatParInitVal[pi] ) ; } // pi. printf("\n\n +++++ Starting scan up from best value.\n\n") ; //-- Now do scan up. double poiVals_scanUp[1000] ; double nllVals_scanUp[1000] ; for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) { double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ; rrv_poiValue -> setVal( poiValue ) ; rrv_poiValue -> setConstant( kTRUE ) ; //+++++++++++++++++++++++++++++++++++ rminuit->migrad() ; rminuit->hesse() ; RooFitResult* rfr = rminuit->save() ; //+++++++++++++++++++++++++++++++++++ if ( verbLevel > 0 ) { rfr->Print("v") ; } float fit_minuit_var_val = rfr->minNll() ; printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n", poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ; cout << flush ; poiVals_scanUp[poivi] = new_poi_rar->getVal() ; nllVals_scanUp[poivi] = plot_var->getVal() ; if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; } delete rfr ; } // poivi double poiVals[1000] ; double nllVals[1000] ; int pointCount(0) ; for ( int pi=0; pi<npoiPoints/2; pi++ ) { poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ; nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ; pointCount++ ; } for ( int pi=1; pi<npoiPoints/2; pi++ ) { poiVals[pointCount] = poiVals_scanUp[pi] ; nllVals[pointCount] = nllVals_scanUp[pi] ; pointCount++ ; } npoiPoints = pointCount ; printf("\n\n --- TGraph arrays:\n") ; for ( int i=0; i<npoiPoints; i++ ) { printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ; } printf("\n\n") ; double nllDiffVals[1000] ; double poiAtMinlnL(-1.) ; double poiAtMinusDelta2(-1.) ; double poiAtPlusDelta2(-1.) ; for ( int poivi=0; poivi < npoiPoints ; poivi++ ) { nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ; double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ; if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; } if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; } if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; } } // poivi printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ; //--- Main canvas TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ; if ( cscan == 0x0 ) { printf("\n Creating canvas.\n\n") ; cscan = new TCanvas("cscan","Delta nll") ; } char gname[1000] ; TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ; sprintf( gname, "scan_%s", new_poi_name ) ; graph->SetName( gname ) ; double poiBest(-1.) ; double poiMinus1stdv(-1.) ; double poiPlus1stdv(-1.) ; double poiMinus2stdv(-1.) ; double poiPlus2stdv(-1.) ; double twoDeltalnLMin(1e9) ; int nscan(1000) ; for ( int xi=0; xi<nscan; xi++ ) { double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ; double twoDeltalnL = graph -> Eval( x, 0, "S" ) ; if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;} if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;} if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; } if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;} if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;} if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; } } printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n", poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv, (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv ) ; printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ; char htitle[1000] ; sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ; TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ; hscan->SetMinimum(0.) ; hscan->SetMaximum(ymax) ; hscan->DrawCopy() ; graph->SetLineColor(4) ; graph->SetLineWidth(3) ; graph->Draw("CP") ; gPad->SetGridx(1) ; gPad->SetGridy(1) ; cscan->Update() ; TLine* line = new