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#include <ToyMCSamplerOpt.h>
Public Types | |
| enum | Mode { Binned, BinnedNoWorkaround, Poisson, Unbinned, Counting } |
Public Member Functions | |
| RooAbsData * | generate (const RooDataSet *protoData=NULL, int forceEvents=0) |
| RooDataSet * | generateAsimov (RooRealVar *&weightVar) |
| RooDataSet * | generatePseudoAsimov (RooRealVar *&weightVar, int nPoints) |
| const RooAbsPdf * | pdf () const |
| void | setCacheTemplates (bool cache) |
| SinglePdfGenInfo (RooAbsPdf &pdf, const RooArgSet &observables, bool preferBinned, const RooDataSet *protoData=NULL, int forceEvents=0) | |
| ~SinglePdfGenInfo () | |
Private Member Functions | |
| RooDataSet * | generateCountingAsimov () |
| RooDataSet * | generateWithHisto (RooRealVar *&weightVar, bool asimov) |
| void | setToExpected (RooProdPdf &prod, RooArgSet &obs) |
| void | setToExpected (RooPoisson &pois, RooArgSet &obs) |
Private Attributes | |
| TH1 * | histoSpec_ |
| bool | keepHistoSpec_ |
| Mode | mode_ |
| RooArgSet | observables_ |
| RooAbsPdf * | pdf_ |
| RooAbsPdf::GenSpec * | spec_ |
| RooRealVar * | weightVar_ |
Definition at line 9 of file ToyMCSamplerOpt.h.
Definition at line 11 of file ToyMCSamplerOpt.h.
{ Binned, BinnedNoWorkaround, Poisson, Unbinned, Counting };
| toymcoptutils::SinglePdfGenInfo::SinglePdfGenInfo | ( | RooAbsPdf & | pdf, |
| const RooArgSet & | observables, | ||
| bool | preferBinned, | ||
| const RooDataSet * | protoData = NULL, |
||
| int | forceEvents = 0 |
||
| ) |
Definition at line 56 of file ToyMCSamplerOpt.cc.
References Binned, BinnedNoWorkaround, runtimedef::get(), mode_, observables_, Poisson, and Unbinned.
: mode_(pdf.canBeExtended() ? (preferBinned ? Binned : Unbinned) : Counting), pdf_(&pdf), spec_(0),histoSpec_(0),keepHistoSpec_(0),weightVar_(0) { if (pdf.canBeExtended()) { if (pdf.getAttribute("forceGenBinned")) mode_ = Binned; else if (pdf.getAttribute("forceGenPoisson")) mode_ = Poisson; else if (pdf.getAttribute("forceGenUnbinned")) mode_ = Unbinned; } RooArgSet *obs = pdf.getObservables(observables); observables_.add(*obs); delete obs; if (mode_ == Binned) { if (runtimedef::get("TMCSO_GenBinned")) mode_ = BinnedNoWorkaround; else if (runtimedef::get("TMCSO_GenBinnedWorkaround")) mode_ = Binned; else mode_ = Poisson; } else if (mode_ == Unbinned) { //if (!runtimedef::get("TMCSO_NoPrepareMultiGen")) { // spec_ = protoData ? pdf.prepareMultiGen(observables_, RooFit::Extended(), RooFit::ProtoData(*protoData, true, true)) // : pdf.prepareMultiGen(observables_, RooFit::Extended()); //} } }
| toymcoptutils::SinglePdfGenInfo::~SinglePdfGenInfo | ( | ) |
Definition at line 82 of file ToyMCSamplerOpt.cc.
{
delete spec_;
delete weightVar_;
delete histoSpec_;
}
| RooAbsData * toymcoptutils::SinglePdfGenInfo::generate | ( | const RooDataSet * | protoData = NULL, |
| int | forceEvents = 0 |
||
| ) |
Definition at line 91 of file ToyMCSamplerOpt.cc.
References data, and run_regression::ret.
