Special pdf's: unbinned maximum likelihood fit of an efficiency eff(x) function to a dataset D(x,cut), cut is a category encoding a selection whose efficiency as function of x should be described by eff(x)
void rf702_efficiencyfit_2D(
bool flat =
false)
{
RooFormulaVar effFunc(
"effFunc",
"((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))",
RooCategory cut(
"cut",
"cutr", { {
"accept", 1}, {
"reject", 0} });
std::unique_ptr<RooDataSet>
data{model.generate({
x,
y, cut}, 10000)};
TCanvas *
ca =
new TCanvas(
"rf702_efficiency_2D",
"rf702_efficiency_2D", 1200, 400);
gPad->SetLeftMargin(0.15);
gPad->SetLeftMargin(0.15);
gPad->SetLeftMargin(0.15);
hh_eff->GetZaxis()->SetTitleOffset(1.8);
return;
}
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void data
RooArgList is a container object that can hold multiple RooAbsArg objects.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Object to represent discrete states.
A PDF helper class to fit efficiencies parameterized by a supplied function F.
RooPolynomial implements a polynomial p.d.f of the form.
Efficient implementation of a product of PDFs of the form.
Variable that can be changed from the outside.
TH1 is the base class of all histogram classes in ROOT.
RooCmdArg YVar(const RooAbsRealLValue &var, const RooCmdArg &arg={})
RooCmdArg Conditional(const RooArgSet &pdfSet, const RooArgSet &depSet, bool depsAreCond=false)
RooConstVar & RooConst(double val)
RooCmdArg PrintLevel(Int_t code)
RooCmdArg ConditionalObservables(Args_t &&... argsOrArgSet)
Create a RooCmdArg to declare conditional observables.
RooCmdArg Binning(const RooAbsBinning &binning)
RooCmdArg Cut(const char *cutSpec)
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
[#0] WARNING:Generation -- RooAcceptReject::ctor(effPdf_Int[]_Norm[cut]) WARNING: performing accept/reject sampling on a p.d.f in 2 dimensions without prior knowledge on maximum value of p.d.f. Determining maximum value by taking 200000 trial samples. If p.d.f contains sharp peaks smaller than average distance between trial sampling points these may be missed and p.d.f. may be sampled incorrectly.
[#0] WARNING:Generation -- RooAcceptReject::ctor(effPdf_Int[]_Norm[cut]): WARNING: 200000 trial samples requested by p.d.f for 2-dimensional accept/reject sampling, this may take some time
[#1] INFO:Fitting -- RooAbsPdf::fitTo(effPdf_over_effPdf_Int[cut]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx512
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_effPdf_over_effPdf_Int[cut]_modelData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
- Date
- February 2018
- Authors
- Clemens Lange, Wouter Verkerke (C++ version)
Definition in file rf702_efficiencyfit_2D.C.