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RooAbsPdf Class Reference

RooAbsPdf, the base class of all PDFs

RooAbsPdf is the abstract interface for all probability density functions. The class provides hybrid analytical/numerical normalization for its implementations, error tracing and a MC generator interface.

A Minimal PDF Implementation

A minimal implementation of a PDF class derived from RooAbsPdf should override the evaluate() function. This function should return the PDF's value (which does not need to be normalised).

Normalization/Integration

Although the normalization of a PDF is an integral part of a probability density function, normalization is treated separately in RooAbsPdf. The reason is that a RooAbsPdf object is more than a PDF: it can be a building block for a more complex, composite PDF if any of its variables are functions instead of variables. In such cases the normalization of the composite may not be simply the integral over the dependents of the top level PDF as these are functions with potentially non-trivial Jacobian terms themselves.

Note
Therefore, no explicit attempt should be made to normalize the function output in evaluate(). In particular, normalisation constants can be omitted to speed up the function evaluations, and included later in the integration of the PDF (see below), which is called rarely in comparison to the evaluate() function.

In addition, RooAbsPdf objects do not have a static concept of what variables are parameters and what variables are dependents (which need to be integrated over for a correct PDF normalization). Instead, the choice of normalization is always specified each time a normalized value is requested from the PDF via the getVal() method.

RooAbsPdf manages the entire normalization logic of each PDF with help of a RooRealIntegral object, which coordinates the integration of a given choice of normalization. By default, RooRealIntegral will perform a fully numeric integration of all dependents. However, PDFs can advertise one or more (partial) analytical integrals of their function, and these will be used by RooRealIntegral, if it determines that this is safe (i.e. no hidden Jacobian terms, multiplication with other PDFs that have one or more dependents in commen etc).

Implementing analytical integrals

To implement analytical integrals, two functions must be implemented. First,

Int_t getAnalyticalIntegral(const RooArgSet& integSet, RooArgSet& anaIntSet)
virtual Int_t getAnalyticalIntegral(RooArgSet &allVars, RooArgSet &analVars, const char *rangeName=0) const
Interface function getAnalyticalIntergral advertises the analytical integrals that are supported.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:35

should return the analytical integrals that are supported. integSet is the set of dependents for which integration is requested. The function should copy the subset of dependents it can analytically integrate to anaIntSet, and return a unique identification code for this integration configuration. If no integration can be performed, zero should be returned. Second,

double Double_t
Definition RtypesCore.h:59
virtual Double_t analyticalIntegral(Int_t code, const char *rangeName=0) const
Implements the actual analytical integral(s) advertised by getAnalyticalIntegral.

implements the actual analytical integral(s) advertised by getAnalyticalIntegral(). This function will only be called with codes returned by getAnalyticalIntegral(), except code zero.

The integration range for each dependent to be integrated can be obtained from the dependent's proxy functions min() and max(). Never call these proxy functions for any proxy not known to be a dependent via the integration code. Doing so may be ill-defined, e.g. in case the proxy holds a function, and will trigger an assert. Integrated category dependents should always be summed over all of their states.

Direct generation of observables

Distributions for any PDF can be generated with the accept/reject method, but for certain PDFs, more efficient methods may be implemented. To implement direct generation of one or more observables, two functions need to be implemented, similar to those for analytical integrals:

Int_t getGenerator(const RooArgSet& generateVars, RooArgSet& directVars)
virtual Int_t getGenerator(const RooArgSet &directVars, RooArgSet &generateVars, Bool_t staticInitOK=kTRUE) const
Load generatedVars with the subset of directVars that we can generate events for, and return a code t...

and

void generateEvent(Int_t code)
virtual void generateEvent(Int_t code)
Interface for generation of an event using the algorithm corresponding to the specified code.

The first function advertises observables, for which distributions can be generated, similar to the way analytical integrals are advertised. The second function implements the actual generator for the advertised observables.

The generated dependent values should be stored in the proxy objects. For this, the assignment operator can be used (i.e. xProxy = 3.0 ). Never call assign to any proxy not known to be a dependent via the generation code. Doing so may be ill-defined, e.g. in case the proxy holds a function, and will trigger an assert.

Batched function evaluations (Advanced usage)

To speed up computations with large numbers of data events in unbinned fits, it is beneficial to override evaluateSpan(). Like this, large spans of computations can be done, without having to call evaluate() for each single data event. evaluateSpan() should execute the same computation as evaluate(), but it may choose an implementation that is capable of SIMD computations. If evaluateSpan is not implemented, the classic and slower evaluate() will be called for each data event.

PyROOT

Some member functions of RooAbsPdf that take a RooCmdArg as argument also support keyword arguments. So far, this applies to RooAbsPdf::fitTo, RooAbsPdf::plotOn, RooAbsPdf::generate, RooAbsPdf::paramOn, RooAbsPdf::createCdf, RooAbsPdf::generateBinned, RooAbsPdf::createChi2, RooAbsPdf::prepareMultiGen and RooAbsPdf::createNLL. For example, the following code is equivalent in PyROOT:

# Directly passing a RooCmdArg:
pdf.fitTo(data, ROOT.RooFit.Range("r1"))
# With keyword arguments:
pdf.fitTo(data, Range="r1")

Definition at line 43 of file RooAbsPdf.h.

Classes

class  CacheElem
 Normalization set with for above integral. More...
 
class  GenSpec
 
struct  MinimizerConfig
 Configuration struct for RooAbsPdf::minimizeNLL with all the default. More...
 

Public Types

enum  ExtendMode { CanNotBeExtended , CanBeExtended , MustBeExtended }
 
- Public Types inherited from RooAbsReal
enum  ErrorLoggingMode { PrintErrors , CollectErrors , CountErrors , Ignore }
 
typedef std::map< constRooAbsArg *, std::pair< std::string, std::list< EvalError > > >::const_iterator EvalErrorIter
 
enum  ScaleType { Raw , Relative , NumEvent , RelativeExpected }
 
using value_type = double
 
- Public Types inherited from RooAbsArg
enum  CacheMode { Always =0 , NotAdvised =1 , Never =2 }
 
enum  ConstOpCode { Activate =0 , DeActivate =1 , ConfigChange =2 , ValueChange =3 }
 
enum  OperMode { Auto =0 , AClean =1 , ADirty =2 }
 
using RefCountList_t = RooSTLRefCountList< RooAbsArg >
 
using RefCountListLegacyIterator_t = TIteratorToSTLInterface< RefCountList_t::Container_t >
 
- Public Types inherited from TObject
enum  {
  kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 ,
  kBitMask = 0x00ffffff
}
 
enum  { kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) }
 
enum  EDeprecatedStatusBits { kObjInCanvas = BIT(3) }
 
enum  EStatusBits {
  kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) ,
  kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13)
}
 
- Public Types inherited from RooPrintable
enum  ContentsOption {
  kName =1 , kClassName =2 , kValue =4 , kArgs =8 ,
  kExtras =16 , kAddress =32 , kTitle =64 , kCollectionHeader =128
}
 
enum  StyleOption {
  kInline =1 , kSingleLine =2 , kStandard =3 , kVerbose =4 ,
  kTreeStructure =5
}
 

Public Member Functions

 RooAbsPdf ()
 Default constructor.
 
 RooAbsPdf (const char *name, const char *title, Double_t minVal, Double_t maxVal)
 Constructor with name, title, and plot range.
 
 RooAbsPdf (const char *name, const char *title=0)
 Constructor with name and title only.
 
virtual ~RooAbsPdf ()
 Destructor.
 
Double_t analyticalIntegralWN (Int_t code, const RooArgSet *normSet, const char *rangeName=0) const
 Analytical integral with normalization (see RooAbsReal::analyticalIntegralWN() for further information)
 
virtual RooAbsGenContextautoGenContext (const RooArgSet &vars, const RooDataSet *prototype=0, const RooArgSet *auxProto=0, Bool_t verbose=kFALSE, Bool_t autoBinned=kTRUE, const char *binnedTag="") const
 
virtual RooAbsGenContextbinnedGenContext (const RooArgSet &vars, Bool_t verbose=kFALSE) const
 Return a binned generator context.
 
Bool_t canBeExtended () const
 If true, PDF can provide extended likelihood term.
 
virtual RooFitResultchi2FitTo (RooDataHist &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Perform a \( \chi^2 \) fit to given histogram.
 
virtual RooFitResultchi2FitTo (RooDataHist &data, const RooLinkedList &cmdList)
 Perform a \( \chi^2 \) fit to given histogram.
 
virtual RooFitResultchi2FitTo (RooDataHist &data, const RooLinkedList &cmdList)
 Calls RooAbsPdf::createChi2(RooDataSet& data, const RooLinkedList& cmdList) and returns fit result.
 
virtual RooFitResultchi2FitTo (RooDataSet &xydata, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Perform a 2-D \( \chi^2 \) fit using a series of x and y values stored in the dataset xydata.
 
virtual RooFitResultchi2FitTo (RooDataSet &xydata, const RooLinkedList &cmdList)
 Perform a 2-D \( \chi^2 \) fit using a series of x and y values stored in the dataset xydata.
 
RooAbsRealcreateCdf (const RooArgSet &iset, const RooArgSet &nset=RooArgSet())
 Create a cumulative distribution function of this p.d.f in terms of the observables listed in iset.
 
RooAbsRealcreateCdf (const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Create an object that represents the integral of the function over one or more observables listed in iset.
 
virtual RooAbsRealcreateChi2 (RooDataHist &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Create a \( \chi^2 \) variable from a histogram and this function.
 
virtual RooAbsRealcreateChi2 (RooDataHist &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Create a \( \chi^2 \) from a histogram and this function.
 
virtual RooAbsRealcreateChi2 (RooDataHist &data, const RooLinkedList &cmdList)
 Create a \( \chi^2 \) variable from a histogram and this function.
 
virtual RooAbsRealcreateChi2 (RooDataSet &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Create a \( \chi^2 \) from a series of x and y values stored in a dataset.
 
virtual RooAbsRealcreateChi2 (RooDataSet &data, const RooLinkedList &cmdList)
 See RooAbsReal::createChi2(RooDataSet&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&)
 
virtual RooAbsRealcreateChi2 (RooDataSet &data, const RooLinkedList &cmdList)
 Argument-list version of RooAbsPdf::createChi2()
 
virtual RooAbsRealcreateNLL (RooAbsData &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Construct representation of -log(L) of PDF with given dataset.
 
virtual RooAbsRealcreateNLL (RooAbsData &data, const RooLinkedList &cmdList)
 Construct representation of -log(L) of PDFwith given dataset.
 
virtual RooAbsPdfcreateProjection (const RooArgSet &iset)
 Return a p.d.f that represent a projection of this p.d.f integrated over given observables.
 
RooAbsRealcreateScanCdf (const RooArgSet &iset, const RooArgSet &nset, Int_t numScanBins, Int_t intOrder)
 
double expectedEvents (const RooArgSet &nset) const
 Return expected number of events to be used in calculation of extended likelihood.
 
virtual Double_t expectedEvents (const RooArgSet *nset) const
 Return expected number of events to be used in calculation of extended likelihood.
 
double extendedTerm (double sumEntries, double expected, double sumEntriesW2=0.0) const
 
double extendedTerm (double sumEntries, RooArgSet const *nset, double sumEntriesW2=0.0) const
 Return the extended likelihood term ( \( N_\mathrm{expect} - N_\mathrm{observed} \cdot \log(N_\mathrm{expect} \)) of this PDF for the given number of observed events.
 
double extendedTerm (RooAbsData const &data, bool weightSquared) const
 Return the extended likelihood term ( \( N_\mathrm{expect} - N_\mathrm{observed} \cdot \log(N_\mathrm{expect} \)) of this PDF for the given number of observed events.
 
virtual ExtendMode extendMode () const
 Returns ability of PDF to provide extended likelihood terms.
 
virtual RooFitResultfitTo (RooAbsData &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Fit PDF to given dataset.
 
virtual RooFitResultfitTo (RooAbsData &data, const RooLinkedList &cmdList)
 Fit PDF to given dataset.
 
virtual RooAbsGenContextgenContext (const RooArgSet &vars, const RooDataSet *prototype=0, const RooArgSet *auxProto=0, Bool_t verbose=kFALSE) const
 Interface function to create a generator context from a p.d.f.
 
RooDataSetgenerate (const RooArgSet &whatVars, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none())
 Generate a new dataset containing the specified variables with events sampled from our distribution.
 
RooDataSetgenerate (const RooArgSet &whatVars, const RooDataSet &prototype, Int_t nEvents=0, Bool_t verbose=kFALSE, Bool_t randProtoOrder=kFALSE, Bool_t resampleProto=kFALSE) const
 Generate a new dataset using a prototype dataset as a model, with values of the variables in whatVars sampled from our distribution.
 
RooDataSetgenerate (const RooArgSet &whatVars, Double_t nEvents=0, Bool_t verbose=kFALSE, Bool_t autoBinned=kTRUE, const char *binnedTag="", Bool_t expectedData=kFALSE, Bool_t extended=kFALSE) const
 Generate a new dataset containing the specified variables with events sampled from our distribution.
 
RooDataSetgenerate (const RooArgSet &whatVars, Int_t nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none())
 See RooAbsPdf::generate(const RooArgSet&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&)
 
RooDataSetgenerate (GenSpec &) const
 Generate according to GenSpec obtained from prepareMultiGen().
 
virtual RooDataHistgenerateBinned (const RooArgSet &whatVars, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none()) const
 Generate a new dataset containing the specified variables with events sampled from our distribution.
 
virtual RooDataHistgenerateBinned (const RooArgSet &whatVars, Double_t nEvents, Bool_t expectedData=kFALSE, Bool_t extended=kFALSE) const
 Generate a new dataset containing the specified variables with events sampled from our distribution.
 
virtual RooDataHistgenerateBinned (const RooArgSet &whatVars, Double_t nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none()) const
 As RooAbsPdf::generateBinned(const RooArgSet&, const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&)
 
virtual void generateEvent (Int_t code)
 Interface for generation of an event using the algorithm corresponding to the specified code.
 
virtual RooDataSetgenerateSimGlobal (const RooArgSet &whatVars, Int_t nEvents)
 Special generator interface for generation of 'global observables' – for RooStats tools.
 
virtual RooArgSetgetAllConstraints (const RooArgSet &observables, RooArgSet &constrainedParams, Bool_t stripDisconnected=kTRUE) const
 This helper function finds and collects all constraints terms of all component p.d.f.s and returns a RooArgSet with all those terms.
 
virtual RooArgSetgetConstraints (const RooArgSet &, RooArgSet &, Bool_t) const
 
virtual Int_t getGenerator (const RooArgSet &directVars, RooArgSet &generateVars, Bool_t staticInitOK=kTRUE) const
 Load generatedVars with the subset of directVars that we can generate events for, and return a code that specifies the generator algorithm we will use.
 
const RooNumGenConfiggetGeneratorConfig () const
 Return the numeric MC generator configuration used for this object.
 
RooSpan< const doublegetLogProbabilities (RooBatchCompute::RunContext &evalData, const RooArgSet *normSet=nullptr) const
 Compute the log-likelihoods for all events in the requested batch.
 
void getLogProbabilities (RooSpan< const double > pdfValues, double *output) const
 
virtual Double_t getLogVal (const RooArgSet *set=0) const
 Return the log of the current value with given normalization An error message is printed if the argument of the log is negative.
 
RooSpan< const doublegetLogValBatch (std::size_t begin, std::size_t batchSize, const RooArgSet *normSet=nullptr) const
 
Double_t getNorm (const RooArgSet &nset) const
 Get normalisation term needed to normalise the raw values returned by getVal().
 
virtual Double_t getNorm (const RooArgSet *set=0) const
 Get normalisation term needed to normalise the raw values returned by getVal().
 
const RooAbsRealgetNormIntegral (const RooArgSet &nset) const
 
virtual const RooAbsRealgetNormObj (const RooArgSet *set, const RooArgSet *iset, const TNamed *rangeName=0) const
 Return pointer to RooAbsReal object that implements calculation of integral over observables iset in range rangeName, optionally taking the integrand normalized over observables nset.
 
std::vector< doublegetValues (RooAbsData const &data, RooFit::BatchModeOption batchMode=RooFit::BatchModeOption::Cpu) const
 
RooSpan< const doublegetValues (RooBatchCompute::RunContext &evalData, const RooArgSet *normSet) const
 Compute batch of values for given input data, and normalise by integrating over the observables in normSet.
 
virtual RooSpan< const doublegetValues (RooBatchCompute::RunContext &evalData, const RooArgSet *normSet=nullptr) const
 
virtual Double_t getValV (const RooArgSet *set=0) const
 Return current value, normalized by integrating over the observables in nset.
 
virtual void initGenerator (Int_t code)
 Interface for one-time initialization to setup the generator for the specified code.
 
virtual Bool_t isDirectGenSafe (const RooAbsArg &arg) const
 Check if given observable can be safely generated using the pdfs internal generator mechanism (if that existsP).
 
std::unique_ptr< RooFitResultminimizeNLL (RooAbsReal &nll, RooAbsData const &data, MinimizerConfig const &cfg)
 Minimizes a given NLL variable by finding the optimal parameters with the RooMinimzer.
 
Bool_t mustBeExtended () const
 If true PDF must provide extended likelihood term.
 
const char * normRange () const
 
virtual RooPlotparamOn (RooPlot *frame, const RooAbsData *data, const char *label="", Int_t sigDigits=2, Option_t *options="NELU", Double_t xmin=0.65, Double_t xmax=0.9, Double_t ymax=0.9)
 
virtual RooPlotparamOn (RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Add a box with parameter values (and errors) to the specified frame.
 
virtual RooPlotplotOn (RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none(), const RooCmdArg &arg9=RooCmdArg::none(), const RooCmdArg &arg10=RooCmdArg::none()) const
 Helper calling plotOn(RooPlot*, RooLinkedList&) const.
 
virtual RooPlotplotOn (RooPlot *frame, RooLinkedList &cmdList) const
 Plot (project) PDF on specified frame.
 
GenSpecprepareMultiGen (const RooArgSet &whatVars, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none())
 Prepare GenSpec configuration object for efficient generation of multiple datasets from identical specification.
 
virtual void printMultiline (std::ostream &os, Int_t contents, Bool_t verbose=kFALSE, TString indent="") const
 Print multi line detailed information of this RooAbsPdf.
 
virtual void printValue (std::ostream &os) const
 Print value of p.d.f, also print normalization integral that was last used, if any.
 
virtual void resetErrorCounters (Int_t resetValue=10)
 Reset error counter to given value, limiting the number of future error messages for this pdf to 'resetValue'.
 
virtual Bool_t selfNormalized () const
 Shows if a PDF is self-normalized, which means that no attempt is made to add a normalization term.
 
void setGeneratorConfig ()
 Remove the specialized numeric MC generator configuration associated with this object.
 
void setGeneratorConfig (const RooNumGenConfig &config)
 Set the given configuration as default numeric MC generator configuration for this object.
 
void setNormRange (const char *rangeName)
 
void setNormRangeOverride (const char *rangeName)
 
void setTraceCounter (Int_t value, Bool_t allNodes=kFALSE)
 Reset trace counter to given value, limiting the number of future trace messages for this pdf to 'value'.
 
RooNumGenConfigspecialGeneratorConfig () const
 Returns the specialized integrator configuration for this RooAbsReal.
 
