RooFitResult* GenericModel::fitTo(RooDataSet* data)
{
// Perform fit of the pseudo-PDF to the data
// On multi-core machines, this automatically uses all available processor cores
SafeDelete(fLastFit);
#ifdef WITH_MULTICORE_CPU
fLastFit = fModelPseudoPDF->fitTo(*data, Save(), NumCPU(WITH_MULTICORE_CPU));
#else
fLastFit = fModelPseudoPDF->fitTo(*data, Save());
#endif
SafeDelete(fParamDataHist);
fParamDataHist = new RooDataHist("params", "params", GetParameters());
// store weights of component pdfs => distribution of parameters
fWeights.removeAll();
const RooArgList& coefs = fModelPseudoPDF->coefList();
for (int i = 0; i < GetNumberOfDataSets(); i++) {
RooAbsReal* coef = (RooAbsReal*)coefs.at(i);
RooRealVar w(Form("w%d", i), Form("Fitted weight of kernel#%d", i), coef->getVal());
if (coef->InheritsFrom(RooRealVar::Class())) {
w.setError(((RooRealVar*)coef)->getError());
} else {
w.setError(coef->getPropagatedError(*fLastFit));
}
fWeights.addClone(w);
fParamDataHist->set(*GetParametersForDataset(i), w.getVal(), w.getError());
}
SafeDelete(fParameterPDF);
fParameterPDF = new RooHistPdf("paramPDF", "paramPDF", GetParameters(), *fParamDataHist);
return fLastFit;
}
void fit( RooAbsReal & chi2, int numberOfBins, const char * outFileNameWithFitResult ){
TFile * out_root_file = new TFile(outFileNameWithFitResult , "recreate");
RooMinuit m_tot(chi2) ;
m_tot.migrad();
// m_tot.hesse();
RooFitResult* r_chi2 = m_tot.save() ;
cout << "==> Chi2 Fit results " << endl ;
r_chi2->Print("v") ;
// r_chi2->floatParsFinal().Print("v") ;
int NumberOfFreeParameters = r_chi2->floatParsFinal().getSize() ;
for (int i =0; i< NumberOfFreeParameters; ++i){
r_chi2->floatParsFinal()[i].Print();
}
cout<<"chi2:" <<chi2.getVal() << ", numberOfBins: " << numberOfBins << ", NumberOfFreeParameters: " << NumberOfFreeParameters << endl;
cout<<"Normalized Chi2 = " << chi2.getVal()/ (numberOfBins - NumberOfFreeParameters)<<endl;
r_chi2->Write( ) ;
delete out_root_file;
}
void Zbi_Zgamma() {
// Make model for prototype on/off problem
// Pois(x | s+b) * Pois(y | tau b )
// for Z_Gamma, use uniform prior on b.
RooWorkspace* w = new RooWorkspace("w",true);
w->factory("Poisson::px(x[150,0,500],sum::splusb(s[0,0,100],b[100,0,300]))");
w->factory("Poisson::py(y[100,0,500],prod::taub(tau[1.],b))");
w->factory("Uniform::prior_b(b)");
// construct the Bayesian-averaged model (eg. a projection pdf)
// p'(x|s) = \int db p(x|s+b) * [ p(y|b) * prior(b) ]
w->factory("PROJ::averagedModel(PROD::foo(px|b,py,prior_b),b)") ;
// plot it, blue is averaged model, red is b known exactly
RooPlot* frame = w->var("x")->frame() ;
w->pdf("averagedModel")->plotOn(frame) ;
w->pdf("px")->plotOn(frame,LineColor(kRed)) ;
frame->Draw() ;
// compare analytic calculation of Z_Bi
// with the numerical RooFit implementation of Z_Gamma
// for an example with x = 150, y = 100
// numeric RooFit Z_Gamma
w->var("y")->setVal(100);
w->var("x")->setVal(150);
RooAbsReal* cdf = w->pdf("averagedModel")->createCdf(*w->var("x"));
cdf->getVal(); // get ugly print messages out of the way
cout << "Hybrid p-value = " << cdf->getVal() << endl;
cout << "Z_Gamma Significance = " <<
PValueToSignificance(1-cdf->getVal()) << endl;
// analytic Z_Bi
double Z_Bi = NumberCountingUtils::BinomialWithTauObsZ(150, 100, 1);
std::cout << "Z_Bi significance estimation: " << Z_Bi << std::endl;
// OUTPUT
// Hybrid p-value = 0.999058
// Z_Gamma Significance = 3.10804
// Z_Bi significance estimation: 3.10804
}
///
/// Make an Asimov toy: set all observables set to truth values.
/// The Asimov point needs to be loaded in the combiner before.
/// \param c - combiner which should be set to an asimov toy
///
void GammaComboEngine::setAsimovObservables(Combiner* c)
{
if ( !c->isCombined() ) {
cout << "GammaComboEngine::setAsimovObservables() : ERROR : Can't set an Asimov toy before "
"the combiner is combined. Call combine() first." << endl;
exit(1);
}
// set observables to asimov values in workspace
RooWorkspace* w = c->getWorkspace();
TIterator* itObs = c->getObservables()->createIterator();
while(RooRealVar* pObs = (RooRealVar*) itObs->Next()) {
// get theory name from the observable name
TString pThName = pObs->GetName();
pThName.ReplaceAll("obs","th");
// get the theory relation
RooAbsReal* th = w->function(pThName);
if ( th==0 ) {
cout << "GammaComboEngine::setAsimovObservables() : ERROR : theory relation not found in workspace: " << pThName << endl;
exit(1);
}
// set the observable to what the theory relation predicts
pObs->setVal(th->getVal());
}
delete itObs;
// write back the asimov values to the PDF object so that when
// the PDF is printed, the asimov values show up
for ( int i=0; i<c->getPdfs().size(); i++ ) {
PDF_Abs* pdf = c->getPdfs()[i];
pdf->setObservableSourceString("Asimov");
TIterator* itObs = pdf->getObservables()->createIterator();
while(RooRealVar* pObs = (RooRealVar*) itObs->Next()) {
RooAbsReal* obs = w->var(pObs->GetName());
if ( obs==0 ) {
cout << "GammaComboEngine::setAsimovObservables() : ERROR : observable not found in workspace: " << pObs->GetName() << endl;
exit(1);
}
pdf->setObservable(pObs->GetName(), obs->getVal());
}
delete itObs;
}
}
// grab the initial normalization from a datacard converted in workspace
// with: scripts/text2workspace.py -b -o model.root datacards/hww-12.1fb.mH125.comb_0j1j2j_shape.txt
void fillInitialNorms(RooArgSet *args, std::map<std::string, std::pair<double,double> > &vals, std::string workspace){
TFile *fw_ = TFile::Open(workspace.c_str());
RooWorkspace *ws = (RooWorkspace*)fw_->Get("w");
TIterator* iter(args->createIterator());
for (TObject *a = iter->Next(); a != 0; a = iter->Next()) {
RooAbsReal *rar = (RooAbsReal*)ws->obj(a->GetName());
std::string name = rar->GetName();
std::pair<double,double> valE(rar->getVal(),0.0);
vals.insert( std::pair<std::string,std::pair<double ,double> > (name,valE)) ;
}
}
double NormalizedIntegral(RooAbsPdf & function, RooRealVar & integrationVar, double lowerLimit, double upperLimit){
integrationVar.setRange("integralRange",lowerLimit,upperLimit);
RooAbsReal* integral = function.createIntegral(integrationVar,NormSet(integrationVar),Range("integralRange"));
double normlizedIntegralValue = integral->getVal();
// cout<<normlizedIntegralValue<<endl;
return normlizedIntegralValue;
}
// get effective sigma from culmalative distribution function
pair<double,double> getEffSigma(RooRealVar *mass, RooAbsPdf *pdf, double wmin=110., double wmax=130., double step=0.002, double epsilon=1.e-4){
RooAbsReal *cdf = pdf->createCdf(RooArgList(*mass));
cout << "Computing effSigma...." << endl;
TStopwatch sw;
sw.Start();
double point=wmin;
vector<pair<double,double> > points;
while (point <= wmax){
mass->setVal(point);
if (pdf->getVal() > epsilon){
points.push_back(pair<double,double>(point,cdf->getVal()));
}
point+=step;
}
double low = wmin;
double high = wmax;
double width = wmax-wmin;
for (unsigned int i=0; i<points.size(); i++){
for (unsigned int j=i; j<points.size(); j++){
double wy = points[j].second - points[i].second;
if (TMath::Abs(wy-0.683) < epsilon){
double wx = points[j].first - points[i].first;
if (wx < width){
low = points[i].first;
high = points[j].first;
width=wx;
}
}
}
}
sw.Stop();
cout << "effSigma: [" << low << "-" << high << "] = " << width/2. << endl;
cout << "\tTook: "; sw.Print();
pair<double,double> result(low,high);
return result;
}
void forData(string channel, string catcut, bool removeMinor=true){
// Suppress all the INFO message
RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING);
// Input files and sum all backgrounds
TChain* treeData = new TChain("tree");
TChain* treeZjets = new TChain("tree");
if( channel == "ele" ){
treeData->Add(Form("%s/data/SingleElectron-Run2015D-05Oct2015-v1_toyMCnew.root", channel.data()));
treeData->Add(Form("%s/data/SingleElectron-Run2015D-PromptReco-V4_toyMCnew.root", channel.data()));
}
else if( channel == "mu" ){
treeData->Add(Form("%s/data/SingleMuon-Run2015D-05Oct2015-v1_toyMCnew.root", channel.data()));
treeData->Add(Form("%s/data/SingleMuon-Run2015D-PromptReco-V4_toyMCnew.root", channel.data()));
}
else return;
treeZjets->Add(Form("%s/Zjets/DYJetsToLL_M-50_HT-100to200_13TeV_toyMCnew.root", channel.data()));
treeZjets->Add(Form("%s/Zjets/DYJetsToLL_M-50_HT-200to400_13TeV_toyMCnew.root", channel.data()));
treeZjets->Add(Form("%s/Zjets/DYJetsToLL_M-50_HT-400to600_13TeV_toyMCnew.root", channel.data()));
treeZjets->Add(Form("%s/Zjets/DYJetsToLL_M-50_HT-600toInf_13TeV_toyMCnew.root", channel.data()));
// To remove minor background contribution in data set (weight is -1)
if( removeMinor ){
treeData->Add(Form("%s/VV/WW_TuneCUETP8M1_13TeV_toyMCnew.root", channel.data()));
treeData->Add(Form("%s/VV/WZ_TuneCUETP8M1_13TeV_toyMCnew.root", channel.data()));
treeData->Add(Form("%s/VV/ZZ_TuneCUETP8M1_13TeV_toyMCnew.root", channel.data()));
treeData->Add(Form("%s/TT/TT_TuneCUETP8M1_13TeV_toyMCnew.root", channel.data()));
}
// Define all the variables from the trees
RooRealVar cat ("cat", "", 0, 2);
RooRealVar mJet("prmass", "M_{jet}", 30., 300., "GeV");
RooRealVar mZH ("mllbb", "M_{ZH}", 900., 3000., "GeV");
RooRealVar evWeight("evweight", "", -1.e3, 1.e3);
// Set the range in jet mass
mJet.setRange("allRange", 30., 300.);
mJet.setRange("lowSB", 30., 65.);
mJet.setRange("highSB", 135., 300.);
mJet.setRange("signal", 105., 135.);
RooBinning binsmJet(54, 30, 300);
RooArgSet variables(cat, mJet, mZH, evWeight);
TCut catCut = Form("cat==%s", catcut.c_str());
TCut sbCut = "prmass>30 && !(prmass>65 && prmass<135) && prmass<300";
TCut sigCut = "prmass>105 && prmass<135";
// Create a dataset from a tree -> to process an unbinned likelihood fitting
RooDataSet dataSetData ("dataSetData", "dataSetData", variables, Cut(catCut), WeightVar(evWeight), Import(*treeData));
RooDataSet dataSetDataSB ("dataSetDataSB", "dataSetDataSB", variables, Cut(catCut && sbCut), WeightVar(evWeight), Import(*treeData));
RooDataSet dataSetZjets ("dataSetZjets", "dataSetZjets", variables, Cut(catCut), WeightVar(evWeight), Import(*treeZjets));
RooDataSet dataSetZjetsSB("dataSetZjetsSB", "dataSetZjetsSB", variables, Cut(catCut && sbCut), WeightVar(evWeight), Import(*treeZjets));
RooDataSet dataSetZjetsSG("dataSetZjetsSG", "dataSetZjetsSG", variables, Cut(catCut && sigCut), WeightVar(evWeight), Import(*treeZjets));
// Total events number
float totalMcEv = dataSetZjetsSB.sumEntries() + dataSetZjetsSG.sumEntries();
float totalDataEv = dataSetData.sumEntries();
RooRealVar nMcEvents("nMcEvents", "nMcEvents", 0., 99999.);
RooRealVar nDataEvents("nDataEvents", "nDataEvents", 0., 99999.);
nMcEvents.setVal(totalMcEv);
nMcEvents.setConstant(true);
nDataEvents.setVal(totalDataEv);
nDataEvents.setConstant(true);
// Signal region jet mass
RooRealVar constant("constant", "constant", -0.02, -1., 0.);
RooRealVar offset ("offset", "offset", 30., -50., 200.);
RooRealVar width ("width", "width", 100., 0., 200.);
if( catcut == "1" ) offset.setConstant(true);
RooErfExpPdf model_mJet("model_mJet", "model_mJet", mJet, constant, offset, width);
RooExtendPdf ext_model_mJet("ext_model_mJet", "ext_model_mJet", model_mJet, nMcEvents);
RooFitResult* mJet_result = ext_model_mJet.fitTo(dataSetZjets, SumW2Error(true), Extended(true), Range("allRange"), Strategy(2), Minimizer("Minuit2"), Save(1));
// Side band jet mass
RooRealVar constantSB("constantSB", "constantSB", constant.getVal(), -1., 0.);
RooRealVar offsetSB ("offsetSB", "offsetSB", offset.getVal(), -50., 200.);
RooRealVar widthSB ("widthSB", "widthSB", width.getVal(), 0., 200.);
offsetSB.setConstant(true);
RooErfExpPdf model_mJetSB("model_mJetSB", "model_mJetSB", mJet, constantSB, offsetSB, widthSB);
RooExtendPdf ext_model_mJetSB("ext_model_mJetSB", "ext_model_mJetSB", model_mJetSB, nMcEvents);
RooFitResult* mJetSB_result = ext_model_mJetSB.fitTo(dataSetZjetsSB, SumW2Error(true), Extended(true), Range("lowSB,highSB"), Strategy(2), Minimizer("Minuit2"), Save(1));
RooAbsReal* nSIGFit = ext_model_mJetSB.createIntegral(RooArgSet(mJet), NormSet(mJet), Range("signal"));
float normFactor = nSIGFit->getVal() * totalMcEv;
// Plot the results on a frame
RooPlot* mJetFrame = mJet.frame();
dataSetZjetsSB. plotOn(mJetFrame, Binning(binsmJet));
ext_model_mJetSB.plotOn(mJetFrame, Range("allRange"), VisualizeError(*mJetSB_result), FillColor(kYellow));
dataSetZjetsSB. plotOn(mJetFrame, Binning(binsmJet));
ext_model_mJetSB.plotOn(mJetFrame, Range("allRange"));
mJetFrame->SetTitle("M_{jet} distribution in Z+jets MC");
// Alpha ratio part
mZH.setRange("fullRange", 900., 3000.);
RooBinning binsmZH(21, 900, 3000);
RooRealVar a("a", "a", 0., -1., 1.);
RooRealVar b("b", "b", 1000, 0., 4000.);
RooGenericPdf model_ZHSB("model_ZHSB", "model_ZHSB", "TMath::Exp(@1*@0+@2/@0)", RooArgSet(mZH,a,b));
RooGenericPdf model_ZHSG("model_ZHSG", "model_ZHSG", "TMath::Exp(@1*@0+@2/@0)", RooArgSet(mZH,a,b));
RooGenericPdf model_ZH ("model_ZH", "model_ZH", "TMath::Exp(@1*@0+@2/@0)", RooArgSet(mZH,a,b));
RooExtendPdf ext_model_ZHSB("ext_model_ZHSB", "ext_model_ZHSB", model_ZHSB, nMcEvents);
RooExtendPdf ext_model_ZHSG("ext_model_ZHSG", "ext_model_ZHSG", model_ZHSG, nMcEvents);
RooExtendPdf ext_model_ZH ("ext_model_ZH", "ext_model_ZH", model_ZH, nDataEvents);
// Fit ZH mass in side band
RooFitResult* mZHSB_result = ext_model_ZHSB.fitTo(dataSetZjetsSB, SumW2Error(true), Extended(true), Range("fullRange"), Strategy(2), Minimizer("Minuit2"), Save(1));
float p0 = a.getVal();
float p1 = b.getVal();
// Fit ZH mass in signal region
RooFitResult* mZHSG_result = ext_model_ZHSG.fitTo(dataSetZjetsSG, SumW2Error(true), Extended(true), Range("fullRange"), Strategy(2), Minimizer("Minuit2"), Save(1));
float p2 = a.getVal();
float p3 = b.getVal();
// Fit ZH mass in side band region (data)
RooFitResult* mZH_result = ext_model_ZH.fitTo(dataSetDataSB, SumW2Error(true), Extended(true), Range("fullRange"), Strategy(2), Minimizer("Minuit2"), Save(1));
// Draw the model of alpha ratio
// Multiply the model of background in data side band with the model of alpha ratio to the a model of background in data signal region
RooGenericPdf model_alpha("model_alpha", "model_alpha", Form("TMath::Exp(%f*@0+%f/@0)/TMath::Exp(%f*@0+%f/@0)", p2,p3,p0,p1), RooArgSet(mZH));
RooProdPdf model_sigData("model_sigData", "ext_model_ZH*model_alpha", RooArgList(ext_model_ZH,model_alpha));
// Plot the results to a frame
RooPlot* mZHFrameMC = mZH.frame();
dataSetZjetsSB.plotOn(mZHFrameMC, Binning(binsmZH));
ext_model_ZHSB.plotOn(mZHFrameMC, VisualizeError(*mZHSB_result), FillColor(kYellow));
dataSetZjetsSB.plotOn(mZHFrameMC, Binning(binsmZH));
ext_model_ZHSB.plotOn(mZHFrameMC, LineStyle(7), LineColor(kBlue));
dataSetZjetsSG.plotOn(mZHFrameMC, Binning(binsmZH));
ext_model_ZHSG.plotOn(mZHFrameMC, VisualizeError(*mZHSG_result), FillColor(kYellow));
dataSetZjetsSG.plotOn(mZHFrameMC, Binning(binsmZH));
ext_model_ZHSG.plotOn(mZHFrameMC, LineStyle(7), LineColor(kRed));
TLegend* leg = new TLegend(0.65,0.77,0.85,0.85);
leg->AddEntry(mZHFrameMC->findObject(mZHFrameMC->nameOf(3)), "side band", "l");
leg->AddEntry(mZHFrameMC->findObject(mZHFrameMC->nameOf(7)), "signal region", "l");
leg->Draw();
mZHFrameMC->addObject(leg);
mZHFrameMC->SetTitle("M_{ZH} distribution in MC");
RooPlot* mZHFrame = mZH.frame();
dataSetDataSB.plotOn(mZHFrame, Binning(binsmZH));
ext_model_ZH .plotOn(mZHFrame, VisualizeError(*mZH_result), FillColor(kYellow));
dataSetDataSB.plotOn(mZHFrame, Binning(binsmZH));
ext_model_ZH .plotOn(mZHFrame, LineStyle(7), LineColor(kBlue));
model_sigData.plotOn(mZHFrame, Normalization(normFactor, RooAbsReal::NumEvent), LineStyle(7), LineColor(kRed));
TLegend* leg1 = new TLegend(0.65,0.77,0.85,0.85);
leg1->AddEntry(mZHFrame->findObject(mZHFrame->nameOf(3)), "side band", "l");
leg1->AddEntry(mZHFrame->findObject(mZHFrame->nameOf(4)), "signal region", "l");
leg1->Draw();
mZHFrame->addObject(leg1);
mZHFrame->SetTitle("M_{ZH} distribution in Data");
TCanvas* c = new TCanvas("c","",0,0,1000,800);
c->cd();
mZHFrameMC->Draw();
c->Print(Form("rooFit_forData_%s_cat%s.pdf(", channel.data(), catcut.data()));
c->cd();
mZHFrame->Draw();
c->Print(Form("rooFit_forData_%s_cat%s.pdf", channel.data(), catcut.data()));
c->cd();
mJetFrame->Draw();
c->Print(Form("rooFit_forData_%s_cat%s.pdf)", channel.data(), catcut.data()));
}
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 ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root",
const char* new_poi_name = "n_M234_H4_3b",
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 20.,
double constraintWidth = 1.5,
double ymax = 10.,
int verbLevel=0 ) {
gStyle->SetOptStat(0) ;
//--- make output directory.
char command[10000] ;
sprintf( command, "basename %s", wsfile ) ;
TString wsfilenopath = gSystem->GetFromPipe( command ) ;
wsfilenopath.ReplaceAll(".root","") ;
char outputdirstr[1000] ;
sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ;
TString outputdir( outputdirstr ) ;
printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ;
sprintf(command, "mkdir -p %s", outputdir.Data() ) ;
gSystem->Exec( command ) ;
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; }
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
if ( verbLevel > 0 ) { ws->Print() ; }
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
if ( verbLevel > 0 ) {
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_all0lep == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
}
//-- do BG only.
rrv_mu_susy_all0lep->setVal(0.) ;
rrv_mu_susy_all0lep->setConstant( kTRUE ) ;
//-- do a prefit.
printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
if ( verbLevel > 0 ) {
dataFitResultSusyFixed->Print("v") ;
}
printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ;
delete dataFitResultSusyFixed ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
if ( npoiPoints <=0 ) {
printf("\n\n Quitting now.\n\n" ) ;
return ;
}
double startPoiVal = new_poi_rar->getVal() ;
//--- The RooNLLVar is NOT equivalent to what minuit uses.
// RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ;
// printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ;
//--- output of createNLL IS what minuit uses, so use that.
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
/// rrv_poiValue->setVal( poiMinVal ) ;
/// rrv_poiValue->setConstant(kTRUE) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
if ( verbLevel > 0 ) {
printf("\n\n ======= debug likelihood print\n\n") ;
likelihood->Print("v") ;
printf("\n\n ======= debug nll print\n\n") ;
nll->Print("v") ;
}
//----------------------------------------------------------------------------------------------
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
//-------
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
//----------------------------------------------------------------------------------------------
//-- If POI range is -1 to -1, automatically determine the range using the set value.
