void ComputeUpperLimit(RooAbsData *data, RooStats::ModelConfig *model, float &UpperLimit, float &signif, RooRealVar *mu, RooArgSet *nullParams,RooWorkspace *ws,REGION region,const char* tag) {
bool StoreEverything=false; // activate if you want to store frames and all
RooStats::ProfileLikelihoodCalculator *plc = new RooStats::ProfileLikelihoodCalculator(*data, *model);
plc->SetParameters(*mu);
plc->SetNullParameters(*nullParams);
plc->SetTestSize(0.05);
RooStats::LikelihoodInterval *interval = plc->GetInterval();
bool ComputationSuccessful=false;
UpperLimit = interval->UpperLimit(*mu,ComputationSuccessful);
signif = 0.0; // plc->GetHypoTest()->Significance(); // deactivated significance (to make algorithm faster)
if(!ComputationSuccessful) {
cout << "There seems to have been a problem. Returned upper limit is " << UpperLimit << " but it will be set to -999" << endl;
UpperLimit=-999;
signif=-999;
}
if(StoreEverything) {
// Store it all
RooRealVar* minv = (RooRealVar*)model->GetObservables()->first();
minv->setBins(static_cast<int>((minv->getMax()-minv->getMin())/5.));
RooPlot* frameEE = minv->frame(RooFit::Title("ee sample"));
frameEE->GetXaxis()->CenterTitle(1);
frameEE->GetYaxis()->CenterTitle(1);
RooPlot* frameMM = minv->frame(RooFit::Title("mm sample"));
frameMM->GetXaxis()->CenterTitle(1);
frameMM->GetYaxis()->CenterTitle(1);
RooPlot* frameOF = minv->frame(RooFit::Title("OF sample"));
frameOF->GetXaxis()->CenterTitle(1);
frameOF->GetYaxis()->CenterTitle(1);
data->plotOn(frameMM,RooFit::Cut("catCentral==catCentral::MMCentral"));
model->GetPdf()->plotOn(frameMM,RooFit::Slice(*ws->cat("catCentral"), "MMCentral"),RooFit::ProjWData(*data));
data->plotOn(frameEE,RooFit::Cut("catCentral==catCentral::EECentral"));
model->GetPdf()->plotOn(frameEE,RooFit::Slice(*ws->cat("catCentral"), "EECentral"),RooFit::ProjWData(*data));
data->plotOn(frameOF,RooFit::Cut("catCentral==catCentral::OFOSCentral"));
model->GetPdf()->plotOn(frameOF,RooFit::Slice(*ws->cat("catCentral"), "OFOSCentral"),RooFit::ProjWData(*data));
TFile *fout = new TFile("fout.root","UPDATE");
frameMM->Write(Concatenate(Concatenate(data->GetName(),"_MM"),tag),TObject::kOverwrite);
frameEE->Write(Concatenate(Concatenate(data->GetName(),"_EE"),tag),TObject::kOverwrite);
frameOF->Write(Concatenate(Concatenate(data->GetName(),"_OF"),tag),TObject::kOverwrite);
fout->Close();
}
delete plc;
plc=0;
}
void THSEventsPDF::adjustBinning(Int_t* offset1) const
{
RooRealVar* xvar = fx_off ;
if (!dynamic_cast<RooRealVar*>(xvar)) {
coutE(InputArguments) << "RooDataHist::adjustBinning(" << GetName() << ") ERROR: dimension " << xvar->GetName() << " must be real" << endl ;
assert(0) ;
}
Double_t xlo = xvar->getMin() ;
Double_t xhi = xvar->getMax() ;
//adjust bin range limits with new scale parameter
//cout<<scale<<" "<<fMean<<" "<<xlo<<" "<<xhi<<endl;
xlo=(xlo-fMean)/scale+fMean;
xhi=(xhi-fMean)/scale+fMean;
if(xvar->getBinning().lowBound()==xlo&&xvar->getBinning().highBound()==xhi) return;
xvar->setRange(xlo,xhi) ;
// Int_t xmin(0) ;
// cout<<"THSEventsPDF::adjustBinning( "<<xlo <<" "<<xhi<<endl;
//now adjust fitting range to bin limits??Possibly not
if (fRHist->GetXaxis()->GetXbins()->GetArray()) {
RooBinning xbins(fRHist->GetNbinsX(),fRHist->GetXaxis()->GetXbins()->GetArray()) ;
Double_t tolerance = 1e-6*xbins.averageBinWidth() ;
// Adjust xlo/xhi to nearest boundary
Double_t xloAdj = xbins.binLow(xbins.binNumber(xlo+tolerance)) ;
Double_t xhiAdj = xbins.binHigh(xbins.binNumber(xhi-tolerance)) ;
xbins.setRange(xloAdj,xhiAdj) ;
xvar->setBinning(xbins) ;
if (fabs(xloAdj-xlo)>tolerance||fabs(xhiAdj-xhi)<tolerance) {
coutI(DataHandling) << "RooDataHist::adjustBinning(" << GetName() << "): fit range of variable " << xvar->GetName() << " expanded to nearest bin boundaries: ["
<< xlo << "," << xhi << "] --> [" << xloAdj << "," << xhiAdj << "]" << endl ;
}
} else {
RooBinning xbins(fRHist->GetXaxis()->GetXmin(),fRHist->GetXaxis()->GetXmax()) ;
xbins.addUniform(fRHist->GetNbinsX(),fRHist->GetXaxis()->GetXmin(),fRHist->GetXaxis()->GetXmax()) ;
Double_t tolerance = 1e-6*xbins.averageBinWidth() ;
// Adjust xlo/xhi to nearest boundary
Double_t xloAdj = xbins.binLow(xbins.binNumber(xlo+tolerance)) ;
Double_t xhiAdj = xbins.binHigh(xbins.binNumber(xhi-tolerance)) ;
xbins.setRange(xloAdj,xhiAdj) ;
xvar->setRange(xloAdj,xhiAdj) ;
//xvar->setRange(xlo,xhi) ;
}
return;
}
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;
}
void createWorkspace(const std::string &infilename, int nState, bool correctCtau, bool drawRapPt2D) {
gROOT->SetStyle("Plain");
gStyle->SetTitleBorderSize(0);
delete gRandom;
gRandom = new TRandom3(23101987);
// Set some strings
const std::string workspacename = "ws_masslifetime",
treename = "selectedData";
// Get the tree from the data file
TFile *f = TFile::Open(infilename.c_str());
TTree *tree = (TTree*)f->Get(treename.c_str());
// Set branch addresses in tree to be able to import tree to roofit
TLorentzVector* chic = new TLorentzVector;
tree->SetBranchAddress("chic",&chic);
TLorentzVector* chic_rf = new TLorentzVector;
tree->SetBranchAddress("chic_rf",&chic_rf);
TLorentzVector* jpsi = new TLorentzVector;
tree->SetBranchAddress("jpsi",&jpsi);
double lifetime = 0;
tree->SetBranchAddress("Jpsict",&lifetime);
double lifetimeErr = 0;
tree->SetBranchAddress("JpsictErr",&lifetimeErr);
char lifetimeTitle[200];
sprintf(lifetimeTitle,"l^{#psi} [mm]");
if(correctCtau) sprintf(lifetimeTitle,"l^{#chi} [mm]");
// define variables necessary for J/Psi(Psi(2S)) mass,lifetime fit
RooRealVar* JpsiMass =
new RooRealVar("JpsiMass", "M^{#psi} [GeV]", onia::massMin, onia::massMax);
RooRealVar* JpsiPt =
new RooRealVar("JpsiPt", "p^{#psi}_{T} [GeV]", 0. ,1000.);
RooRealVar* JpsiRap =
new RooRealVar("JpsiRap", "y^{#psi}", -2., 2.);
RooRealVar* chicMass =
new RooRealVar("chicMass", "M^{#chi} [GeV]", onia::chimassMin, onia::chimassMax);
RooRealVar* chicRap =
new RooRealVar("chicRap", "y^{#chi}", -onia::chirap, onia::chirap);
RooRealVar* chicPt =
new RooRealVar("chicPt", "p^{#chi}_{T} [GeV]", 0. ,100.);
RooRealVar* Jpsict =
new RooRealVar("Jpsict", lifetimeTitle, onia::ctVarMin, onia::ctVarMax);
RooRealVar* JpsictErr =
new RooRealVar("JpsictErr", Form("Error on %s",lifetimeTitle), 0.0001, 1.);
// Set bins
Jpsict->setBins(10000,"cache");
JpsiMass->setBins(10000,"cache");
JpsiPt->setBins(100);
JpsiRap->setBins(10000,"cache");
chicMass->setBins(10000,"cache");
//JpsictErr->setBins(100);
JpsictErr->setBins(10000,"cache");
// The list of data variables
RooArgList dataVars(*JpsiMass,*JpsiPt,*JpsiRap,*chicMass,*chicRap,*chicPt,*Jpsict,*JpsictErr);
// construct dataset to contain events
RooDataSet* fullData = new RooDataSet("fullData","The Full Data From the Input ROOT Trees",dataVars);
int entries = tree->GetEntries();
cout << "entries " << entries << endl;
int numEntriesTotal=0;
int numEntriesInAnalysis=0;
int numEntriesNotInAnalysis=0;
/*
/// Read in 2011 data ctauErr-histos
char saveDir[200];
char PlotID[200];
char savename[200];
sprintf(saveDir,"/afs/hephy.at/scratch/k/knuenz/ChicPol/macros/polFit/Figures/CtauErrModel");
gSystem->mkdir(saveDir);
sprintf(PlotID,"2014May26_MoreLbins");
sprintf(saveDir,"%s/%s",saveDir,PlotID);
gSystem->mkdir(saveDir);
sprintf(savename,"%s/CtauErrModel_histograms.root",saveDir);
TFile *infile = new TFile(savename,"READ");
cout<<"opened file"<<endl;
const int nPT=5;
const int nRAP=2;
const int nL=15;
const double bordersPT[nPT+1] = {0., 12., 16., 20., 30., 100.};
const double bordersRAP[nRAP+1] = {0., 0.6, 2.};
const double bordersL[nL+1] = {onia::ctVarMin, -0.05, -0.03, -0.02, -0.015, -0.01, -0.005, 0., 0.005, 0.01, 0.015, 0.02, 0.03, 0.05, 0.1, onia::ctVarMax};
TH1D* h_ctauerr_2011[nRAP+1][nPT+1][nL+1];
TH1D* h_ctauerr_2012[nRAP+1][nPT+1][nL+1];
for(int iRAP = 0; iRAP < nRAP+1; iRAP++){
for(int iPT = 0; iPT < nPT+1; iPT++){
for(int iL = 0; iL < nL+1; iL++){
h_ctauerr_2011[iRAP][iPT][iL] = (TH1D*)infile->Get(Form("h_ctauerr_2011_rap%d_pt%d_l%d",iRAP, iPT, iL));
h_ctauerr_2012[iRAP][iPT][iL] = (TH1D*)infile->Get(Form("h_ctauerr_2012_rap%d_pt%d_l%d",iRAP, iPT, iL));
}
}
}
cout<<"opened hists"<<endl;
/// Finished reading in 2011 data ctauErr-histos
*/
// loop through events in tree and save them to dataset
for (int ientries = 0; ientries < entries; ientries++) {
numEntriesTotal++;
if (ientries%10000==0) std::cout << "event " << ientries << " of " << entries << std::endl;
tree->GetEntry(ientries);
double M_jpsi =jpsi->M();
double pt_jpsi =jpsi->Pt();
double y_jpsi =jpsi->Rapidity();
double M =chic_rf->M();
//double M =chic->M()-jpsi->M()+onia::MpsiPDG;
double y=chic->Rapidity();
double pt=chic->Pt();
//if (ientries%3==0){
// M_jpsi = gRandom->Uniform(JpsiMass->getMin(), JpsiMass->getMax());
//}
//double JpsictErrRand = h_JpsictErr->GetRandom();
//double JpsictErrRand2 = h_JpsictErr->GetRandom();
//double JpsictMeanRand=0.;
////double pTcorrection=(pt-20.)*0.002;
//
////JpsictErrRand-=pTcorrection;
//if(JpsictErrRand<0) JpsictErrRand=0.001;
////JpsictErrRand2-=pTcorrection;
//if(JpsictErrRand2<0) JpsictErrRand2=0.001;
//
//if (ientries%1000000==0){
// double exponent=0.4;
// JpsictMeanRand=gRandom->Exp(exponent);
//}
//
//
//lifetime = gRandom->Gaus(JpsictMeanRand,0.8*JpsictErrRand);
//lifetimeErr = JpsictErrRand2;
//if (ientries%3==0){
// lifetime = gRandom->Gaus(JpsictMeanRand,1.5*JpsictErrRand);
//}
//
//double resCorrFactor=1.08;
//if(lifetime<0)
// lifetimeErr/=resCorrFactor;
/*
int iRAPindex=0;
int iPTindex=0;
int iLindex=0;
for(int iRAP = 1; iRAP < nRAP+1; iRAP++){
for(int iPT = 1; iPT < nPT+1; iPT++){
for(int iL = 1; iL < nL+1; iL++){
Double_t ptMin = bordersPT[iPT-1];;
Double_t ptMax = bordersPT[iPT];;
Double_t rapMin = bordersRAP[iRAP-1];;
Double_t rapMax = bordersRAP[iRAP]; ;
Double_t lMin = bordersL[iL-1];;
Double_t lMax = bordersL[iL]; ;
if(pt_jpsi>ptMin && pt_jpsi<ptMax && TMath::Abs(y_jpsi)>rapMin && TMath::Abs(y_jpsi)<rapMax && lifetime>lMin && lifetime<lMax){
iRAPindex=iRAP;
iPTindex=iPT;
iLindex=iL;
}
}
}
}
double lifetimeErrRand = h_ctauerr_2011[iRAPindex][iPTindex][iLindex]->GetRandom();
lifetimeErr = lifetimeErrRand;
if (ientries%10000==0){
std::cout << "Test output: lifetimeErr " << lifetimeErr << " randomly drawn from from " << h_ctauerr_2011[iRAPindex][iPTindex][iLindex]->GetName() << std::endl;
}
*/
if (
M > chicMass->getMin() && M < chicMass->getMax()
&& pt > chicPt->getMin() && pt < chicPt->getMax()
&& y > chicRap->getMin() && y < chicRap->getMax()
&& M_jpsi > JpsiMass->getMin() && M_jpsi < JpsiMass->getMax()
&& pt_jpsi > JpsiPt->getMin() && pt_jpsi < JpsiPt->getMax()
&& y_jpsi > JpsiRap->getMin() && y_jpsi < JpsiRap->getMax()
&& lifetime > Jpsict->getMin() && lifetime < Jpsict->getMax()
&& lifetimeErr > JpsictErr->getMin() && lifetimeErr < JpsictErr->getMax()
) {
chicPt ->setVal(pt);
chicRap ->setVal(y);
chicMass ->setVal(M);
JpsiMass ->setVal(M_jpsi);
JpsiPt ->setVal(pt_jpsi);
JpsiRap ->setVal(y_jpsi);
Jpsict ->setVal(lifetime);
JpsictErr ->setVal(lifetimeErr);
//cout<<"JpsiRap->getVal() "<<JpsiRap->getVal()<<endl;
fullData->add(dataVars);
numEntriesInAnalysis++;
}
else {
numEntriesNotInAnalysis++;
//if (M < chicMass->getMin() || M > chicMass->getMax()) cout << "M " << M << endl;
//if (pt < chicPt->getMin() || pt > chicPt->getMax()) cout << "pt " << pt << endl;
//if (y < chicRap->getMin() || y > chicRap->getMax()) cout << "y " << y << endl;
//if (lifetime < Jpsict->getMin() || lifetime > Jpsict->getMax()) cout << "lifetime " << lifetime << endl;
//if (lifetimeErr < JpsictErr->getMin() || lifetimeErr > JpsictErr->getMax()) cout << "lifetimeErr " << lifetimeErr << endl;
//cout << "M " << M << endl;
