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Abstract interface for all probability density functions.

## RooAbsPdf, the base class of all PDFs

RooAbsPdf is the base class for all probability density functions (PDFs). The class provides hybrid analytical/numerical normalization for its implementations, error tracing, and a Monte Carlo 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 PDF may not simply be integral over the dependents of the top-level PDF: 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 rarely called 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 the help of a RooRealIntegral object, which coordinates the integration of a given choice of normalization. By default, RooRealIntegral will perform an entirely 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 common, 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=nullptr) 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:55

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,

virtual double analyticalIntegral(Int_t code, const char *rangeName=nullptr) 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 staticInitOK=true) 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 doEval(). Like this, large spans of computations can be done, without having to call evaluate() for each single data event. doEval() should execute the same computation as evaluate(), but it may choose an implementation that is capable of SIMD computations. If doEval 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::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 40 of file RooAbsPdf.h.

## Classes

class  CacheElem
Normalization set with for above integral. More...

class  GenSpec

## 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 ,
}

enum  { kSingleKey = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) }

enum  EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) }

enum  EStatusBits {
kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) ,
kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 ))
}

Public Types inherited from RooPrintable
enum  ContentsOption {
kName =1 , kClassName =2 , kValue =4 , kArgs =8 ,
}

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 minVal, double maxVal)
Constructor with name, title, and plot range.

RooAbsPdf (const char *name, const char *title=nullptr)
Constructor with name and title only.

~RooAbsPdf () override
Destructor.

double analyticalIntegralWN (Int_t code, const RooArgSet *normSet, const char *rangeName=nullptr) const override
Analytical integral with normalization (see RooAbsReal::analyticalIntegralWN() for further information).

virtual RooAbsGenContextautoGenContext (const RooArgSet &vars, const RooDataSet *prototype=nullptr, const RooArgSet *auxProto=nullptr, bool verbose=false, bool autoBinned=true, const char *binnedTag="") const

virtual RooAbsGenContextbinnedGenContext (const RooArgSet &vars, bool verbose=false) const
Return a binned generator context.

bool canBeExtended () const
If true, PDF can provide extended likelihood term.

std::unique_ptr< RooAbsArgcompileForNormSet (RooArgSet const &normSet, RooFit::Detail::CompileContext &ctx) const override

RooFit::OwningPtr< 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.

RooFit::OwningPtr< RooAbsRealcreateCdf (const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Create an object that represents the integral of the function over one or more observables listed in iset.

virtual std::unique_ptr< RooAbsRealcreateExpectedEventsFunc (const RooArgSet *nset) const
Returns an object that represents the expected number of events for a given normalization set, similar to how createIntegral() returns an object that returns the integral.

template<typename... CmdArgs_t>
RooFit::OwningPtr< RooAbsRealcreateNLL (RooAbsData &data, CmdArgs_t const &... cmdArgs)
Construct representation of -log(L) of PDF with 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.

RooFit::OwningPtr< 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 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, bool doOffset=false) const

double extendedTerm (double sumEntries, RooArgSet const *nset, double sumEntriesW2=0.0, bool doOffset=false) 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, bool doOffset=false) 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.

template<typename... CmdArgs_t>
RooFit::OwningPtr< RooFitResultfitTo (RooAbsData &data, CmdArgs_t const &... cmdArgs)
Fit PDF to given dataset.

virtual RooAbsGenContextgenContext (const RooArgSet &vars, const RooDataSet *prototype=nullptr, const RooArgSet *auxProto=nullptr, bool verbose=false) const
Interface function to create a generator context from a p.d.f.

RooFit::OwningPtr< RooDataSetgenerate (const RooArgSet &whatVars, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={})
Generate a new dataset containing the specified variables with events sampled from our distribution.

RooFit::OwningPtr< RooDataSetgenerate (const RooArgSet &whatVars, const RooDataSet &prototype, Int_t nEvents=0, bool verbose=false, bool randProtoOrder=false, bool resampleProto=false) const
Generate a new dataset using a prototype dataset as a model, with values of the variables in whatVars sampled from our distribution.

RooFit::OwningPtr< RooDataSetgenerate (const RooArgSet &whatVars, double nEvents=0, bool verbose=false, bool autoBinned=true, const char *binnedTag="", bool expectedData=false, bool extended=false) const
Generate a new dataset containing the specified variables with events sampled from our distribution.