RooNumGenConfigspecialGeneratorConfig (Bool_t createOnTheFly)
 Returns the specialized integrator configuration for this RooAbsReal.
 
- Public Member Functions inherited from RooAbsReal
 RooAbsReal ()
 coverity[UNINIT_CTOR] Default constructor
 
 RooAbsReal (const char *name, const char *title, const char *unit="")
 Constructor with unit label.
 
 RooAbsReal (const char *name, const char *title, Double_t minVal, Double_t maxVal, const char *unit="")
 Constructor with plot range and unit label.
 
 RooAbsReal (const RooAbsReal &other, const char *name=0)
 Copy constructor.
 
virtual ~RooAbsReal ()
 Destructor.
 
virtual Double_t analyticalIntegral (Int_t code, const char *rangeName=0) const
 Implements the actual analytical integral(s) advertised by getAnalyticalIntegral.
 
TF1asTF (const RooArgList &obs, const RooArgList &pars=RooArgList(), const RooArgSet &nset=RooArgSet()) const
 Return a ROOT TF1,2,3 object bound to this RooAbsReal with given definition of observables and parameters.
 
virtual std::list< Double_t > * binBoundaries (RooAbsRealLValue &obs, Double_t xlo, Double_t xhi) const
 Retrieve bin boundaries if this distribution is binned in obs.
 
RooAbsFuncbindVars (const RooArgSet &vars, const RooArgSet *nset=0, Bool_t clipInvalid=kFALSE) const
 Create an interface adaptor f(vars) that binds us to the specified variables (in arbitrary order).
 
virtual void computeBatch (cudaStream_t *, double *output, size_t size, RooFit::Detail::DataMap const &) const
 Base function for computing multiple values of a RooAbsReal.
 
RooAbsArgcreateFundamental (const char *newname=0) const
 Create a RooRealVar fundamental object with our properties.
 
TH1createHistogram (const char *name, const RooAbsRealLValue &xvar, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
 Create and fill a ROOT histogram TH1, TH2 or TH3 with the values of this function.
 
TH1createHistogram (const char *name, const RooAbsRealLValue &xvar, RooLinkedList &argList) const
 Internal method implementing createHistogram.
 
TH1createHistogram (const char *varNameList, Int_t xbins=0, Int_t ybins=0, Int_t zbins=0) const
 Create and fill a ROOT histogram TH1, TH2 or TH3 with the values of this function for the variables with given names.
 
RooAbsRealcreateIntegral (const RooArgSet &iset, const char *rangeName) const
 Create integral over observables in iset in range named rangeName.
 
RooAbsRealcreateIntegral (const RooArgSet &iset, const RooArgSet &nset, const char *rangeName=0) const
 Create integral over observables in iset in range named rangeName with integrand normalized over observables in nset.
 
RooAbsRealcreateIntegral (const RooArgSet &iset, const RooArgSet &nset, const RooNumIntConfig &cfg, const char *rangeName=0) const
 Create integral over observables in iset in range named rangeName with integrand normalized over observables in nset while using specified configuration for any numeric integration.
 
virtual RooAbsRealcreateIntegral (const RooArgSet &iset, const RooArgSet *nset=0, const RooNumIntConfig *cfg=0, const char *rangeName=0) const
 Create an object that represents the integral of the function over one or more observables listed in iset.
 
RooAbsRealcreateIntegral (const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
 Create an object that represents the integral of the function over one or more observables listed in iset.
 
RooAbsRealcreateIntegral (const RooArgSet &iset, const RooNumIntConfig &cfg, const char *rangeName=0) const
 Create integral over observables in iset in range named rangeName using specified configuration for any numeric integration.
 
RooAbsRealcreateIntRI (const RooArgSet &iset, const RooArgSet &nset=RooArgSet())
 Utility function for createRunningIntegral.
 
const RooAbsRealcreatePlotProjection (const RooArgSet &dependentVars, const RooArgSet *projectedVars, RooArgSet *&cloneSet, const char *rangeName=0, const RooArgSet *condObs=0) const
 Utility function for plotOn() that creates a projection of a function or p.d.f to be plotted on a RooPlot.
 
const RooAbsRealcreatePlotProjection (const RooArgSet &depVars, const RooArgSet &projVars, RooArgSet *&cloneSet) const
 Utility function for plotOn() that creates a projection of a function or p.d.f to be plotted on a RooPlot.
 
virtual RooAbsRealcreateProfile (const RooArgSet &paramsOfInterest)
 Create a RooProfileLL object that eliminates all nuisance parameters in the present function.
 
RooAbsRealcreateRunningIntegral (const RooArgSet &iset, const RooArgSet &nset=RooArgSet())
 Calls createRunningIntegral(const RooArgSet&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&)
 
RooAbsRealcreateRunningIntegral (const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
 Create an object that represents the running integral of the function over one or more observables listed in iset, i.e.
 
RooAbsRealcreateScanRI (const RooArgSet &iset, const RooArgSet &nset, Int_t numScanBins, Int_t intOrder)
 Utility function for createRunningIntegral that construct an object implementing the numeric scanning technique for calculating the running integral.
 
virtual Double_t defaultErrorLevel () const
 
RooDerivativederivative (RooRealVar &obs, const RooArgSet &normSet, Int_t order, Double_t eps=0.001)
 Return function representing first, second or third order derivative of this function.
 
RooDerivativederivative (RooRealVar &obs, Int_t order=1, Double_t eps=0.001)
 Return function representing first, second or third order derivative of this function.
 
virtual void enableOffsetting (Bool_t)
 
RooDataHistfillDataHist (RooDataHist *hist, const RooArgSet *nset, Double_t scaleFactor, Bool_t correctForBinVolume=kFALSE, Bool_t showProgress=kFALSE) const
 Fill a RooDataHist with values sampled from this function at the bin centers.
 
TH1fillHistogram (TH1 *hist, const RooArgList &plotVars, Double_t scaleFactor=1, const RooArgSet *projectedVars=0, Bool_t scaling=kTRUE, const RooArgSet *condObs=0, Bool_t setError=kTRUE) const
 Fill the ROOT histogram 'hist' with values sampled from this function at the bin centers.
 
Double_t findRoot (RooRealVar &x, Double_t xmin, Double_t xmax, Double_t yval)
 Return value of x (in range xmin,xmax) at which function equals yval.
 
virtual void fixAddCoefNormalization (const RooArgSet &addNormSet=RooArgSet(), Bool_t force=kTRUE)
 Fix the interpretation of the coefficient of any RooAddPdf component in the expression tree headed by this object to the given set of observables.
 
virtual void fixAddCoefRange (const char *rangeName=0, Bool_t force=kTRUE)
 Fix the interpretation of the coefficient of any RooAddPdf component in the expression tree headed by this object to the given set of observables.
 
virtual Bool_t forceAnalyticalInt (const RooAbsArg &) const
 
virtual void forceNumInt (Bool_t flag=kTRUE)
 
RooFunctorfunctor (const RooArgList &obs, const RooArgList &pars=RooArgList(), const RooArgSet &nset=RooArgSet()) const
 Return a RooFunctor object bound to this RooAbsReal with given definition of observables and parameters.
 
virtual Int_t getAnalyticalIntegral (RooArgSet &allVars, RooArgSet &analVars, const char *rangeName=0) const
 Interface function getAnalyticalIntergral advertises the analytical integrals that are supported.
 
virtual Int_t getAnalyticalIntegralWN (RooArgSet &allVars, RooArgSet &analVars, const RooArgSet *normSet, const char *rangeName=0) const
 Variant of getAnalyticalIntegral that is also passed the normalization set that should be applied to the integrand of which the integral is requested.
 
Bool_t getForceNumInt () const
 
RooNumIntConfiggetIntegratorConfig ()
 Return the numeric integration configuration used for this object.
 
const RooNumIntConfiggetIntegratorConfig () const
 Return the numeric integration configuration used for this object.
 
virtual Int_t getMaxVal (const RooArgSet &vars) const
 Advertise capability to determine maximum value of function for given set of observables.
 
const char * getPlotLabel () const
 Get the label associated with the variable.
 
Double_t getPropagatedError (const RooFitResult &fr, const RooArgSet &nset=RooArgSet()) const
 Calculate error on self by linearly propagating errors on parameters using the covariance matrix from a fit result.
 
TString getTitle (Bool_t appendUnit=kFALSE) const
 Return this variable's title string.
 
const Text_tgetUnit () const
 
Double_t getVal (const RooArgSet &normalisationSet) const
 Like getVal(const RooArgSet*), but always requires an argument for normalisation.
 
Double_t getVal (const RooArgSet *normalisationSet=nullptr) const
 Evaluate object.
 
virtual RooSpan< const doublegetValBatch (std::size_t, std::size_t, const RooArgSet *=nullptr)=delete
 
std::vector< doublegetValues (RooAbsData const &data, RooFit::BatchModeOption batchMode=RooFit::BatchModeOption::Cpu) const
 
RooMultiGenFunctioniGenFunction (const RooArgSet &observables, const RooArgSet &nset=RooArgSet())
 
RooGenFunctioniGenFunction (RooRealVar &x, const RooArgSet &nset=RooArgSet())
 
virtual Bool_t isBinnedDistribution (const RooArgSet &) const
 Tests if the distribution is binned. Unless overridden by derived classes, this always returns false.
 
virtual Bool_t isIdentical (const RooAbsArg &other, Bool_t assumeSameType=kFALSE) const
 
virtual Bool_t isOffsetting () const
 
void logEvalError (const char *message, const char *serverValueString=0) const
 Log evaluation error message.
 
virtual Double_t maxVal (Int_t code) const
 Return maximum value for set of observables identified by code assigned in getMaxVal.
 
RooAbsMomentmean (RooRealVar &obs)
 
RooAbsMomentmean (RooRealVar &obs, const RooArgSet &nset)
 
virtual Int_t minTrialSamples (const RooArgSet &) const
 
RooAbsMomentmoment (RooRealVar &obs, const RooArgSet &normObs, Int_t order, Bool_t central, Bool_t takeRoot, Bool_t intNormObs)
 Return function representing moment of p.d.f (normalized w.r.t given observables) of given order.
 
RooAbsMomentmoment (RooRealVar &obs, Int_t order, Bool_t central, Bool_t takeRoot)
 Return function representing moment of function of given order.
 
virtual Double_t offset () const
 
RooAbsRealoperator= (const RooAbsReal &other)
 Assign values, name and configs from another RooAbsReal.
 
virtual Bool_t operator== (const RooAbsArg &other) const
 Equality operator when comparing to another RooAbsArg.
 
Bool_t operator== (Double_t value) const
 Equality operator comparing to a Double_t.
 
virtual std::list< Double_t > * plotSamplingHint (RooAbsRealLValue &obs, Double_t xlo, Double_t xhi) const
 Interface for returning an optional hint for initial sampling points when constructing a curve projected on observable obs.
 
virtual RooPlotplotSliceOn (RooPlot *frame, const RooArgSet &sliceSet, Option_t *drawOptions="L", Double_t scaleFactor=1.0, ScaleType stype=Relative, const RooAbsData *projData=0) const
 
virtual void preferredObservableScanOrder (const RooArgSet &obs, RooArgSet &orderedObs) const
 Interface method for function objects to indicate their preferred order of observables for scanning their values into a (multi-dimensional) histogram or RooDataSet.
 
virtual Bool_t readFromStream (std::istream &is, Bool_t compact, Bool_t verbose=kFALSE)
 Read object contents from stream (dummy for now)
 
void setCachedValue (double value, bool notifyClients=true) final
 Overwrite the value stored in this object's cache.
 
virtual Bool_t setData (RooAbsData &, Bool_t=kTRUE)
 
void setIntegratorConfig ()
 Remove the specialized numeric integration configuration associated with this object.
 
void setIntegratorConfig (const RooNumIntConfig &config)
 Set the given integrator configuration as default numeric integration configuration for this object.
 
void setParameterizeIntegral (const RooArgSet &paramVars)
 
void setPlotLabel (const char *label)
 Set the label associated with this variable.
 
void setUnit (const char *unit)
 
RooAbsMomentsigma (RooRealVar &obs)
 
RooAbsMomentsigma (RooRealVar &obs, const RooArgSet &nset)
 
RooNumIntConfigspecialIntegratorConfig () const
 Returns the specialized integrator configuration for this RooAbsReal.
 
RooNumIntConfigspecialIntegratorConfig (Bool_t createOnTheFly)
 Returns the specialized integrator configuration for this RooAbsReal.
 
virtual void writeToStream (std::ostream &os, Bool_t compact) const
 Write object contents to stream (dummy for now)
 
- Public Member Functions inherited from RooAbsArg
 RooAbsArg ()
 Default constructor.
 
 RooAbsArg (const char *name, const char *title)
 Create an object with the specified name and descriptive title.
 
 RooAbsArg (const RooAbsArg &other, const char *name=0)
 Copy constructor transfers all boolean and string properties of the original object.
 
virtual ~RooAbsArg ()
 Destructor.
 
bool addOwnedComponents (const RooAbsCollection &comps)
 Take ownership of the contents of 'comps'.
 
bool addOwnedComponents (RooAbsCollection &&comps)
 Take ownership of the contents of 'comps'.
 
bool addOwnedComponents (RooArgList &&comps)
 Take ownership of the contents of 'comps'.
 
template<typename... Args_t>
bool addOwnedComponents (std::unique_ptr< Args_t >... comps)
 
virtual void applyWeightSquared (bool flag)
 Disables or enables the usage of squared weights.
 
void attachArgs (const RooAbsCollection &set)
 Bind this node to objects in set.
 
void attachDataSet (const RooAbsData &set)
 Replace server nodes with names matching the dataset variable names with those data set variables, making this PDF directly dependent on the dataset.
 
void attachDataStore (const RooAbsDataStore &set)
 Replace server nodes with names matching the dataset variable names with those data set variables, making this PDF directly dependent on the dataset.
 
const std::set< std::string > & attributes () const
 
virtual bool canComputeBatchWithCuda () const
 
virtual Bool_t checkObservables (const RooArgSet *nset) const
 Overloadable function in which derived classes can implement consistency checks of the variables.
 
virtual TObjectClone (const char *newname=0) const
 Make a clone of an object using the Streamer facility.
 
virtual TObjectclone (const char *newname=0) const =0
 
virtual RooAbsArgcloneTree (const char *newname=0) const
 Clone tree expression of objects.
 
Int_t Compare (const TObject *other) const
 Utility function used by TCollection::Sort to compare contained TObjects We implement comparison by name, resulting in alphabetical sorting by object name.
 
std::size_t dataToken () const
 Returns the token for retrieving results in the BatchMode. For internal use only.
 
virtual Int_t defaultPrintContents (Option_t *opt) const
 Define default contents to print.
 
Bool_t dependsOn (const RooAbsArg &server, const RooAbsArg *ignoreArg=0, Bool_t valueOnly=kFALSE) const
 Test whether we depend on (ie, are served by) the specified object.
 
Bool_t dependsOn (const RooAbsCollection &serverList, const RooAbsArg *ignoreArg=0, Bool_t valueOnly=kFALSE) const
 Test whether we depend on (ie, are served by) any object in the specified collection.
 
Bool_t dependsOnValue (const RooAbsArg &server, const RooAbsArg *ignoreArg=0) const
 Check whether this object depends on values served from the object passed as server.
 
Bool_t dependsOnValue (const RooAbsCollection &serverList, const RooAbsArg *ignoreArg=0) const
 Check whether this object depends on values from an element in the serverList.
 
virtual std::unique_ptr< RooArgSetfillNormSetForServer (RooArgSet const &normSet, RooAbsArg const &server) const
 Fills a RooArgSet to be used as the normalization set for a server, given a normalization set for this RooAbsArg.
 
Bool_t getAttribute (const Text_t *name) const
 Check if a named attribute is set. By default, all attributes are unset.
 
RooLinkedList getCloningAncestors () const
 Return ancestors in cloning chain of this RooAbsArg.
 
RooArgSetgetComponents () const
 Create a RooArgSet with all components (branch nodes) of the expression tree headed by this object.
 
bool getObservables (const RooAbsCollection *depList, RooArgSet &outputSet, bool valueOnly=true) const
 Create a list of leaf nodes in the arg tree starting with ourself as top node that match any of the names the args in the supplied argset.
 
RooArgSetgetObservables (const RooAbsData &data) const
 Return the observables of this pdf given the observables defined by data.
 
RooArgSetgetObservables (const RooAbsData *data) const
 Create a list of leaf nodes in the arg tree starting with ourself as top node that match any of the names of the variable list of the supplied data set (the dependents).
 
RooArgSetgetObservables (const RooArgSet &set, Bool_t valueOnly=kTRUE) const
 Given a set of possible observables, return the observables that this PDF depends on.
 
RooArgSetgetObservables (const RooArgSet *depList, bool valueOnly=true) const
 Create a list of leaf nodes in the arg tree starting with ourself as top node that match any of the names the args in the supplied argset.
 
RooArgSetgetParameters (const RooAbsData &data, bool stripDisconnected=true) const
 Return the parameters of this p.d.f when used in conjuction with dataset 'data'.
 
RooArgSetgetParameters (const RooAbsData *data, bool stripDisconnected=true) const
 Create a list of leaf nodes in the arg tree starting with ourself as top node that don't match any of the names of the variable list of the supplied data set (the dependents).
 
RooArgSetgetParameters (const RooArgSet &observables, bool stripDisconnected=true) const
 Return the parameters of the p.d.f given the provided set of observables.
 
RooArgSetgetParameters (const RooArgSet *observables, bool stripDisconnected=true) const
 Create a list of leaf nodes in the arg tree starting with ourself as top node that don't match any of the names the args in the supplied argset.
 
virtual bool getParameters (const RooArgSet *observables, RooArgSet &outputSet, bool stripDisconnected=true) const
 Fills a list with leaf nodes in the arg tree starting with ourself as top node that don't match any of the names the args in the supplied argset.
 
RooAbsProxygetProxy (Int_t index) const
 Return the nth proxy from the proxy list.
 
const Text_tgetStringAttribute (const Text_t *key) const
 Get string attribute mapped under key 'key'.
 
Bool_t getTransientAttribute (const Text_t *name) const
 Check if a named attribute is set.
 
RooArgSetgetVariables (Bool_t stripDisconnected=kTRUE) const
 Return RooArgSet with all variables (tree leaf nodes of expresssion tree)
 
void graphVizTree (const char *fileName, const char *delimiter="\n", bool useTitle=false, bool useLatex=false)
 Create a GraphViz .dot file visualizing the expression tree headed by this RooAbsArg object.
 
void graphVizTree (std::ostream &os, const char *delimiter="\n", bool useTitle=false, bool useLatex=false)
 Write the GraphViz representation of the expression tree headed by this RooAbsArg object to the given ostream.
 
Bool_t hasClients () const
 
virtual Bool_t hasRange (const char *) const
 
virtual Bool_t importWorkspaceHook (RooWorkspace &ws)
 
virtual Bool_t inRange (const char *) const
 
virtual bool isCategory () const
 
Bool_t isCloneOf (const RooAbsArg &other) const
 Check if this object was created as a clone of 'other'.
 
Bool_t isConstant () const
 Check if the "Constant" attribute is set.
 
virtual Bool_t isDerived () const
 Does value or shape of this arg depend on any other arg?
 
virtual bool isReducerNode () const
 
virtual Bool_t IsSortable () const
 
Bool_t localNoDirtyInhibit () const
 
const TNamednamePtr () const
 De-duplicated pointer to this object's name.
 
