void MakeVZEROTimeDelaysEntryRun2()
{

  AliCDBManager *man = AliCDBManager::Instance();
  man->SetDefaultStorage("local://./OCDB");

  // Creation of the time delays OCDB object

  const Double_t timeShift[66] = {0.0 , 263.453366 , 263.554201 , 263.455896 , 263.705908 , 263.178068 , 263.260511 , 263.352692 , 263.371615 , 264.275609 , 263.022590 , 263.923256 , 263.477724 , 263.328535 , 263.338143 , 263.684347 , 263.636735 , 264.240256 , 264.704787 , 263.341181 , 265.875077 , 264.362158 , 264.009584 , 263.985062 , 264.507631 , 264.711630 , 264.702209 , 264.983539 , 265.156149 , 265.340929 , 265.185957 , 265.402229 , 267.060006 , 260.193899 , 260.831277 , 260.870380 , 260.231921 , 259.848971 , 263.069287 , 262.829099 , 261.297212 , 260.468547 , 260.962657 , 260.754787 , 260.782074 , 260.244392 , 263.248285 , 262.224661 , 259.936356 , 262.307604 , 262.698634 , 262.259535 , 262.425491 , 262.041006 , 264.711811 , 264.537483 , 262.019158 , 263.940932 , 263.309260 , 263.819921 , 264.324985 , 263.419804 , 265.954522 , 266.150658 , 263.942502 , 0.0};
  TH1F *delays = new TH1F("VZEROTimeDelays","VZERO Time delays",64,-0.5,63.5);
  delays->SetContent(timeShift);
	
  AliCDBMetaData *md= new AliCDBMetaData(); // metaData describing the object
  md->SetResponsible("Brigitte Cheynis");
  md->SetBeamPeriod(0);
  md->SetAliRootVersion(gSystem->Getenv("ARVERSION"));
  md->SetComment("Time delays channel by channel for Run2");
  md->PrintMetaData();

  AliCDBId id("VZERO/Calib/TimeDelays",215011,AliCDBRunRange::Infinity());

  man->Put(delays, id, md);

  delete md;

}
Example #2
0
void MakeADLightYieldsEntry(const char *outputCDB = "local://$ALICE_ROOT/../AliRoot/OCDB")
{

  AliCDBManager *man = AliCDBManager::Instance();
  man->SetDefaultStorage(outputCDB);

  // Creation of the light yields OCDB object
  const Double_t lightYieldCorr[18] = {0.0,
				       2.2e-4,2.2e-4,2.2e-4,2.2e-4, 2.2e-4,2.2e-4,2.2e-4,2.2e-4,
                                       2.4e-4,2.4e-4,2.6e-4,2.6e-4, 2.4e-4,2.4e-4,2.6e-4,2.6e-4,
				       0.0};

  TH1F *yields = new TH1F("ADLightYields", "AD Light Yields", 16, -0.5, 15.5);
  yields->SetContent(lightYieldCorr);

  AliCDBMetaData *md = new AliCDBMetaData(); // metaData describing the object
  md->SetResponsible("Michal Broz");
  md->SetBeamPeriod(0);
  md->SetAliRootVersion(gSystem->Getenv("ARVERSION"));
  md->SetComment("Light Yields channel by channel");
  md->PrintMetaData();

  AliCDBId id("AD/Calib/LightYields", 0, AliCDBRunRange::Infinity());
  man->Put(yields, id, md);

  delete md;

}
Example #3
0
void MakeVZEROTimeDelaysEntry()
{

  AliCDBManager *man = AliCDBManager::Instance();
  man->SetDefaultStorage("local://$ALICE_ROOT/OCDB");