//cout << "pt " << pt << endl;
//cout << "y " << y << endl;
//cout << "lifetime " << lifetime << endl;
//cout << "lifetimeErr " << lifetimeErr << endl;
//cout << " " << endl;
}
}//ientries
//infile->Close();
cout << "entries entering all bins " << fullData->sumEntries() << endl;
cout << "numEntriesTotal " << numEntriesTotal << endl;
cout << "numEntriesInAnalysis " << numEntriesInAnalysis << endl;
cout << "numEntriesNotInAnalysis " << numEntriesNotInAnalysis << endl;
//------------------------------------------------------------------------------------------------------------------
// Define workspace and import datasets
////Get datasets binned in pT an y
for(int iRap = 0; iRap <= onia::kNbRapForPTBins; iRap++) {
Double_t yMin;
Double_t yMax;
if(iRap==0) {
yMin = onia::rapForPTRange[0];
yMax = onia::rapForPTRange[onia::kNbRapForPTBins];
} else {
yMin = onia::rapForPTRange[iRap-1];
yMax = onia::rapForPTRange[iRap];
}
for(int iPT = 0; iPT <= onia::kNbPTBins[iRap]; iPT++) {
//for(int iPT = 0; iPT <= 0; iPT++)
Double_t ptMin;
Double_t ptMax;
if(iPT==0) {
ptMin = onia::pTRange[iRap][0];
ptMax = onia::pTRange[iRap][onia::kNbPTBins[0]];
} else {
ptMin = onia::pTRange[iRap][iPT-1];
ptMax = onia::pTRange[iRap][iPT];
}
// output file name and workspace
std::stringstream outfilename;
outfilename << "tmpFiles/backupWorkSpace/ws_createWorkspace_Chi_rap" << iRap << "_pt" << iPT << ".root";
RooWorkspace* ws = new RooWorkspace(workspacename.c_str());
// define pt and y cuts on dataset
std::stringstream cutString;
if(onia::KinParticleChi && !onia::KinParticleChiButJpsiRap) {
cutString << "(chicPt >= " << ptMin << " && chicPt < "<< ptMax << ") && "
<< "(TMath::Abs(chicRap) >= " << yMin << " && TMath::Abs(chicRap) < " << yMax << ")";
}
if(!onia::KinParticleChi) {
cutString << "(JpsiPt >= " << ptMin << " && JpsiPt < "<< ptMax << ") && "
<< "(TMath::Abs(JpsiRap) >= " << yMin << " && TMath::Abs(JpsiRap) < " << yMax << ")";
}
if(onia::KinParticleChi && onia::KinParticleChiButJpsiRap) {
cutString << "(chicPt >= " << ptMin << " && chicPt < "<< ptMax << ") && "
<< "(TMath::Abs(JpsiRap) >= " << yMin << " && TMath::Abs(JpsiRap) < " << yMax << ")";
}
cout << "cutString: " << cutString.str().c_str() << endl;
// get the dataset for the fit
RooDataSet* binData = (RooDataSet*)fullData->reduce(cutString.str().c_str());
std::stringstream name;
name << "jpsi_data_rap" << iRap << "_pt" << iPT;
binData->SetNameTitle(name.str().c_str(), "Data For Fitting");
cout << "numEvents = " << binData->sumEntries() << endl;
double chicMeanPt = binData->mean(*chicPt);
RooRealVar var_chicMeanPt("var_chicMeanPt","var_chicMeanPt",chicMeanPt);
if(!ws->var("var_chicMeanPt")) ws->import(var_chicMeanPt);
else ws->var("var_chicMeanPt")->setVal(chicMeanPt);
cout << "chicMeanPt = " << chicMeanPt << endl;
double jpsiMeanPt = binData->mean(*JpsiPt);
RooRealVar var_jpsiMeanPt("var_jpsiMeanPt","var_jpsiMeanPt",jpsiMeanPt);
if(!ws->var("var_jpsiMeanPt")) ws->import(var_jpsiMeanPt);
else ws->var("var_jpsiMeanPt")->setVal(jpsiMeanPt);
cout << "jpsiMeanPt = " << jpsiMeanPt << endl;
std::stringstream cutStringPosRapChic;
cutStringPosRapChic << "chicRap > 0";
RooDataSet* binDataPosRapChic = (RooDataSet*)binData->reduce(cutStringPosRapChic.str().c_str());
double chicMeanAbsRap = binDataPosRapChic->mean(*chicRap);
cout << "chicMeanAbsRap = " << chicMeanAbsRap << endl;
RooRealVar var_chicMeanAbsRap("var_chicMeanAbsRap","var_chicMeanAbsRap",chicMeanAbsRap);
if(!ws->var("var_chicMeanAbsRap")) ws->import(var_chicMeanAbsRap);
else ws->var("var_chicMeanAbsRap")->setVal(chicMeanAbsRap);
std::stringstream cutStringPosRapJpsi;
cutStringPosRapJpsi << "JpsiRap > 0";
RooDataSet* binDataPosRapJpsi = (RooDataSet*)binData->reduce(cutStringPosRapJpsi.str().c_str());
double jpsiMeanAbsRap = binDataPosRapJpsi->mean(*JpsiRap);
cout << "jpsiMeanAbsRap = " << jpsiMeanAbsRap << endl;
RooRealVar var_jpsiMeanAbsRap("var_jpsiMeanAbsRap","var_jpsiMeanAbsRap",jpsiMeanAbsRap);
if(!ws->var("var_jpsiMeanAbsRap")) ws->import(var_jpsiMeanAbsRap);
else ws->var("var_jpsiMeanAbsRap")->setVal(jpsiMeanAbsRap);
// Import variables to workspace
ws->import(*binData);
ws->writeToFile(outfilename.str().c_str());
}//iPT
}//iRap
////---------------------------------------------------------------
////--Integrating rapidity and pt bins, in +/- 3*sigma mass window
////---------------------------------------------------------------
if(drawRapPt2D) {
double yMin = onia::rapForPTRange[0];
double yMax = 1.6;//onia::rapForPTRange[onia::kNbRapForPTBins];
double ptMin = onia::pTRange[0][0];
double ptMax = onia::pTRange[0][onia::kNbPTBins[0]];
std::stringstream cutRapPt;
cutRapPt << "(chicPt > " << ptMin << " && chicPt < "<< ptMax << ") && "
<< "(TMath::Abs(chicRap) > " << yMin << " && TMath::Abs(chicRap) < " << yMax << ")";
cout<<"cutRapPt: "<<cutRapPt.str().c_str()<<endl;
RooDataSet* rapPtData = (RooDataSet*)fullData->reduce(cutRapPt.str().c_str());
std::stringstream nameRapPt;
nameRapPt << "data_rap0_pt0";
rapPtData->SetNameTitle(nameRapPt.str().c_str(), "Data For full rap and pt");
// output file name and workspace
std::stringstream outfilename;
outfilename << "tmpFiles/backupWorkSpace/ws_createWorkspace_Chi_rap0_pt0.root";
RooWorkspace* ws_RapPt = new RooWorkspace(workspacename.c_str());
//Import variables to workspace
ws_RapPt->import(*rapPtData);
ws_RapPt->writeToFile(outfilename.str().c_str());
TH2D* rapPt;
TH1D* rap1p2;
double MassMin;
double MassMax;
rap1p2 = new TH1D("rap1p2","rap1p2",30,1.2, 1.8);
if(nState==4) {
rapPt = new TH2D( "rapPt", "rapPt", 52,-1.3,1.3,144,0,72);
MassMin=3.011;//massPsi1S-onia::nSigMass*sigma1S;
MassMax=3.174;//massPsi1S+onia::nSigMass*sigma1S;
// sigma 27.2 MeV
// mean 3.093 GeV
}
if(nState==5) {
rapPt = new TH2D( "rapPt", "rapPt", 64,-1.6,1.6,144,0,72); // rap<1.5
//rapPt = new TH2D( "rapPt", "rapPt", 52,-1.3,1.3,144,0,72); // rap<1.2
MassMin=3.576;//massPsi2S-onia::nSigMass*sigma2S;
MassMax=3.786;//massPsi2S+onia::nSigMass*sigma2S;
// sigma 34.9 MeV // pT > 7
// sigma 34.3 MeV // pT > 10
// mean 3.681 GeV
}
cout<<"Plotting rap-Pt for Psi"<<nState-3<<"S"<<endl;
cout<<"MassMin for rap-Pt plot = "<<MassMin<<endl;
cout<<"MassMax for rap-Pt plot = "<<MassMax<<endl;
TTree *rapPtTree = (TTree*)rapPtData->tree();
std::stringstream cutMass;
cutMass<<"(chicMass > " << MassMin << " && chicMass < "<< MassMax << ")";
//following two methods can only be used in root_v30, 34 does not work
rapPtTree->Draw("chicPt:chicRap>>rapPt",cutMass.str().c_str(),"colz");
cout<<"debug"<<endl;
rapPtTree->Draw("TMath::Abs(chicRap)>>rap1p2",cutMass.str().c_str());
TCanvas* c2 = new TCanvas("c2","c2",1200,1500);
rapPt->SetYTitle("p_{T}(#mu#mu) [GeV]");
rapPt->SetXTitle("y(#mu#mu)");
gStyle->SetPalette(1);
gPad->SetFillColor(kWhite);
rapPt->SetTitle(0);
rapPt->SetStats(0);
gPad->SetLeftMargin(0.15);
gPad->SetRightMargin(0.17);
rapPt->GetYaxis()->SetTitleOffset(1.5);
rapPt->Draw("colz");
TLine* rapPtLine;
for(int iRap=0; iRap<onia::kNbRapForPTBins+1; iRap++) {
rapPtLine= new TLine( -onia::rapForPTRange[iRap], onia::pTRange[0][0], -onia::rapForPTRange[iRap], onia::pTRange[0][onia::kNbPTBins[iRap]] );
rapPtLine->SetLineWidth( 2 );
rapPtLine->SetLineStyle( 1 );
rapPtLine->SetLineColor( kWhite );
rapPtLine->Draw();
rapPtLine= new TLine( onia::rapForPTRange[iRap], onia::pTRange[0][0], onia::rapForPTRange[iRap], onia::pTRange[0][onia::kNbPTBins[iRap]] );
rapPtLine->SetLineWidth( 2 );
rapPtLine->SetLineStyle( 1 );
rapPtLine->SetLineColor( kWhite );
rapPtLine->Draw();
int pTBegin = 0;
if(nState==5) pTBegin = 1;
for(int iPt=pTBegin; iPt<onia::kNbPTBins[iRap+1]+1; iPt++) {
rapPtLine= new TLine( -onia::rapForPTRange[onia::kNbRapForPTBins], onia::pTRange[0][iPt], onia::rapForPTRange[onia::kNbRapForPTBins], onia::pTRange[0][iPt] );
rapPtLine->SetLineWidth( 2 );
rapPtLine->SetLineStyle( 1 );
rapPtLine->SetLineColor( kWhite );
rapPtLine->Draw();
}
}
char savename[200];
sprintf(savename,"Figures/rapPt_Chi.pdf");
c2->SaveAs(savename);
TCanvas* c3 = new TCanvas("c3","c3",1500,1200);
rap1p2->SetYTitle("Events");
rap1p2->SetXTitle("y(#mu#mu)");
rap1p2->SetTitle(0);
rap1p2->SetStats(0);
rap1p2->GetYaxis()->SetTitleOffset(1.2);
rap1p2->Draw();
sprintf(savename,"Figures/rap_Chi_1p2.pdf");
c3->SaveAs(savename);
}
f->Close();
}
// internal routine to run the inverter
HypoTestInverterResult * RunInverter(RooWorkspace * w, const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
int npoints, double poimin, double poimax,
int ntoys, bool useCls )
{
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("RA2bHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
// 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("RA2bHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("RA2bHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("RA2bHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("RA2bHypoTestInvDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("RA2bHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
if (!bModel || bModel == sbModel) {
Info("RA2bHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("RA2bHypoTestInvDemo","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("RA2bHypoTestInvDemo","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("RA2bHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(*sbModel->GetSnapshot());
if (bModel->GetSnapshot()) slrts.SetAltParameters(*bModel->GetSnapshot());
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
//MyProfileLikelihoodTestStat profll(*sbModel->GetPdf());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(1);
if (optimize) profll.SetReuseNLL(true);
TestStatistic * testStat = &slrts;
if (testStatType == 1) testStat = &ropl;
if (testStatType == 2 || testStatType == 3) testStat = &profll;
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else hc = new HybridCalculator(*data, *bModel, *sbModel);
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
//=== DEBUG
///// toymcs->SetWS( w ) ;
//=== DEBUG
toymcs->SetNEventsPerToy(1);
toymcs->SetTestStatistic(testStat);
if (optimize) toymcs->SetUseMultiGen(true);
if (type == 1) {
HybridCalculator *hhc = (HybridCalculator*) hc;
hhc->SetToys(ntoys,ntoys);
// check for nuisance prior pdf
if (bModel->GetPriorPdf() && sbModel->GetPriorPdf() ) {
hhc->ForcePriorNuisanceAlt(*bModel->GetPriorPdf());
hhc->ForcePriorNuisanceNull(*sbModel->GetPriorPdf());
}
else {
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
Error("RA2bHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified");
return 0;
}
}
}
else
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
TStopwatch tw; tw.Start();
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
// fit the data first
sbModel->GetPdf()->fitTo(*data);
double poihat = poi->getVal();
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCls);
calc.SetVerbose(true);
// can speed up using proof-lite
if (useProof && nworkers > 1) {
ProofConfig pc(*w, nworkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
printf(" npoints = %d, poimin = %7.2f, poimax = %7.2f\n\n", npoints, poimin, poimax ) ;
cout << flush ;
if ( npoints==1 ) {
std::cout << "Evaluating one point : " << poimax << std::endl;
calc.RunOnePoint(poimax);
} else 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;
}
cout << "\n\n right before calc.GetInterval(), ntoys = " << ntoys << " \n\n" << flush ;
HypoTestInverterResult * r = calc.GetInterval();
return r;
}
void OneSidedFrequentistUpperLimitWithBands_intermediate(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double confidenceLevel=0.95;
// degrade/improve number of pseudo-experiments used to define the confidence belt.