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
}
vector<double> FitInvMass(TH1D* histo){
vector<double> vec;
gROOT->ProcessLine(".x ~/rootlogon.C");
int n = histo->GetEntries();
double w = histo->GetXaxis()->GetBinWidth(1);
int ndf;
RooPlot* frame;
double hmin0 = histo->GetXaxis()->GetXmin();
double hmax0 = histo->GetXaxis()->GetXmax();
histo->GetXaxis()->SetRangeUser(hmin0,hmax0);
// Declare observable x
RooRealVar x("x","x",hmin0,hmax0) ;
RooDataHist dh("dh","dh",x,Import(*histo)) ;
frame = x.frame(Title(histo->GetName())) ;
dh.plotOn(frame,DataError(RooAbsData::SumW2), MarkerColor(1),MarkerSize(0.9),MarkerStyle(7)); //this will show histogram data points on canvas
dh.statOn(frame); //this will display hist stat on canvas
x.setRange("R0",90.5,91) ;
x.setRange("R1",70,110) ;
x.setRange("R2",60,120) ;
x.setRange("R3",50,130) ;
RooRealVar mean("mean","mean",91.186/*histo->GetMean()*/, 70.0, 120.0);
RooRealVar width("width","width",7.5, 0, 30.0);
RooRealVar sigma("sigma","sigma",0, 0.0, 120.0);
mean.setRange(88,94);
width.setRange(0,20);
sigma.setRange(0,10);
//Choose the fitting here
//RooGaussian gauss("gauss","gauss",x,mean,sigma);ndf = 2;
RooBreitWigner gauss("gauss","gauss",x,mean,width);ndf = 2;
//RooVoigtian gauss("gauss","gauss",x,mean,width,sigma); ndf = 3;
RooFitResult* filters = gauss.fitTo(dh,Range("R1"),"qr");
gauss.plotOn(frame,LineColor(4));//this will show fit overlay on canvas
gauss.paramOn(frame); //this will display the fit parameters on canvas
//TCanvas* b1 = new TCanvas("b1","b1",1200,800);
//gPad->SetLeftMargin(0.15);
//frame->GetXaxis()->SetTitle("Z mass (in GeV/c^{2})");
//frame->GetXaxis()->SetTitleOffset(1.2);
//float binsize = histo->GetBinWidth(1);
//frame->Draw() ;
cout<<"The chi2 is:"<<endl;
cout<<frame->chiSquare(ndf)<<endl;
cout<<" "<<endl;
//Do the integral
//Store result in .root file
frame->Write(histo->GetTitle());
RooAbsReal* integral = gauss.createIntegral(x, NormSet(x), Range("R1")) ;
vec.push_back(n*integral->getVal());
//vec.push_back((double)n);
vec.push_back((double)frame->chiSquare(ndf));
return vec;
}
void LL(){
//y0 = 0.000135096401209 sigma_y0 = 0.000103896581837 x0 = 0.000446013873443 sigma_x0 =1.81384394011e-06
//0.014108652249 0.0168368471049 0.0219755396247 0.000120423865262 1.5575931164 1.55759310722 3.41637854038
//0.072569437325 0.084063541977 0.0376693978906 0.000284216132439 0.51908074913 0.519080758095 1.12037749267
// double d = 0.014108652249;
// double sd = 0.0168368471049;
// double mc = 0.0219755396247;
// double smc = 0.000120423865262;
// double r0 = d/mc;
double d = 0.072569437325;
double sd = 0.084063541977;
double mc = 0.0376693978906;
double smc = 0.00028421613243;
double r0 = d/mc;
RooRealVar x("x","x",mc*0.9,mc*1.1);
RooRealVar x0("x0","x0",mc);
RooRealVar sx("sx","sx",smc);
RooRealVar r("r","r",r0,0.,5.);
RooRealVar y0("y0","y0",d);
RooRealVar sy("sy","sy",sd);
RooProduct rx("rx","rx",RooArgList(r,x));
RooGaussian g1("g1","g1",x,x0,sx);
RooGaussian g2("g2","g2",rx,y0,sy);
RooProdPdf LL("LL","LL",g1,g2);
RooArgSet obs(x0,y0); //observables
RooArgSet poi(r); //parameters of interest
RooDataSet data("data", "data", obs);
data.add(obs); //actually add the data
RooFitResult* res = LL.fitTo(data,RooFit::Minos(poi),RooFit::Save(),RooFit::Hesse(false));
if(res->status()==0) {
r.Print();
x.Print();
cout << r.getErrorLo() << " " << r.getErrorHi() << endl;
} else {
cout << "Likelihood maximization failed" << endl;
}
RooAbsReal* nll = LL.createNLL(data);
RooPlot* frame = r.frame();
RooAbsReal* pll = nll->createProfile(poi);
pll->plotOn(frame);//,RooFit::LineColor(ROOT::kRed));
frame->Draw();
r.setVal(0.);
cout << pll->getVal() << endl;
return;
}
void Raa3S_Workspace(const char* name_pbpb="chad_ws_fits/centFits/ws_PbPbData_262548_263757_0cent10_0.0pt50.0_0.0y2.4.root", const char* name_pp="chad_ws_fits/centFits/ws_PPData_262157_262328_-1cent1_0.0pt50.0_0.0y2.4.root", const char* name_out="fitresult_combo.root"){
//TFile File(filename);
//RooWorkspace * ws = test_combine(name_pbpb, name_pp);
TFile *f = new TFile("fitresult_combo_333.root") ;
RooWorkspace * ws1 = (RooWorkspace*) f->Get("wcombo");
//File.GetObject("wcombo", ws);
ws1->Print();
RooAbsData * data = ws1->data("data"); //dataOS, dataSS
// RooDataSet * US_data = (RooDataSet*) data->reduce( "QQsign == QQsign::PlusMinus");
// US_data->SetName("US_data");
// ws->import(* US_data);
// RooDataSet * hi_data = (RooDataSet*) US_data->reduce("dataCat == dataCat::hi");
// hi_data->SetName("hi_data");
// ws->import(* hi_data);
// hi_data->Print();
RooRealVar* raa3 = new RooRealVar("raa3","R_{AA}(#Upsilon (3S))",0.5,-1,1);
RooRealVar* leftEdge = new RooRealVar("leftEdge","leftEdge",0);
RooRealVar* rightEdge = new RooRealVar("rightEdge","rightEdge",1);
RooGenericPdf step("step", "step", "(@0 >= @1) && (@0 < @2)", RooArgList(*raa3, *leftEdge, *rightEdge));
ws1->import(step);
ws1->factory( "Uniform::flat(raa3)" );
//pp Luminosities, Taa and efficiency ratios Systematics
ws1->factory( "Taa_hi[5.662e-9]" );
ws1->factory( "Taa_kappa[1.062]" ); // was 1.057
ws1->factory( "expr::alpha_Taa('pow(Taa_kappa,beta_Taa)',Taa_kappa,beta_Taa[0,-5,5])" );
ws1->factory( "prod::Taa_nom(Taa_hi,alpha_Taa)" );
ws1->factory( "Gaussian::constr_Taa(beta_Taa,glob_Taa[0,-5,5],1)" );
ws1->factory( "lumipp_hi[5.4]" );
ws1->factory( "lumipp_kappa[1.037]" ); // was 1.06
ws1->factory( "expr::alpha_lumipp('pow(lumipp_kappa,beta_lumipp)',lumipp_kappa,beta_lumipp[0,-5,5])" );
ws1->factory( "prod::lumipp_nom(lumipp_hi,alpha_lumipp)" );
ws1->factory( "Gaussian::constr_lumipp(beta_lumipp,glob_lumipp[0,-5,5],1)" );
// ws->factory( "effRat1[1]" );
// ws->factory( "effRat2[1]" );
ws1->factory( "effRat3_hi[0.95]" );
ws1->factory( "effRat_kappa[1.054]" );
ws1->factory( "expr::alpha_effRat('pow(effRat_kappa,beta_effRat)',effRat_kappa,beta_effRat[0,-5,5])" );
// ws->factory( "prod::effRat1_nom(effRat1_hi,alpha_effRat)" );
ws1->factory( "Gaussian::constr_effRat(beta_effRat,glob_effRat[0,-5,5],1)" );
// ws->factory( "prod::effRat2_nom(effRat2_hi,alpha_effRat)" );
ws1->factory( "prod::effRat3_nom(effRat3_hi,alpha_effRat)" );
//
ws1->factory("Nmb_hi[1.161e9]");
ws1->factory("prod::denominator(Taa_nom,Nmb_hi)");
ws1->factory( "expr::lumiOverTaaNmbmodified('lumipp_nom/denominator',lumipp_nom,denominator)");
RooAbsReal *lumiOverTaaNmbmodified = ws1->function("lumiOverTaaNmbmodified"); //RooFormulaVar *lumiOverTaaNmbmodified = ws->function("lumiOverTaaNmbmodified");
//
// RooRealVar *raa1 = ws->var("raa1");
// RooRealVar* nsig1_pp = ws->var("nsig1_pp");
// RooRealVar* effRat1 = ws->function("effRat1_nom");
// RooRealVar *raa2 = ws->var("raa2");
// RooRealVar* nsig2_pp = ws->var("nsig2_pp");
// RooRealVar* effRat2 = ws->function("effRat2_nom");
RooRealVar* nsig3_pp = ws1->var("R_{#frac{3S}{1S}}_pp"); //RooRealVar* nsig3_pp = ws->var("N_{#Upsilon(3S)}_pp");
cout << nsig3_pp << endl;
RooAbsReal* effRat3 = ws1->function("effRat3_nom"); //RooRealVar* effRat3 = ws->function("effRat3_nom");
//
// RooFormulaVar nsig1_hi_modified("nsig1_hi_modified", "@0*@1*@3/@2", RooArgList(*raa1, *nsig1_pp, *lumiOverTaaNmbmodified, *effRat1));
// ws->import(nsig1_hi_modified);
// RooFormulaVar nsig2_hi_modified("nsig2_hi_modified", "@0*@1*@3/@2", RooArgList(*raa2, *nsig2_pp, *lumiOverTaaNmbmodified, *effRat2));
// ws->import(nsig2_hi_modified);
RooFormulaVar nsig3_hi_modified("nsig3_hi_modified", "@0*@1*@3/@2", RooArgList(*raa3, *nsig3_pp, *lumiOverTaaNmbmodified, *effRat3));
ws1->import(nsig3_hi_modified);
// // background yield with systematics
ws1->factory( "nbkg_hi_kappa[1.10]" );
ws1->factory( "expr::alpha_nbkg_hi('pow(nbkg_hi_kappa,beta_nbkg_hi)',nbkg_hi_kappa,beta_nbkg_hi[0,-5,5])" );
ws1->factory( "SUM::nbkg_hi_nom(alpha_nbkg_hi*bkgPdf_hi)" );
ws1->factory( "Gaussian::constr_nbkg_hi(beta_nbkg_hi,glob_nbkg_hi[0,-5,5],1)" );
RooAbsPdf* sig1S_hi = ws1->pdf("sig1S_hi"); //RooAbsPdf* sig1S_hi = ws->pdf("cbcb_hi");
RooAbsPdf* sig2S_hi = ws1->pdf("sig2S_hi");
RooAbsPdf* sig3S_hi = ws1->pdf("sig3S_hi");
RooAbsPdf* LSBackground_hi = ws1->pdf("nbkg_hi_nom");
RooRealVar* nsig1_hi = ws1->var("N_{#Upsilon(1S)}_hi");
RooRealVar* nsig2_hi = ws1->var("R_{#frac{2S}{1S}}_hi");
RooAbsReal* nsig3_hi = ws1->function("nsig3_hi_modified"); //RooFormulaVar* nsig3_hi = ws->function("nsig3_hi_modified");
cout << nsig1_hi << " " << nsig2_hi << " " << nsig3_pp << endl;
RooRealVar* norm_nbkg_hi = ws1->var("n_{Bkgd}_hi");
RooArgList pdfs_hi( *sig1S_hi,*sig2S_hi,*sig3S_hi, *LSBackground_hi);
RooArgList norms_hi(*nsig1_hi,*nsig2_hi,*nsig3_hi, *norm_nbkg_hi);
////////////////////////////////////////////////////////////////////////////////
ws1->factory( "nbkg_pp_kappa[1.03]" );
ws1->factory( "expr::alpha_nbkg_pp('pow(nbkg_pp_kappa,beta_nbkg_pp)',nbkg_pp_kappa,beta_nbkg_pp[0,-5,5])" );
ws1->factory( "SUM::nbkg_pp_nom(alpha_nbkg_pp*bkgPdf_pp)" );
ws1->factory( "Gaussian::constr_nbkg_pp(beta_nbkg_pp,glob_nbkg_pp[0,-5,5],1)" );
RooAbsPdf* sig1S_pp = ws1->pdf("sig1S_pp"); //RooAbsPdf* sig1S_pp = ws1->pdf("cbcb_pp");
RooAbsPdf* sig2S_pp = ws1->pdf("sig2S_pp");
RooAbsPdf* sig3S_pp = ws1->pdf("sig3S_pp");
RooAbsPdf* LSBackground_pp = ws1->pdf("nbkg_pp_nom");
RooRealVar* nsig1_pp = ws1->var("N_{#Upsilon(1S)}_pp");
RooRealVar* nsig2_pp = ws1->var("R_{#frac{2S}{1S}}_pp"); //RooRealVar* nsig2_pp = ws1->var("N_{#Upsilon(2S)}_pp");
// RooRealVar* nsig3_pp = ws1->var("N_{#Upsilon(3S)}_pp");
RooRealVar* norm_nbkg_pp = ws1->var("n_{Bkgd}_pp");
RooArgList pdfs_pp( *sig1S_pp,*sig2S_pp,*sig3S_pp, *LSBackground_pp);
RooArgList norms_pp( *nsig1_pp,*nsig2_pp,*nsig3_pp,*norm_nbkg_pp);
RooAddPdf model_num("model_num", "model_num", pdfs_hi,norms_hi);
ws1->import(model_num);
ws1->factory("PROD::model_hi(model_num, constr_nbkg_hi,constr_lumipp,constr_Taa,constr_effRat)");
RooAddPdf model_den("model_den", "model_den", pdfs_pp,norms_pp);
ws1->import(model_den);
ws1->factory("PROD::model_pp(model_den, constr_nbkg_pp)");
ws1->factory("SIMUL::joint(dataCat,hi=model_hi,pp=model_pp)");
/////////////////////////////////////////////////////////////////////
RooRealVar * pObs = ws1->var("invariantMass"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
obs.add( *ws1->cat("dataCat"));
// /////////////////////////////////////////////////////////////////////
ws1->var("glob_lumipp")->setConstant(true);
ws1->var("glob_Taa")->setConstant(true);
ws1->var("glob_effRat")->setConstant(true);
ws1->var("glob_nbkg_pp")->setConstant(true);
ws1->var("glob_nbkg_hi")->setConstant(true);
RooArgSet globalObs("global_obs");
globalObs.add( *ws1->var("glob_lumipp") );
globalObs.add( *ws1->var("glob_Taa") );
globalObs.add( *ws1->var("glob_effRat") );
globalObs.add( *ws1->var("glob_nbkg_hi") );
globalObs.add( *ws1->var("glob_nbkg_pp") );
cout << "66666" << endl;
// ws1->Print();
RooArgSet poi("poi");
poi.add( *ws1->var("raa3") );
cout << "77777" << endl;
// create set of nuisance parameters
RooArgSet nuis("nuis");
nuis.add( *ws1->var("beta_lumipp") );
nuis.add( *ws1->var("beta_nbkg_hi") );
nuis.add( *ws1->var("beta_nbkg_pp") );
nuis.add( *ws1->var("beta_Taa") );
nuis.add( *ws1->var("beta_effRat") );
cout << "88888" << endl;
ws1->var("#alpha_{CB}_hi")->setConstant(true);
ws1->var("#alpha_{CB}_pp")->setConstant(true);
ws1->var("#sigma_{CB1}_hi")->setConstant(true);
ws1->var("#sigma_{CB1}_pp")->setConstant(true);
ws1->var("#sigma_{CB2}/#sigma_{CB1}_hi")->setConstant(true);
ws1->var("#sigma_{CB2}/#sigma_{CB1}_pp")->setConstant(true);
//ws1->var("Centrality")->setConstant(true); //delete
ws1->var("N_{#varUpsilon(1S)}_hi")->setConstant(true);
ws1->var("N_{#varUpsilon(1S)}_pp")->setConstant(true);
//ws1->var("N_{#Upsilon(2S)}_hi")->setConstant(true);
//ws1->var("N_{#Upsilon(2S)}_pp")->setConstant(true);
//ws1->var("N_{#Upsilon(3S)}_pp")->setConstant(true);
ws1->var("R_{#frac{2S}{1S}}_hi")->setConstant(true); //new
ws1->var("R_{#frac{2S}{1S}}_pp")->setConstant(true); //new
ws1->var("R_{#frac{3S}{1S}}_hi")->setConstant(true); //new
ws1->var("R_{#frac{3S}{1S}}_pp")->setConstant(true); //new
ws1->var("Nmb_hi")->setConstant(true);
// ws1->var("QQsign")->setConstant(true);
ws1->var("Taa_hi")->setConstant(true);
ws1->var("Taa_kappa")->setConstant(true);
// ws1->var("beta_Taa")->setConstant(true);
// ws1->var("beta_effRat")->setConstant(true);
// ws1->var("beta_lumipp")->setConstant(true);
// ws1->var("beta_nbkg_hi")->setConstant(true);
// ws1->var("beta_nbkg_pp")->setConstant(true);
// ws1->var("dataCat")->setConstant(true);
ws1->var("decay_hi")->setConstant(true);
ws1->var("decay_pp")->setConstant(true);
ws1->var("effRat3_hi")->setConstant(true);
ws1->var("effRat_kappa")->setConstant(true);
// ws1->var("glob_Taa")->setConstant(true);
// ws1->var("glob_effRat")->setConstant(true);
// ws1->var("glob_lumipp")->setConstant(true);
// ws1->var("glob_nbkg_hi")->setConstant(true);
// ws1->var("glob_nbkg_pp")->setConstant(true);
// ws1->var("invariantMass")->setConstant(true);
ws1->var("leftEdge")->setConstant(true);
ws1->var("lumipp_hi")->setConstant(true);
ws1->var("lumipp_kappa")->setConstant(true);
ws1->var("m_{ #varUpsilon(1S)}_hi")->setConstant(true); //ws1->var("mass1S_hi")->setConstant(true);
ws1->var("m_{ #varUpsilon(1S)}_pp")->setConstant(true); //ws1->var("mass1S_pp")->setConstant(true);
ws1->var("muMinusPt")->setConstant(true);
ws1->var("muPlusPt")->setConstant(true);
ws1->var("n_{Bkgd}_hi")->setConstant(true);
ws1->var("n_{Bkgd}_pp")->setConstant(true);
ws1->var("nbkg_hi_kappa")->setConstant(true);
ws1->var("nbkg_pp_kappa")->setConstant(true);
//ws1->var("n_{CB}")->setConstant(true); //ws1->var("n_{CB}")->setConstant(true); //ws1->var("npow")->setConstant(true);
ws1->var("n_{CB}_hi")->setConstant(true); //ws1->var("n_{CB}")->setConstant(true); //ws1->var("npow")->setConstant(true);
ws1->var("n_{CB}_pp")->setConstant(true); //ws1->var("n_{CB}")->setConstant(true); //ws1->var("npow")->setConstant(true);
// ws1->var("raa3")->setConstant(true);
ws1->var("rightEdge")->setConstant(true);
ws1->var("sigmaFraction_hi")->setConstant(true);
ws1->var("sigmaFraction_pp")->setConstant(true);
ws1->var("turnOn_hi")->setConstant(true);
ws1->var("turnOn_pp")->setConstant(true);
ws1->var("dimuPt")->setConstant(true); //ws1->var("upsPt")->setConstant(true);
ws1->var("dimuRapidity")->setConstant(true); //ws1->var("upsRapidity")->setConstant(true);
ws1->var("vProb")->setConstant(true);
ws1->var("width_hi")->setConstant(true);
ws1->var("width_pp")->setConstant(true);
// ws1->var("x3raw")->setConstant(true);
// RooArgSet fixed_again("fixed_again");
// fixed_again.add( *ws1->var("leftEdge") );
// fixed_again.add( *ws1->var("rightEdge") );
// fixed_again.add( *ws1->var("Taa_hi") );
// fixed_again.add( *ws1->var("Nmb_hi") );
// fixed_again.add( *ws1->var("lumipp_hi") );
// fixed_again.add( *ws1->var("effRat1_hi") );
// fixed_again.add( *ws1->var("effRat2_hi") );
// fixed_again.add( *ws1->var("effRat3_hi") );
// fixed_again.add( *ws1->var("nsig3_pp") );
// fixed_again.add( *ws1->var("nsig1_pp") );
// fixed_again.add( *ws1->var("nbkg_hi") );
// fixed_again.add( *ws1->var("alpha") );
// fixed_again.add( *ws1->var("nbkg_kappa") );
// fixed_again.add( *ws1->var("Taa_kappa") );
// fixed_again.add( *ws1->var("lumipp_kappa") );
// fixed_again.add( *ws1->var("mean_hi") );
// fixed_again.add( *ws1->var("mean_pp") );
// fixed_again.add( *ws1->var("width_hi") );
// fixed_again.add( *ws1->var("turnOn_hi") );
// fixed_again.add( *ws1->var("bkg_a1_pp") );
// fixed_again.add( *ws1->var("bkg_a2_pp") );
// fixed_again.add( *ws1->var("decay_hi") );
// fixed_again.add( *ws1->var("raa1") );
// fixed_again.add( *ws1->var("raa2") );
// fixed_again.add( *ws1->var("nsig2_pp") );
// fixed_again.add( *ws1->var("sigma1") );
// fixed_again.add( *ws1->var("nbkg_pp") );
// fixed_again.add( *ws1->var("npow") );
// fixed_again.add( *ws1->var("muPlusPt") );
// fixed_again.add( *ws1->var("muMinusPt") );
// fixed_again.add( *ws1->var("mscale_hi") );
// fixed_again.add( *ws1->var("mscale_pp") );
//
// ws1->Print();
cout << "99999" << endl;
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *ws1 );
sbHypo.SetPdf( *ws1->pdf("joint") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *ws1->pdf("step") ); // this is optional
// ws1->Print();
/////////////////////////////////////////////////////////////////////
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data,NumCPU(10) );
cout << "111111" << endl;
RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
cout << "444444" << endl;
RooPlot *framepoi = ((RooRealVar *)poi.first())->frame(Bins(10),Range(0.,0.2),Title("LL and profileLL in raa3"));
cout << "222222" << endl;
pNll->plotOn(framepoi,ShiftToZero());
cout << "333333" << endl;
RooAbsReal * pProfile = pNll->createProfile( globalObs ); // do not profile global observables
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
pProfile->plotOn(framepoi,LineColor(kRed));
framepoi->SetMinimum(0);
framepoi->SetMaximum(3);
TCanvas *cpoi = new TCanvas();
cpoi->cd(); framepoi->Draw();
cpoi->SaveAs("cpoi.pdf");
((RooRealVar *)poi.first())->setMin(0.);
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
// pPoiAndNuisance->add(*sbHypo.GetNuisanceParameters());
// pPoiAndNuisance->add(*sbHypo.GetParametersOfInterest());
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
sbHypo.SetSnapshot(*pPoiAndNuisance);
RooPlot* xframeSB = pObs->frame(Title("SBhypo"));
data->plotOn(xframeSB,Cut("dataCat==dataCat::hi"));
RooAbsPdf *pdfSB = sbHypo.GetPdf();
RooCategory *dataCat = ws1->cat("dataCat");
pdfSB->plotOn(xframeSB,Slice(*dataCat,"hi"),ProjWData(*dataCat,*data));
TCanvas *c1 = new TCanvas();
c1->cd(); xframeSB->Draw();
c1->SaveAs("c1.pdf");
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
ws1->import( sbHypo );
/////////////////////////////////////////////////////////////////////
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*ws1);
pNll = bHypo.GetPdf()->createNLL( *data,NumCPU(2) );
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set raa3=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
RooPlot* xframeB = pObs->frame(Title("Bhypo"));
data->plotOn(xframeB,Cut("dataCat==dataCat::hi"));
RooAbsPdf *pdfB = bHypo.GetPdf();
pdfB->plotOn(xframeB,Slice(*dataCat,"hi"),ProjWData(*dataCat,*data));
TCanvas *c2 = new TCanvas();
c2->cd(); xframeB->Draw();
c2->SaveAs("c2.pdf");
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
bHypo.SetWorkspace(*ws1);
ws1->import( bHypo );
/////////////////////////////////////////////////////////////////////
ws1->Print();
bHypo.Print();
sbHypo.Print();
// save workspace to file
ws1 -> SaveAs(name_out);
return;
}
void build_hbb_workspace1( const char* infile = "outputfiles/input-file.txt", const char* outfile = "outputfiles/ws.root" ) {
//-------------------------------------------------------------------------
//-- Create workspace and other RooStats things.
printf("\n\n Creating workspace.\n\n") ;
RooWorkspace workspace("ws") ;
workspace.autoImportClassCode(true) ;
globalObservables = new RooArgSet("globalObservables");
allNuisances = new RooArgSet("allNuisances");
allNuisancePdfs = new RooArgSet("allNuisancePdfs");
RooArgSet* observedParametersList = new RooArgSet("observables") ;
//-------------------------------------------------------------------------
printf("\n\n Reading input file: %s\n\n", infile ) ;
float fileVal ;
char pname[1000] ;
char formula[1000] ;
sprintf( pname, "bins_of_met" ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
int bins_of_met = TMath::Nint( fileVal ) ;
//-- save bins_of_met in the workspace for convenience.
RooRealVar bom( "bins_of_met", "bins_of_met", bins_of_met, 0., 1000. ) ;
bom.setConstant(kTRUE) ;
workspace.import(bom) ;
//-- save bins_of_nb in the workspace for convenience.
RooRealVar bonb( "bins_of_nb", "bins_of_nb", bins_of_nb, 0., 1000. ) ;
bonb.setConstant(kTRUE) ;
workspace.import(bonb) ;
RooRealVar* rv_N_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_N_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_Rsigsb_corr[bins_of_nb][max_bins_of_met] ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "N_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msig[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msig[nbi][mbi] ) ;
sprintf( pname, "N_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msb[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msb[nbi][mbi] ) ;
sprintf( pname, "smc_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_smc_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_smc_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_smc_msig[nbi][mbi] -> setConstant( kTRUE ) ;
sprintf( pname, "smc_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_smc_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_smc_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_smc_msb[nbi][mbi] -> setConstant( kTRUE ) ;
float corrVal, corrSyst ;
sprintf( pname, "Rsigsb_syst_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrSyst ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
sprintf( pname, "Rsigsb_corr_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_Rsigsb_corr[nbi][mbi] = makeLognormalConstraint( pname, corrVal, corrSyst ) ;
} // mbi.
} // nbi.
//-- Finished reading input from file.
//-------------------------------------------------------------------------
printf("\n\n Creating and importing dataset into workspace.\n\n") ;
RooDataSet* dsObserved = new RooDataSet("hbb_observed_rds", "hbb observed data values", *observedParametersList ) ;
dsObserved -> add( *observedParametersList ) ;
workspace.import( *dsObserved ) ;
//-------------------------------------------------------------------------
//-- Define all floats.
printf("\n\n Defining all unconstrained floats (Ratios, signal strength).\n\n") ;
double R_msigmsb_initialval(0.15) ;
RooRealVar* rv_R_msigmsb[50] ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "R_msigmsb_met%d", mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_R_msigmsb[mbi] = new RooRealVar( pname, pname, R_msigmsb_initialval, 0., 3. ) ;
rv_R_msigmsb[mbi] -> setConstant( kFALSE ) ;
rv_R_msigmsb[mbi] -> Print() ;
} // mbi.
printf("\n") ;
sprintf( pname, "sig_strength" ) ;
RooRealVar* rv_sig_strength = new RooRealVar( pname, pname, 1.0, 0., 10. ) ;
rv_sig_strength -> setConstant(kFALSE) ;
rv_sig_strength -> Print() ;
printf(" %s\n\n", pname ) ;
//-------------------------------------------------------------------------
//-- Define all mu parameters.
printf("\n\n Defining mu parameters.\n\n") ;
RooAbsReal* rv_mu_bg_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_bg_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "mu_bg_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msb[nbi][mbi] = new RooRealVar( pname, pname, rv_N_msb[nbi][mbi] -> getVal(), 0., 1.e6 ) ;
rv_mu_bg_msb[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1 * @2" ) ;
sprintf( pname, "mu_bg_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_Rsigsb_corr[nbi][mbi], *rv_R_msigmsb[mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_mu_bg_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1" ) ;
sprintf( pname, "mu_sig_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rv_smc_msig[nbi][mbi] ) ) ;
rv_mu_sig_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1" ) ;
sprintf( pname, "mu_sig_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rv_smc_msb[nbi][mbi] ) ) ;
rv_mu_sig_msb[nbi][mbi] -> Print() ;
} // mbi.
} // nbi.
//-- Finished defining mu parameters.
