コード例 #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;
}
コード例 #2
0
ファイル: splitws.C プロジェクト: lspiller/limitcode
void splitws(string inFolderName, double mass, string channel) {
  cout << "Splitting workspace in " << channel << endl;

  int flatInterpCode = 4;
  int shapeInterpCode = 4;

  bool do2011 = 0;

  if (inFolderName.find("2011") != string::npos) do2011 = 1;

  bool conditionalAsimov = 0;
  bool doData = 1;
  //if (inFolderName.find("_blind_") != string::npos) {
    //conditionalAsimov = 0;
  //}
  //else {
    //conditionalAsimov = 1;
  //}

  set<string> channelNames;

  if (channel == "01j") {
    channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));

    channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
  }
  else if (channel == "0j") {
    channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
  }
  else if (channel == "1j") {
    channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
  }
  else if (channel == "OF01j") {
    channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_sscr_0j"+string(!do2011?"_2012":""));

    channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_sscr_1j"+string(!do2011?"_2012":""));
  }
  else if (channel == "OF0j") {
    channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_sscr_0j"+string(!do2011?"_2012":""));
  }
  else if (channel == "OF1j") {
    channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_sscr_1j"+string(!do2011?"_2012":""));
  }
  else if (channel == "SF01j") {
    channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));

    channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
  }
  else if (channel == "SF0j") {
    channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));
  }
  else if (channel == "SF1j") {
    channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));
  }
  else if (channel == "2j") {
    channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":""));
    channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
  }
  else if (channel == "OF2j") {
    channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
  }
  else if (channel == "SF2j") {
    channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
  }
  else if (channel == "OF") {
    channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));

    channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":""));
    channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));

    channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
  }
  else if (channel == "SF") {
    channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":""));

    channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":""));
    channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":""));

    channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":""));
    channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":""));
  }
  else {
    cout << "Channel " << channel << " not defined. Please check!" << endl;
    exit(1);
  }

  // bool fix = 1;
  stringstream inFileName;

  inFileName << "workspaces/" << inFolderName << "/" << mass << ".root";
  TFile f(inFileName.str().c_str());
  
  RooWorkspace* w = (RooWorkspace*)f.Get("combWS");
  if (!w) w = (RooWorkspace*)f.Get("combined");
  
  RooDataSet* data = (RooDataSet*)w->data("combData");
  if (!data) data = (RooDataSet*)w->data("obsData");
  
  ModelConfig* mc = (ModelConfig*)w->obj("ModelConfig");
  
  RooRealVar* weightVar = w->var("weightVar");
  
  RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
  if (!mu) mu = w->var("SigXsecOverSM");

  const RooArgSet* mc_obs = mc->GetObservables();
  const RooArgSet* mc_nuis = mc->GetNuisanceParameters();
  const RooArgSet* mc_globs = mc->GetGlobalObservables();
  const RooArgSet* mc_poi = mc->GetParametersOfInterest();

  RooArgSet nuis = *mc_nuis;
  RooArgSet antiNuis = *mc_nuis;

  RooArgSet globs = *mc_globs;
  RooArgSet antiGlobs = *mc_globs;

  RooArgSet allParams;

  RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf();
  RooCategory* cat = (RooCategory*)&simPdf->indexCat();

  RooArgSet nuis_tmp = nuis;
  RooArgSet fullConstraints = *simPdf->getAllConstraints(*mc_obs,nuis_tmp,false);

  vector<string> foundChannels;
  vector<string> skippedChannels;  

  cout << "Getting constraints" << endl;
  map<string, RooDataSet*> data_map;
  map<string, RooAbsPdf*> pdf_map;
  RooCategory* decCat = new RooCategory("dec_channel","dec_channel");
  // int i = 0;
  TIterator* catItr = cat->typeIterator();
  RooCatType* type;
  RooArgSet allConstraints;
  while ((type = (RooCatType*)catItr->Next())) {
    RooAbsPdf* pdf =  simPdf->getPdf(type->GetName());

    string typeName(type->GetName());
    if (channelNames.size() && channelNames.find(typeName) == channelNames.end())  {
      skippedChannels.push_back(typeName);
      continue;
    }
    cout << "On channel " << type->GetName() << endl;
    foundChannels.push_back(typeName);

    decCat->defineType(type->GetName());
    // pdf->getParameters(*data)->Print("v");

    RooArgSet nuis_tmp1 = nuis;
    RooArgSet nuis_tmp2 = nuis;
    RooArgSet* constraints = pdf->getAllConstraints(*mc_obs, nuis_tmp1, true);
    constraints->Print();
    allConstraints.add(*constraints);
  }

  catItr->Reset();

  while ((type = (RooCatType*)catItr->Next())) {
    RooAbsPdf* pdf =  simPdf->getPdf(type->GetName());

    string typeName(type->GetName());
    cout << "Considering type " << typeName << endl;
    if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) continue;
    cout << "On channel " << type->GetName() << endl;

    RooArgSet nuis_tmp1 = nuis;
    RooArgSet nuis_tmp2 = nuis;
    RooArgSet* constraints = pdf->getAllConstraints(*mc_obs, nuis_tmp1, true);

    cout << "Adding pdf to map: " << typeName << " = " << pdf->GetName() << endl;
    pdf_map[typeName] = pdf;

    RooProdPdf prod("prod","prod",*constraints);

    RooArgSet* params = pdf->getParameters(*data);
    antiNuis.remove(*params);
    antiGlobs.remove(*params);

    allParams.add(*params);
    // cout << type->GetName() << endl;
  }
  // return;

  RooArgSet decNuis;
  TIterator* nuiItr = mc_nuis->createIterator();
  TIterator* parItr = allParams.createIterator();
  RooAbsArg* nui, *par;
  while ((par = (RooAbsArg*)parItr->Next())) {
    nuiItr->Reset();
    while ((nui = (RooAbsArg*)nuiItr->Next())) {
      if (par == nui) decNuis.add(*nui);
    }
  }

  RooArgSet decGlobs;
  TIterator* globItr = mc_globs->createIterator();
  parItr->Reset();
  RooAbsArg* glob;
  while ((par = (RooAbsArg*)parItr->Next())) {
    globItr->Reset();
    while ((glob = (RooAbsArg*)globItr->Next())) {
      if (par == glob) decGlobs.add(*glob);
    }
  }

  // antiNuis.Print();

  // nuis.Print();
  // globs.Print();

  // i = 0;
  TList* datalist = data->split(*cat, true);
  TIterator* dataItr = datalist->MakeIterator();
  RooAbsData* ds;
  while ((ds = (RooAbsData*)dataItr->Next())) {
    string typeName(ds->GetName());
    if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) continue;

    cout << "Adding dataset to map: " << ds->GetName() << endl;
    data_map[string(ds->GetName())] = (RooDataSet*)ds;

    cout << ds->GetName() << endl;
  }

  RooSimultaneous* decPdf = new RooSimultaneous("decPdf","decPdf",pdf_map,*decCat); 
  RooArgSet decObs = *decPdf->getObservables(data);
  // decObs.add(*(RooAbsArg*)weightVar);
  decObs.add(*(RooAbsArg*)decCat);
  decObs.Print();

  nuis.remove(antiNuis);
  globs.remove(antiGlobs);
  // nuis.Print("v");

  RooDataSet* decData = new RooDataSet("obsData","obsData",RooArgSet(decObs,*(RooAbsArg*)weightVar),Index(*decCat),Import(data_map),WeightVar(*weightVar));

  decData->Print();

  RooArgSet poi(*(RooAbsArg*)mu);
  RooWorkspace decWS("combined");
  ModelConfig decMC("ModelConfig",&decWS);
  decMC.SetPdf(*decPdf);
  decMC.SetObservables(decObs);
  decMC.SetNuisanceParameters(decNuis);
  decMC.SetGlobalObservables(decGlobs);
  decMC.SetParametersOfInterest(poi);

  decMC.Print();
  decWS.import(*decPdf);
  decWS.import(decMC);
  decWS.import(*decData);
  // decWS.Print();

  ModelConfig* mcInWs = (ModelConfig*)decWS.obj("ModelConfig");
  decPdf = (RooSimultaneous*)mcInWs->GetPdf();