TLine() ; line->SetLineColor(2) ; line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ; line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ; line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ; TText* text = new TText() ; text->SetTextSize(0.04) ; char tstring[1000] ; sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ; text -> DrawTextNDC( 0.15, 0.85, tstring ) ; sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ; text -> DrawTextNDC( 0.15, 0.78, tstring ) ; char hname[1000] ; sprintf( hname, "hscanout_%s", new_poi_name ) ; TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ; double obsVal(-1.) ; hsout->SetBinContent(1, obsVal ) ; hsout->SetBinContent(2, poiPlus1stdv ) ; hsout->SetBinContent(3, poiBest ) ; hsout->SetBinContent(4, poiMinus1stdv ) ; TAxis* xaxis = hsout->GetXaxis() ; xaxis->SetBinLabel(1,"Observed val.") ; xaxis->SetBinLabel(2,"Model+1sd") ; xaxis->SetBinLabel(3,"Model") ; xaxis->SetBinLabel(4,"Model-1sd") ; char outrootfile[10000] ; sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ; char outpdffile[10000] ; sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ; cscan->Update() ; cscan->Draw() ; printf("\n Saving %s\n", outpdffile ) ; cscan->SaveAs( outpdffile ) ; //--- save in root file printf("\n Saving %s\n", outrootfile ) ; TFile fout(outrootfile,"recreate") ; graph->Write() ; hsout->Write() ; fout.Close() ; delete ws ; wstf->Close() ; }
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; }
RooFitResult * safeFit(RooAbsPdf * pdf, RooDataSet * data, Str2VarMap p, ISVALIDF_PTR isValid, string opt = "", int nfree = -1, RooArgSet * cons = NULL, RooAbsReal * nll = NULL) { RooFitResult * res = NULL; RooRealVar cosThetaL("cosThetaL","cosThetaL",0.,-1.,1.); RooRealVar cosThetaB("cosThetaB","cosThetaB",0.,-1.,1.); RooArgSet obs(cosThetaL,cosThetaB); //if(opt.find("-scan")==string::npos) res = pdf->fitTo(*data,PrintLevel(-1),Save(),Extended(true)); if(p.size()==1 && p.find("afb") != p.end()) p["fL"] = GetParam(pdf,"fL"); else if(p.size()==1 && p.find("fL") != p.end()) p["afb"] = GetParam(pdf,"afb"); RooArgSet * nuisances = NULL; /* bool afb_iscost = false, fL_iscost = false, afbB_iscost = false; if (p.find("afb") != p.end()) { afb_iscost = ((RooRealVar*)p["afb"])->getAttribute("Constant"); ((RooRealVar*)p["afb"])->setConstant(); } if (p.find("fL") != p.end()) { fL_iscost = ((RooRealVar*)p["fL"])->getAttribute("Constant"); ((RooRealVar*)p["fL"])->setConstant(); } if (p.find("afbB") != p.end()) { afbB_iscost = ((RooRealVar*)p["afbB"])->getAttribute("Constant"); ((RooRealVar*)p["afbB"])->setConstant(); } RooArgSet * nuisances = copyFreePars(pdf,obs); if (p.find("afb") != p.end()) ((RooRealVar*)p["afb"])->setConstant(afb_iscost); if (p.find("afbB") != p.end()) ((RooRealVar*)p["afbB"])->setConstant(afbB_iscost); if (p.find("fL") != p.end()) ((RooRealVar*)p["fL"])->setConstant(fL_iscost); */ int np = 20; if((!res || res->covQual()!=3 || res->edm() > 0.1) && opt.find("-noscan")==string::npos) { if(!nll) nll = pdf->createNLL(*data); vector < double > mins, maxs, r; Str2VarMap::iterator iter; int pp = 0; for (iter = p.begin(); iter != p.end(); iter++) { RooRealVar * curp = (RooRealVar *)iter->second; maxs.push_back(curp->getMax()); mins.push_back(curp->getMin()); r.push_back((maxs.back() - mins.back())/(double)np); pp++; } findMin(pdf,data,nll,p,mins,maxs,np,isValid,nfree,opt+"-nofit",cons,nuisances); double prec = 1e6; while (prec > 0.001) { double maxr = 0; maxs.clear(); mins.clear(); pp=0; for (iter = p.begin(); iter != p.end(); iter++) { RooRealVar * curp = (RooRealVar *)iter->second; if((curp->getVal() + r[pp]) < curp->getMax()) maxs.push_back(curp->getVal() + r[pp]); else maxs.push_back(curp->getMax()); if((curp->getVal() - r[pp]) > curp->getMin()) mins.push_back(curp->getVal() - r[pp]); else mins.push_back(curp->getMin()); r[pp] = (maxs.back() - mins.back())/(double)np; if(r[pp] > maxr) maxr = r[pp]; pp++; } prec = maxr; res = findMin(pdf,data,nll,p,mins,maxs,np,isValid,nfree,opt,cons,nuisances); } //if(!mynll) delete nll; } return res; }
