void PEs(RooAbsPdf *iGen,RooAbsPdf *iFit,int iN,int iNEvents,RooRealVar &iVar,RooRealVar &iSig,RooRealVar &iMean,
RooRealVar &iScale,RooRealVar &iRes) {
double iM0 = iMean.getVal(); double iS0 = iSig.getVal();
//iScale.setVal(iMeanScale); iRes.setVal(iSigScale);
TRandom1 *lRand = new TRandom1(0xDEADBEEF);
TNtuple * lDN= new TNtuple( "xxx","xxx","ntot:m_r:m:merr:sig_r:sig:sigerr");
for(int i0=0;i0<iN;i0++){
if(i0 % 10 == 0) cout << "+++++++++++++++++++++++++++ running ======> " << i0 << endl;
int lN = lRand->Poisson(iNEvents);
RooDataSet * lSignal = iGen->generate(iVar,lN);
iMean.setVal(iM0); iSig.setVal(iS0);
iFit->fitTo(*lSignal,Strategy(1));//,Save(kTRUE),PrintLevel(1));
if(iMean.getError() < 0.05) iFit->fitTo(*lSignal,Strategy(2));
Float_t values[]={
(Float_t) lN,
(Float_t) 90.78/iScale.getVal(),
(Float_t) iMean.getVal(),
(Float_t) iMean.getError(),
(Float_t) iSig.getVal(),
(Float_t) iSig.getVal(),
(Float_t) iSig.getError()
};
lDN->Fill(values);
}
TFile *lF = new TFile("XXX.root","RECREATE");
lDN->Write();
lF->Close();
}
TGraph *graphLH(std::string nuisname, double err ){
w->loadSnapshot("bestfitall"); // SetTo BestFit values as start
// Get The parameter we want
RooRealVar *nuis =(RooRealVar*) w->var(nuisname.c_str());
double bf = nuis->getVal();
double nll_0=nll->getVal();
TGraph *gr = new TGraph(2*npoints+1);
for (int i=-1*npoints;i<=npoints;i++){
nuis->setVal(bf+err*( ((float)i)*nsigma/npoints));
double nll_v = nll->getVal();
gr->SetPoint(i+npoints,nuis->getVal(),nll_v-nll_0);
}
gr->SetTitle("");
gr->GetYaxis()->SetTitle("NLL - obs data");
gr->GetYaxis()->SetTitleOffset(1.1);
gr->GetXaxis()->SetTitleSize(0.05);
gr->GetYaxis()->SetTitleSize(0.05);
gr->GetXaxis()->SetTitle(nuisname.c_str());
gr->SetLineColor(4);
gr->SetLineWidth(2);
gr->SetMarkerStyle(21);
gr->SetMarkerSize(0.6);
return gr;
}
pair<double,double> bkgEvPerGeV(RooWorkspace *work, int m_hyp, int cat, int spin=false){
RooRealVar *mass = (RooRealVar*)work->var("CMS_hgg_mass");
if (spin) mass = (RooRealVar*)work->var("mass");
mass->setRange(100,180);
RooAbsPdf *pdf = (RooAbsPdf*)work->pdf(Form("pdf_data_pol_model_8TeV_cat%d",cat));
RooAbsData *data = (RooDataSet*)work->data(Form("data_mass_cat%d",cat));
RooPlot *tempFrame = mass->frame();
data->plotOn(tempFrame,Binning(80));
pdf->plotOn(tempFrame);
RooCurve *curve = (RooCurve*)tempFrame->getObject(tempFrame->numItems()-1);
double nombkg = curve->Eval(double(m_hyp));
RooRealVar *nlim = new RooRealVar(Form("nlim%d",cat),"",0.,0.,1.e5);
//double lowedge = tempFrame->GetXaxis()->GetBinLowEdge(FindBin(double(m_hyp)));
//double upedge = tempFrame->GetXaxis()->GetBinUpEdge(FindBin(double(m_hyp)));
//double center = tempFrame->GetXaxis()->GetBinUpCenter(FindBin(double(m_hyp)));
nlim->setVal(nombkg);
mass->setRange("errRange",m_hyp-0.5,m_hyp+0.5);
RooAbsPdf *epdf = 0;
epdf = new RooExtendPdf("epdf","",*pdf,*nlim,"errRange");
RooAbsReal *nll = epdf->createNLL(*data,Extended(),NumCPU(4));
RooMinimizer minim(*nll);
minim.setStrategy(0);
minim.setPrintLevel(-1);
minim.migrad();
minim.minos(*nlim);
double error = (nlim->getErrorLo(),nlim->getErrorHi())/2.;
data->Print();
return pair<double,double>(nombkg,error);
}
RooStats::ModelConfig * Tprime::SetBModel( void ) {
//
// Define model config and parameter snapshot to describe the b model.
// Import to workspace.
//
std::string legend = "[Tprime::SetBModel]: ";
// full signal+background model
//RooStats::ModelConfig * pSBModel = (RooStats::ModelConfig *)pWs->genobj("ModelConfig");
// let's make the b model (bg-only) from the alt model (s+b) with xsec=0
//RooStats::ModelConfig * pBModel =
// new RooStats::ModelConfig(*(RooStats::ModelConfig *)pWs->genobj("ModelConfig"));
RooStats::ModelConfig * _sbModel = (RooStats::ModelConfig *)pWs->genobj("ModelConfig");
RooStats::ModelConfig * pBModel = _sbModel->Clone("BModel");
//pBModel->SetName("BModel");
pBModel->SetWorkspace(*pWs);
//pBModel->SetParametersOfInterest(RooArgSet());
pWs->import(*pBModel);
// set POI to the b model value and take snapshot
RooRealVar * pPoi = (RooRealVar *)pBModel->GetParametersOfInterest()->first();
pPoi->setVal(0.0);
pBModel->SetSnapshot(*pPoi);
pBModel->Print();
return pBModel;
}
void testResolution() {
Prep();
RooRealVar lXVar ("XVar","mass(GeV/c^{2})",100,60,150); lXVar.setBins(1000);
RooRealVar l1Sigma("sigma1","sigma1",1.6 ,0.,15.); //l1Sigma.setConstant(kTRUE);
RooRealVar l2Sigma("sigma2","sigma2",1.6,0.,15.); l2Sigma.setConstant(kTRUE);
RooRealVar l3Sigma("sigma3","sigma3",2.90,0.,35.); l3Sigma.setConstant(kTRUE);
RooRealVar lN ("n" ,"n" ,1.00,-15,15.); lN.setConstant(kTRUE);
RooRealVar lExp ("exp" ,"exp" ,-0.003,-15,15.); //lExp.setConstant(kTRUE);
RooRealVar lR0Mean("xmean","xmean",0,-10,10); lR0Mean.setConstant(kTRUE);
RooRealVar lR1Mean("mean","mean",90.8,60,150); //lR1Mean.setConstant(kTRUE);
RooVoigtianShape lGAdd("Add","Add",lXVar,lR1Mean,l1Sigma,l2Sigma,lN,l3Sigma,true);
RooRealVar lSPar ("SPar","SPar", 1.,0., 2.);
RooFormulaVar lXShift("uparshift","@0*@1",RooArgList(lXVar,lSPar));
TH1F *lMass = getMass(0,-5,5,-1.5,1.5);
RooDataHist *lMHist = new RooDataHist("M" ,"M" ,RooArgSet(lXVar),lMass);
RooHistPdf *lMPdf = new RooHistPdf ("MH","MH",lXShift,lXVar,*lMHist,5);
RooRealVar lGSigma("gsigma","gsigma",1.6 ,0.,15.);
RooGaussian lGaus1("gaus1","gaus1",lXVar,lR0Mean,lGSigma);
RooFFTConvPdf lConv("Conv","Conv",lXVar,*lMPdf,lGaus1);
RooDataSet *lData = new RooDataSet("crap","crap",RooArgSet(lXVar));
fillData(lData,lXVar,0,-5,5,-1.5,1.5);
lConv.fitTo(*lData,Strategy(2));
lGSigma.setVal(lGSigma.getVal());
lSPar.setVal(lSPar.getVal()*1.01);
//cout << "=====> Check " << l1Sigma.getVal() << " --- " << lR1Mean.getVal() << "----" << lSPar.getVal() << endl;
PEs(&lConv,&lGAdd,2000,500,lXVar,l1Sigma,lR1Mean,lSPar,lGSigma);
lData = 0;
Plot(&lConv,&lGAdd,2000,50000,lXVar,l1Sigma,lR1Mean,lSPar,lGSigma,lData);
}
//
// set value and range for a variable in the workspace
//
void setValRange (RooWorkspace* workspace, const char* name, double val, double vmin, double vmax)
{
RooRealVar* var = workspace->var(name);
if ( var ) {
if ( vmax>vmin ) var->setRange(vmin,vmax);
var->setVal(val);
}
}
void UL_significance_Hybrid(Model* model,int n_toys,int random_seed=0,double sig=1){
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
cout<<"Calculating significance with the Hybrid method"<<endl;
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
//set the random seed
RooRandom::randomGenerator()->SetSeed(random_seed);
//get the calculator
HybridCalculatorOriginal myhc(*model->get_data(),*model->get_sb_likelihood(),*model->get_b_likelihood());
//for numbercounting experiments
myhc.PatchSetExtended(false);
//set likelihood ratio as the test statistics
myhc.SetTestStatistic(1);
//define the systematics to be used
if (model->get_nuisance_set()) {
myhc.UseNuisance(true);
myhc.SetNuisancePdf(*model->get_nuisance_prior_pdf());
myhc.SetNuisanceParameters(*model->get_nuisance_set());
} else {
myhc.UseNuisance(false);
}
//define the number of toys to be done
myhc.SetNumberOfToys(n_toys);
//RooArgSet* poi= model->get_POI_set();
// poi->first()->Set(sig);
RooRealVar* firstPOI = (RooRealVar*) model->get_POI_set()->first();
firstPOI->setVal(sig);
//get the Hypotestresult
HybridResult* hcResult = myhc.GetHypoTest();
double significance = hcResult->Significance();
double CLS= hcResult->CLs();
double CLB= hcResult->CLb();
double CLsplusb= hcResult->CLsplusb();
double CLSerror= hcResult->CLsError();
cout<<"POI: "<<sig<<endl;
cout <<"significance:" << significance<<endl;
cout<<"CLs: "<<CLS<<endl;
cout<<"CLb: "<<CLB<<endl;
cout<<"CLsplusb: "<<CLsplusb<<endl;
cout<<"CLserror: "<<CLSerror<<endl;
HybridPlot* plot=hcResult->GetPlot("hcPlot","p Values Plot",100);
TCanvas *c1=new TCanvas;
plot->Draw();
c1->SaveAs("hybrid_REsult");
}
//____________________________________
void MakePlots(RooWorkspace* wks) {
// Make plots of the data and the best fit model in two cases:
// first the signal+background case
// second the background-only case.
// get some things out of workspace
RooAbsPdf* model = wks->pdf("model");
RooAbsPdf* sigModel = wks->pdf("sigModel");
RooAbsPdf* zjjModel = wks->pdf("zjjModel");
RooAbsPdf* qcdModel = wks->pdf("qcdModel");
RooRealVar* mu = wks->var("mu");
RooRealVar* invMass = wks->var("invMass");
RooAbsData* data = wks->data("data");
//////////////////////////////////////////////////////////
// Make plots for the Alternate hypothesis, eg. let mu float
mu->setConstant(kFALSE);
model->fitTo(*data,Save(kTRUE),Minos(kFALSE), Hesse(kFALSE),PrintLevel(-1));
//plot sig candidates, full model, and individual componenets
new TCanvas();
RooPlot* frame = invMass->frame() ;
data->plotOn(frame ) ;
model->plotOn(frame) ;
model->plotOn(frame,Components(*sigModel),LineStyle(kDashed), LineColor(kRed)) ;
model->plotOn(frame,Components(*zjjModel),LineStyle(kDashed),LineColor(kBlack)) ;
model->plotOn(frame,Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
frame->SetTitle("An example fit to the signal + background model");
frame->Draw() ;
// cdata->SaveAs("alternateFit.gif");
//////////////////////////////////////////////////////////
// Do Fit to the Null hypothesis. Eg. fix mu=0
mu->setVal(0); // set signal fraction to 0
mu->setConstant(kTRUE); // set constant
model->fitTo(*data, Save(kTRUE), Minos(kFALSE), Hesse(kFALSE),PrintLevel(-1));
// plot signal candidates with background model and components
new TCanvas();
RooPlot* xframe2 = invMass->frame() ;
data->plotOn(xframe2, DataError(RooAbsData::SumW2)) ;
model->plotOn(xframe2) ;
model->plotOn(xframe2, Components(*zjjModel),LineStyle(kDashed),LineColor(kBlack)) ;
model->plotOn(xframe2, Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
xframe2->SetTitle("An example fit to the background-only model");
xframe2->Draw() ;
// cbkgonly->SaveAs("nullFit.gif");
}
///
/// Set all observables to 'truth' values computed from the
/// current parameters.