RooFit::OwningPtr< RooDataSetgenerate (const RooArgSet &whatVars, Int_t nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={})
See RooAbsPdf::generate(const RooArgSet&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&)

RooFit::OwningPtr< RooDataSetgenerate (GenSpec &) const
Generate according to GenSpec obtained from prepareMultiGen().

virtual RooFit::OwningPtr< RooDataHistgenerateBinned (const RooArgSet &whatVars, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}) const
Generate a new dataset containing the specified variables with events sampled from our distribution.

virtual RooFit::OwningPtr< RooDataHistgenerateBinned (const RooArgSet &whatVars, double nEvents, bool expectedData=false, bool extended=false) const
Generate a new dataset containing the specified variables with events sampled from our distribution.

virtual RooFit::OwningPtr< RooDataHistgenerateBinned (const RooArgSet &whatVars, double nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}) const
As RooAbsPdf::generateBinned(const RooArgSet&, const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&, const RooCmdArg&,const RooCmdArg&) const.

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

virtual RooFit::OwningPtr< RooDataSetgenerateSimGlobal (const RooArgSet &whatVars, Int_t nEvents)
Special generator interface for generation of 'global observables' – for RooStats tools.

RooArgSetgetAllConstraints (const RooArgSet &observables, RooArgSet &constrainedParams, bool stripDisconnected=true, bool removeConstraintsFromPdf=false) 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, bool=false) const

virtual Int_t getGenerator (const RooArgSet &directVars, RooArgSet &generateVars, bool staticInitOK=true) 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.

void getLogProbabilities (std::span< const double > pdfValues, double *output) const

virtual double getLogVal (const RooArgSet *set=nullptr) const
Return the log of the current value with given normalization An error message is printed if the argument of the log is negative.

double getNorm (const RooArgSet &nset) const
Get normalisation term needed to normalise the raw values returned by getVal().

virtual double getNorm (const RooArgSet *set=nullptr) 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=nullptr) 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.

double getValV (const RooArgSet *set=nullptr) const override
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.

TClassIsA () const override

virtual bool isDirectGenSafe (const RooAbsArg &arg) const
Check if given observable can be safely generated using the pdfs internal generator mechanism (if that existsP).

bool mustBeExtended () const
If true PDF must provide extended likelihood term.

const char * normRange () const

virtual RooPlotparamOn (RooPlot *frame, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Add a box with parameter values (and errors) to the specified frame.

RooPlotplotOn (RooPlot *frame, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={}, const RooCmdArg &arg9={}, const RooCmdArg &arg10={}) const override

RooPlotplotOn (RooPlot *frame, RooLinkedList &cmdList) const override
Plot (project) PDF on specified frame.

GenSpecprepareMultiGen (const RooArgSet &whatVars, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={})
Prepare GenSpec configuration object for efficient generation of multiple datasets from identical specification.

void printMultiline (std::ostream &os, Int_t contents, bool verbose=false, TString indent="") const override
Print multi line detailed information of this RooAbsPdf.

void printValue (std::ostream &os) const override
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 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 allNodes=false)
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 createOnTheFly)
Returns the specialized integrator configuration for this RooAbsReal.

void Streamer (TBuffer &) override
Stream an object of class TObject.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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 minVal, double maxVal, const char *unit="")
Constructor with plot range and unit label.

RooAbsReal (const RooAbsReal &other, const char *name=nullptr)
Copy constructor.

~RooAbsReal () override
Destructor.

virtual double analyticalIntegral (Int_t code, const char *rangeName=nullptr) 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 > * binBoundaries (RooAbsRealLValue &obs, double xlo, double xhi) const
Retrieve bin boundaries if this distribution is binned in obs.