  // Creation of the time delays OCDB object
  const Double_t timeShift[66] = {0.0 , 0.477957 , 0.0889999 , 0.757669 , 0.205439 , 0.239666 , -0.183705 , 0.442873 , -0.281366 , 0.260976 , 0.788995 , 0.974758 , 0.548532 , 0.495023 , 0.868472 , 0.661167 , 0.358307 , 0.221243 , 0.530179 , 1.26696 , 1.33082 , 1.27086 , 1.77133 , 1.10253 , 0.634806+0.885 , 2.14838 , 1.50212 , 1.59253 , 1.66122+0.740 , 1.16957 , 1.52056 , 1.47791 , 1.81905 , -1.94123 , -1.29124-0.350 , -2.16045 , -1.78939 , -3.11111 , -1.87178 , -1.57671-0.560 , -1.70311 , -1.81208 , -1.94475 , -2.53058+0.930 , -1.7042 , -2.08109 , -1.84416 , -0.61073 , -1.77145 , 0.16999 , -0.0585339 , 0.00401133 , 0.397726 , 0.851111 , 0.264187 , 0.59573 , -0.158263 , 0.584362 , 1.20835 , 0.927573 , 1.13895 , 0.64648 , 2.18747 , 1.68909 , 0.451194 , 0.0};
  TH1F *delays = new TH1F("VZEROTimeDelays","VZERO Time delays",64,-0.5,63.5);
  delays->SetContent(timeShift);
	
  AliCDBMetaData *md= new AliCDBMetaData(); // metaData describing the object
  md->SetResponsible("Brigitte Cheynis");
  md->SetBeamPeriod(0);
  md->SetAliRootVersion(gSystem->Getenv("ARVERSION"));
  md->SetComment("Time delays channel by channel for year >= 2012");
  md->PrintMetaData();

  AliCDBId id("VZERO/Calib/TimeDelays",0,AliCDBRunRange::Infinity());

  man->Put(delays, id, md);

  delete md;

}
Example #4
0
void MakeADTimeDelaysEntry(const char *outputCDB = "local://$ALICE_ROOT/../AliRoot/OCDB")
{

  AliCDBManager *man = AliCDBManager::Instance();
  man->SetDefaultStorage(outputCDB);

  // Creation of the time delays OCDB object
  //const Double_t timeShift[18] = {0.0, 203.2, 203.4, 203.5, 203.0, 203.4, 203.5, 203.1, 203.2, 194.2, 194.4, 194.5, 194.2, 194.7, 194.5, 194.3, 192.8, 0.0};
  const Double_t timeShift[18] = {0.0, 61.6091, 61.1891, 60.5191, 61.3591, 60.7691, 62.0291, 61.1091, 61.4591, 62.3491, 62.7891, 59.7791, 60.0991, 63.3091, 62.7691, 59.6491, 61.5091, 0.0};
  TH1F *delays = new TH1F("ADTimeDelays", "AD Time delays", 16, -0.5, 15.5);
  delays->SetContent(timeShift);
	
  AliCDBMetaData *md= new AliCDBMetaData(); // metaData describing the object
  md->SetResponsible("Michal Broz");
  md->SetBeamPeriod(0);
  md->SetAliRootVersion(gSystem->Getenv("ARVERSION"));
  md->SetComment("Time delays channel by channel");
  md->PrintMetaData();

  AliCDBStorage *storLoc = man->GetDefaultStorage();
  AliCDBId id("AD/Calib/TimeDelays", 0, AliCDBRunRange::Infinity());

  man->Put(delays, id, md);

  delete md;

}
Example #5
0
AliGenerator* MbPythiaTuneATLAS_Flat()
{
  AliGenPythia* pythia = MbPythiaTuneATLAS();
      
  comment = comment.Append("; flat multiplicity distribution");
      