///
void PDF_Abs::setObservablesTruth()
{
obsValSource = "truth";
for ( int i=0; i<nObs; i++ )
{
RooRealVar* pObs = (RooRealVar*)((RooArgList*)observables)->at(i);
pObs->setVal(((RooRealVar*)((RooArgList*)theory)->at(i))->getVal());
}
}
double effRmsFromSigmaCB(double meanCB, double meanCB_err, double sigmaCB, double sigmaCB_err) {
RooRealVar mass("zmass","m(e^{+}e^{-})",75,105,"GeV/c^{2}");
float zwidth=2.4952;
RooRealVar bwMean("m_{Z}","BW Mean", 91.1876, "GeV/c^{2}");
RooRealVar bwWidth("#Gamma_{Z}", "BW Width", zwidth, "GeV/c^{2}");
RooRealVar cbBias ("#Deltam_{CB}", "CB Bias", 0.00, -10, 10, "GeV/c^{2}"); // typical value
RooRealVar cbSigma("#sigma_{CB}", "CB Width", 1.5, 0.8, 5.0, "GeV/c^{2}");
RooRealVar cbCut ("a_{CB}","CB Cut", 1.0, 1.0, 3.0); // typical value
RooRealVar cbPower("n_{CB}","CB Order", 2.5, 0.1, 20.0); // typical value
float mean_lo = meanCB - meanCB_err;
float mean_hi = meanCB + meanCB_err;
float sigma_lo = sigmaCB - sigmaCB_err;
float sigma_hi = sigmaCB + sigmaCB_err;
int nstep_mean = 10;
int nstep_sigma = 10;
float step_mean = 2*meanCB_err/float(nstep_mean);
float step_sigma = 2*sigmaCB_err/float(nstep_sigma);
double max_rms = 0.0;
double min_rms = 999.;
RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING);
int nsteps=0;
for(float mean=mean_lo; mean<mean_hi; mean+=step_mean) {
for(float sigma=sigma_lo; sigma<sigma_hi; sigma+=step_sigma) {
cbBias.setVal(meanCB);
cbSigma.setVal(sigma);
RooBreitWigner bw("bw", "bw", mass, bwMean, bwWidth);
RooCBShape cball("cball", "Crystal Ball", mass, cbBias, cbSigma, cbCut, cbPower);
RooFFTConvPdf BWxCB("BWxCB", "bw X crystal ball", mass, bw, cball);
RooDataSet *gendata = BWxCB.generate(mass,1000);
TH1D *genh = (TH1D*) gendata->createHistogram("genh",mass,RooFit::Binning(100,75,105));
math::HistogramTools ht(genh);
double rms = ht.effectiveSigma();
rms = sqrt(pow(rms,2)-pow(zwidth,2));
if(rms > max_rms) max_rms=rms;
if(rms < min_rms) min_rms=rms;
delete genh;
nsteps++;
}
}
return (max_rms-min_rms)/sqrt(12)/sqrt(nsteps);
}
void FitterUtils::initiateParams(int nGenSignalZeroGamma, int nGenSignalOneGamma, int nGenSignalTwoGamma, RooRealVar const& expoConstGen, RooRealVar& nSignal, RooRealVar& nPartReco,
RooRealVar& nComb, RooRealVar& fracZero, RooRealVar& fracOne, RooRealVar& expoConst, RooRealVar& nJpsiLeak, bool constPartReco, RooRealVar const& fracPartRecoSigma)
{
TRandom rand;
rand.SetSeed();
int nGenSignal = nGenSignalZeroGamma + nGenSignalOneGamma + nGenSignalTwoGamma;
double nGenSignal2;
double nGenPartReco2;
if(!constPartReco)
{
nGenSignal2 = rand.Uniform(nGenSignal-5*sqrt(nGenSignal), nGenSignal+5*sqrt(nGenSignal));
nGenPartReco2 = rand.Uniform(nGenPartReco-5*sqrt(nGenPartReco), nGenPartReco+5*sqrt(nGenPartReco));
}
if(constPartReco)
{
double nGenSigPartReco( nGenSignal+nGenPartReco );
double nGenSigPartReco2( rand.Uniform( nGenSigPartReco-5*sqrt(nGenSigPartReco), nGenSigPartReco+5*sqrt(nGenSigPartReco) ) );
double fracPartReco1( nGenPartReco/(1.*nGenSignal));
double fracPartReco2( rand.Uniform(fracPartReco1-5*fracPartRecoSigma.getVal(), fracPartReco1+5*fracPartRecoSigma.getVal()) );
nGenPartReco2 = fracPartReco2*nGenSigPartReco2 / (1+fracPartReco2);
nGenSignal2 = nGenSigPartReco2 / (1+fracPartReco2);
}
double nGenComb2 = rand.Uniform(nGenComb-5*sqrt(nGenComb), nGenComb+5*sqrt(nGenComb));
double nGenJpsiLeak2 = rand.Uniform(nGenJpsiLeak-5*sqrt(nGenJpsiLeak), nGenJpsiLeak+5*sqrt(nGenJpsiLeak));
nSignal.setVal(nGenSignal2);
nSignal.setRange(TMath::Max(0.,nGenSignal2-10.*sqrt(nGenSignal)) , nGenSignal2+10*sqrt(nGenSignal));
nPartReco.setVal(nGenPartReco2);
nPartReco.setRange(TMath::Max(0.,nGenPartReco2-10.*sqrt(nGenPartReco)), nGenPartReco2+10*sqrt(nGenPartReco));
nComb.setVal(nGenComb2);
nComb.setRange(TMath::Max(0.,nGenComb2-10.*sqrt(nGenComb)), nGenComb2+10*sqrt(nGenComb));
nJpsiLeak.setVal(nGenJpsiLeak2);
nJpsiLeak.setRange(TMath::Max(0., nGenJpsiLeak2-10*sqrt(nGenJpsiLeak)), nGenJpsiLeak2+10*sqrt(nGenJpsiLeak));
double fracGenZero(nGenSignalZeroGamma/(1.*nGenSignal));
double fracGenOne(nGenSignalOneGamma/(1.*nGenSignal));
fracZero.setVal(rand.Gaus(fracGenZero, sqrt(nGenSignalZeroGamma)/(1.*nGenSignal))) ;
fracZero.setRange(0., 1.);
fracOne.setVal(rand.Gaus(fracGenOne, sqrt(nGenSignalOneGamma)/(1.*nGenSignal))) ;
fracOne.setRange(0., 1.);
expoConst.setVal(rand.Uniform( expoConstGen.getVal() - 5*expoConstGen.getError(), expoConstGen.getVal() + 5*expoConstGen.getError() ) );
expoConst.setRange( expoConstGen.getVal() - 10*expoConstGen.getError(), expoConstGen.getVal() + 10*expoConstGen.getError() );
}
void fillData(RooDataSet *iData,RooRealVar &lXVar,int iCharge,float iPhiMin,float iPhiMax,float iEtaMin,float iEtaMax) {
TFile *lFile = new TFile("ZTP.root");
TTree *lTree = (TTree*) lFile->FindObjectAny("WNtupleIdEffNT");
int lCharge = 0; lTree->SetBranchAddress("charge",&lCharge);
float lEta = 0; lTree->SetBranchAddress("eta" ,&lEta);
float lPhi = 0; lTree->SetBranchAddress("phi" ,&lPhi);
float lMt = 0; lTree->SetBranchAddress("mt" ,&lMt);
float lPt = 0; lTree->SetBranchAddress("pt" ,&lPt);
float lOPt = 0; lTree->SetBranchAddress("jetpt" ,&lOPt);
float lOEta = 0; lTree->SetBranchAddress("jeteta",&lOEta);
float lOPhi = 0; lTree->SetBranchAddress("jetphi",&lOPhi);
for(int i0 = 0; i0 < lTree->GetEntries();i0++) {
lTree->GetEntry(i0);
if(lMt < 60) continue;
if(lCharge > 0 && iCharge < 0) continue;
if(lCharge < 0 && iCharge > 0) continue;
if(lPhi < iPhiMin || lPhi > iPhiMax) continue;
if(lEta < iEtaMin || lEta > iEtaMax) continue;
lXVar.setVal(fabs(lMt));
if(lCharge > 0) lXVar.setVal(correct(lMt,lPhi,lOPhi,lEta,lOEta));
if(lCharge < 0) lXVar.setVal(correct(lMt,lOPhi,lPhi,lOEta,lEta));
iData->add(RooArgSet(lXVar));
}
}
///
/// Set each parameter in workspace to the values found
/// in the fit result
///
void Utils::setParameters(RooWorkspace* w, RooFitResult* values){
RooArgList list = values->floatParsFinal();
list.add(values->constPars());
TIterator* it = list.createIterator();
while(RooRealVar* p = (RooRealVar*)it->Next()){
RooRealVar* var = dynamic_cast<RooRealVar*>(w->allVars().find(p->GetName()));
if(!(var)){
std::cout << "WARNING in Utils::setParameters(RooWorkspace,RooFitResult) -- no Var found with name "
<< p->GetName() << " in Workspace!" << endl;
}
else{
var->setVal(p->getVal());
}
}
return;
};
///
/// Set all observables to 'toy' values drawn from the
/// PDF using the current parameter values. A certain number
/// of toys is pregenerated to speed up when doing mulitple toy fits.
///
void PDF_Abs::setObservablesToy()
{
obsValSource = "toy";
if( !pdf ){ cout<< "PDF_Abs::setObservables(): ERROR: pdf not initialized."<<endl; exit(1); }
if ( toyObservables==0 || iToyObs==nToyObs )
{
RooRandom::randomGenerator()->SetSeed(0);
if ( iToyObs==nToyObs ) delete toyObservables;
toyObservables = pdf->generate(*(RooArgSet*)observables, nToyObs);
iToyObs=0;
}
for ( int i=0; i<nObs; i++ )
{
RooRealVar* pObs = (RooRealVar*)((RooArgList*)observables)->at(i);
pObs->setVal(((RooRealVar*)toyObservables->get(iToyObs)->find(pObs->GetName()))->getVal());
}
iToyObs+=1;
}
double getChisq(RooAbsData &dat, RooAbsPdf &pdf, RooRealVar &var, bool prt=false) {
// Find total number of events
double nEvt;
double nTot=0.0;
for(int j=0; j<dat.numEntries(); j++) {
dat.get(j);
nEvt=dat.weight();
nTot+=nEvt;
}
// Find chi-squared equivalent 2NLL
//RooRealVar *var=(RooRealVar*)(pdf.getParameters(*dat)->find("CMS_hgg_mass"));
double totNLL=0.0;
double prbSum=0.0;
for(int j=0; j<dat.numEntries(); j++) {
double m=dat.get(j)->getRealValue(var.GetName());
if ( m < var.getMin() || m > var.getMax()) continue;
// Find probability density and hence probability
var.setVal(m);
double prb = var.getBinWidth(0)*pdf.getVal(var);
prbSum+=prb;
dat.get(j);
nEvt=dat.weight();
double mubin=nTot*prb;
double contrib(0.);
if (nEvt < 1) contrib = mubin;
else contrib=mubin-nEvt+nEvt*log(nEvt/mubin);
totNLL+=contrib;
if(prt) cout << "Bin " << j << " prob = " << prb << " nEvt = " << nEvt << ", mu = " << mubin << " contribution " << contrib << endl;
}
totNLL*=2.0;
if(prt) cout << pdf.GetName() << " nTot = " << nTot << " 2NLL constant = " << totNLL << endl;
return totNLL;
}
RooDataSet *th22dataset(TH2F *hist){
RooRealVar *v1 = new RooRealVar("constTerm","",0.01,0,0.02);
RooRealVar *v2 = new RooRealVar("alpha","",0.0,0,0.2);
RooRealVar *nll = new RooRealVar("nll",0,0,1e20);
RooArgSet argSet(*v1,*v2, *nll);
RooDataSet *dataset = new RooDataSet(TString(hist->GetName())+"_dataset", "", argSet,"nll");
for(Int_t iBinX=1; iBinX <= hist->GetNbinsX(); iBinX++){
for(Int_t iBinY=1; iBinY <= hist->GetNbinsY(); iBinY++){
Double_t binContent=hist->GetBinContent(iBinX, iBinY);
if(binContent!=0){
v1->setVal(hist->GetXaxis()->GetBinCenter(iBinX));
v2->setVal(hist->GetYaxis()->GetBinCenter(iBinY));
nll->setVal(binContent);
dataset->add(argSet);
}
}
}
return dataset;
}
///
/// Fit a pdf to the minimum, but keep angular parameters in a range of
/// [0,2pi]. If after an initial fit, a parameter has walked outside this
/// interval, add multiples of 2pi to bring it back. Then, refit.
/// All variables that have unit 'rad' are taken to be angles.
///
RooFitResult* Utils::fitToMinBringBackAngles(RooAbsPdf *pdf, bool thorough, int printLevel)
{
countAllFitBringBackAngle++;
RooFitResult* r = fitToMin(pdf, thorough, printLevel);
bool refit = false;
TIterator* it = r->floatParsFinal().createIterator();
while ( RooRealVar* p = (RooRealVar*)it->Next() ){
if ( ! isAngle(p) ) continue;
if ( p->getVal()<0.0 || p->getVal()>2.*TMath::Pi() ){
RooArgSet *pdfPars = pdf->getParameters(RooArgSet());
RooRealVar *pdfPar = (RooRealVar*)pdfPars->find(p->GetName());
pdfPar->setVal(bringBackAngle(p->getVal()));
refit = true;
delete pdfPars;
}
}
if ( refit ){
countFitBringBackAngle++;
delete r;
r = fitToMin(pdf, thorough, printLevel);
}
delete it;
return r;
}
void FitterUtilsSimultaneousExpOfPolyTimesX::initiateParams(int nGenSignalZeroGamma, int nGenSignalOneGamma, int nGenSignalTwoGamma,
RooRealVar& nKemu, RooRealVar& nSignal, RooRealVar& nPartReco,
RooRealVar& nComb, RooRealVar& fracZero, RooRealVar& fracOne,
RooRealVar& nJpsiLeak, bool constPartReco, RooRealVar const& fracPartRecoSigma,
RooRealVar& l1Kee, RooRealVar& l2Kee, RooRealVar& l3Kee, RooRealVar& l4Kee, RooRealVar& l5Kee,
RooRealVar& l1Kemu, RooRealVar& l2Kemu, RooRealVar& l3Kemu, RooRealVar& l4Kemu, RooRealVar& l5Kemu,
RooRealVar const& l1KeeGen, RooRealVar const& l2KeeGen, RooRealVar const& l3KeeGen, RooRealVar const& l4KeeGen, RooRealVar const& l5KeeGen
)
{
FitterUtilsExpOfPolyTimesX::initiateParams(nGenSignalZeroGamma, nGenSignalOneGamma, nGenSignalTwoGamma,
nSignal, nPartReco, nComb, fracZero, fracOne, nJpsiLeak, constPartReco, fracPartRecoSigma,
l1Kee, l2Kee, l3Kee, l4Kee, l5Kee,
l1KeeGen, l2KeeGen, l3KeeGen, l4KeeGen, l5KeeGen );
TRandom rand;
rand.SetSeed();
nKemu.setVal(rand.Uniform(nGenKemu-5*sqrt(nGenKemu), nGenKemu+5*sqrt(nGenKemu)));
nKemu.setRange(nGenKemu-10*sqrt(nGenKemu), nGenKemu+10*sqrt(nGenKemu));
l1Kemu.setVal(rand.Uniform( l1KeeGen.getVal() - 5*l1KeeGen.getError(), l1KeeGen.getVal() + 5*l1KeeGen.getError() ) );
l1Kemu.setRange( l1KeeGen.getVal() - 10*l1KeeGen.getError(), l1KeeGen.getVal() + 10*l1KeeGen.getError() );
l2Kemu.setVal(rand.Uniform( l2KeeGen.getVal() - 5*l2KeeGen.getError(), l2KeeGen.getVal() + 5*l2KeeGen.getError() ) );
l2Kemu.setRange( l2KeeGen.getVal() - 10*l2KeeGen.getError(), l2KeeGen.getVal() + 10*l2KeeGen.getError() );
l3Kemu.setVal(rand.Uniform( l3KeeGen.getVal() - 5*l3KeeGen.getError(), l3KeeGen.getVal() + 5*l3KeeGen.getError() ) );
l3Kemu.setRange( l3KeeGen.getVal() - 10*l3KeeGen.getError(), l3KeeGen.getVal() + 10*l3KeeGen.getError() );
l4Kemu.setVal(rand.Uniform( l4KeeGen.getVal() - 5*l4KeeGen.getError(), l4KeeGen.getVal() + 5*l4KeeGen.getError() ) );
l4Kemu.setRange( l4KeeGen.getVal() - 10*l4KeeGen.getError(), l4KeeGen.getVal() + 10*l4KeeGen.getError() );
l5Kemu.setVal(rand.Uniform( l5KeeGen.getVal() - 5*l5KeeGen.getError(), l5KeeGen.getVal() + 5*l5KeeGen.getError() ) );
l5Kemu.setRange( l5KeeGen.getVal() - 10*l5KeeGen.getError(), l5KeeGen.getVal() + 10*l5KeeGen.getError() );
}
void draw_data_mgg(TString folderName,bool blind=true,float min=103,float max=160)
{
TFile inputFile(folderName+"/data.root");
const int nCat = 5;
TString cats[5] = {"HighPt","Hbb","Zbb","HighRes","LowRes"};
TCanvas cv;
for(int iCat=0; iCat < nCat; iCat++) {
RooWorkspace *ws = (RooWorkspace*)inputFile.Get(cats[iCat]+"_mgg_workspace");
RooFitResult* res = (RooFitResult*)ws->obj("fitresult_pdf_data");
RooRealVar * mass = ws->var("mgg");
mass->setRange("all",min,max);
mass->setRange("blind",121,130);
mass->setRange("low",106,121);
mass->setRange("high",130,160);
mass->setUnit("GeV");
mass->SetTitle("m_{#gamma#gamma}");