//-------------------------------------------------------------------------
//-- Defining small n's
printf("\n\n Defining small n's.\n\n") ;
RooAbsReal* rv_n_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_n_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( formula, "@0 + @1" ) ;
sprintf( pname, "n_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msig[nbi][mbi], *rv_mu_bg_msig[nbi][mbi] ) ) ;
rv_n_msig[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msig[nbi][mbi] ) ;
sprintf( pname, "n_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msb[nbi][mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_n_msb[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Define the Poisson pdfs for the observables.
printf("\n\n Defining Poisson pdfs for the observables.\n\n") ;
RooAbsReal* rv_pdf_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_pdf_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooArgSet pdflist ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "pdf_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msig[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msig[nbi][mbi], *rv_n_msig[nbi][mbi] ) ;
rv_pdf_msig[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msig[nbi][mbi] ) ;
sprintf( pname, "pdf_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msb[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msb[nbi][mbi], *rv_n_msb[nbi][mbi] ) ;
rv_pdf_msb[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Build the likelihood.
printf("\n\n Building the likelihood.\n\n") ;
pdflist.add( *allNuisancePdfs ) ;
pdflist.Print() ;
printf("\n") ;
RooProdPdf* likelihood = new RooProdPdf( "likelihood", "hbb likelihood", pdflist ) ;
likelihood->Print() ;
//-------------------------------------------------------------------------
// printf("\n\n Running a test fit.\n\n") ;
// dsObserved -> Print() ;
// dsObserved -> printMultiline(cout, 1, kTRUE, "") ;
// printf("\n\n =============================================\n\n") ;
// likelihood -> fitTo( *dsObserved, PrintLevel(3), Hesse(0), Minos(0) ) ;
// printf("\n\n =============================================\n\n") ;
//-- Set up RooStats models.
printf("\n\n Setting up S+B model.\n\n") ;
RooArgSet poi( *rv_sig_strength, "poi" ) ;
RooUniform signal_prior( "signal_prior", "signal_prior", *rv_sig_strength ) ;
ModelConfig sbModel ("SbModel");
sbModel.SetWorkspace( workspace ) ;
sbModel.SetPdf( *likelihood ) ;
sbModel.SetParametersOfInterest( poi );
sbModel.SetPriorPdf(signal_prior);
sbModel.SetObservables( *observedParametersList );
sbModel.SetNuisanceParameters( *allNuisances );
sbModel.SetGlobalObservables( *globalObservables );
workspace.Print() ;
printf("\n\n Doing fit for S+B model.\n" ) ; fflush(stdout) ;
RooAbsReal* pNll = sbModel.GetPdf()->createNLL(*dsObserved);
RooAbsReal* pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal();
RooArgSet* pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*sbModel.GetParametersOfInterest());
if(sbModel.GetNuisanceParameters()) pPoiAndNuisance->add(*sbModel.GetNuisanceParameters());
printf("\n\n Will save these parameter points that correspond to the fit to data.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
sbModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (sbModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
printf("\n\n Setting up BG-only model.\n\n") ;
ModelConfig bModel (*(RooStats::ModelConfig *)workspace.obj("SbModel"));
bModel.SetName("BModel");
bModel.SetWorkspace(workspace);
printf("\n\n Doing fit for BG-only model.\n" ) ; fflush(stdout) ;
pNll = bModel.GetPdf()->createNLL(*dsObserved);
pProfile = pNll->createProfile(*bModel.GetParametersOfInterest());
((RooRealVar *)(bModel.GetParametersOfInterest()->first()))->setVal(0.);
pProfile->getVal();
pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*bModel.GetParametersOfInterest());
if(bModel.GetNuisanceParameters()) pPoiAndNuisance->add(*bModel.GetNuisanceParameters());
printf("\n\n Should use these parameter points to generate pseudo data for bkg only.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
bModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (bModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
workspace.Print() ;
printf("\n\n Saving workspace in : %s\n\n", outfile ) ;
gSystem->Exec(" mkdir -p outputfiles " ) ;
workspace.writeToFile( outfile ) ;
} // build_hbb_workspace1.
prepDataFiles(){
// TDirectory *theDr = (TDirectory*) myFile->Get("eleIDdir");///denom_pt/fit_eff_plots");
//theDr->ls();
int myIndex;
TSystemDirectory dir(thePath, thePath);
TSystemFile *file;
TString fname;
TIter next(dir.GetListOfFiles());
while ((file=(TSystemFile*)next())) {
fname = file->GetName();
if (fname.BeginsWith("TnP")&& fname.Contains("mc")) {
ofstream myfile;
TFile *myFile = new TFile(fname);
TIter nextkey(myFile->GetListOfKeys());
TKey *key;
while (key = (TKey*)nextkey()) {
TString theTypeClasse = key->GetClassName();
TString theNomClasse = key->GetTitle();
if ( theTypeClasse == "TDirectoryFile"){
TDirectory *theDr = (TDirectory*) myFile->Get(theNomClasse);
TIter nextkey2(theDr->GetListOfKeys());
TKey *key2;
while (key2 = (TKey*)nextkey2()) {
TString theTypeClasse2 = key2->GetClassName();
TString theNomClasse2 = key2->GetTitle();
myfile.open (theNomClasse2+".info");
if ( theTypeClasse == "TDirectoryFile"){
cout << "avant " << endl;
TDirectory *theDr2 = (TDirectory*) myFile->Get(theNomClasse+"/"+theNomClasse2);
cout << "apres " << endl;
TIter nextkey3(theDr2->GetListOfKeys());
TKey *key3;
while (key3 = (TKey*)nextkey3()) {
TString theTypeClasse3 = key3->GetClassName();
TString theNomClasse3 = key3->GetTitle();
if ((theNomClasse3.Contains("FromMC"))) {
TString localClasse3 = theNomClasse3;
localClasse3.ReplaceAll("__","%");
cout << "apres " << localClasse3 << endl;
TObjArray* listBin = localClasse3.Tokenize('%');
TString first = ((TObjString*)listBin->At(0))->GetString();
TString second = ((TObjString*)listBin->At(2))->GetString();
myfile << first;
myfile << " " << second << " ";
cout << "coucou la on va récupérer le rooFitResult " << endl;
RooFitResult *theResults = (RooFitResult*) myFile->Get(theNomClasse+"/"+theNomClasse2+"/"+theNomClasse3+"/fitresults");
theResults->Print();
RooArgList theParam = theResults->floatParsFinal();
int taille = theParam.getSize();
for (int m = 0 ; m < taille ; m++){
cout << "m=" << m << endl;
RooAbsArg *theArg = (RooAbsArg*) theParam.at(m);
RooAbsReal *theReal = (RooAbsReal*) theArg;
myfile << theReal->getVal() << " " ;
}
myfile << "\n";
}
}
}
myfile.close();
}
}
}
delete myFile;
}
}
}
void plot_pll(TString fname="monoh_withsm_SRCR_bg11.7_bgslop-0.0_nsig0.0.root")
{
SetAtlasStyle();
TFile* file = TFile::Open(fname);
RooWorkspace* wspace = (RooWorkspace*) file->Get("wspace");
cout << "\n\ncheck that eff and reco terms included in BSM component to make fiducial cross-section" <<endl;
wspace->function("nsig")->Print();
RooRealVar* reco = wspace->var("reco");
if( wspace->function("nsig")->dependsOn(*reco) ) {
cout << "all good." <<endl;
} else {
cout << "need to rerun fit_withsm using DO_FIDUCIAL_LIMIT true" <<endl;
return;
}
/*
// DANGER
// TEST WITH EXAGGERATED UNCERTAINTY
wspace->var("unc_theory")->setMax(1);
wspace->var("unc_theory")->setVal(1);
wspace->var("unc_theory")->Print();
*/
// this was for making plot about decoupling/recoupling approach
TCanvas* tc = new TCanvas("tc","",400,400);
RooPlot *frame = wspace->var("xsec_bsm")->frame();
RooAbsPdf* pdfc = wspace->pdf("jointModeld");
RooAbsData* data = wspace->data("data");
RooAbsReal *nllJoint = pdfc->createNLL(*data, RooFit::Constrained()); // slice with fixed xsec_bsm
RooAbsReal *profileJoint = nllJoint->createProfile(*wspace->var("xsec_bsm"));
wspace->allVars().Print("v");
pdfc->fitTo(*data);
wspace->allVars().Print("v");
wspace->var("xsec_bsm")->Print();
double nllmin = 2*nllJoint->getVal();
wspace->var("xsec_bsm")->setVal(0);
double nll0 = 2*nllJoint->getVal();
cout << Form("nllmin = %f, nll0 = %f, Z=%f", nllmin, nll0, sqrt(nll0-nllmin)) << endl;
nllJoint->plotOn(frame, RooFit::LineColor(kGreen), RooFit::LineStyle(kDotted), RooFit::ShiftToZero(), RooFit::Name("nll_statonly")); // no error
profileJoint->plotOn(frame,RooFit::Name("pll") );
wspace->var("xsec_sm")->Print();
wspace->var("theory")->Print();
wspace->var("theory")->setConstant();
profileJoint->plotOn(frame, RooFit::LineColor(kRed), RooFit::LineStyle(kDashed), RooFit::Name("pll_smfixed") );
frame->GetXaxis()->SetTitle("#sigma_{BSM, fid} [fb]");
frame->GetYaxis()->SetTitle("-log #lambda ( #sigma_{BSM, fid} )");
double temp = frame->GetYaxis()->GetTitleOffset();
frame->GetYaxis()->SetTitleOffset( 1.1* temp );
frame->SetMinimum(1e-7);
frame->SetMaximum(4);
// Legend
double x1,y1,x2,y2;
GetX1Y1X2Y2(tc,x1,y1,x2,y2);
TLegend *legend_sr=FastLegend(x2-0.75,y2-0.3,x2-0.25,y2-0.5,0.045);
legend_sr->AddEntry(frame->findObject("pll"),"with #sigma_{SM} uncertainty","L");
legend_sr->AddEntry(frame->findObject("pll_smfixed"),"with #sigma_{SM} constant","L");
legend_sr->AddEntry(frame->findObject("nll_statonly"),"no systematics","L");
frame->Draw();
legend_sr->Draw("SAME");
// descriptive text
vector<TString> pavetext11;
pavetext11.push_back("#bf{#it{ATLAS Internal}}");
pavetext11.push_back("#sqrt{#it{s}} = 8 TeV #scale[0.6]{#int}Ldt = 20.3 fb^{-1}");
pavetext11.push_back("#it{H}+#it{E}_{T}^{miss} , #it{H #rightarrow #gamma#gamma}, #it{m}_{#it{H}} = 125.4 GeV");
TPaveText* text11=CreatePaveText(x2-0.75,y2-0.25,x2-0.25,y2-0.05,pavetext11,0.045);
text11->Draw();
tc->SaveAs("pll.pdf");
/*
wspace->var("xsec_bsm")->setConstant(true);
wspace->var("eff" )->setConstant(true);
wspace->var("mh" )->setConstant(true);
wspace->var("sigma_h" )->setConstant(true);
wspace->var("lumi" )->setConstant(true);
wspace->var("xsec_sm" )->setVal(v_xsec_sm);
wspace->var("eff" )->setVal(1.0);
wspace->var("lumi" )->setVal(v_lumi);
TH1* nllHist = profileJoint->createHistogram("xsec_bsm",100);
TFile* out = new TFile("nllHist.root","REPLACE");
nllHist->Write()
out->Write();
out->Close();
*/
}
void combinedWorkspace_4WS(const char* name_pbpb_pass="******", const char* name_pbpb_fail="fitresult_pbpb_fail.root", const char* name_pp_pass="******", const char* name_pp_fail="fitresult_pp_fail.root", const char* name_out="fitresult_combo.root", const float systval = 0., const char* subDirName ="wsTest", int nCPU=2){
// subdir: Directory to save workspaces under currentPATH/CombinedWorkspaces/subDir/
// set things silent
gErrorIgnoreLevel=kError;
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
bool dosyst = (systval > 0.);
TString nameOut(name_out);
RooWorkspace * ws = test_combine_4WS(name_pbpb_pass, name_pp_pass, name_pbpb_fail, name_pp_fail, false, nCPU);
RooAbsData * data = ws->data("dOS_DATA");
RooRealVar* RFrac2Svs1S_PbPbvsPP_P = ws->var("RFrac2Svs1S_PbPbvsPP_P");
RooRealVar* leftEdge = new RooRealVar("leftEdge","leftEdge",-10);
RooRealVar* rightEdge = new RooRealVar("rightEdge","rightEdge",10);
RooGenericPdf step("step", "step", "(@0 >= @1) && (@0 < @2)", RooArgList(*RFrac2Svs1S_PbPbvsPP_P, *leftEdge, *rightEdge));
ws->import(step);
ws->factory( "Uniform::flat(RFrac2Svs1S_PbPbvsPP_P)" );
// systematics
if (dosyst) {
ws->factory( Form("kappa_syst[%f]",systval) );
ws->factory( "expr::alpha_syst('kappa_syst*beta_syst',kappa_syst,beta_syst[0,-5,5])" );
ws->factory( "Gaussian::constr_syst(beta_syst,glob_syst[0,-5,5],1)" );
// add systematics into the double ratio
ws->factory( "expr::RFrac2Svs1S_PbPbvsPP_P_syst('@0+@1',RFrac2Svs1S_PbPbvsPP_P,alpha_syst)" );
// build the pbpb pdf
RooRealVar* effjpsi_pp_P = (RooRealVar*)ws->var("effjpsi_pp_P");
RooRealVar* effpsip_pp_P = (RooRealVar*)ws->var("effpsip_pp_P");
RooRealVar* effjpsi_pp_NP = (RooRealVar*)ws->var("effjpsi_pp_NP");
Double_t Npsi2SPbPbPass = npsip_pbpb_pass_from_doubleratio_prompt(ws, RooArgList(*effjpsi_pp_P,*effpsip_pp_P,*effjpsi_pp_NP),true); // Create and import N_Psi2S_PbPb_pass_syst
ws->factory( "SUM::pdfMASS_Tot_PbPb_pass_syst(N_Jpsi_PbPb_pass * pdfMASS_Jpsi_PbPb_pass, N_Psi2S_PbPb_pass_syst * pdfMASS_Psi2S_PbPb_pass, N_Bkg_PbPb_pass * pdfMASS_Bkg_PbPb_pass)" );
ws->factory( "PROD::pdfMASS_Tot_PbPb_pass_constr(pdfMASS_Tot_PbPb_pass_syst,constr_syst)" );
// build the combined pdf
ws->factory("SIMUL::simPdf_syst_noconstr(sample,PbPb_pass=pdfMASS_Tot_PbPb_pass_syst,PbPb_fail=pdfMASS_Tot_PbPb_fail,PP_pass=pdfMASS_Tot_PP_pass,PP_fail=pdfMASS_Tot_PP_fail)");
RooSimultaneous *simPdf = (RooSimultaneous*) ws->pdf("simPdf_syst_noconstr");
RooGaussian *constr_syst = (RooGaussian*) ws->pdf("constr_syst");
RooProdPdf *simPdf_constr = new RooProdPdf("simPdf_syst","simPdf_syst",RooArgSet(*simPdf,*constr_syst));
ws->import(*simPdf_constr);
} else {
ws->factory("SIMUL::simPdf_syst(sample,PbPb_pass=pdfMASS_Tot_PbPb_pass,PbPb_fail=pdfMASS_Tot_PbPb_fail,PP_pass=pdfMASS_Tot_PP_pass,PP_fail=pdfMASS_Tot_PP_fail)");
}
ws->Print();
if (dosyst) ws->var("beta_syst")->setConstant(kFALSE);
/////////////////////////////////////////////////////////////////////
RooRealVar * pObs = ws->var("invMass"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
obs.add( *ws->cat("sample"));
// /////////////////////////////////////////////////////////////////////
if (dosyst) ws->var("glob_syst")->setConstant(true);
RooArgSet globalObs("global_obs");
if (dosyst) globalObs.add( *ws->var("glob_syst") );
// ws->Print();
RooArgSet poi("poi");
poi.add( *ws->var("RFrac2Svs1S_PbPbvsPP_P") );
// create set of nuisance parameters
RooArgSet nuis("nuis");
if (dosyst) nuis.add( *ws->var("beta_syst") );
// set parameters constant
RooArgSet allVars = ws->allVars();
TIterator* it = allVars.createIterator();
RooRealVar *theVar = (RooRealVar*) it->Next();
while (theVar) {
TString varname(theVar->GetName());
// if (varname != "RFrac2Svs1S_PbPbvsPP"
// && varname != "invMass"
// && varname != "sample"
// )
// theVar->setConstant();
if ( varname.Contains("f_Jpsi_PP") || varname.Contains("f_Jpsi_PbPb") ||
varname.Contains("rSigma21_Jpsi_PP") ||
varname.Contains("m_Jpsi_PP") || varname.Contains("m_Jpsi_PbPb") ||
varname.Contains("sigma1_Jpsi_PP") || varname.Contains("sigma1_Jpsi_PbPb") ||
(varname.Contains("lambda")) ||
(varname.Contains("_fail") && !varname.Contains("RFrac2Svs1S")))
{
theVar->setConstant();
}
if (varname=="glob_syst"
|| varname=="beta_syst"
) {
cout << varname << endl;
theVar->setConstant(!dosyst);
}
theVar = (RooRealVar*) it->Next();
}
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *ws );
sbHypo.SetPdf( *ws->pdf("simPdf_syst") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *ws->pdf("step") ); // this is optional
/////////////////////////////////////////////////////////////////////
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data,NumCPU(nCPU) );
RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
if (controlPlots)
{
RooPlot *framepoi = ((RooRealVar *)poi.first())->frame(Bins(10),Range(0.,1),Title("LL and profileLL in RFrac2Svs1S_PbPbvsPP_P"));
pNll->plotOn(framepoi,ShiftToZero());
framepoi->SetMinimum(0);
framepoi->SetMaximum(10);
TCanvas *cpoi = new TCanvas();
cpoi->cd(); framepoi->Draw();
cpoi->SaveAs("cpoi.pdf");
}
((RooRealVar *)poi.first())->setMin(0.);
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
sbHypo.SetSnapshot(*pPoiAndNuisance);
if (controlPlots)
{
RooPlot* xframeSB_PP_pass = pObs->frame(Title("SBhypo_PP_pass"));
data->plotOn(xframeSB_PP_pass,Cut("sample==sample::PP_pass"));
RooAbsPdf *pdfSB_PP_pass = sbHypo.GetPdf();
RooCategory *sample = ws->cat("sample");
pdfSB_PP_pass->plotOn(xframeSB_PP_pass,Slice(*sample,"PP_pass"),ProjWData(*sample,*data));
TCanvas *c1 = new TCanvas();
c1->cd(); xframeSB_PP_pass->Draw();
c1->SaveAs("c1.pdf");
RooPlot* xframeSB_PP_fail = pObs->frame(Title("SBhypo_PP_fail"));
data->plotOn(xframeSB_PP_fail,Cut("sample==sample::PP_fail"));
RooAbsPdf *pdfSB_PP_fail = sbHypo.GetPdf();
pdfSB_PP_fail->plotOn(xframeSB_PP_fail,Slice(*sample,"PP_fail"),ProjWData(*sample,*data));
TCanvas *c2 = new TCanvas();
c2->cd(); xframeSB_PP_fail->Draw();
c2->SaveAs("c1.pdf");
RooPlot* xframeB_PbPb_pass = pObs->frame(Title("SBhypo_PbPb_pass"));
data->plotOn(xframeB_PbPb_pass,Cut("sample==sample::PbPb_pass"));
RooAbsPdf *pdfB_PbPb_pass = sbHypo.GetPdf();
pdfB_PbPb_pass->plotOn(xframeB_PbPb_pass,Slice(*sample,"PbPb_pass"),ProjWData(*sample,*data));
TCanvas *c3 = new TCanvas();
c3->cd(); xframeB_PbPb_pass->Draw();
c3->SetLogy();
c3->SaveAs("c2.pdf");
RooPlot* xframeB_PbPb_fail = pObs->frame(Title("SBhypo_PbPb_fail"));
data->plotOn(xframeB_PbPb_fail,Cut("sample==sample::PbPb_fail"));
RooAbsPdf *pdfB_PbPb_fail = sbHypo.GetPdf();
pdfB_PbPb_fail->plotOn(xframeB_PbPb_fail,Slice(*sample,"PbPb_fail"),ProjWData(*sample,*data));
TCanvas *c4 = new TCanvas();
c4->cd(); xframeB_PbPb_fail->Draw();
c4->SetLogy();
c4->SaveAs("c2.pdf");
}
delete pNll;
delete pPoiAndNuisance;
ws->import( sbHypo );
/////////////////////////////////////////////////////////////////////
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*ws);
pNll = bHypo.GetPdf()->createNLL( *data,NumCPU(nCPU) );
// RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
RooAbsReal * pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set RFrac2Svs1S_PbPbvsPP=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
bHypo.SetWorkspace(*ws);
ws->import( bHypo );
/////////////////////////////////////////////////////////////////////
ws->Print();
bHypo.Print();
sbHypo.Print();
// save workspace to file
string mainDIR = gSystem->ExpandPathName(gSystem->pwd());
string wsDIR = mainDIR + "/CombinedWorkspaces/";
string ssubDirName="";
if (subDirName) ssubDirName.append(subDirName);
string subDIR = wsDIR + ssubDirName;
void * dirp = gSystem->OpenDirectory(wsDIR.c_str());
if (dirp) gSystem->FreeDirectory(dirp);
else gSystem->mkdir(wsDIR.c_str(), kTRUE);
void * dirq = gSystem->OpenDirectory(subDIR.c_str());
if (dirq) gSystem->FreeDirectory(dirq);
else gSystem->mkdir(subDIR.c_str(), kTRUE);
const char* saveName = Form("%s/%s",subDIR.c_str(),nameOut.Data());
ws->writeToFile(saveName);
}
void PurityFit(const int _mode){
TChain* tree = new TChain("TEvent");
// tree->Add("/home/vitaly/B0toDh0/TMVA/FIL_b2dh_gen_0-1.root");
tree->Add("/home/vitaly/B0toDh0/TMVA/FIL1_b2dh_uds_2_12.root");
tree->Add("/home/vitaly/B0toDh0/TMVA/FIL1_b2dh_charm_2_12.root");
tree->Add("/home/vitaly/B0toDh0/TMVA/FIL1_b2dh_charged_2_12.root");
tree->Add("/home/vitaly/B0toDh0/TMVA/FIL1_b2dh_mixed_2_12.root");
gROOT->ProcessLine(".L pdfs/RooRhoDeltaEPdf.cxx+");
RooCategory b0f("b0f","b0f");
b0f.defineType("signal",1);
b0f.defineType("fsr",10);
b0f.defineType("bad_pi0",5);
b0f.defineType("rho2",2);
b0f.defineType("rho3",3);
b0f.defineType("rho4",4);
b0f.defineType("rho11",11);
b0f.defineType("comb",-1);
RooCategory mode("mode","mode");
RooCategory h0mode("h0mode","h0mode");
double BDTG_MIN = 0;
double BDTG_MAX = 1;
bool gg_flag = true;
double Mbc_min;
double Mbc_max;
double dE_min;
double dE_max;
int m_mode,m_h0mode;
double mh0_min, mh0_max;
string label;
switch(_mode){
case 1:
label = string("#pi^{0}");
BDTG_MIN = bdt_cut_pi0;
mode.defineType("pi0",1);
h0mode.defineType("gg",10);
Mbc_min = mbc_min_pi0;
Mbc_max = mbc_max_pi0;
dE_min = de_min_pi0;
dE_max = de_max_pi0;
m_mode = 1;
m_h0mode = 10;
mh0_min = mpi0_min;
mh0_max = mpi0_max;
break;
case 2:
label = string("#eta#rightarrow#gamma#gamma");
BDTG_MIN = bdtg_cut_etagg;
mode.defineType("eta",2);
h0mode.defineType("gg",10);
Mbc_min = mbc_min;
Mbc_max = mbc_max;
dE_min = de_min;
dE_max = de_max;
m_mode = 2;
m_h0mode = 10;
mh0_min = EtaGGMass-metagg_cut;
mh0_max = EtaGGMass+metagg_cut;
break;
case 3:
label = string("#eta#rightarrow#pi^{+}#pi^{-}#pi^{0}");
BDTG_MIN = bdtg_cut_etappp;
gg_flag = false;
mode.defineType("eta",2);
h0mode.defineType("ppp",20);
Mbc_min = mbc_min;
Mbc_max = mbc_max;
dE_min = de_min_etappp;
dE_max = de_max_etappp;
m_mode = 2;
m_h0mode = 20;
mh0_min = EtaMass-metappp_cut;
mh0_max = EtaMass+metappp_cut;
break;
case 4:
label = string("#omega");
BDTG_MIN = bdtg_cut_omega;
gg_flag = false;
mode.defineType("omega",3);
h0mode.defineType("ppp",20);
Mbc_min = mbc_min_omega;
Mbc_max = mbc_max_omega;
dE_min = de_min_omega;
dE_max = de_max_omega;
m_mode = 3;
m_h0mode = 20;
mh0_min = OmegaMass-momega_cut;
mh0_max = OmegaMass+momega_cut;
break;
default:
return;
}
RooArgSet argset;
argset.add(mode);
argset.add(h0mode);
argset.add(b0f);
RooCategory flv("flv_mc","flv_mc");
flv.defineType("B0",1);
flv.defineType("anti-B0",-1);
argset.add(flv);
RooCategory bin("bin","bin");
bin.defineType("1",1); bin.defineType("-1",-1);
bin.defineType("2",2); bin.defineType("-2",-2);
bin.defineType("3",3); bin.defineType("-3",-3);
bin.defineType("4",4); bin.defineType("-4",-4);
bin.defineType("5",5); bin.defineType("-5",-5);
bin.defineType("6",6); bin.defineType("-6",-6);
bin.defineType("7",7); bin.defineType("-7",-7);
bin.defineType("8",8); bin.defineType("-8",-8);
argset.add(bin);
RooSuperCategory binflv("binflv","binflv",RooArgSet(bin,flv));
const double mbcMin = 5.20;
const double mbcMax = 5.2885;
const double deMin = -0.15;
const double deMax = 0.3;
const double elliscaleDe = TMath::Sqrt(4./TMath::Pi());
const double elliscaleMbc = TMath::Sqrt(4./TMath::Pi());
RooRealVar mbc_center("mbc_center","mbc_center",0.5*(Mbc_min+Mbc_max),Mbc_min,Mbc_max); mbc_center.setConstant(kTRUE);
RooRealVar mbc_center_eq("mbc_center_eq","mbc_center_eq",mr_argedge_3-0.5*(Mbc_max-Mbc_min)*elliscaleMbc,Mbc_min,Mbc_max); mbc_center_eq.setConstant(kTRUE);
RooRealVar de_center("de_center","de_center",0.5*(dE_min+dE_max),dE_min,dE_max); de_center.setConstant(kTRUE);
RooRealVar mbc_radius("mbc_radius","mbc_radius",0.5*(Mbc_max-Mbc_min)*elliscaleMbc,0,0.5*(mbcMax-mbcMin)); mbc_radius.setConstant(kTRUE);
RooRealVar de_radius("de_radius","de_radius",0.5*(dE_max-dE_min)*elliscaleDe,0.,0.5*(deMax-deMin)); de_radius.setConstant(kTRUE);
RooRealVar mbc_radius1("mbc_radius1","mbc_radius1",0.5*(Mbc_max-Mbc_min),0,0.5*(mbcMax-mbcMin)); mbc_radius1.setConstant(kTRUE);
RooRealVar de_radius1("de_radius1","de_radius1",0.5*(dE_max-dE_min),0.,0.5*(deMax-deMin)); de_radius1.setConstant(kTRUE);
cout << 0.5*(Mbc_min+Mbc_max) << " " << 0.5*(Mbc_max-Mbc_min) << endl;
cout << 0.5*(dE_min+dE_max) << " " << 0.5*(dE_max-dE_min) << endl;
mbc_center.Print();
mbc_center_eq.Print();