  // setup(mcInWs);
  // return;

  mcInWs->GetNuisanceParameters()->Print("v");
  mcInWs->GetGlobalObservables()->Print("v");
  // decData->tree()->Scan("*");

  // Make asimov data
  RooArgSet funcs = decWS.allFunctions();
  TIterator* it = funcs.createIterator();
  TObject* tempObj = 0;
  while((tempObj=it->Next()))
  {
    FlexibleInterpVar* flex = dynamic_cast<FlexibleInterpVar*>(tempObj);
    if(flex) {
      flex->setAllInterpCodes(flatInterpCode);
    }
    PiecewiseInterpolation* piece = dynamic_cast<PiecewiseInterpolation*>(tempObj);
    if(piece) {
      piece->setAllInterpCodes(shapeInterpCode);
    }
  }

  RooDataSet* dataInWs = (RooDataSet*)decWS.data("obsData");
  makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 0);
  makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 1);
  makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 2);

  system(("mkdir -vp workspaces/"+inFolderName+"_"+channel).c_str());
  stringstream outFileName;
  outFileName << "workspaces/" << inFolderName << "_" << channel << "/" << mass << ".root";
  cout << "Exporting" << endl;

  decWS.writeToFile(outFileName.str().c_str());

  cout << "\nIncluded the following channels: " << endl;
  for (int i=0;i<(int)foundChannels.size();i++) {
    cout << "-> " << foundChannels[i] << endl;
  }

  cout << "\nSkipping the following channels: " << endl;
  
  for (int i=0;i<(int)skippedChannels.size();i++) {
    cout << "-> " << skippedChannels[i] << endl;
  }

  cout << "Done" << endl;

  // decPdf->fitTo(*decData, Hesse(0), Minos(0), PrintLevel(0));
}
コード例 #3
0
ファイル: runQ.C プロジェクト: lspiller/limitcode
void runQ(const char* inFileName,
	    const char* wsName = "combined",
	    const char* modelConfigName = "ModelConfig",
	    const char* dataName = "obsData",
	    const char* asimov0DataName = "asimovData_0",
	    const char* conditional0Snapshot = "conditionalGlobs_0",
	    const char* asimov1DataName = "asimovData_1",
	    const char* conditional1Snapshot = "conditionalGlobs_1",
	    const char* nominalSnapshot = "nominalGlobs",
	    string smass = "130",
	    string folder = "test")
{
  double mass;
  stringstream massStr;
  massStr << smass;
  massStr >> mass;

  bool errFast = 0;
  bool goFast = 1;
  bool remakeData = 1;
  bool doRightSided = 1;
  bool doInj = 0;
  bool doObs = 1;
  bool doMedian = 1;

  TStopwatch timer;
  timer.Start();

  TFile f(inFileName);
  RooWorkspace* ws = (RooWorkspace*)f.Get(wsName);
  if (!ws)
  {
    cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl;
    return;
  }
  ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
  if (!mc)
  {
    cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
    return;
  }
  RooDataSet* data = (RooDataSet*)ws->data(dataName);
  if (!data)
  {
    cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
    return;
  }





  mc->GetNuisanceParameters()->Print("v");

  RooNLLVar::SetIgnoreZeroEntries(1);
  ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
  ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
  ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1);
  cout << "Setting max function calls" << endl;
  //ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000);
  RooMinimizer::SetMaxFunctionCalls(10000);

  ws->loadSnapshot("conditionalNuis_0");
  RooArgSet nuis(*mc->GetNuisanceParameters());

  RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();



  if (string(mc->GetPdf()->ClassName()) == "RooSimultaneous" && remakeData)
  {
    RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf();
    double min_mu;
    data = makeData(data, simPdf, mc->GetObservables(), mu, mass, min_mu);
  }







  RooDataSet* asimovData0 = (RooDataSet*)ws->data(asimov0DataName);
  if (!asimovData0)
  {
    cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
    makeAsimovData(mc, true, ws, mc->GetPdf(), data, 1);
    ws->Print();
    asimovData0 = (RooDataSet*)ws->data("asimovData_0");
  }

  RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
  if (!asimovData1)
  {
    cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
    makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0);
    ws->Print();
    asimovData1 = (RooDataSet*)ws->data("asimovData_1");
  }
  
  if (!doRightSided) mu->setRange(0, 40);
  else mu->setRange(-40, 40);






  bool old = false;
  if (old)
  {

    mu->setVal(0);
    RooArgSet poi(*mu);
    ProfileLikelihoodTestStat_modified asimov_testStat_sig(*mc->GetPdf());
    asimov_testStat_sig.SetRightSided(doRightSided);
    asimov_testStat_sig.SetNuis(&nuis);
    if (!doInj) asimov_testStat_sig.SetDoAsimov(true, 1);
    asimov_testStat_sig.SetWorkspace(ws);

    ProfileLikelihoodTestStat_modified testStat(*mc->GetPdf());
    testStat.SetRightSided(doRightSided);
    testStat.SetNuis(&nuis);
    testStat.SetWorkspace(ws);





    //RooMinimizerFcn::SetOverrideEverything(true);
    double med_sig = 0;
    double med_testStat_val = 0;

    //gRandom->SetSeed(1);
    //RooRandom::randomGenerator()->SetSeed(1);


    RooNLLVar::SetIgnoreZeroEntries(1);
    if (asimov1DataName != "" && doMedian)
    {
      mu->setVal(0);
      if (!doInj) mu->setRange(0, 2);
      ws->loadSnapshot("conditionalNuis_0");
      asimov_testStat_sig.SetLoadUncondSnapshot("conditionalNuis_1");
      if (string(conditional1Snapshot) != "") ws->loadSnapshot(conditional1Snapshot);
      med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi);
      if (med_testStat_val < 0 && !doInj) 
      {
	mu->setVal(0);
	med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); // just try again
      }
      int sign = med_testStat_val != 0 ? med_testStat_val/fabs(med_testStat_val) : 0;
      med_sig = sign*sqrt(fabs(med_testStat_val));
      if (string(nominalSnapshot) != "") ws->loadSnapshot(nominalSnapshot);

      if (!doRightSided) mu->setRange(0, 40);
      else mu->setRange(-40, 40);
    }
    RooNLLVar::SetIgnoreZeroEntries(0);


    //gRandom->SetSeed(1);
    //RooRandom::randomGenerator()->SetSeed(1);

    //RooMinimizerFcn::SetOverrideEverything(false);

    cout << "med test stat: " << med_testStat_val << endl;
    ws->loadSnapshot("nominalGlobs");

    ws->loadSnapshot("conditionalNuis_0");
    mu->setVal(0);


    testStat.SetWorkspace(ws);
    testStat.SetLoadUncondSnapshot("ucmles");
    double obsTestStat_val = doObs ? 2*testStat.Evaluate(*data, poi) : 0;
    cout << "obs test stat: " << obsTestStat_val << endl;
//   obsTestStat_val = 2*testStat.Evaluate(*data, poi);
//   cout << "obs test stat: " << obsTestStat_val << endl;
//   obsTestStat_val = 2*testStat.Evaluate(*data, poi);
//   cout << "obs test stat: " << obsTestStat_val << endl;

    double obs_sig;
    int sign = obsTestStat_val == 0 ? 0 : obsTestStat_val / fabs(obsTestStat_val);
    if (!doRightSided && (obsTestStat_val < 0 && obsTestStat_val > -0.1 || mu->getVal() < 0.001)) obs_sig = 0; 
    else obs_sig = sign*sqrt(fabs(obsTestStat_val));
    if (obs_sig != obs_sig) //nan, do by hand
    {
      cout << "Obs test stat gave nan: try by hand" << endl;

      mu->setVal(0);
      mu->setConstant(1);
      mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters()));
      mu->setConstant(0);

      double L_0 = mc->GetPdf()->getVal();

      //mu->setVal(0);
      //mu->setConstant(1);
      mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters()));
      //mu->setConstant(0);
      double L_muhat = mc->GetPdf()->getVal();

      cout << "L_0: " << L_0 << ", L_muhat: " << L_muhat << endl;
      obs_sig = sqrt(-2*TMath::Log(L_0/L_muhat));