  // set high multiplicity trigger
  // this weight achieves a flat multiplicity distribution
  Double_t cont[] =
    {0, 
     0.234836, 0.103484, 0.00984802, 0.0199906, 0.0260018, 0.0208481, 0.0101477, 0.00146998, -0.00681513, -0.0114928,
     -0.0113352, -0.0102012, -0.00895238, -0.00534961, -0.00261648, -0.000819048, 0.00130816, 0.00177978, 0.00373838, 0.00566255,
     0.00628156, 0.00687458, 0.00668017, 0.00702917, 0.00810848, 0.00876167, 0.00832783, 0.00848518, 0.0107709, 0.0106849,
     0.00933702, 0.00912525, 0.0106553, 0.0102785, 0.0101756, 0.010962, 0.00957103, 0.00970448, 0.0117133, 0.012271,
     0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
     0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
     0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
     0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
     0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
     0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113, 0.0113,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
     0};

  Double_t err[] =
    {0, 
     0.00168216, 0.000743548, 0.00103125, 0.00108605, 0.00117101, 0.00124577, 0.00129119, 0.00128341, 0.00121421, 0.00112431,
     0.00100588, 0.000895078, 0.000790314, 0.000711673, 0.000634547, 0.000589133, 0.000542763, 0.000509552, 0.000487375, 0.000468906, 
     0.000460196, 0.000455806, 0.00044843, 0.000449317, 0.00045007, 0.000458016, 0.000461167, 0.000474742, 0.00050161, 0.00051637, 
     0.000542336, 0.000558854, 0.000599169, 0.000611982, 0.000663855, 0.000696322, 0.000722825, 0.000771686, 0.000838023, 0.000908317, 
     0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003,
     0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003,
     0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003,
     0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003,
     0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003,
     0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0};

  TH1F *weight = new TH1F("newweight","newweight",120,-0.5,119.5);

  weight->SetContent(cont);
  weight->SetError(err);
        
  Int_t limit = weight->GetRandom();
  pythia->SetTriggerChargedMultiplicity(limit, 1.4);
      
  comment = comment.Append(Form("; multiplicity threshold set to %d in |eta| < 1.4", limit));

  return pythia;
}
void OneSidedFrequentistUpperLimitWithBands(const char* infile = "",
                                            const char* workspaceName = "combined",
                                            const char* modelConfigName = "ModelConfig",
                                            const char* dataName = "obsData") {



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

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

   }
   else
      filename = infile;

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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


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

   double CLb=0;
   double CLbinclusive=0;

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

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

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

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

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

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

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

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

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

            // Cleanup
            delete globtmp ;
            delete tmp ;
         }
      }

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

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


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

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



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

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

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

      histOfUL->Fill(thisUL);

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

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

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

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

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

   delete profile;
   delete nll;

}
void OneSidedFrequentistUpperLimitWithBands_intermediate(const char* infile = "",
					    const char* workspaceName = "combined",
					    const char* modelConfigName = "ModelConfig",
					    const char* dataName = "obsData"){


  double confidenceLevel=0.95;
  // degrade/improve number of pseudo-experiments used to define the confidence belt.  
  // value of 1 corresponds to default number of toys in the tail, which is 50/(1-confidenceLevel)
  double additionalToysFac = 1.;  
  int nPointsToScan = 30; // number of steps in the parameter of interest 
  int nToyMC = 100; // number of toys used to define the expected limit and band

  TStopwatch t;
  t.Start();
  /////////////////////////////////////////////////////////////
  // First part is just to access a user-defined file 
  // or create the standard example file if it doesn't exist
  ////////////////////////////////////////////////////////////
  const char* filename = "";
  if (!strcmp(infile,""))
    filename = "results/example_combined_GaussExample_model.root";
  else
    filename = infile;
  // Check if example input file exists
  TFile *file = TFile::Open(filename);

  // if input file was specified byt not found, quit
  if(!file && strcmp(infile,"")){
    cout <<"file not found" << endl;
    return;
  } 

  // if default file not found, try to create it
  if(!file ){
    // Normally this would be run on the command line
    cout <<"will run standard hist2workspace example"<<endl;
    gROOT->ProcessLine(".! prepareHistFactory .");
    gROOT->ProcessLine(".! hist2workspace config/example.xml");
    cout <<"\n\n---------------------"<<endl;
    cout <<"Done creating example input"<<endl;
    cout <<"---------------------\n\n"<<endl;
  }