// value of 1 corresponds to default number of toys in the tail, which is 50/(1-confidenceLevel)
double additionalToysFac = 1.;
int nPointsToScan = 30; // number of steps in the parameter of interest
int nToyMC = 100; // number of toys used to define the expected limit and band
TStopwatch t;
t.Start();
/////////////////////////////////////////////////////////////
// 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";
else
filename = infile;
// Check if example input file exists
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file && strcmp(infile,"")){
cout <<"file not found" << endl;
return;
}
// if default file not found, try to create it
if(!file ){
// 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;
}
// now try to access the file again
file = TFile::Open(filename);
if(!file){
// if it is still not there, then we can't continue
cout << "Not able to run hist2workspace to create example input" <<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;
}
cout << "Found data and ModelConfig:" <<endl;
mc->Print();
/////////////////////////////////////////////////////////////
// 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(additionalToysFac); // improve sampling that defines confidence belt
// fc.UseAdaptiveSampling(true); // speed it up a bit, but don't use for expectd 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);
// test speedups:
testStat->SetReuseNLL(true);
// toymcsampler->setUseMultiGen(true); // not fully validated
// 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
ProofConfig pc(*w, 4, "",false);
if(mc->GetGlobalObservables()){
cout << "will use global observables for unconditional ensemble"<<endl;
mc->GetGlobalObservables()->Print();
toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
}
toymcsampler->SetProofConfig(&pc); // enable proof
// Now get the interval
PointSetInterval* interval = fc.GetInterval();
ConfidenceBelt* belt = fc.GetConfidenceBelt();
// print out the iterval 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");
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-constriant
////////////////////////////////////////////////////////////
// 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");
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
w->loadSnapshot("paramsToGenerateData");
// in 5.30 there is a nicer way to generate toy data & randomize global obs
RooAbsData* toyData = toymcsampler->GenerateToyData(*paramsToGenerateData);
// 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) // (should be checked)
CLb+= (1.)/nToyMC;
if(obsTSatObsUL <= toyTSatObsUL) // (should be checked)
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 = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
if(thisTS<=arMax){
thisUL = firstPOI->getVal();
} else{
break;
}
}
histOfUL->Fill(thisUL);
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:
// Now find bands and power constraint
Double_t* bins = histOfUL->GetIntegral();
TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
cumulative->SetContent(bins);
double band2sigDown=0, band1sigDown=0, bandMedian=0, band1sigUp=0,band2sigUp=0;
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);
}
t.Stop();
t.Print();
cout << "-2 sigma band " << band2sigDown << endl;
cout << "-1 sigma band " << band1sigDown << endl;
cout << "median of band " << bandMedian << " [Power Constriant)]" << endl;
cout << "+1 sigma band " << band1sigUp << endl;
cout << "+2 sigma band " << band2sigUp << endl;
// print out the iterval 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;
}
void fitPtOverMCJLST(int mass = 125, int LHCsqrts = 7, int whichtype = 1,
bool correctErrors = false, /* string changeParName = "", */
bool showErrorPDFs = false, string systString = "Default")
// whichtype
// 0 - gg Signal
// 1 - VBF Signal
// 2 - ZZ
// 3 - ZX
// 4 - ggZZ
// 5 - WH
// 6 - ZH
// 7 - ttH
{
string changeParName = "";
if (systString == "Default") changeParName = "up";
string nameSample[8] = {"gg","vbf","zz","zx","ggzz","wh","zh","tth"};
float maxType[8] = {2.4,3.2,1.6,1.6,1.6,3.2,3.2,3.2};
float rebinType[8] = {1,2,1,1,4,10,10,40};
for (int t = 0; t < 8; t++) {
if (mass > 150) maxType[t] = int(117.90*maxType[t]/sqrt(mass-10.91))/10.;
}
char fileToOpen[200];
sprintf(fileToOpen,"selRootFiles/PToverM_%s%d_SEL_%dTeV.root",nameSample[whichtype].c_str(),mass,LHCsqrts);
// if (whichtype == 3) sprintf(fileToOpen,"PTOVERM_%s_SEL_allTeV.root",nameSample[whichtype].c_str());
RooRealVar* ptoverm = new RooRealVar("ptoverm","p_{T}/M^{4l}",0.,maxType[whichtype],"GeV/c");
TFile input(fileToOpen);
// if (systString == "Mass" || systString == "Mela") {
// sprintf(fileToOpen,"ptovermH_%sUp",systString.c_str());
// } else {
sprintf(fileToOpen,"ptovermH_%s",systString.c_str());
//}
TH1F* ptovermH = (TH1F*)input.Get(fileToOpen);
if (rebinType[whichtype] > 1) ptovermH->Rebin(rebinType[whichtype]);
if (maxType[whichtype] < ptovermH->GetBinLowEdge(ptovermH->GetNbinsX() + 1) - ptovermH->GetBinWidth(1)) {
int theBin = ptovermH->FindBin(maxType[whichtype]);
ptovermH->GetXaxis()->SetRange(1,theBin-1);
}
gROOT->ProcessLine(".L mytdrstyle.C");
gROOT->ProcessLine("setTDRStyle()");
// cout << endl << "Signal " << endl;
ptoverm->setBins(ptovermH->GetNbinsX());
RooDataHist* rdh = new RooDataHist("rdh","Some dataset",RooArgList(*ptoverm),Import(*ptovermH,kFALSE));
// fit definitions
// RooWorkspace *ws = new RooWorkspace("ws");
RooRealVar m("m","emme", 1.,0.01, 40.);
RooRealVar n("n","enne", 0.93, 0.05, 15.);
RooRealVar n2("n2","enne2", 0.75, 0.5, 15.);
RooRealVar bb("bb","bibi",0.02, 0.000005, 20.0);
RooRealVar T("T","tti",0.2,0.00000005,1.);
RooRealVar bb2("bb2","bibi2",0.02, 0.0005, 10.0);
RooRealVar fexp("fexp","f_exp",0.02, 0.0, 1.0);
if (whichtype == 0) {
if (LHCsqrts == 8) {
m.setVal(3.319); // m.setConstant(kTRUE);
n.setVal(0.7606); if (systString != "Default" || mass != 125) n.setConstant(kTRUE);
n2.setVal(0.8061); n2.setConstant(kTRUE);
bb.setVal(3.728); // bb.setConstant(kTRUE);
T.setVal(0.00333); // T.setConstant(kTRUE);
bb2.setVal(1.7172); // bb2.setConstant(kTRUE);
fexp.setVal(0.002144); if (systString != "Default" || mass != 125) fexp.setConstant(kTRUE);
} else {
m.setVal(0.061); // m.setConstant(kTRUE);
n.setVal(1.6141); if (systString == "Resummation" || systString == "TopMass") n.setConstant(kTRUE);
n2.setVal(1.3294); n2.setConstant(kTRUE);
bb.setVal(4.2761); // bb.setConstant(kTRUE);
T.setVal(0.0361); // T.setConstant(kTRUE);
bb2.setVal(1.6643); bb2.setConstant(kTRUE);
fexp.setVal(0.0004); // fexp.setConstant(kTRUE);
}
} else if (whichtype == 1) {
m.setVal(1.006); // m.setConstant(kTRUE);
n.setVal(10.939); n.setConstant(kTRUE);
n2.setVal(1.1448); n2.setConstant(kTRUE);
bb.setVal(0.02048); bb.setConstant(kTRUE);
T.setVal(0.16115); if (systString.find("Mela") != string::npos) T.setConstant(kTRUE); // T.setConstant(kTRUE);
bb2.setVal(1.0024); bb2.setConstant(kTRUE);
fexp.setVal(0.005); fexp.setConstant(kTRUE);
if (mass > 300) {
fexp.setVal(0.0); fexp.setConstant(kFALSE);
}
if (mass > 500) {
bb2.setVal(5.0); // bb2.setConstant(kFALSE);
}
if (mass > 500) {
bb.setVal(15.0); // bb.setConstant(kFALSE);
}
} else if (whichtype == 2) {
if (LHCsqrts == 8) {
m.setVal(1.0476); // m.setConstant(kTRUE);
bb.setVal(3.3088); // if (mass != 140) bb.setConstant(kTRUE);
n2.setVal(0.7146); n2.setConstant(kTRUE);
n.setVal(0.9518); n.setConstant(kTRUE);
bb2.setVal(100000.); bb2.setConstant(kTRUE);
T.setVal(0.0021889); if (systString.find("Mela") != string::npos || mass != 140) T.setConstant(kTRUE);
fexp.setVal(0.0); fexp.setConstant(kTRUE);
} else {
m.setVal(1.028); // m.setConstant(kTRUE);
bb.setVal(2.91); // bb.setConstant(kTRUE);
n2.setVal(0.7146); n2.setConstant(kTRUE);
n.setVal(0.9518); n.setConstant(kTRUE);
bb2.setVal(100000.); bb2.setConstant(kTRUE);
T.setVal(0.002248); if (systString.find("Mela") != string::npos) T.setConstant(kTRUE);
fexp.setVal(0.0); fexp.setConstant(kTRUE);
}
} else if (whichtype == 3) {
m.setVal(1.411); // m.setConstant(kTRUE);
n.setVal(3.4523); n.setConstant(kTRUE);
n2.setVal(0.6910); n2.setConstant(kTRUE);
bb.setVal(0.00039); // bb.setConstant(kTRUE);
T.setVal(0.118); // T.setConstant(kTRUE);
bb2.setVal(0.0224); bb2.setConstant(kTRUE);
fexp.setVal(0.0); fexp.setConstant(kTRUE);
} else if (whichtype == 4) {
m.setVal(1.411); // m.setConstant(kTRUE);
n.setVal(5.756); // n.setConstant(kTRUE);
n2.setVal(0.8738); // n2.setConstant(kTRUE);
bb.setVal(0.00039); // bb.setConstant(kTRUE);
T.setVal(0.118); // T.setConstant(kTRUE);
bb2.setVal(0.0224); bb2.setConstant(kTRUE);
fexp.setVal(0.0); fexp.setConstant(kTRUE);
} else if (whichtype == 5 && LHCsqrts == 8) {
m.setVal(1.006); // m.setConstant(kTRUE);
n.setVal(10.939); n.setConstant(kTRUE);
n2.setVal(1.1448); n2.setConstant(kTRUE);
bb.setVal(3.897); bb.setConstant(kTRUE);
T.setVal(0.1009); // T.setConstant(kTRUE);
bb2.setVal(1.0224); bb2.setConstant(kTRUE);
fexp.setVal(0.01); fexp.setConstant(kTRUE);
} else {
// cout << "Entro qui" << endl;
m.setVal(1.006); // m.setConstant(kTRUE);
n.setVal(10.939); n.setConstant(kTRUE);
n2.setVal(1.1448); n2.setConstant(kTRUE);
bb.setVal(0.0129); bb.setConstant(kTRUE);
T.setVal(0.1009); // T.setConstant(kTRUE);
bb2.setVal(1.0224); bb2.setConstant(kTRUE);
fexp.setVal(0.01); fexp.setConstant(kTRUE);
}
RooModifTsallis* rt3 = new RooModifTsallis("rt3","rt3",*ptoverm,m,n,n2,bb,bb2,T,fexp);
// ws->import(*rt3);
// fit
RooFitResult* fit = rt3->fitTo(*rdh,Minos(0),Save(1),SumW2Error(kTRUE),NumCPU(1));
float mVal = m.getVal();
float nVal = n.getVal();
float n2Val = n2.getVal();
float bbVal = bb.getVal();
float bb2Val = bb2.getVal();
float fexpVal = fexp.getVal();
float TVal = T.getVal();
if (correctErrors) {
// Tsallis errors not reliable, use toy MC
TH1F* mHist = new TH1F("mHist","m",21,-0.5*mVal,0.5*mVal);
TH1F* nHist = new TH1F("nHist","n",21,-0.2*nVal,0.2*nVal);
TH1F* n2Hist = new TH1F("n2Hist","n2",21,-0.2*n2Val,0.2*n2Val);
TH1F* bbHist = new TH1F("bbHist","bb",21,-0.2*bbVal,0.2*bbVal);
TH1F* bb2Hist = new TH1F("bb2Hist","bb2",21,-0.2*bb2Val,0.2*bb2Val);
TH1F* fexpHist = new TH1F("fexpHist","fexp",21,-0.2*fexpVal-0.000001,0.2*fexpVal+0.000001);
TH1F* THist = new TH1F("THist","T",21,-0.5*TVal,0.5*TVal);
mHist->GetXaxis()->SetTitle("m-m_{gen}");
nHist->GetXaxis()->SetTitle("n-n_{gen}");
n2Hist->GetXaxis()->SetTitle("n2-n2_{gen}");
bbHist->GetXaxis()->SetTitle("bb-bb_{gen}");
bb2Hist->GetXaxis()->SetTitle("bb2-bb2_{gen}");
THist->GetXaxis()->SetTitle("T-T_{gen}");
fexpHist->GetXaxis()->SetTitle("fexp-fexp_{gen}");
for (unsigned int iToy = 0; iToy < 200; iToy++) {
cout << endl << "####" << endl;
cout << "Generating toy experiment n. " << iToy+1 << endl;
m.setVal(mVal);
n.setVal(nVal);
n2.setVal(n2Val);
bb.setVal(bbVal);
bb2.setVal(bb2Val);
fexp.setVal(fexpVal);
T.setVal(TVal);
TDatime *now = new TDatime();
Int_t seed = now->GetDate() + now->GetTime();
cout << "RooFit Generation Seed = " << seed+iToy << endl;
RooRandom::randomGenerator()->SetSeed(seed+iToy);
cout << "####" << endl << endl;
RooDataSet *dataToy = rt3->generate(RooArgSet(*ptoverm),ptovermH->GetEntries());
RooDataHist *dataToyH = new RooDataHist("dataToyH","toy",RooArgSet(*ptoverm),*dataToy);
rt3->fitTo(*dataToyH,Minos(0),SumW2Error(kTRUE),NumCPU(1));
if (fit->floatParsFinal().find("m")) mHist->Fill(m.getVal()-mVal);
if (fit->floatParsFinal().find("n")) nHist->Fill(n.getVal()-nVal);
if (fit->floatParsFinal().find("n2")) n2Hist->Fill(n2.getVal()-n2Val);
if (fit->floatParsFinal().find("bb")) bbHist->Fill(bb.getVal()-bbVal);
if (fit->floatParsFinal().find("bb2")) bb2Hist->Fill(bb2.getVal()-bb2Val);
if (fit->floatParsFinal().find("fexp")) fexpHist->Fill(fexp.getVal()-fexpVal);
if (fit->floatParsFinal().find("T")) THist->Fill(T.getVal()-TVal);
}
TCanvas cant("cant","Test canvas",5.,5.,900.,500.);
cant.Divide(4,2);
cant.cd(1); mHist->Draw();
cant.cd(2); nHist->Draw();
cant.cd(3); n2Hist->Draw();
cant.cd(4); bbHist->Draw();
cant.cd(5); bb2Hist->Draw();
cant.cd(6); fexpHist->Draw();
cant.cd(7); THist->Draw();
// cant.SaveAs("figs/testToys.pdf");
cant.SaveAs("newfigs/testToys.pdf");
if (fit->floatParsFinal().find("m")) m.setError(mHist->GetRMS());
if (fit->floatParsFinal().find("n")) n.setError(nHist->GetRMS());
if (fit->floatParsFinal().find("n2")) n2.setError(n2Hist->GetRMS());
if (fit->floatParsFinal().find("bb")) bb.setError(bbHist->GetRMS());
if (fit->floatParsFinal().find("bb2")) bb2.setError(bb2Hist->GetRMS());
if (fit->floatParsFinal().find("fexp")) fexp.setError(fexpHist->GetRMS());
if (fit->floatParsFinal().find("T")) T.setError(THist->GetRMS());
}
m.setVal(mVal);
n.setVal(nVal);
n2.setVal(n2Val);
bb.setVal(bbVal);
bb2.setVal(bb2Val);
fexp.setVal(fexpVal);
T.setVal(TVal);
char fileToSave[200];
// if (changeParName != "")
// sprintf(fileToSave,"text/paramsPTOverMCJLST_%s_%dTeV_%s_%s.txt",nameSample[whichtype].c_str(),LHCsqrts,systString.c_str(),changeParName.c_str());
// else
sprintf(fileToSave,"text/paramsPTOverMCJLST_%s%d_%dTeV_%s.txt",nameSample[whichtype].c_str(),mass,LHCsqrts,systString.c_str());
ofstream os1(fileToSave);
if (changeParName != "") {
sprintf(fileToSave,"m%s",changeParName.c_str()); m.SetName(fileToSave);
sprintf(fileToSave,"n%s",changeParName.c_str()); n.SetName(fileToSave);
sprintf(fileToSave,"n2%s",changeParName.c_str()); n2.SetName(fileToSave);
sprintf(fileToSave,"bb%s",changeParName.c_str()); bb.SetName(fileToSave);
sprintf(fileToSave,"bb2%s",changeParName.c_str()); bb2.SetName(fileToSave);
sprintf(fileToSave,"fexp%s",changeParName.c_str()); fexp.SetName(fileToSave);
sprintf(fileToSave,"T%s",changeParName.c_str()); T.SetName(fileToSave);
}
(RooArgSet(m,n,n2,bb,bb2,fexp,T)).writeToStream(os1,false);
os1.close();
RooRealVar mup("mup","emme", 1.,0.01, 30.);
RooRealVar nup("nup","enne", 0.93, 0.5, 15.);
RooRealVar n2up("n2up","enne2", 0.75, 0.5, 15.);
RooRealVar bbup("bbup","bibi",0.02, 0.00005, 20.0);
RooRealVar Tup("Tup","tti",0.2,0.00000005,1.);
RooRealVar bb2up("bb2up","bibi2",0.02, 0.0005, 10.0);
RooRealVar fexpup("fexpup","f_exp",0.02, 0.0, 1.0);
RooModifTsallis* rt3up = new RooModifTsallis("rt3up","rt3up",*ptoverm,mup,nup,n2up,bbup,bb2up,Tup,fexpup);
// ws->import(*rt3up);
RooRealVar mdown("mdown","emme", 1.,0.01, 30.);
RooRealVar ndown("ndown","enne", 0.93, 0.5, 15.);
RooRealVar n2down("n2down","enne2", 0.75, 0.5, 15.);
RooRealVar bbdown("bbdown","bibi",0.02, 0.00005, 20.0);
RooRealVar Tdown("Tdown","tti",0.2,0.00000005,1.);
RooRealVar bb2down("bb2down","bibi2",0.02, 0.0005, 10.0);
RooRealVar fexpdown("fexpdown","f_exp",0.02, 0.0, 1.0);
RooModifTsallis* rt3down = new RooModifTsallis("rt3down","rt3down",*ptoverm,mdown,ndown,n2down,bbdown,bb2down,Tdown,fexpdown);
// ws->import(*rt3down);
RooPlot *frame = ptoverm->frame();
char reducestr[300];
sprintf(reducestr,"ptoverm > %f && ptoverm < %f",ptoverm->getMin(),ptoverm->getMax());
rdh->plotOn(frame,DataError(RooAbsData::SumW2),Cut(reducestr));
static RooHist *hpull;
float chi2 = 0.;
if (changeParName == "") {
sprintf(fileToSave,"text/paramsPTOverMCJLST_%s%d_%dTeV_Default.txt",nameSample[whichtype].c_str(),mass,LHCsqrts);
ifstream is1(fileToSave);
(RooArgSet(mup,nup,n2up,bbup,bb2up,fexpup,Tup)).readFromStream(is1,false);
mdown.setVal(fabs(3*mup.getVal() - 2*m.getVal()));
ndown.setVal(fabs(3*nup.getVal() - 2*n.getVal()));
n2down.setVal(fabs(3*n2up.getVal() - 2*n2.getVal()));
bbdown.setVal(fabs(3*bbup.getVal() - 2*bb.getVal()));
