void clsExample()
{
   // first, the simple case: no binning
   TConfidenceLevel* cl_1bin = TLimit::ComputeLimit(16.0,71.7,70,500000);
   cl_1bin->Draw();
   std::cout << "Statistics: " << cl_1bin->GetStatistic() << std::endl;
   std::cout << "Expected CLs: " << cl_1bin->GetExpectedCLs_b() << std::endl;
   std::cout << "Observed CLb, CLsb, CLs: " << cl_1bin->CLb() << " " << cl_1bin->CLsb() << " " << cl_1bin->CLs() << std::endl;
   // the conclusion here is that one cannot exclude the signal at 95%CL, even with the small downward fluctuation.

  // then with histograms
  new TCanvas;
  TH1F* signal = new TH1F("signal","signal",5,0,5);
  TH1F* bkg = new TH1F("bkg","bkg",5,0,5);
  TH1F* data = new TH1F("data","data",5,0,5);
  signal->SetBinContent(1,1.8);
  signal->SetBinContent(2,4.0);
  signal->SetBinContent(3,5.5);
  signal->SetBinContent(4,4.0);
  signal->SetBinContent(5,0.7);
  bkg->SetBinContent(1,26.1)  ;
  bkg->SetBinContent(2,27.9)  ;
  bkg->SetBinContent(3,14.0)  ;
  bkg->SetBinContent(4,3.4)   ;
  bkg->SetBinContent(5,0.3)   ;
  data->SetBinContent(1,24)   ;
  data->SetBinContent(2,30)   ;
  data->SetBinContent(3,13)   ;
  data->SetBinContent(4,3)    ;
  data->SetBinContent(5,0)    ;
  bkg->SetFillColor(kBlue);
  signal->SetFillColor(kRed);
  data->SetMarkerStyle(29);
  data->SetMarkerSize(1.5);
  THStack* stack = new THStack("simulations","simulations");
  stack->Add(bkg);
  stack->Add(signal);
  stack->Draw();
  data->Draw("same,P");
  TLimitDataSource input(signal,bkg,data);
  TConfidenceLevel* cl_histo = TLimit::ComputeLimit(&input,500000);
  cl_histo->CLs();
  cl_histo->GetExpectedCLs_b();
  new TCanvas;
  cl_histo->Draw();
  std::cout << "Statistics: " << cl_histo->GetStatistic() << std::endl;
  std::cout << "Expected CLs: " << cl_histo->GetExpectedCLs_b() << std::endl;
  std::cout << "Observed CLb, CLsb, CLs: " << cl_histo->CLb() << " " << cl_histo->CLsb() << " " << cl_histo->CLs() << std::endl;

}