void ws_cls_hybrid1_ag( const char* wsfile = "output-files/expected-ws-lm9-2BL.root", bool isBgonlyStudy=false, double poiVal = 150.0, int nToys=100, bool makeTtree=true, int verbLevel=0 ) { TTree* toytt(0x0) ; TFile* ttfile(0x0) ; int tt_gen_Nsig ; int tt_gen_Nsb ; int tt_gen_Nsig_sl ; int tt_gen_Nsb_sl ; int tt_gen_Nsig_ldp ; int tt_gen_Nsb_ldp ; int tt_gen_Nsig_ee ; int tt_gen_Nsb_ee ; int tt_gen_Nsig_mm ; int tt_gen_Nsb_mm ; double tt_testStat ; double tt_dataTestStat ; double tt_hypo_mu_susy_sig ; char ttname[1000] ; char tttitle[1000] ; if ( makeTtree ) { ttfile = gDirectory->GetFile() ; if ( ttfile == 0x0 ) { printf("\n\n\n *** asked for a ttree but no open file???\n\n") ; return ; } if ( isBgonlyStudy ) { sprintf( ttname, "toytt_%.0f_bgo", poiVal ) ; sprintf( tttitle, "Toy study for background only, mu_susy_sig = %.0f", poiVal ) ; } else { sprintf( ttname, "toytt_%.0f_spb", poiVal ) ; sprintf( tttitle, "Toy study for signal+background, mu_susy_sig = %.0f", poiVal ) ; } printf("\n\n Creating TTree : %s : %s\n\n", ttname, tttitle ) ; gDirectory->pwd() ; gDirectory->ls() ; toytt = new TTree( ttname, tttitle ) ; gDirectory->ls() ; toytt -> Branch( "gen_Nsig" , &tt_gen_Nsig , "gen_Nsig/I" ) ; toytt -> Branch( "gen_Nsb" , &tt_gen_Nsb , "gen_Nsb/I" ) ; toytt -> Branch( "gen_Nsig_sl" , &tt_gen_Nsig_sl , "gen_Nsig_sl/I" ) ; toytt -> Branch( "gen_Nsb_sl" , &tt_gen_Nsb_sl , "gen_Nsb_sl/I" ) ; toytt -> Branch( "gen_Nsig_ldp" , &tt_gen_Nsig_ldp , "gen_Nsig_ldp/I" ) ; toytt -> Branch( "gen_Nsb_ldp" , &tt_gen_Nsb_ldp , "gen_Nsb_ldp/I" ) ; toytt -> Branch( "gen_Nsig_ee" , &tt_gen_Nsig_ee , "gen_Nsig_ee/I" ) ; toytt -> Branch( "gen_Nsb_ee" , &tt_gen_Nsb_ee , "gen_Nsb_ee/I" ) ; toytt -> Branch( "gen_Nsig_mm" , &tt_gen_Nsig_mm , "gen_Nsig_mm/I" ) ; toytt -> Branch( "gen_Nsb_mm" , &tt_gen_Nsb_mm , "gen_Nsb_mm/I" ) ; toytt -> Branch( "testStat" , &tt_testStat , "testStat/D" ) ; toytt -> Branch( "dataTestStat" , &tt_dataTestStat , "dataTestStat/D" ) ; toytt -> Branch( "hypo_mu_susy_sig" , &tt_hypo_mu_susy_sig , "hypo_mu_susy_sig/D" ) ; } //--- Tell RooFit to shut up about anything less important than an ERROR. RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ; random_ng = new TRandom2(12345) ; /// char sel[100] ; /// if ( strstr( wsfile, "1BL" ) != 0 ) { /// sprintf( sel, "1BL" ) ; /// } else if ( strstr( wsfile, "2BL" ) != 0 ) { /// sprintf( sel, "2BL" ) ; /// } else if ( strstr( wsfile, "3B" ) != 0 ) { /// sprintf( sel, "3B" ) ; /// } else if ( strstr( wsfile, "1BT" ) != 0 ) { /// sprintf( sel, "1BT" ) ; /// } else if ( strstr( wsfile, "2BT" ) != 0 ) { /// sprintf( sel, "2BT" ) ; /// } else { /// printf("\n\n\n *** can't figure out which selection this is. I quit.\n\n" ) ; /// return ; /// } /// printf("\n\n selection is %s\n\n", sel ) ; TFile* wstf = new TFile( wsfile ) ; RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") ); ws->Print() ; RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ; printf("\n\n\n ===== RooDataSet ====================\n\n") ; rds->Print() ; rds->printMultiline(cout, 1, kTRUE, "") ; ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ; RooAbsPdf* likelihood = modelConfig->GetPdf() ; const RooArgSet* nuisanceParameters = modelConfig->GetNuisanceParameters() ; RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_sig") ; if ( rrv_mu_susy_sig == 0x0 ) { printf("\n\n\n *** can't find mu_susy_sig in workspace. Quitting.