Ejemplo n.º 1
0
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
                                       const char * modelSBName, const char * modelBName, 
                                       const char * dataName, int type,  int testStatType, 
                                       bool useCLs, int npoints, double poimin, double poimax, 
                                       int ntoys,
                                       bool useNumberCounting,
                                       const char * nuisPriorName ){

   std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
  
   w->Print();
  
  
   RooAbsData * data = w->data(dataName); 
   if (!data) { 
      Error("StandardHypoTestDemo","Not existing data %s",dataName);
      return 0;
   }
   else 
      std::cout << "Using data set " << dataName << std::endl;
  
   if (mUseVectorStore) { 
      RooAbsData::setDefaultStorageType(RooAbsData::Vector);
      data->convertToVectorStore() ;
   }
  
  
   // get models from WS
   // get the modelConfig out of the file
   ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
   ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
  
   if (!sbModel) {
      Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
      return 0;
   }
   // check the model 
   if (!sbModel->GetPdf()) { 
      Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
      return 0;
   }
   if (!sbModel->GetParametersOfInterest()) {
      Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
      return 0;
   }
   if (!sbModel->GetObservables()) {
      Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
      return 0;
   }
   if (!sbModel->GetSnapshot() ) { 
      Info("StandardHypoTestInvDemo","Model %s has no snapshot  - make one using model poi",modelSBName);
      sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
   }
  
   // case of no systematics
   // remove nuisance parameters from model
   if (noSystematics) { 
      const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
      if (nuisPar && nuisPar->getSize() > 0) { 
         std::cout << "StandardHypoTestInvDemo" << "  -  Switch off all systematics by setting them constant to their initial values" << std::endl;
         RooStats::SetAllConstant(*nuisPar);
      }
      if (bModel) { 
         const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
         if (bnuisPar) 
            RooStats::SetAllConstant(*bnuisPar);
      }
   }
  
   if (!bModel || bModel == sbModel) {
      Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
      Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
      bModel = (ModelConfig*) sbModel->Clone();
      bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));      
      RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
      if (!var) return 0;
      double oldval = var->getVal();
      var->setVal(0);
      bModel->SetSnapshot( RooArgSet(*var)  );
      var->setVal(oldval);
   }
   else { 
      if (!bModel->GetSnapshot() ) { 
         Info("StandardHypoTestInvDemo","Model %s has no snapshot  - make one using model poi and 0 values ",modelBName);
         RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
         if (var) { 
            double oldval = var->getVal();
            var->setVal(0);
            bModel->SetSnapshot( RooArgSet(*var)  );
            var->setVal(oldval);
         }
         else { 
            Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
            return 0;
         }         
      }
   }

   // check model  has global observables when there are nuisance pdf
   // for the hybrid case the globobs are not needed
   if (type != 1 ) { 
      bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
      bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
      if (hasNuisParam && !hasGlobalObs ) {  
         // try to see if model has nuisance parameters first 
         RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
         if (constrPdf) { 
            Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
            Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
         }
      }
   }


  
   // run first a data fit 
  
   const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
   RooRealVar *poi = (RooRealVar*)poiSet->first();
  
   std::cout << "StandardHypoTestInvDemo : POI initial value:   " << poi->GetName() << " = " << poi->getVal()   << std::endl;  
  
   // fit the data first (need to use constraint )
   TStopwatch tw; 

   bool doFit = initialFit;
   if (testStatType == 0 && initialFit == -1) doFit = false;  // case of LEP test statistic
   if (type == 3  && initialFit == -1) doFit = false;         // case of Asymptoticcalculator with nominal Asimov
   double poihat = 0;

   if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
   else 
      ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
    
   Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
        ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
   
   if (doFit)  { 

      // do the fit : By doing a fit the POI snapshot (for S+B)  is set to the fit value
      // and the nuisance parameters nominal values will be set to the fit value. 
      // This is relevant when using LEP test statistics

      Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
      RooArgSet constrainParams;
      if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
      RooStats::RemoveConstantParameters(&constrainParams);
      tw.Start(); 
      RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
                                                       Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true) );
      if (fitres->status() != 0) { 
         Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
         fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
      }
      if (fitres->status() != 0) 
         Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
  
  
      poihat  = poi->getVal();
      std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "  
                << poihat << " +/- " << poi->getError() << std::endl;
      std::cout << "Time for fitting : "; tw.Print(); 
  
      //save best fit value in the poi snapshot 
      sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
      std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName() 
                << " is set to the best fit value" << std::endl;
  
   }

   // print a message in case of LEP test statistics because it affects result by doing or not doing a fit 
   if (testStatType == 0) {
      if (!doFit) 
         Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
      else 
         Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
   }


   // build test statistics and hypotest calculators for running the inverter 
  
   SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());

   // null parameters must includes snapshot of poi plus the nuisance values 
   RooArgSet nullParams(*sbModel->GetSnapshot());
   if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
   if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
   RooArgSet altParams(*bModel->GetSnapshot());
   if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
   if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
  
   // ratio of profile likelihood - need to pass snapshot for the alt
   RatioOfProfiledLikelihoodsTestStat 
      ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
   ropl.SetSubtractMLE(false);
   if (testStatType == 11) ropl.SetSubtractMLE(true);
   ropl.SetPrintLevel(mPrintLevel);
   ropl.SetMinimizer(minimizerType.c_str());
  
   ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
   if (testStatType == 3) profll.SetOneSided(true);
   if (testStatType == 4) profll.SetSigned(true);
   profll.SetMinimizer(minimizerType.c_str());
   profll.SetPrintLevel(mPrintLevel);

   profll.SetReuseNLL(mOptimize);
   slrts.SetReuseNLL(mOptimize);
   ropl.SetReuseNLL(mOptimize);

   if (mOptimize) { 
      profll.SetStrategy(0);
      ropl.SetStrategy(0);
      ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
   }
  
   if (mMaxPoi > 0) poi->setMax(mMaxPoi);  // increase limit
  
   MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi); 
   NumEventsTestStat nevtts;

   AsymptoticCalculator::SetPrintLevel(mPrintLevel);
  
   // create the HypoTest calculator class 
   HypoTestCalculatorGeneric *  hc = 0;
   if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
   else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
   // else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
   // else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins);  // for using Asimov data generated with nominal values 
   else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
   else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true );  // for using Asimov data generated with nominal values 
   else {
      Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
      return 0;
   }
  
   // set the test statistic 
   TestStatistic * testStat = 0;
   if (testStatType == 0) testStat = &slrts;
   if (testStatType == 1 || testStatType == 11) testStat = &ropl;
   if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
   if (testStatType == 5) testStat = &maxll;
   if (testStatType == 6) testStat = &nevtts;

   if (testStat == 0) { 
      Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
      return 0;
   }
  
  
   ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
   if (toymcs && (type == 0 || type == 1) ) { 
      // look if pdf is number counting or extended
      if (sbModel->GetPdf()->canBeExtended() ) { 
         if (useNumberCounting)   Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
      }
      else { 
         // for not extended pdf
         if (!useNumberCounting  )  { 
            int nEvents = data->numEntries();
            Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken  from observed data set is %d",nEvents);
            toymcs->SetNEventsPerToy(nEvents);
         }
         else {
            Info("StandardHypoTestInvDemo","using a number counting pdf");
            toymcs->SetNEventsPerToy(1);
         }
      }

      toymcs->SetTestStatistic(testStat);
    
      if (data->isWeighted() && !mGenerateBinned) { 
         Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
      }
      toymcs->SetGenerateBinned(mGenerateBinned);
  
      toymcs->SetUseMultiGen(mOptimize);
    
      if (mGenerateBinned &&  sbModel->GetObservables()->getSize() > 2) { 
         Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
      }

      // set the random seed if needed
      if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed); 
    
   }
  
   // specify if need to re-use same toys
   if (reuseAltToys) {
      hc->UseSameAltToys();
   }
  
   if (type == 1) { 
      HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
      assert(hhc);
    
      hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis 
    
      // remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
      bModel->SetGlobalObservables(RooArgSet() );
      sbModel->SetGlobalObservables(RooArgSet() );
    
    
      // check for nuisance prior pdf in case of nuisance parameters 
      if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {

         // fix for using multigen (does not work in this case)
         toymcs->SetUseMultiGen(false);
         ToyMCSampler::SetAlwaysUseMultiGen(false);

         RooAbsPdf * nuisPdf = 0; 
         if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
         // use prior defined first in bModel (then in SbModel)
         if (!nuisPdf)  { 
            Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce  pdf from the model");
            if (bModel->GetPdf() && bModel->GetObservables() ) 
               nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
            else 
               nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
         }   
         if (!nuisPdf ) {
            if (bModel->GetPriorPdf())  { 
               nuisPdf = bModel->GetPriorPdf();
               Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());            
            }
            else { 
               Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
               return 0;
            }
         }
         assert(nuisPdf);
         Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
         nuisPdf->Print();
      
         const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
         RooArgSet * np = nuisPdf->getObservables(*nuisParams);
         if (np->getSize() == 0) { 
            Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
         }
         delete np;
      
         hhc->ForcePriorNuisanceAlt(*nuisPdf);
         hhc->ForcePriorNuisanceNull(*nuisPdf);
      
      
      }
   } 
   else if (type == 2 || type == 3) { 
      if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);  
      if (testStatType != 2 && testStatType != 3)  
         Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
   }
   else if (type == 0 || type == 1) 
      ((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio); 

  
   // Get the result
   RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
  
  
  
   HypoTestInverter calc(*hc);
   calc.SetConfidenceLevel(0.95);
  
  
   calc.UseCLs(useCLs);
   calc.SetVerbose(true);
  
   // can speed up using proof-lite
   if (mUseProof && mNWorkers > 1) { 
      ProofConfig pc(*w, mNWorkers, "", kFALSE);
      toymcs->SetProofConfig(&pc);    // enable proof
   }
  
  
   if (npoints > 0) {
      if (poimin > poimax) { 
         // if no min/max given scan between MLE and +4 sigma 
         poimin = int(poihat);
         poimax = int(poihat +  4 * poi->getError());
      }
      std::cout << "Doing a fixed scan  in interval : " << poimin << " , " << poimax << std::endl;
      calc.SetFixedScan(npoints,poimin,poimax);
   }
   else { 
      //poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
      std::cout << "Doing an  automatic scan  in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
   }
  
   tw.Start();
   HypoTestInverterResult * r = calc.GetInterval();
   std::cout << "Time to perform limit scan \n";
   tw.Print();
  
   if (mRebuild) {
      calc.SetCloseProof(1);
      tw.Start();
      SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
      std::cout << "Time to rebuild distributions " << std::endl;
      tw.Print();
    
      if (limDist) { 
         std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- " 
                   << limDist->InverseCDF(0.16) << "  " 
                   << limDist->InverseCDF(0.84) << "\n"; 
      