RooRealVar mbc("mbc","M_{bc}",0.5*(Mbc_min+Mbc_max),mbcMin,mbcMax,"GeV"); argset.add(mbc);
mbc.setRange("Signal",Mbc_min,Mbc_max);
mbc.setRange("mbcSignal",Mbc_min,Mbc_max);
mbc.setRange("deSignal",mbcMin,mbcMax);
RooRealVar de("de","#DeltaE",deMin,deMax,"GeV"); argset.add(de);
de.setRange("Signal",dE_min,dE_max);
de.setRange("mbcSignal",deMin,deMax);
de.setRange("deSignal",dE_min,dE_max);
// de.setRange("Ellips",dE_min,dE_max);
// RooFormulaVar mbclo("mbclo","@1-@2*TMath::Sqrt(1-(@0-@3)/@4*(@0-@3)/@4+0.00001)",RooArgSet(de,mbc_center,mbc_radius,de_center,de_radius));
// RooFormulaVar mbchi("mbchi","@1+@2*TMath::Sqrt(1-(@0-@3)/@4*(@0-@3)/@4+0.00001)",RooArgSet(de,mbc_center,mbc_radius,de_center,de_radius));
// mbc.setRange("Ellips",mbclo,mbchi);
RooRealVar md("md","md",DMass-md_cut,DMass+md_cut,"GeV"); argset.add(md);
RooRealVar mk("mk","mk",KMass-mk_cut,KMass+mk_cut,"GeV"); argset.add(mk);
RooRealVar mh0("mh0","mh0",mh0_min,mh0_max,"GeV"); argset.add(mh0);
RooRealVar mpi0("mpi0","mpi0",mpi0_min,mpi0_max,"GeV"); if(_mode!=2) argset.add(mpi0);
RooRealVar bdt("bdt","bdt",BDTG_MIN,BDTG_MAX); argset.add(bdt);
argset.add(b0f);
RooDataSet ds_sig("ds_sig","ds_sig",tree,argset,"mbc>0||mbc<=0 && (b0f == 1 || b0f == 5 || b0f == 10)");
RooDataSet ds_bkg("ds_bkg","ds_bkg",tree,argset,"mbc>0||mbc<=0 && !(b0f == 1 || b0f == 5 || b0f == 10)");
RooDataHist dh("dh","dh");
dh.add(ds_sig,"",1./0.563);
dh.add(ds_bkg,"",1./0.949);
stringstream out;
out.str("");
out << "de<" << dE_max << " && de>" << dE_min;
out << " && mbc>" << Mbc_min << " && mbc<" << Mbc_max;
Roo1DTable* sigtable = ds.table(b0f,out.str().c_str());
sigtable->Print();
sigtable->Print("v");
Roo1DTable* fulltable = ds.table(b0f);
fulltable->Print();
fulltable->Print("v");
// RooDataHist* dh = ds0->binnedClone();
ds.Print();
int _b0f = -1;
////////////////
// Signal PDF //
////////////////
////////////
// de pdf //
////////////
if(gg_flag){
RooRealVar de0("de0","de0",get_de0(m_mode,m_h0mode,_b0f),-0.2,0.1); if(cSig) de0.setConstant(kTRUE);
RooRealVar s1("s1","s1",get_s1(m_mode,m_h0mode,_b0f),0.,0.5); if(cSig) s1.setConstant(kTRUE);
RooGaussian g1("g1","g1",de,de0,s1);
RooRealVar deCBl("deCBl","deCBl",get_deCBl(m_mode,m_h0mode,_b0f),-0.2,0.1); if(cSig) deCBl.setConstant(kTRUE);
RooRealVar sCBl("sCBl","sCBl",get_sCBl(m_mode,m_h0mode,_b0f),0.,0.5); if(cSig) sCBl.setConstant(kTRUE);
RooRealVar alphal("alphal","alphal", get_alphal(m_mode,m_h0mode,_b0f), 0.,10.); if(cSIG) alphal.setConstant(kTRUE);
RooRealVar nl("nl","nl",2.,0.,100.); nl.setConstant(kTRUE);
RooRealVar deCBr("deCBr","deCBr",get_deCBr(m_mode,m_h0mode,_b0f),-0.2,0.1); if(cSig) deCBr.setConstant(kTRUE);
RooRealVar sCBr("sCBr","sCBr",get_sCBr(m_mode,m_h0mode,_b0f),0.,0.5); if(cSig) sCBr.setConstant(kTRUE);
RooRealVar alphar("alphar","alphar",get_alphar(m_mode,m_h0mode,_b0f),-10.,0.); if(cSig) alphar.setConstant(kTRUE);
RooRealVar nr("nr","nr",2,0.,100.); nr.setConstant(kTRUE);
RooCBShape CBl("CBl","CBl",de,deCBl,sCBl,alphal,nl);
RooCBShape CBr("CBr","CBr",de,deCBr,sCBr,alphar,nr);
RooRealVar fCBl("fCBl","fCBl",get_fCBl(m_mode,m_h0mode,_b0f),0.,1.); if(cSig) fCBl.setConstant(kTRUE);
RooRealVar fCBr("fCBr","fCBr",get_fCBr(m_mode,m_h0mode,_b0f),0.,1.); if(cSig) fCBr.setConstant(kTRUE);
RooAddPdf pdf_de_sig("pdf_de_sig","pdf_de_sig",RooArgList(CBl,CBr,g1),RooArgSet(fCBl,fCBr));
} else{
RooRealVar de0_201("de0_201","de0_201",get_de0(m_mode,m_h0mode,1),-0.1,0.1); if(cSig) de0_201.setConstant(kTRUE);
RooRealVar s1_201("s1_201","s1_201",get_s1(m_mode,m_h0mode,1),0.,0.5); if(cSig) s1_201.setConstant(kTRUE);
RooGaussian g1_201("g1_201","g1_201",de,de0_201,s1_201);
RooRealVar deCBl_201("deCBl_201","deCBl_201",get_deCBl(m_mode,m_h0mode,1),-0.1,0.1); if(cSig) deCBl_201.setConstant(kTRUE);
RooRealVar sCBl_201("sCBl_201","sCBl_201",get_sCBl(m_mode,m_h0mode,1),0.,0.5); if(cSig) sCBl_201.setConstant(kTRUE);
RooRealVar nl_201("nl_201","nl_201",2.,0.,100.); nl_201.setConstant(kTRUE);
RooRealVar alphal_201("alphal_201","alphal_201",get_alphal(m_mode,m_h0mode,1),-10.,10.); if(cSig) alphal_201.setConstant(kTRUE);
RooRealVar deCBr_201("deCBr_201","deCBr_201",get_deCBr(m_mode,m_h0mode,1),-0.1,0.1); if(cSig) deCBr_201.setConstant(kTRUE);
RooRealVar sCBr_201("sCBr_201","sCBr_201",get_sCBr(m_mode,m_h0mode,1),0.,0.5); if(cSig) sCBr_201.setConstant(kTRUE);
RooRealVar nr_201("nr_201","nr_201",2.,0.,100.); nr_201.setConstant(kTRUE);
RooRealVar alphar_201("alphar_201","alphar_201",get_alphar(m_mode,m_h0mode,1),-10.,10.); if(cSig) alphar_201.setConstant(kTRUE);
RooCBShape CBl_201("CBl_201","CBl_201",de,deCBl_201,sCBl_201,alphal_201,nl_201);
RooCBShape CBr_201("CBr_201","CBr_201",de,deCBr_201,sCBr_201,alphar_201,nr_201);
RooRealVar fCBl_201("fCBl_201","fCBl_201",get_fCBl(m_mode,m_h0mode,1),0.,1.); if(cSig) fCBl_201.setConstant(kTRUE);
if(_mode == 3){
fCBl_201.setVal(0.);
fCBl_201.setConstant(kTRUE);
alphal_201.setConstant(kTRUE);
}
RooRealVar fCBr_201("fCBr_201","fCBr_201",get_fCBr(m_mode,m_h0mode,1),0.,1.); if(cSig) fCBr_201.setConstant(kTRUE);
RooAddPdf pdf_de1("pdf_de1","pdf_de1",RooArgList(CBl_201,CBr_201,g1_201),RooArgSet(fCBl_201,fCBr_201));
RooRealVar de0_205("de0_205","de0_205",get_de0(m_mode,m_h0mode,5),-0.2,0.1); if(cSig) de0_205.setConstant(kTRUE);
RooRealVar s1_205("s1_205","s1_205",get_s1(m_mode,m_h0mode,5),0.,0.5); if(cSig) s1_205.setConstant(kTRUE);
RooGaussian g1_205("g1_205","g1_205",de,de0_205,s1_205);
RooRealVar deCBl_205("deCBl_205","deCBl_205",get_deCBl(m_mode,m_h0mode,5),-0.1,0.1); if(cSig) deCBl_205.setConstant(kTRUE);
RooRealVar sCBl_205("sCBl_205","sCBl_205",get_sCBl(m_mode,m_h0mode,5),0.,0.5); if(cSig) sCBl_205.setConstant(kTRUE);
RooRealVar nl_205("nl_205","nl_205",2,0.,100.); nl_205.setConstant(kTRUE);
RooRealVar alphal_205("alphal_205","alphal_205",get_alphal(m_mode,m_h0mode,5),-10.,10.); if(cSig) alphal_205.setConstant(kTRUE);
RooCBShape CBl_205("CBl_205","CBl_205",de,deCBl_205,sCBl_205,alphal_205,nl_205);
RooRealVar fCBl_205("fCBl_205","fCBl_205",get_fCBl(m_mode,m_h0mode,5),0.,1.); if(cSig) fCBl_205.setConstant(kTRUE);
RooAddPdf pdf_de5("pdf_de5","pdf_de5",RooArgList(CBl_205,g1_205),RooArgSet(fCBl_205));
}
/////////////
// mbc pdf //
/////////////
if(gg_flag){
RooRealVar a_s("a_s","a_s",get_a_s(_mode)); if(cSig) a_s.setConstant(kTRUE);
RooRealVar b_s("b_s","b_s",get_b_s(_mode)); if(cSig) b_s.setConstant(kTRUE);
RooRealVar c_s("c_s","c_s",get_c_s(_mode),0.0015,0.0035);// if(cSig) c_s.setConstant(kTRUE);
RooFormulaVar S("S","S","@1+@2*@0+@3*@0*@0",RooArgList(de,c_s,b_s,a_s));
RooRealVar alpha("alpha","alpha",0.139,0.01,2.); alpha.setConstant(kTRUE);
RooRealVar a_mbc0("a_mbc0","a_mbc0",get_a_mbc0(_mode)); if(cSig) a_mbc0.setConstant(kTRUE);
RooRealVar b_mbc0("b_mbc0","b_mbc0",get_b_mbc0(_mode)); if(cSig) b_mbc0.setConstant(kTRUE);
RooRealVar c_mbc0("c_mbc0","c_mbc0",get_c_mbc0(_mode),5.277,5.285);// if(cSig) c_mbc0.setConstant(kTRUE);
RooFormulaVar MBC0("MBC0","MBC0","@1+@2*@0+@3*@0*@0",RooArgList(de,c_mbc0,b_mbc0,a_mbc0));
RooNovosibirsk pdf_mbc_sig("pdf_mbc_sig","pdf_mbc_sig",mbc,MBC0,S,alpha);
} else{
RooRealVar alpha("alpha","alpha",0.139,0.01,2.); alpha.setConstant(kTRUE);
RooRealVar c0("c0","c0",get_c0(_mode)); if(cSig) c0.setConstant(kTRUE);
RooRealVar c1("c1","c1",get_c1(_mode)); if(cSig) c1.setConstant(kTRUE);
RooRealVar c2("c2","c2",get_c2(_mode)); if(cSig) c2.setConstant(kTRUE);
RooRealVar mbc0("mbc0","mbc0",5.284,5.277,5.29);// if(cSig) mbc0.setConstant(kTRUE);
RooFormulaVar MBC("MBC","MBC","@0+@1*TMath::Erf((@2-@3))/@4",RooArgList(mbc0,c0,c1,de,c2));
RooRealVar a_s1("a_s1","a_s1",get_a_s(_mode),0.15,0.45); if(cSig) a_s1.setConstant(kTRUE);
RooRealVar b_s1("b_s1","b_s1",get_b_s(_mode),-0.05,0.05); if(cSig) b_s1.setConstant(kTRUE);
RooRealVar c_s1("c_s1","c_s1",get_c_s(_mode),0.0015,0.0035);// if(cSig) c_s1.setConstant(kTRUE);
RooFormulaVar S1("S1","S1","@1+@2*@0+@3*@0*@0",RooArgList(de,c_s1,b_s1,a_s1));
RooNovosibirsk pdf_mbc1("pdf_mbc1","pdf_mbc1",mbc,MBC,S1,alpha);
RooRealVar a_s5("a_s5","a_s5",get_a5_s(_mode)); if(cSig) a_s5.setConstant(kTRUE);
RooRealVar b_s5("b_s5","b_s5",get_b5_s(_mode)); if(cSig) b_s5.setConstant(kTRUE);
RooRealVar c_s5("c_s5","c_s5",get_c5_s(_mode),0.0015,0.0055); if(cSig) c_s5.setConstant(kTRUE);
RooFormulaVar S5("S5","S5","@1+@2*@0+@3*@0*@0",RooArgList(de,c_s5,b_s5,a_s5));
RooRealVar a_mbc0("a_mbc0","a_mbc0",get_a5_mbc0(_mode)); if(cSig) a_mbc0.setConstant(kTRUE);
RooRealVar b_mbc0("b_mbc0","b_mbc0",get_b5_mbc0(_mode)); if(cSig) b_mbc0.setConstant(kTRUE);
RooRealVar c_mbc0("c_mbc0","c_mbc0",get_c5_mbc0(_mode),5.27,5.29); if(cSig) c_mbc0.setConstant(kTRUE);
RooFormulaVar MBC0("MBC0","MBC0","@1+@2*@0+@3*@0*@0",RooArgList(de,c_mbc0,b_mbc0,a_mbc0));
RooNovosibirsk pdf_mbc5("pdf_mbc5","pdf_mbc5",mbc,MBC0,S5,alpha);
}
/////////
// pdf //
/////////
if(gg_flag){
RooProdPdf pdf_sig("pdf_sig","pdf_sig",pdf_de_sig,Conditional(pdf_mbc_sig,mbc));
} else{
RooRealVar f_201("f_201","f_201",get_f201(m_mode,m_h0mode),0.,1.); if(cSig) f_201.setConstant(kTRUE);
RooProdPdf pdf1_sig("pdf1_sig","pdf1_sig",pdf_de1,Conditional(pdf_mbc1,mbc));
RooProdPdf pdf5_sig("pdf5_sig","pdf5_sig",pdf_de5,Conditional(pdf_mbc5,mbc));
RooAddPdf pdf_sig("pdf_sig","pdf_sig",RooArgList(pdf1_sig,pdf5_sig),RooArgSet(f_201));
}
//////////////
// Comb PDF //
//////////////
////////////
// de pdf //
////////////
RooRealVar c10("c10","c10",get_cmb_c10(_mode),-10,50.); if(cComb) c10.setConstant(kTRUE);
RooRealVar c11("c11","c11",get_cmb_c11(_mode),-50,0.); if(cComb) c11.setConstant(kTRUE);
RooFormulaVar c1_cmb("c1_cmb","@0+@1*@2",RooArgSet(c10,c11,mbc));
RooRealVar c2_cmb("c2_cmb","c2_cmb",get_cmb_c20(_mode),-0.1,1); if(cComb) c2_cmb.setConstant(kTRUE);
RooChebychev pdf_de_comb_bb("pdf_de_comb_bb","pdf_de_comb_bb",de,RooArgSet(c1_cmb,c2_cmb));
RooRealVar C1("C1","C1",get_cmb_c1(_mode),-10,50.); if(cComb) C1.setConstant(kTRUE);
RooRealVar C2("C2","C2",get_cmb_c2(_mode),-0.1,1); if(cComb) C2.setConstant(kTRUE);
RooChebychev pdf_de_comb_qq("pdf_de_comb_qq","pdf_de_comb_qq",de,RooArgSet(C1,C2));
/////////////
// mbc pdf //
/////////////
RooRealVar argedge("argedge","argedge",5.288,5.285,5.29); //argedge.setConstant(kTRUE);
RooRealVar argpar_cmb_bb("argpar_cmb_bb","argpar_cmb_bb",get_argpar_bb(_mode),-300,-10.); if(cComb) argpar_cmb_bb.setConstant(kTRUE);
RooArgusBG pdf_mbc_comb_ar("pdf_mbc_comb_ar","Argus PDF",mbc,argedge,argpar_cmb_bb);
RooRealVar mbc0_cmb_bb("mbc0_cmb_bb","mbc0_cmb_bb",get_mbc0_cmb_bb(_mode),5.25,5.29,"GeV");// if(cComb) mbc0_cmb_bb.setConstant(kTRUE);
RooRealVar mbcWidth_cmb_bb("mbcWidth","mbcWidth",get_mbcw_cmb_bb(_mode),0.,0.1,"GeV"); if(cComb) mbcWidth_cmb_bb.setConstant(kTRUE);
RooGaussian mbcGaus_cmb_bb("mbcGaus","mbcGaus",mbc,mbc0_cmb_bb,mbcWidth_cmb_bb);
RooRealVar f_g("f_g","f_g",get_f_g_cmb_bb(_mode),0.4,0.7);if(_mode == 2 || !gg_flag){ f_g.setConstant(kTRUE);}
RooAddPdf pdf_mbc_cmb_bb("pdf_mbc_cmb_bb","pdf_mbc_cmb_bb",RooArgList(mbcGaus_cmb_bb,pdf_mbc_comb_ar),RooArgSet(f_g));
RooRealVar argpar_cmb_qq("argpar_cmb_qq","argpar_cmb_qq",get_argpar_qq(_mode),-300,-10.); if(cComb) argpar_cmb_qq.setConstant(kTRUE);
RooArgusBG pdf_mbc_cmb_qq("pdf_mbc_cmb_qq","pdf_mbc_cmb_qq",mbc,argedge,argpar_cmb_qq);
/////////
// pdf //
/////////
RooRealVar f_bb("f_bb","f_bb",0.3,0.,1.);
RooProdPdf pdf_cmb_bb("pdf_cmb_bb","pdf_cmb_bb",pdf_mbc_cmb_bb,Conditional(pdf_de_comb_bb,de));
RooProdPdf pdf_cmb_qq("pdf_cmb_qq","pdf_cmb_qq",pdf_mbc_cmb_qq,Conditional(pdf_de_comb_qq,de));
RooAddPdf pdf_comb("pdf_comb","pdf_comb",RooArgSet(pdf_cmb_bb,pdf_cmb_qq),RooArgList(f_bb));
/////////////////////
// Peaking bkg PDF //
/////////////////////
////////////
// de pdf //
////////////
RooRealVar de0r("de0r","de0r",get_de0r(_mode),-0.2,0.12); if(cPeak) de0r.setConstant(kTRUE);
RooRealVar slopel("slopel","slopel",get_slopel(_mode),-1.e5,0.); if(cPeak) slopel.setConstant(kTRUE);
RooRealVar sloper("sloper","sloper",get_sloper(_mode),-10000,0.); if(cPeak) sloper.setConstant(kTRUE);
RooRealVar steep("steep","steep",get_steep(_mode),0.,1000.); if(cPeak) steep.setConstant(kTRUE);
RooRealVar p5("p5","p5",get_p5(_mode),0.01,1000.); if(cPeak) p5.setConstant(kTRUE);
RooRhoDeltaEPdf pdf_de_peak("pdf_de_peak","pdf_de_peak",de,de0r,slopel,sloper,steep,p5);
// RooGenericPdf pdf_de_peak("pdf_de_peak","1+(@0-@1)*@2+@4*TMath::Log(1+@5*TMath::Exp((@3-@2)*(@0-@1)/@4)) > 0 ? 1+(@0-@1)*@2+@4*TMath::Log(1+@5*TMath::Exp((@3-@2)*(@0-@1)/@4)) : 0.001",RooArgSet(de,de0r,slopel,sloper,steep,p5));
/////////////
// mbc pdf //
/////////////
if(gg_flag){
RooRealVar b_peak_s("b_peak_s","b_peak_s",get_peak_b_s(_mode),-0.1,0.1); if(cPeak) b_peak_s.setConstant(kTRUE);
RooRealVar k_peak_s("k_peak_s","k_peak_s",get_peak_k_s(_mode),-0.1,0.1); if(cPeak) k_peak_s.setConstant(kTRUE);
RooFormulaVar S_peak("S_peak","S_peak","@0+@1*@2",RooArgList(b_peak_s,de,k_peak_s));
RooRealVar alpha_peak("alpha_peak","alpha_peak",0.139,0.01,2.); alpha_peak.setConstant(kTRUE);
RooRealVar b_peak_mbc0("b_peak_mbc0","b_peak_mbc0",get_peak_b_mbc0(_mode),5.25,5.29); if(cPeak) b_peak_mbc0.setConstant(kTRUE);
RooRealVar k_peak_mbc0("k_peak_mbc0","k_peak_mbc0",get_peak_k_mbc0(_mode),-0.1,0.1); if(cPeak) k_peak_mbc0.setConstant(kTRUE);
RooFormulaVar MBC0_peak("MBC0_peak","MBC0_peak","@0+@1*@2",RooArgList(b_peak_mbc0,de,k_peak_mbc0));
RooNovosibirsk pdf_mbc_peak("pdf_mbc_peak","pdf_mbc_peak",mbc,MBC0_peak,S_peak,alpha_peak);
} else{
// RooRealVar argedge("argedge","argedge",5.288,5.285,5.29); //argedge.setConstant(kTRUE);
RooRealVar argpar_peak_bb("argpar_peak_bb","argpar_peak_bb",get_argpar_bb(_mode),-300,-10.); if(cPeak) argpar_peak_bb.setConstant(kTRUE);
RooArgusBG pdf_mbc_peak_ar("pdf_mbc_peak_ar","Argus PDF",mbc,argedge,argpar_peak_bb);
RooRealVar mbc0_peak("mbc0_peak","mbc0_peak",get_peak_b_mbc0(_mode),5.25,5.291,"GeV"); if(cPeak) mbc0_peak.setConstant(kTRUE);
RooRealVar mbcWidth_peak("mbcWidth_peak","mbcWidth_peak",get_peak_b_s(_mode),0.,0.1,"GeV"); if(cPeak) mbcWidth_peak.setConstant(kTRUE);
RooGaussian mbcGaus_peak("mbcGaus_peak","mbcGaus_peak",mbc,mbc0_peak,mbcWidth_peak);
RooRealVar f_g_peak("f_g_peak","f_g_peak",get_f_g_cmb_bb(_mode),0.,1.); if(cPeak) f_g_peak.setConstant(kTRUE);
RooAddPdf pdf_mbc_peak("pdf_mbc_peak","pdf_mbc_peak",RooArgList(mbcGaus_peak,pdf_mbc_peak_ar),RooArgSet(f_g_peak));
}
/////////
// pdf //
/////////
RooProdPdf pdf_peak("pdf_peak","pdf_peak",pdf_de_peak,Conditional(pdf_mbc_peak,mbc));
//////////////////
// Complete PDF //
//////////////////
RooRealVar Nsig("Nsig","Nsig",1150,0.,10000.);
// RooRealVar Npbg("Npbg","Npbg",100,0,100000.);
RooRealVar Ncmb("Ncmb","Ncmb",2288,0,100000);
switch(_mode){
case 1:
RooRealVar Npbg("Npbg","Npbg",100,0,100000.);
break;
case 2:
RooConstVar f_p_f_bbc("f_p_f_bbc","f_p_f_bbc",0.0051);
RooFormulaVar Npbg("Npbg","Npbg","@0*@1*@2",RooArgList(Ncmb,f_bb,f_p_f_bbc));
break;
case 3:
RooConstVar f_p_f_bbc("f_p_f_bbc","f_p_f_bbc",0.0081);
RooFormulaVar Npbg("Npbg","Npbg","@0*@1*@2",RooArgList(Ncmb,f_bb,f_p_f_bbc));
break;
case 4:
RooConstVar f_p_f_bbc("f_p_f_bbc","f_p_f_bbc",0.0031);
RooFormulaVar Npbg("Npbg","Npbg","@0*@1*@2",RooArgList(Ncmb,f_bb,f_p_f_bbc));
break;
default:
return -1;
}
RooAddPdf pdf("pdf","pdf",RooArgList(pdf_sig,pdf_peak,pdf_comb),RooArgList(Nsig,Npbg,Ncmb));
RooArgSet* params = pdf.getParameters(RooArgSet(de,mbc));
// RooArgset* initParams = (RooArgSet*) params->snapshot();
pdf.fitTo(ds,Verbose(),Timer(true));
params->printLatex(OutputFile("PurityFit.tex"));
RooAbsReal* intSig = pdf_sig.createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Signal"));
RooAbsReal* intRho = pdf_peak->createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Signal"));
RooAbsReal* intCmb = pdf_comb.createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Signal"));
const double nsig = intSig->getVal()*Nsig.getVal();
const double nsig_err = intSig->getVal()*Nsig.getError();
const double nsig_err_npq = TMath::Sqrt(nsig*(Nsig.getVal()-nsig)/Nsig.getVal());
const double nsig_err_total = TMath::Sqrt(nsig_err*nsig_err+nsig_err_npq*nsig_err_npq);
const double nrho = intRho->getVal()*Npbg.getVal();
const double nrho_err = _mode == 1 ? intRho->getVal()*Npbg.getError() : intRho->getVal()*f_bb.getError()*Ncmb.getVal()*f_p_f_bbc.getVal();
const double nrho_err_npq = TMath::Sqrt(nrho*(Npbg.getVal()-nrho)/Npbg.getVal());
const double nrho_err_total = TMath::Sqrt(nrho_err*nrho_err+nrho_err_npq*nrho_err_npq);
const double ncmb = intCmb->getVal()*Ncmb.getVal();
const double ncmb_err = intCmb->getVal()*Ncmb.getError();
const double ncmb_err_npq = TMath::Sqrt(ncmb*(Ncmb.getVal()-ncmb)/Ncmb.getVal());
const double ncmb_err_total = TMath::Sqrt(ncmb_err*ncmb_err+ncmb_err_npq*ncmb_err_npq);
const double purity = nsig/(nsig+nrho+ncmb);
const double purity_err = nsig_err_total/(nsig+nrho+ncmb);
de.setRange("Ellips",dE_min,dE_max);
RooFormulaVar mbclo("mbclo","@1-@2*TMath::Sqrt(TMath::Abs(1-(@0-@3)/@4*(@0-@3)/@4)+0.0000001)",RooArgSet(de,mbc_center,mbc_radius,de_center,de_radius));
RooFormulaVar mbchi("mbchi","@1+@2*TMath::Sqrt(TMath::Abs(1-(@0-@3)/@4*(@0-@3)/@4)+0.0000001)",RooArgSet(de,mbc_center,mbc_radius,de_center,de_radius));
mbc.setRange("Ellips",mbclo,mbchi);
de.setRange("Elli",dE_min,dE_max);
RooFormulaVar mbclo1("mbclo1","@1-@2*TMath::Sqrt(TMath::Abs(1-(@0-@3)/@4*(@0-@3)/@4)+0.0000001)",RooArgSet(de,mbc_center,mbc_radius1,de_center,de_radius1));
RooFormulaVar mbchi1("mbchi1","@1+@2*TMath::Sqrt(TMath::Abs(1-(@0-@3)/@4*(@0-@3)/@4)+0.0000001)",RooArgSet(de,mbc_center,mbc_radius1,de_center,de_radius1));
mbc.setRange("Elli",mbclo1,mbchi1);
RooAbsReal* intSigEl = pdf_sig.createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Ellips"));
RooAbsReal* intRhoEl = pdf_peak->createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Ellips"));
RooAbsReal* intCmbEl = pdf_comb.createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Ellips"));
const double nsigEl = intSigEl->getVal()*Nsig.getVal();
const double nsig_errEl = intSigEl->getVal()*Nsig.getError();
const double nsig_errEl_npq = TMath::Sqrt(fabs(nsigEl*(Nsig.getVal()-nsigEl)/Nsig.getVal()));
const double nsig_errEl_total = TMath::Sqrt(fabs(nsig_errEl*nsig_errEl+nsig_errEl_npq*nsig_errEl_npq));
const double nrhoEl = intRhoEl->getVal()*Npbg.getVal();
const double nrho_errEl = _mode == 1 ? intRhoEl->getVal()*Npbg.getError() : intRhoEl->getVal()*f_bb.getError()*Ncmb.getVal()*f_p_f_bbc.getVal();
const double nrho_errEl_npq = TMath::Sqrt(fabs(nrhoEl*(Npbg.getVal()-nrhoEl)/Npbg.getVal()));
const double nrho_errEl_total = TMath::Sqrt(fabs(nrho_errEl*nrho_errEl+nrho_errEl_npq*nrho_errEl_npq));
const double ncmbEl = intCmbEl->getVal()*Ncmb.getVal();
const double ncmb_errEl = intCmbEl->getVal()*Ncmb.getError();
const double ncmb_errEl_npq = TMath::Sqrt(fabs(ncmbEl*(Ncmb.getVal()-ncmbEl)/Ncmb.getVal()));
const double ncmb_errEl_total = TMath::Sqrt(ncmb_errEl*ncmb_errEl+ncmb_errEl_npq*ncmb_errEl_npq);
const double purityEl = nsigEl/(nsigEl+nrhoEl+ncmbEl);
const double purity_errEl = nsig_errEl_total/(nsigEl+nrhoEl+ncmbEl);
RooAbsReal* intSigEl1 = pdf_sig.createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Elli"));
const double intElli = intSigEl1->getVal();
RooAbsReal* intRhoEl1 = pdf_peak->createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Elli"));
RooAbsReal* intCmbEl1 = pdf_comb.createIntegral(RooArgSet(de,mbc),NormSet(RooArgSet(de,mbc)),Range("Elli"));
const double nsigEl1 = intSigEl1->getVal()*Nsig.getVal();
const double nsig_errEl1 = intSigEl1->getVal()*Nsig.getError();
const double nsig_errEl1_npq = TMath::Sqrt(fabs(nsigEl1*(Nsig.getVal()-nsigEl1)/Nsig.getVal()));
const double nsig_errEl1_total = TMath::Sqrt(fabs(nsig_errEl1*nsig_errEl1+nsig_errEl1_npq*nsig_errEl1_npq));
const double nrhoEl1 = intRhoEl1->getVal()*Npbg.getVal();
const double nrho_errEl1 = _mode == 1 ? intRhoEl1->getVal()*Npbg.getError() : intRhoEl1->getVal()*f_bb.getError()*Ncmb.getVal()*f_p_f_bbc.getVal();
const double nrho_errEl1_npq = TMath::Sqrt(fabs(nrhoEl1*(Npbg.getVal()-nrhoEl1)/Npbg.getVal()));
const double nrho_errEl1_total = TMath::Sqrt(fabs(nrho_errEl1*nrho_errEl1+nrho_errEl1_npq*nrho_errEl1_npq));
const double ncmbEl1 = intCmbEl1->getVal()*Ncmb.getVal();
const double ncmb_errEl1 = intCmbEl1->getVal()*Ncmb.getError();
const double ncmb_errEl1_npq = TMath::Sqrt(ncmbEl1*(Ncmb.getVal()-ncmbEl1)/Ncmb.getVal());
const double ncmb_errEl1_total = TMath::Sqrt(ncmb_errEl1*ncmb_errEl1+ncmb_errEl1_npq*ncmb_errEl1_npq);
const double purityEl1 = nsigEl1/(nsigEl1+nrhoEl1+ncmbEl1);
const double purity_errEl1 = nsig_errEl1_total/(nsigEl1+nrhoEl1+ncmbEl1);
/////////////
// Plots //
/////////////
// de //
RooPlot* deFrame = de.frame();
ds.plotOn(deFrame,DataError(RooAbsData::SumW2),MarkerSize(1),CutRange("mbcSignal"));
pdf.plotOn(deFrame,Components(pdf_sig),LineStyle(kDashed),ProjectionRange("mbcSignal"));
pdf.plotOn(deFrame,Components(pdf_peak),LineStyle(kDashed),ProjectionRange("mbcSignal"));
pdf.plotOn(deFrame,Components(pdf_cmb_bb),LineStyle(kDashed),ProjectionRange("mbcSignal"));
pdf.plotOn(deFrame,Components(pdf_cmb_qq),LineStyle(kDashed),ProjectionRange("mbcSignal"));
pdf.plotOn(deFrame,LineWidth(2),ProjectionRange("mbcSignal"));
RooHist* hdepull = deFrame->pullHist();
RooPlot* dePull = de.frame(Title("#Delta E pull distribution"));
dePull->addPlotable(hdepull,"P");
dePull->GetYaxis()->SetRangeUser(-5,5);
TCanvas* cm = new TCanvas("#Delta E, Signal","#Delta E, Signal",600,700);
cm->cd();
TPad *pad3 = new TPad("pad3","pad3",0.01,0.20,0.99,0.99);
TPad *pad4 = new TPad("pad4","pad4",0.01,0.01,0.99,0.20);
pad3->Draw();
pad4->Draw();
pad3->cd();
pad3->SetLeftMargin(0.15);
pad3->SetFillColor(0);
deFrame->GetXaxis()->SetTitleSize(0.05);
deFrame->GetXaxis()->SetTitleOffset(0.85);
deFrame->GetXaxis()->SetLabelSize(0.04);
deFrame->GetYaxis()->SetTitleOffset(1.6);
deFrame->Draw();
out.str("");
out << "(de-" << de_center.getVal() << ")/" << de_radius1.getVal() << "*(de-" << de_center.getVal() << ")/" << de_radius1.getVal() << "+(mbc-"<<mbc_center.getVal()<<")/" << mbc_radius1.getVal() << "*(mbc-" << mbc_center.getVal() << ")/" << mbc_radius1.getVal() << "<1";
Roo1DTable* ellitable1 = ds.table(b0f,out.str().c_str());
out.str("");
out << "(de-" << de_center.getVal() << ")/" << de_radius.getVal() << "*(de-" << de_center.getVal() << ")/" << de_radius.getVal() << "+(mbc-"<<mbc_center.getVal()<<")/" << mbc_radius.getVal() << "*(mbc-" << mbc_center.getVal() << ")/" << mbc_radius.getVal() << "<1";