\n\n\n") ; return ; } //// printf("\n\n\n ===== Doing a fit ====================\n\n") ; //// RooFitResult* preFitResult = likelihood->fitTo( *rds, Save(true) ) ; //// const RooArgList preFitFloatVals = preFitResult->floatParsFinal() ; //// { //// TIterator* parIter = preFitFloatVals.createIterator() ; //// while ( RooRealVar* par = (RooRealVar*) parIter->Next() ) { //// printf(" %20s : %8.2f\n", par->GetName(), par->getVal() ) ; //// } //// } //--- Get pointers to the model predictions of the observables. rfv_n_sig = ws->function("n_sig") ; rfv_n_sb = ws->function("n_sb") ; rfv_n_sig_sl = ws->function("n_sig_sl") ; rfv_n_sb_sl = ws->function("n_sb_sl") ; rfv_n_sig_ldp = ws->function("n_sig_ldp") ; rfv_n_sb_ldp = ws->function("n_sb_ldp") ; rfv_n_sig_ee = ws->function("n_sig_ee") ; rfv_n_sb_ee = ws->function("n_sb_ee") ; rfv_n_sig_mm = ws->function("n_sig_mm") ; rfv_n_sb_mm = ws->function("n_sb_mm") ; if ( rfv_n_sig == 0x0 ) { printf("\n\n\n *** can't find n_sig in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb == 0x0 ) { printf("\n\n\n *** can't find n_sb in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_sl == 0x0 ) { printf("\n\n\n *** can't find n_sig_sl in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_sl == 0x0 ) { printf("\n\n\n *** can't find n_sb_sl in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sig_ldp in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sb_ldp in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_ee == 0x0 ) { printf("\n\n\n *** can't find n_sig_ee in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_ee == 0x0 ) { printf("\n\n\n *** can't find n_sb_ee in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_mm == 0x0 ) { printf("\n\n\n *** can't find n_sig_mm in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_mm == 0x0 ) { printf("\n\n\n *** can't find n_sb_mm in workspace. Quitting.\n\n\n") ; return ; } //--- Get pointers to the observables. const RooArgSet* dsras = rds->get() ; TIterator* obsIter = dsras->createIterator() ; while ( RooRealVar* obs = (RooRealVar*) obsIter->Next() ) { if ( strcmp( obs->GetName(), "Nsig" ) == 0 ) { rrv_Nsig = obs ; } if ( strcmp( obs->GetName(), "Nsb" ) == 0 ) { rrv_Nsb = obs ; } if ( strcmp( obs->GetName(), "Nsig_sl" ) == 0 ) { rrv_Nsig_sl = obs ; } if ( strcmp( obs->GetName(), "Nsb_sl" ) == 0 ) { rrv_Nsb_sl = obs ; } if ( strcmp( obs->GetName(), "Nsig_ldp" ) == 0 ) { rrv_Nsig_ldp = obs ; } if ( strcmp( obs->GetName(), "Nsb_ldp" ) == 0 ) { rrv_Nsb_ldp = obs ; } if ( strcmp( obs->GetName(), "Nsig_ee" ) == 0 ) { rrv_Nsig_ee = obs ; } if ( strcmp( obs->GetName(), "Nsb_ee" ) == 0 ) { rrv_Nsb_ee = obs ; } if ( strcmp( obs->GetName(), "Nsig_mm" ) == 0 ) { rrv_Nsig_mm = obs ; } if ( strcmp( obs->GetName(), "Nsb_mm" ) == 0 ) { rrv_Nsb_mm = obs ; } } if ( rrv_Nsig == 0x0 ) { printf("\n\n\n *** can't find Nsig in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb == 0x0 ) { printf("\n\n\n *** can't find Nsb in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_sl == 0x0 ) { printf("\n\n\n *** can't find Nsig_sl in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_sl == 0x0 ) { printf("\n\n\n *** can't find Nsb_sl in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsig_ldp in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsb_ldp in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_ee == 0x0 ) { printf("\n\n\n *** can't find Nsig_ee in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_ee == 0x0 ) { printf("\n\n\n *** can't find Nsb_ee in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_mm == 0x0 ) { printf("\n\n\n *** can't find Nsig_mm in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_mm == 0x0 ) { printf("\n\n\n *** can't find Nsb_mm in dataset. Quitting.