Ejemplo n.º 1
0
   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;
   }
Ejemplo n.º 2
0
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;
}
Ejemplo n.º 3
0
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
}
Ejemplo n.º 4
0
///
/// 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)) ;
  }
}
Ejemplo n.º 6
0
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;
}
Ejemplo n.º 8
0
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;

}
Ejemplo n.º 10
0
   void ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root",
                                   const char* new_poi_name = "n_M234_H4_3b",
                                   int npoiPoints = 20,
                                   double poiMinVal = 0.,
                                   double poiMaxVal = 20.,
                                   double constraintWidth = 1.5,
                                   double ymax = 10.,
                                   int verbLevel=0 ) {


     gStyle->SetOptStat(0) ;

     //--- make output directory.

     char command[10000] ;
     sprintf( command, "basename %s", wsfile ) ;
     TString wsfilenopath = gSystem->GetFromPipe( command ) ;
     wsfilenopath.ReplaceAll(".root","") ;
     char outputdirstr[1000] ;
     sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ;
     TString outputdir( outputdirstr ) ;


     printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ;
     sprintf(command, "mkdir -p %s", outputdir.Data() ) ;
     gSystem->Exec( command ) ;


     //--- Tell RooFit to shut up about anything less important than an ERROR.
      RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;



       if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; }


       TFile* wstf = new TFile( wsfile ) ;

       RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );

       if ( verbLevel > 0 ) { ws->Print() ; }






       RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;

       if ( verbLevel > 0 ) {
          printf("\n\n\n  ===== RooDataSet ====================\n\n") ;
          rds->Print() ;
          rds->printMultiline(cout, 1, kTRUE, "") ;
       }





       ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
       RooAbsPdf* likelihood = modelConfig->GetPdf() ;

       RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ;
       if ( rrv_mu_susy_all0lep == 0x0 ) {
          printf("\n\n\n *** can't find mu_susy_all0lep in workspace.  Quitting.\n\n\n") ;
          return ;
       }





       //-- do BG only.
       rrv_mu_susy_all0lep->setVal(0.) ;
       rrv_mu_susy_all0lep->setConstant( kTRUE ) ;










       //-- do a prefit.

       printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ;

       RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel));
       int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
       if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit!  Cov qual %d.  Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
       double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;

       if ( verbLevel > 0 ) {
          dataFitResultSusyFixed->Print("v") ;
       }

       printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ;

       delete dataFitResultSusyFixed ;






       //-- Construct the new POI parameter.
       RooAbsReal* new_poi_rar(0x0) ;

       new_poi_rar = ws->var( new_poi_name ) ;
       if ( new_poi_rar == 0x0 ) {
          printf("\n\n New POI %s is not a variable.  Trying function.\n\n", new_poi_name ) ;
          new_poi_rar = ws->function( new_poi_name ) ;
          if ( new_poi_rar == 0x0 ) {
             printf("\n\n New POI %s is not a function.  I quit.\n\n", new_poi_name ) ;
             return ;
          }
       } else {
          printf("\n\n     New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
       }








       if ( npoiPoints <=0 ) {
          printf("\n\n Quitting now.\n\n" ) ;
          return ;
       }


       double startPoiVal = new_poi_rar->getVal() ;



      //--- The RooNLLVar is NOT equivalent to what minuit uses.
  //   RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ;
  //   printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ;

      //--- output of createNLL IS what minuit uses, so use that.
       RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;

       RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
   /// rrv_poiValue->setVal( poiMinVal ) ;
   /// rrv_poiValue->setConstant(kTRUE) ;

       RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
       rrv_constraintWidth -> setVal( constraintWidth ) ;
       rrv_constraintWidth -> setConstant(kTRUE) ;




       if ( verbLevel > 0 ) {
          printf("\n\n ======= debug likelihood print\n\n") ;
          likelihood->Print("v") ;
          printf("\n\n ======= debug nll print\n\n") ;
          nll->Print("v") ;
       }






    //----------------------------------------------------------------------------------------------

       RooMinuit* rminuit( 0x0 ) ;


       RooMinuit* rminuit_uc = new RooMinuit( *nll  ) ;

       rminuit_uc->setPrintLevel(verbLevel-1) ;
       rminuit_uc->setNoWarn() ;

       rminuit_uc->migrad() ;
       rminuit_uc->hesse() ;

       RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;

       double floatParInitVal[10000] ;
       char   floatParName[10000][100] ;
       int nFloatParInitVal(0) ;
       RooArgList ral_floats = rfr_uc->floatParsFinal() ;
       TIterator* floatParIter = ral_floats.createIterator() ;
       {
          RooRealVar* par ;
          while ( (par = (RooRealVar*) floatParIter->Next()) ) {
             sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
             floatParInitVal[nFloatParInitVal] = par->getVal() ;
             nFloatParInitVal++ ;
          }
       }