Roo1DTable* ellitable = ds.table(b0f,out.str().c_str());
const int NSIGNAL_ELLI = ellitable1->get("signal") + ellitable1->get("fsr") + ellitable1->get("bad_pi0");
// const int NSIGNAL_ELLIPS = ellitable->get("signal") + ellitable->get("fsr") + ellitable->get("bad_pi0");
stringstream out1;
TPaveText *pt = new TPaveText(0.5,0.6,0.98,0.9,"brNDC");
pt->SetFillColor(0);
pt->SetTextAlign(12);
out1.str("");
out1 << "#chi^{2}/n.d.f = " << deFrame->chiSquare();
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "S: " << (int)(nsigEl1+0.5) << " #pm " << (int)(nsig_errEl1_total+0.5) << " (" << NSIGNAL_ELLI << ")";
// out1 << "S: " << (int)(nsig+0.5) << " #pm " << (int)(nsig_err_total+0.5);
pt->AddText(out1.str().c_str());
// out1.str("");
// out1 << "S_{2}: " << (int)(nsigEl+0.5) << " #pm " << (int)(nsig_errEl_total+0.5) << " (" << NSIGNAL_ELLIPS << ")";
// pt->AddText(out1.str().c_str());
out1.str("");
// out1 << "Purity: " << std::fixed << std::setprecision(2) << purity*100. << " #pm " << purity_err*100;
out1 << "P: " << std::fixed << std::setprecision(2) << purityEl1*100. << " #pm " << purity_errEl1*100;
pt->AddText(out1.str().c_str());
pt->AddText(label.c_str());
pt->Draw();
TLine *de_line_RIGHT;
de_line_RIGHT = new TLine(dE_max,0,dE_max,120);
de_line_RIGHT->SetLineColor(kRed);
de_line_RIGHT->SetLineStyle(1);
de_line_RIGHT->SetLineWidth((Width_t)2.);
de_line_RIGHT->Draw();
TLine *de_line_LEFT;
de_line_LEFT = new TLine(dE_min,0,dE_min,120);
de_line_LEFT->SetLineColor(kRed);
de_line_LEFT->SetLineStyle(1);
de_line_LEFT->SetLineWidth((Width_t)2.);
de_line_LEFT->Draw();
pad4->cd(); pad4->SetLeftMargin(0.15); pad4->SetFillColor(0);
dePull->SetMarkerSize(0.05); dePull->Draw();
TLine *de_lineUP = new TLine(deMin,3,deMax,3);
de_lineUP->SetLineColor(kBlue);
de_lineUP->SetLineStyle(2);
de_lineUP->Draw();
TLine *de_line = new TLine(deMin,0,deMax,0);
de_line->SetLineColor(kBlue);
de_line->SetLineStyle(1);
de_line->SetLineWidth((Width_t)2.);
de_line->Draw();
TLine *de_lineDOWN = new TLine(deMin,-3,deMax,-3);
de_lineDOWN->SetLineColor(kBlue);
de_lineDOWN->SetLineStyle(2);
de_lineDOWN->Draw();
cm->Update();
// mbc //
RooPlot* mbcFrame = mbc.frame();
ds.plotOn(mbcFrame,DataError(RooAbsData::SumW2),MarkerSize(1),CutRange("deSignal"));
pdf.plotOn(mbcFrame,Components(pdf_cmb_bb),LineStyle(kDashed),ProjectionRange("deSignal"));
pdf.plotOn(mbcFrame,Components(pdf_cmb_qq),LineStyle(kDashed),ProjectionRange("deSignal"));
pdf.plotOn(mbcFrame,Components(pdf_sig),LineStyle(kDashed),ProjectionRange("deSignal"));
pdf.plotOn(mbcFrame,Components(pdf_peak),LineStyle(kDashed),ProjectionRange("deSignal"));
pdf.plotOn(mbcFrame,LineWidth(2),ProjectionRange("deSignal"));
RooHist* hmbcpull = mbcFrame->pullHist();
RooPlot* mbcPull = mbc.frame(Title("#Delta E pull distribution"));
mbcPull->addPlotable(hmbcpull,"P");
mbcPull->GetYaxis()->SetRangeUser(-5,5);
TCanvas* cmmbc = new TCanvas("M_{bc}, Signal","M_{bc}, Signal",600,700);
cmmbc->cd();
TPad *pad1 = new TPad("pad1","pad1",0.01,0.20,0.99,0.99);
TPad *pad2 = new TPad("pad2","pad2",0.01,0.01,0.99,0.20);
pad1->Draw();
pad2->Draw();
pad1->cd();
pad1->SetLeftMargin(0.15);
pad1->SetFillColor(0);
mbcFrame->GetXaxis()->SetTitleSize(0.05);
mbcFrame->GetXaxis()->SetTitleOffset(0.85);
mbcFrame->GetXaxis()->SetLabelSize(0.04);
mbcFrame->GetYaxis()->SetTitleOffset(1.6);
mbcFrame->Draw();
TPaveText *ptmbc = new TPaveText(0.2,0.6,0.7,0.9,"brNDC");
ptmbc->SetFillColor(0);
ptmbc->SetTextAlign(12);
out1.str("");
out1 << "#chi^{2}/n.d.f = " << mbcFrame->chiSquare();
ptmbc->AddText(out1.str().c_str());
out1.str("");
out1 << "S: " << (int)(nsigEl1+0.5) << " #pm " << (int)(nsig_errEl1_total+0.5) << " (" << NSIGNAL_ELLI << ")";
ptmbc->AddText(out1.str().c_str());
// out1.str("");
// out1 << "S_{2}: " << (int)(nsigEl+0.5) << " #pm " << (int)(nsig_errEl_total+0.5) << " (" << NSIGNAL_ELLIPS << ")";
// ptmbc->AddText(out1.str().c_str());
out1.str("");
out1 << "P: " << std::fixed << std::setprecision(2) << purityEl1*100. << " #pm " << purity_errEl1*100;
ptmbc->AddText(out1.str().c_str());
ptmbc->AddText(label.c_str());
ptmbc->Draw();
TLine *mbc_line_RIGHT;
mbc_line_RIGHT = new TLine(Mbc_max,0,Mbc_max,40);
mbc_line_RIGHT->SetLineColor(kRed);
mbc_line_RIGHT->SetLineStyle(1);
mbc_line_RIGHT->SetLineWidth((Width_t)2.);
mbc_line_RIGHT->Draw();
TLine *mbc_line_LEFT;
mbc_line_LEFT = new TLine(Mbc_min,0,Mbc_min,40);
mbc_line_LEFT->SetLineColor(kRed);
mbc_line_LEFT->SetLineStyle(1);
mbc_line_LEFT->SetLineWidth((Width_t)2.);
mbc_line_LEFT->Draw();
pad2->cd();
pad2->SetLeftMargin(0.15);
pad2->SetFillColor(0);
mbcPull->SetMarkerSize(0.05);
mbcPull->Draw();
TLine *mbc_lineUP = new TLine(mbcMin,3,mbcMax,3);
mbc_lineUP->SetLineColor(kBlue);
mbc_lineUP->SetLineStyle(2);
mbc_lineUP->Draw();
TLine *mbc_line = new TLine(mbcMin,0,mbcMax,0);
mbc_line->SetLineColor(kBlue);
mbc_line->SetLineStyle(1);
mbc_line->SetLineWidth((Width_t)2.);
mbc_line->Draw();
TLine *mbc_lineDOWN = new TLine(mbcMin,-3,mbcMax,-3);
mbc_lineDOWN->SetLineColor(kBlue);
mbc_lineDOWN->SetLineStyle(2);
mbc_lineDOWN->Draw();
cmmbc->Update();
double DEMIN = -0.15;
if(keysflag) DEMIN = -0.3;
TH2D* hh_pdf = pdf.createHistogram("hh_data",de,Binning(50,DEMIN,0.1),YVar(mbc,Binning(50,5.26,5.30)));
hh_pdf->SetLineColor(kBlue);
TCanvas* hhc = new TCanvas("hhc","hhc",600,600);
hhc->cd();
hh_pdf->Draw("SURF");
// Show signal ranges
TEllipse* elli = new TEllipse(de_center.getVal(),mbc_center.getVal(),de_radius.getVal(),mbc_radius.getVal());
elli->SetFillColor(0);
elli->SetFillStyle(0);
elli->SetLineColor(kBlue);
elli->SetLineWidth(2);
TEllipse* elli1 = new TEllipse(de_center.getVal(),mbc_center.getVal(),de_radius1.getVal(),mbc_radius1.getVal());
elli1->SetFillColor(0);
elli1->SetFillStyle(0);
// elli1->SetLineColor(kBlue);
elli1->SetLineColor(kRed);
elli1->SetLineWidth(2);
TLine* l1 = new TLine(dE_min,Mbc_min,dE_max,Mbc_min);
l1->SetLineColor(kRed);
l1->SetLineStyle(1);
l1->SetLineWidth(2);
TLine* l2 = new TLine(dE_min,Mbc_max,dE_max,Mbc_max);
l2->SetLineColor(kRed);
l2->SetLineStyle(1);
l2->SetLineWidth(2);
TLine* l3 = new TLine(dE_min,Mbc_min,dE_min,Mbc_max);
l3->SetLineColor(kRed);
l3->SetLineStyle(1);
l3->SetLineWidth(2);
TLine* l4 = new TLine(dE_max,Mbc_min,dE_max,Mbc_max);
l4->SetLineColor(kRed);
l4->SetLineStyle(1);
l4->SetLineWidth(2);
TCanvas* ellican = new TCanvas("ellican","ellican",400,400);
ellican->cd();
out.str("");
out << "bdtg>" << BDTG_MIN << " && de>-0.15 && de<0.20 && mbc>5.265 && b0f != 1 && b0f != 5 && b0f != 10 && b0f != 0";
tree->Draw("mbc:de",out.str().c_str());
tree->SetMarkerStyle(6);
tree->SetMarkerColor(kBlue);
out.str("");
out << "bdtg>" << BDTG_MIN << " && de>-0.15 && de<0.20 && mbc>5.265 && (b0f == 1 || b0f == 5 || b0f == 10)";
tree->Draw("mbc:de",out.str().c_str(),"same");
// elli->Draw();
elli1->Draw();
// ellican->Pad().GetXaxis()->SetTitle("#DeltaE (GeV)");
// l1->Draw(); l2->Draw(); l3->Draw(); l4->Draw();
// TCanvas* sigcan = new TCanvas("sigcan","sigcan",400,400);
// sigcan->cd();
// out <<
// tree->Draw("mbc:de","bdtg>0.98 && de>-0.15 && de<0.20 && mbc>5.265 && (b0f == 1 || b0f == 5 || b0f == 10)");
// elli->Draw(); elli1->Draw(); l1->Draw(); l2->Draw(); l3->Draw(); l4->Draw();
// TCanvas* backcan = new TCanvas("backcan","backcan",400,400);
// backcan->cd();
// tree->Draw("mbc:de","bdtg>0.98 && de>-0.15 && de<0.20 && mbc>5.265 && !(b0f == 1 || b0f == 5 || b0f == 10)");
// elli->Draw(); elli1->Draw(); l1->Draw(); l2->Draw(); l3->Draw(); l4->Draw();
cout << "Rectangle:" << endl;
out.str("");
out << "de<" << dE_max << " && de>" << dE_min;
out << " && mbc>" << Mbc_min << " && mbc<" << Mbc_max;
Roo1DTable* recttable = ds.table(b0f,out.str().c_str());
recttable->Print();
recttable->Print("v");
cout << "Ellips:" << endl;
// out.str("");
// out << "(de-" << de_center.getVal() << ")/" << de_radius.getVal() << "*(de-" << de_center.getVal() << ")/" << de_radius.getVal() << "+(mbc-"<<mbc_center.getVal()<<")/" << mbc_radius.getVal() << "*(mbc-" << mbc_center.getVal() << ")/" << mbc_radius.getVal() << "<1";
// cout << out.str() << endl;
// Roo1DTable* ellitable = ds.table(b0f,out.str().c_str());
ellitable->Print();
ellitable->Print("v");
cout << "Elli:" << endl;
// out.str("");
// out << "(de-" << de_center.getVal() << ")/" << de_radius1.getVal() << "*(de-" << de_center.getVal() << ")/" << de_radius1.getVal() << "+(mbc-"<<mbc_center.getVal()<<")/" << mbc_radius1.getVal() << "*(mbc-" << mbc_center.getVal() << ")/" << mbc_radius1.getVal() << "<1";
// cout << out.str() << endl;
// Roo1DTable* ellitable1 = ds.table(b0f,out.str().c_str());
ellitable1->Print();
ellitable1->Print("v");
Roo1DTable* fulltable = ds.table(b0f);
fulltable->Print();
fulltable->Print("v");
const int NSigTotal = fulltable->get("signal") + fulltable->get("fsr") + fulltable->get("bad_pi0");
const double TruePur = ((double)NSIGNAL_ELLI)/(NSIGNAL_ELLI+ellitable1->get("comb")+ellitable1->get("rho2")+ellitable1->get("rho3")+ellitable1->get("rho4")+ellitable1->get("rho11"));
cout << "Rectangle:" << endl;
cout << "Nsig = " << nsig <<" +- " << nsig_err << " +- " << nsig_err_npq << " (" << nsig_err_total << ")" << endl;
cout << "Npbg = " << nrho <<" +- " << nrho_err << " +- " << nrho_err_npq << " (" << nrho_err_total << ")" << endl;
cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << " +- " << ncmb_err_npq << " (" << ncmb_err_total << ")" << endl;
cout << "Pury = " << purity << " +- " << purity_err << endl;
cout << "Ellips:" << endl;
cout << "Nsig = " << nsigEl <<" +- " << nsig_errEl << " +- " << nsig_errEl_npq << " (" << nsig_errEl_total << ")" << endl;
cout << "Npbg = " << nrhoEl <<" +- " << nrho_errEl << " +- " << nrho_errEl_npq << " (" << nrho_errEl_total << ")" << endl;
cout << "Ncmb = " << ncmbEl <<" +- " << ncmb_errEl << " +- " << ncmb_errEl_npq << " (" << ncmb_errEl_total << ")" << endl;
cout << "Pury = " << purityEl << " +- " << purity_errEl << endl;
cout << "Elli:" << endl;
cout << "Nsig = " << nsigEl1 <<" +- " << nsig_errEl1 << " +- " << nsig_errEl1_npq << " (" << nsig_errEl1_total << ")" << endl;
cout << "Npbg = " << nrhoEl1 <<" +- " << nrho_errEl1 << " +- " << nrho_errEl1_npq << " (" << nrho_errEl1_total << ")" << endl;
cout << "Ncmb = " << ncmbEl1 <<" +- " << ncmb_errEl1 << " +- " << ncmb_errEl1_npq << " (" << ncmb_errEl1_total << ")" << endl;
cout << "Pury = " << purityEl1 << " +- " << purity_errEl1 << endl;
cout << "Elli signal integral: " << intElli << endl;
cout << "Nsig (full range): " << Nsig.getVal() << " +- " << Nsig.getError() << " (" << NSigTotal << ")" << endl;
cout << "True purity: " << TruePur << endl;
}
void deFit(const int mode = 0, const int svd = 2, const bool only_d0 = false){
TChain* tree = new TChain("TEvent");
const double bdtg_min = -0.44;
const double deMin = -0.12;
const double deMax = 0.3;
const double mbcMin = 5.272;
const double mbcMax = 5.287;
// const double sz_sig_max = 0.2;
// const double sz_asc_max = 0.2;
// const double chisq_sig_max = only_d0 ? 10000 : 50;
// const double chisq_asc_max = 50;
const double atckpi_pi_max = 0.8;
const double cm2ps = 78.48566945838871754705;
const double de_min = -0.035;
const double de_max = 0.035;
const bool cComb = false;
const bool cSig = true;
const bool simple_peak = false;
if(mode){
tree->Add("FIL_b2dpi_charged_v2_0_10.root");
tree->Add("FIL_b2dpi_charm_0_v2_10.root");
} else{
tree->Add("FIL_b2dpi_data_v2.root");
}
RooArgSet argset;
RooCategory exp("exp","exp");
if(svd == 1){
exp.defineType("7",7);
exp.defineType("9",9);
exp.defineType("11",11);
exp.defineType("13",13);
exp.defineType("15",15);
exp.defineType("17",17);
exp.defineType("19",19);
exp.defineType("21",21);
exp.defineType("23",23);
exp.defineType("25",25);
exp.defineType("27",27);
} else{
exp.defineType("31",31);
exp.defineType("33",33);
exp.defineType("35",35);
exp.defineType("37",37);
exp.defineType("39",39);
exp.defineType("41",41);
exp.defineType("43",43);
exp.defineType("45",45);
exp.defineType("47",47);
exp.defineType("49",49);
exp.defineType("51",51);
exp.defineType("55",55);
exp.defineType("61",61);
exp.defineType("63",63);
exp.defineType("65",65);
}
argset.add(exp);
RooCategory ndf_z_asc("ndf_z_asc","ndf_z_asc");
ndf_z_asc.defineType("0",0);
ndf_z_asc.defineType("2",2);
ndf_z_asc.defineType("4",4);
ndf_z_asc.defineType("6",6);
ndf_z_asc.defineType("8",8);
ndf_z_asc.defineType("10",10);
ndf_z_asc.defineType("12",12);
ndf_z_asc.defineType("14",14);
ndf_z_asc.defineType("16",16);
ndf_z_asc.defineType("18",18);
argset.add(ndf_z_asc);
RooCategory good_icpv_mlt("good_icpv_mlt","good_icpv_mlt");
good_icpv_mlt.defineType("good",1);
RooCategory good_icpv_sgl("good_icpv_sgl","good_icpv_sgl");
good_icpv_sgl.defineType("good",1);
if(only_d0){
argset.add(good_icpv_sgl);
} else{
argset.add(good_icpv_mlt);
}
RooRealVar mbc("mbc","M_{bc}",mbcMin,mbcMax,"GeV"); argset.add(mbc);
RooRealVar de("de","#DeltaE",deMin,deMax,"GeV"); argset.add(de);
de.setRange("Signal",de_min,de_max);
if(!only_d0){ RooRealVar dz("dz","#Deltaz",-70./cm2ps,70./cm2ps,"cm"); argset.add(dz);}
else { RooRealVar dz("dz_d0","#Deltaz",-70./cm2ps,70./cm2ps,"cm"); argset.add(dz);}
RooRealVar bdtg("bdtg","bdtg",bdtg_min,1.); argset.add(bdtg);
RooRealVar atckpi_pi("atckpi_pi","atckpi_pi",0.,atckpi_pi_max); argset.add(atckpi_pi);
// if(!only_d0){ RooRealVar sz_sig("sz_sig","sz_sig",0.,sz_sig_max,"mm"); argset.add(sz_sig);}
// else { RooRealVar sz_sig("sz_sig_d0","#sigma_{z}^{sig}",0.,sz_sig_max,"mm"); argset.add(sz_sig);}
// RooRealVar sz_asc("sz_asc","sz_asc",0.,sz_asc_max,"mm"); argset.add(sz_asc);
// if(!only_d0 || true){ RooRealVar chisq_z_sig("chisq_z_sig","chisq_z_sig",0.,chisq_sig_max); argset.add(chisq_z_sig);}
// RooRealVar chisq_z_asc("chisq_z_asc","chisq_z_asc",0.,chisq_asc_max); argset.add(chisq_z_asc);
// RooDataSet ds("ds","ds",tree,argset,"mbc>0||mbc<=0 && (ndf_z_asc == 0 || 1.*chisq_z_asc/(ndf_z_asc+0.001)<10)");
RooDataSet ds("ds","ds",tree,argset,"mbc>0||mbc<=0");
RooRealVar de0DK("de0DK","de0DK",-0.049,-0.055,-0.040); de0DK.setConstant(kTRUE);
RooRealVar sDK("sDK","sDK",0.017,0.013,0.016); sDK.setConstant(kTRUE);
RooGaussian gDK("gDK","gDK",de,de0DK,sDK);
RooRealVar Nrho("NDK","NDK",50,0.,5000);// Nrho.setConstant(kTRUE);
RooRealVar c1("c1","c1",-6.96922e-01,-10.,10.); if(cComb) c1.setConstant(kTRUE);
RooRealVar c2("c2","c2",1.72017e-01,-10.,10.); if(cComb) c2.setConstant(kTRUE);
RooChebychev pdf_comb("pdf_comb","pdf_comb",de,RooArgSet(c1,c2));
RooRealVar Ncmb("NComb","NComb",10000,0.,50000);
RooRealVar de0("de0","de0",0.,-0.005,0.005);
RooRealVar s("s","s",1.19644e-02,0.010,0.015);
if(simple_peak){
RooGaussian pdf_sig("pdf_sig","pdf_sig",de,de0,s);
} else{
RooGaussian g1("g1","g1",de,de0,s);
// RooRealVar nl("nl","nl",4.93610e+00,0.,100.); if(cSig) nl.setConstant(kTRUE);
RooRealVar nl("nl","nl",7.78037e+00,0.,100.); if(cSig) nl.setConstant(kTRUE);
RooRealVar alphal("alphal","alphal",-1,-10.,10.); alphal.setConstant(kTRUE);
// RooRealVar nr("nr","nr",4.91073e+00,0.,100.); if(cSig) nr.setConstant(kTRUE);
RooRealVar nr("nr","nr",1.29892e+01,0.,100.); if(cSig) nr.setConstant(kTRUE);
RooRealVar alphar("alphar","alphar",1,-10.,10.); alphar.setConstant(kTRUE);
RooCBShape CBl("CBl","CBl",de,de0,s,alphal,nl);
RooCBShape CBr("CBr","CBr",de,de0,s,alphar,nr);
// RooRealVar fCBl("fCBl","fCBl",2.40571e-01,0.,1.); if(cSig) fCBl.setConstant(kTRUE);
// RooRealVar fCBr("fCBr","fCBr",2.20385e-01,0.,1.); if(cSig) fCBr.setConstant(kTRUE);
RooRealVar fCBl("fCBl","fCBl",2.22046e-01,0.,1.); if(cSig) fCBl.setConstant(kTRUE);
RooRealVar fCBr("fCBr","fCBr",2.21964e-01,0.,1.); if(cSig) fCBr.setConstant(kTRUE);
RooAddPdf pdf_sig("pdf_sig","pdf_sig",RooArgList(CBl,CBr,g1),RooArgSet(fCBl,fCBr));
}
RooRealVar Nsig("NSig","NSig",20000,0.,25000);
RooAddPdf pdf("pdf","pdf",RooArgSet(gDK,pdf_comb,pdf_sig),RooArgList(Nrho,Ncmb,Nsig));
pdf.fitTo(ds,Verbose(),Timer(true));
RooAbsReal* intSig = pdf_sig.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
RooAbsReal* intRho = gDK.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
RooAbsReal* intCmb = pdf_comb.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
const double nsig = intSig->getVal()*Nsig.getVal();
const double nsig_err = intSig->getVal()*Nsig.getError();
const double nsig_err_npq = TMath::Sqrt(nsig*(Nsig.getVal()-nsig)/Nsig.getVal());
const double nsig_err_total = TMath::Sqrt(nsig_err*nsig_err+nsig_err_npq*nsig_err_npq);
const double nrho = intRho->getVal()*Nrho.getVal();
const double nrho_err = intRho->getVal()*Nrho.getError();
const double nrho_err_npq = TMath::Sqrt(nrho*(Nrho.getVal()-nrho)/Nrho.getVal());
const double nrho_err_total = TMath::Sqrt(nrho_err*nrho_err+nrho_err_npq*nrho_err_npq);
const double ncmb = intCmb->getVal()*Ncmb.getVal();
const double ncmb_err = intCmb->getVal()*Ncmb.getError();
const double ncmb_err_npq = TMath::Sqrt(ncmb*(Ncmb.getVal()-ncmb)/Ncmb.getVal());
const double ncmb_err_total = TMath::Sqrt(ncmb_err*ncmb_err+ncmb_err_npq*ncmb_err_npq);
const double purity = nsig/(nsig+nrho+ncmb);
const double purity_err = nsig_err_total/(nsig+nrho+ncmb);
double sig_frac;
double pdf_sig_val;
double pdf_DK_val;
double pdf_smooth_val;
fstream ofile("de_sig_fraction.txt",fstream::out);
for(int i=0; i<1000; i++){
const double dde = 0.2/1000;
de.setVal(-0.1+(i+0.5)*dde);
pdf_sig_val = Nsig.getVal()*pdf_sig.getVal(de);
pdf_DK_val = Nrho.getVal()*gDK.getVal(de);
pdf_smooth_val = Ncmb.getVal()*pdf_comb.getVal(de);
sig_frac = pdf_sig_val/(pdf_sig_val+pdf_DK_val+pdf_smooth_val);
ofile << de.getVal() << " " << sig_frac << endl;
}
ofile.close();
/////////////
// Plots //
/////////////
// de //
RooPlot* deFrame = de.frame();
ds.plotOn(deFrame,DataError(RooAbsData::SumW2),MarkerSize(1));
pdf.plotOn(deFrame,Components(gDK),LineStyle(kDashed));
pdf.plotOn(deFrame,Components(pdf_sig),LineStyle(kDashed));
pdf.plotOn(deFrame,Components(pdf_comb),LineStyle(kDashed));
pdf.plotOn(deFrame,LineWidth(2));
RooHist* hdepull = deFrame->pullHist();
RooPlot* dePull = de.frame(Title("#Delta E pull distribution"));
dePull->addPlotable(hdepull,"P");
dePull->GetYaxis()->SetRangeUser(-5,5);
TCanvas* cm = new TCanvas("Delta E","Delta E",600,700);
cm->cd();
TPad *pad3 = new TPad("pad3","pad3",0.01,0.20,0.99,0.99);
TPad *pad4 = new TPad("pad4","pad4",0.01,0.01,0.99,0.20);
pad3->Draw();
pad4->Draw();
pad3->cd();
pad3->SetLeftMargin(0.15);
pad3->SetFillColor(0);
pad3->SetGrid();
deFrame->GetXaxis()->SetTitleSize(0.05);
deFrame->GetXaxis()->SetTitleOffset(0.85);
deFrame->GetXaxis()->SetLabelSize(0.04);
deFrame->GetYaxis()->SetTitleOffset(1.6);
deFrame->Draw();
const int height = svd == 2 ? 500 : 120;
TLine *de_line_RIGHT = new TLine(de_max,0,de_max,height);
de_line_RIGHT->SetLineColor(kRed);
de_line_RIGHT->SetLineStyle(1);
de_line_RIGHT->SetLineWidth((Width_t)2.);
de_line_RIGHT->Draw();
TLine *de_line_LEFT = new TLine(de_min,0,de_min,height);
de_line_LEFT->SetLineColor(kRed);
de_line_LEFT->SetLineStyle(1);
de_line_LEFT->SetLineWidth((Width_t)2.);
de_line_LEFT->Draw();
stringstream out1;
TPaveText *pt = new TPaveText(0.4,0.65,0.98,0.9,"brNDC");
pt->SetFillColor(0);
pt->SetTextAlign(12);
out1.str("");
out1 << "#chi^{2}/n.d.f = " << deFrame->chiSquare();
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "S: " << (int)(nsig+0.5) << " #pm " << (int)(nsig_err_total+0.5);
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "Purity: " << std::fixed << std::setprecision(2) << purity*100. << " #pm " << purity_err*100;
pt->AddText(out1.str().c_str());
pt->Draw();
pad4->cd(); pad4->SetLeftMargin(0.15); pad4->SetFillColor(0);
dePull->SetMarkerSize(0.05); dePull->Draw();
TLine *de_lineUP = new TLine(deMin,3,deMax,3);
de_lineUP->SetLineColor(kBlue);
de_lineUP->SetLineStyle(2);
de_lineUP->Draw();
TLine *de_line = new TLine(deMin,0,deMax,0);
de_line->SetLineColor(kBlue);
de_line->SetLineStyle(1);
de_line->SetLineWidth((Width_t)2.);
de_line->Draw();
TLine *de_lineDOWN = new TLine(deMin,-3,deMax,-3);
de_lineDOWN->SetLineColor(kBlue);
de_lineDOWN->SetLineStyle(2);
de_lineDOWN->Draw();
cm->Update();
// cout << "Nsig = " << nsig <<" +- " << nsig_err << endl;
// cout << "NDK = " << nrho <<" +- " << nrho_err << endl;
// cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << endl;
// cout << "Purity = " << purity << " +- " << purity_err << endl;
cout << "Nsig = " << nsig <<" +- " << nsig_err << " +- " << nsig_err_npq << " (" << nsig_err_total << ")" << endl;
cout << "NDK = " << nrho <<" +- " << nrho_err << " +- " << nrho_err_npq << " (" << nrho_err_total << ")" << endl;
cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << " +- " << ncmb_err_npq << " (" << ncmb_err_total << ")" << endl;
cout << "Pury = " << purity << " +- " << purity_err << endl;
}
void constrained_scan( const char* wsfile = "outputfiles/ws.root",
const char* new_poi_name="mu_bg_4b_msig_met1",
double constraintWidth=1.5,
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 10.0,
double ymax = 9.,
int verbLevel=1 ) {
TString outputdir("outputfiles") ;
gStyle->SetOptStat(0) ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "hbb_observed_rds" ) ;
cout << "\n\n\n ===== RooDataSet ====================\n\n" << endl ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooRealVar* rv_sig_strength = ws->var("sig_strength") ;
if ( rv_sig_strength == 0x0 ) { printf("\n\n *** can't find sig_strength in workspace.\n\n" ) ; return ; }
RooAbsPdf* likelihood = ws->pdf("likelihood") ;
if ( likelihood == 0x0 ) { printf("\n\n *** can't find likelihood in workspace.\n\n" ) ; return ; }
printf("\n\n Likelihood:\n") ;
likelihood -> Print() ;
/////rv_sig_strength -> setConstant( kFALSE ) ;
rv_sig_strength -> setVal(0.) ;
rv_sig_strength -> setConstant( kTRUE ) ;
likelihood->fitTo( *rds, Save(false), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//double minNllSusyFloat = fitResult->minNll() ;
//double susy_ss_atMinNll = rv_sig_strength -> getVal() ;
RooMsgService::instance().getStream(1).removeTopic(Minimization) ;
RooMsgService::instance().getStream(1).removeTopic(Fitting) ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
} else {
printf("\n Found it.\n\n") ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
double startPoiVal = new_poi_rar->getVal() ;
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
} // constrained_scan.