\n\n\n") ; return ; } printf("\n\n\n === Model values for observables\n\n") ; printObservables() ; //--- save the actual values of the observables. saveObservables() ; //--- evaluate the test stat on the data: fit with susy floating. rrv_mu_susy_sig->setVal( poiVal ) ; rrv_mu_susy_sig->setConstant( kTRUE ) ; printf("\n\n\n ====== Fitting the data with susy fixed.\n\n") ; RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true)); int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ; if ( dataSusyFixedFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; } double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ; rrv_mu_susy_sig->setVal( 0.0 ) ; rrv_mu_susy_sig->setConstant( kFALSE ) ; printf("\n\n\n ====== Fitting the data with susy floating.\n\n") ; RooFitResult* dataFitResultSusyFloat = likelihood->fitTo(*rds, Save(true)); int dataSusyFloatFitCovQual = dataFitResultSusyFloat->covQual() ; if ( dataSusyFloatFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFloatFitCovQual ) ; return ; } double dataFitSusyFloatNll = dataFitResultSusyFloat->minNll() ; double dataTestStat = 2.*( dataFitSusyFixedNll - dataFitSusyFloatNll) ; printf("\n\n\n Data value of test stat : %8.2f\n", dataTestStat ) ; printf("\n\n\n === Nuisance parameters\n\n") ; { int npi(0) ; TIterator* npIter = nuisanceParameters->createIterator() ; while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) { np_initial_val[npi] = np_rrv->getVal() ; //--- I am assuming that the order of the NPs in the iterator does not change. TString npname( np_rrv->GetName() ) ; npname.ReplaceAll("_prim","") ; RooAbsReal* np_rfv = ws->function( npname ) ; TString pdfname( np_rrv->GetName() ) ; pdfname.ReplaceAll("_prim","") ; pdfname.Prepend("pdf_") ; RooAbsPdf* np_pdf = ws->pdf( pdfname ) ; if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; } if ( np_rfv != 0x0 ) { printf(" %20s : %8.2f , %20s, %8.2f\n", np_rrv->GetName(), np_rrv->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ; } else { printf(" %20s : %8.2f\n", np_rrv->GetName(), np_rrv->getVal() ) ; } npi++ ; } // np_rrv iterator. np_count = npi ; } tt_dataTestStat = dataTestStat ; tt_hypo_mu_susy_sig = poiVal ; printf("\n\n\n === Doing the toys\n\n") ; int nToyOK(0) ; int nToyWorseThanData(0) ; for ( int ti=0; ti<nToys; ti++ ) { printf("\n\n\n ======= Toy %4d\n\n\n", ti ) ; //--- 1) pick values for the nuisance parameters from the PDFs and fix them. { TIterator* npIter = nuisanceParameters->createIterator() ; while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) { TString pdfname( np_rrv->GetName() ) ; pdfname.ReplaceAll("_prim","") ; pdfname.Prepend("pdf_") ; RooAbsPdf* np_pdf = ws->pdf( pdfname ) ; if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; return ; } RooDataSet* nprds = np_pdf->generate( RooArgSet(*np_rrv) ,1) ; const RooArgSet* npdsras = nprds->get() ; TIterator* valIter = npdsras->createIterator() ; RooRealVar* val = (RooRealVar*) valIter->Next() ; //--- reset the value of the nuisance parameter and fix it for the toy model definition fit. np_rrv->setVal( val->getVal() ) ; np_rrv->setConstant( kTRUE ) ; TString npname( np_rrv->GetName() ) ; npname.ReplaceAll("_prim","") ; RooAbsReal* np_rfv = ws->function( npname ) ; if ( verbLevel > 0 ) { if ( np_rfv != 0x0 ) { printf(" %20s : %8.2f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ; } else if ( strstr( npname.Data(), "eff_sf" ) != 0 ) { np_rfv = ws->function( "eff_sf_sig" ) ; RooAbsReal* np_rfv2 = ws->function( "eff_sf_sb" ) ; printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ; } else if ( strstr( npname.Data(), "sf_ll" ) != 0 ) { np_rfv = ws->function( "sf_ee" ) ; RooAbsReal* np_rfv2 = ws->function( "sf_mm" ) ; printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ; } else { printf(" %20s : %8.2f\n", val->GetName(), val->getVal() ) ; } } delete nprds ; } // np_rrv iterator } //--- 2) Fit the dataset with these values for the nuisance parameters. if ( isBgonlyStudy ) { //-- fit with susy yield fixed to zero. rrv_mu_susy_sig -> setVal( 0. ) ; if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to zero.