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