         //update r to a new updated result object containing the rebuilt expected p-values distributions
         // (it will not recompute the expected limit)
         if (r) delete r;  // need to delete previous object since GetInterval will return a cloned copy
         r = calc.GetInterval();
      
      }
      else 
         std::cout << "ERROR : failed to re-build distributions " << std::endl; 
   }
  
   return r;
}
Ejemplo n.º 2
0
// internal routine to run the inverter
HypoTestInverterResult *  RunInverter(RooWorkspace * w, const char * modelSBName, const char * modelBName, 
                                      const char * dataName, int type,  int testStatType, 
                                      int npoints, double poimin, double poimax, 
                                      int ntoys, bool useCls ) 
{

   std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;

   w->Print();


   RooAbsData * data = w->data(dataName); 
   if (!data) { 
      Error("RA2bHypoTestDemo","Not existing data %s",dataName);
      return 0;
   }
   else 
      std::cout << "Using data set " << dataName << std::endl;

   
   // get models from WS
   // get the modelConfig out of the file
   ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
   ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);

   if (!sbModel) {
      Error("RA2bHypoTestDemo","Not existing ModelConfig %s",modelSBName);
      return 0;
   }
   // check the model 
   if (!sbModel->GetPdf()) { 
      Error("RA2bHypoTestDemo","Model %s has no pdf ",modelSBName);
      return 0;
   }
   if (!sbModel->GetParametersOfInterest()) {
      Error("RA2bHypoTestDemo","Model %s has no poi ",modelSBName);
      return 0;
   }
   if (!sbModel->GetParametersOfInterest()) {
      Error("RA2bHypoTestInvDemo","Model %s has no poi ",modelSBName);
      return 0;
   }
   if (!sbModel->GetSnapshot() ) { 
      Info("RA2bHypoTestInvDemo","Model %s has no snapshot  - make one using model poi",modelSBName);
      sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
   }


   if (!bModel || bModel == sbModel) {
      Info("RA2bHypoTestInvDemo","The background model %s does not exist",modelBName);
      Info("RA2bHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
      bModel = (ModelConfig*) sbModel->Clone();
      bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));      
      RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
      if (!var) return 0;
      double oldval = var->getVal();
      var->setVal(0);
      bModel->SetSnapshot( RooArgSet(*var)  );
      var->setVal(oldval);
   }
   else { 
      if (!bModel->GetSnapshot() ) { 
         Info("RA2bHypoTestInvDemo","Model %s has no snapshot  - make one using model poi and 0 values ",modelBName);
         RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
         if (var) { 
            double oldval = var->getVal();
            var->setVal(0);
            bModel->SetSnapshot( RooArgSet(*var)  );
            var->setVal(oldval);
         }
         else { 
            Error("RA2bHypoTestInvDemo","Model %s has no valid poi",modelBName);
            return 0;
         }         
      }
   }


   SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
   if (sbModel->GetSnapshot()) slrts.SetNullParameters(*sbModel->GetSnapshot());
   if (bModel->GetSnapshot()) slrts.SetAltParameters(*bModel->GetSnapshot());

   // ratio of profile likelihood - need to pass snapshot for the alt
   RatioOfProfiledLikelihoodsTestStat 
      ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
   ropl.SetSubtractMLE(false);
   
   //MyProfileLikelihoodTestStat profll(*sbModel->GetPdf());
   ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
   if (testStatType == 3) profll.SetOneSided(1);
   if (optimize) profll.SetReuseNLL(true);

   TestStatistic * testStat = &slrts;
   if (testStatType == 1) testStat = &ropl;
   if (testStatType == 2 || testStatType == 3) testStat = &profll;
  
   
   HypoTestCalculatorGeneric *  hc = 0;
   if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
   else hc = new HybridCalculator(*data, *bModel, *sbModel);

   ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
   //=== DEBUG
   ///// toymcs->SetWS( w ) ;
   //=== DEBUG
   toymcs->SetNEventsPerToy(1);
   toymcs->SetTestStatistic(testStat);
   if (optimize) toymcs->SetUseMultiGen(true);


   if (type == 1) { 
      HybridCalculator *hhc = (HybridCalculator*) hc;
      hhc->SetToys(ntoys,ntoys); 

      // check for nuisance prior pdf 
      if (bModel->GetPriorPdf() && sbModel->GetPriorPdf() ) {
         hhc->ForcePriorNuisanceAlt(*bModel->GetPriorPdf());
         hhc->ForcePriorNuisanceNull(*sbModel->GetPriorPdf());
      }
      else {
         if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
            Error("RA2bHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified");
            return 0;
         }
      }
   } 
   else 
      ((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys); 

   // Get the result
   RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);


   TStopwatch tw; tw.Start(); 
   const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
   RooRealVar *poi = (RooRealVar*)poiSet->first();

   // fit the data first
   sbModel->GetPdf()->fitTo(*data);
   double poihat  = poi->getVal();


   HypoTestInverter calc(*hc);
   calc.SetConfidenceLevel(0.95);

   calc.UseCLs(useCls);
   calc.SetVerbose(true);