RooFit::OwningPtr< RooAbsFuncbindVars (const RooArgSet &vars, const RooArgSet *nset=nullptr, bool clipInvalid=false) const
Create an interface adaptor f(vars) that binds us to the specified variables (in arbitrary order).

virtual std::string buildCallToAnalyticIntegral (Int_t code, const char *rangeName, RooFit::Detail::CodeSquashContext &ctx) const
This function defines the analytical integral translation for the class.

virtual RooFit::OwningPtr< RooFitResultchi2FitTo (RooDataHist &data, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Perform a $$\chi^2$$ fit to given histogram.

virtual RooFit::OwningPtr< RooFitResultchi2FitTo (RooDataHist &data, const RooLinkedList &cmdList)
Calls RooAbsReal::createChi2(RooDataSet& data, const RooLinkedList& cmdList) and returns fit result.

virtual RooFit::OwningPtr< RooFitResultchi2FitTo (RooDataSet &xydata, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Perform a 2-D $$\chi^2$$ fit using a series of x and y values stored in the dataset xydata.

virtual RooFit::OwningPtr< 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.

virtual RooFit::OwningPtr< RooAbsRealcreateChi2 (RooDataHist &data, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Create a $$\chi^2$$ variable from a histogram and this function.

virtual RooFit::OwningPtr< RooAbsRealcreateChi2 (RooDataHist &data, const RooLinkedList &cmdList)

virtual RooFit::OwningPtr< RooAbsRealcreateChi2 (RooDataSet &data, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Create a $$\chi^2$$ from a series of x and y values stored in a dataset.

virtual RooFit::OwningPtr< 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&)

RooFit::OwningPtr< RooAbsArgcreateFundamental (const char *newname=nullptr) const override
Create a RooRealVar fundamental object with our properties.

TH1createHistogram (const char *name, const RooAbsRealLValue &xvar, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={}) 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 (RooStringView 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.

RooFit::OwningPtr< RooAbsRealcreateIntegral (const RooArgSet &iset, const char *rangeName) const
Create integral over observables in iset in range named rangeName.

RooFit::OwningPtr< RooAbsRealcreateIntegral (const RooArgSet &iset, const RooArgSet &nset, const char *rangeName=nullptr) const
Create integral over observables in iset in range named rangeName with integrand normalized over observables in nset.

RooFit::OwningPtr< RooAbsRealcreateIntegral (const RooArgSet &iset, const RooArgSet &nset, const RooNumIntConfig &cfg, const char *rangeName=nullptr) 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 RooFit::OwningPtr< RooAbsRealcreateIntegral (const RooArgSet &iset, const RooArgSet *nset=nullptr, const RooNumIntConfig *cfg=nullptr, const char *rangeName=nullptr) const
Create an object that represents the integral of the function over one or more observables listed in iset.

RooFit::OwningPtr< RooAbsRealcreateIntegral (const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={}) const
Create an object that represents the integral of the function over one or more observables listed in iset.

RooFit::OwningPtr< RooAbsRealcreateIntegral (const RooArgSet &iset, const RooNumIntConfig &cfg, const char *rangeName=nullptr) const
Create integral over observables in iset in range named rangeName using specified configuration for any numeric integration.

RooFit::OwningPtr< RooAbsRealcreateIntRI (const RooArgSet &iset, const RooArgSet &nset={})
Utility function for createRunningIntegral.

const RooAbsRealcreatePlotProjection (const RooArgSet &dependentVars, const RooArgSet *projectedVars, RooArgSet *&cloneSet, const char *rangeName=nullptr, const RooArgSet *condObs=nullptr) 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 RooFit::OwningPtr< RooAbsRealcreateProfile (const RooArgSet &paramsOfInterest)
Create a RooProfileLL object that eliminates all nuisance parameters in the present function.

RooFit::OwningPtr< RooAbsRealcreateRunningIntegral (const RooArgSet &iset, const RooArgSet &nset={})
Calls createRunningIntegral(const RooArgSet&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&, const RooCmdArg&)

RooFit::OwningPtr< RooAbsRealcreateRunningIntegral (const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Create an object that represents the running integral of the function over one or more observables listed in iset, i.e.

RooFit::OwningPtr< 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 defaultErrorLevel () const

RooDerivativederivative (RooRealVar &obs, const RooArgSet &normSet, Int_t order, double eps=0.001)
Return function representing first, second or third order derivative of this function.

RooDerivativederivative (RooRealVar &obs, Int_t order=1, double eps=0.001)
Return function representing first, second or third order derivative of this function.

virtual void doEval (RooFit::EvalContext &) const
Base function for computing multiple values of a RooAbsReal.

virtual void enableOffsetting (bool)

RooDataHistfillDataHist (RooDataHist *hist, const RooArgSet *nset, double scaleFactor, bool correctForBinVolume=false, bool showProgress=false) const
Fill a RooDataHist with values sampled from this function at the bin centers.