     //-------

       printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
       double best_poi_val = new_poi_rar->getVal() ;

       char minuit_formula[10000] ;
       sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
         nll->GetName(),
         rrv_constraintWidth->GetName(),
         new_poi_rar->GetName(), rrv_poiValue->GetName(),
         new_poi_rar->GetName(), rrv_poiValue->GetName()
          ) ;

       printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
       RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
           RooArgList( *nll,
                       *rrv_constraintWidth,
                       *new_poi_rar, *rrv_poiValue,
                       *new_poi_rar, *rrv_poiValue
                       ) ) ;

       printf("\n\n Current value is %.2f\n\n",
            new_minuit_var->getVal() ) ;

       rminuit = new RooMinuit( *new_minuit_var ) ;


       RooAbsReal* plot_var = nll ;

       printf("\n\n Current value is %.2f\n\n",
            plot_var->getVal() ) ;




       rminuit->setPrintLevel(verbLevel-1) ;
       if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }

    //----------------------------------------------------------------------------------------------

       //-- If POI range is -1 to -1, automatically determine the range using the set value.

       if ( poiMinVal < 0. && poiMaxVal < 0. ) {

          printf("\n\n Automatic determination of scan range.\n\n") ;

          if ( startPoiVal <= 0. ) {
             printf("\n\n *** POI starting value zero or negative %g.  Quit.\n\n\n", startPoiVal ) ;
             return ;
          }

          poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
          poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;

          if ( poiMinVal < 0. ) { poiMinVal = 0. ; }

          printf("    Start val = %g.   Scan range:   %g  to  %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;


       }



    //----------------------------------------------------------------------------------------------


       double poiVals_scanDown[1000] ;
       double nllVals_scanDown[1000] ;

       //-- Do scan down from best value.

       printf("\n\n +++++ Starting scan down from best value.\n\n") ;

       double minNllVal(1.e9) ;

       for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {

          ////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
          double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;

          rrv_poiValue -> setVal( poiValue ) ;
          rrv_poiValue -> setConstant( kTRUE ) ;


       //+++++++++++++++++++++++++++++++++++

          rminuit->migrad() ;
          rminuit->hesse() ;
          RooFitResult* rfr = rminuit->save() ;

       //+++++++++++++++++++++++++++++++++++


          if ( verbLevel > 0 ) { rfr->Print("v") ; }


          float fit_minuit_var_val = rfr->minNll() ;

          printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI :    %.5f   %.5f   %.5f   %.5f\n",
                poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
          cout << flush ;



          poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
          nllVals_scanDown[poivi] = plot_var->getVal() ;

          if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }

          delete rfr ;


       } // poivi


       printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;

       for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
          RooRealVar* par = ws->var( floatParName[pi] ) ;
          par->setVal( floatParInitVal[pi] ) ;
       } // pi.

       printf("\n\n +++++ Starting scan up from best value.\n\n") ;

      //-- Now do scan up.

       double poiVals_scanUp[1000] ;
       double nllVals_scanUp[1000] ;

       for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {

          double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;

          rrv_poiValue -> setVal( poiValue ) ;
          rrv_poiValue -> setConstant( kTRUE ) ;


       //+++++++++++++++++++++++++++++++++++

          rminuit->migrad() ;
          rminuit->hesse() ;
          RooFitResult* rfr = rminuit->save() ;

       //+++++++++++++++++++++++++++++++++++


          if ( verbLevel > 0 ) { rfr->Print("v") ; }


          float fit_minuit_var_val = rfr->minNll() ;

          printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI :    %.5f   %.5f   %.5f   %.5f\n",
                poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
          cout << flush ;

          poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
          nllVals_scanUp[poivi] = plot_var->getVal() ;

          if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }

          delete rfr ;


       } // poivi





       double poiVals[1000] ;
       double nllVals[1000] ;

       int pointCount(0) ;
       for ( int pi=0; pi<npoiPoints/2; pi++ ) {
          poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
          nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
          pointCount++ ;
       }
       for ( int pi=1; pi<npoiPoints/2; pi++ ) {
          poiVals[pointCount] = poiVals_scanUp[pi] ;
          nllVals[pointCount] = nllVals_scanUp[pi] ;
          pointCount++ ;
       }
       npoiPoints = pointCount ;

       printf("\n\n --- TGraph arrays:\n") ;
       for ( int i=0; i<npoiPoints; i++ ) {
          printf("  %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
       }
       printf("\n\n") ;

       double nllDiffVals[1000] ;

       double poiAtMinlnL(-1.) ;
       double poiAtMinusDelta2(-1.) ;
       double poiAtPlusDelta2(-1.) ;
       for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
          nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
          double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
          if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
          if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
          if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
       } // poivi

       printf("\n\n Estimates for poi at delta ln L = -2, 0, +2:  %g ,   %g ,   %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;