//still nan
      if (obs_sig != obs_sig && fabs(L_0 - L_muhat) < 0.000001) obs_sig = 0;
    }
    cout << "obs: " << obs_sig << endl;

    cout << "Observed significance: " << obs_sig << endl;
    if (med_sig)
    {
      cout << "Median test stat val: " << med_testStat_val << endl;
      cout << "Median significance:   " << med_sig << endl;
    }


    f.Close();

    stringstream fileName;
    fileName << "root_files/" << folder << "/" << mass << ".root";
    system(("mkdir -vp root_files/" + folder).c_str());
    TFile f2(fileName.str().c_str(),"recreate");

//   stringstream fileName;
//   fileName << "results_sig/" << mass << ".root";
//   system("mkdir results_sig");
//   TFile f(fileName.str().c_str(),"recreate");

    TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
    h_hypo->SetBinContent(1, obs_sig);
    h_hypo->SetBinContent(2, med_sig);


    f2.Write();
    f2.Close();
    //mc->GetPdf()->fitTo(*data, PrintLevel(0));

    timer.Stop();
    timer.Print();
  }
  else
  {

    RooAbsPdf* pdf = mc->GetPdf();



    RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
    RooNLLVar* asimov_nll0 = (RooNLLVar*)pdf->createNLL(*asimovData0, Constrain(nuis_tmp1));

    RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
    RooNLLVar* asimov_nll1 = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp2));

    RooArgSet nuis_tmp3 = *mc->GetNuisanceParameters();
    RooNLLVar* obs_nll = (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp3));

    
//do asimov

    int status;




//get sigma_b

    ws->loadSnapshot(conditional0Snapshot);
    status = ws->loadSnapshot("conditionalNuis_0");
    if (status != 0 && goFast) errFast = 1;

    mu->setVal(0);
    mu->setConstant(1);
    status = goFast ? 0 : minimize(asimov_nll0, ws);
    if (status < 0) 
    {
      cout << "Retrying" << endl;
      //ws->loadSnapshot("conditionalNuis_0");
      status = minimize(asimov_nll0, ws);
      if (status >= 0) cout << "Success!" << endl;
    }
    double asimov0_nll0 = asimov_nll0->getVal();

    mu->setVal(1);
    ws->loadSnapshot("conditionalNuis_1");
    status = minimize(asimov_nll0, ws);
    if (status < 0) 
    {
      cout << "Retrying" << endl;
      //ws->loadSnapshot("conditionalNuis_0");
      status = minimize(asimov_nll0, ws);
      if (status >= 0) cout << "Success!" << endl;
    }

    double asimov0_nll1 = asimov_nll0->getVal();
    double asimov0_q = 2*(asimov0_nll1 - asimov0_nll0);
    double sigma_b = sqrt(1./asimov0_q);

    ws->loadSnapshot(nominalSnapshot);





//get sigma_sb

    ws->loadSnapshot(conditional1Snapshot);
    ws->loadSnapshot("conditionalNuis_0");

    mu->setVal(0);
    mu->setConstant(1);
    status = minimize(asimov_nll1, ws);
    if (status < 0) 
    {
      cout << "Retrying" << endl;
      //ws->loadSnapshot("conditionalNuis_0");
      status = minimize(asimov_nll1, ws);
      if (status >= 0) cout << "Success!" << endl;
    }
    double asimov1_nll0 = asimov_nll1->getVal();

    mu->setVal(1);
    status = ws->loadSnapshot("conditionalNuis_1");
    if (status != 0 && goFast) errFast = 1;
    status = goFast ? 0 : minimize(asimov_nll1, ws);
    if (status < 0) 
    {
      cout << "Retrying" << endl;
      //ws->loadSnapshot("conditionalNuis_0");
      status = minimize(asimov_nll1, ws);
      if (status >= 0) cout << "Success!" << endl;
    }

    double asimov1_nll1 = asimov_nll1->getVal();
    double asimov1_q = 2*(asimov1_nll1 - asimov1_nll0);
    double sigma_sb = sqrt(-1./asimov1_q);

    ws->loadSnapshot(nominalSnapshot);



//do obs

    mu->setVal(0);
    status = ws->loadSnapshot("conditionalNuis_0");
    if (status != 0 && goFast) errFast = 1;
    mu->setConstant(1);
    status = goFast ? 0 : minimize(obs_nll, ws);
    if (status < 0) 
    {
      cout << "Retrying with conditional snapshot at mu=1" << endl;
      ws->loadSnapshot("conditionalNuis_0");
      status = minimize(obs_nll, ws);
      if (status >= 0) cout << "Success!" << endl;
    }
    double obs_nll0 = obs_nll->getVal();



    status = ws->loadSnapshot("conditionalNuis_1");
    if (status != 0 && goFast) errFast = 1;
    mu->setVal(1);
    status = goFast ? 0 : minimize(obs_nll, ws);
    if (status < 0) 
    {
      cout << "Retrying with conditional snapshot at mu=1" << endl;
      ws->loadSnapshot("conditionalNuis_0");
      status = minimize(obs_nll, ws);
      if (status >= 0) cout << "Success!" << endl;
    }

    double obs_nll1 = obs_nll->getVal();
    double obs_q = 2*(obs_nll1 - obs_nll0);



    double Zobs = (1./sigma_b/sigma_b - obs_q) / (2./sigma_b);
    double Zexp = (1./sigma_b/sigma_b - asimov1_q) / (2./sigma_b);

    double pb_obs = 1-ROOT::Math::gaussian_cdf(Zobs);
    double pb_exp = 1-ROOT::Math::gaussian_cdf(Zexp);


    cout << "asimov0_q = " << asimov0_q << endl;
    cout << "asimov1_q = " << asimov1_q << endl;
    cout << "obs_q     = " << obs_q << endl;
    cout << "sigma_b   = " << sigma_b << endl;
    cout << "sigma_sb  = " << sigma_sb << endl;
    cout << "Z obs     = " << Zobs << endl;
    cout << "Z exp     = " << Zexp << endl;



    f.Close();

    stringstream fileName;
    fileName << "root_files/" << folder << "/" << mass << ".root";
    system(("mkdir -vp root_files/" + folder).c_str());
    TFile f2(fileName.str().c_str(),"recreate");

    TH1D* h_hypo = new TH1D("hypo_tev","hypo_tev",2,0,2);
    h_hypo->SetBinContent(1, pb_obs);
    h_hypo->SetBinContent(2, pb_exp);


    f2.Write();
    f2.Close();

    stringstream fileName3;
    fileName3 << "root_files/" << folder << "_llr/" << mass << ".root";
    system(("mkdir -vp root_files/" + folder + "_llr").c_str());
    TFile f3(fileName3.str().c_str(),"recreate");

    TH1D* h_hypo3 = new TH1D("hypo_llr","hypo_llr",7,0,7);
    h_hypo3->SetBinContent(1, -obs_q);
    h_hypo3->SetBinContent(2, -asimov1_q);
    h_hypo3->SetBinContent(3, -asimov0_q);
    h_hypo3->SetBinContent(4, -asimov0_q-2*2/sigma_b);
    h_hypo3->SetBinContent(5, -asimov0_q-1*2/sigma_b);
    h_hypo3->SetBinContent(6, -asimov0_q+1*2/sigma_b);
    h_hypo3->SetBinContent(7, -asimov0_q+2*2/sigma_b);


    f3.Write();
    f3.Close();

    timer.Stop();
    timer.Print();




  }
}
コード例 #4
0
void OneSidedFrequentistUpperLimitWithBands(const char* infile = "",
                                            const char* workspaceName = "combined",
                                            const char* modelConfigName = "ModelConfig",
                                            const char* dataName = "obsData") {



   double confidenceLevel=0.95;
   int nPointsToScan = 20;
   int nToyMC = 200;

   // -------------------------------------------------------
   // First part is just to access a user-defined file
   // or create the standard example file if it doesn't exist
   const char* filename = "";
   if (!strcmp(infile,"")) {
      filename = "results/example_combined_GaussExample_model.root";
      bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
      // if file does not exists generate with histfactory
      if (!fileExist) {
#ifdef _WIN32
         cout << "HistFactory file cannot be generated on Windows - exit" << endl;
         return;
#endif
         // Normally this would be run on the command line
         cout <<"will run standard hist2workspace example"<<endl;
         gROOT->ProcessLine(".! prepareHistFactory .");
         gROOT->ProcessLine(".! hist2workspace config/example.xml");
         cout <<"\n\n---------------------"<<endl;
         cout <<"Done creating example input"<<endl;
         cout <<"---------------------\n\n"<<endl;
      }