TH1fillHistogram (TH1 *hist, const RooArgList &plotVars, double scaleFactor=1, const RooArgSet *projectedVars=nullptr, bool scaling=true, const RooArgSet *condObs=nullptr, bool setError=true) const
Fill the ROOT histogram 'hist' with values sampled from this function at the bin centers.

double findRoot (RooRealVar &x, double xmin, double xmax, double yval)
Return value of x (in range xmin,xmax) at which function equals yval.

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=nullptr, bool force=true)
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 forceAnalyticalInt (const RooAbsArg &) const

virtual void forceNumInt (bool flag=true)

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=nullptr) 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=nullptr) 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 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 getPropagatedError (const RooFitResult &fr, const RooArgSet &nset={}) const
Propagates parameter uncertainties to an uncertainty estimate for this RooAbsReal.

TString getTitle (bool appendUnit=false) const
Return this variable's title string.

const Text_tgetUnit () const

double getVal (const RooArgSet &normalisationSet) const
Like getVal(const RooArgSet*), but always requires an argument for normalisation.

double getVal (const RooArgSet *normalisationSet=nullptr) const
Evaluate object.

virtual void gradient (double *) const

TClassIsA () const override

virtual bool isBinnedDistribution (const RooArgSet &) const
Tests if the distribution is binned. Unless overridden by derived classes, this always returns false.

bool isIdentical (const RooAbsArg &other, bool assumeSameType=false) const override

virtual bool isOffsetting () const

bool isSelectedComp () const
If true, the current pdf is a selected component (for use in plotting)

void logEvalError (const char *message, const char *serverValueString=nullptr) const
Log evaluation error message.

virtual double 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 central, bool takeRoot, bool 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 central, bool takeRoot)
Return function representing moment of function of given order.

virtual double offset () const

bool operator== (const RooAbsArg &other) const override
Equality operator when comparing to another RooAbsArg.

bool operator== (double value) const
Equality operator comparing to a double.

virtual std::list< double > * plotSamplingHint (RooAbsRealLValue &obs, double xlo, double 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 scaleFactor=1.0, ScaleType stype=Relative, const RooAbsData *projData=nullptr) 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.

void printMultiline (std::ostream &os, Int_t contents, bool verbose=false, TString indent="") const override
Structure printing.

void printValue (std::ostream &os) const override
Print object value.

bool readFromStream (std::istream &is, bool compact, bool verbose=false) override
Read object contents from stream (dummy for now)

void selectComp (bool flag)

void setCachedValue (double value, bool notifyClients=true) final
Overwrite the value stored in this object's cache.

virtual bool setData (RooAbsData &, bool=true)

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 createOnTheFly)
Returns the specialized integrator configuration for this RooAbsReal.

void Streamer (TBuffer &) override
Stream an object of class TObject.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void writeToStream (std::ostream &os, bool compact) const override
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=nullptr)
Copy constructor transfers all boolean and string properties of the original object.

~RooAbsArg () override
Destructor.

Take ownership of the contents of 'comps'.

Take ownership of the contents of '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 checkObservables (const RooArgSet *nset) const
Overloadable function in which derived classes can implement consistency checks of the variables.

virtual TObjectclone (const char *newname=nullptr) const =0

TObjectClone (const char *newname=nullptr) const override
Make a clone of an object using the Streamer facility.

virtual RooAbsArgcloneTree (const char *newname=nullptr) const
Clone tree expression of objects.

Int_t Compare (const TObject *other) const override
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.

Int_t defaultPrintContents (Option_t *opt) const override
Define default contents to print.

bool dependsOn (const RooAbsArg &server, const RooAbsArg *ignoreArg=nullptr, bool valueOnly=false) const
Test whether we depend on (ie, are served by) the specified object.

bool dependsOn (const RooAbsCollection &serverList, const RooAbsArg *ignoreArg=nullptr, bool valueOnly=false) const
Test whether we depend on (ie, are served by) any object in the specified collection.

bool dependsOn (TNamed const *namePtr, const RooAbsArg *ignoreArg=nullptr, bool valueOnly=false) const
Test whether we depend on (ie, are served by) an object with a specific name.

bool dependsOnValue (const RooAbsArg &server, const RooAbsArg *ignoreArg=nullptr) const
Check whether this object depends on values served from the object passed as server.

bool dependsOnValue (const RooAbsCollection &serverList, const RooAbsArg *ignoreArg=nullptr) const
Check whether this object depends on values from an element in the serverList.

bool getAttribute (const Text_t *name) const
Check if a named attribute is set. By default, all attributes are unset.