Int_t numProxies () const
 Return the number of registered proxies.
 
Bool_t observableOverlaps (const RooAbsData *dset, const RooAbsArg &testArg) const
 Test if any of the dependents of the arg tree (as determined by getObservables) overlaps with those of the testArg.
 
Bool_t observableOverlaps (const RooArgSet *depList, const RooAbsArg &testArg) const
 Test if any of the dependents of the arg tree (as determined by getObservables) overlaps with those of the testArg.
 
RooAbsArgoperator= (const RooAbsArg &other)
 Assign all boolean and string properties of the original object.
 
Bool_t overlaps (const RooAbsArg &testArg, Bool_t valueOnly=kFALSE) const
 Test if any of the nodes of tree are shared with that of the given tree.
 
const RooArgSetownedComponents () const
 
virtual void Print (Option_t *options=0) const
 Print the object to the defaultPrintStream().
 
virtual void printAddress (std::ostream &os) const
 Print class name of object.
 
virtual void printArgs (std::ostream &os) const
 Print object arguments, ie its proxies.
 
virtual void printClassName (std::ostream &os) const
 Print object class name.
 
void printCompactTree (const char *indent="", const char *fileName=0, const char *namePat=0, RooAbsArg *client=0)
 Print tree structure of expression tree on stdout, or to file if filename is specified.
 
void printCompactTree (std::ostream &os, const char *indent="", const char *namePat=0, RooAbsArg *client=0)
 Print tree structure of expression tree on given ostream.
 
virtual void printCompactTreeHook (std::ostream &os, const char *ind="")
 Hook function interface for object to insert additional information when printed in the context of a tree structure.
 
void printComponentTree (const char *indent="", const char *namePat=0, Int_t nLevel=999)
 Print tree structure of expression tree on given ostream, only branch nodes are printed.
 
void printDirty (Bool_t depth=kTRUE) const
 Print information about current value dirty state information.
 
virtual void printMetaArgs (std::ostream &) const
 
virtual void printName (std::ostream &os) const
 Print object name.
 
virtual void printTitle (std::ostream &os) const
 Print object title.
 
virtual void printTree (std::ostream &os, TString indent="") const
 Print object tree structure.
 
Bool_t recursiveCheckObservables (const RooArgSet *nset) const
 Recursively call checkObservables on all nodes in the expression tree.
 
void setAttribute (const Text_t *name, Bool_t value=kTRUE)
 Set (default) or clear a named boolean attribute of this object.
 
void setDataToken (std::size_t index)
 Sets the token for retrieving results in the BatchMode. For internal use only.
 
void setLocalNoDirtyInhibit (Bool_t flag) const
 
void SetName (const char *name)
 Set the name of the TNamed.
 
void SetNameTitle (const char *name, const char *title)
 Set all the TNamed parameters (name and title).
 
void setProhibitServerRedirect (Bool_t flag)
 
void setStringAttribute (const Text_t *key, const Text_t *value)
 Associate string 'value' to this object under key 'key'.
 
void setTransientAttribute (const Text_t *name, Bool_t value=kTRUE)
 Set (default) or clear a named boolean attribute of this object.
 
void setWorkspace (RooWorkspace &ws)
 
const std::map< std::string, std::string > & stringAttributes () const
 
const std::set< std::string > & transientAttributes () const
 
TIteratorclientIterator () const
 Retrieve a client iterator.
 
TIteratorvalueClientIterator () const
 
TIteratorshapeClientIterator () const
 
TIteratorserverIterator () const
 
RooFIter valueClientMIterator () const
 
RooFIter shapeClientMIterator () const
 
RooFIter serverMIterator () const
 
RooArgSetgetDependents (const RooArgSet &set) const
 
RooArgSetgetDependents (const RooAbsData *set) const
 
RooArgSetgetDependents (const RooArgSet *depList) const
 
Bool_t dependentOverlaps (const RooAbsData *dset, const RooAbsArg &testArg) const
 
Bool_t dependentOverlaps (const RooArgSet *depList, const RooAbsArg &testArg) const
 
Bool_t checkDependents (const RooArgSet *nset) const
 
Bool_t recursiveCheckDependents (const RooArgSet *nset) const
 
const RefCountList_tclients () const
 List of all clients of this object.
 
const RefCountList_tvalueClients () const
 List of all value clients of this object. Value clients receive value updates.
 
const RefCountList_tshapeClients () const
 List of all shape clients of this object.
 
const RefCountList_tservers () const
 List of all servers of this object.
 
RooAbsArgfindServer (const char *name) const
 Return server of this with name name. Returns nullptr if not found.
 
RooAbsArgfindServer (const RooAbsArg &arg) const
 Return server of this that has the same name as arg. Returns nullptr if not found.
 
RooAbsArgfindServer (Int_t index) const
 Return i-th server from server list.
 
Bool_t isValueServer (const RooAbsArg &arg) const
 Check if this is serving values to arg.
 
Bool_t isValueServer (const char *name) const
 Check if this is serving values to an object with name name.
 
Bool_t isShapeServer (const RooAbsArg &arg) const
 Check if this is serving shape to arg.
 
Bool_t isShapeServer (const char *name) const
 Check if this is serving shape to an object with name name.
 
void leafNodeServerList (RooAbsCollection *list, const RooAbsArg *arg=0, Bool_t recurseNonDerived=kFALSE) const
 Fill supplied list with all leaf nodes of the arg tree, starting with ourself as top node.
 
void branchNodeServerList (RooAbsCollection *list, const RooAbsArg *arg=0, Bool_t recurseNonDerived=kFALSE) const
 Fill supplied list with all branch nodes of the arg tree starting with ourself as top node.
 
void treeNodeServerList (RooAbsCollection *list, const RooAbsArg *arg=0, Bool_t doBranch=kTRUE, Bool_t doLeaf=kTRUE, Bool_t valueOnly=kFALSE, Bool_t recurseNonDerived=kFALSE) const
 Fill supplied list with nodes of the arg tree, following all server links, starting with ourself as top node.
 
virtual Bool_t isFundamental () const
 Is this object a fundamental type that can be added to a dataset? Fundamental-type subclasses override this method to return kTRUE.
 
virtual Bool_t isLValue () const
 Is this argument an l-value, i.e., can it appear on the left-hand side of an assignment expression? LValues are also special since they can potentially be analytically integrated and generated.
 
Bool_t redirectServers (const RooAbsCollection &newServerList, Bool_t mustReplaceAll=kFALSE, Bool_t nameChange=kFALSE, Bool_t isRecursionStep=kFALSE)
 Replace all direct servers of this object with the new servers in newServerList.
 
Bool_t recursiveRedirectServers (const RooAbsCollection &newServerList, Bool_t mustReplaceAll=kFALSE, Bool_t nameChange=kFALSE, Bool_t recurseInNewSet=kTRUE)
 Recursively replace all servers with the new servers in newSet.
 
virtual void serverNameChangeHook (const RooAbsArg *, const RooAbsArg *)
 
void addServer (RooAbsArg &server, Bool_t valueProp=kTRUE, Bool_t shapeProp=kFALSE, std::size_t refCount=1)
 Register another RooAbsArg as a server to us, ie, declare that we depend on it.
 
void addServerList (RooAbsCollection &serverList, Bool_t valueProp=kTRUE, Bool_t shapeProp=kFALSE)
 Register a list of RooAbsArg as servers to us by calling addServer() for each arg in the list.
 
void replaceServer (RooAbsArg &oldServer, RooAbsArg &newServer, Bool_t valueProp, Bool_t shapeProp)
 Replace 'oldServer' with 'newServer'.
 
void changeServer (RooAbsArg &server, Bool_t valueProp, Bool_t shapeProp)
 Change dirty flag propagation mask for specified server.
 
void removeServer (RooAbsArg &server, Bool_t force=kFALSE)
 Unregister another RooAbsArg as a server to us, ie, declare that we no longer depend on its value and shape.
 
RooAbsArgfindNewServer (const RooAbsCollection &newSet, Bool_t nameChange) const
 Find the new server in the specified set that matches the old server.
 
virtual void optimizeCacheMode (const RooArgSet &observables)
 Activate cache mode optimization with given definition of observables.
 
virtual void optimizeCacheMode (const RooArgSet &observables, RooArgSet &optNodes, RooLinkedList &processedNodes)
 Activate cache mode optimization with given definition of observables.
 
Bool_t findConstantNodes (const RooArgSet &observables, RooArgSet &cacheList)
 Find branch nodes with all-constant parameters, and add them to the list of nodes that can be cached with a dataset in a test statistic calculation.
 
Bool_t findConstantNodes (const RooArgSet &observables, RooArgSet &cacheList, RooLinkedList &processedNodes)
 Find branch nodes with all-constant parameters, and add them to the list of nodes that can be cached with a dataset in a test statistic calculation.
 
virtual void constOptimizeTestStatistic (ConstOpCode opcode, Bool_t doAlsoTrackingOpt=kTRUE)
 Interface function signaling a request to perform constant term optimization.
 
virtual CacheMode canNodeBeCached () const
 
virtual void setCacheAndTrackHints (RooArgSet &)
 
Bool_t isShapeDirty () const
 
Bool_t isValueDirty () const
 
Bool_t isValueDirtyAndClear () const
 
Bool_t isValueOrShapeDirtyAndClear () const
 
void registerCache (RooAbsCache &cache)
 Register RooAbsCache with this object.
 
void unRegisterCache (RooAbsCache &cache)
 Unregister a RooAbsCache. Called from the RooAbsCache destructor.
 
Int_t numCaches () const
 Return number of registered caches.
 
RooAbsCachegetCache (Int_t index) const
 Return registered cache object by index.
 
OperMode operMode () const
 Query the operation mode of this node.
 
void setOperMode (OperMode mode, Bool_t recurseADirty=kTRUE)
 Set the operation mode of this node.
 
void setValueDirty ()
 Mark the element dirty. This forces a re-evaluation when a value is requested.
 
void setShapeDirty ()
 Notify that a shape-like property (e.g. binning) has changed.
 
const char * aggregateCacheUniqueSuffix () const
 
virtual const char * cacheUniqueSuffix () const
 
void wireAllCaches ()
 
RooExpensiveObjectCacheexpensiveObjectCache () const
 
virtual void setExpensiveObjectCache (RooExpensiveObjectCache &cache)
 
- Public Member Functions inherited from TNamed
 TNamed ()
 
 TNamed (const char *name, const char *title)
 
 TNamed (const TNamed &named)
 TNamed copy ctor.
 
 TNamed (const TString &name, const TString &title)
 
virtual ~TNamed ()
 TNamed destructor.
 
virtual void Clear (Option_t *option="")
 Set name and title to empty strings ("").
 
virtual void Copy (TObject &named) const
 Copy this to obj.
 
virtual void FillBuffer (char *&buffer)
 Encode TNamed into output buffer.
 
virtual const char * GetName () const
 Returns name of object.
 
virtual const char * GetTitle () const
 Returns title of object.
 
virtual ULong_t Hash () const
 Return hash value for this object.
 
virtual void ls (Option_t *option="") const
 List TNamed name and title.
 
TNamedoperator= (const TNamed &rhs)
 TNamed assignment operator.
 
virtual void SetTitle (const char *title="")
 Set the title of the TNamed.
 
virtual Int_t Sizeof () const
 Return size of the TNamed part of the TObject.
 
- Public Member Functions inherited from TObject
 TObject ()
 TObject constructor.
 
 TObject (const TObject &object)
 TObject copy ctor.
 
virtual ~TObject ()
 TObject destructor.
 
void AbstractMethod (const char *method) const
 Use this method to implement an "abstract" method that you don't want to leave purely abstract.
 
virtual void AppendPad (Option_t *option="")
 Append graphics object to current pad.
 
virtual void Browse (TBrowser *b)
 Browse object. May be overridden for another default action.
 
ULong_t CheckedHash ()
 Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.
 
virtual const char * ClassName () const
 Returns name of class to which the object belongs.
 
virtual void Delete (Option_t *option="")
 Delete this object.
 
virtual Int_t DistancetoPrimitive (Int_t px, Int_t py)
 Computes distance from point (px,py) to the object.
 
virtual void Draw (Option_t *option="")
 Default Draw method for all objects.
 
virtual void DrawClass () const
 Draw class inheritance tree of the class to which this object belongs.
 
virtual TObjectDrawClone (Option_t *option="") const
 Draw a clone of this object in the current selected pad for instance with: gROOT->SetSelectedPad(gPad).
 
virtual void Dump () const
 Dump contents of object on stdout.
 
virtual void Error (const char *method, const char *msgfmt,...) const
 Issue error message.
 
virtual void Execute (const char *method, const char *params, Int_t *error=0)
 Execute method on this object with the given parameter string, e.g.
 
virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=0)
 Execute method on this object with parameters stored in the TObjArray.
 
virtual void ExecuteEvent (Int_t event, Int_t px, Int_t py)
 Execute action corresponding to an event at (px,py).
 
virtual void Fatal (const char *method, const char *msgfmt,...) const
 Issue fatal error message.
 
virtual TObjectFindObject (const char *name) const
 Must be redefined in derived classes.
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes.
 
virtual Option_tGetDrawOption () const
 Get option used by the graphics system to draw this object.
 
virtual const char * GetIconName () const
 Returns mime type name of object.
 
virtual char * GetObjectInfo (Int_t px, Int_t py) const
 Returns string containing info about the object at position (px,py).
 
virtual Option_tGetOption () const
 
virtual UInt_t GetUniqueID () const
 Return the unique object id.
 
virtual Bool_t HandleTimer (TTimer *timer)
 Execute action in response of a timer timing out.
 
Bool_t HasInconsistentHash () const
 Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.
 
virtual void Info (const char *method, const char *msgfmt,...) const
 Issue info message.
 
virtual Bool_t InheritsFrom (const char *classname) const
 Returns kTRUE if object inherits from class "classname".
 
virtual Bool_t InheritsFrom (const TClass *cl) const
 Returns kTRUE if object inherits from TClass cl.
 
virtual void Inspect () const
 Dump contents of this object in a graphics canvas.
 
void InvertBit (UInt_t f)
 
Bool_t IsDestructed () const
 IsDestructed.
 
virtual Bool_t IsEqual (const TObject *obj) const
 Default equal comparison (objects are equal if they have the same address in memory).
 
virtual Bool_t IsFolder () const
 Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).
 
R__ALWAYS_INLINE Bool_t IsOnHeap () const
 
R__ALWAYS_INLINE Bool_t IsZombie () const
 
void MayNotUse (const char *method) const
 Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).
 
virtual Bool_t Notify ()
 This method must be overridden to handle object notification.
 
void Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
 Use this method to declare a method obsolete.
 
void operator delete (void *ptr)
 Operator delete.
 
void operator delete[] (void *ptr)
 Operator delete [].
 
voidoperator new (size_t sz)
 
voidoperator new (size_t sz, void *vp)
 
voidoperator new[] (size_t sz)
 
voidoperator new[] (size_t sz, void *vp)
 
TObjectoperator= (const TObject &rhs)
 TObject assignment operator.
 
virtual void Paint (Option_t *option="")
 This method must be overridden if a class wants to paint itself.
 
virtual void Pop ()
 Pop on object drawn in a pad to the top of the display list.
 
virtual Int_t Read (const char *name)
 Read contents of object with specified name from the current directory.
 
virtual void RecursiveRemove (TObject *obj)
 Recursively remove this object from a list.
 
void ResetBit (UInt_t f)
 
virtual void SaveAs (const char *filename="", Option_t *option="") const
 Save this object in the file specified by filename.
 
virtual void SavePrimitive (std::ostream &out, Option_t *option="")
 Save a primitive as a C++ statement(s) on output stream "out".
 
void SetBit (UInt_t f)
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f.
 
virtual void SetDrawOption (Option_t *option="")
 Set drawing option for object.
 
virtual void SetUniqueID (UInt_t uid)
 Set the unique object id.
 
virtual void SysError (const char *method, const char *msgfmt,...) const
 Issue system error message.
 
R__ALWAYS_INLINE Bool_t TestBit (UInt_t f) const
 
Int_t TestBits (UInt_t f) const
 
virtual void UseCurrentStyle ()
 Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.
 
virtual void Warning (const char *method, const char *msgfmt,...) const
 Issue warning message.
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
 Write this object to the current directory.
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
 Write this object to the current directory.
 
- Public Member Functions inherited from RooPrintable
 RooPrintable ()
 
virtual ~RooPrintable ()
 
virtual StyleOption defaultPrintStyle (Option_t *opt) const
 
virtual void printExtras (std::ostream &os) const
 Interface to print extras of object.
 
virtual void printStream (std::ostream &os, Int_t contents, StyleOption style, TString indent="") const
 Print description of object on ostream, printing contents set by contents integer, which is interpreted as an OR of 'enum ContentsOptions' values and in the style given by 'enum StyleOption'.
 

Static Public Member Functions

static RooNumGenConfigdefaultGeneratorConfig ()
 Returns the default numeric MC generator configuration for all RooAbsReals.
 
static int verboseEval ()
 Return global level of verbosity for p.d.f. evaluations.
 
static void verboseEval (Int_t stat)
 Change global level of verbosity for p.d.f. evaluations.
 
- Static Public Member Functions inherited from RooAbsReal
static void clearEvalErrorLog ()
 Clear the stack of evaluation error messages.
 
static RooNumIntConfigdefaultIntegratorConfig ()
 Returns the default numeric integration configuration for all RooAbsReals.
 
static EvalErrorIter evalErrorIter ()
 
static ErrorLoggingMode evalErrorLoggingMode ()
 Return current evaluation error logging mode.
 
static Bool_t hideOffset ()
 
static void logEvalError (const RooAbsReal *originator, const char *origName, const char *message, const char *serverValueString=0)
 Interface to insert remote error logging messages received by RooRealMPFE into current error loggin stream.
 
static Int_t numEvalErrorItems ()
 
static Int_t numEvalErrors ()
 Return the number of logged evaluation errors since the last clearing.
 
static void printEvalErrors (std::ostream &os=std::cout, Int_t maxPerNode=10000000)
 Print all outstanding logged evaluation error on the given ostream.
 
static void setEvalErrorLoggingMode (ErrorLoggingMode m)
 Set evaluation error logging mode.
 
static void setHideOffset (Bool_t flag)
 
- Static Public Member Functions inherited from RooAbsArg
static void setDirtyInhibit (Bool_t flag)
 Control global dirty inhibit mode.
 
static void verboseDirty (Bool_t flag)
 Activate verbose messaging related to dirty flag propagation.
 
- Static Public Member Functions inherited from TObject
static Longptr_t GetDtorOnly ()
 Return destructor only flag.
 
static Bool_t GetObjectStat ()
 Get status of object stat flag.
 
static void SetDtorOnly (void *obj)
 Set destructor only flag.
 
static void SetObjectStat (Bool_t stat)
 Turn on/off tracking of objects in the TObjectTable.
 
- Static Public Member Functions inherited from RooPrintable
static std::ostream & defaultPrintStream (std::ostream *os=0)
 Return a reference to the current default stream to use in Print().
 
static void nameFieldLength (Int_t newLen)
 Set length of field reserved from printing name of RooAbsArgs in multi-line collection printing to given amount.
 