// implementation
void TwoBinInstructional( void ){
// let's time this example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace * pWs = new RooWorkspace("ws");
// derived from data
pWs->factory("xsec[0.2,0,2]"); // POI
pWs->factory("bg_b[10,0,50]"); // data driven nuisance
// predefined nuisances
pWs->factory("lumi[100,0,1000]");
pWs->factory("eff_a[0.2,0,1]");
pWs->factory("eff_b[0.05,0,1]");
pWs->factory("tau[0,1]");
pWs->factory("xsec_bg_a[0.05]"); // constant
pWs->var("xsec_bg_a")->setConstant(1);
// channel a (signal): lumi*xsec*eff_a + lumi*bg_a + tau*bg_b
pWs->factory("prod::sig_a(lumi,xsec,eff_a)");
pWs->factory("prod::bg_a(lumi,xsec_bg_a)");
pWs->factory("prod::tau_bg_b(tau, bg_b)");
pWs->factory("Poisson::pdf_a(na[14,0,100],sum::mu_a(sig_a,bg_a,tau_bg_b))");
// channel b (control): lumi*xsec*eff_b + bg_b
pWs->factory("prod::sig_b(lumi,xsec,eff_b)");
pWs->factory("Poisson::pdf_b(nb[11,0,100],sum::mu_b(sig_b,bg_b))");
// nuisance constraint terms (systematics)
pWs->factory("Lognormal::l_lumi(lumi,nom_lumi[100,0,1000],sum::kappa_lumi(1,d_lumi[0.1]))");
pWs->factory("Lognormal::l_eff_a(eff_a,nom_eff_a[0.20,0,1],sum::kappa_eff_a(1,d_eff_a[0.05]))");
pWs->factory("Lognormal::l_eff_b(eff_b,nom_eff_b[0.05,0,1],sum::kappa_eff_b(1,d_eff_b[0.05]))");
pWs->factory("Lognormal::l_tau(tau,nom_tau[0.50,0,1],sum::kappa_tau(1,d_tau[0.05]))");
//pWs->factory("Lognormal::l_bg_a(bg_a,nom_bg_a[0.05,0,1],sum::kappa_bg_a(1,d_bg_a[0.10]))");
// complete model PDF
pWs->factory("PROD::model(pdf_a,pdf_b,l_lumi,l_eff_a,l_eff_b,l_tau)");
// Now create sets of variables. Note that we could use the factory to
// create sets but in that case many of the sets would be duplicated
// when the ModelConfig objects are imported into the workspace. So,
// we create the sets outside the workspace, and only the needed ones
// will be automatically imported by ModelConfigs
// observables
RooArgSet obs(*pWs->var("na"), *pWs->var("nb"), "obs");
// global observables
RooArgSet globalObs(*pWs->var("nom_lumi"), *pWs->var("nom_eff_a"), *pWs->var("nom_eff_b"),
*pWs->var("nom_tau"),
"global_obs");
// parameters of interest
RooArgSet poi(*pWs->var("xsec"), "poi");
// nuisance parameters
RooArgSet nuis(*pWs->var("lumi"), *pWs->var("eff_a"), *pWs->var("eff_b"), *pWs->var("tau"), "nuis");
// priors (for Bayesian calculation)
pWs->factory("Uniform::prior_xsec(xsec)"); // for parameter of interest
pWs->factory("Uniform::prior_bg_b(bg_b)"); // for data driven nuisance parameter
pWs->factory("PROD::prior(prior_xsec,prior_bg_b)"); // total prior
// create data
pWs->var("na")->setVal(14);
pWs->var("nb")->setVal(11);
RooDataSet * pData = new RooDataSet("data","",obs);
pData->add(obs);
pWs->import(*pData);
//pData->Print();
// signal+background model
ModelConfig * pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*pWs);
pSbModel->SetPdf(*pWs->pdf("model"));
pSbModel->SetPriorPdf(*pWs->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
// set all but obs, poi and nuisance to const
SetConstants(pWs, pSbModel);
pWs->import(*pSbModel);
// background-only model
// use the same PDF as s+b, with xsec=0
// POI value under the background hypothesis
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)pWs->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*pWs);
pWs->import(*pBModel);
// find global maximum with the signal+background model
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
// - safer to keep a default name because some RooStats calculators
// will anticipate it
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*pData);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
pNll = pBModel->GetPdf()->createNLL(*pData);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
pWs->Print();
// save workspace to file
pWs->writeToFile("ws_twobin.root");
// clean up
delete pWs;
delete pData;
delete pSbModel;
delete pBModel;
} // ----- end of tutorial ----------------------------------------
int main(int argc, char *argv[])
{
//gROOT->ProcessLine("using namespace RooFit;");
RooRealVar* xt=new RooRealVar("xt","x for spec fit",0,1.e6);//should be big=1.e6!needed to calculate integral of pdf = coe
RooRealVar* xe=new RooRealVar("xe","x for spec fit",0,1.e6);//should be big=1.e6!needed to calculate integral of pdf = coe
TString nameStr;
string isoname="B12";
RooRealVar* rateMu=new RooRealVar("rateMu","rateMu",-0.0629969);
RooRealVar* B12Tau= new RooRealVar("B12Tau", "B12Tau", -0.0291,"s");
RooRealVar* B12FitNum=new RooRealVar("B12FitNum","B12FitNum",159061.3,100,6000000);
RooFormulaVar* B12Lambda=new RooFormulaVar("B12Lambda","B12Lambda","1/@0 + @1",RooArgList(*B12Tau, *rateMu));
RooExponential* B12ExpPdf=new RooExponential("B12ExpPdf","B12ExpPdf", *xt,*B12Lambda);
RooExtendPdf* B12ExtendPdf=new RooExtendPdf("B12ExtendPdf","B12ExtendPdf",*B12ExpPdf,*B12FitNum) ;
RooRealVar* BkgTau= new RooRealVar("BkgTau", "BkgTau", -0.5,"s");
RooRealVar* BkgFitNum=new RooRealVar("BkgFitNum","BkgFitNum",66002,100,6000000);
RooFormulaVar* BkgLambda=new RooFormulaVar("BkgLambda","BkgLambda","@0 + @1",RooArgList(*BkgTau, *rateMu));
RooExponential* BkgExpPdf=new RooExponential("BkgExpPdf","BkgExpPdf", *xt,*BkgLambda);
RooExtendPdf* BkgExtendPdf=new RooExtendPdf("BkgExtendPdf","BkgExtendPdf",*BkgExpPdf,*BkgFitNum) ;
RooArgList timeFitComList;
timeFitComList.add(*B12ExtendPdf);
timeFitComList.add(*BkgExtendPdf);
RooAddPdf* timeFitPdf=new RooAddPdf("timeModel","timeModel",timeFitComList) ;
//RooRealVar* xe=new RooRealVar("xe","x for spec fit",0,1.e6);
xt->setRange("tRange",0.001,0.501);
RooAbsReal* tTmpCoe =B12ExpPdf->createIntegral(*xt,NormSet(*xt),Range("tRange"));
std::cout<<"tTmpCoe : "<<tTmpCoe->getVal()<<endl;
RooRealVar* tCutCoe=new RooRealVar("tCutCoe","tCutCoe",tTmpCoe->getVal());
TFile* f=new TFile("IsoTheoreticalSpec.root","read");
nameStr=Form("%sspecHistogramVsp.d.f",isoname.c_str());
TCanvas* ce2 = new TCanvas(nameStr,nameStr,1200,400) ;
ce2->Divide(3) ;
nameStr=Form("%sSpectraAfterCor",isoname.c_str());
TH1F* h=(TH1F*)f->Get(nameStr);
h->Rebin(250);
ce2->cd(1) ; h->Draw() ;
RooDataHist* ehd=new RooDataHist("hd","hd",*xe,h);
RooPlot* framee = xe->frame(Title("spec histogram")) ;
ehd->plotOn(framee,LineColor(kRed),DataError(RooAbsData::None));
ce2->cd(2) ; framee->Draw() ;
std::cout<<"ehd->numEntries() : "<<ehd->numEntries()<<endl;
nameStr=Form("%shpdf",isoname.c_str());
RooHistPdf* fitHistPdf=new RooHistPdf(nameStr,nameStr,*xe,*ehd,2) ;
RooPlot* framee1 = xe->frame(Title("spec p.d.f")) ;
fitHistPdf->plotOn(framee1),LineColor(kBlue) ;
ce2->cd(3) ; framee1->Draw() ;
nameStr=Form("P14A/dataEps/test%sspecHistogramVsp.d.f.eps",isoname.c_str());
ce2->SaveAs(nameStr);
xe->setRange("eRange",5.0,20.0);
RooAbsReal* eTmpCoe =fitHistPdf->createIntegral(*xe,NormSet(*xe),Range("eRange"));
RooRealVar* eCutCoe=new RooRealVar("eCutCoe","eCutCoe",eTmpCoe->getVal());
//RooFormulaVar* eFitNum=new RooFormulaVar("eFitNum","eFitNum","@0/@1*@2",RooArgList(*B12FitNum,*tCutCoe,*eCutCoe));
RooRealVar* eFitNum=new RooRealVar("eFitNum","eFitNum",159061.3,100,6000000);
RooExtendPdf* hdExtendPdf=new RooExtendPdf("hdExtendPdf","hdExtendPdf",*fitHistPdf,*eFitNum) ;
RooArgList specFitComList;
specFitComList.add(*hdExtendPdf);
RooAddPdf* specFitPdf=new RooAddPdf("specMode","specMode",specFitComList);
/*
TFile* f=new TFile("IsoTheoreticalSpec.root","read");
nameStr=Form("%sSpectraAfterCor",isoname.c_str());
TH1F* h=(TH1F*)f->Get(nameStr);
//h->Scale(10000000);
ce->cd(1) ; h->Draw() ;
RooDataHist* hd =new RooDataHist("hd","hd",*xe,h);
RooPlot* framee = xe->frame() ;
hd->plotOn(framee,DataError(RooAbsData::None),MarkerSize(0.1));
ce->cd(2) ; framee->Draw() ;
*/
/*
TH1F* hh=new TH1F("hh","hh",100,1,10);
for( int i=1 ; i<=100 ; i++ )
{
//hh->SetBinContent(i,i);
hh->SetBinContent(i,(-(i/10.-5)*(i/10.-5)+25)/100000.);
}
ce->cd(3);
hh->Draw();
RooDataHist* hd2 =new RooDataHist("hd2","hd2",*xe,hh);
RooPlot* frame2 = xe->frame() ;
hd2->plotOn(frame2,DataError(RooAbsData::None));
ce->cd(4) ; frame2->Draw() ;
*/
//nameStr=Form("P14A/EH1iso_P14A.root",dataVer.c_str(),site.c_str(),dataVer.c_str());
nameStr=Form("%sspecHistogramVsp.d.f",isoname.c_str());
TCanvas* ce = new TCanvas(nameStr,nameStr,1200,800) ;
ce->Divide(3,2) ;
TFile* f2=new TFile("P14A/EH1iso_P14A.root","read");
TH1F* h4=(TH1F*)f2->Get("time2lastshowermuonNoRed4_5.0_20.0");
int hmax=h4->FindBin(0.501);
std::cout<<"hman : "<<hmax<<endl;
int hmin=h4->FindBin(0.001);
std::cout<<"hmin : "<<hmin<<endl;
int hBinNum=hmax-hmin;
std::cout<<"hBinNum : "<<hBinNum<<endl;
TH1F* ht=new TH1F("slice4","slice4",hBinNum,0.001,0.501);
for( int j=1 ; j<=hBinNum ; j++ )
{
ht->SetBinContent(j,h4->GetBinContent(hmin+j-1));
}
//ht->Rebin(8);
ce->cd(1) ; ht->Draw() ;
RooDataHist* hd1=new RooDataHist(Form("%stime2lastmuonBinned",isoname.c_str()),"time2lastmuon binned data",*xt,ht);
RooPlot* frame1 = xt->frame() ;
hd1->plotOn(frame1,DataError(RooAbsData::None),MarkerSize(0.1));
ce->cd(2) ; frame1->Draw() ;
nameStr=Form("%sSpecNoRedSlice4_5.0_20.0",isoname.c_str());
TH1F* hs=(TH1F*)f2->Get(nameStr);
ce->cd(3);
hs->Draw();
xe->setRange(5,20);
RooDataHist* hd3=new RooDataHist(Form("%sspecBinned",isoname.c_str()),"spec binned data",*xe,hs);
RooPlot* frame3 = xe->frame() ;
hd3->plotOn(frame3,DataError(RooAbsData::None),MarkerSize(0.1));
ce->cd(4) ; frame3->Draw() ;
RooCategory sample("sample","sample") ;
sample.defineType("time");
sample.defineType("spec");
//std::map<string,TH1F*> histMap;
//histMap.insert(pair<string,TH1F*>("time",ht));
//histMap.insert(pair<string,TH1F*>("spec",hs));
std::cout<<"hd1->numEntries() : "<<hd1->numEntries()<<endl;
std::cout<<"hd3->numEntries() : "<<hd3->numEntries()<<endl;
xt->setBins(hd1->numEntries());
xe->setBins(hd3->numEntries());
//RooDataHist combData("combData","combined data",RooArgSet(*xt,*xe),Index(sample),Import("time",*(hd1)),Import("spec",*(hd3))) ;
//RooDataHist combData("combData","combined data",RooArgSet(*xt,*xe),Index(sample),Import("time",*(ht)),Import("spec",*(hs))) ;
//map<int,string> mabb;
//RooDataHist combData("combData","combined data",RooArgSet(*xt,*xe),Index(sample),histMap,1) ;
//nameStr=Form("P14A/dataEps/%sspecHistogramVsp.d.f.eps",isoname.c_str());
//ce->SaveAs(nameStr);
//RooRealVar* xt=new RooRealVar("xt","x for time fit",0,1.e6);
//RooSimultaneous simPdf("simPdf","simultaneous pdf",sample) ;
//simPdf.addPdf(*specFitPdf,"spec") ;
//simPdf.addPdf(*timeFitPdf,"time") ;
//simPdf.fitTo(combData,SumW2Error(kTRUE),PrintEvalErrors(10)) ;
RooAbsReal* nll1 = timeFitPdf->createNLL(*(hd1)) ;
//RooAbsReal* nll2 = specFitPdf->createNLL(*(hd3)) ;
RooAbsReal* nll2 = hdExtendPdf->createNLL(*(hd3)) ;
RooAddition nll("nll","nll",RooArgSet(*nll1,*nll2)) ;
RooMinuit m1(*nll1) ;
m1.migrad() ;
m1.hesse() ;
RooFitResult* r1 = m1.save() ;
std::cout<<"!!!cout m1"<<endl;
r1->Print("v") ;
std::cout<<"!!!end m1"<<endl;
RooMinuit m2(*nll2) ;
m2.migrad() ;
m2.hesse() ;
RooFitResult* r2 = m2.save() ;
std::cout<<"!!!cout m2"<<endl;
r2->Print("v") ;
std::cout<<"!!!end m2"<<endl;
//RooMinuit m(nll) ;
//m.migrad() ;
//m.hesse() ;
//RooFitResult* r = m.save() ;
//r->Print("v") ;
RooPlot* frame4 = xt->frame(Bins(500),Title("Time fit")) ;
//combData.plotOn(frame4,Cut("sample==sample::time"),DataError(RooAbsData::None),MarkerSize(0.1)) ;
//simPdf.plotOn(frame4,Slice(sample,"time"),ProjWData(sample,combData)) ;
//simPdf.plotOn(frame4,Slice(sample,"time"),Components(*(B12ExpPdf)),ProjWData(sample,combData),Name(Form("%s",isoname.c_str())),LineStyle(kDashed),LineColor(kRed)) ;
//simPdf.plotOn(frame4,Slice(sample,"time"),Components(*(BkgExpPdf)),ProjWData(sample,combData),Name(Form("%s",isoname.c_str())),LineStyle(kDashed),LineColor(kBlue)) ;
ce->cd(5) ; frame4->Draw() ;
RooPlot* frame5 = xe->frame(Bins(60),Title("Spectrum fit")) ;
//combData.plotOn(frame5,Cut("sample==sample::spec"),DataError(RooAbsData::None),MarkerSize(0.1)) ;
//simPdf.plotOn(frame5,Slice(sample,"spec"),ProjWData(sample,combData)) ;
ce->cd(6) ; frame5->Draw() ;
nameStr=Form("P14A/dataEps/%stestRooDataHist.eps",isoname.c_str());
ce->SaveAs(nameStr);
std::cout<<"all done "<<endl;
return 1;
}
void rf308_normintegration2d()
{
// S e t u p m o d e l
// ---------------------
// Create observables x,y
RooRealVar x("x","x",-10,10) ;
RooRealVar y("y","y",-10,10) ;
// Create p.d.f. gaussx(x,-2,3), gaussy(y,2,2)
RooGaussian gx("gx","gx",x,RooConst(-2),RooConst(3)) ;
RooGaussian gy("gy","gy",y,RooConst(+2),RooConst(2)) ;
// Create gxy = gx(x)*gy(y)
RooProdPdf gxy("gxy","gxy",RooArgSet(gx,gy)) ;
// R e t r i e v e r a w & n o r m a l i z e d v a l u e s o f R o o F i t p . d . f . s
// --------------------------------------------------------------------------------------------------
// Return 'raw' unnormalized value of gx
cout << "gxy = " << gxy.getVal() << endl ;
// Return value of gxy normalized over x _and_ y in range [-10,10]
RooArgSet nset_xy(x,y) ;
cout << "gx_Norm[x,y] = " << gxy.getVal(&nset_xy) << endl ;
// Create object representing integral over gx
// which is used to calculate gx_Norm[x,y] == gx / gx_Int[x,y]
RooAbsReal* igxy = gxy.createIntegral(RooArgSet(x,y)) ;
cout << "gx_Int[x,y] = " << igxy->getVal() << endl ;
// NB: it is also possible to do the following
// Return value of gxy normalized over x in range [-10,10] (i.e. treating y as parameter)
RooArgSet nset_x(x) ;
cout << "gx_Norm[x] = " << gxy.getVal(&nset_x) << endl ;
// Return value of gxy normalized over y in range [-10,10] (i.e. treating x as parameter)
RooArgSet nset_y(y) ;
cout << "gx_Norm[y] = " << gxy.getVal(&nset_y) << endl ;
// I n t e g r a t e n o r m a l i z e d p d f o v e r s u b r a n g e
// ----------------------------------------------------------------------------
// Define a range named "signal" in x from -5,5
x.setRange("signal",-5,5) ;
y.setRange("signal",-3,3) ;
// Create an integral of gxy_Norm[x,y] over x and y in range "signal"
// This is the fraction of of p.d.f. gxy_Norm[x,y] which is in the
// range named "signal"
RooAbsReal* igxy_sig = gxy.createIntegral(RooArgSet(x,y),NormSet(RooArgSet(x,y)),Range("signal")) ;
cout << "gx_Int[x,y|signal]_Norm[x,y] = " << igxy_sig->getVal() << endl ;
// C o n s t r u c t c u m u l a t i v e d i s t r i b u t i o n f u n c t i o n f r o m p d f
// -----------------------------------------------------------------------------------------------------
// Create the cumulative distribution function of gx
// i.e. calculate Int[-10,x] gx(x') dx'
RooAbsReal* gxy_cdf = gxy.createCdf(RooArgSet(x,y)) ;
// Plot cdf of gx versus x
TH1* hh_cdf = gxy_cdf->createHistogram("hh_cdf",x,Binning(40),YVar(y,Binning(40))) ;
hh_cdf->SetLineColor(kBlue) ;
new TCanvas("rf308_normintegration2d","rf308_normintegration2d",600,600) ;
gPad->SetLeftMargin(0.15) ; hh_cdf->GetZaxis()->SetTitleOffset(1.8) ;
hh_cdf->Draw("surf") ;
}
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 Purity_1d_fit(int type = 0){
TChain* tree = new TChain("TEvent");
if(!type) tree->Add("/home/vitaly/B0toDh0/TMVA/FIL_b2dh_gen_0-1_full.root");
else tree->Add("/home/vitaly/B0toDh0/TMVA/FIL_b2dh_data.root");
RooCategory b0f("b0f","b0f");
b0f.defineType("signal",1);
b0f.defineType("fsr",10);
b0f.defineType("bad_pi0",5);
b0f.defineType("rho",3);
b0f.defineType("comb",-1);
RooArgSet argset;
const double deMin = -0.15;
const double deMax = 0.3;
RooRealVar mbc("mbc","M_{bc}",mbc_min,mbc_max,"GeV"); argset.add(mbc);
RooRealVar de("de","#DeltaE",deMin,deMax,"GeV"); argset.add(de);
de.setRange("Signal",de_min,de_max);
RooRealVar md("md","md",DMass-md_cut,DMass+md_cut,"GeV"); argset.add(md);
RooRealVar mk("mk","mk",KMass-mk_cut,KMass+mk_cut,"GeV"); argset.add(mk);
RooRealVar mpi0("mpi0","mpi0",Pi0Mass-mpi0_cut,Pi0Mass+mpi0_cut,"GeV"); argset.add(mpi0);
RooRealVar bdtgs("bdtgs","bdtgs",bdtgs_cut,1.); argset.add(bdtgs);
RooRealVar atckpi_max("atckpi_max","atckpi_max",0.,atckpi_cut); argset.add(atckpi_max);
if(!type) argset.add(b0f);
RooDataSet ds("ds","ds",tree,argset,"mbc>0||mbc<=0");
// RooDataSet* ds0 = ds.reduce(RooArgSet(de));
stringstream out;
if(!type){
out.str("");
out << "de<" << de_max << " && de>" << de_min;
Roo1DTable* sigtable = ds.table(b0f,out.str().c_str());
sigtable->Print();
sigtable->Print("v");
Roo1DTable* fulltable = ds.table(b0f);
fulltable->Print();
fulltable->Print("v");
}
// RooDataHist* dh = ds0->binnedClone();
// ds0->Print();
////////////////
// Signal PDF //
////////////////
////////////
// de pdf //
////////////
RooRealVar de0("de0","de0",m_de0,-0.1,0.1); if(cSig) de0.setConstant(kTRUE);
RooRealVar s1("s1","s1",m_s1,0.,0.5); if(cSig) s1.setConstant(kTRUE);
RooGaussian g1("g1","g1",de,de0,s1);
RooRealVar deCBl("deCBl","deCBl",m_deCBl,-0.1,0.1); if(cSig) deCBl.setConstant(kTRUE);
RooRealVar sCBl("sCBl","sCBl",m_sCBl,0.,0.5); if(cSig) sCBl.setConstant(kTRUE);
RooRealVar nl("nl","nl",m_nl,0.,100.); if(cSig) nl.setConstant(kTRUE);
RooRealVar alphal("alphal","alphal",m_alphal,-10.,10.); if(cSig) alphal.setConstant(kTRUE);
RooRealVar deCBr("deCBr","deCBr",m_deCBr,-0.1,0.1); if(cSig) deCBr.setConstant(kTRUE);
RooRealVar sCBr("sCBr","sCBr",m_sCBr,0.,0.5); if(cSig) sCBr.setConstant(kTRUE);