  // now try to access the file again
  file = TFile::Open(filename);
  if(!file){
    // if it is still not there, then we can't continue
    cout << "Not able to run hist2workspace to create example input" <<endl;
    return;
  }

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

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

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

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

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

  cout << "Found data and ModelConfig:" <<endl;
  mc->Print();

  /////////////////////////////////////////////////////////////
  // Now get the POI for convenience
  // you may want to adjust the range of your POI
  ////////////////////////////////////////////////////////////
  RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
  //  firstPOI->setMin(0);
  //  firstPOI->setMax(10);

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

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


  // test speedups:
  testStat->SetReuseNLL(true);
  //  toymcsampler->setUseMultiGen(true); // not fully validated

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

  // We can use PROOF to speed things along in parallel
  ProofConfig pc(*w, 4, "",false); 
  if(mc->GetGlobalObservables()){
    cout << "will use global observables for unconditional ensemble"<<endl;
    mc->GetGlobalObservables()->Print();
    toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
  }
  toymcsampler->SetProofConfig(&pc);	// enable proof


  // Now get the interval
  PointSetInterval* interval = fc.GetInterval();
  ConfidenceBelt* belt = fc.GetConfidenceBelt();
 
  // print out the iterval on the first Parameter of Interest
  cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
    interval->LowerLimit(*firstPOI) << ", "<<
    interval->UpperLimit(*firstPOI) <<"] "<<endl;

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


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

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

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

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

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


  double CLb=0;
  double CLbinclusive=0;

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

    // set parameters back to values for generating pseudo data
    w->loadSnapshot("paramsToGenerateData");

    // in 5.30 there is a nicer way to generate toy data  & randomize global obs
    RooAbsData* toyData = toymcsampler->GenerateToyData(*paramsToGenerateData);

    // get test stat at observed UL in observed data
    firstPOI->setVal(observedUL);
    double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
    //    toyData->get()->Print("v");
    //    cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
    if(obsTSatObsUL < toyTSatObsUL) // (should be checked)
      CLb+= (1.)/nToyMC;
    if(obsTSatObsUL <= toyTSatObsUL) // (should be checked)
      CLbinclusive+= (1.)/nToyMC;


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

      if(thisTS<=arMax){
	thisUL = firstPOI->getVal();
      } else{
	break;
      }
    }
    

    histOfUL->Fill(thisUL);

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

  // if you want to see a plot of the sampling distribution for a particular scan point:

  // Now find bands and power constraint
  Double_t* bins = histOfUL->GetIntegral();
  TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
  cumulative->SetContent(bins);
  double band2sigDown=0, band1sigDown=0, bandMedian=0, band1sigUp=0,band2sigUp=0;
  for(int i=1; i<=cumulative->GetNbinsX(); ++i){
    if(bins[i]<RooStats::SignificanceToPValue(2))
      band2sigDown=cumulative->GetBinCenter(i);
    if(bins[i]<RooStats::SignificanceToPValue(1))
      band1sigDown=cumulative->GetBinCenter(i);
    if(bins[i]<0.5)
      bandMedian=cumulative->GetBinCenter(i);
    if(bins[i]<RooStats::SignificanceToPValue(-1))
      band1sigUp=cumulative->GetBinCenter(i);
    if(bins[i]<RooStats::SignificanceToPValue(-2))
      band2sigUp=cumulative->GetBinCenter(i);
  }

  t.Stop();
  t.Print();

  cout << "-2 sigma  band " << band2sigDown << endl;
  cout << "-1 sigma  band " << band1sigDown  << endl;
  cout << "median of band " << bandMedian << " [Power Constriant)]" << endl;
  cout << "+1 sigma  band " << band1sigUp << endl;
  cout << "+2 sigma  band " << band2sigUp << endl;

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

  delete profile;
  delete nll;

}