Protected Member Functions

 RooAbsPdf (const RooAbsPdf &other, const char *name=0)
 Copy constructor.
 
double normalizeWithNaNPacking (double rawVal, double normVal) const
 
virtual RooPlotplotOn (RooPlot *frame, PlotOpt o) const
 Plot oneself on 'frame'.
 
Int_trandomizeProtoOrder (Int_t nProto, Int_t nGen, Bool_t resample=kFALSE) const
 Return lookup table with randomized order for nProto prototype events.
 
virtual Bool_t redirectServersHook (const RooAbsCollection &, Bool_t, Bool_t, Bool_t)
 Function that is called at the end of redirectServers().
 
virtual Bool_t syncNormalization (const RooArgSet *dset, Bool_t adjustProxies=kTRUE) const
 Verify that the normalization integral cached with this PDF is valid for given set of normalization observables.
 
- Protected Member Functions inherited from RooAbsReal
virtual void attachToTree (TTree &t, Int_t bufSize=32000)
 Attach object to a branch of given TTree.
 
virtual void attachToVStore (RooVectorDataStore &vstore)
 
RooFitResultchi2FitDriver (RooAbsReal &fcn, RooLinkedList &cmdList)
 Internal driver function for chi2 fits.
 
virtual void copyCache (const RooAbsArg *source, Bool_t valueOnly=kFALSE, Bool_t setValDirty=kTRUE)
 Copy the cached value of another RooAbsArg to our cache.
 
RooAbsRealcreateIntObj (const RooArgSet &iset, const RooArgSet *nset, const RooNumIntConfig *cfg, const char *rangeName) const
 Internal utility function for createIntegral() that creates the actual integral object.
 
virtual Double_t evaluate () const =0
 Evaluate this PDF / function / constant. Needs to be overridden by all derived classes.
 
virtual RooSpan< doubleevaluateBatch (std::size_t, std::size_t)=delete
 
virtual RooSpan< doubleevaluateSpan (RooBatchCompute::RunContext &evalData, const RooArgSet *normSet) const
 Evaluate this object for a batch/span of data points.
 
virtual void fillTreeBranch (TTree &t)
 Fill the tree branch that associated with this object with its current value.
 
void findInnerMostIntegration (const RooArgSet &allObs, RooArgSet &innerObs, const char *rangeName) const
 Utility function for createIntObj() that aids in the construct of recursive integrals over functions with multiple observables with parameterized ranges.
 
TString integralNameSuffix (const RooArgSet &iset, const RooArgSet *nset=0, const char *rangeName=0, Bool_t omitEmpty=kFALSE) const
 Construct string with unique suffix name to give to integral object that encodes integrated observables, normalization observables and the integration range name.
 
Bool_t isSelectedComp () const
 If true, the current pdf is a selected component (for use in plotting)
 
virtual bool isValid () const
 Check if current value is valid.
 
virtual bool isValidReal (double, bool printError=false) const
 Interface function to check if given value is a valid value for this object. Returns true unless overridden.
 
void makeProjectionSet (const RooAbsArg *plotVar, const RooArgSet *allVars, RooArgSet &projectedVars, Bool_t silent) const
 Utility function for plotOn() that constructs the set of observables to project when plotting ourselves as function of 'plotVar'.
 
Bool_t matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a) const
 Utility function for use in getAnalyticalIntegral().
 
Bool_t matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a, const RooArgProxy &b) const
 Utility function for use in getAnalyticalIntegral().
 
Bool_t matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a, const RooArgProxy &b, const RooArgProxy &c) const
 Utility function for use in getAnalyticalIntegral().
 
Bool_t matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a, const RooArgProxy &b, const RooArgProxy &c, const RooArgProxy &d) const
 Utility function for use in getAnalyticalIntegral().
 
Bool_t matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgSet &set) const
 Utility function for use in getAnalyticalIntegral().
 
virtual RooPlotplotAsymOn (RooPlot *frame, const RooAbsCategoryLValue &asymCat, PlotOpt o) const
 
void plotOnCompSelect (RooArgSet *selNodes) const
 Helper function for plotting of composite p.d.fs.
 
RooPlotplotOnWithErrorBand (RooPlot *frame, const RooFitResult &fr, Double_t Z, const RooArgSet *params, const RooLinkedList &argList, Bool_t method1) const
 Plot function or PDF on frame with support for visualization of the uncertainty encoded in the given fit result fr.
 
Bool_t plotSanityChecks (RooPlot *frame) const
 Utility function for plotOn(), perform general sanity check on frame to ensure safe plotting operations.
 
void selectComp (Bool_t flag)
 
virtual void selectNormalization (const RooArgSet *depSet=0, Bool_t force=kFALSE)
 Interface function to force use of a given set of observables to interpret function value.
 
virtual void selectNormalizationRange (const char *rangeName=0, Bool_t force=kFALSE)
 Interface function to force use of a given normalization range to interpret function value.
 
virtual void setTreeBranchStatus (TTree &t, Bool_t active)
 (De)Activate associated tree branch
 
virtual void syncCache (const RooArgSet *set=0)
 
Double_t traceEval (const RooArgSet *set) const
 Calculate current value of object, with error tracing wrapper.
 
- Protected Member Functions inherited from RooAbsArg
void attachToStore (RooAbsDataStore &store)
 Attach this argument to the data store such that it reads data from there.
 
TString cleanBranchName () const
 Construct a mangled name from the actual name that is free of any math symbols that might be interpreted by TTree.
 
void clearShapeDirty () const
 
void clearValueAndShapeDirty () const
 
void clearValueDirty () const
 
virtual void getObservablesHook (const RooArgSet *, RooArgSet *) const
 
virtual void getParametersHook (const RooArgSet *, RooArgSet *, Bool_t) const
 
void graphVizAddConnections (std::set< std::pair< RooAbsArg *, RooAbsArg * > > &)
 Utility function that inserts all point-to-point client-server connections between any two RooAbsArgs in the expression tree headed by this object in the linkSet argument.
 
Bool_t inhibitDirty () const
 Delete watch flag.
 
virtual void ioStreamerPass2 ()
 Method called by workspace container to finalize schema evolution issues that cannot be handled in a single ioStreamer pass.
 
virtual void operModeHook ()
 
virtual void optimizeDirtyHook (const RooArgSet *)
 
void printAttribList (std::ostream &os) const
 Transient boolean attributes (not copied in ctor)
 
void registerProxy (RooArgProxy &proxy)
 Register an RooArgProxy in the proxy list.
 
void registerProxy (RooListProxy &proxy)
 Register an RooListProxy in the proxy list.
 
void registerProxy (RooSetProxy &proxy)
 Register an RooSetProxy in the proxy list.
 
void setProxyNormSet (const RooArgSet *nset)
 Forward a change in the cached normalization argset to all the registered proxies.
 
void setShapeDirty (const RooAbsArg *source)
 Notify that a shape-like property (e.g. binning) has changed.
 
void setValueDirty (const RooAbsArg *source)
 Force element to re-evaluate itself when a value is requested.
 
void unRegisterProxy (RooArgProxy &proxy)
 Remove proxy from proxy list.
 
void unRegisterProxy (RooListProxy &proxy)
 Remove proxy from proxy list.
 
void unRegisterProxy (RooSetProxy &proxy)
 Remove proxy from proxy list.
 
- Protected Member Functions inherited from TObject
virtual void DoError (int level, const char *location, const char *fmt, va_list va) const
 Interface to ErrorHandler (protected).
 
void MakeZombie ()
 

Protected Attributes

Int_t _errorCount
 
Int_t _negCount
 
RooAbsReal_norm = nullptr
 
RooObjCacheManager _normMgr
 
TString _normRange
 MC generator configuration specific for this object.
 
RooArgSet const * _normSet = nullptr
 Normalization integral (owned by _normMgr)
 
Double_t _rawValue
 
Bool_t _selectComp
 
RooNumGenConfig_specGeneratorConfig
 
Int_t _traceCount
 
- Protected Attributes inherited from RooAbsReal
Bool_t _forceNumInt
 
TString _label
 
RooArgSet_lastNSet
 
Int_t _plotBins
 
Double_t _plotMax
 
Double_t _plotMin
 
Bool_t _selectComp
 
RooNumIntConfig_specIntegratorConfig
 
TString _unit
 
Double_t _value
 
- Protected Attributes inherited from RooAbsArg
std::set< std::string > _boolAttrib
 
std::set< std::string > _boolAttribTransient
 
std::vector< RooAbsCache * > _cacheList
 
RefCountList_t _clientList
 
RefCountList_t _clientListShape
 
RefCountList_t _clientListValue
 
std::size_t _dataToken = 0
 In which workspace do I live, if any.
 
Bool_t _deleteWatch
 
RooExpensiveObjectCache_eocache {nullptr}
 Prohibit server redirects – Debugging tool.
 
Bool_t _fast = false
 
Bool_t _isConstant
 De-duplicated name pointer. This will be equal for all objects with the same name.
 
Bool_t _localNoInhibitDirty
 Cached isConstant status.
 
RooWorkspace_myws
 Prevent 'AlwaysDirty' mode for this node.
 
const TNamed_namePtr
 
OperMode _operMode
 
RooArgSet_ownedComponents
 
Bool_t _prohibitServerRedirect
 Set of owned component.
 
RooRefArray _proxyList
 
ProxyListCache _proxyListCache
 
RefCountList_t _serverList
 
Bool_t _shapeDirty
 
std::map< std::string, std::string > _stringAttrib
 
Bool_t _valueDirty
 
- Protected Attributes inherited from TNamed
TString fName
 
TString fTitle
 

Static Protected Attributes

static TString _normRangeOverride
 
static Int_t _verboseEval = 0
 
- Static Protected Attributes inherited from RooAbsReal
static Bool_t _globalSelectComp = false
 Component selection flag for RooAbsPdf::plotCompOn.
 
static Bool_t _hideOffset = kTRUE
 
- Static Protected Attributes inherited from RooAbsArg
static Bool_t _inhibitDirty
 
static Bool_t _verboseDirty
 cache of the list of proxies. Avoids type casting.
 
- Static Protected Attributes inherited from RooPrintable
static Int_t _nameLength
 

Private Member Functions

int calcAsymptoticCorrectedCovariance (RooMinimizer &minimizer, RooAbsData const &data)
 Use the asymptotically correct approach to estimate errors in the presence of weights.
 
int calcSumW2CorrectedCovariance (RooMinimizer &minimizer, RooAbsReal &nll) const
 Apply correction to errors and covariance matrix.
 
RooDataSetgenerate (RooAbsGenContext &context, const RooArgSet &whatVars, const RooDataSet *prototype, Double_t nEvents, Bool_t verbose, Bool_t randProtoOrder, Bool_t resampleProto, Bool_t skipInit=kFALSE, Bool_t extended=kFALSE) const
 Internal method.
 
void logBatchComputationErrors (RooSpan< const double > &outputs, std::size_t begin) const
 Scan through outputs and fix+log all nans and negative values.
 
virtual RooPlotparamOn (RooPlot *frame, const RooArgSet &params, Bool_t showConstants=kFALSE, const char *label="", Int_t sigDigits=2, Option_t *options="NELU", Double_t xmin=0.65, Double_t xmax=0.99, Double_t ymax=0.95, const RooCmdArg *formatCmd=0)
 Add a text box with the current parameter values and their errors to the frame.
 
Bool_t traceEvalPdf (Double_t value) const
 Check that passed value is positive and not 'not-a-number'.
 

Friends

class CacheElem
 The cache manager.
 
class RooAbsAnaConvPdf
 
class RooAddGenContext
 
class RooAddGenContextOrig
 
class RooConvGenContext
 
class RooEffGenContext
 
class RooExtendPdf
 
class RooMCStudy
 
class RooProdGenContext
 
class RooProdPdf
 
class RooRealIntegral
 
class RooSimGenContext
 
class RooSimSplitGenContext
 
class RooSimultaneous
 

Additional Inherited Members

- Protected Types inherited from TObject
enum  { kOnlyPrepStep = BIT(3) }
 
- Static Protected Member Functions inherited from RooAbsReal
static void globalSelectComp (Bool_t flag)
 Global switch controlling the activation of the selectComp() functionality.
 
- Static Protected Member Functions inherited from RooAbsArg
static void ioStreamerPass2Finalize ()
 Method called by workspace container to finalize schema evolution issues that cannot be handled in a single ioStreamer pass.
 

#include <RooAbsPdf.h>

Inheritance diagram for RooAbsPdf:
[legend]

Member Enumeration Documentation

◆ ExtendMode

Enumerator
CanNotBeExtended 
CanBeExtended 
MustBeExtended 

Definition at line 256 of file RooAbsPdf.h.

Constructor & Destructor Documentation

◆ RooAbsPdf() [1/4]

RooAbsPdf::RooAbsPdf ( )

Default constructor.

Definition at line 255 of file RooAbsPdf.cxx.

◆ RooAbsPdf() [2/4]

RooAbsPdf::RooAbsPdf ( const char *  name,
const char *  title = 0 
)

Constructor with name and title only.

Definition at line 269 of file RooAbsPdf.cxx.

◆ RooAbsPdf() [3/4]

RooAbsPdf::RooAbsPdf ( const char *  name,
const char *  title,
Double_t  minVal,
Double_t  maxVal 
)

Constructor with name, title, and plot range.

Definition at line 281 of file RooAbsPdf.cxx.

◆ ~RooAbsPdf()

RooAbsPdf::~RooAbsPdf ( )
virtual

Destructor.

Definition at line 313 of file RooAbsPdf.cxx.

◆ RooAbsPdf() [4/4]

RooAbsPdf::RooAbsPdf ( const RooAbsPdf other,
const char *  name = 0 
)
protected

Copy constructor.

Definition at line 294 of file RooAbsPdf.cxx.

Member Function Documentation

◆ analyticalIntegralWN()

Double_t RooAbsPdf::analyticalIntegralWN ( Int_t  code,
const RooArgSet normSet,
const char *  rangeName = 0 
) const
virtual

Analytical integral with normalization (see RooAbsReal::analyticalIntegralWN() for further information)

This function applies the normalization specified by 'normSet' to the integral returned by RooAbsReal::analyticalIntegral(). The passthrough scenario (code=0) is also changed to return a normalized answer

Reimplemented from RooAbsReal.

Reimplemented in RooNormalizedPdf, RooBinSamplingPdf, RooWrapperPdf, RooAddModel, RooExtendPdf, RooProdPdf, RooProjectedPdf, RooRealSumPdf, RooSimultaneous, and RooAddPdf.

Definition at line 427 of file RooAbsPdf.cxx.

◆ autoGenContext()

RooAbsGenContext * RooAbsPdf::autoGenContext ( const RooArgSet vars,
const RooDataSet prototype = 0,
const RooArgSet auxProto = 0,
Bool_t  verbose = kFALSE,
Bool_t  autoBinned = kTRUE,
const char *  binnedTag = "" 
) const
virtual

Reimplemented in RooSimultaneous.

Definition at line 1941 of file RooAbsPdf.cxx.

◆ binnedGenContext()

RooAbsGenContext * RooAbsPdf::binnedGenContext ( const RooArgSet vars,
Bool_t  verbose = kFALSE 
) const
virtual

Return a binned generator context.

Definition at line 1922 of file RooAbsPdf.cxx.

◆ calcAsymptoticCorrectedCovariance()

int RooAbsPdf::calcAsymptoticCorrectedCovariance ( RooMinimizer minimizer,
RooAbsData const &  data 
)
private

Use the asymptotically correct approach to estimate errors in the presence of weights.

This is slower but more accurate than SumW2Error. See also https://arxiv.org/abs/1911.01303). Applies the calculated covaraince matrix to the RooMinimizer and returns the quality of the covariance matrix. See also the documentation of RooAbsPdf::fitTo(), where this function is used.

Parameters
[in]minimizerThe RooMinimizer to get the fit result from. The state of the minimizer will be altered by this function: the covariance matrix caltulated here will be applied to it via RooMinimizer::applyCovarianceMatrix().
[in]dataThe dataset that was used for the fit.

Definition at line 1207 of file RooAbsPdf.cxx.

◆ calcSumW2CorrectedCovariance()

int RooAbsPdf::calcSumW2CorrectedCovariance ( RooMinimizer minimizer,
RooAbsReal nll 
) const
private

Apply correction to errors and covariance matrix.

This uses two covariance matrices, one with the weights, the other with squared weights, to obtain the correct errors for weighted likelihood fits. Applies the calculated covaraince matrix to the RooMinimizer and returns the quality of the covariance matrix. See also the documentation of RooAbsPdf::fitTo(), where this function is used.

Parameters
[in]minimizerThe RooMinimizer to get the fit result from. The state of the minimizer will be altered by this function: the covariance matrix caltulated here will be applied to it via RooMinimizer::applyCovarianceMatrix().
[in]nllThe NLL object that was used for the fit.

Definition at line 1287 of file RooAbsPdf.cxx.

◆ canBeExtended()

Bool_t RooAbsPdf::canBeExtended ( ) const
inline

If true, PDF can provide extended likelihood term.

Definition at line 262 of file RooAbsPdf.h.

◆ chi2FitTo() [1/5]

RooFitResult * RooAbsReal::chi2FitTo ( RooDataHist data,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Perform a \( \chi^2 \) fit to given histogram.

By default the fit is executed through the MINUIT commands MIGRAD, HESSE in succession

The following named arguments are supported

Options to control construction of chi2
Range(const char* name) Fit only data inside range with given name
Range(Double_t lo, Double_t hi) Fit only data inside given range. A range named "fit" is created on the fly on all observables. Multiple comma separated range names can be specified.
NumCPU(int num) Parallelize NLL calculation on num CPUs
Optimize(Bool_t flag) Activate constant term optimization (on by default)
IntegrateBins()

Integrate PDF within each bin. This sets the desired precision.

Options to control flow of fit procedure
InitialHesse(Bool_t flag) Flag controls if HESSE before MIGRAD as well, off by default
Hesse(Bool_t flag) Flag controls if HESSE is run after MIGRAD, on by default
Minos(Bool_t flag) Flag controls if MINOS is run after HESSE, on by default
Minos(const RooArgSet& set) Only run MINOS on given subset of arguments
Save(Bool_t flag) Flac controls if RooFitResult object is produced and returned, off by default
Strategy(Int_t flag) Set Minuit strategy (0 through 2, default is 1)
FitOptions(const char* optStr)

Steer fit with classic options string (for backward compatibility). Use of this option excludes use of any of the new style steering options.

Options to control informational output
Verbose(Bool_t flag) Flag controls if verbose output is printed (NLL, parameter changes during fit
Timer(Bool_t flag) Time CPU and wall clock consumption of fit steps, off by default
PrintLevel(Int_t level) Set Minuit print level (-1 through 3, default is 1). At -1 all RooFit informational messages are suppressed as well
Warnings(Bool_t flag) Enable or disable MINUIT warnings (enabled by default)
PrintEvalErrors(Int_t numErr) Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative value suppress output completely, a zero value will only print the error count per p.d.f component, a positive value is will print details of each error up to numErr messages per p.d.f component.

Reimplemented from RooAbsReal.

Definition at line 184 of file RooAbsReal.cxx.

◆ chi2FitTo() [2/5]

RooFitResult * RooAbsReal::chi2FitTo ( RooDataHist data,
const RooLinkedList cmdList 
)
virtual

Perform a \( \chi^2 \) fit to given histogram.