\n\n") ; } } else { //-- fit with susy yield fixed to predicted value. rrv_mu_susy_sig -> setVal( poiVal ) ; if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to %8.1f.\n\n", poiVal) ; } } rrv_mu_susy_sig->setConstant( kTRUE ) ; if ( verbLevel > 0 ) { printf("\n\n") ; printf(" Fitting with these values for the observables to define the model for toy generation.\n") ; rds->printMultiline(cout, 1, kTRUE, "") ; printf("\n\n") ; } RooFitResult* toyModelDefinitionFitResult(0x0) ; if ( verbLevel < 2 ) { toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1)); } else { toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true)); } int toyModelDefFitCovQual = toyModelDefinitionFitResult->covQual() ; if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d\n\n", toyModelDefFitCovQual ) ; } if ( toyModelDefFitCovQual != 3 ) { printf("\n\n\n *** Bad toy model definition fit. Cov qual %d. Aborting this toy.\n\n\n", toyModelDefFitCovQual ) ; continue ; } delete toyModelDefinitionFitResult ; if ( verbLevel > 0 ) { printf("\n\n\n === Model values for observables. These will be used to generate the toy dataset.\n\n") ; printObservables() ; } //--- 3) Generate a new set of observables based on this model. generateObservables() ; printf("\n\n\n Generated dataset\n") ; rds->Print() ; rds->printMultiline(cout, 1, kTRUE, "") ; //--- Apparently, I need to make a new RooDataSet... Resetting the // values in the old one doesn't stick. If you do likelihood->fitTo(*rds), it // uses the original values, not the reset ones, in the fit. RooArgSet toyFitobservedParametersList ; toyFitobservedParametersList.add( *rrv_Nsig ) ; toyFitobservedParametersList.add( *rrv_Nsb ) ; toyFitobservedParametersList.add( *rrv_Nsig_sl ) ; toyFitobservedParametersList.add( *rrv_Nsb_sl ) ; toyFitobservedParametersList.add( *rrv_Nsig_ldp ) ; toyFitobservedParametersList.add( *rrv_Nsb_ldp ) ; toyFitobservedParametersList.add( *rrv_Nsig_ee ) ; toyFitobservedParametersList.add( *rrv_Nsb_ee ) ; toyFitobservedParametersList.add( *rrv_Nsig_mm ) ; toyFitobservedParametersList.add( *rrv_Nsb_mm ) ; RooDataSet* toyFitdsObserved = new RooDataSet("toyfit_ra2b_observed_rds", "RA2b toy observed data values", toyFitobservedParametersList ) ; toyFitdsObserved->add( toyFitobservedParametersList ) ; //--- 4) Reset and free the nuisance parameters. { if ( verbLevel > 0 ) { printf("\n\n") ; } int npi(0) ; TIterator* npIter = nuisanceParameters->createIterator() ; while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) { np_rrv -> setVal( np_initial_val[npi] ) ; // assuming that the order in the iterator does not change. np_rrv -> setConstant( kFALSE ) ; npi++ ; if ( verbLevel > 0 ) { printf(" reset %20s to %8.2f and freed it.\n", np_rrv->GetName() , np_rrv->getVal() ) ; } } // np_rrv iterator. if ( verbLevel > 0 ) { printf("\n\n") ; } } //--- 5a) Evaluate the test statistic: Fit with susy yield floating to get the absolute maximum log likelihood. if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy floating.