remove a failing tutorial

git-svn-id: http://root.cern.ch/svn/root/trunk@31352 27541ba8-7e3a-0410-8455-c3a389f83636

tutorials/roostats/rs503_ProfileLikelihoodCalculator_averageLimit.C

-//////////////////////////////////////////////////////////////////////////
-//
-// RooStats tutorial macro #503
-// 2009/08 - Nils Ruthmann, Gregory Schott
-//
-///////////////////////////////////////////////////////////////////////
-
-#include "RooRealVar.h"
-#include "RooProdPdf.h"
-#include "RooWorkspace.h"
-#include "RooRandom.h"
-#include "RooMCStudy.h"
-#include "RooDataSet.h"
-
-#include "RooStats/ProfileLikelihoodCalculator.h"
-#include "RooStats/LikelihoodIntervalPlot.h"
-#include "RooStats/HypoTestResult.h"
-#include "RooStats/UpperLimitMCSModule.h"
-
-
-
-#include "TFile.h"
-#include "TStopwatch.h"
-#include "TCanvas.h"
-
-
-
-void rs503_ProfileLikelihoodCalculator_averageLimit(const char* fname="WS_GaussOverFlat.root",int ntoys=1000,const char* outputplot="pll_avLimit.ps"){
-  using namespace RooFit ;
-  using namespace RooStats ;
-  TStopwatch t;
-  t.Start();
-
-  TFile* file =new TFile(fname);
-  RooWorkspace* my_WS = (RooWorkspace*) file->Get("myWS");
-  std::cout<<"ws open"<<endl;
-  //Import the objects needed
-  RooAbsPdf* model_naked=my_WS->pdf("model");
-  RooAbsPdf* priorNuisance=my_WS->pdf("priorNuisance");
-  RooAbsPdf* modelBkg_naked=my_WS->pdf("modelBkg");
-  const RooArgSet* paramInterestSet=my_WS->set("POI");
-  RooRealVar* paramInterest= (RooRealVar*) paramInterestSet->first();
-  const RooArgSet* observable=my_WS->set("observables");
-  const RooArgSet* nuisanceParam=my_WS->set("parameters");
-  //If there are nuisance parameters present, multiply their prior to the model
-  RooAbsPdf* model=model_naked;
-  RooAbsPdf* modelBkg=modelBkg_naked;
-  if(priorNuisance!=0) {
-     model=new RooProdPdf("constrainedModel","Model with nuisance parameters",*model_naked,*priorNuisance);
-     //From now work with the product of both
-     modelBkg=new RooProdPdf("constrainedBkgModel","Bkg Model with nuisance parameters",*modelBkg_naked,*priorNuisance);
-  }
-  else{
-    std::cout<<"No Nuisance Parameters present"<<std::endl;
-  }
-
-  //Save the default values of the parameters:
-  RooArgSet* parameters=model->getVariables();
-  RooArgSet* default_parameters=new RooArgSet("default_parameters");
-  TIterator* it=parameters->createIterator();
-  RooRealVar* currentparam=(RooRealVar*) it->Next();
-  do {
-    default_parameters->addClone(*currentparam,false);
-    currentparam=(RooRealVar*) it->Next();
-  }while(currentparam!=0);
- 
- 
-
-  
- 
-  RooRandom::randomGenerator()->SetSeed(100);
-
-  //--------------------------------------------------------------------
-  //ROOMCSTUDY 
-  //For simplicity use RooMCStudy, a tool to perform several Toy MCs.
-  //If there are systematics constrain them
-  //Generate the background and fit for the signal to test limitsetting
-  RooMCStudy* mcs = 0; 
-  if(priorNuisance!=0) {
-    mcs = new RooMCStudy(*modelBkg,*observable,Extended(kTRUE),FitModel(*model),
-				     FitOptions(Extended(kTRUE),PrintEvalErrors(1)),Constrain(*nuisanceParam)) ;
-  }
-  else {
-    mcs = new RooMCStudy(*modelBkg,*observable,Extended(kTRUE),FitModel(*model),
-				     FitOptions(Extended(kTRUE),PrintEvalErrors(1))); 
-  }
-
-  //Adding a module which allows to compute the upper limit in every generation cycle
-  UpperLimitMCSModule limitModule(paramInterestSet,0.95) ;
-  mcs->addModule(limitModule) ;
-  
-  cout<<"start fit process"<<endl;
-  mcs->generateAndFit(ntoys);
-
-  TString limitstr("ul_");
-  TH1* mcslimit_histo=(TH1F*)mcs->fitParDataSet().createHistogram(limitstr+paramInterest->GetName());
-  //Make a histogram of the upperlimit vs the number of generated events
-  TString limitstr2("ngen,ul_");
-  TH1* mcslimitvsevt_histo=(TH1F*)mcs->fitParDataSet().createHistogram(limitstr2+paramInterest->GetName());
-  TCanvas* c2 =new TCanvas();
-  c2->Divide(1,2);
-  c2->cd(1);
-  mcslimit_histo->Draw();
-  c2->cd(2);
-  mcslimitvsevt_histo->Draw();
-  c2->Print(outputplot);
-  //file->Close();
-  t.Stop();
-  t.Print();
-
-}
-
-