#include <ToyMCSamplerOpt.h>

Public Member Functions
RooAbsData *	generate (RooRealVar &weightVar, const RooDataSet protoData=NULL, int forceEvents=0)

RooAbsData *	generateAsimov (RooRealVar *&weightVar)

void	setCacheTemplates (bool cache)

void	setCopyData (bool copyData)

	SimPdfGenInfo (RooAbsPdf &pdf, const RooArgSet &observables, bool preferBinned, const RooDataSet *protoData=NULL, int forceEvents=0)

	~SimPdfGenInfo ()

Private Attributes
RooAbsCategoryLValue *	cat_

bool	copyData_

std::map< std::string, RooAbsData * >	datasetPieces_

RooArgSet	observables_

RooArgSet	ownedCrap_

RooAbsPdf *	pdf_

std::vector< SinglePdfGenInfo * >	pdfs_

Detailed Description

Definition at line 32 of file ToyMCSamplerOpt.h.

Constructor & Destructor Documentation

toymcoptutils::SimPdfGenInfo::SimPdfGenInfo	(	RooAbsPdf &	pdf,
		const RooArgSet &	observables,
		bool	preferBinned,
		const RooDataSet *	protoData = `NULL`,
		int	forceEvents = `0`
	)

Definition at line 273 of file ToyMCSamplerOpt.cc.

References cat_, utils::factorizePdf(), pileupCalc::nbins, ownedCrap_, and pdfs_.

                                                                                                                                                        :
     pdf_(&pdf),
     cat_(0),
     observables_(observables),
     copyData_(true)
 {
     assert(forceEvents == 0 && "SimPdfGenInfo: forceEvents must be zero at least for now");
     RooSimultaneous *simPdf = dynamic_cast<RooSimultaneous *>(&pdf);
     if (simPdf) {
         cat_ = const_cast<RooAbsCategoryLValue *>(&simPdf->indexCat());
         int nbins = cat_->numBins((const char *)0);
         pdfs_.resize(nbins, 0);
         RooArgList dummy;
         for (int ic = 0; ic < nbins; ++ic) {
             cat_->setBin(ic);
             RooAbsPdf *pdfi = simPdf->getPdf(cat_->getLabel());
             if (pdfi == 0) throw std::logic_error(std::string("Unmapped category state: ") + cat_->getLabel());
             RooAbsPdf *newpdf = utils::factorizePdf(observables, *pdfi, dummy);
             pdfs_[ic] = new SinglePdfGenInfo(*newpdf, observables, preferBinned);
             if (newpdf != 0 && newpdf != pdfi) {
                 ownedCrap_.addOwned(*newpdf); 
             }
         }
     } else {
         pdfs_.push_back(new SinglePdfGenInfo(pdf, observables, preferBinned, protoData, forceEvents));
     }
 }

toymcoptutils::SimPdfGenInfo::~SimPdfGenInfo ( )

Definition at line 301 of file ToyMCSamplerOpt.cc.

 {
     for (std::vector<SinglePdfGenInfo *>::iterator it = pdfs_.begin(), ed = pdfs_.end(); it != ed; ++it) {
         delete *it;
     }
     pdfs_.clear();
     //for (std::map<std::string,RooDataSet*>::iterator it = datasetPieces_.begin(), ed = datasetPieces_.end(); it != ed; ++it) {
     for (std::map<std::string,RooAbsData*>::iterator it = datasetPieces_.begin(), ed = datasetPieces_.end(); it != ed; ++it) {
         delete it->second;
     }
     datasetPieces_.clear();
 }

Member Function Documentation

RooAbsData * toymcoptutils::SimPdfGenInfo::generate	(	RooRealVar *&	weightVar,
		const RooDataSet *	protoData = `NULL`,
		int	forceEvents = `0`
	)

Definition at line 316 of file ToyMCSamplerOpt.cc.

References data, i, n, and run_regression::ret.

Referenced by ToyMCSamplerOpt::Generate(), and Combine::run().