Tdown.setVal(fabs(3*Tup.getVal() - 2*T.getVal()));
bb2down.setVal(fabs(3*bb2up.getVal() - 2*bb2.getVal()));
fexpdown.setVal(fabs(3*fexpup.getVal() - 2*fexp.getVal()));
if (showErrorPDFs) {
rt3->plotOn(frame,LineColor(kRed),LineStyle(kDashed),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
hpull = frame->pullHist();
rt3up->plotOn(frame,LineColor(kBlue),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
if (systString.find("Mela") == string::npos) rt3down->plotOn(frame,LineColor(kRed),LineStyle(kDashed),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
} else {
rt3->plotOn(frame,LineColor(kBlue),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
hpull = frame->pullHist();
}
} else {
mup.setVal(m.getVal() + m.getError()); cout << "mup = " << mup.getVal() << endl;
nup.setVal(n.getVal() + n.getError());
n2up.setVal(n2.getVal() + n2.getError());
bbup.setVal(bb.getVal() + bb.getError());
Tup.setVal(T.getVal() + T.getError());
bb2up.setVal(bb2.getVal() + bb2.getError());
fexpup.setVal(fexp.getVal() + fexp.getError());
mdown.setVal(m.getVal() - m.getError()); cout << "mdown = " << mdown.getVal() << endl;
ndown.setVal(n.getVal() - n.getError());
n2down.setVal(n2.getVal() - n2.getError());
bbdown.setVal(bb.getVal() - bb.getError());
Tdown.setVal(T.getVal() - T.getError());
bb2down.setVal(bb2.getVal() - bb2.getError());
fexpdown.setVal(fexp.getVal() - fexp.getError());
rt3->plotOn(frame,LineColor(kBlue),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
hpull = frame->pullHist();
if (showErrorPDFs) {
rt3up->plotOn(frame,LineColor(kRed),LineStyle(kDashed),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
rt3down->plotOn(frame,LineColor(kRed),LineStyle(kDashed),Normalization(rdh->sumEntries(),RooAbsReal::NumEvent));
}
}
double *ypulls = hpull->GetY();
unsigned int nBins = rdh->numEntries();
unsigned int nFullBins = 0;
for (unsigned int i = 0; i < nBins; i++) {
cout << "Pull of bin " << i << " = " << ypulls[i] << endl;
if (fabs(ypulls[i]) < 5.0) chi2 += ypulls[i]*ypulls[i];
cout << "Partial chi2 = " << chi2 << endl;
if (fabs(ypulls[i]) > 0.0001 && fabs(ypulls[i]) < 5.0) nFullBins++;
}
for (unsigned int i = 0; i < nBins; i++) {
if (fabs(ypulls[i]) < 0.0001) ypulls[i] = 999.;
hpull->SetPointError(i,0.,0.,0.,0.);
}
int nFitPar = fit->floatParsFinal().getSize() - 1;
TCanvas can("can","The canvas",5.,5.,500.,900.);
can.Divide(1,3);
TLatex *t = new TLatex();
t->SetNDC();
t->SetTextAlign(22);
t->SetTextSize(0.06);
can.cd(1);
gPad->SetBottomMargin(0.0);
frame->Draw();
// gPad->SetLogy();
// Htest->Draw();
sprintf(fileToSave,"%s %d GeV at %d TeV",nameSample[whichtype].c_str(),mass,LHCsqrts);
t->DrawLatex(0.6,0.8,fileToSave);
can.cd(2);
gPad->SetLogy();
gPad->SetTopMargin(0.0);
frame->Draw();
RooPlot* pull = ptoverm->frame(Title("Pull Distribution")) ;
pull->GetYaxis()->SetTitle("Pull");
/* pull->SetLabelSize(0.08,"XYZ");
pull->SetTitleSize(0.08,"XYZ");
pull->SetTitleOffset(0.6,"Y");
pull->SetTitleOffset(1.0,"X"); */
pull->addPlotable(hpull,"P") ;
pull->SetMinimum(-6.);
pull->SetMaximum(6.);
can.cd(3);
gPad->SetGridy();
pull->Draw();
sprintf(fileToSave,"#chi^{2}/n_{DoF} = %4.1f/%d",chi2,nFullBins - nFitPar);
if (chi2 < 1000.) t->DrawLatex(0.80,0.86,fileToSave);
// sprintf(fileToSave,"figs/fitPTOverMCJLST_%s%d_%dTeV_%s.pdf",nameSample[whichtype].c_str(),mass,LHCsqrts,systString.c_str());
sprintf(fileToSave,"newfigs/fitPTOverMCJLST_%s%d_%dTeV_%s.pdf",nameSample[whichtype].c_str(),mass,LHCsqrts,systString.c_str());
can.SaveAs(fileToSave);
}
// 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;
}
int DiagnosisMacro(int Nbins = 10, int Nsigma = 10, int CPUused = 1, TString Filename = "FIT_DATA_Psi2SJpsi_PPPrompt_Bkg_SecondOrderChebychev_pt65300_rap016_cent0200_262620_263757.root", TString Outputdir = "./")
//Nbins: Number of points for which to calculate profile likelihood. Time required is about (1/CPU) minutes per point per parameter. 0 means do plain likelihood only
//Nsigma: The range in which the scan is performed (value-Nsigma*sigma, value+Nsigma*sigma)
//CPUused: anything larger than 1 causes weird fit results on my laptop, runs fine on lxplus with more (16)
{
// R e a d w o r k s p a c e f r o m f i l e
// -----------------------------------------------
// Open input file with workspace
//Filename = "FIT_DATA_Psi2SJpsi_PP_Jpsi_DoubleCrystalBall_Psi2S_DoubleCrystalBall_Bkg_Chebychev2_pt6590_rap016_cent0200.root";
//Filename = "FIT_DATA_Psi2SJpsi_PbPb_Jpsi_DoubleCrystalBall_Psi2S_DoubleCrystalBall_Bkg_Chebychev1_pt6590_rap016_cent0200.root";
TFile *f = new TFile(Filename);
// Retrieve workspace from file
RooWorkspace* w = (RooWorkspace*)f->Get("workspace");
// Retrieve x,model and data from workspace
RooRealVar* x = w->var("invMass");
RooAbsPdf* model = w->pdf("simPdf_syst");
if (model == 0) {
model = w->pdf("simPdf");
}
if (model == 0) {
model = w->pdf("pdfMASS_Tot_PP");
}
if (model == 0) {
model = w->pdf("pdfMASS_Tot_PbPb");
}
if (model == 0) {
cout << "[ERROR] pdf failed to load from the workspace" << endl;
return false;
}
RooAbsData* data = w->data("dOS_DATA");
if (data == 0) {
data = w->data("dOS_DATA_PP");
}
if (data == 0) {
data = w->data("dOS_DATA_PbPb");
}
if (data == 0) {
cout << "[ERROR] data failed to load from the workspace" << endl;
return false;
}
// Print structure of composite p.d.f.
model->Print("t");
/*
// P l o t m o d e l
// ---------------------------------------------------------
// Plot data and PDF overlaid
RooPlot* xframe = x->frame(Title("J/psi Model and Data"));
data->plotOn(xframe);
model->plotOn(xframe);
// Draw the frame on the canvas
TCanvas* c2 = new TCanvas("PlotModel", "PlotModel", 1000, 1000);
gPad->SetLeftMargin(0.15);
xframe->GetYaxis()->SetTitleOffset(2.0);
xframe->Draw();//*/
///// Check parameters
RooArgSet* paramSet1 = model->getDependents(data);
paramSet1->Print("v"); // Just check
RooArgSet* paramSet2 = model->getParameters(data);
paramSet2->Print("v");
int Nparams = paramSet2->getSize();
cout << "Number of parameters: " << Nparams<<endl<<endl;
// C o n s t r u c t p l a i n l i k e l i h o o d
// ---------------------------------------------------
// Construct unbinned likelihood
RooAbsReal* nll = model->createNLL(*data, NumCPU(CPUused));
// Minimize likelihood w.r.t all parameters before making plots
RooMinuit(*nll).migrad();
//////////////////////////////////////////////////////
/////////////////// L O O P O V E R P A R A M E T E R S
/////////////////////////////////////////////////////
/// Set up loop over parameters
TString ParamName;
double ParamValue;
double ParamError;
double ParamLimitLow;
double ParamLimitHigh;
double FitRangeLow;
double FitRangeHigh;
RooRealVar* vParam;
int counter = 0;
// Loop start
TIterator* iter = paramSet2->createIterator();
TObject* var = iter->Next();
while (var != 0) {
counter++;
ParamName = var->GetName();
vParam = w->var(ParamName);
ParamValue = vParam->getVal();
ParamError = vParam->getError();
ParamLimitLow = vParam->getMin();
ParamLimitHigh = vParam->getMax();
cout << ParamName << " has value " << ParamValue << " with error: " << ParamError << " and limits: " << ParamLimitLow << " to " << ParamLimitHigh << endl << endl;
if (ParamError == 0) { //Skipping fixed parameters
cout << "Parameter was fixed, skipping its fitting" << endl;
cout << endl << "DONE WITH " << counter << " PARAMETER OUT OF " << Nparams << endl << endl;
var = iter->Next();
continue;
}
// determining fit range: Nsigma sigma on each side unless it would be outside of parameter limits
if ((ParamValue - Nsigma * ParamError) > ParamLimitLow) {
FitRangeLow = (ParamValue - Nsigma * ParamError);
}
else {
FitRangeLow = ParamLimitLow;
}
if ((ParamValue + Nsigma * ParamError) < ParamLimitHigh) {
FitRangeHigh = (ParamValue + Nsigma * ParamError);
}
else {
FitRangeHigh = ParamLimitHigh;
}
// P l o t p l a i n l i k e l i h o o d a n d C o n s t r u c t p r o f i l e l i k e l i h o o d
// ---------------------------------------------------
RooPlot* frame1;
RooAbsReal* pll=NULL;
if (Nbins != 0) {
frame1 = vParam->frame(Bins(Nbins), Range(FitRangeLow, FitRangeHigh), Title(TString::Format("LL and profileLL in %s", ParamName.Data())));
nll->plotOn(frame1, ShiftToZero());
pll = nll->createProfile(*vParam);
// Plot the profile likelihood
pll->plotOn(frame1, LineColor(kRed), RooFit::Precision(-1));
}
else { //Skip profile likelihood
frame1 = vParam->frame(Bins(10), Range(FitRangeLow, FitRangeHigh), Title(TString::Format("LL and profileLL in %s", ParamName.Data())));
nll->plotOn(frame1, ShiftToZero());
}
// D r a w a n d s a v e p l o t s
// -----------------------------------------------------------------------
// Adjust frame maximum for visual clarity
frame1->SetMinimum(0);
frame1->SetMaximum(20);
TCanvas* c = new TCanvas("CLikelihoodResult", "CLikelihoodResult", 800, 600);
c->cd(1);
gPad->SetLeftMargin(0.15);
frame1->GetYaxis()->SetTitleOffset(1.4);
frame1->Draw();
TLegend* leg = new TLegend(0.70, 0.70, 0.95, 0.88, "");
leg->SetFillColor(kWhite);
leg->SetBorderSize(0);
leg->SetTextSize(0.035);
TLegendEntry *le1 = leg->AddEntry(nll, "Plain likelihood", "l");
le1->SetLineColor(kBlue);
le1->SetLineWidth(3);
TLegendEntry *le2 = leg->AddEntry(pll, "Profile likelihood", "l");
le2->SetLineColor(kRed);
le2->SetLineWidth(3);
leg->Draw("same");
//Save plot
TString StrippedName = TString(Filename(Filename.Last('/')+1,Filename.Length()));
StrippedName = StrippedName.ReplaceAll(".root","");
cout << StrippedName << endl;
gSystem->mkdir(Form("%s/root/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/root/%s/Likelihood_scan_%s.root", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
gSystem->mkdir(Form("%s/pdf/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/pdf/%s/Likelihood_scan_%s.pdf", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
gSystem->mkdir(Form("%s/png/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/png/%s/Likelihood_scan_%s.png", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
delete c;
delete frame1;
if (pll) delete pll;
cout << endl << "DONE WITH " << counter << " PARAMETER OUT OF " << Nparams << endl << endl;
//if (counter == 2){ break; } //Exit - for testing
var = iter->Next();
} // End of the loop
return true;
}
void test_counting_experiment() {
////////////////////// MODEL BUILDING /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
/*
N_s = N_tot_theory(Mass,Xsec) * Acceptance_SR * Eff_AmBe_bin_i * mu
N_b = N_Co_SR_bin_i * Norm_factor
Xesec: considered 10^-40 cm^2
Norm_factor = N_Data_CR / N_Co_CR --> assuming no difference between Co and Data in CR and SR.
N_tot_theory(Mass,Xsec): for 225 livedays, 45kg and considering Xsec. It is a constant, no uncertainty at the moment.
---Costraint Signal
nuissance parameter = Acceptance_SR, Eff_AmBe_bin_i
Gauss(Acceptance_SR_obs | Acceptance_SR, err.)
Poisson(S0_i | S_tot_SR * Eff_AmBe_bin_i)
---Costraint Bkg
nuissance parameter = N_Co_SR_bin_i, Norm_factor
Gauss(Norm_factor_obs | Norm_factor, err)
Poisson(B0_i | N_Co_SR_bin_i)
---- WARNING:: convergence problems: mu_hat should always be >> 1, too small values have problem in finding minimum
because mu is set >0. ---> Try to fix Xsec in order to have mu_hat ~ 10
*/
RooWorkspace w("w");
//gROOT->ProcessLine(".L retrieve_input_from_histo_NoSys.C+");
gROOT->ProcessLine(".L retrieve_input_from_histo.C+");
retrieve_input_from_histo(w);
// Building the model
ModelConfig mc("ModelConfig",&w);
mc.SetPdf(*w.pdf("model"));
mc.SetParametersOfInterest(*w.var("mu"));
// Setting nuissance parameter
mc.SetNuisanceParameters(*w.set("nuissance_parameter"));
// need now to set the global observable
mc.SetGlobalObservables(*w.set("g_observables"));
mc.SetObservables(*w.set("observables"));
// this is needed for the hypothesis tests
mc.SetSnapshot(*w.var("mu"));
// make data set with the number of observed events
RooDataSet data("data","", *w.set("observables"));
data.add(*w.set("observables"));
// import data set in workspace and save it in a file
w.import(data);
// import model in the workspace
w.import(mc);
w.writeToFile("CountingModel.root", true);
w.Print();
data.Print();
/*
cout << w.var("S_i")->getValV() << endl;//<< " " << w.var("S_i_exp")->getValV() << endl;
///////////////////////////////////////////////////////////////////////
ProfileLikelihoodCalculator pl(data,mc);
pl.SetConfidenceLevel(0.95);
LikelihoodInterval* interval = pl.GetInterval();
// find the iterval on the first Parameter of Interest
RooRealVar* firstPOI = (RooRealVar*) mc.GetParametersOfInterest()->first();
double lowerLimit = interval->LowerLimit(*firstPOI);
double upperLimit = interval->UpperLimit(*firstPOI);
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
lowerLimit << ", "<<
upperLimit <<"] "<<endl;
LikelihoodIntervalPlot * plot = new LikelihoodIntervalPlot(interval);
// plot->SetRange(0,50); // possible eventually to change ranges
//plot->SetNPoints(50); // do not use too many points, it could become very slow for some models
plot->Draw(""); // use option TF1 if too slow (plot.Draw("tf1")
*/
////////////////////////// hypo test
// get the modelConfig (S+B) out of the file
// and create the B model from the S+B model
ModelConfig * sbModel = (ModelConfig*) mc.Clone();
sbModel->SetName("S+B Model");
RooRealVar* poi = (RooRealVar*) sbModel->GetParametersOfInterest()->first();
poi->setVal(1); // set POI snapshot in S+B model for expected significance
sbModel->SetSnapshot(*poi);
ModelConfig * bModel = (ModelConfig*) mc.Clone();
bModel->SetName("B Model");
RooRealVar* poi2 = (RooRealVar*) bModel->GetParametersOfInterest()->first();
poi2->setVal(0);
bModel->SetSnapshot( *poi2 );
//------------------Limit calculation for N_th event expected = 10
AsymptoticCalculator ac(data, *bModel, *sbModel);
//ac.SetOneSidedDiscovery(true); // for one-side discovery test
// ac.SetOneSided(true); // for one-side tests (limits)
ac.SetQTilde(true);
ac.SetPrintLevel(2); // to suppress print level
// create hypotest inverter
// passing the desired calculator
HypoTestInverter *calc = new HypoTestInverter(ac); // for asymptotic
//HypoTestInverter calc(fc); // for frequentist
calc->SetConfidenceLevel(0.90);
//calc->UseCLs(false);
calc->UseCLs(true);
int npoints = 500; // number of points to scan
//int npoints = 1000; // number of points to scan default 1000
// min and max (better to choose smaller intervals)
double poimin = poi->getMin();
double poimax = poi->getMax();
//poimin = 0; poimax=10;
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc->SetFixedScan(npoints,poimin,poimax);
calc->SetVerbose(2);
HypoTestInverterResult * r = calc->GetInterval();
double upperLimit = r->UpperLimit();
std::cout << "The computed Expected upper limit is: " << r->GetExpectedUpperLimit(0) << std::endl;
//------------ Getting the interval as function of m --------------//
/* ifstream in;
in.open("integral_mass.dat");
vector <double> masses_v;
vector <double> observed_v;
vector <double> expected_v;
vector <double> expected_gaud_v;
vector <double> expected_S1_up_v;
vector <double> expected_S1_dw_v;
vector <double> expected_S2_up_v;
vector <double> expected_S2_dw_v;
double mass_itr =0.;
double Nev_exp_th_itr =0.;
double xsec_modifier = 10.;
double N_tot_theory = w.var("N_tot_theory")->getValV();
while(mass_itr <1000.){
in >> mass_itr;
in >> Nev_exp_th_itr;
xsec_modifier = Nev_exp_th_itr * 225.009 * 34.; //225.009 livedays and 34 kg and 10^-40 cm2 Xsec.