   }
   else
      filename = infile;

   // Try to open the file
   TFile *file = TFile::Open(filename);

   // if input file was specified byt not found, quit
   if(!file ){
      cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
      return;
   }


   // -------------------------------------------------------
   // Now get the data and workspace

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

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

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

   // make sure ingredients are found
   if(!data || !mc){
      w->Print();
      cout << "data or ModelConfig was not found" <<endl;
      return;
   }

   // -------------------------------------------------------
   // Now get the POI for convenience
   // you may want to adjust the range of your POI

   RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
   /*  firstPOI->setMin(0);*/
   /*  firstPOI->setMax(10);*/

   // --------------------------------------------
   // Create and use the FeldmanCousins tool
   // to find and plot the 95% confidence interval
   // on the parameter of interest as specified
   // in the model config
   // REMEMBER, we will change the test statistic
   // so this is NOT a Feldman-Cousins interval
   FeldmanCousins fc(*data,*mc);
   fc.SetConfidenceLevel(confidenceLevel);
   /*  fc.AdditionalNToysFactor(0.25); // degrade/improve sampling that defines confidence belt*/
   /*  fc.UseAdaptiveSampling(true); // speed it up a bit, don't use for expected limits*/
   fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan
   fc.CreateConfBelt(true); // save the information in the belt for plotting

   // -------------------------------------------------------
   // Feldman-Cousins is a unified limit by definition
   // but the tool takes care of a few things for us like which values
   // of the nuisance parameters should be used to generate toys.
   // so let's just change the test statistic and realize this is
   // no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction.
   /*  ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());*/
   /*  fc.GetTestStatSampler()->SetTestStatistic(&onesided);*/
   /* ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true); */
   ToyMCSampler*  toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
   ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic());
   testStat->SetOneSided(true);

   // Since this tool needs to throw toy MC the PDF needs to be
   // extended or the tool needs to know how many entries in a dataset
   // per pseudo experiment.
   // In the 'number counting form' where the entries in the dataset
   // are counts, and not values of discriminating variables, the
   // datasets typically only have one entry and the PDF is not
   // extended.
   if(!mc->GetPdf()->canBeExtended()){
      if(data->numEntries()==1)
         fc.FluctuateNumDataEntries(false);
      else
         cout <<"Not sure what to do about this model" <<endl;
   }

   // We can use PROOF to speed things along in parallel
   // However, the test statistic has to be installed on the workers
   // so either turn off PROOF or include the modified test statistic
   // in your `$ROOTSYS/roofit/roostats/inc` directory,
   // add the additional line to the LinkDef.h file,
   // and recompile root.
   if (useProof) {
      ProofConfig pc(*w, nworkers, "", false);
      toymcsampler->SetProofConfig(&pc); // enable proof
   }

   if(mc->GetGlobalObservables()){
      cout << "will use global observables for unconditional ensemble"<<endl;
      mc->GetGlobalObservables()->Print();
      toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
   }


   // Now get the interval
   PointSetInterval* interval = fc.GetInterval();
   ConfidenceBelt* belt = fc.GetConfidenceBelt();

   // print out the interval on the first Parameter of Interest
   cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
      interval->LowerLimit(*firstPOI) << ", "<<
      interval->UpperLimit(*firstPOI) <<"] "<<endl;

   // get observed UL and value of test statistic evaluated there
   RooArgSet tmpPOI(*firstPOI);
   double observedUL = interval->UpperLimit(*firstPOI);
   firstPOI->setVal(observedUL);
   double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI);


   // Ask the calculator which points were scanned
   RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan();
   RooArgSet* tmpPoint;

   // make a histogram of parameter vs. threshold
   TH1F* histOfThresholds = new TH1F("histOfThresholds","",
                                       parameterScan->numEntries(),
                                       firstPOI->getMin(),
                                       firstPOI->getMax());
   histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
   histOfThresholds->GetYaxis()->SetTitle("Threshold");

   // loop through the points that were tested and ask confidence belt
   // what the upper/lower thresholds were.
   // For FeldmanCousins, the lower cut off is always 0
   for(Int_t i=0; i<parameterScan->numEntries(); ++i){
      tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
      //cout <<"get threshold"<<endl;
      double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
      double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ;
      histOfThresholds->Fill(poiVal,arMax);
   }
   TCanvas* c1 = new TCanvas();
   c1->Divide(2);
   c1->cd(1);
   histOfThresholds->SetMinimum(0);
   histOfThresholds->Draw();
   c1->cd(2);

   // -------------------------------------------------------
   // Now we generate the expected bands and power-constraint

   // First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
   RooAbsReal* nll = mc->GetPdf()->createNLL(*data);
   RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest());
   firstPOI->setVal(0.);
   profile->getVal(); // this will do fit and set nuisance parameters to profiled values
   RooArgSet* poiAndNuisance = new RooArgSet();
   if(mc->GetNuisanceParameters())
      poiAndNuisance->add(*mc->GetNuisanceParameters());
   poiAndNuisance->add(*mc->GetParametersOfInterest());
   w->saveSnapshot("paramsToGenerateData",*poiAndNuisance);
   RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot();
   cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
   paramsToGenerateData->Print("v");


   RooArgSet unconditionalObs;
   unconditionalObs.add(*mc->GetObservables());
   unconditionalObs.add(*mc->GetGlobalObservables()); // comment this out for the original conditional ensemble

   double CLb=0;
   double CLbinclusive=0;

   // Now we generate background only and find distribution of upper limits
   TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax());
   histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
   histOfUL->GetYaxis()->SetTitle("Entries");
   for(int imc=0; imc<nToyMC; ++imc){

      // set parameters back to values for generating pseudo data
      //    cout << "\n get current nuis, set vals, print again" << endl;
      w->loadSnapshot("paramsToGenerateData");
      //    poiAndNuisance->Print("v");

      RooDataSet* toyData = 0;
      // now generate a toy dataset
      if(!mc->GetPdf()->canBeExtended()){
         if(data->numEntries()==1)
            toyData = mc->GetPdf()->generate(*mc->GetObservables(),1);
         else
            cout <<"Not sure what to do about this model" <<endl;
      } else{
         //      cout << "generating extended dataset"<<endl;
         toyData = mc->GetPdf()->generate(*mc->GetObservables(),Extended());
      }

      // generate global observables
      // need to be careful for simpdf
      //    RooDataSet* globalData = mc->GetPdf()->generate(*mc->GetGlobalObservables(),1);

      RooSimultaneous* simPdf = dynamic_cast<RooSimultaneous*>(mc->GetPdf());
      if(!simPdf){
         RooDataSet *one = mc->GetPdf()->generate(*mc->GetGlobalObservables(), 1);
         const RooArgSet *values = one->get();
         RooArgSet *allVars = mc->GetPdf()->getVariables();
         *allVars = *values;
         delete allVars;
         delete values;
         delete one;
      } else {

         //try fix for sim pdf
         TIterator* iter = simPdf->indexCat().typeIterator() ;
         RooCatType* tt = NULL;
         while((tt=(RooCatType*) iter->Next())) {

            // Get pdf associated with state from simpdf
            RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;

            // Generate only global variables defined by the pdf associated with this state
            RooArgSet* globtmp = pdftmp->getObservables(*mc->GetGlobalObservables()) ;
            RooDataSet* tmp = pdftmp->generate(*globtmp,1) ;

            // Transfer values to output placeholder
            *globtmp = *tmp->get(0) ;

            // Cleanup
            delete globtmp ;
            delete tmp ;
         }
      }

      //    globalData->Print("v");
      //    unconditionalObs = *globalData->get();
      //    mc->GetGlobalObservables()->Print("v");
      //    delete globalData;
      //    cout << "toy data = " << endl;
      //    toyData->get()->Print("v");