      //--- Main canvas

       TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
       if ( cscan == 0x0 ) {
          printf("\n Creating canvas.\n\n") ;
          cscan = new TCanvas("cscan","Delta nll") ;
       }


       char gname[1000] ;

       TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
       sprintf( gname, "scan_%s", new_poi_name ) ;
       graph->SetName( gname ) ;

       double poiBest(-1.) ;
       double poiMinus1stdv(-1.) ;
       double poiPlus1stdv(-1.) ;
       double poiMinus2stdv(-1.) ;
       double poiPlus2stdv(-1.) ;
       double twoDeltalnLMin(1e9) ;

       int nscan(1000) ;
       for ( int xi=0; xi<nscan; xi++ ) {

          double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;

          double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;

          if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
          if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
          if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
          if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
          if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}

          if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f,  2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }

       }
       printf("\n\n POI estimate :  %g  +%g  -%g    [%g,%g],   two sigma errors: +%g  -%g   [%g,%g]\n\n",
               poiBest,
               (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
               (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
               ) ;

       printf(" %s val,pm1sig,pm2sig: %7.2f  %7.2f  %7.2f  %7.2f  %7.2f\n",
          new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;

       char htitle[1000] ;
       sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
       TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
       hscan->SetMinimum(0.) ;
       hscan->SetMaximum(ymax) ;


       hscan->DrawCopy() ;
       graph->SetLineColor(4) ;
       graph->SetLineWidth(3) ;
       graph->Draw("CP") ;
       gPad->SetGridx(1) ;
       gPad->SetGridy(1) ;
       cscan->Update() ;

       TLine* line = new TLine() ;
       line->SetLineColor(2) ;
       line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
       line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
       line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;

       TText* text = new TText() ;
       text->SetTextSize(0.04) ;
       char tstring[1000] ;

       sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
       text -> DrawTextNDC( 0.15, 0.85, tstring ) ;

       sprintf( tstring, "68%% interval [%.1f,  %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
       text -> DrawTextNDC( 0.15, 0.78, tstring ) ;


       char hname[1000] ;
       sprintf( hname, "hscanout_%s", new_poi_name ) ;
       TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
       double obsVal(-1.) ;
       hsout->SetBinContent(1, obsVal ) ;
       hsout->SetBinContent(2, poiPlus1stdv ) ;
       hsout->SetBinContent(3, poiBest ) ;
       hsout->SetBinContent(4, poiMinus1stdv ) ;
       TAxis* xaxis = hsout->GetXaxis() ;
       xaxis->SetBinLabel(1,"Observed val.") ;
       xaxis->SetBinLabel(2,"Model+1sd") ;
       xaxis->SetBinLabel(3,"Model") ;
       xaxis->SetBinLabel(4,"Model-1sd") ;

       char outrootfile[10000] ;
       sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;

       char outpdffile[10000] ;
       sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;

       cscan->Update() ; cscan->Draw() ;

       printf("\n Saving %s\n", outpdffile ) ;
       cscan->SaveAs( outpdffile ) ;



     //--- save in root file

       printf("\n Saving %s\n", outrootfile ) ;
       TFile fout(outrootfile,"recreate") ;
       graph->Write() ;
       hsout->Write() ;
       fout.Close() ;

       delete ws ;
       wstf->Close() ;

   }
Ejemplo n.º 11
0
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;
}
Ejemplo n.º 12
0
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;
    
    


}
Ejemplo n.º 13
0
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;
}
Ejemplo n.º 14
0
   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.
Ejemplo n.º 15
0
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;
		}
	
	}

}
Ejemplo n.º 16
0
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();
  */

}
Ejemplo n.º 17
0
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);
}
Ejemplo n.º 18
0
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;
}
Ejemplo n.º 19
0
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;
}
Ejemplo n.º 20
0
   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.
Ejemplo n.º 21
0
// 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 ----------------------------------------
Ejemplo n.º 22
0
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;
}
Ejemplo n.º 23
0
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;

}
Ejemplo n.º 25
0
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;
}
Ejemplo n.º 26
0
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;

}
Ejemplo n.º 27
0
/*
 * 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 ----------------------------------------
Ejemplo n.º 28
0
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;
}
Ejemplo n.º 29
0
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",&centmin,"centmin/F");
   tr->Branch("centmax",&centmax,"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();
}
Ejemplo n.º 30
0
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();
}