masses_v.push_back(mass_itr);
observed_v.push_back( 1.e-40 * N_tot_theory / xsec_modifier * upperLimit );
expected_v.push_back( 1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(0) );
expected_gaud_v.push_back(7e-38 * 1.37590955945e-05 / Nev_exp_th_itr );
expected_S1_up_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(1));
expected_S2_up_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(2));
expected_S2_dw_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(-2));
expected_S1_dw_v.push_back(1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(-1));
cout << "Expected median limit for mass " << mass_itr << " GeV = " << 1.e-40 * N_tot_theory / xsec_modifier * r->GetExpectedUpperLimit(0) << " cm^2 " << endl;
// observed_v.push_back( w.var("Xsec")->getValV() * w.var("K_m")->getValV()* upperLimit );
// expected_v.push_back( w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(0) );
// expected_S1_up_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(1));
// expected_S2_up_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(2));
// expected_S2_dw_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(-2));
// expected_S1_dw_v.push_back(w.var("Xsec")->getValV() * w.var("K_m")->getValV()* r->GetExpectedUpperLimit(-1));
}
in.close();
const int n = masses_v.size();
double xe[n];
double mA[n];
double observed[n];
double expected[n];
double expected_gaudenz[n];
double exSigma1_l[n];
double exSigma1_u[n];
double exSigma2_l[n];
double exSigma2_u[n];
for(int k=0; k< n; k++){
mA[k] = masses_v[k];
observed[k] = observed_v[k];
expected[k] = expected_v[k];
expected_gaudenz[k] = expected_gaud_v[k];
exSigma1_l[k] =expected_v[k] - expected_S1_dw_v[k] ;
exSigma1_u[k] = expected_S1_up_v[k] - expected_v[k];
exSigma2_l[k] = expected_v[k] - expected_S2_dw_v[k];
exSigma2_u[k] = expected_S2_up_v[k] - expected_v[k] ;
}
TGraphErrors *obs_limits = new TGraphErrors(n, mA, observed);
TGraphErrors *Exp_limits = new TGraphErrors(n, mA, expected );
TGraphAsymmErrors *Exp_limitsS1 = new TGraphAsymmErrors(n, mA, expected ,xe, xe, exSigma1_l, exSigma1_u );
TGraphAsymmErrors *Exp_limitsS2 = new TGraphAsymmErrors(n, mA, expected ,xe, xe, exSigma2_l, exSigma2_u);
TGraphErrors *Exp_limits_gaudenz = new TGraphErrors( n, mA, expected_gaudenz);
//double expected_xmass[15] = {8e-36,7e-37, 2e-37, 1e-37, 8e-38, 6e-38, 5.5e-38, 5e-38, 4.3e-38, 5e-38, 6e-38, 7e-38, 9e-38, 1.2e-37, 1.5e-37};
//double m_xmass[15] = { 20, 30., 40., 50., 60., 70., 80., 90., 100., 200., 300., 400, 500.,700., 1000.};
TGraphErrors *Exp_limits_xmass = new TGraphErrors(16);
Exp_limits_xmass->SetPoint(0,20,8e-36);
Exp_limits_xmass->SetPointError(0,0,0);
Exp_limits_xmass->SetPoint(1,29.8071,7.162923e-37);
Exp_limits_xmass->SetPointError(1,0,0);
Exp_limits_xmass->SetPoint(2,39.90202,2.027528e-37);
Exp_limits_xmass->SetPointError(2,0,0);
Exp_limits_xmass->SetPoint(3,53.41583,9.91722e-38);
Exp_limits_xmass->SetPointError(3,0,0);
Exp_limits_xmass->SetPoint(4,62.16429,7.461589e-38);
Exp_limits_xmass->SetPointError(4,0,0);
Exp_limits_xmass->SetPoint(5,69.85718,6.3506e-38);
Exp_limits_xmass->SetPointError(5,0,0);
Exp_limits_xmass->SetPoint(6,83.21777,5.354015e-38);
Exp_limits_xmass->SetPointError(6,0,0);
Exp_limits_xmass->SetPoint(7,90,5e-38);
Exp_limits_xmass->SetPointError(7,0,0);
Exp_limits_xmass->SetPoint(8,105.0887,4.600252e-38);
Exp_limits_xmass->SetPointError(8,0,0);
Exp_limits_xmass->SetPoint(9,200,5e-38);
Exp_limits_xmass->SetPointError(9,0,0);
Exp_limits_xmass->SetPoint(10,300,6e-38);
Exp_limits_xmass->SetPointError(10,0,0);
Exp_limits_xmass->SetPoint(11,388.2045,7.252295e-38);
Exp_limits_xmass->SetPointError(11,0,0);
Exp_limits_xmass->SetPoint(12,590.8438,9.823615e-38);
Exp_limits_xmass->SetPointError(12,0,0);
Exp_limits_xmass->SetPoint(13,746.1269,1.210266e-37);
Exp_limits_xmass->SetPointError(13,0,0);
Exp_limits_xmass->SetPoint(14,1000,1.5e-37);
Exp_limits_xmass->SetPointError(14,0,0);
Exp_limits_xmass->SetPoint(15,4244.204,4.354065e-37);
Exp_limits_xmass->SetPointError(15,0,0);
TCanvas *c1 = new TCanvas("limits", "limit", 600, 600);
Exp_limitsS1->SetFillColor(3);
Exp_limitsS1->SetLineColor(3);
Exp_limitsS1->SetMarkerColor(3);
Exp_limitsS1->SetMarkerSize(0);
Exp_limitsS2->SetFillColor(5);
Exp_limitsS2->SetLineColor(5);
Exp_limitsS2->SetMarkerColor(5);
Exp_limitsS2->SetMarkerSize(0);
obs_limits->SetFillColor(0);
obs_limits->SetLineWidth(3);
obs_limits->SetMarkerSize(0);
Exp_limits->SetFillColor(0);
Exp_limits->SetMarkerSize(0);
Exp_limits->SetLineStyle(7);
Exp_limits->SetLineWidth(3);
Exp_limits_gaudenz->SetFillColor(0);
Exp_limits_gaudenz->SetMarkerSize(0);
Exp_limits_gaudenz->SetLineWidth(3);
Exp_limits_gaudenz->SetLineColor(4);
Exp_limits_xmass->SetFillColor(0);
Exp_limits_xmass->SetMarkerSize(0);
Exp_limits_xmass->SetLineWidth(3);
Exp_limits_xmass->SetLineColor(2);
//Exp_limitsS2->GetYaxis()->SetTitle("#sigma#timesBR( #phi #rightarrow #tau#tau ) [pb]");
Exp_limitsS2->GetYaxis()->SetTitle("#sigma");
Exp_limitsS2->GetXaxis()->SetTitle("M [GeV]");
Exp_limitsS2->GetXaxis()->SetLimits(9.,1000.);
Exp_limitsS2->GetYaxis()->SetRangeUser(1E-38,1E-30);
Exp_limits->GetXaxis()->SetLimits(9.,1000.);
Exp_limits->GetYaxis()->SetRangeUser(1E-38,1E-30);
Exp_limitsS2->Draw("Al3");
Exp_limitsS1->Draw("sameL3");
Exp_limits->Draw("PL");
Exp_limits_gaudenz->Draw("PC");
Exp_limits_xmass->Draw("PC");
//obs_limits->Draw("PL");
TLegend* lego = new TLegend(0.2,0.9,0.5,0.7);
lego->SetTextSize(0.033);
lego->SetFillColor(0);
lego->SetBorderSize(0);
lego->AddEntry(obs_limits,"Observed 90\% CLs limit");
lego->AddEntry(Exp_limits_gaudenz, "Expected 90\% Gaudenz");
lego->AddEntry(Exp_limits_xmass, "Expected 90\% XMASS");
lego->AddEntry(Exp_limits, "Expected 90\% CLs limit");
lego->AddEntry(Exp_limitsS1,"1 #sigma","f");
lego->AddEntry(Exp_limitsS2,"2 #sigma","f");
lego->Draw();
gPad->SetLogy();
gPad->SetLogx();
gPad->RedrawAxis("g");
myText(0.4,0.86,2,"Test");
*/
// now use the profile inspector
ProfileInspector p;
TList* list = p.GetListOfProfilePlots(data,&mc);
// now make plots
TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200);
if(list->GetSize()>4){
double n = list->GetSize();
int nx = (int)sqrt(n) ;
int ny = TMath::CeilNint(n/nx);
nx = TMath::CeilNint( sqrt(n) );
c1->Divide(ny,nx);
} else
c1->Divide(list->GetSize());
for(int i=0; i<list->GetSize(); ++i){
c1->cd(i+1);
list->At(i)->Draw("al");
}
cout << endl;
/* // plot now the result of the scan
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot","HypoTest Scan Result",r);
// plot in a new canvas with style
TCanvas * c1 = new TCanvas("HypoTestInverter Scan");
c1->SetLogy(false);
plot->Draw("2CL"); // plot also CLb and CLs+b
//plot->Draw("OBS"); // plot only observed p-value
*/
// plot also in a new canvas the test statistics distributions
// plot test statistics distributions for the two hypothesis
/* // when distribution is generated (case of FrequentistCalculators)
const int n = r->ArraySize();
if (n> 0 && r->GetResult(0)->GetNullDistribution() ) {
TCanvas * c2 = new TCanvas("Test Statistic Distributions","",2);
if (n > 1) {
int ny = TMath::CeilNint( sqrt(n) );
int nx = TMath::CeilNint(double(n)/ny);
c2->Divide( nx,ny);
}
for (int i=0; i<n; i++) {
if (n > 1) c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
}
*/
}
///
/// Perform the 1d Prob scan.
/// Saves chi2 values and the prob-Scan p-values in a root tree
/// For the datasets stuff, we do not yet have a MethodDatasetsProbScan class, so we do it all in
/// MethodDatasetsProbScan
/// \param nRun Part of the root tree file name to facilitate parallel production.
///
int MethodDatasetsProbScan::scan1d(bool fast, bool reverse)
{
if (fast) return 0; // tmp
if ( arg->debug ) cout << "MethodDatasetsProbScan::scan1d() : starting ... " << endl;
// Set limit to all parameters.
this->loadParameterLimits(); /// Default is "free", if not changed by cmd-line parameter
// Define scan parameter and scan range.
RooRealVar *parameterToScan = w->var(scanVar1);
float parameterToScan_min = hCL->GetXaxis()->GetXmin();
float parameterToScan_max = hCL->GetXaxis()->GetXmax();
// do a free fit
RooFitResult *result = this->loadAndFit(this->pdf); // fit on data
assert(result);
RooSlimFitResult *slimresult = new RooSlimFitResult(result,true);
slimresult->setConfirmed(true);
solutions.push_back(slimresult);
double freeDataFitValue = w->var(scanVar1)->getVal();
// Define outputfile
system("mkdir -p root");
TString probResName = Form("root/scan1dDatasetsProb_" + this->pdf->getName() + "_%ip" + "_" + scanVar1 + ".root", arg->npoints1d);
TFile* outputFile = new TFile(probResName, "RECREATE");
// Set up toy root tree
this->probScanTree = new ToyTree(this->pdf, arg);
this->probScanTree->init();
this->probScanTree->nrun = -999; //\todo: why does this branch even exist in the output tree of the prob scan?
// Save parameter values that were active at function
// call. We'll reset them at the end to be transparent
// to the outside.
RooDataSet* parsFunctionCall = new RooDataSet("parsFunctionCall", "parsFunctionCall", *w->set(pdf->getParName()));
parsFunctionCall->add(*w->set(pdf->getParName()));
// start scan
cout << "MethodDatasetsProbScan::scan1d_prob() : starting ... with " << nPoints1d << " scanpoints..." << endl;
ProgressBar progressBar(arg, nPoints1d);
for ( int i = 0; i < nPoints1d; i++ )
{
progressBar.progress();
// scanpoint is calculated using min, max, which are the hCL x-Axis limits set in this->initScan()
// this uses the "scan" range, as expected
// don't add half the bin size. try to solve this within plotting method
float scanpoint = parameterToScan_min + (parameterToScan_max - parameterToScan_min) * (double)i / ((double)nPoints1d - 1);
if (arg->debug) cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() " << scanpoint << " " << parameterToScan_min << " " << parameterToScan_max << endl;
this->probScanTree->scanpoint = scanpoint;
if (arg->debug) cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() - scanpoint in step " << i << " : " << scanpoint << endl;
// don't scan in unphysical region
// by default this means checking against "free" range
if ( scanpoint < parameterToScan->getMin() || scanpoint > parameterToScan->getMax() + 2e-13 ) {
cout << "it seems we are scanning in an unphysical region: " << scanpoint << " < " << parameterToScan->getMin() << " or " << scanpoint << " > " << parameterToScan->getMax() + 2e-13 << endl;
exit(EXIT_FAILURE);
}
// FIT TO REAL DATA WITH FIXED HYPOTHESIS(=SCANPOINT).
// THIS GIVES THE NUMERATOR FOR THE PROFILE LIKELIHOOD AT THE GIVEN HYPOTHESIS
// THE RESULTING NUISANCE PARAMETERS TOGETHER WITH THE GIVEN HYPOTHESIS ARE ALSO
// USED WHEN SIMULATING THE TOY DATA FOR THE FELDMAN-COUSINS METHOD FOR THIS HYPOTHESIS(=SCANPOINT)
// Here the scanvar has to be fixed -> this is done once per scanpoint
// and provides the scanner with the DeltaChi2 for the data as reference
// additionally the nuisances are set to the resulting fit values
parameterToScan->setVal(scanpoint);
parameterToScan->setConstant(true);
RooFitResult *result = this->loadAndFit(this->pdf); // fit on data
assert(result);
if (arg->debug) {
cout << "DEBUG in MethodDatasetsProbScan::scan1d_prob() - minNll data scan at scan point " << scanpoint << " : " << 2 * result->minNll() << ": "<< 2 * pdf->getMinNll() << endl;
}
this->probScanTree->statusScanData = result->status();
// set chi2 of fixed fit: scan fit on data
// CAVEAT: chi2min from fitresult gives incompatible results to chi2min from pdf
// this->probScanTree->chi2min = 2 * result->minNll();
this->probScanTree->chi2min = 2 * pdf->getMinNll();
this->probScanTree->covQualScanData = result->covQual();
this->probScanTree->scanbest = freeDataFitValue;
// After doing the fit with the parameter of interest constrained to the scanpoint,
// we are now saving the fit values of the nuisance parameters. These values will be
// used to generate toys according to the PLUGIN method.
this->probScanTree->storeParsScan(); // \todo : figure out which one of these is semantically the right one
this->pdf->deleteNLL();
// also save the chi2 of the free data fit to the tree:
this->probScanTree->chi2minGlobal = this->getChi2minGlobal();
this->probScanTree->chi2minBkg = this->getChi2minBkg();
this->probScanTree->genericProbPValue = this->getPValueTTestStatistic(this->probScanTree->chi2min - this->probScanTree->chi2minGlobal);
this->probScanTree->fill();
if(arg->debug && pdf->getBkgPdf())
{
float pval_cls = this->getPValueTTestStatistic(this->probScanTree->chi2min - this->probScanTree->chi2minBkg, true);
cout << "DEBUG in MethodDatasetsProbScan::scan1d() - p value CLs: " << pval_cls << endl;
}
// reset
setParameters(w, pdf->getParName(), parsFunctionCall->get(0));
//setParameters(w, pdf->getObsName(), obsDataset->get(0));
} // End of npoints loop
probScanTree->writeToFile();
if (bkgOnlyFitResult) bkgOnlyFitResult->Write();
if (dataFreeFitResult) dataFreeFitResult->Write();
outputFile->Close();
std::cout << "Wrote ToyTree to file" << std::endl;
delete parsFunctionCall;
// This is kind of a hack. The effect is supposed to be the same as callincg
// this->sethCLFromProbScanTree(); here, but the latter gives a segfault somehow....
