// @(#)root/roostats:$Id: HybridResult.cxx 35810 2010-09-27 20:05:22Z moneta $

/*************************************************************************
 * Project: RooStats                                                     *
 * Package: RooFit/RooStats                                              *
 * Authors:                                                              *
 *   Kyle Cranmer, Lorenzo Moneta, Gregory Schott, Wouter Verkerke       *
 *************************************************************************
 * Copyright (C) 1995-2008, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/

//____________________________________________________________________
/*
HybridResult class: this class is a fresh rewrite in RooStats of
	RooStatsCms/LimitResults developped by D. Piparo and G. Schott
New contributions to this class have been written by Matthias Wolf (error estimation)

The objects of this class store and access with lightweight methods the
information calculated by LimitResults through a Lent calculation using
MC toy experiments.
In some ways can be considered an extended and extensible implementation of the
TConfidenceLevel class (http://root.cern.ch/root/html/TConfidenceLevel.html).

*/

#include "RooDataHist.h"
#include "RooDataSet.h"
#include "RooGlobalFunc.h" // for RooFit::Extended()
#include "RooNLLVar.h"
#include "RooRealVar.h"
#include "RooAbsData.h"

#include "RooStats/HybridResult.h"
#include "RooStats/HybridPlot.h"


/// ClassImp for building the THtml documentation of the class 
ClassImp(RooStats::HybridResult)

using namespace RooStats;

///////////////////////////////////////////////////////////////////////////

HybridResult::HybridResult( const char *name) :
   HypoTestResult(name),
   fTestStat_data(-999.),
   fComputationsNulDoneFlag(false),
   fComputationsAltDoneFlag(false),
   fSumLargerValues(false)
{
   // HybridResult default constructor (with name )
}

///////////////////////////////////////////////////////////////////////////

HybridResult::HybridResult( const char *name,
                            const std::vector<double>& testStat_sb_vals,
                            const std::vector<double>& testStat_b_vals,
			    bool sumLargerValues ) :
   HypoTestResult(name,0,0),
   fTestStat_data(-999.),
   fComputationsNulDoneFlag(false),
   fComputationsAltDoneFlag(false),
   fSumLargerValues(sumLargerValues)
{
   // HybridResult constructor (with name, title and vectors of S+B and B values)

   int vector_size_sb = testStat_sb_vals.size();
   assert(vector_size_sb>0);

   int vector_size_b = testStat_b_vals.size();
   assert(vector_size_b>0);

   fTestStat_sb.reserve(vector_size_sb);
   for (int i=0;i<vector_size_sb;++i)
      fTestStat_sb.push_back(testStat_sb_vals[i]);

   fTestStat_b.reserve(vector_size_b);
   for (int i=0;i<vector_size_b;++i)
      fTestStat_b.push_back(testStat_b_vals[i]);
}


///////////////////////////////////////////////////////////////////////////

HybridResult::~HybridResult()
{
   // HybridResult destructor

   fTestStat_sb.clear();
   fTestStat_b.clear();
}

///////////////////////////////////////////////////////////////////////////

void HybridResult::SetDataTestStatistics(double testStat_data_val)
{
   // set the value of the test statistics on data

   fComputationsAltDoneFlag = false;
   fComputationsNulDoneFlag = false;
   fTestStat_data = testStat_data_val;
   return;
}

///////////////////////////////////////////////////////////////////////////

double HybridResult::NullPValue() const
{
   // return 1-CL_b : the B p-value

   if (fComputationsNulDoneFlag==false) {
      int nToys = fTestStat_b.size();
      if (nToys==0) {
         std::cout << "Error: no toy data present. Returning -1.\n";
         return -1;
      }

      double larger_than_measured=0;
      if (fSumLargerValues) {
	for (int iToy=0;iToy<nToys;++iToy)
	  if ( fTestStat_b[iToy] >= fTestStat_data ) ++larger_than_measured;
      } else {
	for (int iToy=0;iToy<nToys;++iToy)
	  if ( fTestStat_b[iToy] <= fTestStat_data ) ++larger_than_measured;
      }

      if (larger_than_measured==0) std::cout << "Warning: CLb = 0 ... maybe more toys are needed!\n";

      fComputationsNulDoneFlag = true;
      fNullPValue = 1-larger_than_measured/nToys;
   }

   return fNullPValue;
}

///////////////////////////////////////////////////////////////////////////

double HybridResult::AlternatePValue() const
{
   // return CL_s+b : the S+B p-value

   if (fComputationsAltDoneFlag==false) {
      int nToys = fTestStat_b.size();
      if (nToys==0) {
         std::cout << "Error: no toy data present. Returning -1.\n";
         return -1;
      }

      double larger_than_measured=0;
      if (fSumLargerValues) {
	for (int iToy=0;iToy<nToys;++iToy)
	  if ( fTestStat_sb[iToy] >= fTestStat_data ) ++larger_than_measured;
      } else {
	for (int iToy=0;iToy<nToys;++iToy)
	  if ( fTestStat_sb[iToy] <= fTestStat_data ) ++larger_than_measured;
      }