By default the fit is executed through the MINUIT commands MIGRAD, HESSE in succession

The following named arguments are supported

Options to control construction of chi2
Range(const char* name) Fit only data inside range with given name
Range(Double_t lo, Double_t hi) Fit only data inside given range. A range named "fit" is created on the fly on all observables. Multiple comma separated range names can be specified.
NumCPU(int num) Parallelize NLL calculation on num CPUs
Optimize(Bool_t flag) Activate constant term optimization (on by default)
IntegrateBins()

Integrate PDF within each bin. This sets the desired precision.

Options to control flow of fit procedure
InitialHesse(Bool_t flag) Flag controls if HESSE before MIGRAD as well, off by default
Hesse(Bool_t flag) Flag controls if HESSE is run after MIGRAD, on by default
Minos(Bool_t flag) Flag controls if MINOS is run after HESSE, on by default
Minos(const RooArgSet& set) Only run MINOS on given subset of arguments
Save(Bool_t flag) Flac controls if RooFitResult object is produced and returned, off by default
Strategy(Int_t flag) Set Minuit strategy (0 through 2, default is 1)
FitOptions(const char* optStr)

Steer fit with classic options string (for backward compatibility). Use of this option excludes use of any of the new style steering options.

Options to control informational output
Verbose(Bool_t flag) Flag controls if verbose output is printed (NLL, parameter changes during fit
Timer(Bool_t flag) Time CPU and wall clock consumption of fit steps, off by default
PrintLevel(Int_t level) Set Minuit print level (-1 through 3, default is 1). At -1 all RooFit informational messages are suppressed as well
Warnings(Bool_t flag) Enable or disable MINUIT warnings (enabled by default)
PrintEvalErrors(Int_t numErr) Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative value suppress output completely, a zero value will only print the error count per p.d.f component, a positive value is will print details of each error up to numErr messages per p.d.f component.

Reimplemented from RooAbsReal.

Definition at line 187 of file RooAbsReal.cxx.

◆ chi2FitTo() [3/5]

RooFitResult * RooAbsPdf::chi2FitTo ( RooDataHist data,
const RooLinkedList cmdList 
)
virtual

Calls RooAbsPdf::createChi2(RooDataSet& data, const RooLinkedList& cmdList) and returns fit result.

Reimplemented from RooAbsReal.

Definition at line 1735 of file RooAbsPdf.cxx.

◆ chi2FitTo() [4/5]

RooAbsReal::chi2FitTo ( RooDataSet xydata,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Perform a 2-D \( \chi^2 \) fit using a series of x and y values stored in the dataset xydata.

The y values can either be the event weights, or can be another column designated by the YVar() argument. The y value must have errors defined for the \( \chi^2 \) to be well defined.

Options to control construction of the \( \chi^2 \)
YVar(RooRealVar& yvar) Designate given column in dataset as Y value
Integrate(Bool_t flag)

Integrate function over range specified by X errors rather than take value at bin center.

Options to control flow of fit procedure
InitialHesse(Bool_t flag) Flag controls if HESSE before MIGRAD as well, off by default
Hesse(Bool_t flag) Flag controls if HESSE is run after MIGRAD, on by default
Minos(Bool_t flag) Flag controls if MINOS is run after HESSE, on by default
Minos(const RooArgSet& set) Only run MINOS on given subset of arguments
Save(Bool_t flag) Flac controls if RooFitResult object is produced and returned, off by default
Strategy(Int_t flag) Set Minuit strategy (0 through 2, default is 1)
FitOptions(const char* optStr)

Steer fit with classic options string (for backward compatibility). Use of this option excludes use of any of the new style steering options.

Options to control informational output
Verbose(Bool_t flag) Flag controls if verbose output is printed (NLL, parameter changes during fit
Timer(Bool_t flag) Time CPU and wall clock consumption of fit steps, off by default
PrintLevel(Int_t level) Set Minuit print level (-1 through 3, default is 1). At -1 all RooFit informational messages are suppressed as well
Warnings(Bool_t flag) Enable or disable MINUIT warnings (enabled by default)
PrintEvalErrors(Int_t numErr) Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative value suppress output completely, a zero value will only print the error count per p.d.f component, a positive value is will print details of each error up to numErr messages per p.d.f component.

PyROOT

The RooAbsReal::chi2FitTo() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Reimplemented from RooAbsReal.

Definition at line 195 of file RooAbsReal.cxx.

◆ chi2FitTo() [5/5]

RooFitResult * RooAbsReal::chi2FitTo ( RooDataSet xydata,
const RooLinkedList cmdList 
)
virtual

Perform a 2-D \( \chi^2 \) fit using a series of x and y values stored in the dataset xydata.

The y values can either be the event weights, or can be another column designated by the YVar() argument. The y value must have errors defined for the \( \chi^2 \) to be well defined.

Options to control construction of the \( \chi^2 \)
YVar(RooRealVar& yvar) Designate given column in dataset as Y value
Integrate(Bool_t flag)

Integrate function over range specified by X errors rather than take value at bin center.

Options to control flow of fit procedure
InitialHesse(Bool_t flag) Flag controls if HESSE before MIGRAD as well, off by default
Hesse(Bool_t flag) Flag controls if HESSE is run after MIGRAD, on by default
Minos(Bool_t flag) Flag controls if MINOS is run after HESSE, on by default
Minos(const RooArgSet& set) Only run MINOS on given subset of arguments
Save(Bool_t flag) Flac controls if RooFitResult object is produced and returned, off by default
Strategy(Int_t flag) Set Minuit strategy (0 through 2, default is 1)
FitOptions(const char* optStr)

Steer fit with classic options string (for backward compatibility). Use of this option excludes use of any of the new style steering options.

Options to control informational output
Verbose(Bool_t flag) Flag controls if verbose output is printed (NLL, parameter changes during fit
Timer(Bool_t flag) Time CPU and wall clock consumption of fit steps, off by default
PrintLevel(Int_t level) Set Minuit print level (-1 through 3, default is 1). At -1 all RooFit informational messages are suppressed as well
Warnings(Bool_t flag) Enable or disable MINUIT warnings (enabled by default)
PrintEvalErrors(Int_t numErr) Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative value suppress output completely, a zero value will only print the error count per p.d.f component, a positive value is will print details of each error up to numErr messages per p.d.f component.

PyROOT

The RooAbsReal::chi2FitTo() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Reimplemented from RooAbsReal.

Definition at line 198 of file RooAbsReal.cxx.

◆ createCdf() [1/2]

RooAbsReal * RooAbsPdf::createCdf ( const RooArgSet iset,
const RooArgSet nset = RooArgSet() 
)

Create a cumulative distribution function of this p.d.f in terms of the observables listed in iset.

If no nset argument is given the c.d.f normalization is constructed over the integrated observables, so that its maximum value is precisely 1. It is also possible to choose a different normalization for multi-dimensional p.d.f.s: eg. for a pdf f(x,y,z) one can construct a partial cdf c(x,y) that only when integrated itself over z results in a maximum value of 1. To construct such a cdf pass z as argument to the optional nset argument

Definition at line 3345 of file RooAbsPdf.cxx.

◆ createCdf() [2/2]

RooAbsPdf::createCdf ( const RooArgSet iset,
const RooCmdArg arg1,
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)

Create an object that represents the integral of the function over one or more observables listed in iset.

The actual integration calculation is only performed when the return object is evaluated. The name of the integral object is automatically constructed from the name of the input function, the variables it integrates and the range integrates over

The following named arguments are accepted

Type of CmdArg Effect on CDF
SupNormSet(const RooArgSet&) Observables over which should be normalized in addition to the integration observables
ScanNumCdf() Apply scanning technique if cdf integral involves numeric integration [ default ]
ScanAllCdf() Always apply scanning technique
ScanNoCdf() Never apply scanning technique
ScanParameters(Int_t nbins, Int_t intOrder) Parameters for scanning technique of making CDF: number of sampled bins and order of interpolation applied on numeric cdf

PyROOT

The RooAbsPdf::createCdf() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Definition at line 3367 of file RooAbsPdf.cxx.

◆ createChi2() [1/6]

RooAbsReal::createChi2 ( RooDataHist data,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Create a \( \chi^2 \) variable from a histogram and this function.

The following named arguments are supported

Options to control construction of the \( \chi^2 \)
DataError(RooAbsData::ErrorType) Choose between Poisson errors and Sum-of-weights errors
NumCPU(Int_t) Activate parallel processing feature on N processes
Range() Calculate \( \chi^2 \) only in selected region
IntegrateBins() Integrate PDF within each bin. This sets the desired precision.
Parameters
dataHistogram with data
Returns
\( \chi^2 \) variable

PyROOT

The RooAbsReal::createChi2() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Reimplemented from RooAbsReal.

Definition at line 190 of file RooAbsReal.cxx.

◆ createChi2() [2/6]

RooAbsPdf::createChi2 ( RooDataHist data,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Create a \( \chi^2 \) from a histogram and this function.

Options to control construction of the \f$ \chi^2 \f$

Type of CmdArg Effect on \( \chi^2 \)
Extended() Use expected number of events of an extended p.d.f as normalization
DataError() Choose between:
NumCPU() Activate parallel processing feature
Range() Fit only selected region
SumCoefRange() Set the range in which to interpret the coefficients of RooAddPdf components
SplitRange() Fit ranges used in different categories get named after the category. Using Range("range"), SplitRange() as switches, different ranges could be set like this:
myVariable.setRange("range_pi0", 135, 210);
myVariable.setRange("range_gamma", 50, 210);
ConditionalObservables(Args_t &&... argsOrArgSet) Define projected observables.

PyROOT

The RooAbsPdf::createChi2() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Reimplemented from RooAbsReal.

Definition at line 1781 of file RooAbsPdf.cxx.

◆ createChi2() [3/6]

RooAbsReal * RooAbsReal::createChi2 ( RooDataHist data,
const RooLinkedList cmdList 
)
virtual

Create a \( \chi^2 \) variable from a histogram and this function.

The following named arguments are supported

Options to control construction of the \( \chi^2 \)
DataError(RooAbsData::ErrorType) Choose between Poisson errors and Sum-of-weights errors
NumCPU(Int_t) Activate parallel processing feature on N processes
Range() Calculate \( \chi^2 \) only in selected region
IntegrateBins() Integrate PDF within each bin. This sets the desired precision.
Parameters
dataHistogram with data
Returns
\( \chi^2 \) variable

PyROOT

The RooAbsReal::createChi2() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Parameters
cmdListList with RooCmdArg() from the table

Reimplemented from RooAbsReal.

Definition at line 189 of file RooAbsReal.cxx.

◆ createChi2() [4/6]

RooAbsReal * RooAbsReal::createChi2 ( RooDataSet data,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Create a \( \chi^2 \) from a series of x and y values stored in a dataset.

The y values can either be the event weights (default), or can be another column designated by the YVar() argument. The y value must have errors defined for the \( \chi^2 \) to be well defined.

The following named arguments are supported

Options to control construction of the \( \chi^2 \)
YVar(RooRealVar& yvar) Designate given column in dataset as Y value
Integrate(Bool_t flag) Integrate function over range specified by X errors rather than take value at bin center.

Reimplemented from RooAbsReal.

Definition at line 201 of file RooAbsReal.cxx.

◆ createChi2() [5/6]

◆ createChi2() [6/6]

RooAbsReal * RooAbsPdf::createChi2 ( RooDataSet data,
const RooLinkedList cmdList 
)
virtual

Argument-list version of RooAbsPdf::createChi2()

Reimplemented from RooAbsReal.

Definition at line 1853 of file RooAbsPdf.cxx.

◆ createNLL() [1/2]

RooAbsPdf::createNLL ( RooAbsData data,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Construct representation of -log(L) of PDF with given dataset.

If dataset is unbinned, an unbinned likelihood is constructed. If the dataset is binned, a binned likelihood is constructed.

The following named arguments are supported

Type of CmdArg Effect on nll
ConditionalObservables(Args_t &&... argsOrArgSet) Do not normalize PDF over listed observables.
Extended(Bool_t flag) Add extended likelihood term, off by default
Range(const char* name) Fit only data inside range with given name
Range(Double_t lo, Double_t hi) Fit only data inside given range. A range named "fit" is created on the fly on all observables. Multiple comma separated range names can be specified.
SumCoefRange(const char* name) Set the range in which to interpret the coefficients of RooAddPdf components
NumCPU(int num, int strat) Parallelize NLL calculation on num CPUs
Strategy Effect
0 = RooFit::BulkPartition (Default) Divide events in N equal chunks
1 = RooFit::Interleave Process event iN in process N. Recommended for binned data with a substantial number of zero-bins, which will be distributed across processes more equitably in this strategy
2 = RooFit::SimComponents Process each component likelihood of a RooSimultaneous fully in a single process and distribute components over processes. This approach can be benificial if normalization calculation time dominates the total computation time of a component (since the normalization calculation must be performed in each process in strategies 0 and 1. However beware that if the RooSimultaneous components do not share many parameters this strategy is inefficient: as most minuit-induced likelihood calculations involve changing a single parameter, only 1 of the N processes will be active most of the time if RooSimultaneous components do not share many parameters
3 = RooFit::Hybrid Follow strategy 0 for all RooSimultaneous components, except those with less than 30 dataset entries, for which strategy 2 is followed.
BatchMode(bool on) Batch evaluation mode. See fitTo().
Optimize(Bool_t flag) Activate constant term optimization (on by default)
SplitRange(Bool_t flag) Use separate fit ranges in a simultaneous fit. Actual range name for each subsample is assumed to be rangeName_indexState, where indexState is the state of the master index category of the simultaneous fit. Using Range("range"), SplitRange() as switches, different ranges could be set like this:
myVariable.setRange("range_pi0", 135, 210);
myVariable.setRange("range_gamma", 50, 210);
Constrain(const RooArgSet&pars) For p.d.f.s that contain internal parameter constraint terms (that is usually product PDFs, where one term of the product depends on parameters but not on the observable(s),), only apply constraints to the given subset of parameters.
ExternalConstraints(const RooArgSet& ) Include given external constraints to likelihood by multiplying them with the original likelihood.
GlobalObservables(const RooArgSet&) Define the set of normalization observables to be used for the constraint terms. If none are specified the constrained parameters are used.
GlobalObservablesSource(const char* sourceName) Which source to prioritize for global observable values. Can be either:
  • data: to take the values from the dataset, falling back to the pdf value if a given global observable is not available. If no GlobalObservables or GlobalObservablesTag command argument is given, the set of global observables will be automatically defined to be the set stored in the data.
  • model: to take all values from the pdf and completely ignore the set of global observables stored in the data (not even using it to automatically define the set of global observables if the GlobalObservables or GlobalObservablesTag command arguments are not given). The default option is data.
GlobalObservablesTag(const char* tagName) Define the set of normalization observables to be used for the constraint terms by a string attribute associated with pdf observables that match the given tagName.
Verbose(Bool_t flag) Controls RooFit informational messages in likelihood construction
CloneData(Bool flag) Use clone of dataset in NLL (default is true)
Offset(Bool_t) Offset likelihood by initial value (so that starting value of FCN in minuit is zero). This can improve numeric stability in simultaneous fits with components with large likelihood values
IntegrateBins(double precision) In binned fits, integrate the PDF over the bins instead of using the probability density at the bin centre. This can reduce the bias observed when fitting functions with high curvature to binned data.
  • precision > 0: Activate bin integration everywhere. Use precision between 0.01 and 1.E-6, depending on binning. Note that a low precision such as 0.01 might yield identical results to 1.E-4, since the integrator might reach 1.E-4 already in its first integration step. If lower precision is desired (more speed), a RooBinSamplingPdf has to be created manually, and its integrator has to be manipulated directly.
  • precision = 0: Activate bin integration only for continuous PDFs fit to a RooDataHist.
  • precision < 0: Deactivate.
    See also
    RooBinSamplingPdf

PyROOT

The RooAbsPdf::createNLL() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Reimplemented in RooStats::HistFactory::HistFactorySimultaneous.

Definition at line 936 of file RooAbsPdf.cxx.

◆ createNLL() [2/2]

RooAbsReal * RooAbsPdf::createNLL ( RooAbsData data,
const RooLinkedList cmdList 
)
virtual

Construct representation of -log(L) of PDFwith given dataset.

If dataset is unbinned, an unbinned likelihood is constructed. If the dataset is binned, a binned likelihood is constructed.

See RooAbsPdf::createNLL(RooAbsData& data, RooCmdArg arg1, RooCmdArg arg2, RooCmdArg arg3, RooCmdArg arg4, RooCmdArg arg5, RooCmdArg arg6, RooCmdArg arg7, RooCmdArg arg8) for documentation of options

Reimplemented in RooStats::HistFactory::HistFactorySimultaneous.

Definition at line 1001 of file RooAbsPdf.cxx.

◆ createProjection()

RooAbsPdf * RooAbsPdf::createProjection ( const RooArgSet iset)
virtual

Return a p.d.f that represent a projection of this p.d.f integrated over given observables.

Reimplemented in RooProjectedPdf.

Definition at line 3310 of file RooAbsPdf.cxx.

◆ createScanCdf()

RooAbsReal * RooAbsPdf::createScanCdf ( const RooArgSet iset,
const RooArgSet nset,
Int_t  numScanBins,
Int_t  intOrder 
)

Definition at line 3422 of file RooAbsPdf.cxx.

◆ defaultGeneratorConfig()

RooNumGenConfig * RooAbsPdf::defaultGeneratorConfig ( )
static

Returns the default numeric MC generator configuration for all RooAbsReals.

Definition at line 3461 of file RooAbsPdf.cxx.

◆ expectedEvents() [1/2]

double RooAbsPdf::expectedEvents ( const RooArgSet nset) const
inline

Return expected number of events to be used in calculation of extended likelihood.

This function should not be overridden, as it just redirects to the actual virtual function but takes a RooArgSet reference instead of pointer (

See also
expectedEvents(const RooArgSet*) const).

Definition at line 276 of file RooAbsPdf.h.

◆ expectedEvents() [2/2]

Double_t RooAbsPdf::expectedEvents ( const RooArgSet nset) const
virtual

Return expected number of events to be used in calculation of extended likelihood.

Return expected number of events from this p.d.f for use in extended likelihood calculations.

This default implementation returns zero

Reimplemented in RooAddModel, RooExtendedTerm, RooExtendPdf, RooProdPdf, RooRealSumPdf, RooSimultaneous, RooAddPdf, RooBinSamplingPdf, and RooNormalizedPdf.

Definition at line 3260 of file RooAbsPdf.cxx.

◆ extendedTerm() [1/3]

double RooAbsPdf::extendedTerm ( double  sumEntries,
double  expected,
double  sumEntriesW2 = 0.0 
) const

Definition at line 791 of file RooAbsPdf.cxx.

◆ extendedTerm() [2/3]

double RooAbsPdf::extendedTerm ( double  sumEntries,
RooArgSet const *  nset,
double  sumEntriesW2 = 0.0 
) const

Return the extended likelihood term ( \( N_\mathrm{expect} - N_\mathrm{observed} \cdot \log(N_\mathrm{expect} \)) of this PDF for the given number of observed events.

For successful operation, the PDF implementation must indicate that it is extendable by overloading canBeExtended(), and must implement the expectedEvents() function.

Parameters
[in]observedThe number of observed events.
[in]nsetThe normalization set when asking the pdf for the expected number of events.
[in]observedSumW2The number of observed events when weighting with squared weights. If non-zero, the weight-squared error correction is applied to the extended term.