RooAbsPdf * pdf = ws->pdf("pdf");
RooPlot *plot = mass->frame(min,max,max-min);
plot->SetTitle("");
RooAbsData* data = ws->data("data")->reduce(Form("mgg > %f && mgg < %f",min,max));
double nTot = data->sumEntries();
if(blind) data = data->reduce("mgg < 121 || mgg>130");
double nBlind = data->sumEntries();
double norm = nTot/nBlind; //normalization for the plot
data->plotOn(plot);
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"),RooFit::LineWidth(0.1) );
plot->Print();
//add the fix error band
RooCurve* c = plot->getCurve("pdf_Norm[mgg]_Range[Full]_NormRange[Full]");
const int Nc = c->GetN();
//TGraphErrors errfix(Nc);
//TGraphErrors errfix2(Nc);
TGraphAsymmErrors errfix(Nc);
TGraphAsymmErrors errfix2(Nc);
Double_t *x = c->GetX();
Double_t *y = c->GetY();
double NtotalFit = ws->var("Nbkg1")->getVal()*ws->var("Nbkg1")->getVal() + ws->var("Nbkg2")->getVal()*ws->var("Nbkg2")->getVal();
for( int i = 0; i < Nc; i++ )
{
errfix.SetPoint(i,x[i],y[i]);
errfix2.SetPoint(i,x[i],y[i]);
mass->setVal(x[i]);
double shapeErr = pdf->getPropagatedError(*res)*NtotalFit;
//double totalErr = TMath::Sqrt( shapeErr*shapeErr + y[i] );
//total normalization error
double totalErr = TMath::Sqrt( shapeErr*shapeErr + y[i]*y[i]/NtotalFit );
if ( y[i] - totalErr > .0 )
{
errfix.SetPointError(i, 0, 0, totalErr, totalErr );
}
else
{
errfix.SetPointError(i, 0, 0, y[i] - 0.01, totalErr );
}
//2sigma
if ( y[i] - 2.*totalErr > .0 )
{
errfix2.SetPointError(i, 0, 0, 2.*totalErr, 2.*totalErr );
}
else
{
errfix2.SetPointError(i, 0, 0, y[i] - 0.01, 2.*totalErr );
}
/*
std::cout << x[i] << " " << y[i] << " "
<< " ,pdf get Val: " << pdf->getVal()
<< " ,pdf get Prop Err: " << pdf->getPropagatedError(*res)*NtotalFit
<< " stat uncertainty: " << TMath::Sqrt(y[i]) << " Ntot: " << NtotalFit << std::endl;
*/
}
errfix.SetFillColor(kYellow);
errfix2.SetFillColor(kGreen);
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kFALSE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kFALSE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kTRUE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kTRUE));
plot->addObject(&errfix,"4");
plot->addObject(&errfix2,"4");
plot->addObject(&errfix,"4");
data->plotOn(plot);
TBox blindBox(121,plot->GetMinimum()-(plot->GetMaximum()-plot->GetMinimum())*0.015,130,plot->GetMaximum());
blindBox.SetFillColor(kGray);
if(blind) {
plot->addObject(&blindBox);
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kTRUE));
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kTRUE));
}
//plot->addObject(&errfix,"4");
//data->plotOn(plot);
//pdf->plotOn(plot,RooFit::Normalization( norm ) );
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"),RooFit::LineWidth(1.5) );
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"), RooFit::LineWidth(1));
data->plotOn(plot);
/*
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::Range("all"),RooFit::LineWidth(0.8) );
//pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kGreen),RooFit::Range("all"), RooFit::VisualizeError(*res,2.0,kFALSE));
//pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kYellow),RooFit::Range("all"), RooFit::VisualizeError(*res,1.0,kFALSE));
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kGreen),RooFit::Range("all"), RooFit::VisualizeError(*res,2.0,kTRUE));
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kYellow),RooFit::Range("all"), RooFit::VisualizeError(*res,1.0,kTRUE));
data->plotOn(plot);
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::Range("all"),RooFit::LineWidth(0.8) );
*/
TLatex lbl0(0.1,0.96,"CMS Preliminary");
lbl0.SetNDC();
lbl0.SetTextSize(0.042);
plot->addObject(&lbl0);
TLatex lbl(0.4,0.96,Form("%s Box",cats[iCat].Data()));
lbl.SetNDC();
lbl.SetTextSize(0.042);
plot->addObject(&lbl);
TLatex lbl2(0.6,0.96,"#sqrt{s}=8 TeV L = 19.78 fb^{-1}");
lbl2.SetNDC();
lbl2.SetTextSize(0.042);
plot->addObject(&lbl2);
int iObj=-1;
TNamed *obj;
while( (obj = (TNamed*)plot->getObject(++iObj)) ) {
obj->SetName(Form("Object_%d",iObj));
}
plot->Draw();
TString tag = (blind ? "_BLIND" : "");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".png");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".pdf");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".C");
}
}
void compute_p0(const char* inFileName,
const char* wsName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData",
const char* asimov1DataName = "asimovData_1",
const char* conditional1Snapshot = "conditionalGlobs_1",
const char* nominalSnapshot = "nominalGlobs",
string smass = "130",
string folder = "test")
{
double mass;
stringstream massStr;
massStr << smass;
massStr >> mass;
double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected
bool doConditional = 1; // do conditional expected data
bool remakeData = 0; // handle unphysical pdf cases in H->ZZ->4l
bool doUncap = 1; // uncap p0
bool doInj = 0; // setup the poi for injection study (zero is faster if you're not)
bool doObs = 1; // compute median significance
bool doMedian = 1; // compute observed significance
TStopwatch timer;
timer.Start();
TFile f(inFileName);
RooWorkspace* ws = (RooWorkspace*)f.Get(wsName);
if (!ws)
{
cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl;
return;
}
ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
if (!mc)
{
cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
return;
}
RooDataSet* data = (RooDataSet*)ws->data(dataName);
if (!data)
{
cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
return;
}
mc->GetNuisanceParameters()->Print("v");
ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1);
cout << "Setting max function calls" << endl;
ws->loadSnapshot("conditionalNuis_0");
RooArgSet nuis(*mc->GetNuisanceParameters());
RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
RooAbsPdf* pdf = mc->GetPdf();
string condSnapshot(conditional1Snapshot);
RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp2)) : NULL;
RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
RooRealVar* emb = (RooRealVar*)mc->GetNuisanceParameters()->find("ATLAS_EMB");
if (!asimovData1 || (string(inFileName).find("ic10") != string::npos && emb))
{
if (emb) emb->setVal(0.7);
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
string mu_str, mu_prof_str;
asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true);
condSnapshot="conditionalGlobs"+mu_prof_str;
}
if (!doUncap) mu->setRange(0, 40);
else mu->setRange(-40, 40);
RooAbsPdf* pdf = mc->GetPdf();
RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1));
//do asimov
mu->setVal(1);
mu->setConstant(0);
if (!doInj) mu->setConstant(1);
int status,sign;
double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0;
if (doMedian)
{
ws->loadSnapshot(condSnapshot.c_str());
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
mc->GetGlobalObservables()->Print("v");
mu->setVal(0);
mu->setConstant(1);
status = minimize(asimov_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_cond = asimov_nll->getVal();
mu->setVal(1);
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
if (doInj) mu->setConstant(0);
status = minimize(asimov_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_min = asimov_nll->getVal();
asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min);
if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0;
sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0);
med_sig = sign*sqrt(fabs(asimov_q0));
ws->loadSnapshot(nominalSnapshot);
}
if (doObs)
{
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
mu->setConstant(1);
status = minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll_cond = obs_nll->getVal();
mu->setConstant(0);
status = minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll_min = obs_nll->getVal();
obs_q0 = 2*(obs_nll_cond - obs_nll_min);
if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0;
sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0));
if (!doUncap && (obs_q0 < 0 && obs_q0 > -0.1 || mu->getVal() < 0.001)) obs_sig = 0;
else obs_sig = sign*sqrt(fabs(obs_q0));
}
// Report results
cout << "obs: " << obs_sig << endl;
cout << "Observed significance: " << obs_sig << endl;
cout << "Corresponding to a p-value of " << (1-ROOT::Math::gaussian_cdf( obs_sig )) << endl;
if (med_sig)
{
cout << "Median test stat val: " << asimov_q0 << endl;
cout << "Median significance: " << med_sig << endl;
}
f.Close();
stringstream fileName;
fileName << "root-files/" << folder << "/" << mass << ".root";
system(("mkdir -vp root-files/" + folder).c_str());
TFile f2(fileName.str().c_str(),"recreate");
TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
h_hypo->SetBinContent(1, obs_sig);
h_hypo->SetBinContent(2, med_sig);
f2.Write();
f2.Close();
timer.Stop();
timer.Print();
}
int main(int argc, char **argv)
{
bool printSw = true;
//TString massModel = "Gauss-m[5622]";
string massModel = "DCB-m[5622]";
TString effbase = "/afs/cern.ch/user/p/pluca/work/Lb/Lmumu/results/";
bool printeff = false;
TString dodata = "data";
bool fitsingle = false;
TString wstr = "physRate_polp006";
TString decayToDo = "Lb2Lmumu";
if(dodata=="genMC") wstr += "_noDecay";
gROOT->ProcessLine(".x lhcbStyle.C");
RooRealVar * cosThetaL = new RooRealVar("cosThetaL","cosThetaL",0.,-1.,1.);
RooRealVar * cosThetaB = new RooRealVar("cosThetaB","cosThetaB",0.,-1.,1.);
RooRealVar * nsig_sw = new RooRealVar("nsig_sw","nsig_sw",1,-1.e6,1.e6);
RooRealVar * MCweight = new RooRealVar(wstr,wstr,1.,-1.e10,1.e10);
RooRealVar * MM = new RooRealVar("Lb_MassConsLambda","Lb_MassConsLambda",5620.,5500.,5900.);
TString datafilename = "/afs/cern.ch/user/p/pluca/work/Lb/Lmumu/candLb.root";
if(dodata=="MC") datafilename = "/afs/cern.ch/user/p/pluca/work/Lb/Lmumu/candLb_MC.root";
if(dodata=="genMC") datafilename = "/afs/cern.ch/work/p/pluca/weighted/Lmumu/"+(string)decayToDo+"_geomMC_Pythia8_NBweighted.root";
TreeReader * data;
if(dodata!="genMC") data = new TreeReader("cand"+decayToDo);
else data = new TreeReader("MCtree");
data->AddFile(datafilename);
TFile * histFile = new TFile("Afb_hist.root","recreate");
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
int nbins = 1;//CutsDef::nq2bins;
double q2min[] = {15.,11.0,15,16,18};//&CutsDef::q2min_highfirst[0];
double q2max[] = {20.,12.5,16,18,20};//&CutsDef::q2max_highfirst[0];
//int nbins = CutsDef::nq2bins
//double *q2min = &CutsDef::q2min[0];
//double *q2max = &CutsDef::q2max[0];
TGraphErrors * Afb_vs_q2 = new TGraphErrors();
TGraphErrors * AfbB_vs_q2 = new TGraphErrors();
TGraphErrors * fL_vs_q2 = new TGraphErrors();
TCanvas * ceff = new TCanvas();
RooCategory * samples = new RooCategory("samples","samples");
samples->defineType("DD");
samples->defineType("LL");
RooRealVar * afb = new RooRealVar("afb","afb",0.,-100,100);
RooRealVar * fL = new RooRealVar("fL","fL",0.7,-1.,10.);
//RooRealVar * afb = new RooRealVar("afb","afb",0.,-1.,1.);
//RooRealVar * fL = new RooRealVar("fL","fL",0.7,0.,1.);
RooRealVar * origafb = new RooRealVar("afb","afb",0.,-1.,1.);
RooRealVar * origfL = new RooRealVar("fL","fL",0.7,-1.,10.);
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.,-100,100);
//RooRealVar * afbB = new RooRealVar("afbB","afbB",0.,-1.,1.);
RooRealVar * origafbB = new RooRealVar("afbB","afbB",0.,-1.,1.);
TString afbBpdf = "(1 + 2*afbB*cosThetaB)";
vector< vector< double > > afb_errs, afbB_errs, fL_errs;
TList * LLlist = new TList, * DDlist = new TList;
TCanvas * cDD = new TCanvas();
TCanvas * cLL = new TCanvas();
TCanvas * cDDB = new TCanvas();
TCanvas * cLLB = new TCanvas();
for(int i = 0; i < nbins; i++)
{
//if(q2min[i] < 8) continue;
TString q2name = ((TString)Form("q2_%4.2f_%4.2f",q2min[i],q2max[i])).ReplaceAll(".","");
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]);
//TString curq2cut = Form("TMath::Power(J_psi_1S_MM/1000,2) >= %e && TMath::Power(J_psi_1S_MM/1000,2) < %e && (Lb_MassConsLambda > 5680 || Lb_MassConsLambda < 5590)",q2min[i],q2max[i]);
cout << "------------------- q2 bin: " << q2min[i] << " - " << q2max[i] << " -----------------------" << endl;
TFile * effFile = NULL;
TH1F * effDD = NULL, * effLL = NULL, * effLLB = NULL, * effDDB = NULL;
if(q2min[i] == 15 && q2max[i] == 20)
{
effFile = TFile::Open(effbase+"LbeffvscosThetaL_DD.root");
effDD = (TH1F *)effFile->Get("htoteff");
effFile = TFile::Open(effbase+"LbeffvscosThetaL_LL.root");
effLL = (TH1F *)effFile->Get("htoteff");
effFile = TFile::Open(effbase+"LbeffvscosThetaB_DD.root");
effDDB = (TH1F *)effFile->Get("htot_nodet_eff");
effFile = TFile::Open(effbase+"LbeffvscosThetaB_LL.root");
effLLB = (TH1F *)effFile->Get("htot_nodet_eff");
}
else
{
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaL_vs_q2_DD.root");
TH2F * effDD2D = (TH2F *)effFile->Get("htot_eff");
effDD = (TH1F*)GetSliceX(effDD2D,(q2max[i]+q2min[i])/2.);
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaL_vs_q2_LL.root");
TH2F * effLL2D = (TH2F *)effFile->Get("htot_eff");
effLL = (TH1F*)GetSliceX(effLL2D,(q2max[i]+q2min[i])/2.);
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaB_vs_q2_DD.root");
TH2F * effDDB2D = (TH2F *)effFile->Get("hupper_eff");
effDDB = (TH1F*)GetSliceX(effDDB2D,(q2max[i]+q2min[i])/2.);
effFile = TFile::Open(effbase+"Lbeff2D_cosThetaB_vs_q2_LL.root");
TH2F * effLLB2D = (TH2F *)effFile->Get("hupper_eff");
effLLB = (TH1F*)GetSliceX(effLLB2D,(q2max[i]+q2min[i])/2.);
}
ceff->cd();
/** FIT EFFICIENCY **/
RooDataHist * hLL = new RooDataHist("hLL","hLL",*cosThetaL,effLL);
RooDataHist * hDD = new RooDataHist("hDD","hDD",*cosThetaL,effDD);
RooRealVar * c1LL = new RooRealVar("c1LL","",0.,-1.,1);
RooRealVar * c1DD = new RooRealVar("c1DD","",0.,-1.,1);
RooRealVar * c2LL = new RooRealVar("c2LL","",0.,-1.,1);
RooRealVar * c2DD = new RooRealVar("c2DD","",0.,-1.,1);
TString effLLstr = "(1 + c1LL*cosThetaL + c2LL*TMath::Power(cosThetaL,2))";
TString effDDstr = "(1 + c1DD*cosThetaL + c2DD*TMath::Power(cosThetaL,2))";
RooAbsPdf * effLLpdf = new RooGenericPdf("effLLpdf", "", effLLstr, RooArgSet(*cosThetaL, *c1LL, *c2LL));
RooAbsPdf * effDDpdf = new RooGenericPdf("effDDpdf", "", effDDstr, RooArgSet(*cosThetaL, *c1DD, *c2DD));
effLLpdf->fitTo(*hLL,PrintLevel(-1));
effDDpdf->fitTo(*hDD,PrintLevel(-1));
fixParams(effLLpdf,cosThetaL);
fixParams(effDDpdf,cosThetaL);
RooDataHist * hLLB = new RooDataHist("hLLB","hLLB",*cosThetaB,effLLB);
RooDataHist * hDDB = new RooDataHist("hDDB","hDDB",*cosThetaB,effDDB);
RooRealVar * cB1LL = new RooRealVar("cB1LL","",0,-1.,1);
RooRealVar * cB1DD = new RooRealVar("cB1DD","",0,-1.,1);
RooRealVar * cB2LL = new RooRealVar("cB2LL","",0,-1.,1);
RooRealVar * cB2DD = new RooRealVar("cB2DD","",0,-1.,1);
TString effLLBstr = "(1 + cB1LL*cosThetaB + cB2LL*TMath::Power(cosThetaB,2))";
TString effDDBstr = "(1 + cB1DD*cosThetaB + cB2DD*TMath::Power(cosThetaB,2))";
RooAbsPdf * effLLpdfB = new RooGenericPdf("effLLpdfB", "", effLLBstr, RooArgSet(*cosThetaB, *cB1LL, *cB2LL));
RooAbsPdf * effDDpdfB = new RooGenericPdf("effDDpdfB", "", effDDBstr, RooArgSet(*cosThetaB, *cB1DD, *cB2DD));
effLLpdfB->fitTo(*hLLB,PrintLevel(-1));
effDDpdfB->fitTo(*hDDB,PrintLevel(-1));
fixParams(effLLpdfB,cosThetaB);
fixParams(effDDpdfB,cosThetaB);
//cout << q2min[i] << " - " << q2max[i] << " LL cosThetaL -> " << c1LL->getVal() << " " << c2LL->getVal() << endl;
//cout << q2min[i] << " - " << q2max[i] << " DD cosThetaL -> " << c1DD->getVal() << " " << c2DD->getVal() << endl;
//cout << q2min[i] << " - " << q2max[i] << " LL cosThetaB -> " << cB1LL->getVal() << " " << cB2LL->getVal() << endl;
//cout << q2min[i] << " - " << q2max[i] << " DD cosThetaB -> " << cB1DD->getVal() << " " << cB2DD->getVal() << endl;
if(printeff) {
GetFrame(cosThetaL, hLL,effLLpdf,"-nochi2",0,NULL,0,"cos#theta_{l}","Tot. eff.")->Draw();
ceff->Print("DDeffFit"+q2name+".pdf");
GetFrame(cosThetaL, hDD,effDDpdf,"-nochi2",0,NULL,0,"cos#theta_{l}","Tot. eff.")->Draw();
ceff->Print("LLeffFit"+q2name+".pdf");
GetFrame(cosThetaB, hLLB,effLLpdfB,"-nochi2",0,NULL,0,"cos#theta_{#Lambda}","Tot. eff.")->Draw();
ceff->Print("DDeffFitB"+q2name+".pdf");
GetFrame(cosThetaB, hDDB,effDDpdfB,"-nochi2",0,NULL,0,"cos#theta_{#Lambda}","Tot. eff.")->Draw();
ceff->Print("LLeffFitB"+q2name+".pdf"); }
/** FIT AFB **/
afb->setVal(0);
afbB->setVal(0);
fL->setVal(0.7);
TString LLnorm = "1./( 1. + (2./3.)*afb*c1LL + (2./5.)*c2LL - (1./5.)*c2LL*fL )*"+effLLstr;
TString DDnorm = "1./( 1. + (2./3.)*afb*c1DD + (2./5.)*c2DD - (1./5.)*c2DD*fL )*"+effDDstr;
RooAbsPdf * corrPdfLL = new RooGenericPdf(Form("corrPdfLL_%i",i),LLnorm+"*"+afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1LL, *c2LL) );
RooAbsPdf * corrPdfDD = new RooGenericPdf(Form("corrPdfDD_%i",i),DDnorm+"*"+afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1DD, *c2DD) );
TString LLnormB = "1./( (2./3.)*( 2*afbB*cB1LL + cB2LL + 3.) )*"+effLLBstr;
TString DDnormB = "1./( (2./3.)*( 2*afbB*cB1DD + cB2DD + 3.) )*"+effDDBstr;
RooAbsPdf * corrPdfLLB = new RooGenericPdf(Form("corrPdfLLB_%i",i),LLnormB+"*"+afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1LL, *cB2LL) );
RooAbsPdf * corrPdfDDB = new RooGenericPdf(Form("corrPdfDDB_%i",i),DDnormB+"*"+afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1DD, *cB2DD) );
TCut cutLL = CutsDef::LLcut + (TCut)curq2cut;
TCut cutDD = CutsDef::DDcut + (TCut)curq2cut;
if(dodata=="genMC")
{
corrPdfLLB = new RooGenericPdf("corrPdfLL",afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1LL, *cB2LL) );
corrPdfDDB = new RooGenericPdf("corrPdfDD",afbBpdf,RooArgSet(*cosThetaB, *afbB, *cB1DD, *cB2DD) );
corrPdfLL = new RooGenericPdf("corrPdfLL",afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1LL, *c2LL) );
corrPdfDD = new RooGenericPdf("corrPdfDD",afbLpdf,RooArgSet(*cosThetaL, *afb, *fL, *c1DD, *c2DD) );
cutLL = (TCut)curq2cut;
cutDD = (TCut)curq2cut;
}
Analysis * anaLL = new Analysis(Form("LL_mass_%i",i),"Lb",data,&cutLL,MM);
anaLL->AddVariable(cosThetaL);
anaLL->AddVariable(cosThetaB);
anaLL->AddVariable("J_psi_1S_MM");
if(dodata!="data") anaLL->SetWeight(wstr);
RooDataSet * dataLL = anaLL->GetDataSet("-recalc-docuts");
Analysis * anaDD = new Analysis(Form("DD_mass_%i",i),"Lb",data,&cutDD,MM);
anaDD->AddVariable(cosThetaL);
anaDD->AddVariable(cosThetaB);
anaDD->AddVariable("J_psi_1S_MM");
if(dodata!="data") anaDD->SetWeight(wstr);
RooDataSet * dataDD = anaDD->GetDataSet("-recalc-docuts");
RooDataSet * sdataDD, * sdataLL;
if(dodata=="data")
{
sdataLL = anaLL->CalcSweight("",massModel.c_str(),"Exp");
if(printSw) {
GetFrame(MM,NULL,sdataLL,"-nochi2",30,NULL,0,"M(#Lambda#mu#mu) (MeV/c^{2})")->Draw();
ceff->Print("Mass_LL_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,sdataLL,"-nochi2",6,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_LL_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,dataLL,"-nochi2",6,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_LL_"+q2name+".pdf");
}
sdataDD = anaDD->CalcSweight("",massModel.c_str(),"Exp");
if(printSw) {
GetFrame(MM,NULL,sdataDD,"-nochi2",30,NULL,0,"M(#Lambda#mu#mu) (MeV/c^{2})")->Draw();
ceff->Print("Mass_DD_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,sdataDD,"-nochi2",10,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_DD_sWeighted"+q2name+".pdf");
GetFrame(cosThetaL,NULL,dataDD,"-nochi2",10,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("cosThetaL_DD_"+q2name+".pdf");
}
}
else { sdataLL = dataLL; sdataDD = dataDD; }
histFile->cd();
TTree * LLTree = (TTree*)sdataLL->tree();
LLTree->SetName(Form("treeLL_%i",i));
LLlist->Add(LLTree);
TTree * DDTree = (TTree*)sdataDD->tree();
DDTree->SetName(Form("treeDD_%i",i));
DDlist->Add(DDTree);
// CREATE COMBINED DATASET
RooDataSet * combData;
if(dodata=="data") combData = new RooDataSet(Form("combData_%i",i),"combined data",RooArgSet(*cosThetaL,*cosThetaB,*nsig_sw),Index(*samples),Import("DD",*sdataDD),Import("LL",*sdataLL),WeightVar("nsig_sw"));
else combData = new RooDataSet(Form("combData_%i",i),"combined data",RooArgSet(*cosThetaL,*cosThetaB,*MCweight),Index(*samples),Import("DD",*sdataDD),Import("LL",*sdataLL),WeightVar(wstr));
// FIT COS LEPTON
RooSimultaneous * combModel = new RooSimultaneous(Form("combModel_%i",i),"",*samples);
combModel->addPdf(*corrPdfLL,"LL");
combModel->addPdf(*corrPdfDD,"DD");
combModel->fitTo(*combData,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
if(fitsingle) corrPdfLL->fitTo(*sdataLL,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaL,corrPdfLL,sdataLL,"-sumW2err-nochi2-noCost",6,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("Afb_LL_"+q2name+".pdf");
if(fitsingle) corrPdfDD->fitTo(*sdataDD,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaL,corrPdfDD,sdataDD,"-sumW2err-nochi2-noCost",10,NULL,0,"cos#theta_{l}")->Draw();
ceff->Print("Afb_DD_"+q2name+".pdf");
Afb_vs_q2->SetPoint(i,(q2max[i] + q2min[i])/2.,afb->getVal());
Afb_vs_q2->SetPointError(i,(q2max[i] - q2min[i])/2.,afb->getError());
fL_vs_q2->SetPoint(i,(q2max[i] + q2min[i])/2.,fL->getVal());
fL_vs_q2->SetPointError(i,(q2max[i] - q2min[i])/2.,fL->getError());
// FIT COS HADRON
RooSimultaneous * combModelB = new RooSimultaneous(Form("combModelB_%i",i),"",*samples);
combModelB->addPdf(*corrPdfLLB,"LL");
combModelB->addPdf(*corrPdfDDB,"DD");
combModelB->fitTo(*combData,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
if(fitsingle) corrPdfLLB->fitTo(*sdataLL,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaB,corrPdfLLB,sdataLL,"-sumW2err-nochi2-noCost",6,NULL,0,"cos#theta_{#Lambda}")->Draw();
ceff->Print("AfbB_LL_"+q2name+".pdf");
if(fitsingle) corrPdfDDB->fitTo(*sdataDD,PrintLevel(-1),Verbose(kFALSE),SumW2Error(kTRUE));
GetFrame(cosThetaB,corrPdfDDB,sdataDD,"-sumW2err-nochi2-noCost",10,NULL,0,"cos#theta_{#Lambda}")->Draw();
ceff->Print("AfbB_DD_"+q2name+".pdf");
AfbB_vs_q2->SetPoint(i,(q2max[i] + q2min[i])/2.,afbB->getVal());
AfbB_vs_q2->SetPointError(i,(q2max[i] - q2min[i])/2.,afbB->getError());
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 << "------------------------ FELDMAN AND COUSINS ------------------------" << endl;
vector < RooDataSet * > datas;
vector < RooAbsPdf * > pdfs, pdfsB;
vector < TString > cat;
cat.push_back("LL");
cat.push_back("DD");
datas.push_back(sdataLL);
datas.push_back(sdataDD);
RooArgSet * origPars = new RooArgSet();
origPars->add(*origafb);
origPars->add(*origfL);
pdfs.push_back(corrPdfLL);
pdfs.push_back(corrPdfDD);
vector< double > afb_err, afbB_err, fL_err;
/*
double fLval = fL->getVal(), fLerr = fL->getError();
FeldmanCousins * FC = new FeldmanCousins(q2name,cat,datas,pdfs,cosThetaL,afb,"nsig_sw");
//FC->SetNPointsToScan(20);
//FC->SetNExp(1000);
if(q2min[i]==18) afb_err = FC->ExtractLimits(origPars,-0.3,0.3);
else if( (afb->getVal()-1.4*afb->getError()) > -1 && (afb->getVal()+1.4*afb->getError()) < 1 )
afb_err = FC->ExtractLimits(origPars,afb->getVal()-1.4*afb->getError(),afb->getVal()+1.4*afb->getError());
else afb_err = FC->ExtractLimits(origPars,-0.4,0.4);
//FeldmanCousins * FCfL = new FeldmanCousins(q2name,cat,datas,pdfs,cosThetaL,fL,"nsig_sw");
//if(q2min[i]==11) fL_err = FCfL->ExtractLimits(origPars,0.,0.6);
//else if (q2min[i]==18) fL_err = FCfL->ExtractLimits(origPars,0.75,0.992);
//( (fLval-1.3*fLerr) > 0 && (fLval+1.3*fLerr) <= 1 )
//else fL_err = FCfL->ExtractLimits(origPars,fLval-1.3*fLerr,fLval+1.3*fLerr);
afb_errs.push_back(afb_err);
//fL_errs.push_back(fL_err);
RooArgSet * origParsB = new RooArgSet();
origParsB->add(*origafbB);
pdfsB.push_back(corrPdfLLB);
pdfsB.push_back(corrPdfDDB);
FeldmanCousins * FCB = new FeldmanCousins(q2name,cat,datas,pdfsB,cosThetaB,afbB,"nsig_sw");
if( (afbB->getVal()-1.5*afbB->getError()) > -1 && (afbB->getVal()+1.5*afbB->getError()) < 1 )
afbB_err = FCB->ExtractLimits(origParsB,afbB->getVal()-1.5*afbB->getError(),afbB->getVal()+1.5*afbB->getError());
else afbB_err = FCB->ExtractLimits(origParsB,-0.4,0.4);
afbB_errs.push_back(afbB_err);
*/
delete effDD;
delete effLL;
delete effLLB;
delete effDDB;
}
cDD->Print("DDeff.pdf");
cLL->Print("LLeff.pdf");
cDDB->Print("DDBeff.pdf");
cLLB->Print("LLBeff.pdf");
Afb_vs_q2->GetXaxis()->SetTitle("q^{2}");
Afb_vs_q2->GetYaxis()->SetTitle("Afb");
Afb_vs_q2->SetMaximum(1);
Afb_vs_q2->SetMinimum(-1);
Afb_vs_q2->Draw("AP");
ceff->Print("Afb_vs_q2.pdf");
AfbB_vs_q2->GetXaxis()->SetTitle("q^{2}");
AfbB_vs_q2->GetYaxis()->SetTitle("AfbB");
AfbB_vs_q2->SetMaximum(1);
AfbB_vs_q2->SetMinimum(-1);
AfbB_vs_q2->Draw("AP");
ceff->Print("AfbB_vs_q2.pdf");
fL_vs_q2->GetXaxis()->SetTitle("q^{2}");
fL_vs_q2->GetYaxis()->SetTitle("fL");
fL_vs_q2->Draw("AP");
ceff->Print("fL_vs_q2.pdf");
for(int bb = 0; bb < Afb_vs_q2->GetN(); bb++)
{
double qq, qqerr, afbv, afbBv, fLv;
Afb_vs_q2->GetPoint(bb,qq,afbv);
qqerr = Afb_vs_q2->GetErrorX(bb);
AfbB_vs_q2->GetPoint(bb,qq,afbBv);
fL_vs_q2->GetPoint(bb,qq,fLv);
cout << fixed << setprecision(1) << qq-qqerr << " - " << qq+qqerr;
cout << fixed << setprecision(4);
//cout << " & $" << afbv << "_{-" << TMath::Abs(afb_errs[bb][0] - afbv) << "}^{+" << TMath::Abs(afb_errs[bb][1] - afbv) << "} \\text{(stat)} \\pm \\text{(sys)}$ ";
//cout << " & $" << afbBv << "_{-" << TMath::Abs(afbB_errs[bb][0] - afbBv) << "}^{+" << TMath::Abs(afbB_errs[bb][1]-afbBv) << "} \\text{(stat)} \\pm \\text{(sys)}$ " ;
//cout << " & $" << fLv << "_{-" << TMath::Abs(fL_errs[bb][0] - fLv) << "}^{+" << TMath::Abs(fL_errs[bb][1] - fLv) << "} \\text{(stat)} \\pm \\text{(sys)}$ ";
cout << " \\\\ " << endl;
}
histFile->cd();
TTree * finalLLtree = (TTree*)TTree::MergeTrees(LLlist);
TTree * finalDDtree = (TTree*)TTree::MergeTrees(DDlist);
finalLLtree->SetName("LL_data");
finalDDtree->SetName("DD_data");
finalLLtree->Write();
finalDDtree->Write();
delete ceff;
histFile->Write();