// \todo: use this->sethCLFromProbScanTree() directly after figuring out the cause of the segfault.
this->loadScanFromFile();
return 0;
}
void createWorkspace(const std::string &infilename, int nState, bool correctCtau, bool drawRapPt2D, bool drawPtCPM2D){
gROOT->SetStyle("Plain");
gStyle->SetTitleBorderSize(0);
// Set some strings
const std::string workspacename = "ws_masslifetime",
treename = "selectedData";
// Get the tree from the data file
TFile *f = TFile::Open(infilename.c_str());
TTree *tree = (TTree*)f->Get(treename.c_str());
// Set branch addresses in tree to be able to import tree to roofit
TLorentzVector* jpsi = new TLorentzVector;
tree->SetBranchAddress("JpsiP",&jpsi);
double CPMval = 0;
tree->SetBranchAddress("CPM",&CPMval);
double massErr = 0;
tree->SetBranchAddress("JpsiMassErr",&massErr);
double Vprob = 0;
tree->SetBranchAddress("JpsiVprob",&Vprob);
double lifetime = 0;
tree->SetBranchAddress("Jpsict",&lifetime);
double lifetimeErr = 0;
tree->SetBranchAddress("JpsictErr",&lifetimeErr);
// define variables necessary for J/Psi(Psi(2S)) mass,lifetime fit
RooRealVar* JpsiMass =
new RooRealVar("JpsiMass", "M [GeV]", onia::massMin, onia::massMax);
RooRealVar* JpsiMassErr =
new RooRealVar("JpsiMassErr", "#delta M [GeV]", 0, 5);
RooRealVar* JpsiRap =
new RooRealVar("JpsiRap", "y", -onia::rap, onia::rap);
RooRealVar* JpsiPt =
new RooRealVar("JpsiPt", "p_{T} [GeV]", 0. ,100.);
RooRealVar* JpsiCPM =
new RooRealVar("JpsiCPM", "N_{ch}", 0. ,100.);
RooRealVar* Jpsict =
new RooRealVar("Jpsict", "lifetime [mm]", -1., 2.5);
RooRealVar* JpsictErr =
new RooRealVar("JpsictErr", "Error on lifetime [mm]", 0.0001, 1);
RooRealVar* JpsiVprob =
new RooRealVar("JpsiVprob", "", 0.01, 1.);
// Set bins
Jpsict->setBins(10000,"cache");
Jpsict->setBins(100);
JpsiMass->setBins(100);
JpsictErr->setBins(100);
// The list of data variables
RooArgList dataVars(*JpsiMass,*JpsiMassErr,*JpsiRap,*JpsiPt,*JpsiCPM,*Jpsict,*JpsictErr,*JpsiVprob);
// construct dataset to contain events
RooDataSet* fullData = new RooDataSet("fullData","The Full Data From the Input ROOT Trees",dataVars);
int entries = tree->GetEntries();
cout << "entries " << entries << endl;
// loop through events in tree and save them to dataset
for (int ientries = 0; ientries < entries; ientries++) {
if (ientries%100000==0) std::cout << "event " << ientries << " of " << entries << std::endl;
tree->GetEntry(ientries);
double M =jpsi->M();
double y=jpsi->Rapidity();
double pt=jpsi->Pt();
double cpm=CPMval;
if (M > JpsiMass->getMin() && M < JpsiMass->getMax()
&& massErr > JpsiMassErr->getMin() && massErr < JpsiMassErr->getMax()
&& pt > JpsiPt->getMin() && pt < JpsiPt->getMax()
&& cpm > JpsiCPM->getMin() && cpm < JpsiCPM->getMax()
&& y > JpsiRap->getMin() && y < JpsiRap->getMax()
&& lifetime > Jpsict->getMin() && lifetime < Jpsict->getMax()
&& lifetimeErr > JpsictErr->getMin() && lifetimeErr < JpsictErr->getMax()
&& Vprob > JpsiVprob->getMin() && Vprob < JpsiVprob->getMax()
){
JpsiPt ->setVal(pt);
JpsiCPM ->setVal(cpm);
JpsiRap ->setVal(y);
JpsiMass ->setVal(M);
JpsiMassErr ->setVal(massErr);
JpsiVprob ->setVal(Vprob);
//cout<<"before lifetime correction \n"
// <<"Jpsict: "<<lifetime<<" JpsictErr: "<<lifetimeErr<<endl;
if(correctCtau){
lifetime = lifetime * onia::MpsiPDG / M ;
lifetimeErr = lifetimeErr * onia::MpsiPDG / M ;
Jpsict ->setVal(lifetime);
JpsictErr ->setVal(lifetimeErr);
//cout<<"MpsiPDG: "<<onia::MpsiPDG<<endl;
//cout<<"after lifetime correction \n"
// <<"Jpsict: "<<lifetime<<" JpsictErr: "<<lifetimeErr<<endl;
}
else{
Jpsict ->setVal(lifetime);
JpsictErr ->setVal(lifetimeErr);
}
fullData->add(dataVars);
}
}//ientries
//------------------------------------------------------------------------------------------------------------------
// Define workspace and import datasets
////Get datasets binned in pT, cpm, and y
for(int iRap = 1; iRap <= onia::kNbRapForPTBins; iRap++){
Double_t yMin;
Double_t yMax;
if(iRap==0){
yMin = onia::rapForPTRange[0];
yMax = onia::rapForPTRange[onia::kNbRapForPTBins];
} else{
yMin = onia::rapForPTRange[iRap-1];
yMax = onia::rapForPTRange[iRap];
}
for(int iPT = 1; iPT <= onia::kNbPTBins[iRap]; iPT++){
//for(int iPT = 0; iPT <= 0; iPT++)
Double_t ptMin;
Double_t ptMax;
if(iPT==0){
ptMin = onia::pTRange[iRap][0];
ptMax = onia::pTRange[iRap][onia::kNbPTBins[0]];
} else{
ptMin = onia::pTRange[iRap][iPT-1];
ptMax = onia::pTRange[iRap][iPT];
}
for(int iCPM = 1; iCPM <= onia::NchBins; iCPM++){
Double_t cpmMin;
Double_t cpmMax;
if(iCPM==0){
cpmMin = onia::cpmRange[0];
cpmMax = onia::cpmRange[onia::NchBins];
} else{
cpmMin = onia::cpmRange[iCPM-1];
cpmMax = onia::cpmRange[iCPM];
}
// output file name and workspace
std::stringstream outfilename;
outfilename << "tmpFiles/backupWorkSpace/fit_Psi" << nState-3 << "S_rap" << iRap << "_pt" << iPT << "_cpm" << iCPM << ".root";
// outfilename << "tmpFiles/fit_Psi" << nState-3 << "S_rap" << iRap << "_pt" << iPT << ".root";
RooWorkspace* ws = new RooWorkspace(workspacename.c_str());
// define pt and y cuts on dataset
std::stringstream cutString;
cutString << "(JpsiCPM > " << cpmMin << " && JpsiCPM < "<< cpmMax << ") && " << "(JpsiPt >= " << ptMin << " && JpsiPt < "<< ptMax << ") && "
<< "(TMath::Abs(JpsiRap) >= " << yMin << " && TMath::Abs(JpsiRap) < " << yMax << ")";
cout << "cutString: " << cutString.str().c_str() << endl;
// get the dataset for the fit
RooDataSet* binData = (RooDataSet*)fullData->reduce(cutString.str().c_str());
std::stringstream name;
name << "data_rap" << iRap << "_pt" << iPT << "_cpm" << iCPM;;
binData->SetNameTitle(name.str().c_str(), "Data For Fitting");
// Import variables to workspace
ws->import(*binData);
ws->writeToFile(outfilename.str().c_str());
}//iCPM
}//iPT
}//iRap
////---------------------------------------------------------------
////--Integrating rapidity and pt bins, in +/- 3*sigma mass window
////---------------------------------------------------------------
if(drawRapPt2D){
double yMin = onia::rapForPTRange[0];
double yMax = onia::rapForPTRange[onia::kNbRapForPTBins];
double ptMin = onia::pTRange[0][0];
double ptMax = onia::pTRange[0][onia::kNbPTBins[0]];
double cpmMin = onia::cpmRange[0];
double cpmMax = onia::cpmRange[onia::NchBins];
std::stringstream cutRapPt;
cutRapPt << "(JpsiCPM > " << cpmMin << " && JpsiCPM < "<< cpmMax << ") && "
<< "(JpsiPt > " << ptMin << " && JpsiPt < "<< ptMax << ") && "
<< "(TMath::Abs(JpsiRap) > " << yMin << " && TMath::Abs(JpsiRap) < " << yMax << ")";
cout<<"cutRapPt: "<<cutRapPt.str().c_str()<<endl;
RooDataSet* rapPtData = (RooDataSet*)fullData->reduce(cutRapPt.str().c_str());
std::stringstream nameRapPt;
nameRapPt << "data_rap0_pt0_cpm0";
rapPtData->SetNameTitle(nameRapPt.str().c_str(), "Data For full rap and pt");
// output file name and workspace
std::stringstream outfilename;
outfilename << "tmpFiles/backupWorkSpace/fit_Psi" << nState-3 << "S_rap0_pt0_cpm0.root";
RooWorkspace* ws_RapPt = new RooWorkspace(workspacename.c_str());
//Import variables to workspace
ws_RapPt->import(*rapPtData);
ws_RapPt->writeToFile(outfilename.str().c_str());
TH2D* rapPt;
TH1D* rap1p2;
double MassMin;
double MassMax;
rap1p2 = new TH1D("rap1p2","rap1p2",30,0, 1.8);
if(nState==4){
rapPt = new TH2D( "rapPt", "rapPt", 52,-1.3,1.3,144,0,72);
MassMin=3.011;//massPsi1S-onia::nSigMass*sigma1S;
MassMax=3.174;//massPsi1S+onia::nSigMass*sigma1S;
// sigma 27.2 MeV
// mean 3.093 GeV
}
if(nState==5){
rapPt = new TH2D( "rapPt", "rapPt", 64,-1.6,1.6,144,0,72); // rap<1.5
//rapPt = new TH2D( "rapPt", "rapPt", 52,-1.3,1.3,144,0,72); // rap<1.2
MassMin=3.576;//massPsi2S-onia::nSigMass*sigma2S;
MassMax=3.786;//massPsi2S+onia::nSigMass*sigma2S;
// sigma 34.9 MeV // pT > 7
// sigma 34.3 MeV // pT > 10
// mean 3.681 GeV
}
cout<<"Plotting rap-Pt for Psi"<<nState-3<<"S"<<endl;
cout<<"MassMin for rap-Pt plot = "<<MassMin<<endl;
cout<<"MassMax for rap-Pt plot = "<<MassMax<<endl;
TTree *rapPtTree = (TTree*)rapPtData->tree();
std::stringstream cutMass;
cutMass<<"(JpsiMass > " << MassMin << " && JpsiMass < "<< MassMax << ")";
//following two methods can only be used in root_v30, 34 does not work
rapPtTree->Draw("JpsiPt:JpsiRap>>rapPt",cutMass.str().c_str(),"colz");
cout<<"debug"<<endl;
rapPtTree->Draw("TMath::Abs(JpsiRap)>>rap1p2",cutMass.str().c_str());
TCanvas* c2 = new TCanvas("c2","c2",1200,1500);
rapPt->SetYTitle("p_{T}(#mu#mu) [GeV]");
rapPt->SetXTitle("y(#mu#mu)");
gStyle->SetPalette(1);
gPad->SetFillColor(kWhite);
rapPt->SetTitle(0);
rapPt->SetStats(0);
gPad->SetLeftMargin(0.15);
gPad->SetRightMargin(0.17);
rapPt->GetYaxis()->SetTitleOffset(1.5);
rapPt->Draw("colz");
TLine* rapPtLine;
for(int iRap=0;iRap<onia::kNbRapForPTBins+1;iRap++){
rapPtLine= new TLine( -onia::rapForPTRange[iRap], onia::pTRange[0][0], -onia::rapForPTRange[iRap], onia::pTRange[0][onia::kNbPTBins[iRap]] );
rapPtLine->SetLineWidth( 2 );
rapPtLine->SetLineStyle( 1 );
rapPtLine->SetLineColor( kWhite );
rapPtLine->Draw();
rapPtLine= new TLine( onia::rapForPTRange[iRap], onia::pTRange[0][0], onia::rapForPTRange[iRap], onia::pTRange[0][onia::kNbPTBins[iRap]] );
rapPtLine->SetLineWidth( 2 );
rapPtLine->SetLineStyle( 1 );
rapPtLine->SetLineColor( kWhite );
rapPtLine->Draw();
int pTBegin = 0;
if(nState==5) pTBegin = 1;
for(int iPt=pTBegin;iPt<onia::kNbPTBins[iRap]+1;iPt++){
rapPtLine= new TLine( -onia::rapForPTRange[onia::kNbRapForPTBins], onia::pTRange[0][iPt], onia::rapForPTRange[onia::kNbRapForPTBins], onia::pTRange[0][iPt] );
rapPtLine->SetLineWidth( 2 );
rapPtLine->SetLineStyle( 1 );
rapPtLine->SetLineColor( kWhite );
rapPtLine->Draw();
}
}
char savename[200];
sprintf(savename,"Fit/rapPt_Psi%dS.pdf",nState-3);
c2->SaveAs(savename);
TCanvas* c3 = new TCanvas("c3","c3",1500,1200);
rap1p2->SetYTitle("Events");
rap1p2->SetXTitle("y(#mu#mu)");
rap1p2->SetTitle(0);
rap1p2->SetStats(0);
rap1p2->GetYaxis()->SetTitleOffset(1.2);
rap1p2->Draw();
sprintf(savename,"Fit/rapDimuon_1p2_Psi%dS.pdf",nState-3);
c3->SaveAs(savename);
}
if(drawPtCPM2D){
double yMin = onia::rapForPTRange[0];
double yMax = onia::rapForPTRange[onia::kNbRapForPTBins];
double ptMin = onia::pTRange[0][0];
double ptMax = onia::pTRange[0][onia::kNbPTBins[0]];
double cpmMin = onia::cpmRange[0];
double cpmMax = onia::cpmRange[onia::NchBins];
std::stringstream cutRapPt;
cutRapPt << "(JpsiCPM > " << cpmMin << " && JpsiCPM < "<< cpmMax << ") && "
<< "(JpsiPt > " << ptMin << " && JpsiPt < "<< ptMax << ") && "
<< "(TMath::Abs(JpsiRap) > " << yMin << " && TMath::Abs(JpsiRap) < " << yMax << ")";
cout<<"cutRapPt: "<<cutRapPt.str().c_str()<<endl;
RooDataSet* rapPtData = (RooDataSet*)fullData->reduce(cutRapPt.str().c_str());
std::stringstream nameRapPt;
nameRapPt << "data_rap0_pt0_cpm0";
rapPtData->SetNameTitle(nameRapPt.str().c_str(), "Data For full rap and pt");
// output file name and workspace
std::stringstream outfilename;
outfilename << "tmpFiles/backupWorkSpace/fit_Psi" << nState-3 << "S_rap0_pt0_cpm0.root";
RooWorkspace* ws_RapPt = new RooWorkspace(workspacename.c_str());
//Import variables to workspace
ws_RapPt->import(*rapPtData);
ws_RapPt->writeToFile(outfilename.str().c_str());
TH2D* PtCPM;
double MassMin;
double MassMax;
if(nState==4){
PtCPM = new TH2D( "PtCPM", "PtCPM", 100,0,50,200,0,100);
MassMin=3.011;//massPsi1S-onia::nSigMass*sigma1S;
MassMax=3.174;//massPsi1S+onia::nSigMass*sigma1S;
// sigma 27.2 MeV
// mean 3.093 GeV
}
if(nState==5){
PtCPM = new TH2D( "PtCPM", "PtCPM", 100,0,50,200,0,100); // rap<1.5
//rapPt = new TH2D( "rapPt", "rapPt", 52,-1.3,1.3,144,0,72); // rap<1.2
MassMin=3.576;//massPsi2S-onia::nSigMass*sigma2S;
MassMax=3.786;//massPsi2S+onia::nSigMass*sigma2S;
// sigma 34.9 MeV // pT > 7
// sigma 34.3 MeV // pT > 10
// mean 3.681 GeV
}
cout<<"Plotting Pt-CPM for Psi"<<nState-3<<"S"<<endl;
cout<<"MassMin for Pt-CPM plot = "<<MassMin<<endl;
cout<<"MassMax for Pt-CPM plot = "<<MassMax<<endl;
TTree *rapPtTree = (TTree*)rapPtData->tree();
std::stringstream cutMass;
cutMass<<"(JpsiMass > " << MassMin << " && JpsiMass < "<< MassMax << ")";
//following two methods can only be used in root_v30, 34 does not work
rapPtTree->Draw("JpsiCPM:JpsiPt>>PtCPM",cutMass.str().c_str(),"colz");
cout<<"debug"<<endl;
TCanvas* c2 = new TCanvas("c2","c2",1200,1500);
PtCPM->SetYTitle("N_{ch}");
PtCPM->SetXTitle("p_{T}(#mu#mu) [GeV]");
gStyle->SetPalette(1);
gPad->SetFillColor(kWhite);
PtCPM->SetTitle(0);
PtCPM->SetStats(0);
gPad->SetLeftMargin(0.15);
gPad->SetRightMargin(0.17);
PtCPM->GetYaxis()->SetTitleOffset(1.5);
PtCPM->Draw("colz");
TLine* PtCPMLine;
int iRap=0;
for(int iPt=0;iPt<onia::kNbPTMaxBins+1;iPt++){
int cpmBegin = 0;
if(nState==5) cpmBegin = 1;
for(int icpm=cpmBegin;icpm<onia::NchBins+1;icpm++){
PtCPMLine= new TLine( onia::pTRange[iRap][0], onia::cpmRange[icpm], onia::pTRange[iRap][onia::kNbPTMaxBins], onia::cpmRange[icpm] );
PtCPMLine->SetLineWidth( 2 );
PtCPMLine->SetLineStyle( 1 );
PtCPMLine->SetLineColor( kWhite );
PtCPMLine->Draw();
PtCPMLine= new TLine( onia::pTRange[iRap][iPt], onia::cpmRange[0], onia::pTRange[iRap][iPt], onia::cpmRange[onia::NchBins] );
PtCPMLine->SetLineWidth( 2 );
PtCPMLine->SetLineStyle( 1 );
PtCPMLine->SetLineColor( kWhite );
PtCPMLine->Draw();
// PtCPMLine= new TLine( onia::pTRange[0][onia::kNbPTMaxBins], onia::cpmRange[icpm], onia::pTRange[0][onia::kNbPTMaxBins], onia::cpmRange[icpm] );
// PtCPMLine->SetLineWidth( 2 );
// PtCPMLine->SetLineStyle( 1 );
// PtCPMLine->SetLineColor( kWhite );
// PtCPMLine->Draw();
}
}
char savename[200];
sprintf(savename,"Fit/PtCPM_Psi%dS.pdf",nState-3);
c2->SaveAs(savename);
}
f->Close();
}
void StandardTestStatDistributionDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
// the number of toy MC used to generate the distribution
int nToyMC = 1000;
// The parameter below is needed for asymptotic distribution to be chi-square,
// but set to false if your model is not numerically stable if mu<0
bool allowNegativeMu=true;
/////////////////////////////////////////////////////////////
// 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;
}
mc->Print();
/////////////////////////////////////////////////////////////
// Now find the upper limit based on the asymptotic results
////////////////////////////////////////////////////////////
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
ProfileLikelihoodCalculator plc(*data,*mc);
LikelihoodInterval* interval = plc.GetInterval();
double plcUpperLimit = interval->UpperLimit(*firstPOI);
delete interval;
cout << "\n\n--------------------------------------"<<endl;
cout <<"Will generate sampling distribution at " << firstPOI->GetName() << " = " << plcUpperLimit <<endl;
int nPOI = mc->GetParametersOfInterest()->getSize();
if(nPOI>1){
cout <<"not sure what to do with other parameters of interest, but here are their values"<<endl;
mc->GetParametersOfInterest()->Print("v");
}
/////////////////////////////////////////////
// create thte test stat sampler
ProfileLikelihoodTestStat ts(*mc->GetPdf());
// to avoid effects from boundary and simplify asymptotic comparison, set min=-max
if(allowNegativeMu)
firstPOI->setMin(-1*firstPOI->getMax());
// temporary RooArgSet
RooArgSet poi;
poi.add(*mc->GetParametersOfInterest());
// create and configure the ToyMCSampler
ToyMCSampler sampler(ts,nToyMC);
sampler.SetPdf(*mc->GetPdf());
sampler.SetObservables(*mc->GetObservables());
sampler.SetGlobalObservables(*mc->GetGlobalObservables());
if(!mc->GetPdf()->canBeExtended() && (data->numEntries()==1)){
cout << "tell it to use 1 event"<<endl;
sampler.SetNEventsPerToy(1);
}
firstPOI->setVal(plcUpperLimit); // set POI value for generation
sampler.SetParametersForTestStat(*mc->GetParametersOfInterest()); // set POI value for evaluation
if (useProof) {
ProofConfig pc(*w, nworkers, "",false);
sampler.SetProofConfig(&pc); // enable proof
}
firstPOI->setVal(plcUpperLimit);
RooArgSet allParameters;
allParameters.add(*mc->GetParametersOfInterest());
allParameters.add(*mc->GetNuisanceParameters());
allParameters.Print("v");
SamplingDistribution* sampDist = sampler.GetSamplingDistribution(allParameters);
SamplingDistPlot plot;
plot.AddSamplingDistribution(sampDist);
plot.GetTH1F(sampDist)->GetYaxis()->SetTitle(Form("f(-log #lambda(#mu=%.2f) | #mu=%.2f)",plcUpperLimit,plcUpperLimit));
plot.SetAxisTitle(Form("-log #lambda(#mu=%.2f)",plcUpperLimit));
TCanvas* c1 = new TCanvas("c1");
c1->SetLogy();
plot.Draw();
double min = plot.GetTH1F(sampDist)->GetXaxis()->GetXmin();
double max = plot.GetTH1F(sampDist)->GetXaxis()->GetXmax();
TF1* f = new TF1("f",Form("2*ROOT::Math::chisquared_pdf(2*x,%d,0)",nPOI),min,max);
f->Draw("same");
c1->SaveAs("standard_test_stat_distribution.pdf");
}
///
/// Perform 1d Prob scan.
///
/// - Scan range defined through limit "scan".
/// - Will fill the hCL histogram with the 1-CL curve.
/// - Start at a scan value that is in the middle of the allowed
/// range, preferably a solution, and scan up and down from there.
/// - use the "probforce" command line flag to enable force minimum finding
///
/// \param fast This will scan each scanpoint only once.
/// \param reverse This will scan in reverse direction.
/// When using the drag mode, this can sometimes make a difference.
/// \return status: 2 = new global minimum found, 1 = error
///
int MethodProbScan::scan1d(bool fast, bool reverse)
{
if ( arg->debug ) cout << "MethodProbScan::scan1d() : starting ... " << endl;
nScansDone++;
// The "improve" method doesn't need multiple scans.
if ( arg->probforce || arg->probimprove ) fast = true;
if ( arg->probforce ) scanDisableDragMode = true;
// Save parameter values that were active at function call.
if ( startPars ) delete startPars;
startPars = new RooDataSet("startPars", "startPars", *w->set(parsName));
startPars->add(*w->set(parsName));
// // start scan from global minimum (not always a good idea as we need to set from other places as well)
// setParameters(w, parsName, globalMin);
// load scan parameter and scan range
setLimit(w, scanVar1, "scan");
RooRealVar *par = w->var(scanVar1);
assert(par);
float min = hCL->GetXaxis()->GetXmin();
float max = hCL->GetXaxis()->GetXmax();
if ( fabs(par->getMin()-min)>1e-6 || fabs(par->getMax()-max)>1e-6 ){
cout << "MethodProbScan::scan1d() : WARNING : Scan range was changed after initScan()" << endl;
cout << " was called so the old range will be used." << endl;
}
if ( arg->verbose ){
cout << "\nProb configuration:" << endl;
cout << " combination : " << title << endl;
cout << " scan variable : " << scanVar1 << endl;
cout << " scan range : " << min << " ... " << max << endl;
cout << " scan steps : " << nPoints1d << endl;
cout << " fast mode : " << fast << endl;
cout << endl;
}
// Set limit to all parameters.
combiner->loadParameterLimits();
// fix scan parameter
par->setConstant(true);
// j =
// 0 : start value -> upper limit
// 1 : upper limit -> start value
// 2 : start value -> lower limit
// 3 : lower limit -> start value
float startValue = par->getVal();
bool scanUp;
// for the status bar
float nTotalSteps = nPoints1d;
nTotalSteps *= fast ? 1 : 2;
float nStep = 0;
float printFreq = nTotalSteps>15 ? 10 : nTotalSteps;
// Report on the smallest new minimum we come across while scanning.
// Sometimes the scan doesn't find the minimum
// that was found before. Warn if this happens.
double bestMinOld = chi2minGlobal;
double bestMinFoundInScan = 100.;
for ( int jj=0; jj<4; jj++ )
{
int j = jj;
if ( reverse ) switch(jj)
{
case 0: j = 2; break;
case 1: j = 3; break;
case 2: j = 0; break;
case 3: j = 1; break;
}
float scanStart, scanStop;
switch(j)
{
case 0:
// UP
setParameters(w, parsName, startPars->get(0));
scanStart = startValue;
scanStop = par->getMax();
scanUp = true;
break;
case 1:
// DOWN
scanStart = par->getMax();
scanStop = startValue;
scanUp = false;
break;
case 2:
// DOWN
setParameters(w, parsName, startPars->get(0));
scanStart = startValue;
scanStop = par->getMin();
scanUp = false;
break;
case 3:
// UP
scanStart = par->getMin();
scanStop = startValue;
scanUp = true;
break;
}
if ( fast && ( j==1 || j==3 ) ) continue;
for ( int i=0; i<nPoints1d; i++ )
{
float scanvalue;
if ( scanUp )
{
scanvalue = min + (max-min)*(double)i/(double)nPoints1d + hCL->GetBinWidth(1)/2.;
if ( scanvalue < scanStart ) continue;
if ( scanvalue > scanStop ) break;
}
else
{
scanvalue = max - (max-min)*(double)(i+1)/(double)nPoints1d + hCL->GetBinWidth(1)/2.;
if ( scanvalue > scanStart ) continue;
if ( scanvalue < scanStop ) break;
}
// disable drag mode
// (the improve method doesn't work with drag mode as parameter run
// at their limits)
if ( scanDisableDragMode ) setParameters(w, parsName, startPars->get(0));
// set the parameter of interest to the scan point
par->setVal(scanvalue);
// don't scan in unphysical region
if ( scanvalue < par->getMin() || scanvalue > par->getMax() ) continue;
// status bar
if ( (((int)nStep % (int)(nTotalSteps/printFreq)) == 0))
cout << "MethodProbScan::scan1d() : scanning " << (float)nStep/(float)nTotalSteps*100. << "% \r" << flush;
// fit!
RooFitResult *fr = 0;
if ( arg->probforce ) fr = fitToMinForce(w, combiner->getPdfName());
else if ( arg->probimprove ) fr = fitToMinImprove(w, combiner->getPdfName());
else fr = fitToMinBringBackAngles(w->pdf(pdfName), false, -1);
double chi2minScan = fr->minNll();
if ( std::isinf(chi2minScan) ) chi2minScan=1e4; // else the toys in PDF_testConstraint don't work
RooSlimFitResult *r = new RooSlimFitResult(fr); // try to save memory by using the slim fit result
delete fr;
allResults.push_back(r);
bestMinFoundInScan = TMath::Min((double)chi2minScan, (double)bestMinFoundInScan);
TString warningChi2Neg;
if ( chi2minScan < 0 ){
float newChi2minScan = chi2minGlobal + 25.; // 5sigma more than best point
warningChi2Neg = "MethodProbScan::scan1d() : WARNING : " + title;
warningChi2Neg += TString(Form(" chi2 negative for scan point %i: %f",i,chi2minScan));
warningChi2Neg += " setting to: " + TString(Form("%f",newChi2minScan));
//cout << warningChi2Neg << "\r" << flush;
cout << warningChi2Neg << endl;
chi2minScan = newChi2minScan;
}
// If we find a minimum smaller than the old "global" minimum, this means that all
// previous 1-CL values are too high.
if ( chi2minScan<chi2minGlobal ){
if ( arg->verbose ) cout << "MethodProbScan::scan1d() : WARNING : '" << title << "' new global minimum found! "
<< " chi2minScan=" << chi2minScan << endl;
chi2minGlobal = chi2minScan;
// recompute previous 1-CL values
for ( int k=1; k<=hCL->GetNbinsX(); k++ ){
hCL->SetBinContent(k, TMath::Prob(hChi2min->GetBinContent(k)-chi2minGlobal, 1));
}
}
double deltaChi2 = chi2minScan - chi2minGlobal;
double oneMinusCL = TMath::Prob(deltaChi2, 1);
// Save the 1-CL value and the corresponding fit result.
// But only if better than before!
if ( hCL->GetBinContent(hCL->FindBin(scanvalue)) <= oneMinusCL ){
hCL->SetBinContent(hCL->FindBin(scanvalue), oneMinusCL);
hChi2min->SetBinContent(hCL->FindBin(scanvalue), chi2minScan);
int iRes = hCL->FindBin(scanvalue)-1;
curveResults[iRes] = r;
}
nStep++;
}
}
cout << "MethodProbScan::scan1d() : scan done. " << endl;
if ( bestMinFoundInScan-bestMinOld > 0.01 ){
cout << "MethodProbScan::scan1d() : WARNING: Scan didn't find similar minimum to what was found before!" << endl;
cout << "MethodProbScan::scan1d() : Too strict parameter limits? Too coarse scan steps? Didn't load global minimum?" << endl;
cout << "MethodProbScan::scan1d() : chi2 bestMinFoundInScan=" << bestMinFoundInScan << ", bestMinOld=" << bestMinOld << endl;
}
// attempt to correct for undercoverage
if (pvalueCorrectorSet) {
for ( int k=1; k<=hCL->GetNbinsX(); k++ ){
double pvalueProb = hCL->GetBinContent(k);
pvalueProb = pvalueCorrector->transform(pvalueProb);
hCL->SetBinContent(k, pvalueProb);
}
}
setParameters(w, parsName, startPars->get(0));
saveSolutions();
if (arg->confirmsols) confirmSolutions();
if ( (bestMinFoundInScan-bestMinOld)/bestMinOld > 0.01 ) return 1;
return 0;
}
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;
}
void StandardFeldmanCousinsDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
// -------------------------------------------------------
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
// -------------------------------------------------------
// Tutorial starts here
// -------------------------------------------------------
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// -------------------------------------------------------
// create and use the FeldmanCousins tool
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
FeldmanCousins fc(*data,*mc);
fc.SetConfidenceLevel(0.95); // 95% interval
//fc.AdditionalNToysFactor(0.1); // to speed up the result
fc.UseAdaptiveSampling(true); // speed it up a bit
fc.SetNBins(10); // set how many points per parameter of interest to scan
fc.CreateConfBelt(true); // save the information in the belt for plotting
// 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
// ProofConfig pc(*w, 1, "workers=4", kFALSE);
// ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
// toymcsampler->SetProofConfig(&pc); // enable proof
// Now get the interval
PointSetInterval* interval = fc.GetInterval();
ConfidenceBelt* belt = fc.GetConfidenceBelt();
// print out the iterval on the first Parameter of Interest
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"] "<<endl;
// ---------------------------------------------
// No nice plots yet, so plot the belt by hand
// 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());
// 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");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
double arMin = belt->GetAcceptanceRegionMax(*tmpPoint);
double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ;
histOfThresholds->Fill(poiVal,arMax);
}
histOfThresholds->SetMinimum(0);
histOfThresholds->Draw();
}
void MakeSpinPlots::DrawBlindFit(TString tag, TString mcName,TString cosThetaBin){
TString fitTag="FULLFIT";
TString cat = "evtcat";
if(cosThetaBin!=""){
tag+="_"+cosThetaBin;
fitTag="FULLCOSTFIT";
cat = "evtcat_cosT";
}
TString dataTag = "_Combined";
if(cosThetaBin!="") dataTag+="_CosTBin";
TCanvas *cv = new TCanvas(Form("%s_%s",mcName.Data(),tag.Data()));
RooRealVar* mass = ws->var("mass");
mass->setBins( (mass->getMax() - mass->getMin())/1.5 ); //enfore 1.5GeV bin width
RooPlot* frame = mass->frame();
double Nb = ws->var(Form("Data_BKGFIT_%s_Nbkg",tag.Data()))->getVal();
cout << Nb << endl;
RooDataSet *blind = (RooDataSet*)ws->data("Data"+dataTag)->reduce(TString("((mass<119) || (mass>135.5)) && ")+cat+"=="+cat+"::"+tag);
blind->plotOn(frame);
tPair lbl(mcName,tag);
double nBkg = ws->data("Data"+dataTag)->sumEntries(cat+"=="+cat+"::"+tag);
ws->pdf( Form("Data_BKGFIT_%s_bkgModel",tag.Data()) )->plotOn(frame,RooFit::Range("all"),RooFit::Normalization(nBkg/blind->sumEntries()),
RooFit::LineColor(kRed));
//TLatex *prelim = new TLatex(250,x->GetXmax()-40.,"CMS Preliminary");
TLatex *prelim = new TLatex(0.12,0.96,"CMS Preliminary");
TLatex *lum = new TLatex(0.7,0.96,Form("#sqrt{s}=8 TeV L = %0.1f fb^{-1}",lumi));
prelim->SetNDC();
lum->SetNDC();
prelim->SetTextSize(0.045);
prelim->SetTextColor(kBlack);
lum->SetTextSize(0.045);
lum->SetTextColor(kBlack);
TLatex *owner = new TLatex(0.6,0.88,"Caltech-CMS Preliminary");
owner->SetNDC();
owner->SetTextSize(0.045);
owner->SetTextColor(kBlack);
TLatex *Nbkg = new TLatex(0.7,0.8,Form("N_{bkg}= %0.1f #pm %0.1f",nBackground[lbl].first,nBackground[lbl].second));
Nbkg->SetNDC();
Nbkg->SetTextSize(0.045);
TLatex *sig = new TLatex(0.7,0.72,Form("#sigma_{eff} = %0.1f #pm %0.2f",fitSigEff[lbl].first,fitSigEff[lbl].second));
sig->SetNDC();
sig->SetTextSize(0.045);
TLatex *expBkg = new TLatex(0.7,0.64,Form("B @ 125 = %0.1f",fitBkg1Sigma[lbl].first));
expBkg->SetNDC();
expBkg->SetTextSize(0.045);
frame->addObject(prelim);
frame->addObject(lum);
//frame->addObject(owner);
frame->addObject(Nbkg);
frame->addObject(sig);
frame->addObject(expBkg);
frame->Draw();
cv->SaveAs( basePath+Form("/mgg-%s-%s-%s_BLIND.png",outputTag.Data(),mcName.Data(),tag.Data()) );
cv->SaveAs( basePath+Form("/C/mgg-%s-%s-%s_BLIND.C",outputTag.Data(),mcName.Data(),tag.Data()) );
cv->SaveAs( basePath+Form("/mgg-%s-%s-%s_BLIND.pdf",outputTag.Data(),mcName.Data(),tag.Data()) );
delete cv;
}
void MakeSpinPlots::DrawIndFit(TString tag, TString mcName){
TCanvas *cv = new TCanvas(Form("%s_%s",mcName.Data(),tag.Data()));
if(ws->var( Form("Data_%s_INDFIT_%s_Nsig",mcName.Data(),tag.Data()) ) == 0) return;
RooRealVar* mass = ws->var("mass");
mass->setBins( (mass->getMax() - mass->getMin())/1.5 ); //enfore 1.5GeV bin width
RooPlot* frame = mass->frame();
tPair lbl(mcName,tag);
double Ns = ws->var( Form("Data_%s_INDFIT_%s_Nsig",mcName.Data(),tag.Data()) )->getVal();
double Nb = ws->var( Form("Data_%s_INDFIT_%s_Nbkg",mcName.Data(),tag.Data()) )->getVal();
double Nblind = ws->data("Data_Combined")->reduce("(mass>100 && mass<119) || (mass>135.5 && mass<170)")->sumEntries(TString("evtcat==evtcat::")+tag);
double Ntot = ws->data("Data_Combined")->sumEntries(TString("evtcat==evtcat::")+tag);
RooFitResult* fitres = (RooFitResult*)ws->obj(Form("Data_%s_INDFIT_fitResult",mcName.Data()));
std::cout << fitres << std::endl;
ws->data("Data_Combined")->reduce(TString("evtcat==evtcat::")+tag)->plotOn(frame,RooFit::LineColor(kWhite),RooFit::MarkerColor(kWhite));
//Data_Hgg125_INDFIT_EB_0
ws->pdf(Form("Data_%s_INDFIT_%s",mcName.Data(),tag.Data()))->plotOn(frame, RooFit::FillColor(kGreen),RooFit::VisualizeError(*fitres,2.0));
ws->pdf(Form("Data_%s_INDFIT_%s",mcName.Data(),tag.Data()))->plotOn(frame, RooFit::FillColor(kYellow),RooFit::VisualizeError(*fitres,1.0));
ws->pdf(Form("Data_%s_INDFIT_%s",mcName.Data(),tag.Data()))->plotOn(frame, RooFit::LineColor(kRed));
std::cout << "1" << std::endl;
ws->pdf(Form("Data_BKGFIT_%s_bkgModel",tag.Data()))->plotOn(frame, RooFit::Normalization(Nb/(Nb+Ns)),RooFit::LineColor(kRed),RooFit::LineStyle(kDashed));
std::cout << "2" << std::endl;
ws->data("Data_Combined")->reduce(TString("evtcat==evtcat::")+tag)->plotOn(frame);
frame->Draw();
//TLatex *prelim = new TLatex(250,x->GetXmax()-40.,"CMS Preliminary");
TLatex *prelim = new TLatex(0.12,0.96,"CMS Preliminary");
TLatex *lum = new TLatex(0.7,0.96,Form("#sqrt{s}=8 TeV L = %0.1f fb^{-1}",lumi));
prelim->SetNDC();
lum->SetNDC();
prelim->SetTextSize(0.045);
prelim->SetTextColor(kBlack);
lum->SetTextSize(0.045);
lum->SetTextColor(kBlack);
TLatex *owner = new TLatex(0.6,0.88,"Alex Mott (Nov. 13, 2012)");
owner->SetNDC();
owner->SetTextSize(0.045);
owner->SetTextColor(kBlack);
TLatex *mu = new TLatex(0.7,0.8,Form("#mu = %0.1f #pm %0.2f", fitMean[lbl].first,fitMean[lbl].second));
mu->SetNDC();
mu->SetTextSize(0.045);
TLatex *sig = new TLatex(0.7,0.72,Form("#sigma_{eff} = %0.1f #pm %0.2f", fitSigEff[lbl].first,fitSigEff[lbl].second));
sig->SetNDC();
sig->SetTextSize(0.045);
float nSig = ws->var( Form("Data_%s_INDFIT_%s_Nsig",mcName.Data(),tag.Data()) )->getVal();
float nSigErr = ws->var( Form("Data_%s_INDFIT_%s_Nsig",mcName.Data(),tag.Data()) )->getError();
TLatex *Nsig = new TLatex(0.7,0.64,Form("N_{sig}= %0.1f #pm %0.1f",nSig,nSigErr));
Nsig->SetNDC();
Nsig->SetTextSize(0.045);
frame->addObject(prelim);
frame->addObject(lum);
//frame->addObject(owner);
frame->addObject(mu);
frame->addObject(sig);
frame->addObject(Nsig);
frame->Draw();
cv->SaveAs( basePath+Form("/mgg-FloatedFraction-%s-%s-%s.png",outputTag.Data(),mcName.Data(),tag.Data()) );
cv->SaveAs( basePath+Form("/C/mgg-FloatedFraction-%s-%s-%s.C",outputTag.Data(),mcName.Data(),tag.Data()) );
cv->SaveAs( basePath+Form("/mgg-FloatedFraction-%s-%s-%s.pdf",outputTag.Data(),mcName.Data(),tag.Data()) );
delete cv;
}
///
/// Find the global minimum in a more thorough way.
/// First fit with external start parameters, then
/// for each parameter that starts with "d" or "r" (typically angles and ratios):
/// - at upper scan range, rest at start parameters
/// - at lower scan range, rest at start parameters
/// This amounts to a maximum of 1+2^n fits, where n is the number
/// of parameters to be varied.
///
/// \param w Workspace holding the pdf.
/// \param name Name of the pdf without leading "pdf_".
/// \param forceVariables Apply the force method for these variables only. Format
/// "var1,var2,var3," (list must end with comma). Default is to apply for all angles,
/// all ratios except rD_k3pi and rD_kpi, and the k3pi coherence factor.
///
RooFitResult* Utils::fitToMinForce(RooWorkspace *w, TString name, TString forceVariables)
{
bool debug = true;
TString parsName = "par_"+name;
TString obsName = "obs_"+name;
TString pdfName = "pdf_"+name;
RooFitResult *r = 0;
int printlevel = -1;
RooMsgService::instance().setGlobalKillBelow(ERROR);
// save start parameters
if ( !w->set(parsName) ){
cout << "MethodProbScan::scan2d() : ERROR : parsName not found: " << parsName << endl;
exit(1);
}
RooDataSet *startPars = new RooDataSet("startParsForce", "startParsForce", *w->set(parsName));
startPars->add(*w->set(parsName));
// set up parameters and ranges
RooArgList *varyPars = new RooArgList();
TIterator* it = w->set(parsName)->createIterator();
while ( RooRealVar* p = (RooRealVar*)it->Next() )
{
if ( p->isConstant() ) continue;
if ( forceVariables=="" && ( false
|| TString(p->GetName()).BeginsWith("d") ///< use these variables
// || TString(p->GetName()).BeginsWith("r")
|| TString(p->GetName()).BeginsWith("k")
|| TString(p->GetName()) == "g"
) && ! (
TString(p->GetName()) == "rD_k3pi" ///< don't use these
|| TString(p->GetName()) == "rD_kpi"
// || TString(p->GetName()) == "dD_kpi"
|| TString(p->GetName()) == "d_dk"
|| TString(p->GetName()) == "d_dsk"
))
{
varyPars->add(*p);
}
else if ( forceVariables.Contains(TString(p->GetName())+",") )
{
varyPars->add(*p);
}
}
delete it;
int nPars = varyPars->getSize();
if ( debug ) cout << "Utils::fitToMinForce() : nPars = " << nPars << " => " << pow(2.,nPars) << " fits" << endl;
if ( debug ) cout << "Utils::fitToMinForce() : varying ";
if ( debug ) varyPars->Print();
//////////
r = fitToMinBringBackAngles(w->pdf(pdfName), false, printlevel);
//////////
int nErrors = 0;
// We define a binary mask where each bit corresponds
// to parameter at max or at min.
for ( int i=0; i<pow(2.,nPars); i++ )
{
if ( debug ) cout << "Utils::fitToMinForce() : fit " << i << " \r" << flush;
setParameters(w, parsName, startPars->get(0));
for ( int ip=0; ip<nPars; ip++ )
{
RooRealVar *p = (RooRealVar*)varyPars->at(ip);
float oldMin = p->getMin();
float oldMax = p->getMax();
setLimit(w, p->GetName(), "force");
if ( i/(int)pow(2.,ip) % 2==0 ) { p->setVal(p->getMin()); }
if ( i/(int)pow(2.,ip) % 2==1 ) { p->setVal(p->getMax()); }
p->setRange(oldMin, oldMax);
}
// check if start parameters are sensible, skip if they're not
double startParChi2 = getChi2(w->pdf(pdfName));
if ( startParChi2>2000 ){
nErrors += 1;
continue;
}
// refit
RooFitResult *r2 = fitToMinBringBackAngles(w->pdf(pdfName), false, printlevel);
// In case the initial fit failed, accept the second one.
// If both failed, still select the second one and hope the
// next fit succeeds.
if ( !(r->edm()<1 && r->covQual()==3) ){
delete r;
r = r2;
}
else if ( r2->edm()<1 && r2->covQual()==3 && r2->minNll()<r->minNll() ){
// better minimum found!
delete r;
r = r2;
}
else{
delete r2;
}
}
if ( debug ) cout << endl;
if ( debug ) cout << "Utils::fitToMinForce() : nErrors = " << nErrors << endl;
RooMsgService::instance().setGlobalKillBelow(INFO);
// (re)set to best parameters
setParameters(w, parsName, r);
delete startPars;
return r;
}
void StandardBayesianNumericalDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData") {
// option definitions
double confLevel = optBayes.confLevel;
TString integrationType = optBayes.integrationType;
int nToys = optBayes.nToys;
bool scanPosterior = optBayes.scanPosterior;
int nScanPoints = optBayes.nScanPoints;
int intervalType = optBayes.intervalType;
int maxPOI = optBayes.maxPOI;
double nSigmaNuisance = optBayes.nSigmaNuisance;
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
/////////////////////////////////////////////
// create and use the BayesianCalculator
// to find and plot the 95% credible interval
// on the parameter of interest as specified
// in the model config
// before we do that, we must specify our prior
// it belongs in the model config, but it may not have
// been specified
RooUniform prior("prior","",*mc->GetParametersOfInterest());
w->import(prior);
mc->SetPriorPdf(*w->pdf("prior"));
// do without systematics
//mc->SetNuisanceParameters(RooArgSet() );
if (nSigmaNuisance > 0) {
RooAbsPdf * pdf = mc->GetPdf();
assert(pdf);
RooFitResult * res = pdf->fitTo(*data, Save(true), Minimizer(ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str()), Hesse(true),
PrintLevel(ROOT::Math::MinimizerOptions::DefaultPrintLevel()-1) );
res->Print();
RooArgList nuisPar(*mc->GetNuisanceParameters());
for (int i = 0; i < nuisPar.getSize(); ++i) {
RooRealVar * v = dynamic_cast<RooRealVar*> (&nuisPar[i] );
assert( v);
v->setMin( TMath::Max( v->getMin(), v->getVal() - nSigmaNuisance * v->getError() ) );
v->setMax( TMath::Min( v->getMax(), v->getVal() + nSigmaNuisance * v->getError() ) );
std::cout << "setting interval for nuisance " << v->GetName() << " : [ " << v->getMin() << " , " << v->getMax() << " ]" << std::endl;
}
}
BayesianCalculator bayesianCalc(*data,*mc);
bayesianCalc.SetConfidenceLevel(confLevel); // 95% interval
// default of the calculator is central interval. here use shortest , central or upper limit depending on input
// doing a shortest interval might require a longer time since it requires a scan of the posterior function
if (intervalType == 0) bayesianCalc.SetShortestInterval(); // for shortest interval
if (intervalType == 1) bayesianCalc.SetLeftSideTailFraction(0.5); // for central interval
if (intervalType == 2) bayesianCalc.SetLeftSideTailFraction(0.); // for upper limit
if (!integrationType.IsNull() ) {
bayesianCalc.SetIntegrationType(integrationType); // set integrationType
bayesianCalc.SetNumIters(nToys); // set number of ietrations (i.e. number of toys for MC integrations)
}
// in case of toyMC make a nnuisance pdf
if (integrationType.Contains("TOYMC") ) {
RooAbsPdf * nuisPdf = RooStats::MakeNuisancePdf(*mc, "nuisance_pdf");
cout << "using TOYMC integration: make nuisance pdf from the model " << std::endl;
nuisPdf->Print();
bayesianCalc.ForceNuisancePdf(*nuisPdf);
scanPosterior = true; // for ToyMC the posterior is scanned anyway so used given points
}
// compute interval by scanning the posterior function
if (scanPosterior)
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooRealVar* poi = (RooRealVar*) mc->GetParametersOfInterest()->first();
if (maxPOI != -999 && maxPOI > poi->getMin())
poi->setMax(maxPOI);
SimpleInterval* interval = bayesianCalc.GetInterval();
// print out the iterval on the first Parameter of Interest
cout << "\n>>>> RESULT : " << confLevel*100 << "% interval on " << poi->GetName()<<" is : ["<<
interval->LowerLimit() << ", "<<
interval->UpperLimit() <<"] "<<endl;
// make a plot
// since plotting may take a long time (it requires evaluating
// the posterior in many points) this command will speed up
// by reducing the number of points to plot - do 50
// ignore errors of PDF if is zero
RooAbsReal::setEvalErrorLoggingMode(RooAbsReal::Ignore) ;
cout << "\nDrawing plot of posterior function....." << endl;
// always plot using numer of scan points
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooPlot * plot = bayesianCalc.GetPosteriorPlot();
plot->Draw();
}
RooFitResult * safeFit(RooAbsPdf * pdf, RooDataSet * data, Str2VarMap p, ISVALIDF_PTR isValid, string opt = "", int nfree = -1, RooArgSet * cons = NULL, RooAbsReal * nll = NULL)
{
RooFitResult * res = NULL;
RooRealVar cosThetaL("cosThetaL","cosThetaL",0.,-1.,1.);
RooRealVar cosThetaB("cosThetaB","cosThetaB",0.,-1.,1.);
RooArgSet obs(cosThetaL,cosThetaB);
//if(opt.find("-scan")==string::npos) res = pdf->fitTo(*data,PrintLevel(-1),Save(),Extended(true));
if(p.size()==1 && p.find("afb") != p.end()) p["fL"] = GetParam(pdf,"fL");
else if(p.size()==1 && p.find("fL") != p.end()) p["afb"] = GetParam(pdf,"afb");
RooArgSet * nuisances = NULL;
/*
bool afb_iscost = false, fL_iscost = false, afbB_iscost = false;
if (p.find("afb") != p.end()) { afb_iscost = ((RooRealVar*)p["afb"])->getAttribute("Constant"); ((RooRealVar*)p["afb"])->setConstant(); }
if (p.find("fL") != p.end()) { fL_iscost = ((RooRealVar*)p["fL"])->getAttribute("Constant"); ((RooRealVar*)p["fL"])->setConstant(); }
if (p.find("afbB") != p.end()) { afbB_iscost = ((RooRealVar*)p["afbB"])->getAttribute("Constant"); ((RooRealVar*)p["afbB"])->setConstant(); }
RooArgSet * nuisances = copyFreePars(pdf,obs);
if (p.find("afb") != p.end()) ((RooRealVar*)p["afb"])->setConstant(afb_iscost);
if (p.find("afbB") != p.end()) ((RooRealVar*)p["afbB"])->setConstant(afbB_iscost);
if (p.find("fL") != p.end()) ((RooRealVar*)p["fL"])->setConstant(fL_iscost);
*/
int np = 20;
if((!res || res->covQual()!=3 || res->edm() > 0.1) && opt.find("-noscan")==string::npos)
{
if(!nll) nll = pdf->createNLL(*data);
vector < double > mins, maxs, r;
Str2VarMap::iterator iter; int pp = 0;
for (iter = p.begin(); iter != p.end(); iter++)
{
RooRealVar * curp = (RooRealVar *)iter->second;
maxs.push_back(curp->getMax());
mins.push_back(curp->getMin());
r.push_back((maxs.back() - mins.back())/(double)np);
pp++;
}
findMin(pdf,data,nll,p,mins,maxs,np,isValid,nfree,opt+"-nofit",cons,nuisances);
double prec = 1e6;
while (prec > 0.001)
{
double maxr = 0;
maxs.clear(); mins.clear(); pp=0;
for (iter = p.begin(); iter != p.end(); iter++)
{
RooRealVar * curp = (RooRealVar *)iter->second;
if((curp->getVal() + r[pp]) < curp->getMax()) maxs.push_back(curp->getVal() + r[pp]);
else maxs.push_back(curp->getMax());
if((curp->getVal() - r[pp]) > curp->getMin()) mins.push_back(curp->getVal() - r[pp]);
else mins.push_back(curp->getMin());
r[pp] = (maxs.back() - mins.back())/(double)np;
if(r[pp] > maxr) maxr = r[pp];
pp++;
}
prec = maxr;
res = findMin(pdf,data,nll,p,mins,maxs,np,isValid,nfree,opt,cons,nuisances);
}
//if(!mynll) delete nll;
}
return res;
}