{
assert(forceEvents == 0 && "SinglePdfGenInfo: forceEvents must be zero at least for now");
RooAbsData *ret = 0;
switch (mode_) {
case Unbinned:
if (spec_ == 0) spec_ = protoData ? pdf_->prepareMultiGen(observables_, RooFit::Extended(), RooFit::ProtoData(*protoData, true, true))
: pdf_->prepareMultiGen(observables_, RooFit::Extended());
if (spec_) ret = pdf_->generate(*spec_);
else ret = pdf_->generate(observables_, RooFit::Extended());
break;
case Binned:
{ // aka generateBinnedWorkaround
RooDataSet *data = pdf_->generate(observables_, RooFit::Extended());
ret = new RooDataHist(data->GetName(), "", *data->get(), *data);
delete data;
}
break;
case BinnedNoWorkaround:
ret = protoData ? pdf_->generateBinned(observables_, RooFit::Extended(), RooFit::ProtoData(*protoData, true, true))
: pdf_->generateBinned(observables_, RooFit::Extended());
break;
case Poisson:
ret = generateWithHisto(weightVar_, false);
break;
case Counting:
ret = pdf_->generate(observables_, 1);
break;
default:
throw std::logic_error("Mode not foreseen in SinglePdfGenInfo::generate");
}
//std::cout << "Dataset generated from " << pdf_->GetName() << " (weighted? " << ret->isWeighted() << ")" << std::endl;
//utils::printRAD(ret);
return ret;
}
| RooDataSet * toymcoptutils::SinglePdfGenInfo::generateAsimov | ( | RooRealVar *& | weightVar | ) |
Definition at line 128 of file ToyMCSamplerOpt.cc.
References reco::get().
{
static int nPA = runtimedef::get("TMCSO_PseudoAsimov");
if (nPA) return generatePseudoAsimov(weightVar, nPA);
return generateWithHisto(weightVar, true);
}
| RooDataSet * toymcoptutils::SinglePdfGenInfo::generateCountingAsimov | ( | ) | [private] |
Definition at line 219 of file ToyMCSamplerOpt.cc.
References parseEventContent::prod, and run_regression::ret.
{
RooArgSet obs(observables_);
RooProdPdf *prod = dynamic_cast<RooProdPdf *>(pdf_);
RooPoisson *pois = 0;
if (prod != 0) {
setToExpected(*prod, observables_);
} else if ((pois = dynamic_cast<RooPoisson *>(pdf_)) != 0) {
setToExpected(*pois, observables_);
} else throw std::logic_error("A counting model pdf must be either a RooProdPdf or a RooPoisson");
RooDataSet *ret = new RooDataSet(TString::Format("%sData", pdf_->GetName()), "", obs);
ret->add(obs);
return ret;
}
| RooDataSet * toymcoptutils::SinglePdfGenInfo::generatePseudoAsimov | ( | RooRealVar *& | weightVar, |
| int | nPoints | ||
| ) |
Definition at line 136 of file ToyMCSamplerOpt.cc.
{
if (mode_ == Unbinned) {
double expEvents = pdf_->expectedEvents(observables_);
std::auto_ptr<RooDataSet> data(pdf_->generate(observables_, nPoints));
if (weightVar == 0) weightVar = new RooRealVar("_weight_","",1.0);
RooArgSet obsPlusW(observables_); obsPlusW.add(*weightVar);
RooDataSet *rds = new RooDataSet(data->GetName(), "", obsPlusW, weightVar->GetName());
for (int i = 0; i < nPoints; ++i) {
observables_ = *data->get(i);
rds->add(observables_, expEvents/nPoints);
}
return rds;
} else {
return generateWithHisto(weightVar, true);
}
}
| RooDataSet * toymcoptutils::SinglePdfGenInfo::generateWithHisto | ( | RooRealVar *& | weightVar, |
| bool | asimov | ||
| ) | [private] |
Definition at line 156 of file ToyMCSamplerOpt.cc.