RooFit::OwningPtr< 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.

RooFit::OwningPtr< RooArgSetgetObservables (const RooAbsData &data) const
Return the observables of this pdf given the observables defined by data.

RooFit::OwningPtr< 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).

RooFit::OwningPtr< RooArgSetgetObservables (const RooArgSet &set, bool valueOnly=true) const
Given a set of possible observables, return the observables that this PDF depends on.

RooFit::OwningPtr< 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.

RooFit::OwningPtr< RooArgSetgetParameters (const RooAbsData &data, bool stripDisconnected=true) const
Return the parameters of this p.d.f when used in conjunction with dataset 'data'.

RooFit::OwningPtr< 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).

RooFit::OwningPtr< RooArgSetgetParameters (const RooArgSet &observables, bool stripDisconnected=true) const
Return the parameters of the p.d.f given the provided set of observables.

RooFit::OwningPtr< 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 getTransientAttribute (const Text_t *name) const
Check if a named attribute is set.

RooFit::OwningPtr< RooArgSetgetVariables (bool stripDisconnected=true) const
Return RooArgSet with all variables (tree leaf nodes of expression 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 hasClients () const

bool hasDataToken () const

virtual bool hasRange (const char *) const

virtual bool importWorkspaceHook (RooWorkspace &ws)

virtual bool inRange (const char *) const

virtual bool isCategory () const

bool isConstant () const
Check if the "Constant" attribute is set.

virtual bool isDerived () const
Does value or shape of this arg depend on any other arg?

virtual bool isReducerNode () const

bool IsSortable () const override

bool localNoDirtyInhibit () const

const TNamednamePtr () const
De-duplicated pointer to this object's name.

Int_t numProxies () const
Return the number of registered proxies.

bool 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 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)=delete

bool overlaps (const RooAbsArg &testArg, bool valueOnly=false) const
Test if any of the nodes of tree are shared with that of the given tree.

const RooArgSetownedComponents () const

void Print (Option_t *options=nullptr) const override
Print the object to the defaultPrintStream().

void printAddress (std::ostream &os) const override

void printArgs (std::ostream &os) const override
Print object arguments, ie its proxies.

void printClassName (std::ostream &os) const override
Print object class name.

void printCompactTree (const char *indent="", const char *fileName=nullptr, const char *namePat=nullptr, RooAbsArg *client=nullptr)
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=nullptr, RooAbsArg *client=nullptr)
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=nullptr, Int_t nLevel=999)
Print tree structure of expression tree on given ostream, only branch nodes are printed.

void printDirty (bool depth=true) const
Print information about current value dirty state information.

virtual void printMetaArgs (std::ostream &) const

void printName (std::ostream &os) const override
Print object name.

void printTitle (std::ostream &os) const override
Print object title.

void printTree (std::ostream &os, TString indent="") const override
Print object tree structure.

bool recursiveCheckObservables (const RooArgSet *nset) const
Recursively call checkObservables on all nodes in the expression tree.

void removeStringAttribute (const Text_t *key)
Delete a string attribute with a given key.

void resetDataToken ()

void setAttribute (const Text_t *name, bool value=true)
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 flag) const

void SetName (const char *name) override
Set the name of the TNamed.

void SetNameTitle (const char *name, const char *title) override
Set all the TNamed parameters (name and title).

void setProhibitServerRedirect (bool 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 value=true)
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

virtual void translate (RooFit::Detail::CodeSquashContext &ctx) const
This function defines a translation for each RooAbsReal based object that can be used to express the class as simple C++ code.

RooWorkspaceworkspace () const

TIteratorclientIterator () const R__DEPRECATED(6
Retrieve a client iterator.

TIterator Use clients () and begin()

TIterator Use end () or range-based loops.")