RooRealVar nr("nr","nr",m_nr,0.,100.); if(cSig) nr.setConstant(kTRUE);
RooRealVar alphar("alphar","alphar",m_alphar,-10.,10.); if(cSig) alphar.setConstant(kTRUE);
RooCBShape CBl("CBl","CBl",de,deCBl,sCBl,alphal,nl);
RooCBShape CBr("CBr","CBr",de,deCBr,sCBr,alphar,nr);
RooRealVar fCBl("fCBl","fCBl",m_fCBl,0.,1.); if(cSig) fCBl.setConstant(kTRUE);
RooRealVar fCBr("fCBr","fCBr",m_fCBr,0.,1.); if(cSig) fCBr.setConstant(kTRUE);
RooAddPdf pdf_sig("pdf_sig","pdf_sig",RooArgList(CBl,CBr,g1),RooArgSet(fCBl,fCBr));
//////////////
// Comb PDF //
//////////////
////////////
// de pdf //
////////////
RooRealVar c1("c1","c1",mc_c1_1d,-10.,10.); if(cComb) c1.setConstant(kTRUE);
RooRealVar c2("c2","c2",mc_c2_1d,-10.,10.); if(cComb) c2.setConstant(kTRUE);
RooChebychev pdf_comb("pdf_comb","pdf_comb",de,RooArgSet(c1,c2));
/////////////
// Rho PDF //
/////////////
////////////
// de pdf //
////////////
if(de_rho_param == 0){
RooRealVar exppar("exppar","exppar",mr_exppar,-40.,-25.);// if(cRho) exppar.setConstant(kTRUE);
RooExponential pdf_rho("pdf_rho","pdf_rho",de,exppar);
}
RooRealVar de0r("de0r","de0r",mr_de0r,-0.2,0.12); if(cRho) de0r.setConstant(kTRUE);
if(de_rho_param == 1){
RooRealVar slopel("slopel","slopel",mr_slopel,-1000,-500.); if(cRho) slopel.setConstant(kTRUE);
RooRealVar sloper("sloper","sloper",mr_sloper,-10000,0.); if(cRho) sloper.setConstant(kTRUE);
RooRealVar steep("steep","steep",mr_steep,7.,9.); if(cRho) steep.setConstant(kTRUE);
RooRealVar p5("p5","p5",mr_p5,0.01,1000.); if(cRho) p5.setConstant(kTRUE);
RooRhoDeltaEPdf pdf_rho("pdf_rho","pdf_rho",de,de0r,slopel,sloper,steep,p5);
}
if(de_rho_param == -1){
RooRealVar x0("x0","x0",mr_x0_1d,-0.2,0.12); if(cRho) x0.setConstant(kTRUE);
RooRealVar p1("p1","p1",mr_p1_1d,-1000.,100.); if(cRho) p1.setConstant(kTRUE);
RooRealVar p2("p2","p2",mr_p2_1d,0.,100.); if(cRho) p2.setConstant(kTRUE);
RooGenericPdf pdf_rho("pdf_rho","1+@0*@1-@2*TMath::Log(1+TMath::Exp(@2*(@0-@1)/@3))",RooArgSet(de,x0,p1,p2));
}
//////////////////
// Complete PDF //
//////////////////
RooRealVar Nsig("Nsig","Nsig",700,100.,1500.);// fsig.setConstant(kTRUE);
RooRealVar Nrho("Nrho","Nrho",400,100,1500.);// frho.setConstant(kTRUE);
RooRealVar Ncmb("Ncmb","Ncmb",1000,100,100000);// frho.setConstant(kTRUE);
RooAddPdf pdf("pdf","pdf",RooArgList(pdf_sig,pdf_rho,pdf_comb),RooArgList(Nsig,Nrho,Ncmb));
pdf.fitTo(ds,Verbose(),Timer(true));
RooAbsReal* intSig = pdf_sig.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
RooAbsReal* intRho = pdf_rho.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
RooAbsReal* intCmb = pdf_comb.createIntegral(RooArgSet(de),NormSet(RooArgSet(de)),Range("Signal"));
const double nsig = intSig->getVal()*Nsig.getVal();
const double nsig_err = intSig->getVal()*Nsig.getError();
const double nsig_err_npq = TMath::Sqrt(nsig*(Nsig.getVal()-nsig)/Nsig.getVal());
const double nsig_err_total = TMath::Sqrt(nsig_err*nsig_err+nsig_err_npq*nsig_err_npq);
const double nrho = intRho->getVal()*Nrho.getVal();
const double nrho_err = intRho->getVal()*Nrho.getError();
const double nrho_err_npq = TMath::Sqrt(nrho*(Nrho.getVal()-nrho)/Nrho.getVal());
const double nrho_err_total = TMath::Sqrt(nrho_err*nrho_err+nrho_err_npq*nrho_err_npq);
const double ncmb = intCmb->getVal()*Ncmb.getVal();
const double ncmb_err = intCmb->getVal()*Ncmb.getError();
const double ncmb_err_npq = TMath::Sqrt(ncmb*(Ncmb.getVal()-ncmb)/Ncmb.getVal());
const double ncmb_err_total = TMath::Sqrt(ncmb_err*ncmb_err+ncmb_err_npq*ncmb_err_npq);
const double purity = nsig/(nsig+nrho+ncmb);
const double purity_err = nsig_err_total/(nsig+nrho+ncmb);
cout << "Nsig = " << nsig <<" +- " << nsig_err << endl;
cout << "Nrho = " << nrho <<" +- " << nrho_err << endl;
cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << endl;
/////////////
// Plots //
/////////////
// de //
RooPlot* deFrame = de.frame();
ds.plotOn(deFrame,DataError(RooAbsData::SumW2),MarkerSize(1));
pdf.plotOn(deFrame,Components(pdf_sig),LineStyle(kDashed));
pdf.plotOn(deFrame,Components(pdf_rho),LineStyle(kDashed));
pdf.plotOn(deFrame,Components(pdf_comb),LineStyle(kDashed));
pdf.plotOn(deFrame,LineWidth(2));
RooHist* hdepull = deFrame->pullHist();
RooPlot* dePull = de.frame(Title("#Delta E pull distribution"));
dePull->addPlotable(hdepull,"P");
dePull->GetYaxis()->SetRangeUser(-5,5);
TCanvas* cm = new TCanvas("Delta E","Delta E",600,700);
cm->cd();
TPad *pad3 = new TPad("pad3","pad3",0.01,0.20,0.99,0.99);
TPad *pad4 = new TPad("pad4","pad4",0.01,0.01,0.99,0.20);
pad3->Draw();
pad4->Draw();
pad3->cd();
pad3->SetLeftMargin(0.15);
pad3->SetFillColor(0);
deFrame->GetXaxis()->SetTitleSize(0.05);
deFrame->GetXaxis()->SetTitleOffset(0.85);
deFrame->GetXaxis()->SetLabelSize(0.04);
deFrame->GetYaxis()->SetTitleOffset(1.6);
deFrame->Draw();
stringstream out1;
TPaveText *pt = new TPaveText(0.6,0.75,0.98,0.9,"brNDC");
pt->SetFillColor(0);
pt->SetTextAlign(12);
out1.str("");
out1 << "#chi^{2}/n.d.f = " << deFrame->chiSquare();
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "S: " << (int)(nsig+0.5) << " #pm " << (int)(nsig_err_total+0.5);
pt->AddText(out1.str().c_str());
out1.str("");
out1 << "Purity: " << std::fixed << std::setprecision(2) << purity*100. << " #pm " << purity_err*100;
pt->AddText(out1.str().c_str());
pt->Draw();
TLine *de_line_RIGHT = new TLine(de_max,0,de_max,50);
de_line_RIGHT->SetLineColor(kRed);
de_line_RIGHT->SetLineStyle(1);
de_line_RIGHT->SetLineWidth((Width_t)2.);
de_line_RIGHT->Draw();
TLine *de_line_LEFT = new TLine(de_min,0,de_min,50);
de_line_LEFT->SetLineColor(kRed);
de_line_LEFT->SetLineStyle(1);
de_line_LEFT->SetLineWidth((Width_t)2.);
de_line_LEFT->Draw();
pad4->cd(); pad4->SetLeftMargin(0.15); pad4->SetFillColor(0);
dePull->SetMarkerSize(0.05); dePull->Draw();
TLine *de_lineUP = new TLine(deMin,3,deMax,3);
de_lineUP->SetLineColor(kBlue);
de_lineUP->SetLineStyle(2);
de_lineUP->Draw();
TLine *de_line = new TLine(deMin,0,deMax,0);
de_line->SetLineColor(kBlue);
de_line->SetLineStyle(1);
de_line->SetLineWidth((Width_t)2.);
de_line->Draw();
TLine *de_lineDOWN = new TLine(deMin,-3,deMax,-3);
de_lineDOWN->SetLineColor(kBlue);
de_lineDOWN->SetLineStyle(2);
de_lineDOWN->Draw();
cm->Update();
if(!type){
out.str("");
out << "de<" << de_max << " && de>" << de_min;
Roo1DTable* sigtable = ds.table(b0f,out.str().c_str());
sigtable->Print();
sigtable->Print("v");
Roo1DTable* fulltable = ds.table(b0f);
fulltable->Print();
fulltable->Print("v");
}
cout << "Nsig = " << nsig <<" +- " << nsig_err << " +- " << nsig_err_npq << " (" << nsig_err_total << ")" << endl;
cout << "Nrho = " << nrho <<" +- " << nrho_err << " +- " << nrho_err_npq << " (" << nrho_err_total << ")" << endl;
cout << "Ncmb = " << ncmb <<" +- " << ncmb_err << " +- " << ncmb_err_npq << " (" << ncmb_err_total << ")" << endl;
cout << "Pury = " << purity << " +- " << purity_err << endl;
}
void new_RA4(){
// let's time this challenging example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace* wspace = new RooWorkspace("wspace");
wspace->factory("Gaussian::sigCons(prime_SigEff[0,-5,5], nom_SigEff[0,-5,5], 1)");
wspace->factory("expr::SigEff('1.0*pow(1.20,@0)',prime_SigEff)"); // // 1+-20%, 1.20=exp(20%)
wspace->factory("Poisson::on(non[0,50], sum::splusb(prod::SigUnc(s[0,0,50],SigEff),mainb[8.8,0,50],dilep[0.9,0,20],tau[2.3,0,20],QCD[0.,0,10],MC[0.1,0,4]))");
wspace->factory("Gaussian::mcCons(prime_rho[0,-5,5], nom_rho[0,-5,5], 1)");
wspace->factory("expr::rho('1.0*pow(1.39,@0)',prime_rho)"); // // 1+-39%
wspace->factory("Poisson::off(noff[0,200], prod::rhob(mainb,rho,mu_plus_e[0.74,0.01,10],1.08))");
wspace->factory("Gaussian::mcCons2(mu_plus_enom[0.74,0.01,4], mu_plus_e, sigmatwo[.05])");
wspace->factory("Gaussian::dilep_pred(dilep_nom[0.9,0,20], dilep, sigma3[2.2])");
wspace->factory("Gaussian::tau_pred(tau_nom[2.3,0,20], tau, sigma4[0.5])");
wspace->factory("Gaussian::QCD_pred(QCD_nom[0.0,0,10], QCD, sigma5[1.0])");
wspace->factory("Gaussian::MC_pred(MC_nom[0.1,0.01,4], MC, sigma7[0.14])");
wspace->factory("PROD::model(on,off,mcCons,mcCons2,sigCons,dilep_pred,tau_pred,QCD_pred,MC_pred)");
RooArgSet obs(*wspace->var("non"), *wspace->var("noff"), *wspace->var("mu_plus_enom"), *wspace->var("dilep_nom"), *wspace->var("tau_nom"), "obs");
obs.add(*wspace->var("QCD_nom")); obs.add(*wspace->var("MC_nom"));
RooArgSet globalObs(*wspace->var("nom_SigEff"), *wspace->var("nom_rho"), "global_obs");
// fix global observables to their nominal values
wspace->var("nom_SigEff")->setConstant();
wspace->var("nom_rho")->setConstant();
RooArgSet poi(*wspace->var("s"), "poi");
RooArgSet nuis(*wspace->var("mainb"), *wspace->var("prime_rho"), *wspace->var("prime_SigEff"), *wspace->var("mu_plus_e"), *wspace->var("dilep"), *wspace->var("tau"), "nuis");
nuis.add(*wspace->var("QCD")); nuis.add(*wspace->var("MC"));
wspace->factory("Uniform::prior_poi({s})");
wspace->factory("Uniform::prior_nuis({mainb,mu_plus_e,dilep,tau,QCD,MC})");
wspace->factory("PROD::prior(prior_poi,prior_nuis)");
wspace->var("non")->setVal(8); //observed
//wspace->var("non")->setVal(12); //expected observation
wspace->var("noff")->setVal(7); //observed events in control region
wspace->var("mu_plus_enom")->setVal(0.74);
wspace->var("dilep_nom")->setVal(0.9);
wspace->var("tau_nom")->setVal(2.3);
wspace->var("QCD")->setVal(0.0);
wspace->var("MC")->setVal(0.1);
RooDataSet * data = new RooDataSet("data","",obs);
data->add(obs);
wspace->import(*data);
/////////////////////////////////////////////////////
// Now the statistical tests
// model config
ModelConfig* pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*wspace);
pSbModel->SetPdf(*wspace->pdf("model"));
pSbModel->SetPriorPdf(*wspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
wspace->import(*pSbModel);
// set all but obs, poi and nuisance to const
SetConstants(wspace, pSbModel);
wspace->import(*pSbModel);
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)wspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*wspace);
wspace->import(*pBModel);
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*data);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
//if(pSbModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
pNll = pBModel->GetPdf()->createNLL(*data);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
//if(pBModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
wspace->Print();
// save workspace to file
wspace->writeToFile("tight.root");
//wspace->writeToFile("tight_median.root");
// clean up
delete wspace;
delete data;
delete pSbModel;
delete pBModel;
}
/*
* Prepares the workspace to be used by the hypothesis test calculator
*/
void workspace_preparer(char *signal_file_name, char *signal_hist_name_in_file, char *background_file_name, char *background_hist_name_in_file, char *data_file_name, char *data_hist_name_in_file, char *config_file) {
// Include the config_reader class.
TString path = gSystem->GetIncludePath();
path.Append(" -I/home/max/cern/cls/mario");
gSystem->SetIncludePath(path);
gROOT->LoadMacro("config_reader.cxx");
// RooWorkspace used to store values.
RooWorkspace * pWs = new RooWorkspace("ws");
// Create a config_reader (see source for details) to read the config
// file.
config_reader reader(config_file, pWs);
// Read MR and RR bounds from the config file.
double MR_lower = reader.find_double("MR_lower");
double MR_upper = reader.find_double("MR_upper");
double RR_lower = reader.find_double("RR_lower");
double RR_upper = reader.find_double("RR_upper");
double MR_initial = (MR_lower + MR_upper)/2;
double RR_initial = (RR_lower + RR_upper)/2;
// Define the Razor Variables
RooRealVar MR = RooRealVar("MR", "MR", MR_initial, MR_lower, MR_upper);
RooRealVar RR = RooRealVar("RSQ", "RSQ", RR_initial, RR_lower, RR_upper);
// Argument lists
RooArgList pdf_arg_list(MR, RR, "input_args_list");
RooArgSet pdf_arg_set(MR, RR, "input_pdf_args_set");
/***********************************************************************/
/* PART 1: IMPORTING SIGNAL AND BACKGROUND HISTOGRAMS */
/***********************************************************************/
/*
* Get the signal's unextended pdf by converting the TH2D in the file
* into a RooHistPdf
*/
TFile *signal_file = new TFile(signal_file_name);
TH2D *signal_hist = (TH2D *)signal_file->Get(signal_hist_name_in_file);
RooDataHist *signal_RooDataHist = new RooDataHist("signal_roodatahist",
"signal_roodatahist",
pdf_arg_list,
signal_hist);
RooHistPdf *unextended_sig_pdf = new RooHistPdf("unextended_sig_pdf",
"unextended_sig_pdf",
pdf_arg_set,
*signal_RooDataHist);
/*
* Repeat this process for the background.
*/
TFile *background_file = new TFile(background_file_name);
TH2D *background_hist =
(TH2D *)background_file->Get(background_hist_name_in_file);
RooDataHist *background_RooDataHist =
new RooDataHist("background_roodatahist", "background_roodatahist",
pdf_arg_list, background_hist);
RooHistPdf *unextended_bkg_pdf = new RooHistPdf("unextended_bkg_pdf",
"unextended_bkg_pdf",
pdf_arg_set,
*background_RooDataHist);
/*
* Now, we want to create the bprime variable, which represents the
* integral over the background-only sample. We will perform the
* integral automatically (that's why this is the only nuisance
* parameter declared in this file - its value can be determined from
* the input histograms).
*/
ostringstream bprime_string;
ostringstream bprime_pdf_string;
bprime_string << "bprime[" << background_hist->Integral() << ", 0, 999999999]";
bprime_pdf_string << "Poisson::bprime_pdf(bprime, " << background_hist->Integral() << ")";
pWs->factory(bprime_string.str().c_str());
pWs->factory(bprime_pdf_string.str().c_str());
/*
* This simple command will create all values from the config file
* with 'make:' at the beginning and a delimiter at the end (see config
* _reader if you don't know what a delimiter is). In other
* words, the luminosity, efficiency, transfer factors, and their pdfs
* are created from this command. The declarations are contained in the
* config file to be changed easily without having to modify this code.
*/
reader.factory_all();
/*
* Now, we want to create the extended pdfs from the unextended pdfs, as
* well as from the S and B values we manufactured in the config file.
* S and B are the values by which the signal and background pdfs,
* respectively, are extended. Recall that they were put in the
* workspace in the reader.facotry_all() command.
*/
RooAbsReal *S = pWs->function("S");
RooAbsReal *B = pWs->function("B");
RooExtendPdf *signalpart = new RooExtendPdf("signalpart", "signalpart",
*unextended_sig_pdf, *S);
RooExtendPdf *backgroundpart =
new RooExtendPdf("backgroundpart", "backgroundpart",
*unextended_bkg_pdf, *B);
RooArgList *pdf_list = new RooArgList(*signalpart, *backgroundpart,
"list");
// Add the signal and background pdfs to make a TotalPdf
RooAddPdf *TotalPdf = new RooAddPdf("TotalPdf", "TotalPdf", *pdf_list);
RooArgList *pdf_prod_list = new RooArgList(*TotalPdf,
*pWs->pdf("lumi_pdf"),
*pWs->pdf("eff_pdf"),
*pWs->pdf("rho_pdf"),
*pWs->pdf("bprime_pdf"));
// This creates the final model pdf.
RooProdPdf *model = new RooProdPdf("model", "model", *pdf_prod_list);
/*
* Up until now, we have been using the workspace pWs to contain all of
* our values. Now, all of our values that we require are in use in the
* RooProdPdf called "model". So, we need to import "model" into a
* RooWorkspace. To avoid recopying values into the rooworkspace, when
* the values may already be present (which can cause problems), we will
* simply create a new RooWorkspace to avoid confusion and problems. The
* new RooWorkspace is created here.
*/
RooWorkspace *newworkspace = new RooWorkspace("newws");
newworkspace->import(*model);
// Immediately delete pWs, so we don't accidentally use it again.
delete pWs;
// Show off the newworkspace
newworkspace->Print();
// observables
RooArgSet obs(*newworkspace->var("MR"), *newworkspace->var("RSQ"), "obs");
// global observables
RooArgSet globalObs(*newworkspace->var("nom_lumi"), *newworkspace->var("nom_eff"), *newworkspace->var("nom_rho"));
//fix global observables to their nominal values
newworkspace->var("nom_lumi")->setConstant();
newworkspace->var("nom_eff")->setConstant();
newworkspace->var("nom_rho")->setConstant();
//Set Parameters of interest
RooArgSet poi(*newworkspace->var("sigma"), "poi");
//Set Nuisnaces
RooArgSet nuis(*newworkspace->var("prime_lumi"), *newworkspace->var("prime_eff"), *newworkspace->var("prime_rho"), *newworkspace->var("bprime"));
// priors (for Bayesian calculation)
newworkspace->factory("Uniform::prior_signal(sigma)"); // for parameter of interest
newworkspace->factory("Uniform::prior_bg_b(bprime)"); // for data driven nuisance parameter
newworkspace->factory("PROD::prior(prior_signal,prior_bg_b)"); // total prior
//Observed data is pulled from histogram.
//TFile *data_file = new TFile(data_file_name);
TFile *data_file = new TFile(data_file_name);
TH2D *data_hist = (TH2D *)data_file->Get(data_hist_name_in_file);
RooDataHist *pData = new RooDataHist("data", "data", obs, data_hist);
newworkspace->import(*pData);
// Now, we will draw our data from a RooDataHist.