      // get test stat at observed UL in observed data
      firstPOI->setVal(observedUL);
      double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
      //    toyData->get()->Print("v");
      //    cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
      if(obsTSatObsUL < toyTSatObsUL) // not sure about <= part yet
         CLb+= (1.)/nToyMC;
      if(obsTSatObsUL <= toyTSatObsUL) // not sure about <= part yet
         CLbinclusive+= (1.)/nToyMC;


      // loop over points in belt to find upper limit for this toy data
      double thisUL = 0;
      for(Int_t i=0; i<parameterScan->numEntries(); ++i){
         tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
         double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
         firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
         //   double thisTS = profile->getVal();
         double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);

         //   cout << "poi = " << firstPOI->getVal()
         // << " max is " << arMax << " this profile = " << thisTS << endl;
         //      cout << "thisTS = " << thisTS<<endl;
         if(thisTS<=arMax){
            thisUL = firstPOI->getVal();
         } else{
            break;
         }
      }



      /*
      // loop over points in belt to find upper limit for this toy data
      double thisUL = 0;
      for(Int_t i=0; i<histOfThresholds->GetNbinsX(); ++i){
         tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
         cout <<"----------------  "<<i<<endl;
         tmpPoint->Print("v");
         cout << "from hist " << histOfThresholds->GetBinCenter(i+1) <<endl;
         double arMax = histOfThresholds->GetBinContent(i+1);
         // cout << " threhold from Hist = aMax " << arMax<<endl;
         // double arMax2 = belt->GetAcceptanceRegionMax(*tmpPoint);
         // cout << "from scan arMax2 = "<< arMax2 << endl; // not the same due to TH1F not TH1D
         // cout << "scan - hist" << arMax2-arMax << endl;
         firstPOI->setVal( histOfThresholds->GetBinCenter(i+1));
         //   double thisTS = profile->getVal();
         double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);

         //   cout << "poi = " << firstPOI->getVal()
         // << " max is " << arMax << " this profile = " << thisTS << endl;
         //      cout << "thisTS = " << thisTS<<endl;

         // NOTE: need to add a small epsilon term for single precision vs. double precision
         if(thisTS<=arMax + 1e-7){
            thisUL = firstPOI->getVal();
         } else{
            break;
         }
      }
      */

      histOfUL->Fill(thisUL);

      // for few events, data is often the same, and UL is often the same
      //    cout << "thisUL = " << thisUL<<endl;

      delete toyData;
   }
   histOfUL->Draw();
   c1->SaveAs("one-sided_upper_limit_output.pdf");

   // if you want to see a plot of the sampling distribution for a particular scan point:
   /*
   SamplingDistPlot sampPlot;
   int indexInScan = 0;
   tmpPoint = (RooArgSet*) parameterScan->get(indexInScan)->clone("temp");
   firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
   toymcsampler->SetParametersForTestStat(tmpPOI);
   SamplingDistribution* samp = toymcsampler->GetSamplingDistribution(*tmpPoint);
   sampPlot.AddSamplingDistribution(samp);
   sampPlot.Draw();
      */

   // Now find bands and power constraint
   Double_t* bins = histOfUL->GetIntegral();
   TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
   cumulative->SetContent(bins);
   double band2sigDown, band1sigDown, bandMedian, band1sigUp,band2sigUp;
   for(int i=1; i<=cumulative->GetNbinsX(); ++i){
      if(bins[i]<RooStats::SignificanceToPValue(2))
         band2sigDown=cumulative->GetBinCenter(i);
      if(bins[i]<RooStats::SignificanceToPValue(1))
         band1sigDown=cumulative->GetBinCenter(i);
      if(bins[i]<0.5)
         bandMedian=cumulative->GetBinCenter(i);
      if(bins[i]<RooStats::SignificanceToPValue(-1))
         band1sigUp=cumulative->GetBinCenter(i);
      if(bins[i]<RooStats::SignificanceToPValue(-2))
         band2sigUp=cumulative->GetBinCenter(i);
   }
   cout << "-2 sigma  band " << band2sigDown << endl;
   cout << "-1 sigma  band " << band1sigDown << " [Power Constraint)]" << endl;
   cout << "median of band " << bandMedian << endl;
   cout << "+1 sigma  band " << band1sigUp << endl;
   cout << "+2 sigma  band " << band2sigUp << endl;

   // print out the interval on the first Parameter of Interest
   cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl;
   cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl;
   cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl;

   delete profile;
   delete nll;

}
コード例 #5
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; 
         
      }
   }

}
コード例 #6
0
ファイル: IntegralError.C プロジェクト: neggert/MCTSusy
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;
}
コード例 #7
0
ファイル: PlotAll.C プロジェクト: gerbaudo/hlfv-fitmodel
void PlotAll(TString wsname)
{
	char* binLabels[19] = {"60","70","80","90","100","110","120","130","140","150","160","170","180","190","200","250","300","400","1000"};	

	//get the stuff from the workspace:
	
	TFile* file=TFile::Open(wsname);
	RooWorkspace* ws = (RooWorkspace*)file->Get("combined");
	ModelConfig  *mc = (ModelConfig*)ws->obj("ModelConfig");
	RooAbsData   *data = ws->data("obsData");
	RooSimultaneous* simPdf=(RooSimultaneous*)(mc->GetPdf());
	RooAbsReal* nll=simPdf->createNLL(*data);

	// FPT 0 **************************************	
	// EM channel
	
	RooCategory* chanCat = (RooCategory*) (&simPdf->indexCat());
        TIterator* iterat = chanCat->typeIterator() ;
        RooCatType* ttype = (RooCatType*)iterat->Next();

	RooAbsPdf  *pdf_stateEM  = simPdf->getPdf(ttype->GetName()) ;
	RooArgSet  *obstmpEM  = pdf_stateEM->getObservables( *mc->GetObservables() ) ;
	
	// get EM data
       	RooAbsData *dataEM = data->reduce(Form("%s==%s::%s",chanCat->GetName(),chanCat->GetName(),ttype->GetName()));
		
	RooRealVar *obsEM     = ((RooRealVar*) obstmpEM->first());
	TString chanName1(ttype->GetName());

	// create data histogram
	TH1* hdataEM = dataEM->createHistogram("Data "+chanName1,*obsEM);
	// set errors to gaussian
        for (int ib=0 ; ib<hdataEM->GetNbinsX()+1 ; ib++) hdataEM->SetBinError(ib, sqrt(hdataEM->GetBinContent(ib)));

	double EMnorm = pdf_stateEM->expectedEvents(*obsEM);
	
	//****************************
	// ME channel
	ttype = (RooCatType*)iterat->Next();
	RooAbsPdf* pdf_stateME  = simPdf->getPdf(ttype->GetName()) ;
        RooArgSet* obstmpME  = pdf_stateME->getObservables( *mc->GetObservables() ) ;

	// get ME data
	RooAbsData *dataME = data->reduce(Form("%s==%s::%s",chanCat->GetName(),chanCat->GetName(),ttype->GetName()));	
	RooRealVar* obsME = ((RooRealVar*) obstmpME->first());
	TString chanName2(ttype->GetName());

        // create data histogram
        TH1* hdataME = dataME->createHistogram("Data "+chanName2,*obsME);
        // set errors to gaussian
        for (int ib=0 ; ib<hdataME->GetNbinsX()+1 ; ib++) hdataME->SetBinError(ib, sqrt(hdataME->GetBinContent(ib)));
        
	
	// get initial BG histogram
	//TH1* h_initial_BG_EM = pdf_stateEM->createHistogram("initial_BG_EM",*obsEM);
	//TH1* h_initial_BG_ME = pdf_stateME->createHistogram("initial_BG_ME",*obsME);
	
	double MEnorm = pdf_stateME->expectedEvents(*obsME);
	cout << "EM expected events = " << EMnorm << ", ME expected events = " << MEnorm << "." << endl;
	//h_initial_BG_EM->Scale(EMnorm);
	//h_initial_BG_ME->Scale(MEnorm);	

	// get initial gammas
	int nbins = hdataEM->GetNbinsX();
        double InitGamma[nbins];
        for (int i=0; i<nbins; i++)
        {
               	TString varname = "gamma_B0_l1pt0_bin_"+NumberToString(i);
               	InitGamma[i] = ws->var(varname)->getVal();
               	cout << "initial gamma"+NumberToString(i)+" = " << InitGamma[i] << endl;
        }
        double InitFpt = ws->var("fl1pt_l1pt0")->getVal();
        cout << "initial fpt_l1pt0 = " << InitFpt <<  endl;