The weight-squared error correction works as follows: adjust poisson such that estimate of \(N_\mathrm{expect}\) stays at the same value, but has a different variance, rescale both the observed and expected count of the Poisson with a factor \( \sum w_{i} / \sum w_{i}^2 \) (the effective weight of the Poisson term), i.e., change \(\mathrm{Poisson}(N_\mathrm{observed} = \sum w_{i} | N_\mathrm{expect} )\) to \( \mathrm{Poisson}(\sum w_{i} \cdot \sum w_{i} / \sum w_{i}^2 | N_\mathrm{expect} \cdot \sum w_{i} / \sum w_{i}^2 ) \), weighted by the effective weight \( \sum w_{i}^2 / \sum w_{i} \) in the likelihood. Since here we compute the likelihood with the weight square, we need to multiply by the square of the effective weight:

  • \( W_\mathrm{expect} = N_\mathrm{expect} \cdot \sum w_{i} / \sum w_{i}^2 \) : effective expected entrie
  • \( W_\mathrm{observed} = \sum w_{i} \cdot \sum w_{i} / \sum w_{i}^2 \) : effective observed entries

The extended term for the likelihood weighted by the square of the weight will be then:

\( \left(\sum w_{i}^2 / \sum w_{i}\right)^2 \cdot W_\mathrm{expect} - (\sum w_{i}^2 / \sum w_{i})^2 \cdot W_\mathrm{observed} \cdot \log{W_\mathrm{expect}} \)

aund this is using the previous expressions for \( W_\mathrm{expect} \) and \( W_\mathrm{observed} \):

\( \sum w_{i}^2 / \sum w_{i} \cdot N_\mathrm{expect} - \sum w_{i}^2 \cdot \log{W_\mathrm{expect}} \)

Since the weights are constants in the likelihood we can use \(\log{N_\mathrm{expect}}\) instead of \(\log{W_\mathrm{expect}}\).

See also RooAbsPdf::extendedTerm(RooAbsData const& data, bool weightSquared), which takes a dataset to extract \(N_\mathrm{observed}\) and the normalization set.

Definition at line 786 of file RooAbsPdf.cxx.

◆ extendedTerm() [3/3]

double RooAbsPdf::extendedTerm ( RooAbsData const &  data,
bool  weightSquared 
) const

Return the extended likelihood term ( \( N_\mathrm{expect} - N_\mathrm{observed} \cdot \log(N_\mathrm{expect} \)) of this PDF for the given number of observed events.

This function is a wrapper around RooAbsPdf::extendedTerm(double observed, const RooArgSet* nset), where the number of observed events and observables to be used as the normalization set for the pdf is extracted from a RooAbsData.

For successful operation, the PDF implementation must indicate that it is extendable by overloading canBeExtended(), and must implement the expectedEvents() function.

Parameters
[in]dataThe RooAbsData to retrieve the set of observables and number of expected events.
[in]weightSquaredIf set to true, the extended term will be scaled by the ratio of squared event weights over event weights: \( \sum w_{i}^2 / \sum w_{i} \). Indended to be used by fits with the SumW2Error() option that can be passed to RooAbsPdf::fitTo(RooAbsData&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&) (see the documentation of said function to learn more about the interpretation of fits with squared weights).

Definition at line 852 of file RooAbsPdf.cxx.

◆ extendMode()

virtual ExtendMode RooAbsPdf::extendMode ( ) const
inlinevirtual

Returns ability of PDF to provide extended likelihood terms.

Possible answers are in the enumerator RooAbsPdf::ExtendMode. This default implementation always returns CanNotBeExtended.

Reimplemented in RooAddModel, RooExtendedTerm, RooExtendPdf, RooProdPdf, RooRealSumPdf, RooSimultaneous, RooAddPdf, RooBinSamplingPdf, and RooNormalizedPdf.

Definition at line 260 of file RooAbsPdf.h.

◆ fitTo() [1/2]

RooAbsPdf::fitTo ( RooAbsData data,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Fit PDF to given dataset.

If dataset is unbinned, an unbinned maximum likelihood is performed. If the dataset is binned, a binned maximum likelihood is performed. By default the fit is executed through the MINUIT commands MIGRAD, HESSE in succession.

Parameters
[in]dataData to fit the PDF to
[in]arg1One or more arguments to control the behaviour of the fit
Returns
RooFitResult with fit status and parameters if option Save() is used, nullptr otherwise. The user takes ownership of the fit result.

The following named arguments are supported

Type of CmdArg Options to control construction of -log(L)
ConditionalObservables(Args_t &&... argsOrArgSet) Do not normalize PDF over listed observables.
Extended(Bool_t flag) Add extended likelihood term, off by default
Range(const char* name) Fit only data inside range with given name. Multiple comma-separated range names can be specified. In this case, the unnormalized PDF \(f(x)\) is normalized by the integral over all ranges \(r_i\):

\[ p(x) = \frac{f(x)}{\sum_i \int_{r_i} f(x) dx}. \]

Range(Double_t lo, Double_t hi) Fit only data inside given range. A range named "fit" is created on the fly on all observables.
SumCoefRange(const char* name) Set the range in which to interpret the coefficients of RooAddPdf components
NumCPU(int num, int strat) Parallelize NLL calculation on num CPUs
Strategy Effect
0 = RooFit::BulkPartition (Default) Divide events in N equal chunks
1 = RooFit::Interleave Process event iN in process N. Recommended for binned data with a substantial number of zero-bins, which will be distributed across processes more equitably in this strategy
2 = RooFit::SimComponents Process each component likelihood of a RooSimultaneous fully in a single process and distribute components over processes. This approach can be benificial if normalization calculation time dominates the total computation time of a component (since the normalization calculation must be performed in each process in strategies 0 and 1. However beware that if the RooSimultaneous components do not share many parameters this strategy is inefficient: as most minuit-induced likelihood calculations involve changing a single parameter, only 1 of the N processes will be active most of the time if RooSimultaneous components do not share many parameters
3 = RooFit::Hybrid Follow strategy 0 for all RooSimultaneous components, except those with less than 30 dataset entries, for which strategy 2 is followed.
SplitRange(Bool_t flag) Use separate fit ranges in a simultaneous fit. Actual range name for each subsample is assumed to by rangeName_indexState where indexState is the state of the master index category of the simultaneous fit. Using Range("range"), SplitRange() as switches, different ranges could be set like this:
myVariable.setRange("range_pi0", 135, 210);
myVariable.setRange("range_gamma", 50, 210);
Constrain(const RooArgSet&pars) For p.d.f.s that contain internal parameter constraint terms (that is usually product PDFs, where one term of the product depends on parameters but not on the observable(s),), only apply constraints to the given subset of parameters.
ExternalConstraints(const RooArgSet& ) Include given external constraints to likelihood by multiplying them with the original likelihood.
GlobalObservables(const RooArgSet&) Define the set of normalization observables to be used for the constraint terms. If none are specified the constrained parameters are used.
Offset(Bool_t) Offset likelihood by initial value (so that starting value of FCN in minuit is zero). This can improve numeric stability in simultaneously fits with components with large likelihood values
BatchMode(bool on) Experimental batch evaluation mode. This computes a batch of likelihood values at a time, uses faster math functions and possibly auto vectorisation (this depends on the compiler flags). Depending on hardware capabilities, the compiler flags and whether a batch evaluation function was implemented for the PDFs of the model, likelihood computations are 2x to 10x faster. The relative difference of the single log-likelihoods w.r.t. the legacy mode is usually better than 1.E-12, and fit parameters usually agree to better than 1.E-6.
IntegrateBins(double precision) In binned fits, integrate the PDF over the bins instead of using the probability density at the bin centre. This can reduce the bias observed when fitting functions with high curvature to binned data.
  • precision > 0: Activate bin integration everywhere. Use precision between 0.01 and 1.E-6, depending on binning. Note that a low precision such as 0.01 might yield identical results to 1.E-4, since the integrator might reach 1.E-4 already in its first integration step. If lower precision is desired (more speed), a RooBinSamplingPdf has to be created manually, and its integrator has to be manipulated directly.
  • precision = 0: Activate bin integration only for continuous PDFs fit to a RooDataHist.
  • precision < 0: Deactivate.
    See also
    RooBinSamplingPdf
Options to control flow of fit procedure
Minimizer("<type>", "<algo>")

Choose minimization package and optionally the algorithm to use. Default is MINUIT/MIGRAD through the RooMinimizer interface, but others can be specified (through RooMinimizer interface).

Type Algorithm
Minuit migrad, simplex, minimize (=migrad+simplex), migradimproved (=migrad+improve)
Minuit2 migrad, simplex, minimize, scan
GSLMultiMin conjugatefr, conjugatepr, bfgs, bfgs2, steepestdescent
GSLSimAn -

InitialHesse(Bool_t flag) Flag controls if HESSE before MIGRAD as well, off by default
Optimize(Bool_t flag) Activate constant term optimization of test statistic during minimization (on by default)
Hesse(Bool_t flag) Flag controls if HESSE is run after MIGRAD, on by default
Minos(Bool_t flag) Flag controls if MINOS is run after HESSE, off by default
Minos(const RooArgSet& set) Only run MINOS on given subset of arguments
Save(Bool_t flag) Flag controls if RooFitResult object is produced and returned, off by default
Strategy(Int_t flag) Set Minuit strategy (0 to 2, default is 1)
EvalErrorWall(bool flag=true) When parameters are in disallowed regions (e.g. PDF is negative), return very high value to fitter to force it out of that region. This can, however, mean that the fitter gets lost in this region. If this happens, try switching it off.
RecoverFromUndefinedRegions(double strength) When PDF is invalid (e.g. parameter in undefined region), try to direct minimiser away from that region. strength controls the magnitude of the penalty term. Leaving out this argument defaults to 10. Switch off with strength = 0..
FitOptions(const char* optStr)
Deprecated:
Steer fit with classic options string (for backward compatibility).
Attention
Use of this option excludes use of any of the new style steering options.
SumW2Error(Bool_t flag) Apply correction to errors and covariance matrix. This uses two covariance matrices, one with the weights, the other with squared weights, to obtain the correct errors for weighted likelihood fits. If this option is activated, the corrected covariance matrix is calculated as \( V_\mathrm{corr} = V C^{-1} V \), where \( V \) is the original covariance matrix and \( C \) is the inverse of the covariance matrix calculated using the squared weights. This allows to switch between two interpretations of errors:
SumW2Error Interpretation
true

The errors reflect the uncertainty of the Monte Carlo simulation. Use this if you want to know how much accuracy you can get from the available Monte Carlo statistics.

Example: Simulation with 1000 events, the average weight is 0.1. The errors are as big as if one fitted to 1000 events.

false

The errors reflect the errors of a dataset, which is as big as the sum of weights. Use this if you want to know what statistical errors you would get if you had a dataset with as many events as the (weighted) Monte Carlo simulation represents.

Example (Data as above): The errors are as big as if one fitted to 100 events.

Note
If the SumW2Error correction is enabled, the covariance matrix quality stored in the RooFitResult object will be the minimum of the original covariance matrix quality and the quality of the covariance matrix calculated with the squared weights.
AsymptoticError() Use the asymptotically correct approach to estimate errors in the presence of weights. This is slower but more accurate than SumW2Error. See also https://arxiv.org/abs/1911.01303).
PrefitDataFraction(double fraction) Runs a prefit on a small dataset of size fraction*(actual data size). This can speed up fits by finding good starting values for the parameters for the actual fit.
Warning
Prefitting may give bad results when used in binned analysis.
Options to control informational output
Verbose(Bool_t flag) Flag controls if verbose output is printed (NLL, parameter changes during fit).
Timer(Bool_t flag) Time CPU and wall clock consumption of fit steps, off by default.
PrintLevel(Int_t level) Set Minuit print level (-1 to 3, default is 1). At -1 all RooFit informational messages are suppressed as well. See RooMinimizer::PrintLevel for the meaning of the levels.
Warnings(Bool_t flag) Enable or disable MINUIT warnings (enabled by default)
PrintEvalErrors(Int_t numErr) Control number of p.d.f evaluation errors printed per likelihood evaluation. A negative value suppresses output completely, a zero value will only print the error count per p.d.f component, a positive value will print details of each error up to numErr messages per p.d.f component.

PyROOT

The RooAbsPdf::fitTo() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Definition at line 1465 of file RooAbsPdf.cxx.

◆ fitTo() [2/2]

RooFitResult * RooAbsPdf::fitTo ( RooAbsData data,
const RooLinkedList cmdList 
)
virtual

Fit PDF to given dataset.

If dataset is unbinned, an unbinned maximum likelihood is performed. If the dataset is binned, a binned maximum likelihood is performed. By default the fit is executed through the MINUIT commands MIGRAD, HESSE and MINOS in succession.

See RooAbsPdf::fitTo(RooAbsData&,RooCmdArg&,RooCmdArg&,RooCmdArg&,RooCmdArg&,RooCmdArg&,RooCmdArg&,RooCmdArg&,RooCmdArg&)

for documentation of options

Definition at line 1601 of file RooAbsPdf.cxx.

◆ genContext()

RooAbsGenContext * RooAbsPdf::genContext ( const RooArgSet vars,
const RooDataSet prototype = 0,
const RooArgSet auxProto = 0,
Bool_t  verbose = kFALSE 
) const
virtual

Interface function to create a generator context from a p.d.f.

This default implementation returns a 'standard' context that works for any p.d.f

Reimplemented in RooEffProd, RooAddPdf, RooAbsAnaConvPdf, RooAddModel, RooFFTConvPdf, RooNumConvPdf, RooProdPdf, and RooSimultaneous.

Definition at line 1932 of file RooAbsPdf.cxx.

◆ generate() [1/6]

RooAbsPdf::generate ( const RooArgSet whatVars,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none() 
)

Generate a new dataset containing the specified variables with events sampled from our distribution.

Generate the specified number of events or expectedEvents() if not specified.

Parameters
[in]whatVarsChoose variables in which to generate events. Variables not listed here will remain constant and not be used for event generation.
[in]argxxOptional RooCmdArg() to change behaviour of generate().
Returns
RooDataSet *, owned by caller.

Any variables of this PDF that are not in whatVars will use their current values and be treated as fixed parameters. Returns zero in case of an error.

Type of CmdArg Effect on generate
Name(const char* name) Name of the output dataset
Verbose(Bool_t flag) Print informational messages during event generation
NumEvent(int nevt) Generate specified number of events
Extended() If no number of events to be generated is given, use expected number of events from extended likelihood term. This evidently only works for extended PDFs.
GenBinned(const char* tag) Use binned generation for all component pdfs that have 'setAttribute(tag)' set
AutoBinned(Bool_t flag) Automatically deploy binned generation for binned distributions (e.g. RooHistPdf, sums and products of RooHistPdfs etc)
Note
Datasets that are generated in binned mode are returned as weighted unbinned datasets. This means that for each bin, there will be one event in the dataset with a weight corresponding to the (possibly randomised) bin content.
AllBinned() As above, but for all components.
Note
The notion of components is only meaningful for simultaneous PDFs as binned generation is always executed at the top-level node for a regular PDF, so for those it only mattes that the top-level node is tagged.
ProtoData(const RooDataSet& data, Bool_t randOrder) Use specified dataset as prototype dataset. If randOrder in ProtoData() is set to true, the order of the events in the dataset will be read in a random order if the requested number of events to be generated does not match the number of events in the prototype dataset.
Note
If ProtoData() is used, the specified existing dataset as a prototype: the new dataset will contain the same number of events as the prototype (unless otherwise specified), and any prototype variables not in whatVars will be copied into the new dataset for each generated event and also used to set our PDF parameters. The user can specify a number of events to generate that will override the default. The result is a copy of the prototype dataset with only variables in whatVars randomized. Variables in whatVars that are not in the prototype will be added as new columns to the generated dataset.

Accessing the underlying event generator

Depending on the fit model (if it is difficult to sample), it may be necessary to change generator settings. For the default generator (RooFoamGenerator), the number of samples or cells could be increased by e.g. using myPdf->specialGeneratorConfig()->getConfigSection("RooFoamGenerator").setRealValue("nSample",1e4);

The foam generator e.g. has the following config options:

PyROOT

The RooAbsPdf::generate() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Definition at line 2015 of file RooAbsPdf.cxx.

◆ generate() [2/6]

RooDataSet * RooAbsPdf::generate ( const RooArgSet whatVars,
const RooDataSet prototype,
Int_t  nEvents = 0,
Bool_t  verbose = kFALSE,
Bool_t  randProtoOrder = kFALSE,
Bool_t  resampleProto = kFALSE 
) const

Generate a new dataset using a prototype dataset as a model, with values of the variables in whatVars sampled from our distribution.

Parameters
[in]whatVarsGenerate for these variables.
[in]prototypeUse this dataset as a prototype: the new dataset will contain the same number of events as the prototype (by default), and any prototype variables not in whatVars will be copied into the new dataset for each generated event and also used to set our PDF parameters. The user can specify a number of events to generate that will override the default. The result is a copy of the prototype dataset with only variables in whatVars randomized. Variables in whatVars that are not in the prototype will be added as new columns to the generated dataset.
[in]nEventsNumber of events to generate. Defaults to 0, which means number of event in prototype dataset.
[in]verboseShow which generator strategies are being used.
[in]randProtoOrderRandomise order of retrieval of events from proto dataset.
[in]resampleProtoResample from the proto dataset.
Returns
The new dataset. Returns zero in case of an error. The caller takes ownership of the returned dataset.

Definition at line 2275 of file RooAbsPdf.cxx.

◆ generate() [3/6]

RooDataSet * RooAbsPdf::generate ( const RooArgSet whatVars,
Double_t  nEvents = 0,
Bool_t  verbose = kFALSE,
Bool_t  autoBinned = kTRUE,
const char *  binnedTag = "",
Bool_t  expectedData = kFALSE,
Bool_t  extended = kFALSE 
) const

Generate a new dataset containing the specified variables with events sampled from our distribution.

Parameters
[in]whatVarsGenerate a dataset with the variables (and categories) in this set. Any variables of this PDF that are not in whatVars will use their current values and be treated as fixed parameters.
[in]nEventsGenerate the specified number of events or else try to use expectedEvents() if nEvents <= 0 (default).
[in]verboseShow which generator strategies are being used.
[in]autoBinnedIf original distribution is binned, return bin centers and randomise weights instead of generating single events.
[in]binnedTag
[in]expectedDataCall setExpectedData on the genContext.
[in]extendedRandomise number of events generated according to Poisson(nEvents). Only useful if PDF is extended.
Returns
New dataset. Returns zero in case of an error. The caller takes ownership of the returned dataset.

Definition at line 2190 of file RooAbsPdf.cxx.

◆ generate() [4/6]

RooDataSet * RooAbsPdf::generate ( const RooArgSet whatVars,
Int_t  nEvents,
const RooCmdArg arg1,
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none() 
)
inline

◆ generate() [5/6]

RooDataSet * RooAbsPdf::generate ( RooAbsPdf::GenSpec spec) const

Generate according to GenSpec obtained from prepareMultiGen().

If many identical generation requests are needed, e.g.

in toy MC studies, it is more efficient to use the prepareMultiGen()/generate() combination than calling the standard generate() multiple times as initialization overhead is only incurred once.

Definition at line 2153 of file RooAbsPdf.cxx.