TIteratorvalueClientIterator () const R__DEPRECATED(6

TIterator Use valueClients () and begin()

TIterator Use end () or range-based loops.")

TIteratorshapeClientIterator () const R__DEPRECATED(6

TIterator Use shapeClients () and begin()

TIterator Use end () or range-based loops.")

TIteratorserverIterator () const R__DEPRECATED(6

TIterator Use servers () and begin()

TIterator Use end () or range-based loops.")

RooFIter valueClientMIterator () const R__DEPRECATED(6

RooFIter Use valueClients () and begin()

RooFIter Use end () or range-based loops.")

RooFIter shapeClientMIterator () const R__DEPRECATED(6

RooFIter Use shapeClients () and begin()

RooFIter Use end () or range-based loops.")

RooFIter serverMIterator () const R__DEPRECATED(6

RooFIter Use servers () and begin()

RooFIter Use end () or range-based loops.")

RooFit::OwningPtr< RooArgSetgetDependents (const RooArgSet &set) const

RooFit::OwningPtr< RooArgSetgetDependents (const RooAbsData *set) const

RooFit::OwningPtr< RooArgSetgetDependents (const RooArgSet *depList) const

bool dependentOverlaps (const RooAbsData *dset, const RooAbsArg &testArg) const

bool dependentOverlaps (const RooArgSet *depList, const RooAbsArg &testArg) const

bool checkDependents (const RooArgSet *nset) const

bool 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 isValueServer (const RooAbsArg &arg) const
Check if this is serving values to arg.

bool isValueServer (const char *name) const
Check if this is serving values to an object with name name.

bool isShapeServer (const RooAbsArg &arg) const
Check if this is serving shape to arg.

bool isShapeServer (const char *name) const
Check if this is serving shape to an object with name name.

void leafNodeServerList (RooAbsCollection *list, const RooAbsArg *arg=nullptr, bool recurseNonDerived=false) 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=nullptr, bool recurseNonDerived=false) 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=nullptr, bool doBranch=true, bool doLeaf=true, bool valueOnly=false, bool recurseNonDerived=false) const
Fill supplied list with nodes of the arg tree, following all server links, starting with ourself as top node.

virtual bool isFundamental () const
Is this object a fundamental type that can be added to a dataset? Fundamental-type subclasses override this method to return true.

virtual bool 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 redirectServers (const RooAbsCollection &newServerList, bool mustReplaceAll=false, bool nameChange=false, bool isRecursionStep=false)
Replace all direct servers of this object with the new servers in newServerList.

bool redirectServers (std::unordered_map< RooAbsArg *, RooAbsArg * > const &replacements)
Replace some servers of this object.

bool recursiveRedirectServers (const RooAbsCollection &newServerList, bool mustReplaceAll=false, bool nameChange=false, bool recurseInNewSet=true)
Recursively replace all servers with the new servers in newSet.

virtual void serverNameChangeHook (const RooAbsArg *, const RooAbsArg *)

void addServer (RooAbsArg &server, bool valueProp=true, bool shapeProp=false, 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 valueProp=true, bool shapeProp=false)
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 valueProp, bool shapeProp)
Replace 'oldServer' with 'newServer', specifying whether the new server has value or shape server properties.

void changeServer (RooAbsArg &server, bool valueProp, bool shapeProp)
Change dirty flag propagation mask for specified server.

void removeServer (RooAbsArg &server, bool force=false)
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 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 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 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 doAlsoTrackingOpt=true)
Interface function signaling a request to perform constant term optimization.

virtual CacheMode canNodeBeCached () const

virtual void setCacheAndTrackHints (RooArgSet &)

bool isShapeDirty () const

bool isValueDirty () const

bool isValueDirtyAndClear () const

bool 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 recurseADirty=true)
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.

void Clear (Option_t *option="") override
Set name and title to empty strings ("").

TObjectClone (const char *newname="") const override
Make a clone of an object using the Streamer facility.

Int_t Compare (const TObject *obj) const override
Compare two TNamed objects.

void Copy (TObject &named) const override
Copy this to obj.

virtual void FillBuffer (char *&buffer)
Encode TNamed into output buffer.

const char * GetName () const override
Returns name of object.

const char * GetTitle () const override
Returns title of object.