References data, i, n, none, x, detailsBasic3DVector::y, and z.
{
if (mode_ == Counting) return generateCountingAsimov();
if (observables_.getSize() > 3) throw std::invalid_argument(std::string("ERROR in SinglePdfGenInfo::generateWithHisto for ") + pdf_->GetName() + ", more than 3 observable");
RooArgList obs(observables_);
RooRealVar *x = (RooRealVar*)obs.at(0);
RooRealVar *y = obs.getSize() > 1 ? (RooRealVar*)obs.at(1) : 0;
RooRealVar *z = obs.getSize() > 2 ? (RooRealVar*)obs.at(2) : 0;
if (weightVar == 0) weightVar = new RooRealVar("_weight_","",1.0);
RooCmdArg ay = (y ? RooFit::YVar(*y) : RooCmdArg::none());
RooCmdArg az = (z ? RooFit::ZVar(*z) : RooCmdArg::none());
if (histoSpec_ == 0) {
histoSpec_ = pdf_->createHistogram("htemp", *x, ay, az);
histoSpec_->SetDirectory(0);
}
double expectedEvents = pdf_->expectedEvents(observables_);
histoSpec_->Scale(expectedEvents/ histoSpec_->Integral());
RooArgSet obsPlusW(obs); obsPlusW.add(*weightVar);
RooDataSet *data = new RooDataSet(TString::Format("%sData", pdf_->GetName()), "", obsPlusW, weightVar->GetName());
RooAbsArg::setDirtyInhibit(true); // don't propagate dirty flags while filling histograms
switch (obs.getSize()) {
case 1:
for (int i = 1, n = histoSpec_->GetNbinsX(); i <= n; ++i) {
x->setVal(histoSpec_->GetXaxis()->GetBinCenter(i));
data->add(observables_, asimov ? histoSpec_->GetBinContent(i) : RooRandom::randomGenerator()->Poisson(histoSpec_->GetBinContent(i)) );
}
break;
case 2:
{
TH2& h2 = dynamic_cast<TH2&>(*histoSpec_);
for (int ix = 1, nx = h2.GetNbinsX(); ix <= nx; ++ix) {
for (int iy = 1, ny = h2.GetNbinsY(); iy <= ny; ++iy) {
x->setVal(h2.GetXaxis()->GetBinCenter(ix));
y->setVal(h2.GetYaxis()->GetBinCenter(iy));
data->add(observables_, asimov ? h2.GetBinContent(ix,iy) : RooRandom::randomGenerator()->Poisson(h2.GetBinContent(ix,iy)) );
} }
}
break;
case 3:
{
TH3& h3 = dynamic_cast<TH3&>(*histoSpec_);
for (int ix = 1, nx = h3.GetNbinsX(); ix <= nx; ++ix) {
for (int iy = 1, ny = h3.GetNbinsY(); iy <= ny; ++iy) {
for (int iz = 1, nz = h3.GetNbinsZ(); iz <= nz; ++iz) {
x->setVal(h3.GetXaxis()->GetBinCenter(ix));
y->setVal(h3.GetYaxis()->GetBinCenter(iy));
z->setVal(h3.GetYaxis()->GetBinCenter(iz));
data->add(observables_, asimov ? h3.GetBinContent(ix,iy,iz) : RooRandom::randomGenerator()->Poisson(h3.GetBinContent(ix,iy,iz)) );
} } }
}
}
RooAbsArg::setDirtyInhibit(false); // restore proper propagation of dirty flags
if (!keepHistoSpec_) { delete histoSpec_; histoSpec_ = 0; }
//std::cout << "Asimov dataset generated from " << pdf_->GetName() << " (sumw? " << data->sumEntries() << ", expected events " << expectedEvents << ")" << std::endl;
//utils::printRDH(data);
return data;
}
| const RooAbsPdf* toymcoptutils::SinglePdfGenInfo::pdf | ( | ) | const [inline] |
| void toymcoptutils::SinglePdfGenInfo::setCacheTemplates | ( | bool | cache | ) | [inline] |
Definition at line 18 of file ToyMCSamplerOpt.h.