◆ generate() [6/6]

RooDataSet * RooAbsPdf::generate ( RooAbsGenContext context,
const RooArgSet whatVars,
const RooDataSet prototype,
Double_t  nEvents,
Bool_t  verbose,
Bool_t  randProtoOrder,
Bool_t  resampleProto,
Bool_t  skipInit = kFALSE,
Bool_t  extended = kFALSE 
) const
private

Internal method.

Definition at line 2219 of file RooAbsPdf.cxx.

◆ generateBinned() [1/3]

RooAbsPdf::generateBinned ( const RooArgSet whatVars,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none() 
) const
virtual

Generate a new dataset containing the specified variables with events sampled from our distribution.

Parameters
[in]whatVarsChoose variables in which to generate events. Variables not listed here will remain constant and not be used for event generation
[in]arg1Optional RooCmdArg to change behaviour of generateBinned()
Returns
RooDataHist *, to be managed by caller.

Generate the specified number of events or expectedEvents() if not specified.

Any variables of this PDF that are not in whatVars will use their current values and be treated as fixed parameters. Returns zero in case of an error. The caller takes ownership of the returned dataset.

The following named arguments are supported

Type of CmdArg Effect on generation
Name(const char* name) Name of the output dataset
Verbose(Bool_t flag) Print informational messages during event generation
NumEvent(int nevt) Generate specified number of events
Extended() The actual number of events generated will be sampled from a Poisson distribution with mu=nevt.

This can be much faster for peaked PDFs, but the number of events is not exactly what was requested. | ExpectedData() | Return a binned dataset without statistical fluctuations (also aliased as Asimov())

PyROOT

The RooAbsPdf::generateBinned() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Definition at line 2408 of file RooAbsPdf.cxx.

◆ generateBinned() [2/3]

RooDataHist * RooAbsPdf::generateBinned ( const RooArgSet whatVars,
Double_t  nEvents,
Bool_t  expectedData = kFALSE,
Bool_t  extended = kFALSE 
) const
virtual

Generate a new dataset containing the specified variables with events sampled from our distribution.

Parameters
[in]whatVarsVariables that values should be generated for.
[in]nEventsHow many events to generate. If nEvents <=0, use the value returned by expectedEvents() as target.
[in]expectedDataIf set to true (false by default), the returned histogram returns the 'expected' data sample, i.e. no statistical fluctuations are present.
[in]extendedFor each bin, generate Poisson(x, mu) events, where mu is chosen such that on average, one would obtain nEvents events. This means that the true number of events will fluctuate around the desired value, but the generation happens a lot faster. Especially if the PDF is sharply peaked, the multinomial event generation necessary to generate exactly nEvents events can be very slow.

The binning used for generation of events is the currently set binning for the variables. It can e.g. be changed using

x.setBins(15);
x.setRange(-5., 5.);
pdf.generateBinned(RooArgSet(x), 1000);
friend class RooArgSet
Definition RooAbsArg.h:642
Double_t x[n]
Definition legend1.C:17

Any variables of this PDF that are not in whatVars will use their current values and be treated as fixed parameters.

Returns
RooDataHist* owned by the caller. Returns nullptr in case of an error.

Definition at line 2490 of file RooAbsPdf.cxx.

◆ generateBinned() [3/3]

virtual RooDataHist * RooAbsPdf::generateBinned ( const RooArgSet whatVars,
Double_t  nEvents,
const RooCmdArg arg1,
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none() 
) const
inlinevirtual

As RooAbsPdf::generateBinned(const RooArgSet&, const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&)

Parameters
[in]nEventsHow many events to generate

Definition at line 107 of file RooAbsPdf.h.

◆ generateEvent()

void RooAbsPdf::generateEvent ( Int_t  code)
virtual

Interface for generation of an event using the algorithm corresponding to the specified code.

The meaning of each code is defined by the getGenerator() implementation. The default implementation does nothing.

Reimplemented in RooBCPEffDecay, RooBCPGenDecay, RooBDecay, RooBMixDecay, RooDecay, RooGamma, RooGaussModel, RooGExpModel, RooLandau, RooLognormal, RooNonCPEigenDecay, RooUniform, RooAddModel, RooMultiVarGaussian, RooProdPdf, RooProjectedPdf, RooTruthModel, RooGaussian, RooJohnson, RooPoisson, and RooBinSamplingPdf.

Definition at line 2353 of file RooAbsPdf.cxx.

◆ generateSimGlobal()

RooDataSet * RooAbsPdf::generateSimGlobal ( const RooArgSet whatVars,
Int_t  nEvents 
)
virtual

Special generator interface for generation of 'global observables' – for RooStats tools.

Reimplemented in RooSimultaneous.

Definition at line 2610 of file RooAbsPdf.cxx.

◆ getAllConstraints()

RooArgSet * RooAbsPdf::getAllConstraints ( const RooArgSet observables,
RooArgSet constrainedParams,
Bool_t  stripDisconnected = kTRUE 
) const
virtual

This helper function finds and collects all constraints terms of all component p.d.f.s and returns a RooArgSet with all those terms.

Definition at line 3439 of file RooAbsPdf.cxx.

◆ getConstraints()

virtual RooArgSet * RooAbsPdf::getConstraints ( const RooArgSet ,
RooArgSet ,
Bool_t   
) const
inlinevirtual

Reimplemented in RooProdPdf.

Definition at line 210 of file RooAbsPdf.h.

◆ getGenerator()

Int_t RooAbsPdf::getGenerator ( const RooArgSet directVars,
RooArgSet generateVars,
Bool_t  staticInitOK = kTRUE 
) const
virtual

Load generatedVars with the subset of directVars that we can generate events for, and return a code that specifies the generator algorithm we will use.

A code of zero indicates that we cannot generate any of the directVars (in this case, nothing should be added to generatedVars). Any non-zero codes will be passed to our generateEvent() implementation, but otherwise its value is arbitrary. The default implemetation of this method returns zero. Subclasses will usually implement this method using the matchArgs() methods to advertise the algorithms they provide.

Reimplemented in RooBinSamplingPdf, RooBCPEffDecay, RooBCPGenDecay, RooBDecay, RooBMixDecay, RooDecay, RooGamma, RooGaussModel, RooGExpModel, RooLandau, RooLognormal, RooNonCPEigenDecay, RooUniform, RooAddModel, RooMultiVarGaussian, RooProdPdf, RooProjectedPdf, RooTruthModel, RooGaussian, RooJohnson, and RooPoisson.

Definition at line 2331 of file RooAbsPdf.cxx.

◆ getGeneratorConfig()

const RooNumGenConfig * RooAbsPdf::getGeneratorConfig ( ) const

Return the numeric MC generator configuration used for this object.

If a specialized configuration was associated with this object, that configuration is returned, otherwise the default configuration for all RooAbsReals is returned

Definition at line 3499 of file RooAbsPdf.cxx.

◆ getLogProbabilities() [1/2]

RooSpan< const double > RooAbsPdf::getLogProbabilities ( RooBatchCompute::RunContext evalData,
const RooArgSet normSet = nullptr 
) const

Compute the log-likelihoods for all events in the requested batch.

The arguments are passed over to getValues().

Parameters
[in]evalDataStruct with data that should be used for evaluation.
[in]normSetOptional normalisation set to be used during computations.
Returns
Returns a batch of doubles that contains the log probabilities.

Definition at line 729 of file RooAbsPdf.cxx.

◆ getLogProbabilities() [2/2]

void RooAbsPdf::getLogProbabilities ( RooSpan< const double pdfValues,
double output 
) const

Definition at line 739 of file RooAbsPdf.cxx.

◆ getLogVal()

Double_t RooAbsPdf::getLogVal ( const RooArgSet set = 0) const
virtual

Return the log of the current value with given normalization An error message is printed if the argument of the log is negative.

Reimplemented in RooHistConstraint.

Definition at line 672 of file RooAbsPdf.cxx.

◆ getLogValBatch()

RooSpan< const double > RooAbsPdf::getLogValBatch ( std::size_t  begin,
std::size_t  batchSize,
const RooArgSet normSet = nullptr 
) const

◆ getNorm() [1/2]

Double_t RooAbsPdf::getNorm ( const RooArgSet nset) const
inline

Get normalisation term needed to normalise the raw values returned by getVal().

Note that getVal(normalisationVariables) will automatically apply the normalisation term returned here.

Parameters
nsetSet of variables to normalise over.

Definition at line 239 of file RooAbsPdf.h.

◆ getNorm() [2/2]

Double_t RooAbsPdf::getNorm ( const RooArgSet nset = 0) const
virtual

Get normalisation term needed to normalise the raw values returned by getVal().

Note that getVal(normalisationVariables) will automatically apply the normalisation term returned here.

Parameters
nsetSet of variables to normalise over.

Reimplemented in RooResolutionModel.

Definition at line 482 of file RooAbsPdf.cxx.

◆ getNormIntegral()

const RooAbsReal * RooAbsPdf::getNormIntegral ( const RooArgSet nset) const
inline

Definition at line 297 of file RooAbsPdf.h.

◆ getNormObj()

const RooAbsReal * RooAbsPdf::getNormObj ( const RooArgSet set,
const RooArgSet iset,
const TNamed rangeName = 0 
) const
virtual

Return pointer to RooAbsReal object that implements calculation of integral over observables iset in range rangeName, optionally taking the integrand normalized over observables nset.

Definition at line 506 of file RooAbsPdf.cxx.

◆ getValues() [1/3]

std::vector< double > RooAbsReal::getValues ( RooAbsData const &  data,
RooFit::BatchModeOption  batchMode = RooFit::BatchModeOption::Cpu 
) const

Definition at line 141 of file RooAbsReal.cxx.

◆ getValues() [2/3]

RooSpan< const double > RooAbsPdf::getValues ( RooBatchCompute::RunContext evalData,
const RooArgSet normSet 
) const
virtual

Compute batch of values for given input data, and normalise by integrating over the observables in normSet.

Store result in evalData, and return a span pointing to it. This uses evaluateSpan() to perform an (unnormalised) computation of data points. This computation is finalised by normalising the bare values, and by checking for computation errors. Derived classes should override evaluateSpan() to reach maximal performance.

Parameters
[in,out]evalDataObject holding data that should be used in computations. Results are also stored here.
[in]normSetIf not nullptr, normalise results by integrating over the variables in this set. The normalisation is only computed once, and applied to the full batch.
Returns
RooSpan with probabilities. The memory of this span is owned by evalData.
See also
RooAbsReal::getValues().

Reimplemented from RooAbsReal.

Definition at line 408 of file RooAbsPdf.cxx.

◆ getValues() [3/3]

RooSpan< const double > RooAbsReal::getValues ( RooBatchCompute::RunContext evalData,
const RooArgSet normSet = nullptr 
) const
virtual

Compute batch of values for input data stored in evalData.

Deprecated:
getValBatch() has been removed in favour of the faster getValues(). If your code is affected by this change, please consult the release notes for ROOT 6.24 for guidance on how to make this transition. https://root.cern/doc/v624/release-notes.html

This is a faster, multi-value version of getVal(). It calls evaluateSpan() to trigger computations, and finalises those (e.g. error checking or automatic normalisation) before returning a span with the results. This span will also be stored in evalData, so subsquent calls of getValues() will return immediately.

If evalData is empty, a single value will be returned, which is the result of evaluating the current value of each object that's serving values to us. If evalData contains a batch of values for one or more of the objects serving values to us, a batch of values for each entry stored in evalData is returned. To fill a RunContext with values from a dataset, use RooAbsData::getBatches().

Parameters
[in]evalDataObject holding spans of input data. The results are also stored here.
[in]normSetUse these variables for normalisation (relevant for PDFs), and pass this normalisation on to object serving values to us.
Returns
RooSpan pointing to the computation results. The memory this span points to is owned by evalData.

Reimplemented from RooAbsReal.

Definition at line 140 of file RooAbsReal.cxx.

◆ getValV()

Double_t RooAbsPdf::getValV ( const RooArgSet nset = 0) const
virtual

Return current value, normalized by integrating over the observables in nset.

If nset is 0, the unnormalized value is returned. All elements of nset must be lvalues.

Unnormalized values are not cached. Doing so would be complicated as _norm->getVal() could spoil the cache and interfere with returning the cached return value. Since unnormalized calls are typically done in integration calls, there is no performance hit.

Reimplemented from RooAbsReal.

Reimplemented in RooNormalizedPdf, RooResolutionModel, RooAbsCachedPdf, and RooAddPdf.

Definition at line 356 of file RooAbsPdf.cxx.

◆ initGenerator()

void RooAbsPdf::initGenerator ( Int_t  code)
virtual

Interface for one-time initialization to setup the generator for the specified code.

Reimplemented in RooBCPEffDecay, RooBCPGenDecay, RooBMixDecay, RooNonCPEigenDecay, RooMultiVarGaussian, RooProdPdf, RooBinSamplingPdf, and RooProjectedPdf.

Definition at line 2341 of file RooAbsPdf.cxx.

◆ isDirectGenSafe()

Bool_t RooAbsPdf::isDirectGenSafe ( const RooAbsArg arg) const
virtual

Check if given observable can be safely generated using the pdfs internal generator mechanism (if that existsP).

Observables on which a PDF depends via more than route are not safe for use with internal generators because they introduce correlations not known to the internal generator

Reimplemented in RooAbsAnaConvPdf, RooAddModel, RooProdPdf, and RooBinSamplingPdf.

Definition at line 2366 of file RooAbsPdf.cxx.

◆ logBatchComputationErrors()

void RooAbsPdf::logBatchComputationErrors ( RooSpan< const double > &  outputs,
std::size_t  begin 
) const
private

Scan through outputs and fix+log all nans and negative values.

Parameters
[in,out]outputsArray to be scanned & fixed.
[in]beginBegin of event range. Only needed to print the correct event number where the error occurred.

Definition at line 706 of file RooAbsPdf.cxx.

◆ minimizeNLL()

std::unique_ptr< RooFitResult > RooAbsPdf::minimizeNLL ( RooAbsReal nll,
RooAbsData const &  data,
MinimizerConfig const &  cfg 
)

Minimizes a given NLL variable by finding the optimal parameters with the RooMinimzer.

The NLL variable can be created with RooAbsPdf::createNLL. If you are looking for a function that combines likelihood creation with fitting, see RooAbsPdf::fitTo.

Parameters
[in]nllThe negative log-likelihood variable to minimize.
[in]dataThe dataset that was als used for the NLL. It's a necessary parameter because it is used in the asymptotic error correction.
[in]cfgConfiguration struct with all the configuration options for the RooMinimizer. These are a subset of the options that you can also pass to RooAbsPdf::fitTo via the RooFit command arguments.

Definition at line 1488 of file RooAbsPdf.cxx.

◆ mustBeExtended()

Bool_t RooAbsPdf::mustBeExtended ( ) const
inline

If true PDF must provide extended likelihood term.

Definition at line 266 of file RooAbsPdf.h.

◆ normalizeWithNaNPacking()

double RooAbsPdf::normalizeWithNaNPacking ( double  rawVal,
double  normVal 
) const
protected

Definition at line 319 of file RooAbsPdf.cxx.

◆ normRange()

const char * RooAbsPdf::normRange ( ) const
inline

Definition at line 292 of file RooAbsPdf.h.

◆ paramOn() [1/3]

RooPlot * RooAbsPdf::paramOn ( RooPlot frame,
const RooAbsData data,
const char *  label = "",
Int_t  sigDigits = 2,
Option_t options = "NELU",
Double_t  xmin = 0.65,
Double_t  xmax = 0.9,
Double_t  ymax = 0.9 
)
virtual
Deprecated:
Obsolete, provided for backward compatibility. Don't use.

Definition at line 3178 of file RooAbsPdf.cxx.

◆ paramOn() [2/3]

RooPlot * RooAbsPdf::paramOn ( RooPlot frame,
const RooArgSet params,
Bool_t  showConstants = kFALSE,
const char *  label = "",
Int_t  sigDigits = 2,
Option_t options = "NELU",
Double_t  xmin = 0.65,
Double_t  xmax = 0.99,
Double_t  ymax = 0.95,
const RooCmdArg formatCmd = 0 
)
privatevirtual

Add a text box with the current parameter values and their errors to the frame.

Observables of this PDF appearing in the 'data' dataset will be omitted.

An optional label will be inserted if passed. Multi-line labels can be generated by adding \n to the label string. Use 'sigDigits' to modify the default number of significant digits printed. The 'xmin,xmax,ymax' values specify the initial relative position of the text box in the plot frame.

Definition at line 3199 of file RooAbsPdf.cxx.

◆ paramOn() [3/3]

RooAbsPdf::paramOn ( RooPlot frame,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none() 
)
virtual

Add a box with parameter values (and errors) to the specified frame.

The following named arguments are supported.

Type of CmdArg Effect on parameter box
Parameters(const RooArgSet& param) Only the specified subset of parameters will be shown. By default all non-constant parameters are shown.
ShowConstants(Bool_t flag) Also display constant parameters
Format(const char* optStr)
Deprecated:
Classing parameter formatting options, provided for backward compatibility

Format(const char* what,...) Parameter formatting options.
Parameter Format
const char* what Controls what is shown. "N" adds name, "E" adds error, "A" shows asymmetric error, "U" shows unit, "H" hides the value
FixedPrecision(int n) Controls precision, set fixed number of digits
AutoPrecision(int n) Controls precision. Number of shown digits is calculated from error + n specified additional digits (1 is sensible default)
Label(const chat* label) Add label to parameter box. Use \n for multi-line labels.
Layout(Double_t xmin, Double_t xmax, Double_t ymax) Specify relative position of left/right side of box and top of box. Coordinates are given as position on the pad between 0 and 1. The lower end of the box is calculated automatically from the number of lines in the box.

Example use:

pdf.paramOn(frame, Label("fit result"), Format("NEU",AutoPrecision(1)) ) ;
static char * Format(const char *format, va_list ap)
Format a string in a circular formatting buffer (using a printf style format descriptor).
Definition TString.cxx:2400

PyROOT

The RooAbsPdf::paramOn() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Definition at line 3108 of file RooAbsPdf.cxx.

◆ plotOn() [1/3]

RooAbsPdf::plotOn ( RooPlot frame,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none(),
const RooCmdArg arg7 = RooCmdArg::none(),
const RooCmdArg arg8 = RooCmdArg::none(),
const RooCmdArg arg9 = RooCmdArg::none(),
const RooCmdArg arg10 = RooCmdArg::none() 
) const
inlinevirtual

Helper calling plotOn(RooPlot*, RooLinkedList&) const.

PyROOT

The RooAbsPdf::plotOn() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Reimplemented from RooAbsReal.

Reimplemented in RooSimultaneous, and RooSimultaneous.

Definition at line 121 of file RooAbsPdf.h.

◆ plotOn() [2/3]

RooPlot * RooAbsPdf::plotOn ( RooPlot frame,
PlotOpt  o 
) const
protectedvirtual

Plot oneself on 'frame'.

In addition to features detailed in RooAbsReal::plotOn(), the scale factor for a PDF can be interpreted in three different ways. The interpretation is controlled by ScaleType

Relative - Scale factor is applied on top of PDF normalization scale factor
NumEvent - Scale factor is interpreted as a number of events. The surface area
under the PDF curve will match that of a histogram containing the specified
number of event
Raw - Scale factor is applied to the raw (projected) probability density.
Not too useful, option provided for completeness.
#define a(i)
Definition RSha256.hxx:99

Reimplemented from RooAbsReal.

Reimplemented in RooSimultaneous.