   // can speed up using proof-lite
   if (useProof && nworkers > 1) { 
      ProofConfig pc(*w, nworkers, "", kFALSE);
      toymcs->SetProofConfig(&pc);    // enable proof
   }


   printf(" npoints = %d, poimin = %7.2f, poimax = %7.2f\n\n", npoints, poimin, poimax ) ;
   cout << flush ;

   if ( npoints==1 ) {

      std::cout << "Evaluating one point : " << poimax << std::endl;
      calc.RunOnePoint(poimax);

   } else if (npoints > 0) {
      if (poimin >= poimax) { 
         // if no min/max given scan between MLE and +4 sigma 
         poimin = int(poihat);
         poimax = int(poihat +  4 * poi->getError());
      }
      std::cout << "Doing a fixed scan  in interval : " << poimin << " , " << poimax << std::endl;
      calc.SetFixedScan(npoints,poimin,poimax);
   }
   else { 
      //poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
      std::cout << "Doing an  automatic scan  in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
   }

   cout << "\n\n right before calc.GetInterval(), ntoys = " << ntoys << " \n\n" << flush ;
   HypoTestInverterResult * r = calc.GetInterval();


   return r; 
}
Ejemplo n.º 3
0
result fit_toy(RooWorkspace* wspace, int n, const RooArgSet* globals) {
    RooRandom::randomGenerator()->SetSeed(0);
    // TFile f(filename);
    // RooWorkspace *wspace = (RooWorkspace*)f.Get("combined");
    ModelConfig* model = (ModelConfig*)wspace->obj("ModelConfig");

    RooAbsPdf* pdf;
    pdf = model->GetPdf();

    RooAbsPdf* top_constraint = (RooAbsPdf*)wspace->obj("top_ratio_constraint");
    RooAbsPdf* vv_constraint = (RooAbsPdf*)wspace->obj("vv_ratio_constraint");
    RooAbsPdf* top_vv_constraint_sf = (RooAbsPdf*)wspace->obj("top_vv_ratio_sf_constraint");
    RooAbsPdf* top_vv_constraint_of = (RooAbsPdf*)wspace->obj("top_vv_ratio_of_constraint");


    // generate constraint global observables
    RooRealVar *nom_top_ratio = (RooRealVar*)wspace->obj("nom_top_ratio");
    nom_top_ratio->setRange(0, 100);
    RooRealVar *nom_vv_ratio = (RooRealVar*)wspace->obj("nom_vv_ratio");
    nom_vv_ratio->setRange(0,100);
    RooRealVar *nom_top_vv_ratio_sf = (RooRealVar*)wspace->obj("nom_top_vv_ratio_sf");
    nom_top_vv_ratio_sf->setRange(0,100);
    RooRealVar *nom_top_vv_ratio_of = (RooRealVar*)wspace->obj("nom_top_vv_ratio_of");
    nom_top_vv_ratio_of->setRange(0,100);

    RooDataSet *nom_top_generated = top_constraint->generateSimGlobal(RooArgSet(*nom_top_ratio), 1);
    nom_top_ratio->setVal(((RooRealVar*)nom_top_generated->get(0)->find("nom_top_ratio"))->getVal());

    RooDataSet *nom_vv_generated = vv_constraint->generateSimGlobal(RooArgSet(*nom_vv_ratio), 1);
    nom_vv_ratio->setVal(((RooRealVar*)nom_vv_generated->get(0)->find("nom_vv_ratio"))->getVal());

    RooDataSet *nom_top_vv_sf_generated = top_vv_constraint_sf->generateSimGlobal(RooArgSet(*nom_top_vv_ratio_sf), 1);
    nom_top_vv_ratio_sf->setVal(((RooRealVar*)nom_top_vv_sf_generated->get(0)->find("nom_top_vv_ratio_sf"))->getVal());

    RooDataSet *nom_top_vv_of_generated = top_vv_constraint_of->generateSimGlobal(RooArgSet(*nom_top_vv_ratio_of), 1);
    nom_top_vv_ratio_of->setVal(((RooRealVar*)nom_top_vv_of_generated->get(0)->find("nom_top_vv_ratio_of"))->getVal());

    NumEventsTestStat* dummy = new NumEventsTestStat(*pdf);

    ToyMCSampler* mc = new ToyMCSampler(*dummy, 1);
    mc->SetPdf(*pdf);
    mc->SetObservables(*model->GetObservables());
    mc->SetGlobalObservables(*globals);
    mc->SetNuisanceParameters(*model->GetNuisanceParameters());
    mc->SetParametersForTestStat(*model->GetParametersOfInterest());
    mc->SetNEventsPerToy(n);

    RooArgSet constr;
    constr.add(*(model->GetNuisanceParameters()));
    RemoveConstantParameters(&constr);

    RooDataSet* toy_data = (RooDataSet*)mc->GenerateToyData(*const_cast<RooArgSet*>(model->GetSnapshot()));

