<commit_before>/*
StandardTestStatDistributionDemo.C
author <NAME>
date: summer solstice, 2011

This simple script plots the sampling distribution of the profile likelihood
ratio test statistic based on the input Model File.  To do this one needs to
specify the value of the parameter of interest that will be used for evaluating
the test statistic and the value of the parameters used for generating the toy data.
In this case, it uses the upper-limit estimated from the ProfileLikleihoodCalculator,
which assumes the asymptotic chi-square distribution for -2 log profile likleihood ratio.
Thus, the script is handy for checking to see if the asymptotic approximations are valid.
To aid, that comparison, the script overlays a chi-square distribution as well.
The most common parameter of interest is a parameter proportional to the signal rate,
and often that has a lower-limit of 0, which breaks the standard chi-square distribution.
Thus the script allows the parameter to be negative so that the overlay chi-square is
the correct asymptotic distribution.
*/

#include "TFile.h"
#include "TROOT.h"
#include "TH1F.h"
#include "TCanvas.h"
#include "TSystem.h"
#include "TF1.h"
#include "TSystem.h"

#include "RooWorkspace.h"
#include "RooAbsData.h"

#include "RooStats/ModelConfig.h"
#include "RooStats/FeldmanCousins.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/PointSetInterval.h"
#include "RooStats/ConfidenceBelt.h"

#include "RooStats/ProfileLikelihoodCalculator.h"
#include "RooStats/LikelihoodInterval.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SamplingDistribution.h"
#include "RooStats/SamplingDistPlot.h"

using namespace RooFit;
using namespace RooStats;


/////////////////////////////////////////////////////////////////////////
// The actual macro

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

  ProofConfig pc(*w, 4, "workers=4",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();<commit_msg>Disable usage of PROOF in tutorial<commit_after>/*
StandardTestStatDistributionDemo.C
author <NAME>
date: summer solstice, 2011

This simple script plots the sampling distribution of the profile likelihood
ratio test statistic based on the input Model File.  To do this one needs to
specify the value of the parameter of interest that will be used for evaluating
the test statistic and the value of the parameters used for generating the toy data.
In this case, it uses the upper-limit estimated from the ProfileLikleihoodCalculator,
which assumes the asymptotic chi-square distribution for -2 log profile likleihood ratio.
Thus, the script is handy for checking to see if the asymptotic approximations are valid.
To aid, that comparison, the script overlays a chi-square distribution as well.
The most common parameter of interest is a parameter proportional to the signal rate,
and often that has a lower-limit of 0, which breaks the standard chi-square distribution.
Thus the script allows the parameter to be negative so that the overlay chi-square is
the correct asymptotic distribution.
*/

#include "TFile.h"
#include "TROOT.h"
#include "TH1F.h"
#include "TCanvas.h"
#include "TSystem.h"
#include "TF1.h"
#include "TSystem.h"

#include "RooWorkspace.h"
#include "RooAbsData.h"

#include "RooStats/ModelConfig.h"
#include "RooStats/FeldmanCousins.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/PointSetInterval.h"
#include "RooStats/ConfidenceBelt.h"

#include "RooStats/ProfileLikelihoodCalculator.h"
#include "RooStats/LikelihoodInterval.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SamplingDistribution.h"
#include "RooStats/SamplingDistPlot.h"

using namespace RooFit;
using namespace RooStats;


bool useProof = false;  // flag to control whether to use Proof
int nworkers = 0;   // number of workers (default use all available cores)

/////////////////////////////////////////////////////////////////////////
// The actual macro

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();
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