Definition at line 3042 of file RooAbsPdf.cxx.

◆ plotOn() [3/3]

RooPlot * RooAbsPdf::plotOn ( RooPlot frame,
RooLinkedList cmdList 
) const
virtual

Plot (project) PDF on specified frame.

  • If a PDF is plotted in an empty frame, it will show a unit-normalized curve in the frame variable. When projecting a multi- dimensional PDF onto the frame axis, hidden parameters are taken are taken at their current value.
  • If a PDF is plotted in a frame in which a dataset has already been plotted, it will show a projection integrated over all variables that were present in the shown dataset (except for the one on the x-axis). The normalization of the curve will be adjusted to the event count of the plotted dataset. An informational message will be printed for each projection step that is performed.
  • If a PDF is plotted in a frame showing a dataset after a fit, the above happens, but the PDF will be drawn and normalised only in the fit range. If this is not desired, plotting and normalisation range can be overridden using Range() and NormRange() as documented in the table below.

This function takes the following named arguments (for more arguments, see also RooAbsReal::plotOn(RooPlot*,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&, const RooCmdArg&) const )

Type of argument Controlling normalisation
NormRange(const char* name) Calculate curve normalization w.r.t. specified range[s]. See the tutorial rf212_plottingInRanges_blinding.C
Note
Setting a Range() by default also sets a NormRange() on the same range, meaning that the PDF is plotted and normalised in the same range. Overriding this can be useful if the PDF was fit in limited range[s] such as side bands, NormRange("sidebandLeft,sidebandRight"), but the PDF should be drawn in the full range, Range("").
Normalization(Double_t scale, ScaleType code)

Adjust normalization by given scale factor. Interpretation of number depends on code: RooAbsReal::Relative: relative adjustment factor RooAbsReal::NumEvent: scale to match given number of events.

Type of argument Misc control
Name(const chat* name) Give curve specified name in frame. Useful if curve is to be referenced later
Asymmetry(const RooCategory& c) Show the asymmetry of the PDF in given two-state category \( \frac{F(+)-F(-)}{F(+)+F(-)} \) rather than the PDF projection. Category must have two states with indices -1 and +1 or three states with indeces -1,0 and +1.
ShiftToZero(Bool_t flag) Shift entire curve such that lowest visible point is at exactly zero. Mostly useful when plotting -log(L) or \( \chi^2 \) distributions
AddTo(const char* name, double_t wgtSelf, double_t wgtOther) Create a projection of this PDF onto the x-axis, but instead of plotting it directly, add it to an existing curve with given name (and relative weight factors).
Components(const char* names) When plotting sums of PDFs, plot only the named components (e.g. only the signal of a signal+background model).
Components(const RooArgSet& compSet)

As above, but pass a RooArgSet of the components themselves.

Type of argument Projection control
Slice(const RooArgSet& set) Override default projection behaviour by omitting observables listed in set from the projection, i.e. by not integrating over these. Slicing is usually only sensible in discrete observables, by e.g. creating a slice of the PDF at the current value of the category observable.
Slice(RooCategory& cat, const char* label) Override default projection behaviour by omitting the specified category observable from the projection, i.e., by not integrating over all states of this category. The slice is positioned at the given label value. Multiple Slice() commands can be given to specify slices in multiple observables, e.g.
pdf.plotOn(frame, Slice(tagCategory, "2tag"), Slice(jetCategory, "3jet"));
Project(const RooArgSet& set) Override default projection behaviour by projecting over observables given in set, completely ignoring the default projection behavior. Advanced use only.
ProjWData(const RooAbsData& d) Override default projection technique (integration). For observables present in given dataset projection of PDF is achieved by constructing an average over all observable values in given set. Consult RooFit plotting tutorial for further explanation of meaning & use of this technique
ProjWData(const RooArgSet& s, const RooAbsData& d) As above but only consider subset 's' of observables in dataset 'd' for projection through data averaging
ProjectionRange(const char* rn)

When projecting the PDF onto the plot axis, it is usually integrated over the full range of the invisible variables. The ProjectionRange overrides this. This is useful if the PDF was fitted in a limited range in y, but it is now projected onto x. If ProjectionRange("<name of fit range>") is passed, the projection is normalised correctly.

Type of argument Plotting control
LineStyle(Int_t style) Select line style by ROOT line style code, default is solid
LineColor(Int_t color) Select line color by ROOT color code, default is blue
LineWidth(Int_t width) Select line with in pixels, default is 3
FillStyle(Int_t style) Select fill style, default is not filled. If a filled style is selected, also use VLines() to add vertical downward lines at end of curve to ensure proper closure
FillColor(Int_t color) Select fill color by ROOT color code
Range(const char* name) Only draw curve in range defined by given name. Multiple comma-separated ranges can be given. An empty string "" or nullptr means to use the default range of the variable.
Range(double lo, double hi) Only draw curve in specified range
VLines() Add vertical lines to y=0 at end points of curve
Precision(Double_t eps) Control precision of drawn curve w.r.t to scale of plot, default is 1e-3. A higher precision will result in more and more densely spaced curve points. A negative precision value will disable adaptive point spacing and restrict sampling to the grid point of points defined by the binning of the plotted observable (recommended for expensive functions such as profile likelihoods)
Invisible(Bool_t flag) Add curve to frame, but do not display. Useful in combination AddTo()
VisualizeError(const RooFitResult& fitres, Double_t Z=1, Bool_t linearMethod=kTRUE) Visualize the uncertainty on the parameters, as given in fitres, at 'Z' sigma. The linear method is fast but may not be accurate in the presence of strong correlations (~>0.9) and at Z>2 due to linear and Gaussian approximations made. Intervals from the sampling method can be asymmetric, and may perform better in the presence of strong correlations, but may take (much) longer to calculate
Note
To include the uncertainty from the expected number of events, the Normalization() argument with ScaleType RooAbsReal::RelativeExpected has to be passed, e.g.
pdf.plotOn(frame, VisualizeError(fitResult), Normalization(1.0, RooAbsReal::RelativeExpected));
VisualizeError(const RooFitResult& fitres, const RooArgSet& param, Double_t Z=1, Bool_t linearMethod=kTRUE) Visualize the uncertainty on the subset of parameters 'param', as given in fitres, at 'Z' sigma

Reimplemented from RooAbsReal.

Reimplemented in RooSimultaneous, and RooSimultaneous.

Definition at line 2748 of file RooAbsPdf.cxx.

◆ prepareMultiGen()

RooAbsPdf::prepareMultiGen ( const RooArgSet whatVars,
const RooCmdArg arg1 = RooCmdArg::none(),
const RooCmdArg arg2 = RooCmdArg::none(),
const RooCmdArg arg3 = RooCmdArg::none(),
const RooCmdArg arg4 = RooCmdArg::none(),
const RooCmdArg arg5 = RooCmdArg::none(),
const RooCmdArg arg6 = RooCmdArg::none() 
)

Prepare GenSpec configuration object for efficient generation of multiple datasets from identical specification.

Note
This method does not perform any generation. To generate according to generations specification call RooAbsPdf::generate(RooAbsPdf::GenSpec&) const

Details copied from RooAbsPdf::generate():

Generate the specified number of events or expectedEvents() if not specified.

Parameters
[in]whatVarsChoose variables in which to generate events. Variables not listed here will remain constant and not be used for event generation.
[in]argxxOptional RooCmdArg() to change behaviour of generate().
Returns
RooDataSet *, owned by caller.

Any variables of this PDF that are not in whatVars will use their current values and be treated as fixed parameters. Returns zero in case of an error.

Type of CmdArg Effect on generate
Name(const char* name) Name of the output dataset
Verbose(Bool_t flag) Print informational messages during event generation
NumEvent(int nevt) Generate specified number of events
Extended() If no number of events to be generated is given, use expected number of events from extended likelihood term. This evidently only works for extended PDFs.
GenBinned(const char* tag) Use binned generation for all component pdfs that have 'setAttribute(tag)' set
AutoBinned(Bool_t flag) Automatically deploy binned generation for binned distributions (e.g. RooHistPdf, sums and products of RooHistPdfs etc)
Note
Datasets that are generated in binned mode are returned as weighted unbinned datasets. This means that for each bin, there will be one event in the dataset with a weight corresponding to the (possibly randomised) bin content.
AllBinned() As above, but for all components.
Note
The notion of components is only meaningful for simultaneous PDFs as binned generation is always executed at the top-level node for a regular PDF, so for those it only mattes that the top-level node is tagged.
ProtoData(const RooDataSet& data, Bool_t randOrder) Use specified dataset as prototype dataset. If randOrder in ProtoData() is set to true, the order of the events in the dataset will be read in a random order if the requested number of events to be generated does not match the number of events in the prototype dataset.
Note
If ProtoData() is used, the specified existing dataset as a prototype: the new dataset will contain the same number of events as the prototype (unless otherwise specified), and any prototype variables not in whatVars will be copied into the new dataset for each generated event and also used to set our PDF parameters. The user can specify a number of events to generate that will override the default. The result is a copy of the prototype dataset with only variables in whatVars randomized. Variables in whatVars that are not in the prototype will be added as new columns to the generated dataset.

Accessing the underlying event generator

Depending on the fit model (if it is difficult to sample), it may be necessary to change generator settings. For the default generator (RooFoamGenerator), the number of samples or cells could be increased by e.g. using myPdf->specialGeneratorConfig()->getConfigSection("RooFoamGenerator").setRealValue("nSample",1e4);

The foam generator e.g. has the following config options:

PyROOT

The RooAbsPdf::generate() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

PyROOT

The RooAbsPdf::prepareMultiGen() function is pythonized with the command argument pythonization. The keywords must correspond to the CmdArgs of the function.

Definition at line 2104 of file RooAbsPdf.cxx.

◆ printMultiline()

void RooAbsPdf::printMultiline ( std::ostream &  os,
Int_t  contents,
Bool_t  verbose = kFALSE,
TString  indent = "" 
) const
virtual

Print multi line detailed information of this RooAbsPdf.

Reimplemented from RooAbsReal.

Reimplemented in RooGenericPdf, RooResolutionModel, and RooAbsAnaConvPdf.

Definition at line 1905 of file RooAbsPdf.cxx.

◆ printValue()

void RooAbsPdf::printValue ( std::ostream &  os) const
virtual

Print value of p.d.f, also print normalization integral that was last used, if any.

Reimplemented from RooAbsReal.

Definition at line 1886 of file RooAbsPdf.cxx.

◆ randomizeProtoOrder()

Int_t * RooAbsPdf::randomizeProtoOrder ( Int_t  nProto,
Int_t  nGen,
Bool_t  resample = kFALSE 
) const
protected

Return lookup table with randomized order for nProto prototype events.

Definition at line 2294 of file RooAbsPdf.cxx.

◆ redirectServersHook()

virtual Bool_t RooAbsPdf::redirectServersHook ( const RooAbsCollection ,
Bool_t  ,
Bool_t  ,
Bool_t   
)
inlineprotectedvirtual

Function that is called at the end of redirectServers().

Can be overloaded to inject some class-dependent behavior after server redirection, e.g. resetting of caches. The return value is meant to be an error flag, so in case something goes wrong the function should return true.

See also
redirectServers() For a detailed explanation of the function parameters.
Parameters
[in]newServerListOne of the original parameters passed to redirectServers().
[in]mustReplaceAllOne of the original parameters passed to redirectServers().
[in]nameChangeOne of the original parameters passed to redirectServers().
[in]isRecursiveStepOne of the original parameters passed to redirectServers().

Reimplemented from RooAbsArg.

Reimplemented in RooAddPdf, RooProdPdf, RooFFTConvPdf, RooGenericPdf, RooResolutionModel, and RooProjectedPdf.

Definition at line 367 of file RooAbsPdf.h.

◆ resetErrorCounters()

void RooAbsPdf::resetErrorCounters ( Int_t  resetValue = 10)
virtual

Reset error counter to given value, limiting the number of future error messages for this pdf to 'resetValue'.

Reimplemented in RooAddModel, and RooAddPdf.

Definition at line 634 of file RooAbsPdf.cxx.

◆ selfNormalized()

virtual Bool_t RooAbsPdf::selfNormalized ( ) const
inlinevirtual

Shows if a PDF is self-normalized, which means that no attempt is made to add a normalization term.

Always returns false, unless a PDF overrides this function.

Reimplemented in RooIntegralMorph, RooMomentMorph, RooMomentMorphND, RooAbsCachedPdf, RooAddModel, RooExtendPdf, RooHistPdf, RooProdPdf, RooProjectedPdf, RooRealSumPdf, RooResolutionModel, RooSimultaneous, RooAddPdf, RooBinSamplingPdf, and RooNormalizedPdf.

Definition at line 251 of file RooAbsPdf.h.

◆ setGeneratorConfig() [1/2]

void RooAbsPdf::setGeneratorConfig ( )

Remove the specialized numeric MC generator configuration associated with this object.

Definition at line 3526 of file RooAbsPdf.cxx.

◆ setGeneratorConfig() [2/2]

void RooAbsPdf::setGeneratorConfig ( const RooNumGenConfig config)

Set the given configuration as default numeric MC generator configuration for this object.

Definition at line 3512 of file RooAbsPdf.cxx.

◆ setNormRange()

void RooAbsPdf::setNormRange ( const char *  rangeName)

Definition at line 3557 of file RooAbsPdf.cxx.

◆ setNormRangeOverride()

void RooAbsPdf::setNormRangeOverride ( const char *  rangeName)

Definition at line 3574 of file RooAbsPdf.cxx.

◆ setTraceCounter()

void RooAbsPdf::setTraceCounter ( Int_t  value,
Bool_t  allNodes = kFALSE 
)

Reset trace counter to given value, limiting the number of future trace messages for this pdf to 'value'.

Definition at line 646 of file RooAbsPdf.cxx.

◆ specialGeneratorConfig() [1/2]

RooNumGenConfig * RooAbsPdf::specialGeneratorConfig ( ) const

Returns the specialized integrator configuration for this RooAbsReal.

If this object has no specialized configuration, a null pointer is returned

Definition at line 3471 of file RooAbsPdf.cxx.

◆ specialGeneratorConfig() [2/2]

RooNumGenConfig * RooAbsPdf::specialGeneratorConfig ( Bool_t  createOnTheFly)

Returns the specialized integrator configuration for this RooAbsReal.

If this object has no specialized configuration, a null pointer is returned, unless createOnTheFly is kTRUE in which case a clone of the default integrator configuration is created, installed as specialized configuration, and returned

Definition at line 3484 of file RooAbsPdf.cxx.

◆ syncNormalization()

Bool_t RooAbsPdf::syncNormalization ( const RooArgSet nset,
Bool_t  adjustProxies = kTRUE 
) const
protectedvirtual

Verify that the normalization integral cached with this PDF is valid for given set of normalization observables.

If not, the cached normalization integral (if any) is deleted and a new integral is constructed for use with 'nset'. Elements in 'nset' can be discrete and real, but must be lvalues.

For functions that declare to be self-normalized by overloading the selfNormalized() function, a unit normalization is always constructed.

Definition at line 541 of file RooAbsPdf.cxx.

◆ traceEvalPdf()

Bool_t RooAbsPdf::traceEvalPdf ( Double_t  value) const
private

Check that passed value is positive and not 'not-a-number'.

If not, print an error, until the error counter reaches its set maximum.

Definition at line 447 of file RooAbsPdf.cxx.

◆ verboseEval() [1/2]

Int_t RooAbsPdf::verboseEval ( )
static

Return global level of verbosity for p.d.f. evaluations.

Definition at line 3280 of file RooAbsPdf.cxx.

◆ verboseEval() [2/2]

void RooAbsPdf::verboseEval ( Int_t  stat)
static

Change global level of verbosity for p.d.f. evaluations.

Definition at line 3270 of file RooAbsPdf.cxx.

Friends And Related Symbol Documentation

◆ CacheElem

friend class CacheElem
friend

The cache manager.

Definition at line 365 of file RooAbsPdf.h.

◆ RooAbsAnaConvPdf

friend class RooAbsAnaConvPdf
friend

Definition at line 351 of file RooAbsPdf.h.

◆ RooAddGenContext

friend class RooAddGenContext
friend

Definition at line 330 of file RooAbsPdf.h.

◆ RooAddGenContextOrig

friend class RooAddGenContextOrig
friend

Definition at line 336 of file RooAbsPdf.h.

◆ RooConvGenContext

friend class RooConvGenContext
friend

Definition at line 334 of file RooAbsPdf.h.

◆ RooEffGenContext

friend class RooEffGenContext
friend

Definition at line 329 of file RooAbsPdf.h.

◆ RooExtendPdf

friend class RooExtendPdf
friend

Definition at line 342 of file RooAbsPdf.h.

◆ RooMCStudy

friend class RooMCStudy
friend

Definition at line 338 of file RooAbsPdf.h.

◆ RooProdGenContext

friend class RooProdGenContext
friend

Definition at line 331 of file RooAbsPdf.h.

◆ RooProdPdf

friend class RooProdPdf
friend

Definition at line 337 of file RooAbsPdf.h.

◆ RooRealIntegral

friend class RooRealIntegral
friend

Definition at line 346 of file RooAbsPdf.h.

◆ RooSimGenContext

friend class RooSimGenContext
friend

Definition at line 332 of file RooAbsPdf.h.

◆ RooSimSplitGenContext

friend class RooSimSplitGenContext
friend

Definition at line 333 of file RooAbsPdf.h.

◆ RooSimultaneous

friend class RooSimultaneous
friend

Definition at line 335 of file RooAbsPdf.h.

Member Data Documentation

◆ _errorCount

Int_t RooAbsPdf::_errorCount
mutableprotected

Definition at line 383 of file RooAbsPdf.h.

◆ _negCount

Int_t RooAbsPdf::_negCount
mutableprotected

Definition at line 385 of file RooAbsPdf.h.

◆ _norm

RooAbsReal* RooAbsPdf::_norm = nullptr
mutableprotected

Definition at line 353 of file RooAbsPdf.h.

◆ _normMgr

RooObjCacheManager RooAbsPdf::_normMgr
mutableprotected

Definition at line 363 of file RooAbsPdf.h.

◆ _normRange

TString RooAbsPdf::_normRange
protected

MC generator configuration specific for this object.

Definition at line 391 of file RooAbsPdf.h.

◆ _normRangeOverride

TString RooAbsPdf::_normRangeOverride
staticprotected

Definition at line 392 of file RooAbsPdf.h.

◆ _normSet

RooArgSet const* RooAbsPdf::_normSet = nullptr
mutableprotected

Normalization integral (owned by _normMgr)

Definition at line 354 of file RooAbsPdf.h.

◆ _rawValue

Double_t RooAbsPdf::_rawValue
mutableprotected

Definition at line 352 of file RooAbsPdf.h.

◆ _selectComp

Bool_t RooAbsPdf::_selectComp
protected

Definition at line 387 of file RooAbsPdf.h.

◆ _specGeneratorConfig

RooNumGenConfig* RooAbsPdf::_specGeneratorConfig
protected

Definition at line 389 of file RooAbsPdf.h.

◆ _traceCount

Int_t RooAbsPdf::_traceCount
mutableprotected

Definition at line 384 of file RooAbsPdf.h.

◆ _verboseEval

Int_t RooAbsPdf::_verboseEval = 0
staticprotected

Definition at line 347 of file RooAbsPdf.h.

Libraries for RooAbsPdf:

The documentation for this class was generated from the following files: