//=============================================================================
/** 
 * @file DoAsymptoticErrFit.C
 * @brief Do a fit to weighted data using AsymptoticError and show 
 * that it crashes
 *
 * @author Tim-Philip Huecking, <huecking@hep.physik.uni-siegen.de>
 * 
 * Created:
 * @date 20-05-27 (TPH)
 *
 * Changed:
 *
 */
//=============================================================================

// system includes

// ROOT includes 

// local includes
// system includes
#include<iomanip>

// ROOT includes 
#include <RooStats/SPlot.h>
#include <RooArgSet.h>

// local includes
#include "modelBuilder.C"
//#include "Config.C"

RooDataSet* getSWeightedData(ModelBuilder& model, RooDataSet* data);
void compareFitRes(RooFitResult* withMinos, RooFitResult* noMinos);

void DoAsymptoticErrFit()
{
    RooTrace::active(kTRUE);
    RooTrace::verbose(kTRUE);
   // Model parameters are stored in Configurator
   Configurator cfg{};
   cfg.prntModelPars();
   // Build a model with 3 variables:
   //    - mass (bkg: Exponential, sig: Gaussian)
   //    - vexex (bkg: Exponential, sig: Exponential)
   //    - vgg (bkg: Gaussian, sig: Gaussian)
   ModelBuilder model{cfg};
   bool useRandomSeed = false;
   model.buildFullModel(useRandomSeed);
   
   // generate a data set 
   model.m_pdfFull->Print();
   model.m_nSigFit->Print();
   model.m_nBkgFit->Print();
   RooDataSet* data = model.m_pdfFull->generate(RooArgList(
            *model.m_mass.get(), *model.m_vgg.get()
            , *model.m_vexex.get()), Extended(kTRUE));

   // Get a data set weighted with the signal sWeights
   RooDataSet *sigSwData = getSWeightedData(model, data);

   // Now fit the signal Gaussian of vgg to it 
   model.m_pdfExtVggSig->Print();
   RooTrace::dump();
   cout << "\n---- USE SUMWERROR ----\n" << endl;
   RooFitResult* wFitSumW2 = model.m_pdfExtVggSig->fitTo( *sigSwData
         , Extended(kTRUE), AsymptoticError(kFALSE), SumW2Error(kTRUE)
         , PrintLevel(0), Verbose(kFALSE), Save(kTRUE), Minos(kFALSE));
   cout << "\n\t => IF YOU CAN READ THIS, SUMW2ERROR WORKED\n" << endl;
   cout << "\n---- USE ASYMPTOTICERROR----\n" << endl;
   RooFitResult* wFitAsymVxx = model.m_pdfExtVexexSig->fitTo(*sigSwData
         , Extended(kTRUE), AsymptoticError(kTRUE), SumW2Error(kFALSE)
         , PrintLevel(0), Verbose(kFALSE), Save(kFALSE), Minos(kFALSE));
   RooFitResult* wFitAsym = model.m_pdfExtVggSig->fitTo( *sigSwData
         , Extended(kTRUE), AsymptoticError(kTRUE), SumW2Error(kFALSE)
         , PrintLevel(0), Verbose(kFALSE), Save(kTRUE), Minos(kFALSE));
   
   return;
}


RooDataSet* getSWeightedData(ModelBuilder& model, RooDataSet* data)
{
   // Do a fit in the discriminating variable, as it is needed for 
   // sPlot method
   //RooFitResult* discrRes = model.m_pdfMassSum->fitTo(*data 
         //, Extended(kTRUE), Save(kTRUE), Verbose(kFALSE), Minos(kFALSE));

   // add sWeights to data set
   auto splot = new RooStats::SPlot("splot", "splot");
   splot->SetSData(data);
   splot->AddSWeight( model.m_pdfMassSum.get() //model.m_pdfFull.get()
         , RooArgList(*model.m_nSigFit.get(), *model.m_nBkgFit.get())
         , RooArgSet{}, kFALSE);
   data->Print();
   delete splot;

   // now perform a fit to weighted events
   // Therefore create a data set weighted with signal sWeights
   auto sigSwData = new RooDataSet("sigSwData"
         , "Data weighted with signal sWeights", RooArgSet(*data->get())
         , Import(*data), WeightVar("nSigFit_sw"));
   return sigSwData;
}