 {
     RooAbsData *ret = 0;
     TString retName =  TString::Format("%sData", pdf_->GetName());
     if (cat_ != 0) {
         //bool needsWeights = false;
         for (int i = 0, n = cat_->numBins((const char *)0); i < n; ++i) {
             if (pdfs_[i] == 0) continue;
             cat_->setBin(i);
             RooAbsData *&data =  datasetPieces_[cat_->getLabel()]; delete data;
             assert(protoData == 0);
             data = pdfs_[i]->generate(protoData); // I don't really know if protoData != 0 would make sense here
             if (data->isWeighted()) {
                 if (weightVar == 0) weightVar = new RooRealVar("_weight_","",1.0);
                 RooArgSet obs(*data->get()); 
                 obs.add(*weightVar);
                 RooDataSet *wdata = new RooDataSet(data->GetName(), "", obs, "_weight_");
                 for (int i = 0, n = data->numEntries(); i < n; ++i) {
                     obs = *data->get(i);
                     if (data->weight()) wdata->add(obs, data->weight());
                 }
                 //std::cout << "DataHist was " << std::endl; utils::printRAD(data);
                 delete data;
                 data = wdata;
                 //std::cout << "DataSet is " << std::endl; utils::printRAD(data);
             } 
             //if (data->isWeighted()) needsWeights = true;
         }
         if (copyData_) {
             RooArgSet vars(observables_), varsPlusWeight(observables_); 
             if (weightVar) varsPlusWeight.add(*weightVar);
             ret = new RooDataSet(retName, "", varsPlusWeight, (weightVar ? weightVar->GetName() : 0));
             for (std::map<std::string,RooAbsData*>::iterator it = datasetPieces_.begin(), ed = datasetPieces_.end(); it != ed; ++it) {
                 cat_->setLabel(it->first.c_str());
                 for (unsigned int i = 0, n = it->second->numEntries(); i < n; ++i) {
                     vars = *it->second->get(i);
                     ret->add(vars, it->second->weight());
                 }
             }
         } else {
             // not copyData is the "fast" mode used when generating toys as a ToyMCSampler.
             // this doesn't copy the data, so the toys cannot outlive this class and each new
             // toy over-writes the memory of the previous one.
             ret = new RooDataSet(retName, "", observables_, RooFit::Index((RooCategory&)*cat_), RooFit::Link(datasetPieces_) /*, RooFit::OwnLinked()*/);
         }
     } else ret = pdfs_[0]->generate(protoData, forceEvents);
     //std::cout << "Dataset generated from sim pdf (weighted? " << ret->isWeighted() << ")" << std::endl; utils::printRAD(ret);
     return ret;
 }

RooAbsData * toymcoptutils::SimPdfGenInfo::generateAsimov ( RooRealVar *& weightVar )

Definition at line 368 of file ToyMCSamplerOpt.cc.

References data, i, n, and run_regression::ret.

Referenced by asimovutils::asimovDatasetNominal(), asimovutils::asimovDatasetWithFit(), and Combine::run().

 {
     RooAbsData *ret = 0;
     TString retName =  TString::Format("%sData", pdf_->GetName());
     if (cat_ != 0) {
         //bool needsWeights = false;
         for (int i = 0, n = cat_->numBins((const char *)0); i < n; ++i) {
             if (pdfs_[i] == 0) continue;
             cat_->setBin(i);
             RooAbsData *&data =  datasetPieces_[cat_->getLabel()]; delete data;
             data = pdfs_[i]->generateAsimov(weightVar); 
         }
         if (copyData_) { 
             RooArgSet vars(observables_), varsPlusWeight(observables_); varsPlusWeight.add(*weightVar);
             ret = new RooDataSet(retName, "", varsPlusWeight, (weightVar ? weightVar->GetName() : 0));
             for (std::map<std::string,RooAbsData*>::iterator it = datasetPieces_.begin(), ed = datasetPieces_.end(); it != ed; ++it) {
                 cat_->setLabel(it->first.c_str());
                 for (unsigned int i = 0, n = it->second->numEntries(); i < n; ++i) {
                     vars = *it->second->get(i);
                     ret->add(vars, it->second->weight());
                 }
             }
         } else {
             // not copyData is the "fast" mode used when generating toys as a ToyMCSampler.
             // this doesn't copy the data, so the toys cannot outlive this class and each new
             // toy over-writes the memory of the previous one.
             ret = new RooDataSet(retName, "", observables_, RooFit::Index((RooCategory&)*cat_), RooFit::Link(datasetPieces_) /*, RooFit::OwnLinked()*/);
         }
     } else ret = pdfs_[0]->generateAsimov(weightVar);
     //std::cout << "Asimov dataset generated from sim pdf " << pdf_->GetName() << " (sumw? " << ret->sumEntries() << ")" << std::endl; 
     //utils::printRAD(ret);
     return ret;
 }

void toymcoptutils::SimPdfGenInfo::setCacheTemplates ( bool cache )

Definition at line 403 of file ToyMCSamplerOpt.cc.

Referenced by ToyMCSamplerOpt::Generate().

 {
     for (std::vector<SinglePdfGenInfo *>::const_iterator it = pdfs_.begin(), ed = pdfs_.end(); it != ed; ++it) {
         if (*it) (*it)->setCacheTemplates(cache);
     }
 }