References tests::test_CacheProxy::cache, and keepHistoSpec_.
{ keepHistoSpec_ = cache; }
| void toymcoptutils::SinglePdfGenInfo::setToExpected | ( | RooProdPdf & | prod, |
| RooArgSet & | obs | ||
| ) | [private] |
Definition at line 235 of file ToyMCSamplerOpt.cc.
References a.
{
std::auto_ptr<TIterator> iter(prod.pdfList().createIterator());
for (RooAbsArg *a = (RooAbsArg *) iter->Next(); a != 0; a = (RooAbsArg *) iter->Next()) {
if (!a->dependsOn(obs)) continue;
RooPoisson *pois = 0;
if ((pois = dynamic_cast<RooPoisson *>(a)) != 0) {
setToExpected(*pois, obs);
} else {
RooProdPdf *subprod = dynamic_cast<RooProdPdf *>(a);
if (subprod) setToExpected(*subprod, obs);
else throw std::logic_error("Illegal term in counting model: depends on observables, but not Poisson or Product");
}
}
}
| void toymcoptutils::SinglePdfGenInfo::setToExpected | ( | RooPoisson & | pois, |
| RooArgSet & | obs | ||
| ) | [private] |
Definition at line 252 of file ToyMCSamplerOpt.cc.
References a.
{
RooRealVar *myobs = 0;
RooAbsReal *myexp = 0;
std::auto_ptr<TIterator> iter(pois.serverIterator());
for (RooAbsArg *a = (RooAbsArg *) iter->Next(); a != 0; a = (RooAbsArg *) iter->Next()) {
if (obs.contains(*a)) {
assert(myobs == 0 && "SinglePdfGenInfo::setToExpected(RooPoisson): Two observables??");
myobs = dynamic_cast<RooRealVar *>(a);
assert(myobs != 0 && "SinglePdfGenInfo::setToExpected(RooPoisson): Observables is not a RooRealVar??");
} else {
assert(myexp == 0 && "SinglePdfGenInfo::setToExpected(RooPoisson): Two expecteds??");
myexp = dynamic_cast<RooAbsReal *>(a);
assert(myexp != 0 && "SinglePdfGenInfo::setToExpected(RooPoisson): Expectedis not a RooAbsReal??");
}
}
assert(myobs != 0 && "SinglePdfGenInfo::setToExpected(RooPoisson): No observable?");
assert(myexp != 0 && "SinglePdfGenInfo::setToExpected(RooPoisson): No expected?");
myobs->setVal(myexp->getVal());
}
TH1* toymcoptutils::SinglePdfGenInfo::histoSpec_ [private] |
Definition at line 24 of file ToyMCSamplerOpt.h.
bool toymcoptutils::SinglePdfGenInfo::keepHistoSpec_ [private] |
Definition at line 25 of file ToyMCSamplerOpt.h.
Referenced by setCacheTemplates().
Mode toymcoptutils::SinglePdfGenInfo::mode_ [private] |
Definition at line 20 of file ToyMCSamplerOpt.h.
Referenced by SinglePdfGenInfo().
RooArgSet toymcoptutils::SinglePdfGenInfo::observables_ [private] |
Definition at line 22 of file ToyMCSamplerOpt.h.
Referenced by SinglePdfGenInfo().
RooAbsPdf* toymcoptutils::SinglePdfGenInfo::pdf_ [private] |
Definition at line 21 of file ToyMCSamplerOpt.h.
Referenced by pdf().
RooAbsPdf::GenSpec* toymcoptutils::SinglePdfGenInfo::spec_ [private] |
Definition at line 23 of file ToyMCSamplerOpt.h.
RooRealVar* toymcoptutils::SinglePdfGenInfo::weightVar_ [private] |
Definition at line 26 of file ToyMCSamplerOpt.h.
1.7.3