    RooFitResult *res = pdf->fitTo(*toy_data, Constrain(constr), PrintLevel(0), Save(),
                                               Range("fitRange"), InitialHesse(),
                                               ExternalConstraints(RooArgSet(*top_constraint, *vv_constraint, *top_vv_constraint_sf, *top_vv_constraint_of)));
    result yield = get_results(wspace, res);
    yield.of.generated_sum.val = toy_data->sumEntries("(channelCat==channelCat::of) & (obs_x_of>120)");
    yield.sf.generated_sum.val = toy_data->sumEntries("(channelCat==channelCat::sf) & (obs_x_sf>120)");

    delete mc;
    delete dummy;
    // f.Close();

    return yield;
}
Ejemplo n.º 4
0
void StandardHypoTestDemo(const char* infile = "",
                          const char* workspaceName = "combined",
                          const char* modelSBName = "ModelConfig",
                          const char* modelBName = "",
                          const char* dataName = "obsData", 
                          int calcType = 0, // 0 freq 1 hybrid, 2 asymptotic
                          int testStatType = 3,   // 0 LEP, 1 TeV, 2 LHC, 3 LHC - one sided
                          int ntoys = 5000, 
                          bool useNC = false, 
                          const char * nuisPriorName = 0)
{

/*

  Other Parameter to pass in tutorial
  apart from standard for filename, ws, modelconfig and data

  type = 0 Freq calculator 
  type = 1 Hybrid calculator
  type = 2 Asymptotic calculator  

  testStatType = 0 LEP
  = 1 Tevatron 
  = 2 Profile Likelihood
  = 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)

  ntoys:         number of toys to use 

  useNumberCounting:  set to true when using number counting events 

  nuisPriorName:   name of prior for the nnuisance. This is often expressed as constraint term in the global model
  It is needed only when using the HybridCalculator (type=1)
  If not given by default the prior pdf from ModelConfig is used. 

  extra options are available as global paramwters of the macro. They major ones are: 
 
  generateBinned       generate binned data sets for toys (default is false) - be careful not to activate with 
  a too large (>=3) number of observables 
  nToyRatio            ratio of S+B/B toys (default is 2)
  printLevel
  
*/

   // disable - can cause some problems
   //ToyMCSampler::SetAlwaysUseMultiGen(true);

   SimpleLikelihoodRatioTestStat::SetAlwaysReuseNLL(true);
   ProfileLikelihoodTestStat::SetAlwaysReuseNLL(true);
   RatioOfProfiledLikelihoodsTestStat::SetAlwaysReuseNLL(true);

   //RooRandom::randomGenerator()->SetSeed(0);

   // to change minimizers 
   // ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
   // ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
   // ROOT::Math::MinimizerOptions::SetDefaultTolerance(1);

  /////////////////////////////////////////////////////////////
  // 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;
  }

  
  /////////////////////////////////////////////////////////////
  // Tutorial starts here
  ////////////////////////////////////////////////////////////

  // get the workspace out of the file
  RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
  if(!w){
    cout <<"workspace not found" << endl;
    return;
  }
  w->Print();

  // get the modelConfig out of the file
  ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);


  // get the modelConfig out of the file
  RooAbsData* data = w->data(dataName);

  // make sure ingredients are found
  if(!data || !sbModel){
    w->Print();
    cout << "data or ModelConfig was not found" <<endl;
    return;
  }
  // make b model
  ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);


   // case of no systematics
   // remove nuisance parameters from model
   if (noSystematics) { 
      const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
      if (nuisPar && nuisPar->getSize() > 0) { 
         std::cout << "StandardHypoTestInvDemo" << "  -  Switch off all systematics by setting them constant to their initial values" << std::endl;
         RooStats::SetAllConstant(*nuisPar);
      }
      if (bModel) { 
         const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
         if (bnuisPar) 
            RooStats::SetAllConstant(*bnuisPar);
      }
   }


  if (!bModel ) {
      Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
      Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
      bModel = (ModelConfig*) sbModel->Clone();
      bModel->SetName(TString(modelSBName)+TString("B_only"));      
      RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
      if (!var) return;
      double oldval = var->getVal();
      var->setVal(0);
      //bModel->SetSnapshot( RooArgSet(*var, *w->var("lumi"))  );
      bModel->SetSnapshot( RooArgSet(*var)  );
      var->setVal(oldval);
  }
  
   if (!sbModel->GetSnapshot() || poiValue > 0) { 
      Info("StandardHypoTestDemo","Model %s has no snapshot  - make one using model poi",modelSBName);
      RooRealVar * var = dynamic_cast<RooRealVar*>(sbModel->GetParametersOfInterest()->first());
      if (!var) return;
      double oldval = var->getVal();
      if (poiValue > 0)  var->setVal(poiValue);
      //sbModel->SetSnapshot( RooArgSet(*var, *w->var("lumi") ) );
      sbModel->SetSnapshot( RooArgSet(*var) );
      if (poiValue > 0) var->setVal(oldval);
      //sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
   }

   



   // part 1, hypothesis testing 
   SimpleLikelihoodRatioTestStat * slrts = new SimpleLikelihoodRatioTestStat(*bModel->GetPdf(), *sbModel->GetPdf());
   // null parameters must includes snapshot of poi plus the nuisance values 
   RooArgSet nullParams(*bModel->GetSnapshot());
   if (bModel->GetNuisanceParameters()) nullParams.add(*bModel->GetNuisanceParameters());
   
   slrts->SetNullParameters(nullParams);
   RooArgSet altParams(*sbModel->GetSnapshot());
   if (sbModel->GetNuisanceParameters()) altParams.add(*sbModel->GetNuisanceParameters());
   slrts->SetAltParameters(altParams);