delete histFile;
}
RooWorkspace* makeInvertedANFit(TTree* tree, float forceSigma=-1, bool constrainMu=false, float forceMu=-1) {
RooWorkspace *ws = new RooWorkspace("ws","");
std::vector< TString (*)(TString, RooRealVar&, RooWorkspace&) > bkgPdfList;
bkgPdfList.push_back(makeSingleExp);
bkgPdfList.push_back(makeDoubleExp);
#if DEBUG==0
//bkgPdfList.push_back(makeTripleExp);
bkgPdfList.push_back(makeModExp);
bkgPdfList.push_back(makeSinglePow);
bkgPdfList.push_back(makeDoublePow);
bkgPdfList.push_back(makePoly2);
bkgPdfList.push_back(makePoly3);
#endif
RooRealVar mgg("mgg","m_{#gamma#gamma}",103,160,"GeV");
mgg.setBins(38);
mgg.setRange("sideband_low", 103,120);
mgg.setRange("sideband_high",131,160);
mgg.setRange("signal",120,131);
RooRealVar MR("MR","",0,3000,"GeV");
MR.setBins(60);
RooRealVar Rsq("t1Rsq","",0,1,"GeV");
Rsq.setBins(20);
RooRealVar hem1_M("hem1_M","",-1,2000,"GeV");
hem1_M.setBins(40);
RooRealVar hem2_M("hem2_M","",-1,2000,"GeV");
hem2_M.setBins(40);
RooRealVar ptgg("ptgg","p_{T}^{#gamma#gamma}",0,500,"GeV");
ptgg.setBins(50);
RooDataSet data("data","",tree,RooArgSet(mgg,MR,Rsq,hem1_M,hem2_M,ptgg));
RooDataSet* blind_data = (RooDataSet*)data.reduce("mgg<121 || mgg>130");
std::vector<TString> tags;
//fit many different background models
for(auto func = bkgPdfList.begin(); func != bkgPdfList.end(); func++) {
TString tag = (*func)("bonly",mgg,*ws);
tags.push_back(tag);
ws->pdf("bonly_"+tag+"_ext")->fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
RooFitResult* bres = ws->pdf("bonly_"+tag+"_ext")->fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
bres->SetName(tag+"_bonly_fitres");
ws->import(*bres);
//make blinded fit
RooPlot *fmgg_b = mgg.frame();
blind_data->plotOn(fmgg_b,RooFit::Range("sideband_low,sideband_high"));
TBox blindBox(121,fmgg_b->GetMinimum()-(fmgg_b->GetMaximum()-fmgg_b->GetMinimum())*0.015,130,fmgg_b->GetMaximum());
blindBox.SetFillColor(kGray);
fmgg_b->addObject(&blindBox);
ws->pdf("bonly_"+tag+"_ext")->plotOn(fmgg_b,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("sideband_low,sideband_high"));
fmgg_b->SetName(tag+"_blinded_frame");
ws->import(*fmgg_b);
delete fmgg_b;
//set all the parameters constant
RooArgSet* vars = ws->pdf("bonly_"+tag)->getVariables();
RooFIter iter = vars->fwdIterator();
RooAbsArg* a;
while( (a = iter.next()) ){
if(string(a->GetName()).compare("mgg")==0) continue;
static_cast<RooRealVar*>(a)->setConstant(kTRUE);
}
//make the background portion of the s+b fit
(*func)("b",mgg,*ws);
RooRealVar sigma(tag+"_s_sigma","",5,0,100);
if(forceSigma!=-1) {
sigma.setVal(forceSigma);
sigma.setConstant(true);
}
RooRealVar mu(tag+"_s_mu","",126,120,132);
if(forceMu!=-1) {
mu.setVal(forceMu);
mu.setConstant(true);
}
RooGaussian sig(tag+"_sig_model","",mgg,mu,sigma);
RooRealVar Nsig(tag+"_sb_Ns","",5,0,100);
RooRealVar Nbkg(tag+"_sb_Nb","",100,0,100000);
RooRealVar HiggsMass("HiggsMass","",125.1);
RooRealVar HiggsMassError("HiggsMassError","",0.24);
RooGaussian HiggsMassConstraint("HiggsMassConstraint","",mu,HiggsMass,HiggsMassError);
RooAddPdf fitModel(tag+"_sb_model","",RooArgList( *ws->pdf("b_"+tag), sig ),RooArgList(Nbkg,Nsig));
RooFitResult* sbres;
RooAbsReal* nll;
if(constrainMu) {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
} else {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE));
}
sbres->SetName(tag+"_sb_fitres");
ws->import(*sbres);
ws->import(fitModel);
RooPlot *fmgg = mgg.frame();
data.plotOn(fmgg);
fitModel.plotOn(fmgg);
ws->pdf("b_"+tag+"_ext")->plotOn(fmgg,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("Full"));
fmgg->SetName(tag+"_frame");
ws->import(*fmgg);
delete fmgg;
RooMinuit(*nll).migrad();
RooPlot *fNs = Nsig.frame(0,25);
fNs->SetName(tag+"_Nsig_pll");
RooAbsReal *pll = nll->createProfile(Nsig);
//nll->plotOn(fNs,RooFit::ShiftToZero(),RooFit::LineColor(kRed));
pll->plotOn(fNs);
ws->import(*fNs);
delete fNs;
RooPlot *fmu = mu.frame(125,132);
fmu->SetName(tag+"_mu_pll");
RooAbsReal *pll_mu = nll->createProfile(mu);
pll_mu->plotOn(fmu);
ws->import(*fmu);
delete fmu;
}
RooArgSet weights("weights");
RooArgSet pdfs_bonly("pdfs_bonly");
RooArgSet pdfs_b("pdfs_b");
RooRealVar minAIC("minAIC","",1E10);
//compute AIC stuff
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooAbsPdf *p_bonly = ws->pdf("bonly_"+*t);
RooAbsPdf *p_b = ws->pdf("b_"+*t);
RooFitResult *sb = (RooFitResult*)ws->obj(*t+"_bonly_fitres");
RooRealVar k(*t+"_b_k","",p_bonly->getParameters(RooArgSet(mgg))->getSize());
RooRealVar nll(*t+"_b_minNll","",sb->minNll());
RooRealVar Npts(*t+"_b_N","",blind_data->sumEntries());
RooFormulaVar AIC(*t+"_b_AIC","2*@0+2*@1+2*@1*(@1+1)/(@2-@1-1)",RooArgSet(nll,k,Npts));
ws->import(AIC);
if(AIC.getVal() < minAIC.getVal()) {
minAIC.setVal(AIC.getVal());
}
//aicExpSum+=TMath::Exp(-0.5*AIC.getVal()); //we will need this precomputed for the next step
pdfs_bonly.add(*p_bonly);
pdfs_b.add(*p_b);
}
ws->import(minAIC);
//compute the AIC weight
float aicExpSum=0;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
aicExpSum+=TMath::Exp(-0.5*(AIC->getVal()-minAIC.getVal())); //we will need this precomputed for the next step
}
std::cout << "aicExpSum: " << aicExpSum << std::endl;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
RooRealVar *AICw = new RooRealVar(*t+"_b_AICWeight","",TMath::Exp(-0.5*(AIC->getVal()-minAIC.getVal()))/aicExpSum);
if( TMath::IsNaN(AICw->getVal()) ) {AICw->setVal(0);}
ws->import(*AICw);
std::cout << *t << ": " << AIC->getVal()-minAIC.getVal() << " " << AICw->getVal() << std::endl;
weights.add(*AICw);
}
RooAddPdf bonly_AIC("bonly_AIC","",pdfs_bonly,weights);
RooAddPdf b_AIC("b_AIC","",pdfs_b,weights);
//b_AIC.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
//RooFitResult* bres = b_AIC.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
//bres->SetName("AIC_b_fitres");
//ws->import(*bres);
//make blinded fit
RooPlot *fmgg_b = mgg.frame(RooFit::Range("sideband_low,sideband_high"));
blind_data->plotOn(fmgg_b,RooFit::Range("sideband_low,sideband_high"));
TBox blindBox(121,fmgg_b->GetMinimum()-(fmgg_b->GetMaximum()-fmgg_b->GetMinimum())*0.015,130,fmgg_b->GetMaximum());
blindBox.SetFillColor(kGray);
fmgg_b->addObject(&blindBox);
bonly_AIC.plotOn(fmgg_b,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("sideband_low,sideband_high"));
fmgg_b->SetName("AIC_blinded_frame");
ws->import(*fmgg_b);
delete fmgg_b;
#if 1
RooRealVar sigma("AIC_s_sigma","",5,0,100);
if(forceSigma!=-1) {
sigma.setVal(forceSigma);
sigma.setConstant(true);
}
RooRealVar mu("AIC_s_mu","",126,120,132);
if(forceMu!=-1) {
mu.setVal(forceMu);
mu.setConstant(true);
}
RooGaussian sig("AIC_sig_model","",mgg,mu,sigma);
RooRealVar Nsig("AIC_sb_Ns","",5,0,100);
RooRealVar Nbkg("AIC_sb_Nb","",100,0,100000);
RooRealVar HiggsMass("HiggsMass","",125.1);
RooRealVar HiggsMassError("HiggsMassError","",0.24);
RooGaussian HiggsMassConstraint("HiggsMassConstraint","",mu,HiggsMass,HiggsMassError);
RooAddPdf fitModel("AIC_sb_model","",RooArgList( b_AIC, sig ),RooArgList(Nbkg,Nsig));
RooFitResult* sbres;
RooAbsReal *nll;
if(constrainMu) {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
} else {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE));
}
assert(nll!=0);
sbres->SetName("AIC_sb_fitres");
ws->import(*sbres);
ws->import(fitModel);
RooPlot *fmgg = mgg.frame();
data.plotOn(fmgg);
fitModel.plotOn(fmgg);
ws->pdf("b_AIC")->plotOn(fmgg,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("Full"));
fmgg->SetName("AIC_frame");
ws->import(*fmgg);
delete fmgg;
RooMinuit(*nll).migrad();
RooPlot *fNs = Nsig.frame(0,25);
fNs->SetName("AIC_Nsig_pll");
RooAbsReal *pll = nll->createProfile(Nsig);
//nll->plotOn(fNs,RooFit::ShiftToZero(),RooFit::LineColor(kRed));
pll->plotOn(fNs);
ws->import(*fNs);
delete fNs;
RooPlot *fmu = mu.frame(125,132);
fmu->SetName("AIC_mu_pll");
RooAbsReal *pll_mu = nll->createProfile(mu);
pll_mu->plotOn(fmu);
ws->import(*fmu);
delete fmu;
std::cout << "min AIC: " << minAIC.getVal() << std::endl;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
RooRealVar *AICw = ws->var(*t+"_b_AICWeight");
RooRealVar* k = ws->var(*t+"_b_k");
printf("%s & %0.0f & %0.2f & %0.2f \\\\\n",t->Data(),k->getVal(),AIC->getVal()-minAIC.getVal(),AICw->getVal());
//std::cout << k->getVal() << " " << AIC->getVal()-minAIC.getVal() << " " << AICw->getVal() << std::endl;
}
#endif
return ws;
}
//put very small data entries in a binned dataset to avoid unphysical pdfs, specifically for H->ZZ->4l
RooDataSet* makeData(RooDataSet* orig, RooSimultaneous* simPdf, const RooArgSet* observables, RooRealVar* firstPOI, double mass, double& mu_min)
{
double max_soverb = 0;
mu_min = -10e9;
map<string, RooDataSet*> data_map;
firstPOI->setVal(0);
RooCategory* cat = (RooCategory*)&simPdf->indexCat();
TList* datalist = orig->split(*(RooAbsCategory*)cat, true);
TIterator* dataItr = datalist->MakeIterator();
RooAbsData* ds;
RooRealVar* weightVar = new RooRealVar("weightVar","weightVar",1);
while ((ds = (RooAbsData*)dataItr->Next()))
{
string typeName(ds->GetName());
cat->setLabel(typeName.c_str());
RooAbsPdf* pdf = simPdf->getPdf(typeName.c_str());
cout << "pdf: " << pdf << endl;
RooArgSet* obs = pdf->getObservables(observables);
cout << "obs: " << obs << endl;
RooArgSet obsAndWeight(*obs, *weightVar);
obsAndWeight.add(*cat);
stringstream datasetName;
datasetName << "newData_" << typeName;
RooDataSet* thisData = new RooDataSet(datasetName.str().c_str(),datasetName.str().c_str(), obsAndWeight, WeightVar(*weightVar));
RooRealVar* firstObs = (RooRealVar*)obs->first();
//int ibin = 0;
int nrEntries = ds->numEntries();
for (int ib=0;ib<nrEntries;ib++)
{
const RooArgSet* event = ds->get(ib);
const RooRealVar* thisObs = (RooRealVar*)event->find(firstObs->GetName());
firstObs->setVal(thisObs->getVal());
firstPOI->setVal(0);
double b = pdf->expectedEvents(*firstObs)*pdf->getVal(obs);
firstPOI->setVal(1);
double s = pdf->expectedEvents(*firstObs)*pdf->getVal(obs) - b;
if (s > 0)
{
mu_min = max(mu_min, -b/s);
double soverb = s/b;
if (soverb > max_soverb)
{
max_soverb = soverb;
cout << "Found new max s/b: " << soverb << " in pdf " << pdf->GetName() << " at m = " << thisObs->getVal() << endl;
}
}
if (b == 0 && s != 0)
{
cout << "Expecting non-zero signal and zero bg at m=" << firstObs->getVal() << " in pdf " << pdf->GetName() << endl;
}
if (s+b <= 0)
{
cout << "expecting zero" << endl;
continue;
}
double weight = ds->weight();
if ((typeName.find("ATLAS_H_4mu") != string::npos ||
typeName.find("ATLAS_H_4e") != string::npos ||
typeName.find("ATLAS_H_2mu2e") != string::npos ||
typeName.find("ATLAS_H_2e2mu") != string::npos) && fabs(firstObs->getVal() - mass) < 10 && weight == 0)
{
cout << "adding event: " << firstObs->getVal() << endl;
thisData->add(*event, pow(10., -9.));
}
else
{
//weight = max(pow(10.0, -9), weight);
thisData->add(*event, weight);
}
}
data_map[string(ds->GetName())] = (RooDataSet*)thisData;
}
RooDataSet* newData = new RooDataSet("newData","newData",RooArgSet(*observables, *weightVar),
Index(*cat), Import(data_map), WeightVar(*weightVar));
orig->Print();
newData->Print();
//newData->tree()->Scan("*");
return newData;
}
void generateSigHist_for_combineMaker(Float_t lum =10, Int_t cc1 = 0, Int_t cc2 = 9) {
const Int_t NCAT(4); // 4 categories in the source file
TString myCut[NCAT] = {"EBHighR9","EBLowR9","EEHighR9","EELowR9"};
//Float_t lum =10;
TString lum_string = TString::Format("%g", lum);
TFile *file=TFile::Open("workspaces/HighMassGG_m1500_01_mgg_lum_"+lum_string+".root");
RooWorkspace *w_all = (RooWorkspace*)file->Get("w_all");
RooRealVar *mgg = (RooRealVar*)w_all->var("mgg");
RooRealVar *MH = (RooRealVar*)w_all->var("MH");
RooRealVar *kpl = (RooRealVar*)w_all->var("kpl");
RooNumConvPdf* numConv[NCAT];
RooFormulaVar* norm[NCAT];
// necessary to set a window as it is not saved when RooNumConvPdf is saved
RooRealVar* W = new RooRealVar("W","W",500);
W->setConstant();
RooRealVar* C = new RooRealVar("C","C",0);
C->setConstant();
cout << "READ PDFs FROM WORKSPACE : SIGNAL FROM 4 CATEGORIES" << endl;
cout << "READ ROOFORMULAs FROM WORKSPACE : NORMALIZATION OF 4 CATEGORIES" << endl;
cout << "SET WINDOW FOR THE 4 CONVOLUTIONS (PDFs)" << endl;
cout << "..." << endl;
for(Int_t c = 0; c<NCAT; c++) {
numConv[c] = (RooNumConvPdf*)w_all->pdf("mggSig_cat"+myCut[c]);
norm[c] = (RooFormulaVar*)w_all->function("mggSig_cat"+myCut[c]+"_norm");
numConv[c]->setConvolutionWindow(*C,*W);
}
cout << endl;
cout << "ADD PDFs TO FORM THE TWO CATEGORIES EBEB AND EBEE" << endl;
RooFormulaVar *mggSig_catEBEB_norm = new RooFormulaVar("mggSig_catEBEB_norm","mggSig_catEBEB_norm","@0+@1",RooArgList(*norm[0],*norm[1]));
RooFormulaVar *mggSig_catEBEE_norm = new RooFormulaVar("mggSig_catEBEE_norm","mggSig_catEBEE_norm","@0+@1",RooArgList(*norm[2],*norm[3]));