      if (larger_than_measured==0) std::cout << "Warning: CLsb = 0 ... maybe more toys are needed!\n";

      fComputationsAltDoneFlag = true;
      fAlternatePValue = larger_than_measured/nToys;
   }

   return fAlternatePValue;
}

///////////////////////////////////////////////////////////////////////////

Double_t HybridResult::CLbError() const
{
  // Returns an estimate of the error on CLb assuming a binomial error on
  // CLb:
  // BEGIN_LATEX
  // #sigma_{CL_{b}} &=& #sqrt{CL_{b} #left( 1 - CL_{b} #right) / n_{toys}}
  // END_LATEX
  unsigned const int n = fTestStat_b.size();
  return TMath::Sqrt(CLb() * (1. - CLb()) / n);
}

///////////////////////////////////////////////////////////////////////////

Double_t HybridResult::CLsplusbError() const
{
  // Returns an estimate of the error on CLsplusb assuming a binomial
  // error on CLsplusb:
  // BEGIN_LATEX
  // #sigma_{CL_{s+b}} &=& #sqrt{CL_{s+b} #left( 1 - CL_{s+b} #right) / n_{toys}}
  // END_LATEX
  unsigned const int n = fTestStat_sb.size();
  return TMath::Sqrt(CLsplusb() * (1. - CLsplusb()) / n);
}

///////////////////////////////////////////////////////////////////////////

Double_t HybridResult::CLsError() const
{
  // Returns an estimate of the error on CLs through combination of the
  // errors on CLb and CLsplusb:
  // BEGIN_LATEX
  // #sigma_{CL_s} &=& CL_s #sqrt{#left( #frac{#sigma_{CL_{s+b}}}{CL_{s+b}} #right)^2 + #left( #frac{#sigma_{CL_{b}}}{CL_{b}} #right)^2}
  // END_LATEX
  unsigned const int n_b = fTestStat_b.size();
  unsigned const int n_sb = fTestStat_sb.size();
  
  if (CLb() == 0 || CLsplusb() == 0)
    return 0;
  
  double cl_b_err = (1. - CLb()) / (n_b * CLb());
  double cl_sb_err = (1. - CLsplusb()) / (n_sb * CLsplusb());
  
  return CLs() * TMath::Sqrt(cl_b_err + cl_sb_err);
}

///////////////////////////////////////////////////////////////////////////

void HybridResult::Add(HybridResult* other)
{
   // add additional toy-MC experiments to the current results
   // use the data test statistics of the added object if none is already present (otherwise, ignore the new one)

   int other_size_sb = other->GetTestStat_sb().size();
   for (int i=0;i<other_size_sb;++i)
      fTestStat_sb.push_back(other->GetTestStat_sb()[i]);

   int other_size_b = other->GetTestStat_b().size();
   for (int i=0;i<other_size_b;++i)
      fTestStat_b.push_back(other->GetTestStat_b()[i]);

   // if no data is present use the other's HybridResult's data
   if (fTestStat_data==-999.)
      fTestStat_data = other->GetTestStat_data();

   fComputationsAltDoneFlag = false;
   fComputationsNulDoneFlag = false;

   return;
}

///////////////////////////////////////////////////////////////////////////

HybridPlot* HybridResult::GetPlot(const char* name,const char* title, int n_bins)
{
   // prepare a plot showing a result and return a pointer to a HybridPlot object
   // the needed arguments are: an object name, a title and the number of bins in the plot

   // default plot name
   TString plot_name;
   if ( TString(name)=="" ) {
      plot_name += GetName();
      plot_name += "_plot";
   } else plot_name = name;

   // default plot title
   TString plot_title;
   if ( TString(title)=="" ) {
      plot_title += GetTitle();
      plot_title += "_plot (";
      plot_title += fTestStat_b.size();
      plot_title += " toys)";
   } else plot_title = title;

   HybridPlot* plot = new HybridPlot( plot_name.Data(),
                                      plot_title.Data(),
                                      fTestStat_sb,
                                      fTestStat_b,
                                      fTestStat_data,
                                      n_bins,
                                      true );
   return plot;
}

///////////////////////////////////////////////////////////////////////////

void HybridResult::PrintMore(const char* /*options */)
{
   // Print out some information about the results

   std::cout << "\nResults " << GetName() << ":\n"
             << " - Number of S+B toys: " << fTestStat_b.size() << std::endl
             << " - Number of B toys: " << fTestStat_sb.size() << std::endl
             << " - test statistics evaluated on data: " << fTestStat_data << std::endl
             << " - CL_b " << CLb() << std::endl
             << " - CL_s+b " << CLsplusb() << std::endl
             << " - CL_s " << CLs() << std::endl;

   return;
}