   ProfileLikelihoodTestStat * profll = new ProfileLikelihoodTestStat(*bModel->GetPdf());


   RatioOfProfiledLikelihoodsTestStat * 
      ropl = new RatioOfProfiledLikelihoodsTestStat(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot());
   ropl->SetSubtractMLE(false);

   if (testStatType == 3) profll->SetOneSidedDiscovery(1);
   profll->SetPrintLevel(printLevel);

   // profll.SetReuseNLL(mOptimize);
   // slrts.SetReuseNLL(mOptimize);
   // ropl.SetReuseNLL(mOptimize);

   AsymptoticCalculator::SetPrintLevel(printLevel);

   HypoTestCalculatorGeneric *  hypoCalc = 0;
   // note here Null is B and Alt is S+B
   if (calcType == 0) hypoCalc = new  FrequentistCalculator(*data, *sbModel, *bModel);
   else if (calcType == 1) hypoCalc= new  HybridCalculator(*data, *sbModel, *bModel);
   else if (calcType == 2) hypoCalc= new  AsymptoticCalculator(*data, *sbModel, *bModel);

   if (calcType == 0) 
       ((FrequentistCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
   if (calcType == 1) 
       ((HybridCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
   if (calcType == 2 ) { 
      if (testStatType == 3) ((AsymptoticCalculator*) hypoCalc)->SetOneSidedDiscovery(true);  
      if (testStatType != 2 && testStatType != 3)  
         Warning("StandardHypoTestDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
      

   }


   // check for nuisance prior pdf in case of nuisance parameters 
   if (calcType == 1 && (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() )) {
         RooAbsPdf * nuisPdf = 0; 
         if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
         // use prior defined first in bModel (then in SbModel)
         if (!nuisPdf)  { 
            Info("StandardHypoTestDemo","No nuisance pdf given for the HybridCalculator - try to deduce  pdf from the   model");
            if (bModel->GetPdf() && bModel->GetObservables() ) 
               nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
            else 
               nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
         }   
         if (!nuisPdf ) {
            if (bModel->GetPriorPdf())  { 
               nuisPdf = bModel->GetPriorPdf();
               Info("StandardHypoTestDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());            
            }
            else { 
               Error("StandardHypoTestDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
               return;
            }
         }
         assert(nuisPdf);
         Info("StandardHypoTestDemo","Using as nuisance Pdf ... " );
         nuisPdf->Print();
      
         const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
         RooArgSet * np = nuisPdf->getObservables(*nuisParams);
         if (np->getSize() == 0) { 
            Warning("StandardHypoTestDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
         }
         delete np;
      
         ((HybridCalculator*)hypoCalc)->ForcePriorNuisanceAlt(*nuisPdf);
         ((HybridCalculator*)hypoCalc)->ForcePriorNuisanceNull(*nuisPdf);
   }

   // hypoCalc->ForcePriorNuisanceAlt(*sbModel->GetPriorPdf());
   // hypoCalc->ForcePriorNuisanceNull(*bModel->GetPriorPdf());

   ToyMCSampler * sampler = (ToyMCSampler *)hypoCalc->GetTestStatSampler();

   if (sampler && (calcType == 0 || calcType == 1) ) { 

      // look if pdf is number counting or extended
      if (sbModel->GetPdf()->canBeExtended() ) { 
         if (useNC)   Warning("StandardHypoTestDemo","Pdf is extended: but number counting flag is set: ignore it ");
      }
      else {
         // for not extended pdf
         if (!useNC)  { 
            int nEvents = data->numEntries();
            Info("StandardHypoTestDemo","Pdf is not extended: number of events to generate taken  from observed data set is %d",nEvents);
            sampler->SetNEventsPerToy(nEvents);
         }
         else {
            Info("StandardHypoTestDemo","using a number counting pdf");
            sampler->SetNEventsPerToy(1);
         }
      }
      
      if (data->isWeighted() && !generateBinned) { 
         Info("StandardHypoTestDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set generateBinned to true\n",data->numEntries(), data->sumEntries());
      }
      if (generateBinned)  sampler->SetGenerateBinned(generateBinned);


      // set the test statistic
      if (testStatType == 0) sampler->SetTestStatistic(slrts); 
      if (testStatType == 1) sampler->SetTestStatistic(ropl); 
      if (testStatType == 2 || testStatType == 3) sampler->SetTestStatistic(profll); 

   }
   
   HypoTestResult *  htr = hypoCalc->GetHypoTest();
   htr->SetPValueIsRightTail(true);
   htr->SetBackgroundAsAlt(false);
   htr->Print(); // how to get meaningfull CLs at this point?

   delete sampler;
   delete slrts; 
   delete ropl; 
   delete profll;

   if (calcType != 2) {
      HypoTestPlot * plot = new HypoTestPlot(*htr,100);
      plot->SetLogYaxis(true);
      plot->Draw();
   }
   else { 
      std::cout << "Asymptotic results " << std::endl;
      
   }

   // look at expected significances 
   // found median of S+B distribution
   if (calcType != 2) { 