\n\n") ; } rrv_mu_susy_sig->setVal( 0.0 ) ; rrv_mu_susy_sig->setConstant( kFALSE ) ; if ( verbLevel > 0 ) { printf("\n toy dataset\n\n") ; toyFitdsObserved->printMultiline(cout, 1, kTRUE, "") ; } /////---- nfg. Need to create a new dataset ---------- /////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1)); /////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true)); /////-------------- RooFitResult* maxLikelihoodFitResult(0x0) ; if ( verbLevel < 2 ) { maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1)); } else { maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true)); } if ( verbLevel > 0 ) { printObservables() ; } int mlFitCovQual = maxLikelihoodFitResult->covQual() ; if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", mlFitCovQual, maxLikelihoodFitResult->minNll() ) ; } if ( mlFitCovQual != 3 ) { printf("\n\n\n *** Bad maximum likelihood fit (susy floating). Cov qual %d. Aborting this toy.\n\n\n", mlFitCovQual ) ; continue ; } double maxL_susyFloat = maxLikelihoodFitResult->minNll() ; double maxL_mu_susy_sig = rrv_mu_susy_sig->getVal() ; delete maxLikelihoodFitResult ; //--- 5b) Evaluate the test statistic: Fit with susy yield fixed to hypothesis value. // This is only necessary if the maximum likelihood fit value of the susy yield // is less than the hypothesis value (to get a one-sided limit). double testStat(0.0) ; double maxL_susyFixed(0.0) ; if ( maxL_mu_susy_sig < poiVal ) { if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy fixed to %8.2f.\n\n", poiVal ) ; } rrv_mu_susy_sig->setVal( poiVal ) ; rrv_mu_susy_sig->setConstant( kTRUE ) ; if ( verbLevel > 0 ) { printf("\n toy dataset\n\n") ; rds->printMultiline(cout, 1, kTRUE, "") ; } ////--------- nfg. need to make a new dataset --------------- ////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1)); ////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true)); ////----------------------------- RooFitResult* susyFixedFitResult(0x0) ; if ( verbLevel < 2 ) { susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1)); } else { susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true)); } if ( verbLevel > 0 ) { printObservables() ; } int susyFixedFitCovQual = susyFixedFitResult->covQual() ; if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", susyFixedFitCovQual, susyFixedFitResult->minNll() ) ; } if ( susyFixedFitCovQual != 3 ) { printf("\n\n\n *** Bad maximum likelihood fit (susy fixed). Cov qual %d. Aborting this toy.\n\n\n", susyFixedFitCovQual ) ; continue ; } maxL_susyFixed = susyFixedFitResult->minNll() ; testStat = 2. * (maxL_susyFixed - maxL_susyFloat) ; delete susyFixedFitResult ; } else { if ( verbLevel > 0 ) { printf("\n\n Floating value of susy yield greater than hypo value (%8.2f > %8.2f). Setting test stat to zero.\n\n", maxL_mu_susy_sig, poiVal ) ; } testStat = 0.0 ; } printf(" --- test stat for toy %4d : %8.2f\n", ti, testStat ) ; nToyOK++ ; if ( testStat > dataTestStat ) { nToyWorseThanData++ ; } if ( makeTtree ) { tt_testStat = testStat ; tt_gen_Nsig = rrv_Nsig->getVal() ; tt_gen_Nsb = rrv_Nsb->getVal() ; tt_gen_Nsig_sl = rrv_Nsig_sl->getVal() ; tt_gen_Nsb_sl = rrv_Nsb_sl->getVal() ; tt_gen_Nsig_ldp = rrv_Nsig_ldp->getVal() ; tt_gen_Nsb_ldp = rrv_Nsb_ldp->getVal() ; tt_gen_Nsig_ee = rrv_Nsig_ee->getVal() ; tt_gen_Nsb_ee = rrv_Nsb_ee->getVal() ; tt_gen_Nsig_mm = rrv_Nsig_mm->getVal() ; tt_gen_Nsb_mm = rrv_Nsb_mm->getVal() ; toytt->Fill() ; } //--- *) reset things for the next toy. resetObservables() ; delete toyFitdsObserved ; } // ti. wstf->Close() ; printf("\n\n\n") ; if ( nToyOK == 0 ) { printf("\n\n\n *** All toys bad !?!?!\n\n\n") ; return ; } double pValue = (1.0*nToyWorseThanData) / (1.0*nToyOK) ; if ( isBgonlyStudy ) { printf("\n\n\n p-value result, BG-only , poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ; } else { printf("\n\n\n p-value result, S-plus-B, poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ; } if ( makeTtree ) { printf("\n\n Writing TTree : %s : %s\n\n", ttname, tttitle ) ; ttfile->cd() ; toytt->Write() ; } } // ws_cls_hybrid1