	// DO THE GLOBAL FIT
	
	minimize(nll);	
       
	// get final BG histograms
	TH1* h_final_BG_EM = pdf_stateEM->createHistogram("final_BG_EM",*obsEM);
        TH1* h_final_BG_ME = pdf_stateME->createHistogram("final_BG_ME",*obsME); 
	h_final_BG_EM->Scale(EMnorm);
	h_final_BG_ME->Scale(MEnorm);
	
	// uncertainty bands
	TH1D* BuncertaintyEM = new TH1D("BuncertaintyEM","BuncertaintyEM",nbins,0,nbins);
	TH1D* BuncertaintyME = new TH1D("BuncertaintyME","BuncertaintyME",nbins,0,nbins);
	for (int i=1; i<=nbins; i++){
		double sigbEM = h_final_BG_EM->GetBinError(i);
		double bEM = h_final_BG_EM->GetBinContent(i);
		BuncertaintyEM->SetBinError(i,sigbEM); BuncertaintyEM->SetBinContent(i,bEM);
		double sigbME = h_final_BG_ME->GetBinError(i);
                double bME = h_final_BG_ME->GetBinContent(i);
                BuncertaintyME->SetBinError(i,sigbME); BuncertaintyME->SetBinContent(i,bME);
	}
	//BuncertaintyEM->SetFillStyle(3004); 
	BuncertaintyEM->SetFillColor(kGreen-9);
	BuncertaintyEM->SetLineColor(kBlack); BuncertaintyEM->SetLineStyle(2);
	//BuncertaintyME->SetFillStyle(3004); 
	BuncertaintyME->SetFillColor(kBlue-9);
        BuncertaintyME->SetLineColor(kBlack); BuncertaintyME->SetLineStyle(2);

	// get gammas after fit
	double FinalGamma[nbins];
	//TH1* h_initBG_times_gamma = (TH1*)h_initial_BG_EM->Clone("initBGEM_times_gamma");
	for (int i=0; i<nbins; i++)
       	{
               	TString varname = "gamma_B0_l1pt0_bin_"+NumberToString(i);
               	FinalGamma[i] = ws->var(varname)->getVal();
               	cout << "Final gamma in bin "+NumberToString(i)+" = " << FinalGamma[i] << endl;
       	//	h_initBG_times_gamma->SetBinContent(i+1,h_initial_BG_EM->GetBinContent(i+1)*FinalGamma[i]);
	}
	//double FinalFpt = ws->var("fl1pt_l1pt0")->getVal();
	
	// get final alpha (pull)
	RooRealVar* alphaVar = ws->var("alpha_l1ptsys_l1pt0");
	double alpha, alphaErr;
	if (alphaVar != NULL) {
		alpha = ws->var("alpha_l1ptsys_l1pt0")->getVal();
		alphaErr = ws->var("alpha_l1ptsys_l1pt0")->getError();
	}

	//FOR UNCONSTRAINED FPT - get final fpts
	double FinalFpt[5];
	double FinalFptErr[5];
	for (int k=0; k<5; k++){
		TString varname = "fl1pt_l1pt"+NumberToString(k);
		FinalFpt[k] = ws->var(varname)->getVal();
		FinalFptErr[k] =  ws->var(varname)->getError();
		cout << varname << " = "  << FinalFpt[k] << " +- " << FinalFptErr[k] << endl;
	}
	
	// get POI value
	double mu = ws->var("mu_BR_htm")->getVal();
	double muErr = ws->var("mu_BR_htm")->getError();
	
	// Draw
	TCanvas* c1 = new TCanvas("BG and Data "+chanName1+" "+chanName2,"BG and Data "+chanName1+" "+chanName2,600,600);
	BuncertaintyEM->Draw("E3 sames"); BuncertaintyME->Draw("E3 sames");
	//h_initial_BG_EM->SetLineColor(kGreen+2); h_initial_BG_EM->SetLineStyle(2); h_initial_BG_EM->Draw("sames");
	hdataEM->SetLineColor(kGreen+2); hdataEM->SetMarkerStyle(20); hdataEM->SetMarkerColor(kGreen+2);
	hdataEM->Draw("e1 sames");
	//h_initial_BG_ME->SetLineColor(kBlue); h_initial_BG_ME->SetLineStyle(2); h_initial_BG_ME->Draw("sames");
        hdataME->SetLineColor(kBlue); hdataME->SetMarkerStyle(20);  hdataME->SetMarkerColor(kBlue);
	hdataME->Draw("e1 sames");

	h_final_BG_EM->SetLineColor(kGreen+2); h_final_BG_EM->SetLineWidth(2); h_final_BG_EM->Draw("sames");
	h_final_BG_ME->SetLineColor(kBlue); h_final_BG_ME->SetLineWidth(2); h_final_BG_ME->Draw("sames");

	TLegend* leg = new TLegend(0.5,0.45,0.85,0.65);
        leg->SetFillColor(kWhite); leg->SetBorderSize(1); leg->SetLineColor(0); //leg->SetTextFont(14);
        leg->SetTextSize(.03);

	leg->AddEntry(hdataME,"DATA #mue","lep");
	leg->AddEntry(hdataEM,"DATA e#mu","lep");
	//leg->AddEntry(h_initial_BG_ME,"Initial #mue PDF","l");
	//leg->AddEntry(h_initial_BG_EM,"Initial e#mu PDF","l");
	leg->AddEntry(h_final_BG_ME,"#mue PDF = #gamma_{i}B_{i} + #muS_{i}","l");
	leg->AddEntry(h_final_BG_EM,"e#mu PDF = f(1+#alpha#sigma)(#gamma_{i}B_{i}+#muW_{i})","l");
	leg->Draw();

	cout << " ********************* Fit Values **************************** " <<  endl;
	if (alphaVar != NULL){cout << "alpha = " << alpha << " +- " << alphaErr << endl;}
	cout << "mu    = " << mu << " +- " << muErr << endl;

	TString WriteDownAlphaValue;
	TString WriteDownMuValue;
	WriteDownAlphaValue = "Fpt0 = ";
	WriteDownMuValue = "#mu = ";
	WriteDownAlphaValue += Form("%4.4f",FinalFpt[0]);
	WriteDownAlphaValue += "#pm";
	WriteDownAlphaValue += Form("%4.4f",FinalFptErr[0]);
	WriteDownMuValue += Form("%4.4f",mu);
        WriteDownMuValue += "#pm";
        WriteDownMuValue += Form("%4.4f",muErr);

	TLatex *texl = new TLatex(12,25,WriteDownAlphaValue);
   	texl->SetTextAlign(22); texl->SetTextSize(0.03); 
   	TLatex *texl2 = new TLatex(12,23,WriteDownMuValue);
        texl2->SetTextAlign(22); texl2->SetTextSize(0.03);
	texl->Draw(); 
	texl2->Draw();



	//FPT 1 ***********************************
	ttype = (RooCatType*)iterat->Next();

        RooAbsPdf  *pdf_stateEM1  = simPdf->getPdf(ttype->GetName()) ;
        RooArgSet  *obstmpEM1  = pdf_stateEM1->getObservables( *mc->GetObservables() ) ;
	RooAbsData *dataEM1 = data->reduce(Form("%s==%s::%s",chanCat->GetName(),chanCat->GetName(),ttype->GetName()));