      SamplingDistribution * altDist = htr->GetAltDistribution();   
      HypoTestResult htExp("Expected Result");
      htExp.Append(htr);
      // find quantiles in alt (S+B) distribution 
      double p[5];
      double q[5];
      for (int i = 0; i < 5; ++i) { 
         double sig = -2  + i;
         p[i] = ROOT::Math::normal_cdf(sig,1);
      }
      std::vector<double> values = altDist->GetSamplingDistribution();
      TMath::Quantiles( values.size(), 5, &values[0], q, p, false);  

      for (int i = 0; i < 5; ++i) { 
         htExp.SetTestStatisticData( q[i] );
         double sig = -2  + i;      
         std::cout << " Expected p -value and significance at " << sig << " sigma = " 
                   << htExp.NullPValue() << " significance " << htExp.Significance() << " sigma " << std::endl; 
         
      }
   }
   else { 
      // case of asymptotic calculator 
      for (int i = 0; i < 5; ++i) { 
         double sig = -2  + i;      
         // sigma is inverted here 
         double pval = AsymptoticCalculator::GetExpectedPValues( htr->NullPValue(), htr->AlternatePValue(), -sig, false);
         std::cout << " Expected p -value and significance at " << sig << " sigma = " 
                   << pval << " significance " << ROOT::Math::normal_quantile_c(pval,1) << " sigma " << std::endl; 
         
      }
   }

}
void HypoTestInvDemo(const char * fileName ="GausModel_b.root",
                     const char * wsName = "w",
                     const char * modelSBName = "model_sb",
                     const char * modelBName = "model_b",
                     const char * dataName = "data_obs",                  
                     int type = 0,  // calculator type 
                     int testStatType = 0, // test stat type
                     int npoints = 10,   
                     int ntoys=1000,
                     bool useCls = true )
{ 
   /*
    type = 0 Freq calculator 
    type = 1 Hybrid 

    testStatType = 0 LEP
                 = 1 Tevatron 
                 = 2 PL


   */

   if (fileName==0) { 
      std::cout << "give input filename " << std::endl;
      return; 
   }
   TFile * file = new TFile(fileName); 

   RooWorkspace * w = dynamic_cast<RooWorkspace*>( file->Get(wsName) );
   if (!w) {      
      return; 
   }
   w->Print();


   RooAbsData * data = w->data(dataName); 
   if (!data) { 
      Error("HypoTestDemo","Not existing data %s",dataName);
   }

   
   // get models from WS
  // get the modelConfig out of the file
  ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
  ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);


   SimpleLikelihoodRatioTestStat slrts(*bModel->GetPdf(),*sbModel->GetPdf());
   slrts.SetNullParameters(*bModel->GetSnapshot());
   slrts.SetAltParameters(*sbModel->GetSnapshot());

   RatioOfProfiledLikelihoodsTestStat 
   ropl(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot());
   ropl.SetSubtractMLE(false);
   
   ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
   profll.SetOneSided(0);

   TestStatistic * testStat = &slrts;
   if (testStatType == 1) testStat = &ropl;
   if (testStatType == 2) testStat = &profll;
  
   
   HypoTestCalculatorGeneric *  hc = 0;
   if (type == 0) hc = new FrequentistCalculator(*data, *sbModel, *bModel);
   else new HybridCalculator(*data, *sbModel, *bModel);

   ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
   //toymcs->SetNEventsPerToy(1);
   toymcs->SetTestStatistic(testStat);


    if (type == 1) { 
      HybridCalculator *hhc = (HybridCalculator*) hc;
      hhc->SetToys(ntoys,ntoys); 
      // hhc->ForcePriorNuisanceAlt(*pdfNuis);
      // hhc->ForcePriorNuisanceNull(*pdfNuis);
   } 
   else 
      ((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys); 

  // Get the result
   RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);


   TStopwatch tw; tw.Start(); 
   const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
   RooRealVar *poi = (RooRealVar*)poiSet->first();

   // fit the data first
   sbModel->GetPdf()->fitTo(*data);
   double poihat  = poi->getVal();
   //poi->setVal(30);
   //poi->setError(10);


   HypoTestInverter calc(*hc);
   // GENA: for two-sided interval
   //calc.SetConfidenceLevel(0.95);
   // GENA: for 95% upper limit
   calc.SetConfidenceLevel(0.90);

   calc.UseCLs(useCls);
   calc.SetVerbose(true);

   // can spped up using proof
   ProofConfig pc(*w, 2, "workers=2", kFALSE);
   //ProofConfig pc(*w, 30, "localhost", kFALSE);
   //ToyMCSampler * toymcs = dynamic_cast<ToyMCSampler *> (calc.GetHypoTestCalculator()->GetTestStatSampler() );
   // GENA: disable proof for now
   //toymcs->SetProofConfig(&pc);    // enable proof

   
   if (npoints > 0) {
     // GENA
     double poimin = TMath::Max(poihat -   4 * poi->getError(), 0.0);
     //poimin = poihat;
     double poimax = poihat +  4 * poi->getError();
     poimin = 0; 
     poimax = 20;
     //double poimin = poi->getMin();
     //double poimax = poi->getMax();
     std::cout << "Doing a fixed scan  in interval : " << poimin << " , " << poimax << std::endl;
     calc.SetFixedScan(npoints,poimin,poimax);
   }

   HypoTestInverterResult * r = calc.GetInterval();

   // write to a file the results
   TString resultFileName = (useCls) ? "CLs_" : "Cls+b_";
   resultFileName += fileName;