/*TFile *data_file = new TFile(data_file_name);
TTree *data_tree = (TTree *) data_file->Get(data_hist_name_in_file);
RooDataSet *pData = new RooDataSet("data", "data", data_tree, obs);
newworkspace->import(*pData);*/
// Craft the signal+background model
ModelConfig * pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*newworkspace);
pSbModel->SetPdf(*newworkspace->pdf("model"));
pSbModel->SetPriorPdf(*newworkspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
// set all but obs, poi and nuisance to const
SetConstants(newworkspace, pSbModel);
newworkspace->import(*pSbModel);
// background-only model
// use the same PDF as s+b, with sig=0
// POI value under the background hypothesis
// (We will set the value to 0 later)
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)newworkspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*newworkspace);
newworkspace->import(*pBModel);
// find global maximum with the signal+background model
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
// - safer to keep a default name because some RooStats calculators
// will anticipate it
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*pData);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
pNll = pBModel->GetPdf()->createNLL(*pData);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// save workspace to file
newworkspace->writeToFile("ws_twobin.root");
// clean up
delete newworkspace;
delete pData;
delete pSbModel;
delete pBModel;
} // ----- end of tutorial ----------------------------------------
void MakeWorkspace( void ){
//
// this function implements a RooFit model for a counting experiment
//
// create workspace
RooWorkspace * pWs = new RooWorkspace("myWS");
// observable: number of events
pWs->factory( "n[0.0]" );
// integrated luminosity with systematics
pWs->factory( "lumi_nom[5000.0, 4000.0, 6000.0]" );
pWs->factory( "lumi_kappa[1.045]" );
pWs->factory( "cexpr::alpha_lumi('pow(lumi_kappa,beta_lumi)',lumi_kappa,beta_lumi[0,-5,5])" );
pWs->factory( "prod::lumi(lumi_nom,alpha_lumi)" );
pWs->factory( "Gaussian::constr_lumi(beta_lumi,glob_lumi[0,-5,5],1)" );
// cross section - parameter of interest
pWs->factory( "xsec[0.001,0.0,0.1]" );
// selection efficiency * acceptance with systematics
pWs->factory( "efficiency_nom[0.1, 0.05, 0.15]" );
pWs->factory( "efficiency_kappa[1.10]" );
pWs->factory( "cexpr::alpha_efficiency('pow(efficiency_kappa,beta_efficiency)',efficiency_kappa,beta_efficiency[0,-5,5])" );
pWs->factory( "prod::efficiency(efficiency_nom,alpha_efficiency)" );
pWs->factory( "Gaussian::constr_efficiency(beta_efficiency,glob_efficiency[0,-5,5],1)" );
// signal yield
pWs->factory( "prod::nsig(lumi,xsec,efficiency)" );
// background yield with systematics
pWs->factory( "nbkg_nom[10.0, 5.0, 15.0]" );
pWs->factory( "nbkg_kappa[1.10]" );
pWs->factory( "cexpr::alpha_nbkg('pow(nbkg_kappa,beta_nbkg)',nbkg_kappa,beta_nbkg[0,-5,5])" );
pWs->factory( "prod::nbkg(nbkg_nom,alpha_lumi,alpha_nbkg)" );
pWs->factory( "Gaussian::constr_nbkg(beta_nbkg,glob_nbkg[0,-5,5],1)" );
// full event yield
pWs->factory("sum::yield(nsig,nbkg)");
// Core model: Poisson probability with mean signal+bkg
pWs->factory( "Poisson::model_core(n,yield)" );
// define Bayesian prior PDF for POI
pWs->factory( "Uniform::prior(xsec)" );
// model with systematics
pWs->factory( "PROD::model(model_core,constr_lumi,constr_efficiency,constr_nbkg)" );
// create set of observables (will need it for datasets and ModelConfig later)
RooRealVar * pObs = pWs->var("n"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
// create the dataset
pObs->setVal(11); // this is your observed data: we counted ten events
RooDataSet * data = new RooDataSet("data", "data", obs);
data->add( *pObs );
// import dataset into workspace
pWs->import(*data);
// create set of global observables (need to be defined as constants)
pWs->var("glob_lumi")->setConstant(true);
pWs->var("glob_efficiency")->setConstant(true);
pWs->var("glob_nbkg")->setConstant(true);
RooArgSet globalObs("global_obs");
globalObs.add( *pWs->var("glob_lumi") );
globalObs.add( *pWs->var("glob_efficiency") );
globalObs.add( *pWs->var("glob_nbkg") );
// create set of parameters of interest (POI)
RooArgSet poi("poi");
poi.add( *pWs->var("xsec") );
// create set of nuisance parameters
RooArgSet nuis("nuis");
nuis.add( *pWs->var("beta_lumi") );
nuis.add( *pWs->var("beta_efficiency") );
nuis.add( *pWs->var("beta_nbkg") );
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *pWs );
sbHypo.SetPdf( *pWs->pdf("model") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *pWs->pdf("prior") ); // this is optional
// fix all other variables in model:
// everything except observables, POI, and nuisance parameters
// must be constant
pWs->var("lumi_nom")->setConstant(true);
pWs->var("efficiency_nom")->setConstant(true);
pWs->var("nbkg_nom")->setConstant(true);
pWs->var("lumi_kappa")->setConstant(true);
pWs->var("efficiency_kappa")->setConstant(true);
pWs->var("nbkg_kappa")->setConstant(true);
RooArgSet fixed("fixed");
fixed.add( *pWs->var("lumi_nom") );
fixed.add( *pWs->var("efficiency_nom") );
fixed.add( *pWs->var("nbkg_nom") );
fixed.add( *pWs->var("lumi_kappa") );
fixed.add( *pWs->var("efficiency_kappa") );
fixed.add( *pWs->var("nbkg_kappa") );
// set parameter snapshot that corresponds to the best fit to data
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data );
RooAbsReal * pProfile = pNll->createProfile( globalObs ); // do not profile global observables
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
pPoiAndNuisance->add(*sbHypo.GetNuisanceParameters());
pPoiAndNuisance->add(*sbHypo.GetParametersOfInterest());
sbHypo.SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import S+B ModelConfig into workspace
pWs->import( sbHypo );
// create background-only Model Config from the S+B one
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*pWs);
// set parameter snapshot for bHypo, setting xsec=0
// it is useful to understand how this block of code works
// but you can also use it as a recipe to make a parameter snapshot
pNll = bHypo.GetPdf()->createNLL( *data );
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set xsec=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
pWs->import( bHypo );
// print out the workspace contents
pWs->Print();
// save workspace to file
pWs -> SaveAs("workspace.root");
return;
}
void results2tree(
const char* workDirName,
bool isMC=false,
const char* thePoiNames="RFrac2Svs1S,N_Jpsi,f_Jpsi,m_Jpsi,sigma1_Jpsi,alpha_Jpsi,n_Jpsi,sigma2_Jpsi,MassRatio,rSigma21_Jpsi,lambda1_Bkg,lambda2_Bkg,lambda3_Bkg,lambda4_Bkg,lambda5__Bkg,N_Bkg"
) {
// workDirName: usual tag where to look for files in Output
// thePoiNames: comma-separated list of parameters to store ("par1,par2,par3"). Default: all
TFile *f = new TFile(treeFileName(workDirName,isMC),"RECREATE");
TTree *tr = new TTree("fitresults","fit results");
// bin edges
float ptmin, ptmax, ymin, ymax, centmin, centmax;
// model names
Char_t jpsiName[128], psipName[128], bkgName[128];
// collision system
Char_t collSystem[8];
// goodness of fit
float nll, chi2, normchi2; int npar, ndof;
// parameters to store: make it a vector
vector<poi> thePois;
TString thePoiNamesStr(thePoiNames);
TString t; Int_t from = 0;
while (thePoiNamesStr.Tokenize(t, from , ",")) {
poi p; strcpy(p.name, t.Data());
cout << p.name << endl;
thePois.push_back(p);
}
// create tree branches
tr->Branch("ptmin",&ptmin,"ptmin/F");
tr->Branch("ptmax",&ptmax,"ptmax/F");
tr->Branch("ymin",&ymin,"ymin/F");
tr->Branch("ymax",&ymax,"ymax/F");
tr->Branch("centmin",¢min,"centmin/F");
tr->Branch("centmax",¢max,"centmax/F");
tr->Branch("jpsiName",jpsiName,"jpsiName/C");
tr->Branch("psipName",psipName,"psipName/C");
tr->Branch("bkgName",bkgName,"bkgName/C");
tr->Branch("collSystem",collSystem,"collSystem/C");
tr->Branch("nll",&nll,"nll/F");
tr->Branch("chi2",&chi2,"chi2/F");
tr->Branch("normchi2",&normchi2,"normchi2/F");
tr->Branch("npar",&npar,"npar/I");
tr->Branch("ndof",&ndof,"ndof/I");
for (vector<poi>::iterator it=thePois.begin(); it!=thePois.end(); it++) {
tr->Branch(Form("%s_val",it->name),&(it->val),Form("%s_val/F",it->name));
tr->Branch(Form("%s_err",it->name),&(it->err),Form("%s_err/F",it->name));
}
// list of files
vector<TString> theFiles = fileList(workDirName,"",isMC);
int cnt=0;
for (vector<TString>::const_iterator it=theFiles.begin(); it!=theFiles.end(); it++) {
cout << "Parsing file " << cnt << " / " << theFiles.size() << ": " << *it << endl;
// parse the file name to get info
anabin thebin = binFromFile(*it);
ptmin = thebin.ptbin().low();
ptmax = thebin.ptbin().high();
ymin = thebin.rapbin().low();
ymax = thebin.rapbin().high();
centmin = thebin.centbin().low();
centmax = thebin.centbin().high();
strcpy(collSystem, (it->Index("PbPb")>0) ? "PbPb" : "PP");
// get the model names
from = 0;
bool catchjpsi=false, catchpsip=false, catchbkg=false;
while (it->Tokenize(t, from, "_")) {
if (catchjpsi) {strcpy(jpsiName, t.Data()); catchjpsi=false;}
if (catchpsip) {strcpy(psipName, t.Data()); catchpsip=false;}
if (catchbkg) {strcpy(bkgName, t.Data()); catchbkg=false;}
if (t=="Jpsi") catchjpsi=true;
if (t=="Psi2S") catchpsip=true;
if (t=="Bkg") catchbkg=true;
}
TFile *f = new TFile(*it); RooWorkspace *ws = NULL;
if (!f) {
cout << "Error, file " << *it << " does not exist." << endl;
} else {
ws = (RooWorkspace*) f->Get("workspace");
if (!ws) {
cout << "Error, workspace not found in " << *it << "." << endl;
}
}
nll=0; chi2=0; npar=0; ndof=0;
if (f && ws) {
// get the model for nll and npar
RooAbsPdf *model = pdfFromWS(ws, Form("_%s",collSystem), "pdfMASS_Tot");
if (model) {
RooAbsData *dat = dataFromWS(ws, Form("_%s",collSystem), "dOS_DATA");
if (dat) {
RooAbsReal *NLL = model->createNLL(*dat);
if (NLL) nll = NLL->getVal();
npar = model->getParameters(dat)->selectByAttrib("Constant",kFALSE)->getSize();
// compute the chi2 and the ndof
RooPlot* frame = ws->var("invMass")->frame(Bins(nBins));
dat->plotOn(frame);
model->plotOn(frame);
TH1 *hdatact = dat->createHistogram("hdatact", *(ws->var("invMass")), Binning(nBins));
RooHist *hpull = frame->pullHist(0,0, true);
double* ypulls = hpull->GetY();
unsigned int nFullBins = 0;
for (int i = 0; i < nBins; i++) {
if (hdatact->GetBinContent(i+1) > 0.0) {
chi2 += ypulls[i]*ypulls[i];
nFullBins++;
}
}
ndof = nFullBins - npar;
normchi2 = chi2/ndof;
}
}
// get the POIs
for (vector<poi>::iterator itpoi=thePois.begin(); itpoi!=thePois.end(); itpoi++) {
RooRealVar *thevar = poiFromWS(ws, Form("_%s",collSystem), itpoi->name);
itpoi->val = thevar ? thevar->getVal() : 0;
itpoi->err = thevar ? thevar->getError() : 0;
}
f->Close();
delete f;
} else {
for (vector<poi>::iterator itpoi=thePois.begin(); itpoi!=thePois.end(); itpoi++) {
itpoi->val = 0;
itpoi->err = 0;
}
}
// fill the tree
tr->Fill();
cnt++;
} // loop on the files
f->Write();
f->Close();
}
void higgsMassFit(const int& mH, const std::string& mode = "exclusion", const bool& drawPlots = false, const int& nToys = 10000)
{
using namespace RooFit;
RooMsgService::instance().deleteStream(0);
RooMsgService::instance().deleteStream(1);
std::string varName = "lepNuW_m";
int step = 16;
char treeName[50];
sprintf(treeName, "ntu_%d", step);
float lumi = 1000.;
int nBins = 50;
double xMin = 0.;
double xMax = 1000.;
double xMin_signal = 0.;
double xMax_signal = 0.;
SetXSignal(xMin_signal,xMax_signal,mH);
RooRealVar x("x",varName.c_str(),xMin,xMax);
x.setRange("low", xMin, xMin_signal);
x.setRange("signal",xMin_signal,xMax_signal);
x.setRange("high", xMax_signal,xMax);
RooRealVar w("w","weight",0.,1000000000.);
char signalCut[50];
sprintf(signalCut,"x > %f && x < %f",xMin_signal,xMax_signal);
//-------------------
// define the outfile
char outFileName[50];
sprintf(outFileName,"higgsMassFit_H%d_%s.root",mH,mode.c_str());
TFile* outFile = new TFile(outFileName,"RECREATE");
outFile -> cd();
//-------------------
// define the infiles
char higgsMass[50];
sprintf(higgsMass,"%d",mH);
std::string BKGPath = "/grid_mnt/vol__vol1__u/llr/cms/abenagli/COLLISIONS7TeV/Fall10/VBFAnalysisPackage/data/VBFAnalysis_AK5PF_H" +
std::string(higgsMass) +
"_ET30_maxDeta_minDeta_Spring11_EGMu_noHiggsMassCut/";
std::string WJetsFolder = "WJetsToLNu_TuneZ2_7TeV-madgraph-tauola_Spring11-PU_S1_START311_V1G1-v1/";
std::string TTJetsFolder = "TTJets_TuneZ2_7TeV-madgraph-tauola_Spring11-PU_S1_START311_V1G1-v1/";
//std::string ZJetsFolder =
//std::string GJets_HT40To100Folder
//std::string GJets_HT100To200Folder
//std::string GJets_HT200Folder
//std::string WWFolder
//std::string WZFolder
//std::string TJets_schannelFolder
//std::string TJets_tchannelFolder
//std::string TJets_tWchannelFolder
std::string GluGluHToLNuQQFolder = "GluGluToHToWWToLNuQQ_M-" + std::string(higgsMass) + "_7TeV-powheg-pythia6_Spring11-PU_S1_START311_V1G1-v1/";
std::string GluGluHToTauNuQQFolder = "GluGluToHToWWToTauNuQQ_M-" + std::string(higgsMass) + "_7TeV-powheg-pythia6_Spring11-PU_S1_START311_V1G1-v1/";
std::string VBFHToLNuQQFolder = "VBF_HToWWToLNuQQ_M-" + std::string(higgsMass) + "_7TeV-powheg-pythia6_Spring11-PU_S1_START311_V1G1-v1/";
std::string VBFHToTauNuQQFolder = "VBF_HToWWToTauNuQQ_M-" + std::string(higgsMass) + "_7TeV-powheg-pythia6_Spring11-PU_S1_START311_V1G1-v1/";
//---------------------------------------
// define the background shape histograms
int nBKG = 2;
TH1F** BKGShapeHisto = new TH1F*[nBKG];
TH1F* BKGTotShapeHisto = new TH1F("BKGTotShapeHisto","",nBins,xMin,xMax);
THStack* BKGShapeStack = new THStack();
RooDataSet** rooBKGDataSet = new RooDataSet*[nBKG];
std::string* BKGNames = new std::string[nBKG];
BKGNames[1] = BKGPath+WJetsFolder+"VBFAnalysis_AK5PF.root";
BKGNames[0] = BKGPath+TTJetsFolder+"VBFAnalysis_AK5PF.root";
//BKGNames[1] = BKGPath+ZJetsFolder+"VBFAnalysis_AK5PF.root";
//BKGNames[2] = BKGPath+GJets_HT40To100Folder+"VBFAnalysis_AK5PF.root";
//BKGNames[3] = BKGPath+GJets_HT100To200Folder+"VBFAnalysis_AK5PF.root";
//BKGNames[4] = BKGPath+GJets_HT200Folder+"VBFAnalysis_AK5PF.root";
//BKGNames[6] = BKGPath+WWFolder+"VBFAnalysis_AK5PF.root";
//BKGNames[7] = BKGPath+WZFolder+"VBFAnalysis_AK5PF.root";
//BKGNames[8] = BKGPath+TJets_schannelFolder+"VBFAnalysis_AK5PF.root";
//BKGNames[9] = BKGPath+TJets_tchannelFolder+"VBFAnalysis_AK5PF.root";
//BKGNames[10] = BKGPath+TJets_tWchannelFolder+"VBFAnalysis_AK5PF.root";
std::string* BKGShortNames = new std::string[nBKG];
BKGShortNames[1] = "WJets";
BKGShortNames[0] = "TTJets";
//BKGShortNames[1] = "ZJets";
//BKGShortNames[2] = "GJets_HT40To100";
//BKGShortNames[3] = "GJets_HT100To200";
//BKGShortNames[4] = "GJets_HT200";
//BKGShortNames[6] = "WW";
//BKGShortNames[7] = "WZ";
//BKGShortNames[8] = "TJets_schannel";
//BKGShortNames[9] = "TJets_tchannel";
//BKGShortNames[10] = "TJets_tWchannel";
Color_t* BKGColors = new Color_t[nBKG];
BKGColors[1] = kOrange-708;
BKGColors[0] = kAzure-795;
//-----------------------------------
// define the signal shape histograms
int nSIG = 2;
TH1F* SIGShapeHisto = new TH1F("SIGShapeHisto","",4*nBins,xMin,xMax);
SIGShapeHisto -> Sumw2();
SIGShapeHisto -> SetLineWidth(1);
SIGShapeHisto -> SetLineStyle(1);
RooDataSet* rooSIGDataSet = new RooDataSet("rooSIGDataSet","",RooArgSet(x,w),WeightVar(w));
std::string* SIGNames = new std::string[nSIG];
SIGNames[0] = BKGPath+GluGluHToLNuQQFolder+"VBFAnalysis_AK5PF.root";
SIGNames[1] = BKGPath+VBFHToLNuQQFolder+"VBFAnalysis_AK5PF.root";
std::string* SIGShortNames = new std::string[nSIG];
SIGShortNames[1] = "ggH";
SIGShortNames[0] = "qqH";
//----------------------
// loop over backgrounds
std::cout << "***********************************************************************" << std::endl;
std::cout << ">>> Fill the background shapes" << std::endl;
for(int i = 0; i < nBKG; ++i)
{
TFile* inFile_BKGShape = TFile::Open((BKGNames[i]).c_str());
inFile_BKGShape -> cd();
TTree* BKGShapeTree = (TTree*)(inFile_BKGShape -> Get(treeName));
BKGShapeHisto[i] = new TH1F(("BKGShapeHisto_"+BKGShortNames[i]).c_str(),"",nBins,xMin,xMax);
enum EColor color = (enum EColor)(BKGColors[i]);
BKGShapeHisto[i] -> SetFillColor(color);
BKGShapeHisto[i] -> Sumw2();
rooBKGDataSet[i] = new RooDataSet(("rooBKGDataSet_"+BKGShortNames[i]).c_str(),"",RooArgSet(x,w),WeightVar(w));
TH1F* eventsHisto;
inFile_BKGShape -> GetObject("events", eventsHisto);
float totEvents = eventsHisto -> GetBinContent(1);
// set branch addresses
float crossSection;
float var;
BKGShapeTree -> SetBranchAddress("crossSection", &crossSection);
BKGShapeTree -> SetBranchAddress(varName.c_str(),&var);
// loop over the entries
for(int entry = 0; entry < BKGShapeTree->GetEntries(); ++entry)
{
BKGShapeTree -> GetEntry(entry);
x = var;
w = 1./totEvents*crossSection*lumi;
BKGShapeHisto[i] -> Fill(var, 1./totEvents*crossSection*lumi);
BKGTotShapeHisto -> Fill(var, 1./totEvents*crossSection*lumi);
rooBKGDataSet[i] -> add(RooArgSet(x,w));
}
BKGShapeStack -> Add(BKGShapeHisto[i]);
}
//------------------
// loop over signals
std::cout << ">>> Fill the signal shapes" << std::endl;
for(int i = 0; i < nSIG; ++i)
{
TFile* inFile_SIGShape = TFile::Open((SIGNames[i]).c_str());
inFile_SIGShape -> cd();
TTree* SIGShapeTree = (TTree*)(inFile_SIGShape -> Get(treeName));
TH1F* eventsHisto = (TH1F*)(inFile_SIGShape -> Get("events"));
float totEvents = eventsHisto -> GetBinContent(1);
// set branch addresses
float crossSection;
float var;
SIGShapeTree -> SetBranchAddress("crossSection", &crossSection);
SIGShapeTree -> SetBranchAddress(varName.c_str(),&var);
// loop over the entries
for(int entry = 0; entry < SIGShapeTree->GetEntries(); ++entry)
{
SIGShapeTree -> GetEntry(entry);
x = var;
w= 1./totEvents*crossSection*lumi;
SIGShapeHisto -> Fill(var, 1./totEvents*crossSection*lumi);
rooSIGDataSet -> add(RooArgSet(x,w));
}
}
//-----------------------------------
// draw the background + signal stack
if( drawPlots )
{
TCanvas* c1 = new TCanvas("BKGShapeStack","BKGShapeStack");
c1 -> SetGridx();
c1 -> SetGridy();
BKGShapeStack -> Draw("HIST");
SIGShapeHisto -> Draw("HIST,same");
char pngFileName[50];
sprintf(pngFileName,"BKGShapeStack_H%d_%s.png",mH,mode.c_str());
c1 -> Print(pngFileName,"png");
}
//---------------------------------
// define the bkg shape with roofit
std::cout << ">>> Define the background pdf" << std::endl;
RooKeysPdf** rooBKGPdf = new RooKeysPdf*[nBKG];
RooRealVar** rooNBKG = new RooRealVar*[nBKG];
RooRealVar* rooNBKGTot = new RooRealVar("rooNBKGTot","",BKGTotShapeHisto->Integral(),0.,1000000.);
for(int i = 0; i < nBKG; ++i)
{
rooBKGPdf[i] = new RooKeysPdf(("rooBKGPdf_"+BKGShortNames[i]).c_str(),"",x,*rooBKGDataSet[i]);
rooNBKG[i] = new RooRealVar(("rooNBKG_"+BKGShortNames[i]).c_str(),"",BKGShapeHisto[i]->Integral(),BKGShapeHisto[i]->Integral()),BKGShapeHisto[i]->Integral();
}
RooAddPdf* rooBKGTotPdf = new RooAddPdf("rooBKGTotPdf","",RooArgList(*rooBKGPdf[0],*rooBKGPdf[1]),RooArgList(*rooNBKG[0],*rooNBKG[1]));
//---------------------------------
// define the sig shape with roofit
std::cout << ">>> Define the signal pdf" << std::endl;
RooKeysPdf* rooSIGPdf = new RooKeysPdf("rooSIGPdf","",x,*rooSIGDataSet);
RooRealVar* rooNSIG = new RooRealVar("rooNSIG","",1.,-1000000.,1000000.);
//---------------------------------
// define the tot shape with roofit
std::cout << ">>> Define the total pdf" << std::endl;
RooAddPdf* rooTotPdf = NULL;
if( mode == "exclusion") rooTotPdf = new RooAddPdf("rooTotPdf","",RooArgList(*rooBKGTotPdf),RooArgList(*rooNBKGTot));
if( mode == "discovery") rooTotPdf = new RooAddPdf("rooTotPdf","",RooArgList(*rooBKGTotPdf,*rooSIGPdf),RooArgList(*rooNBKGTot,*rooNSIG));
//----
// plot
if( drawPlots )
{
TCanvas* c2 = new TCanvas("rooTotPdf","rooTotPdf");
c2 -> SetGridx();
c2 -> SetGridy();
RooPlot* rooBKGPlot = x.frame();
rooBKGTotPdf -> plotOn(rooBKGPlot,LineColor(kBlack));
enum EColor color = (enum EColor)(BKGColors[0]);
rooBKGTotPdf -> plotOn(rooBKGPlot,Components(("rooBKGPdf_"+BKGShortNames[0]).c_str()),LineColor(color));
color = (enum EColor)(BKGColors[1]);
rooBKGTotPdf -> plotOn(rooBKGPlot,Components(("rooBKGPdf_"+BKGShortNames[1]).c_str()),LineColor(color));
rooSIGPdf -> plotOn(rooBKGPlot,LineColor(kBlack),LineStyle(1),LineWidth(1));
rooBKGPlot->Draw();
TH1F* BKGShapeHistoNorm = (TH1F*) BKGTotShapeHisto -> Clone();
BKGShapeHistoNorm -> Scale(1./BKGTotShapeHisto->Integral()/nBKG);
BKGShapeHistoNorm -> Draw("HIST,same");
char pngFileName[50];
sprintf(pngFileName,"BKGShapeNorm_H%d_%s.png",mH,mode.c_str());
c2 -> Print(pngFileName,"png");
}
//------------------------
// generate toy experiment
std::cout << "***********************************************************************" << std::endl;
std::cout << ">>> 1st toy experiment - " << mode << " mode" << std::endl;
int NBKGToy = int(BKGTotShapeHisto->Integral());
int NSIGToy = 0;
if( mode == "discovery" ) NSIGToy = int(SIGShapeHisto->Integral());
RooDataSet* rooBKGToyDataSet = rooBKGTotPdf->generate(RooArgSet(x),NBKGToy);
RooDataSet* rooSIGToyDataSet = rooSIGPdf->generate(RooArgSet(x),NSIGToy);
rooBKGToyDataSet -> append(*rooSIGToyDataSet);
float NBKGToy_signal = rooBKGToyDataSet->sumEntries(signalCut);
float NBKGToy_signal_fit = 0.;
// fit
if( mode == "exclusion" ) rooTotPdf -> fitTo(*rooBKGToyDataSet,Extended(kTRUE),PrintLevel(-1),Range("low,high"));
if( mode == "discovery" ) rooTotPdf -> fitTo(*rooBKGToyDataSet,Extended(kTRUE),PrintLevel(-1));
// count events
if( mode == "exclusion" )
{
RooAbsReal* rooTotIntegral = rooTotPdf -> createIntegral(x,NormSet(x),Range("signal"));
NBKGToy_signal_fit = rooTotIntegral->getVal() * rooNBKGTot->getVal();
std::cout << ">>>>>> BKG toy events (true) in signal region in " << lumi << "/pb: " << NBKGToy_signal << std::endl;
std::cout << ">>>>>> BKG toy events (fit) in signal region in " << lumi << "/pb: " << NBKGToy_signal_fit << std::endl;
}
if( mode == "discovery" )
{
std::cout << ">>>>>> BKG toy events (true) in " << lumi << "/pb: " << NBKGToy << std::endl;
std::cout << ">>>>>> BKG toy events (fit) in " << lumi << "/pb: " << rooNBKGTot->getVal() << std::endl;
std::cout << ">>>>>> SIG toy events (true) in " << lumi << "/pb: " << NSIGToy << std::endl;
std::cout << ">>>>>> SIG toy events (fit) in " << lumi << "/pb: " << rooNSIG->getVal() << std::endl;
}
if( drawPlots )
{
TCanvas* c3 = new TCanvas("TOY","TOY");
c3 -> SetGridx();
c3 -> SetGridy();
RooPlot* rooTOYPlot = x.frame();
rooBKGToyDataSet -> plotOn(rooTOYPlot,MarkerSize(0.7));
rooTotPdf -> plotOn(rooTOYPlot, LineColor(kRed));
rooTotPdf -> plotOn(rooTOYPlot, Components("rooSIGPdf"), LineColor(kRed));
rooTOYPlot->Draw();
char pngFileName[50];
sprintf(pngFileName,"BKGToyFit_H%d_%s.png",mH,mode.c_str());
c3 -> Print(pngFileName,"png");
}
//-------------------------
// generate toy experiments
TH1F* h_BKGRes = new TH1F("h_BKGRes","",200,-400,400);
TH1F* h_SIGRes = new TH1F("h_SIGRes","",200,-400,400);
TRandom3 B;
TRandom3 S;
for(int j = 0; j < nToys; ++j)
{
if( j%100 == 0 )
std::cout << ">>>>>> generating toy experiment " << j << std::endl;
NBKGToy = B.Poisson(BKGTotShapeHisto->Integral());
NSIGToy = 0;
if( mode == "discovery" ) NSIGToy = S.Poisson(SIGShapeHisto->Integral());
rooBKGToyDataSet = rooBKGTotPdf->generate(RooArgSet(x),NBKGToy);
rooSIGToyDataSet = rooSIGPdf->generate(RooArgSet(x),NSIGToy);
rooBKGToyDataSet -> append(*rooSIGToyDataSet);
NBKGToy_signal = rooBKGToyDataSet->sumEntries(signalCut);
NBKGToy_signal_fit = 0.;
// fit
if( mode == "exclusion" ) rooTotPdf -> fitTo(*rooBKGToyDataSet,Extended(kTRUE),PrintLevel(-1),Range("low,high"));
if( mode == "discovery" ) rooTotPdf -> fitTo(*rooBKGToyDataSet,Extended(kTRUE),PrintLevel(-1));
// count events
if( mode == "exclusion" )
{
RooAbsReal* rooTotIntegral = rooTotPdf -> createIntegral(x,NormSet(x),Range("signal"));
NBKGToy_signal_fit = rooTotIntegral->getVal() * rooNBKGTot->getVal();
h_BKGRes -> Fill(NBKGToy_signal_fit - NBKGToy_signal);
h_SIGRes -> Fill(0.);
}
if( mode == "discovery" )
{
h_BKGRes -> Fill(rooNBKGTot->getVal() - NBKGToy);
h_SIGRes -> Fill(rooNSIG->getVal() - NSIGToy);
}
}
outFile -> cd();
h_BKGRes -> Write();
h_SIGRes -> Write();
outFile -> Close();
}