RooFormulaVar *frac1EBEB = new RooFormulaVar("frac1EBEB","frac1EBEB","@0/@1",RooArgList(*norm[0],*mggSig_catEBEB_norm));
RooFormulaVar *frac2EBEB = new RooFormulaVar("frac2EBEB","frac2EBEB","@0/@1",RooArgList(*norm[1],*mggSig_catEBEB_norm));
RooFormulaVar *frac1EBEE = new RooFormulaVar("frac1EBEE","frac1EBE","@0/@1",RooArgList(*norm[2],*mggSig_catEBEE_norm));
RooFormulaVar *frac2EBEE = new RooFormulaVar("frac2EBEE","frac2EBEE","@0/@1",RooArgList(*norm[3],*mggSig_catEBEE_norm));
RooAddPdf* mggSig_catEBEB = new RooAddPdf("mggSig_catEBEB","mggSig_catEBEB",RooArgList(*numConv[0],*numConv[1]),RooArgList(*frac1EBEB,*frac2EBEB));
RooAddPdf* mggSig_catEBEE = new RooAddPdf("mggSig_catEBEE","mggSig_catEBEE",RooArgList(*numConv[2],*numConv[3]),RooArgList(*frac1EBEE,*frac2EBEE));
cout << endl << endl ;
cout << "READ CFG FROM BACKGROUND_FINAL TO BE USED IN COMBINE_MAKER.PY" << endl;
TFile *fcfg = TFile::Open("../../Analysis/macros/workspace_cic2_dijet_lum_10/full_analysis_anv1_v18_bkg_ws_300.root");
TObjString *cfg = (TObjString*)fcfg->Get("cfg");
const Int_t nCouplings(9);
Double_t couplings[nCouplings] = {0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2};
cout << endl << endl ;
cout << "SCAN OVER MH AND KAPPA VALUES TO GET DIFFERENT PDFs FOR EACH SET OF PARAMETERS..." << endl;
for(Int_t icoupling = cc1; icoupling < cc2; icoupling++) {
//CREATE FOLDER FOR THE COUPLING
TString coupling_string = TString::Format("%g",couplings[icoupling]);
coupling_string.ReplaceAll(".","");
TString inDir("../../Analysis/macros/workspace_cic2_signalDataHist_lum_"+lum_string+"/");
gSystem->mkdir(inDir);
for(Int_t mass = 1000; mass < 5100 ; mass+=100) {
TString mass_string = TString::Format("%d",mass);
TString signame("grav_"+coupling_string+"_"+mass_string);
cout << endl;
cout << "********************************************" << endl;
cout << "SET MH and KAPPA values" << endl << "kappa: " << couplings[icoupling] << endl << "mass: " << mass << endl;
cout << "********************************************" << endl;
MH->setVal(mass);
kpl->setVal(couplings[icoupling]);
cout << "CREATE ROODATAHIST WITH NORM..." << endl;
RooDataHist* dhEBEB = mggSig_catEBEB->generateBinned(RooArgSet(*mgg),mggSig_catEBEB_norm->getVal(), RooFit::ExpectedData());
RooDataHist* dhEBEE = mggSig_catEBEE->generateBinned(RooArgSet(*mgg),mggSig_catEBEE_norm->getVal(), RooFit::ExpectedData());
dhEBEB->SetName("signal_"+signame+"_EBEB");
dhEBEB->SetTitle("signal_"+signame+"_EBEB");
dhEBEE->SetName("signal_"+signame+"_EBEE");
dhEBEE->SetTitle("signal_"+signame+"_EBEE");
//CREATE ROOHISTPDF
//RooHistPdf *fhEBEB = new RooHistPdf("mggHistPdf_catEBEB","mggHistPdf_catEBEB",RooArgSet(*mgg),*dhEBEB);
//RooHistPdf *fhEBEE = new RooHistPdf("mggHistPdf_catEBEE","mggHistPdf_catEBEE",RooArgSet(*mgg),*dhEBEE);
//SAVE ALL IN A WORKSPACE WITH FILE NAME corresponding to MASS AND COUPLING VALUE
RooWorkspace *wtemplates = new RooWorkspace("wtemplates","wtemplates");
//wtemplates->import(*fhEBEB);
//wtemplates->import(*fhEBEE);
//wtemplates->import(*mggSig_catEBEB_norm,RecycleConflictNodes());
//wtemplates->import(*mggSig_catEBEE_norm,RecycleConflictNodes());
wtemplates->import(*dhEBEB);
wtemplates->import(*dhEBEE);
//wtemplates->import(*numConv[0],RecycleConflictNodes());
cout << "******************************************" << endl;
cout << "WORKSPACE CONTENT FOR COUPLING " << couplings[icoupling] << " AND MASS " << mass << endl;
wtemplates->Print("V");
//WRITE IN FILE
TString filename = inDir+"300_"+signame+".root";
TFile *fileOutput = TFile::Open(filename,"recreate");
cfg->Write("cfg");
wtemplates->Write("wtemplates");
cout << "workspace imported to " << filename << endl;
}
}
}
void OneSidedFrequentistUpperLimitWithBands(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData") {
double confidenceLevel=0.95;
int nPointsToScan = 20;
int nToyMC = 200;
// -------------------------------------------------------
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
// -------------------------------------------------------
// Now get the data and workspace
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// -------------------------------------------------------
// Now get the POI for convenience
// you may want to adjust the range of your POI
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
/* firstPOI->setMin(0);*/
/* firstPOI->setMax(10);*/
// --------------------------------------------
// Create and use the FeldmanCousins tool
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
// REMEMBER, we will change the test statistic
// so this is NOT a Feldman-Cousins interval
FeldmanCousins fc(*data,*mc);
fc.SetConfidenceLevel(confidenceLevel);
/* fc.AdditionalNToysFactor(0.25); // degrade/improve sampling that defines confidence belt*/
/* fc.UseAdaptiveSampling(true); // speed it up a bit, don't use for expected limits*/
fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan
fc.CreateConfBelt(true); // save the information in the belt for plotting
// -------------------------------------------------------
// Feldman-Cousins is a unified limit by definition
// but the tool takes care of a few things for us like which values
// of the nuisance parameters should be used to generate toys.
// so let's just change the test statistic and realize this is
// no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction.
/* ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());*/
/* fc.GetTestStatSampler()->SetTestStatistic(&onesided);*/
/* ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true); */
ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic());
testStat->SetOneSided(true);
// Since this tool needs to throw toy MC the PDF needs to be
// extended or the tool needs to know how many entries in a dataset
// per pseudo experiment.
// In the 'number counting form' where the entries in the dataset
// are counts, and not values of discriminating variables, the
// datasets typically only have one entry and the PDF is not
// extended.
if(!mc->GetPdf()->canBeExtended()){
if(data->numEntries()==1)
fc.FluctuateNumDataEntries(false);
else
cout <<"Not sure what to do about this model" <<endl;
}
// We can use PROOF to speed things along in parallel
// However, the test statistic has to be installed on the workers
// so either turn off PROOF or include the modified test statistic
// in your `$ROOTSYS/roofit/roostats/inc` directory,
// add the additional line to the LinkDef.h file,
// and recompile root.
if (useProof) {
ProofConfig pc(*w, nworkers, "", false);
toymcsampler->SetProofConfig(&pc); // enable proof
}
if(mc->GetGlobalObservables()){
cout << "will use global observables for unconditional ensemble"<<endl;
mc->GetGlobalObservables()->Print();
toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
}
// Now get the interval
PointSetInterval* interval = fc.GetInterval();
ConfidenceBelt* belt = fc.GetConfidenceBelt();
// print out the interval on the first Parameter of Interest
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"] "<<endl;
// get observed UL and value of test statistic evaluated there
RooArgSet tmpPOI(*firstPOI);
double observedUL = interval->UpperLimit(*firstPOI);
firstPOI->setVal(observedUL);
double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI);
// Ask the calculator which points were scanned
RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan();
RooArgSet* tmpPoint;
// make a histogram of parameter vs. threshold
TH1F* histOfThresholds = new TH1F("histOfThresholds","",
parameterScan->numEntries(),
firstPOI->getMin(),
firstPOI->getMax());
histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
histOfThresholds->GetYaxis()->SetTitle("Threshold");
// loop through the points that were tested and ask confidence belt
// what the upper/lower thresholds were.
// For FeldmanCousins, the lower cut off is always 0
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
//cout <<"get threshold"<<endl;
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ;
histOfThresholds->Fill(poiVal,arMax);
}
TCanvas* c1 = new TCanvas();
c1->Divide(2);
c1->cd(1);
histOfThresholds->SetMinimum(0);
histOfThresholds->Draw();
c1->cd(2);
// -------------------------------------------------------
// Now we generate the expected bands and power-constraint
// First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
RooAbsReal* nll = mc->GetPdf()->createNLL(*data);
RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest());
firstPOI->setVal(0.);
profile->getVal(); // this will do fit and set nuisance parameters to profiled values
RooArgSet* poiAndNuisance = new RooArgSet();
if(mc->GetNuisanceParameters())
poiAndNuisance->add(*mc->GetNuisanceParameters());
poiAndNuisance->add(*mc->GetParametersOfInterest());
w->saveSnapshot("paramsToGenerateData",*poiAndNuisance);
RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot();
cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
paramsToGenerateData->Print("v");
RooArgSet unconditionalObs;
unconditionalObs.add(*mc->GetObservables());
unconditionalObs.add(*mc->GetGlobalObservables()); // comment this out for the original conditional ensemble
double CLb=0;
double CLbinclusive=0;
// Now we generate background only and find distribution of upper limits
TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax());
histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
histOfUL->GetYaxis()->SetTitle("Entries");
for(int imc=0; imc<nToyMC; ++imc){
// set parameters back to values for generating pseudo data
// cout << "\n get current nuis, set vals, print again" << endl;
w->loadSnapshot("paramsToGenerateData");
// poiAndNuisance->Print("v");
RooDataSet* toyData = 0;
// now generate a toy dataset
if(!mc->GetPdf()->canBeExtended()){
if(data->numEntries()==1)
toyData = mc->GetPdf()->generate(*mc->GetObservables(),1);
else
cout <<"Not sure what to do about this model" <<endl;
} else{
// cout << "generating extended dataset"<<endl;
toyData = mc->GetPdf()->generate(*mc->GetObservables(),Extended());
}
// generate global observables
// need to be careful for simpdf
// RooDataSet* globalData = mc->GetPdf()->generate(*mc->GetGlobalObservables(),1);
RooSimultaneous* simPdf = dynamic_cast<RooSimultaneous*>(mc->GetPdf());
if(!simPdf){
RooDataSet *one = mc->GetPdf()->generate(*mc->GetGlobalObservables(), 1);
const RooArgSet *values = one->get();
RooArgSet *allVars = mc->GetPdf()->getVariables();
*allVars = *values;
delete allVars;
delete values;
delete one;
} else {
//try fix for sim pdf
TIterator* iter = simPdf->indexCat().typeIterator() ;
RooCatType* tt = NULL;
while((tt=(RooCatType*) iter->Next())) {
// Get pdf associated with state from simpdf
RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;
// Generate only global variables defined by the pdf associated with this state
RooArgSet* globtmp = pdftmp->getObservables(*mc->GetGlobalObservables()) ;
RooDataSet* tmp = pdftmp->generate(*globtmp,1) ;
// Transfer values to output placeholder
*globtmp = *tmp->get(0) ;
// Cleanup
delete globtmp ;
delete tmp ;
}
}
// globalData->Print("v");
// unconditionalObs = *globalData->get();
// mc->GetGlobalObservables()->Print("v");
// delete globalData;
// cout << "toy data = " << endl;
// toyData->get()->Print("v");
// get test stat at observed UL in observed data
firstPOI->setVal(observedUL);
double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// toyData->get()->Print("v");
// cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
if(obsTSatObsUL < toyTSatObsUL) // not sure about <= part yet
CLb+= (1.)/nToyMC;
if(obsTSatObsUL <= toyTSatObsUL) // not sure about <= part yet
CLbinclusive+= (1.)/nToyMC;
// loop over points in belt to find upper limit for this toy data
double thisUL = 0;
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
// double thisTS = profile->getVal();
double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// cout << "poi = " << firstPOI->getVal()
// << " max is " << arMax << " this profile = " << thisTS << endl;
// cout << "thisTS = " << thisTS<<endl;
if(thisTS<=arMax){
thisUL = firstPOI->getVal();
} else{
break;
}
}
/*
// loop over points in belt to find upper limit for this toy data
double thisUL = 0;
for(Int_t i=0; i<histOfThresholds->GetNbinsX(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
cout <<"---------------- "<<i<<endl;
tmpPoint->Print("v");
cout << "from hist " << histOfThresholds->GetBinCenter(i+1) <<endl;
double arMax = histOfThresholds->GetBinContent(i+1);
// cout << " threhold from Hist = aMax " << arMax<<endl;
// double arMax2 = belt->GetAcceptanceRegionMax(*tmpPoint);
// cout << "from scan arMax2 = "<< arMax2 << endl; // not the same due to TH1F not TH1D
// cout << "scan - hist" << arMax2-arMax << endl;
firstPOI->setVal( histOfThresholds->GetBinCenter(i+1));