   // GENA
   //TFile * file = new TFile(resultFileName,"RECREATE");
   file = new TFile(resultFileName,"RECREATE");
   r->Write();
   file->Close();                                                                     

  double ulError = r->UpperLimitEstimatedError();
  double upperLimit = r->UpperLimit();
  std::cout << "The computed upper limit is: " << upperLimit << std::endl;
  std::cout << "an estimated error on this upper limit is: " << ulError << std::endl;

  // check using interpolation
  // double interpLimit = r->FindInterpolatedLimit(1.-r->ConfidenceLevel() );
  // cout << "The computer interpolated limits is " << interpLimit << endl;

  const int nEntries = r->ArraySize();

  std::vector<Double_t> xArray(nEntries);
  std::vector<Double_t> yArray(nEntries);
  std::vector<Double_t> yErrArray(nEntries);
  for (int i=0; i<nEntries; i++) {
    xArray[i] = r->GetXValue(i);
    yArray[i] = r->GetYValue(i);
    yErrArray[i] = r->GetYError(i);
    std::cout << xArray[i] << " , " << yArray[i] << " err = " << yErrArray[i] << std::endl;
  }
 

   // see expected result (bands)
   TGraph * g0 = new TGraph(nEntries);
   TGraphAsymmErrors * g1 = new TGraphAsymmErrors(nEntries);
   TGraphAsymmErrors * g2l = new TGraphAsymmErrors(nEntries);
   TGraphAsymmErrors * g2u = new TGraphAsymmErrors(nEntries);
   double p[7]; 
   double q[7];
   p[0] = ROOT::Math::normal_cdf(-2);
   p[1] = ROOT::Math::normal_cdf(-1.5);
   p[2] = ROOT::Math::normal_cdf(-1);
   p[3] = 0.5;
   p[4] = ROOT::Math::normal_cdf(1);
   p[5] = ROOT::Math::normal_cdf(1.5);
   p[6] = ROOT::Math::normal_cdf(2);
   for (int i=0; i<nEntries; i++) {
      SamplingDistribution * s = r->GetExpectedDistribution(i);
      // GENA
      //const std::vector<double> & values = s->GetSamplingDistribution();
      const std::vector<Double_t> & cValues = s->GetSamplingDistribution();
      std::vector<Double_t> values;
      for (std::vector<Double_t>::const_iterator val = cValues.begin();
	   val != cValues.end();
	   ++val) values.push_back(*val);
      TMath::Quantiles(values.size(), 7, &values[0],q,p,false);
      double p0 = q[3];
      double p2l =  q[1];
      double p2u =  q[5];
      g0->SetPoint(i, r->GetXValue(i), p0 ) ;
      g1->SetPoint(i, r->GetXValue(i),  p0);
      g2l->SetPoint(i, r->GetXValue(i), p2l);
      g2u->SetPoint(i, r->GetXValue(i), p2u);
      //g2->SetPoint(i, r->GetXValue(i), s->InverseCDF(0.50));
      g1->SetPointEYlow(i, q[3] - q[2]); // -1 sigma errorr   
      g1->SetPointEYhigh(i, q[4] - q[3]);//+1 sigma error

      g2l->SetPointEYlow(i, q[1]-q[0]);   // -2 -- -1 sigma error
      g2l->SetPointEYhigh(i, q[2]-q[1]);

      g2u->SetPointEYlow(i, q[5]-q[4]);
      g2u->SetPointEYhigh(i, q[6]-q[5]);


      if (plotHypoTestResult) { 
         HypoTestResult * hr = new HypoTestResult();
         hr->SetNullDistribution( r->GetBackgroundDistribution() );
         hr->SetAltDistribution( r->GetSignalAndBackgroundDistribution(i) );
         new TCanvas();
         HypoTestPlot * pl = new HypoTestPlot(*hr);
         pl->Draw();
      }
  }

   HypoTestInverterPlot *plot = new HypoTestInverterPlot("result","",r);
   TGraphErrors * g = plot->MakePlot();

   g->Draw("APL");   
   g2l->SetFillColor(kYellow);
   g2l->Draw("3");
   g2u->SetFillColor(kYellow);
   g2u->Draw("3");
   g1->SetFillColor(kGreen);
   g1->Draw("3");
   g0->SetLineColor(kBlue);
   g0->SetLineStyle(2);
   g0->SetLineWidth(2);
   g0->Draw("L");

   //g1->Draw("P");
   //g2->Draw("P");
   g->SetLineWidth(2);
   g->Draw("PL");   

   // GENA: two-sided interval
   //double alpha = 1.-r->ConfidenceLevel();
   // GENA: upper limit
   double alpha = (1.-r->ConfidenceLevel())/2.0;
   double x1 = g->GetXaxis()->GetXmin();
   double x2 = g->GetXaxis()->GetXmax();
   TLine * line = new TLine(x1, alpha, x2,alpha);
   line->SetLineColor(kRed);
   line->Draw();

   // see the expected limit and -1 +1 sigma bands
   // SamplingDistribution * limits = r->GetUpperLimitDistribution();

   // std::cout << " expected limit (median) " << limits->InverseCDF(0.50) << std::endl;
   // std::cout << " expected limit (-1 sig) " << limits->InverseCDF((ROOT::Math::normal_cdf(-1))) << std::endl;
   // std::cout << " expected limit (+1 sig) " << limits->InverseCDF((ROOT::Math::normal_cdf(+1))) << std::endl;
   
   tw.Print();

}