        RooRealVar *obsEM1     = ((RooRealVar*) obstmpEM1->first());
        TString chanName11(ttype->GetName());	
	TH1* hdataEM1 = dataEM1->createHistogram("Data "+chanName11,*obsEM1);
	for (int ib=0 ; ib<hdataEM1->GetNbinsX()+1 ; ib++) hdataEM1->SetBinError(ib, sqrt(hdataEM1->GetBinContent(ib)));

        double EMnorm1 = pdf_stateEM1->expectedEvents(*obsEM1);
	ttype = (RooCatType*)iterat->Next();
        RooAbsPdf* pdf_stateME1  = simPdf->getPdf(ttype->GetName()) ;
        RooArgSet* obstmpME1  = pdf_stateME1->getObservables( *mc->GetObservables() ) ;
	RooAbsData *dataME1 = data->reduce(Form("%s==%s::%s",chanCat->GetName(),chanCat->GetName(),ttype->GetName()));
        RooRealVar* obsME1 = ((RooRealVar*) obstmpME1->first());
        TString chanName21(ttype->GetName());
	TH1* hdataME1 = dataME1->createHistogram("Data "+chanName21,*obsME1);

	for (int ib=0 ; ib<hdataME1->GetNbinsX()+1 ; ib++) hdataME1->SetBinError(ib, sqrt(hdataME1->GetBinContent(ib)));
	double MEnorm1 = pdf_stateME1->expectedEvents(*obsME1);
	TH1* h_final_BG_EM1 = pdf_stateEM1->createHistogram("final_BG_EM1",*obsEM1);
        TH1* h_final_BG_ME1 = pdf_stateME1->createHistogram("final_BG_ME1",*obsME1);
        h_final_BG_EM1->Scale(EMnorm1);
        h_final_BG_ME1->Scale(MEnorm1);
	TH1D* BuncertaintyEM1 = new TH1D("BuncertaintyEM1","BuncertaintyEM1",nbins,0,nbins);
        TH1D* BuncertaintyME1 = new TH1D("BuncertaintyME1","BuncertaintyME1",nbins,0,nbins);
        for (int i=1; i<=nbins; i++){
                double sigbEM = h_final_BG_EM1->GetBinError(i);
                double bEM = h_final_BG_EM1->GetBinContent(i);
                BuncertaintyEM1->SetBinError(i,sigbEM); BuncertaintyEM1->SetBinContent(i,bEM);
                double sigbME = h_final_BG_ME1->GetBinError(i);
                double bME = h_final_BG_ME1->GetBinContent(i);
                BuncertaintyME1->SetBinError(i,sigbME); BuncertaintyME1->SetBinContent(i,bME);
        }
	BuncertaintyEM1->SetFillColor(kGreen-9);
        BuncertaintyEM1->SetLineColor(kBlack); BuncertaintyEM1->SetLineStyle(2);
	BuncertaintyME1->SetFillColor(kBlue-9);
        BuncertaintyME1->SetLineColor(kBlack); BuncertaintyME1->SetLineStyle(2);
	double FinalGamma1[nbins];
        for (int i=0; i<nbins; i++)
        {
                TString varname = "gamma_B0_l1pt1_bin_"+NumberToString(i);
                FinalGamma1[i] = ws->var(varname)->getVal();
                cout << "Final gamma in bin "+NumberToString(i)+" = " << FinalGamma1[i] << endl;
        }
	TCanvas* c2 = new TCanvas("BG and Data "+chanName11+" "+chanName21,"BG and Data "+chanName11+" "+chanName21,600,600);
        BuncertaintyEM1->Draw("E3 sames"); BuncertaintyME1->Draw("E3 sames");
        hdataEM1->SetLineColor(kGreen+2); hdataEM1->SetMarkerStyle(20); hdataEM1->SetMarkerColor(kGreen+2);
        hdataEM1->Draw("e1 sames");
        hdataME1->SetLineColor(kBlue); hdataME1->SetMarkerStyle(20);  hdataME1->SetMarkerColor(kBlue);
        hdataME1->Draw("e1 sames");

        h_final_BG_EM1->SetLineColor(kGreen+2); h_final_BG_EM1->SetLineWidth(2); h_final_BG_EM1->Draw("sames");
        h_final_BG_ME1->SetLineColor(kBlue); h_final_BG_ME1->SetLineWidth(2); h_final_BG_ME1->Draw("sames");

        leg->Draw();

        cout << " ********************* Fit Values **************************** " <<  endl;
        cout << "mu    = " << mu << " +- " << muErr << endl;
	TString WriteDownAlphaValue1;
        WriteDownAlphaValue1 = "Fpt1 = ";
        WriteDownAlphaValue1 += Form("%4.4f",FinalFpt[1]);
        WriteDownAlphaValue1 += "#pm";
        WriteDownAlphaValue1 += Form("%4.4f",FinalFptErr[1]);

        TLatex *texl11 = new TLatex(12,25,WriteDownAlphaValue1);
        texl11->SetTextAlign(22); texl11->SetTextSize(0.03);
        texl11->Draw(); 
        texl2->Draw();

}
コード例 #8
0
void StandardHistFactoryPlotsWithCategories(const char* infile = "",
                                            const char* workspaceName = "combined",
                                            const char* modelConfigName = "ModelConfig",
                                            const char* dataName = "obsData"){


   double nSigmaToVary=5.;
   double muVal=0;
   bool doFit=false;

   // -------------------------------------------------------
   // First part is just to access a user-defined file
   // or create the standard example file if it doesn't exist
   const char* filename = "";
   if (!strcmp(infile,"")) {
      filename = "results/example_combined_GaussExample_model.root";
      bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
                                                           // if file does not exists generate with histfactory
      if (!fileExist) {
#ifdef _WIN32
         cout << "HistFactory file cannot be generated on Windows - exit" << endl;
         return;
#endif
         // Normally this would be run on the command line
         cout <<"will run standard hist2workspace example"<<endl;
         gROOT->ProcessLine(".! prepareHistFactory .");
         gROOT->ProcessLine(".! hist2workspace config/example.xml");
         cout <<"\n\n---------------------"<<endl;
         cout <<"Done creating example input"<<endl;
         cout <<"---------------------\n\n"<<endl;
      }

   }
   else
      filename = infile;

   // Try to open the file
   TFile *file = TFile::Open(filename);

   // if input file was specified byt not found, quit
   if(!file ){
      cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
      return;
   }

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

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

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

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

   // make sure ingredients are found
   if(!data || !mc){
      w->Print();
      cout << "data or ModelConfig was not found" <<endl;
      return;
   }

   // -------------------------------------------------------
   // now use the profile inspector

   RooRealVar* obs = (RooRealVar*)mc->GetObservables()->first();
   TList* list = new TList();


   RooRealVar * firstPOI = dynamic_cast<RooRealVar*>(mc->GetParametersOfInterest()->first());

   firstPOI->setVal(muVal);
   //  firstPOI->setConstant();
   if(doFit){
      mc->GetPdf()->fitTo(*data);
   }

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


   mc->GetNuisanceParameters()->Print("v");
   int  nPlotsMax = 1000;
   cout <<" check expectedData by category"<<endl;
   RooDataSet* simData=NULL;
   RooSimultaneous* simPdf = NULL;
   if(strcmp(mc->GetPdf()->ClassName(),"RooSimultaneous")==0){
      cout <<"Is a simultaneous PDF"<<endl;
      simPdf = (RooSimultaneous *)(mc->GetPdf());
   } else {
      cout <<"Is not a simultaneous PDF"<<endl;
   }



   if(doFit) {
      RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
      TIterator* iter = channelCat->typeIterator() ;
      RooCatType* tt = NULL;
      tt=(RooCatType*) iter->Next();
      RooAbsPdf* pdftmp = ((RooSimultaneous*)mc->GetPdf())->getPdf(tt->GetName()) ;
      RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
      obs = ((RooRealVar*)obstmp->first());
      RooPlot* frame = obs->frame();
      cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
      cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
      data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));

      Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;

      pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
      frame->Draw();
      cout <<"expected events = " << mc->GetPdf()->expectedEvents(*data->get()) <<endl;
      return;
   }



   int nPlots=0;
   if(!simPdf){

      TIterator* it = mc->GetNuisanceParameters()->createIterator();
      RooRealVar* var = NULL;
      while( (var = (RooRealVar*) it->Next()) != NULL){
         RooPlot* frame = obs->frame();
         frame->SetYTitle(var->GetName());
         data->plotOn(frame,MarkerSize(1));
         var->setVal(0);
         mc->GetPdf()->plotOn(frame,LineWidth(1.));
         var->setVal(1);
         mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(1));
         var->setVal(-1);
         mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(1));
         list->Add(frame);
         var->setVal(0);
      }


   } else {
      RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
      //    TIterator* iter = simPdf->indexCat().typeIterator() ;
      TIterator* iter = channelCat->typeIterator() ;
      RooCatType* tt = NULL;
      while(nPlots<nPlotsMax && (tt=(RooCatType*) iter->Next())) {

         cout << "on type " << tt->GetName() << " " << endl;
         // Get pdf associated with state from simpdf
         RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;

         // Generate observables defined by the pdf associated with this state
         RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
         //      obstmp->Print();


         obs = ((RooRealVar*)obstmp->first());

         TIterator* it = mc->GetNuisanceParameters()->createIterator();
         RooRealVar* var = NULL;
         while(nPlots<nPlotsMax && (var = (RooRealVar*) it->Next())){
            TCanvas* c2 = new TCanvas("c2");
            RooPlot* frame = obs->frame();
            frame->SetName(Form("frame%d",nPlots));
            frame->SetYTitle(var->GetName());

            cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
            cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
            data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));

            Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;

            if(strcmp(var->GetName(),"Lumi")==0){
               cout <<"working on lumi"<<endl;
               var->setVal(w->var("nominalLumi")->getVal());
               var->Print();
            } else{
               var->setVal(0);
            }
            // w->allVars().Print("v");
            // mc->GetNuisanceParameters()->Print("v");
            // pdftmp->plotOn(frame,LineWidth(2.));
            // mc->GetPdf()->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
            //pdftmp->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
            normCount = pdftmp->expectedEvents(*obs);
            pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;

            if(strcmp(var->GetName(),"Lumi")==0){
               cout <<"working on lumi"<<endl;
               var->setVal(w->var("nominalLumi")->getVal()+0.05);
               var->Print();
            } else{
               var->setVal(nSigmaToVary);
            }
            // pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2));
            // mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
            //pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
            normCount = pdftmp->expectedEvents(*obs);
            pdftmp->plotOn(frame,LineWidth(2.),LineColor(kRed),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;

            if(strcmp(var->GetName(),"Lumi")==0){
               cout <<"working on lumi"<<endl;
               var->setVal(w->var("nominalLumi")->getVal()-0.05);
               var->Print();
            } else{
               var->setVal(-nSigmaToVary);
            }
            // pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2));
            // mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
            //pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
            normCount = pdftmp->expectedEvents(*obs);
            pdftmp->plotOn(frame,LineWidth(2.),LineColor(kGreen),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;



            // set them back to normal
            if(strcmp(var->GetName(),"Lumi")==0){
               cout <<"working on lumi"<<endl;
               var->setVal(w->var("nominalLumi")->getVal());
               var->Print();
            } else{
               var->setVal(0);
            }

            list->Add(frame);

            // quit making plots
            ++nPlots;

            frame->Draw();
            c2->SaveAs(Form("%s_%s_%s.pdf",tt->GetName(),obs->GetName(),var->GetName()));
            delete c2;
         }
      }
   }



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


   // now make plots
   TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200);
   if(list->GetSize()>4){
      double n = list->GetSize();
      int nx = (int)sqrt(n) ;
      int ny = TMath::CeilNint(n/nx);
      nx = TMath::CeilNint( sqrt(n) );
      c1->Divide(ny,nx);
   } else
      c1->Divide(list->GetSize());
   for(int i=0; i<list->GetSize(); ++i){
      c1->cd(i+1);
      list->At(i)->Draw();
   }





}
コード例 #9
0
void StandardTestStatDistributionDemo(const char* infile = "",
                                      const char* workspaceName = "combined",
                                      const char* modelConfigName = "ModelConfig",
                                      const char* dataName = "obsData"){


  // the number of toy MC used to generate the distribution
  int nToyMC = 1000;
  // The parameter below is needed for asymptotic distribution to be chi-square,
  // but set to false if your model is not numerically stable if mu<0
  bool allowNegativeMu=true;


  /////////////////////////////////////////////////////////////
  // First part is just to access a user-defined file
  // or create the standard example file if it doesn't exist
  ////////////////////////////////////////////////////////////
   const char* filename = "";
   if (!strcmp(infile,"")) {
      filename = "results/example_combined_GaussExample_model.root";
      bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
      // if file does not exists generate with histfactory
      if (!fileExist) {
#ifdef _WIN32
         cout << "HistFactory file cannot be generated on Windows - exit" << endl;
         return;
#endif
         // Normally this would be run on the command line
         cout <<"will run standard hist2workspace example"<<endl;
         gROOT->ProcessLine(".! prepareHistFactory .");
         gROOT->ProcessLine(".! hist2workspace config/example.xml");
         cout <<"\n\n---------------------"<<endl;
         cout <<"Done creating example input"<<endl;
         cout <<"---------------------\n\n"<<endl;
      }

   }
   else
      filename = infile;

   // Try to open the file
   TFile *file = TFile::Open(filename);

   // if input file was specified byt not found, quit
   if(!file ){
      cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
      return;
   }


  /////////////////////////////////////////////////////////////
  // Now get the data and workspace
  ////////////////////////////////////////////////////////////

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

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

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

  // make sure ingredients are found
  if(!data || !mc){
    w->Print();
    cout << "data or ModelConfig was not found" <<endl;
    return;
  }

  mc->Print();
  /////////////////////////////////////////////////////////////
  // Now find the upper limit based on the asymptotic results
  ////////////////////////////////////////////////////////////
  RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
  ProfileLikelihoodCalculator plc(*data,*mc);
  LikelihoodInterval* interval = plc.GetInterval();
  double plcUpperLimit = interval->UpperLimit(*firstPOI);
  delete interval;
  cout << "\n\n--------------------------------------"<<endl;
  cout <<"Will generate sampling distribution at " << firstPOI->GetName() << " = " << plcUpperLimit <<endl;
  int nPOI = mc->GetParametersOfInterest()->getSize();
  if(nPOI>1){
    cout <<"not sure what to do with other parameters of interest, but here are their values"<<endl;
    mc->GetParametersOfInterest()->Print("v");
  }

  /////////////////////////////////////////////
  // create thte test stat sampler
  ProfileLikelihoodTestStat ts(*mc->GetPdf());

  // to avoid effects from boundary and simplify asymptotic comparison, set min=-max
  if(allowNegativeMu)
    firstPOI->setMin(-1*firstPOI->getMax());

  // temporary RooArgSet
  RooArgSet poi;
  poi.add(*mc->GetParametersOfInterest());

  // create and configure the ToyMCSampler
  ToyMCSampler sampler(ts,nToyMC);
  sampler.SetPdf(*mc->GetPdf());
  sampler.SetObservables(*mc->GetObservables());
  sampler.SetGlobalObservables(*mc->GetGlobalObservables());
  if(!mc->GetPdf()->canBeExtended() && (data->numEntries()==1)){
    cout << "tell it to use 1 event"<<endl;
    sampler.SetNEventsPerToy(1);
  }
  firstPOI->setVal(plcUpperLimit); // set POI value for generation
  sampler.SetParametersForTestStat(*mc->GetParametersOfInterest()); // set POI value for evaluation

  if (useProof) {
     ProofConfig pc(*w, nworkers, "",false);
     sampler.SetProofConfig(&pc); // enable proof
  }

  firstPOI->setVal(plcUpperLimit);
  RooArgSet allParameters;
  allParameters.add(*mc->GetParametersOfInterest());
  allParameters.add(*mc->GetNuisanceParameters());
  allParameters.Print("v");

  SamplingDistribution* sampDist = sampler.GetSamplingDistribution(allParameters);
  SamplingDistPlot plot;
  plot.AddSamplingDistribution(sampDist);
  plot.GetTH1F(sampDist)->GetYaxis()->SetTitle(Form("f(-log #lambda(#mu=%.2f) | #mu=%.2f)",plcUpperLimit,plcUpperLimit));
  plot.SetAxisTitle(Form("-log #lambda(#mu=%.2f)",plcUpperLimit));

  TCanvas* c1 = new TCanvas("c1");
  c1->SetLogy();
  plot.Draw();
  double min = plot.GetTH1F(sampDist)->GetXaxis()->GetXmin();
  double max = plot.GetTH1F(sampDist)->GetXaxis()->GetXmax();

  TF1* f = new TF1("f",Form("2*ROOT::Math::chisquared_pdf(2*x,%d,0)",nPOI),min,max);
  f->Draw("same");
  c1->SaveAs("standard_test_stat_distribution.pdf");

}