// double thisTS = profile->getVal();
double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// cout << "poi = " << firstPOI->getVal()
// << " max is " << arMax << " this profile = " << thisTS << endl;
// cout << "thisTS = " << thisTS<<endl;
// NOTE: need to add a small epsilon term for single precision vs. double precision
if(thisTS<=arMax + 1e-7){
thisUL = firstPOI->getVal();
} else{
break;
}
}
*/
histOfUL->Fill(thisUL);
// for few events, data is often the same, and UL is often the same
// cout << "thisUL = " << thisUL<<endl;
delete toyData;
}
histOfUL->Draw();
c1->SaveAs("one-sided_upper_limit_output.pdf");
// if you want to see a plot of the sampling distribution for a particular scan point:
/*
SamplingDistPlot sampPlot;
int indexInScan = 0;
tmpPoint = (RooArgSet*) parameterScan->get(indexInScan)->clone("temp");
firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
toymcsampler->SetParametersForTestStat(tmpPOI);
SamplingDistribution* samp = toymcsampler->GetSamplingDistribution(*tmpPoint);
sampPlot.AddSamplingDistribution(samp);
sampPlot.Draw();
*/
// Now find bands and power constraint
Double_t* bins = histOfUL->GetIntegral();
TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
cumulative->SetContent(bins);
double band2sigDown, band1sigDown, bandMedian, band1sigUp,band2sigUp;
for(int i=1; i<=cumulative->GetNbinsX(); ++i){
if(bins[i]<RooStats::SignificanceToPValue(2))
band2sigDown=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(1))
band1sigDown=cumulative->GetBinCenter(i);
if(bins[i]<0.5)
bandMedian=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-1))
band1sigUp=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-2))
band2sigUp=cumulative->GetBinCenter(i);
}
cout << "-2 sigma band " << band2sigDown << endl;
cout << "-1 sigma band " << band1sigDown << " [Power Constraint)]" << endl;
cout << "median of band " << bandMedian << endl;
cout << "+1 sigma band " << band1sigUp << endl;
cout << "+2 sigma band " << band2sigUp << endl;
// print out the interval on the first Parameter of Interest
cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl;
cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl;
cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl;
delete profile;
delete nll;
}
int main(int argc, char* argv[]) {
doofit::builder::EasyPdf *epdf = new doofit::builder::EasyPdf();
epdf->Var("sig_yield");
epdf->Var("sig_yield").setVal(153000);
epdf->Var("sig_yield").setConstant(false);
//decay time
epdf->Var("obsTime");
epdf->Var("obsTime").SetTitle("t_{#kern[-0.2]{B}_{#kern[-0.1]{ d}}^{#kern[-0.1]{ 0}}}");
epdf->Var("obsTime").setUnit("ps");
epdf->Var("obsTime").setRange(0.,16.);
// tag, respectively the initial state of the produced B meson
epdf->Cat("obsTag");
epdf->Cat("obsTag").defineType("B_S",1);
epdf->Cat("obsTag").defineType("Bbar_S",-1);
//finalstate
epdf->Cat("catFinalState");
epdf->Cat("catFinalState").defineType("f",1);
epdf->Cat("catFinalState").defineType("fbar",-1);
epdf->Var("obsEtaOS");
epdf->Var("obsEtaOS").setRange(0.0,0.5);
std::vector<double> knots;
knots.push_back(0.07);
knots.push_back(0.10);
knots.push_back(0.138);
knots.push_back(0.16);
knots.push_back(0.23);
knots.push_back(0.28);
knots.push_back(0.35);
knots.push_back(0.42);
knots.push_back(0.44);
knots.push_back(0.48);
knots.push_back(0.5);
// empty arg list for coefficients
RooArgList* list = new RooArgList();
// create first coefficient
RooRealVar* coeff_first = &(epdf->Var("parCSpline1"));
coeff_first->setRange(0,10000);
coeff_first->setVal(1);
coeff_first->setConstant(false);
list->add( *coeff_first );
for (unsigned int i=1; i <= knots.size(); ++i){
std::string number = boost::lexical_cast<std::string>(i);
RooRealVar* coeff = &(epdf->Var("parCSpline"+number));
coeff->setRange(0,10000);
coeff->setVal(1);
coeff->setConstant(false);
list->add( *coeff );
}
// create last coefficient
RooRealVar* coeff_last = &(epdf->Var("parCSpline"+boost::lexical_cast<std::string>(knots.size())));
coeff_last->setRange(0,10000);
coeff_last->setVal(1);
coeff_last->setConstant(false);
list->add( *coeff_last );
list->Print();
doofit::roofit::pdfs::DooCubicSplinePdf splinePdf("splinePdf",epdf->Var("obsEtaOS"),knots,*list,0,0.5);
//doofit::roofit::pdfs::DooCubicSplinePdf* splinePdf = new doofit::roofit::pdfs::DooCubicSplinePdf("splinePdf", epdf->Var("obsEtaOS"), knots, *list,0,0.5);
//Koeffizienten
DecRateCoeff *coeff_c = new DecRateCoeff("coef_cos","coef_cos",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("C_f"),epdf->Var("C_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff"));
DecRateCoeff *coeff_s = new DecRateCoeff("coef_sin","coef_sin",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("S_f"),epdf->Var("S_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff"));
DecRateCoeff *coeff_sh = new DecRateCoeff("coef_sinh","coef_sinh",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f1_f"),epdf->Var("f1_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff"));
DecRateCoeff *coeff_ch = new DecRateCoeff("coef_cosh","coef_cosh",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f0_f"),epdf->Var("f0_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff"));
epdf->AddRealToStore(coeff_ch);
epdf->AddRealToStore(coeff_sh);
epdf->AddRealToStore(coeff_c);
epdf->AddRealToStore(coeff_s);
///////////////////Generiere PDF's/////////////////////
//Zeit
epdf->GaussModel("resTimeGauss",epdf->Var("obsTime"),epdf->Var("allTimeResMean"),epdf->Var("allTimeReso"));
epdf->BDecay("pdfSigTime",epdf->Var("obsTime"),epdf->Var("tau"),epdf->Var("dgamma"),epdf->Real("coef_cosh"),epdf->Real("coef_sinh"),epdf->Real("coef_cos"),epdf->Real("coef_sin"),epdf->Var("deltaM"),epdf->Model("resTimeGauss"));
//Zusammenfassen der Parameter in einem RooArgSet
RooArgSet Observables;
Observables.add(RooArgSet( epdf->Var("obsTime"),epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("obsEtaOS")));
epdf->Extend("pdfExtend", epdf->Pdf("pdfSigTime"),epdf->Real("sig_yield"));
//Multipliziere Signal und Untergrund PDF mit ihrer jeweiligen Zerfalls PDF//
//Untergrund * Zerfall
/*epdf->Product("pdf_bkg", RooArgSet(epdf->Pdf("pdf_bkg_mass_expo"), epdf->Pdf("pdf_bkg_mass_time")));
//Signal * Zerfall
epdf->Product("pdf_sig", RooArgSet(epdf->Pdf("pdf_sig_mass_gauss"),epdf->Pdf("pdfSigTime")));
//Addiere PDF's
epdf->Add("pdf_total", RooArgSet(epdf->Pdf("pdf_sig_mass_gauss*pdf_sig_time_decay"), epdf->Pdf("pdf_bkg_mass*pdf_bkg_time_decay")), RooArgSet(epdf->Var("bkg_Yield"),epdf->Var("sig_Yield")));*/
RooWorkspace ws;
ws.import(epdf->Pdf("pdfExtend"));
ws.defineSet("Observables",Observables, true);
ws.Print();
doofit::config::CommonConfig cfg_com("common");
cfg_com.InitializeOptions(argc, argv);
doofit::toy::ToyFactoryStdConfig cfg_tfac("toyfac");
cfg_tfac.InitializeOptions(cfg_com);
doofit::toy::ToyStudyStdConfig cfg_tstudy("toystudy");
cfg_tstudy.InitializeOptions(cfg_tfac);
// set a previously defined workspace to get PDF from (not mandatory, but convenient)
cfg_tfac.set_workspace(&ws);
// Check for a set --help flag and if so, print help and exit gracefully
// (recommended).
cfg_com.CheckHelpFlagAndPrintHelp();
// More custom code, e.g. to set options internally.
// Not required as configuration via command line/config file is enough.
cfg_com.PrintAll();
// Print overview of all options (optional)
// cfg_com.PrintAll();
// Initialize the toy factory module with the config objects and start
// generating toy samples.
doofit::toy::ToyFactoryStd tfac(cfg_com, cfg_tfac);
doofit::toy::ToyStudyStd tstudy(cfg_com, cfg_tstudy);
RooDataSet* data = tfac.Generate();
data->Print();
epdf->Pdf("pdfExtend").getParameters(data)->readFromFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter_spline.txt");
epdf->Pdf("pdfExtend").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter_spline.txt.new");
//epdf->Pdf("pdfExtend").fitTo(*data);
//epdf->Pdf("pdfExtend").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-fit-result.txt");
RooFitResult* fit_result = epdf->Pdf("pdfExtend").fitTo(*data, RooFit::Save(true));
tstudy.StoreFitResult(fit_result);
/*using namespace doofit::plotting;
PlotConfig cfg_plot("cfg_plot");
cfg_plot.InitializeOptions();
cfg_plot.set_plot_directory("/net/lhcb-tank/home/chasenberg/Ergebnis/dootoycp_spline-lhcb/time/");
// plot PDF and directly specify components
Plot myplot(cfg_plot, epdf->Var("obsTime"), *data, RooArgList(epdf->Pdf("pdfExtend")));
myplot.PlotItLogNoLogY();
PlotConfig cfg_plotEta("cfg_plotEta");
cfg_plotEta.InitializeOptions();
cfg_plotEta.set_plot_directory("/net/lhcb-tank/home/chasenberg/Ergebnis/dootoycp_spline-lhcb/eta/");
// plot PDF and directly specify components
Plot myplotEta(cfg_plotEta, epdf->Var("obsEtaOS"), *data, RooArgList(splinePdf));
myplotEta.PlotIt();*/
}
exampleScript()
{
gSystem->CompileMacro("betaHelperFunctions.h" ,"kO") ;
gSystem->CompileMacro("RooNormalFromFlatPdf.cxx" ,"kO") ;
gSystem->CompileMacro("RooBetaInverseCDF.cxx" ,"kO") ;
gSystem->CompileMacro("RooBetaPrimeInverseCDF.cxx" ,"kO") ;
gSystem->CompileMacro("RooCorrelatedBetaGeneratorHelper.cxx" ,"kO") ;
gSystem->CompileMacro("RooCorrelatedBetaPrimeGeneratorHelper.cxx" ,"kO") ;
gSystem->CompileMacro("rooFitBetaHelperFunctions.h","kO") ;
TFile betaTest("betaTest.root","RECREATE");
betaTest.cd();
RooWorkspace workspace("workspace");
TString correlatedName("testVariable");
TString observables("observables");
TString nuisances("nuisances");
RooAbsArg* betaOne = getCorrelatedBetaConstraint(workspace,"betaOne","",
0.5 , 0.1 ,
observables, nuisances,
correlatedName );
printf("\n\n *** constraint name is %s from betaOne and %s\n\n", betaOne->GetName(), correlatedName.Data() ) ;
RooAbsArg* betaTwo = getCorrelatedBetaConstraint(workspace,"betaTwo","",
0 , 0 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaThree = getCorrelatedBetaConstraint(workspace,"betaThree","",
0.2 , 0.01 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaFour = getCorrelatedBetaConstraint(workspace,"betaFour","",
0.7 , 0.1 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaFourC = getCorrelatedBetaConstraint(workspace,"betaFourC","",
0.7 , 0.1 ,
observables, nuisances,
correlatedName, kTRUE );
RooAbsArg* betaPrimeOne = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeOne","",
1.0 , 0.5 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaPrimeOneC = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeOneC","",
1.0 , 0.5 ,
observables, nuisances,
correlatedName, kTRUE );
RooAbsArg* betaPrimeTwo = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeTwo","",
0.7 , 0.5 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaPrimeThree = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeThree","",
0.1 , 0.05 ,
observables, nuisances,
correlatedName );
RooAbsArg* betaPrimeFour = getCorrelatedBetaPrimeConstraint(workspace,"betaPrimeFour","",
7 , 1 ,
observables, nuisances,
correlatedName );
RooRealVar* correlatedParameter = workspace.var(correlatedName);
RooAbsPdf* normalFromFlat = workspace.pdf(correlatedName+"_Constraint");
RooDataSet* data = normalFromFlat->generate(RooArgSet(*correlatedParameter),1e5);
data->addColumn(*normalFromFlat);
data->addColumn(*betaOne);
data->addColumn(*betaTwo);
data->addColumn(*betaThree);
data->addColumn(*betaFour);
data->addColumn(*betaFourC);
data->addColumn(*betaPrimeOne);
data->addColumn(*betaPrimeTwo);
data->addColumn(*betaPrimeThree);
data->addColumn(*betaPrimeFour);
data->addColumn(*betaPrimeOneC);
data->Print("v");
workspace.Print() ;
//Setup Plotting Kluges:
RooRealVar normalPlotter (correlatedName+"_Constraint" , correlatedName+"_Constraint" ,0,1);
RooPlot* normalPlot = normalPlotter.frame();
data->plotOn(normalPlot);
RooRealVar betaOnePlotter ("betaOne_BetaInverseCDF" ,"betaOne_BetaInverseCDF" ,0,1);
RooRealVar betaTwoPlotter ("betaTwo_BetaInverseCDF" ,"betaTwo_BetaInverseCDF" ,0,1);
RooRealVar betaThreePlotter("betaThree_BetaInverseCDF","betaThree_BetaInverseCDF",0,1);
RooRealVar betaFourPlotter ("betaFour_BetaInverseCDF" ,"betaFour_BetaInverseCDF" ,0,1);
RooRealVar betaFourCPlotter ("betaFourC_BetaInverseCDF" ,"betaFourC_BetaInverseCDF" ,0,1);
RooRealVar betaPrimeOnePlotter ("betaPrimeOne_BetaPrimeInverseCDF" ,"betaPrimeOne_BetaPrimeInverseCDF" ,0,4);
RooRealVar betaPrimeOneCPlotter ("betaPrimeOneC_BetaPrimeInverseCDF" ,"betaPrimeOneC_BetaPrimeInverseCDF" ,0,4);
RooRealVar betaPrimeTwoPlotter ("betaPrimeTwo_BetaPrimeInverseCDF" ,"betaPrimeTwo_BetaPrimeInverseCDF" ,0,4);
RooRealVar betaPrimeThreePlotter("betaPrimeThree_BetaPrimeInverseCDF","betaPrimeThree_BetaPrimeInverseCDF",0,0.3);
RooRealVar betaPrimeFourPlotter ("betaPrimeFour_BetaPrimeInverseCDF" ,"betaPrimeFour_BetaPrimeInverseCDF" ,4,12);
RooPlot* betaOnePlot = betaOnePlotter .frame();
RooPlot* betaTwoPlot = betaTwoPlotter .frame();
RooPlot* betaThreePlot = betaThreePlotter.frame();
RooPlot* betaFourPlot = betaFourPlotter .frame();
RooPlot* betaFourCPlot = betaFourCPlotter .frame();
data->plotOn(betaOnePlot );
data->plotOn(betaTwoPlot );
data->plotOn(betaThreePlot);
data->plotOn(betaFourPlot );
data->plotOn(betaFourCPlot );
RooPlot* betaPrimeOnePlot = betaPrimeOnePlotter .frame();
RooPlot* betaPrimeOneCPlot = betaPrimeOneCPlotter .frame();
RooPlot* betaPrimeTwoPlot = betaPrimeTwoPlotter .frame();
RooPlot* betaPrimeThreePlot = betaPrimeThreePlotter.frame();
RooPlot* betaPrimeFourPlot = betaPrimeFourPlotter .frame();
data->plotOn(betaPrimeOnePlot );
data->plotOn(betaPrimeOneCPlot );
data->plotOn(betaPrimeTwoPlot );
data->plotOn(betaPrimeThreePlot);
data->plotOn(betaPrimeFourPlot );
TCanvas* underlyingVariable = new TCanvas("underlyingVariable","underlyingVariable",800,800);
underlyingVariable->Divide(2,2);
underlyingVariable->cd(1);
RooPlot* underlyingPlot = correlatedParameter->frame();
data->plotOn(underlyingPlot);
underlyingPlot->Draw();
underlyingVariable->cd(2);
normalPlot->Draw();
underlyingVariable->cd(3);
TH2F* underlying = data->createHistogram(*correlatedParameter,normalPlotter,50,50);
underlying->Draw("col");
TH2F* legoUnderlying = (TH2F*)underlying->Clone();
underlyingVariable->cd(4);
legoUnderlying->Draw("lego");
underlyingVariable->SaveAs("underlyingVariable.pdf");
TCanvas* betaCanvas = new TCanvas("betaCanvas","betaCanvas",800,800);
betaCanvas->Divide(3,2);
betaCanvas->cd(1);
betaOnePlot->Draw();
betaCanvas->cd(2);
betaTwoPlot->Draw();
betaCanvas->cd(3);
betaThreePlot->Draw();
betaCanvas->cd(4);
betaFourPlot->Draw();
betaCanvas->cd(5);
betaFourCPlot->Draw();
betaCanvas->SaveAs("betaVariables.pdf");
TCanvas* betaPrimeCanvas = new TCanvas("betaPrimeCanvas","betaPrimeCanvas",1200,800);
betaPrimeCanvas->Divide(3,2);
betaPrimeCanvas->cd(1);
betaPrimeOnePlot->Draw();
betaPrimeCanvas->cd(2);
betaPrimeTwoPlot->Draw();
betaPrimeCanvas->cd(3);
betaPrimeThreePlot->Draw();
betaPrimeCanvas->cd(4);
betaPrimeFourPlot->Draw();
betaPrimeCanvas->cd(5);
betaPrimeOneCPlot->Draw();
betaPrimeCanvas->SaveAs("betaPrimeVariables.pdf");
TCanvas* betaCorrelationsCanvas = new TCanvas("betaCorrelationsCanvas","betaCorrelationsCanvas",1600,800);
betaCorrelationsCanvas->Divide(4,2);
TH2F* oneTwo = data->createHistogram(betaOnePlotter,betaTwoPlotter,30,30);
TH2F* oneThree = data->createHistogram(betaOnePlotter,betaThreePlotter,30,30);
TH2F* oneFour = data->createHistogram(betaOnePlotter,betaFourPlotter,30,30);
TH2F* twoThree = data->createHistogram(betaTwoPlotter,betaThreePlotter,30,30);
TH2F* twoFour = data->createHistogram(betaTwoPlotter,betaFourPlotter,30,30);
TH2F* threeFour = data->createHistogram(betaThreePlotter,betaFourPlotter,30,30);
TH2F* twoFourC = data->createHistogram(betaTwoPlotter,betaFourCPlotter,30,30);
TH2F* fourFourC = data->createHistogram(betaFourPlotter,betaFourCPlotter,30,30);
betaCorrelationsCanvas->cd(1);
oneTwo->DrawCopy("lego");
betaCorrelationsCanvas->cd(2);
oneThree->DrawCopy("lego");
betaCorrelationsCanvas->cd(3);
oneFour->DrawCopy("lego");
betaCorrelationsCanvas->cd(4);
twoThree->DrawCopy("lego");
betaCorrelationsCanvas->cd(5);
twoFour->DrawCopy("lego");
betaCorrelationsCanvas->cd(6);
threeFour->DrawCopy("lego");
betaCorrelationsCanvas->cd(7);
twoFourC->DrawCopy("lego");
betaCorrelationsCanvas->cd(8);
fourFourC->DrawCopy("lego");
betaCorrelationsCanvas->SaveAs("betaCorrelations.pdf");
TCanvas* betaPrimeCorrelationsCanvas = new TCanvas("betaPrimeCorrelationsCanvas","betaPrimeCorrelationsCanvas",1600,800);
betaPrimeCorrelationsCanvas->Divide(4,2);
TH2F* oneTwo = data->createHistogram(betaPrimeOnePlotter,betaPrimeTwoPlotter,30,30);
TH2F* oneThree = data->createHistogram(betaPrimeOnePlotter,betaPrimeThreePlotter,30,30);
TH2F* oneFour = data->createHistogram(betaPrimeOnePlotter,betaPrimeFourPlotter,30,30);
TH2F* twoThree = data->createHistogram(betaPrimeTwoPlotter,betaPrimeThreePlotter,30,30);
TH2F* twoFour = data->createHistogram(betaPrimeTwoPlotter,betaPrimeFourPlotter,30,30);
TH2F* threeFour = data->createHistogram(betaPrimeThreePlotter,betaPrimeFourPlotter,30,30);
TH2F* oneOneC = data->createHistogram(betaPrimeOnePlotter,betaPrimeOneCPlotter,30,30);
betaPrimeCorrelationsCanvas->cd(1);
oneTwo->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(2);
oneThree->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(3);
oneFour->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(4);
twoThree->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(5);
twoFour->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(6);
threeFour->DrawCopy("lego");
betaPrimeCorrelationsCanvas->cd(7);
oneOneC->DrawCopy("lego");
betaPrimeCorrelationsCanvas->SaveAs("betaPrimeCorrelations.pdf");
RooProdPdf totalPdf("totalPdf","totalPdf",workspace.allPdfs());
totalPdf.Print("v");
RooArgSet* observableSet = workspace.set("observables");
observableSet->Print();
RooDataSet* allDataOne = totalPdf.generate(*observableSet,1);
allDataOne->Print("v");
correlatedParameter->setVal(0.25);
RooDataSet* allDataTwo = totalPdf.generate(*observableSet,1);
allDataTwo->Print("v");
correlatedParameter->setVal(0.75);
RooDataSet* allDataThree = totalPdf.generate(*observableSet,1);
allDataThree->Print("v");
//Testing for extreme values!
for(int i = 0; i< 101; i++)
{
correlatedParameter->setVal((double)i/100.);
cout << "Correlation parameter has value of " << correlatedParameter->getVal();
cout << " and the pdf has an unnormalized value of " << normalFromFlat->getVal() << endl;
}
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
}
// print a message in case of LEP test statistics because it affects result by doing or not doing a fit
if (testStatType == 0) {
if (!doFit)
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
else
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
}
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
if (testStatType == 11) ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1)
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof && mNWorkers > 1) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- "
<< limDist->InverseCDF(0.16) << " "
<< limDist->InverseCDF(0.84) << "\n";
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
void fitSingleMass( const std::string& basedir, float mass, const std::string& width, TGraphErrors* gr_mean, TGraphErrors* gr_sigma, TGraphErrors* gr_width, TGraphErrors* gr_alpha1, TGraphErrors* gr_n1, TGraphErrors* gr_alpha2, TGraphErrors* gr_n2 ) {
std::string outdir = "genSignalShapes";
system( Form("mkdir -p %s", outdir.c_str()) );
std::string dataset( Form( "GluGluSpin0ToZGamma_ZToLL_W_%s_M_%.0f_TuneCUEP8M1_13TeV_pythia8", width.c_str(), mass ) );
std::cout << "-> Starting: " << dataset << std::endl;
system( Form("ls %s/%s/crab_%s/*/0000/genAna_1.root >> toBeAdded.txt", basedir.c_str(), dataset.c_str(), dataset.c_str() ) );
TChain* tree = new TChain("mt2");
ifstream ifs("toBeAdded.txt");
while( ifs.good() ) {
std::string fileName;
ifs >> fileName;
TString fileName_tstr(fileName);
if( !fileName_tstr.Contains("pnfs") ) continue;
tree->Add(Form("$DCAP/%s/mt2", fileName.c_str()) );
}
system( "rm toBeAdded.txt" );
if( tree->GetEntries()==0 ) return;
int ngenPart;
tree->SetBranchAddress( "ngenPart", &ngenPart );
float genPart_pt[100];
tree->SetBranchAddress( "genPart_pt", genPart_pt );
float genPart_eta[100];
tree->SetBranchAddress( "genPart_eta", genPart_eta );
float genPart_phi[100];
tree->SetBranchAddress( "genPart_phi", genPart_phi );
float genPart_mass[100];
tree->SetBranchAddress( "genPart_mass", genPart_mass );
int genPart_pdgId[100];
tree->SetBranchAddress( "genPart_pdgId", genPart_pdgId );
int genPart_motherId[100];
tree->SetBranchAddress( "genPart_motherId", genPart_motherId );
int genPart_status[100];
tree->SetBranchAddress( "genPart_status", genPart_status );
RooRealVar* x = new RooRealVar("boss_mass", "boss_mass", mass, 0.5*mass, 1.5*mass );
RooDataSet* data = new RooDataSet( "data", "data", RooArgSet(*x) );
int nentries = tree->GetEntries();
for( int iEntry = 0; iEntry<nentries; ++iEntry ) {
if( iEntry % 25000 == 0 ) std::cout << " Entry: " << iEntry << " / " << nentries << std::endl;
tree->GetEntry(iEntry);
TLorentzVector leptPlus;
TLorentzVector leptMinus;
TLorentzVector photon;
bool foundLeptPlus = false;
bool foundLeptMinus = false;
bool foundPhoton = false;
bool tauEvent = false;
for( int iPart=0; iPart<ngenPart; ++iPart ) {
if( genPart_status[iPart]!=1 ) continue;
if( abs(genPart_pdgId[iPart])==15 ) {
tauEvent = true;
break;
}
if( (genPart_pdgId[iPart]==+11 || genPart_pdgId[iPart]==+13) && genPart_motherId[iPart]==23 ) {
leptMinus.SetPtEtaPhiM( genPart_pt[iPart], genPart_eta[iPart], genPart_phi[iPart], genPart_mass[iPart] );
foundLeptMinus = true;
}
if( (genPart_pdgId[iPart]==-11 || genPart_pdgId[iPart]==-13) && genPart_motherId[iPart]==23 ) {
leptPlus.SetPtEtaPhiM( genPart_pt[iPart], genPart_eta[iPart], genPart_phi[iPart], genPart_mass[iPart] );
foundLeptPlus = true;
}
if( genPart_pdgId[iPart]==22 && genPart_motherId[iPart]==25 ) {
photon.SetPtEtaPhiM( genPart_pt[iPart], genPart_eta[iPart], genPart_phi[iPart], genPart_mass[iPart] );
foundPhoton = true;
}
} // for genparts
if( tauEvent ) continue;
if( !foundLeptPlus || !foundLeptMinus || !foundPhoton ) continue;
if( photon.Pt() < 40. ) continue;
float ptMax = TMath::Max( leptPlus.Pt(), leptMinus.Pt() );
float ptMin = TMath::Min( leptPlus.Pt(), leptMinus.Pt() );
if( ptMax<25. ) continue;
if( ptMin<20. ) continue;
if( fabs( photon.Eta() ) > 2.5 ) continue;
if( fabs( photon.Eta())>1.44 && fabs( photon.Eta())<1.57 ) continue;
if( fabs( leptPlus.Eta() ) > 2.4 ) continue;
if( fabs( leptMinus.Eta() ) > 2.4 ) continue;
if( photon.DeltaR( leptPlus ) < 0.4 ) continue;
if( photon.DeltaR( leptMinus ) < 0.4 ) continue;
TLorentzVector zBoson = leptPlus + leptMinus;
if( zBoson.M() < 50. ) continue;
TLorentzVector boss = zBoson + photon;
if( boss.M() < 200. ) continue;
if( photon.Pt()/boss.M()< 40./150. ) continue;
x->setVal(boss.M());
data->add(RooArgSet(*x));
}
//RooRealVar* bw_mean = new RooRealVar( "bw_mean", "Breit-Wigner Mean" , mass, 0.2*mass, 1.8*mass );
//RooRealVar* bw_gamma = new RooRealVar( "bw_gamma", "Breit-Wigner Width", 0.01*mass, 0., 0.3*mass );
//RooBreitWigner* model = new RooBreitWigner( "bw", "Breit-Wigner", *x, *bw_mean, *bw_gamma);
// Crystal-Ball
RooRealVar mean( "mean", "mean", mass, 0.9*mass, 1.1*mass );
RooRealVar sigma( "sigma", "sigma", 0.015*mass, 0., 0.07*mass );
RooRealVar alpha1( "alpha1", "alpha1", 1.2, 0., 2.5 );
RooRealVar n1( "n1", "n1", 3., 0., 5. );
RooRealVar alpha2( "alpha2", "alpha2", 1.2, 0., 2.5 );
RooRealVar n2( "n2", "n2", 3., 0., 10. );
RooDoubleCBShape* model = new RooDoubleCBShape( "cb", "cb", *x, mean, sigma, alpha1, n1, alpha2, n2 );
model->fitTo( *data );
int npoints = gr_mean->GetN();
gr_mean ->SetPoint( npoints, mass, mean.getVal() );
gr_sigma ->SetPoint( npoints, mass, sigma.getVal() );
gr_width ->SetPoint( npoints, mass, sigma.getVal()/mean.getVal() );
gr_alpha1->SetPoint( npoints, mass, alpha1.getVal() );
gr_alpha2->SetPoint( npoints, mass, alpha2.getVal() );
gr_n1 ->SetPoint( npoints, mass, n1.getVal() );
gr_n2 ->SetPoint( npoints, mass, n2.getVal() );
gr_mean ->SetPointError( npoints, 0., mean.getError() );
gr_sigma ->SetPointError( npoints, 0., sigma.getError() );
gr_width ->SetPointError( npoints, 0., sigma.getError()/mean.getVal() );
gr_alpha1->SetPointError( npoints, 0., alpha1.getError() );
gr_alpha2->SetPointError( npoints, 0., alpha2.getError() );
gr_n1 ->SetPointError( npoints, 0., n1.getError() );
gr_n2 ->SetPointError( npoints, 0., n2.getError() );
//gr_mean ->SetPoint ( npoints, mass, bw_mean->getVal() );
//gr_gamma->SetPoint ( npoints, mass, bw_gamma->getVal() );
//gr_width->SetPoint ( npoints, mass, bw_gamma->getVal()/bw_mean->getVal() );
//gr_mean ->SetPointError( npoints, 0., bw_mean->getError() );
//gr_gamma->SetPointError( npoints, 0., bw_gamma->getError()/bw_mean->getVal() );
//gr_width->SetPointError( npoints, 0., bw_gamma->getError()/bw_mean->getVal() );
RooPlot* plot = x->frame();
data->plotOn(plot);
model->plotOn(plot);
TCanvas* c1 = new TCanvas( "c1", "", 600, 600 );
c1->cd();
plot->Draw();
c1->SaveAs( Form("%s/fit_m%.0f_%s.eps", outdir.c_str(), mass, width.c_str()) );
c1->SaveAs( Form("%s/fit_m%.0f_%s.pdf", outdir.c_str(), mass, width.c_str()) );
c1->SetLogy();
c1->SaveAs( Form("%s/fit_m%.0f_%s_log.eps", outdir.c_str(), mass, width.c_str()) );
c1->SaveAs( Form("%s/fit_m%.0f_%s_log.pdf", outdir.c_str(), mass, width.c_str()) );
//delete bw_mean;
//delete bw_gamma;
delete c1;
delete data;
delete model;
delete plot;
delete x;
}
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;
}
}