ULong_t Hash () const override
Return hash value for this object.

TClassIsA () const override

Bool_t IsSortable () const override

void ls (Option_t *option="") const override
List TNamed name and title.

TNamedoperator= (const TNamed &rhs)
TNamed assignment operator.

void Print (Option_t *option="") const override
Print TNamed name and title.

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.

void Streamer (TBuffer &) override
Stream an object of class TObject.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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.

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 with: gROOT->SetSelectedPad(c1).

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=nullptr)
Execute method on this object with the given parameter string, e.g.

virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=nullptr)
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 (the base implementation is no-op).

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, void *vp)
Only called by placement new when throwing an exception.

void operator delete[] (void *ptr)
Operator delete [].

void operator delete[] (void *ptr, void *vp)
Only called by placement new[] when throwing an exception.

void * operator new (size_t sz)

void * operator new (size_t sz, void *vp)

void * operator new[] (size_t sz)

void * operator 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.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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=nullptr, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.

virtual Int_t Write (const char *name=nullptr, 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'.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

## Static Public Member Functions

static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

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 TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static void clearEvalErrorLog ()
Clear the stack of evaluation error messages.

static const char * DeclFileName ()

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 hideOffset ()

static void logEvalError (const RooAbsReal *originator, const char *origName, const char *message, const char *serverValueString=nullptr)
Interface to insert remote error logging messages received by RooRealMPFE into current error logging 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 flag)

Static Public Member Functions inherited from RooAbsArg
static void setDirtyInhibit (bool flag)
Control global dirty inhibit mode.

static void verboseDirty (bool flag)
Activate verbose messaging related to dirty flag propagation.

Static Public Member Functions inherited from TNamed
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

Static Public Member Functions inherited from TObject
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

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 TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

static std::ostream & defaultPrintStream (std::ostream *os=nullptr)
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=nullptr)
Copy constructor.

virtual std::unique_ptr< RooAbsRealcreateNLLImpl (RooAbsData &data, const RooLinkedList &cmdList)
Protected implementation of the NLL creation routine.

virtual std::unique_ptr< RooFitResultfitToImpl (RooAbsData &data, const RooLinkedList &cmdList)
Protected implementation of the likelihood fitting routine.

bool isActiveNormSet (RooArgSet const *normSet) const
Checks if normSet is the currently active normalization set of this PDF, meaning is exactly the same object as the one the _normSet member points to (or both are nullptr).

double normalizeWithNaNPacking (double rawVal, double normVal) const

RooPlotplotOn (RooPlot *frame, PlotOpt o) const override
Plot oneself on 'frame'.

Int_trandomizeProtoOrder (Int_t nProto, Int_t nGen, bool resample=false) const
Return lookup table with randomized order for nProto prototype events.

bool redirectServersHook (const RooAbsCollection &newServerList, bool mustReplaceAll, bool nameChange, bool isRecursiveStep) override
The cache manager.

virtual bool syncNormalization (const RooArgSet *dset, bool adjustProxies=true) const
Verify that the normalization integral cached with this PDF is valid for given set of normalization observables.

Protected Member Functions inherited from RooAbsReal
void attachToTree (TTree &t, Int_t bufSize=32000) override
Attach object to a branch of given TTree.

void attachToVStore (RooVectorDataStore &vstore) override

void copyCache (const RooAbsArg *source, bool valueOnly=false, bool setValDirty=true) override
Copy the cached value of another RooAbsArg to our cache.

RooFit::OwningPtr< 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 evaluate () const =0
Evaluate this PDF / function / constant. Needs to be overridden by all derived classes.

void fillTreeBranch (TTree &t) override
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=nullptr, const char *rangeName=nullptr, bool omitEmpty=false) const
Construct string with unique suffix name to give to integral object that encodes integrated observables, normalization observables and the integration range name.

bool isValid () const override
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 silent) const
Utility function for plotOn() that constructs the set of observables to project when plotting ourselves as function of 'plotVar'.

bool matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a) const
Utility function for use in getAnalyticalIntegral().

bool matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a, const RooArgProxy &b) const
Utility function for use in getAnalyticalIntegral().

bool matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgProxy &a, const RooArgProxy &b, const RooArgProxy &c) const
Utility function for use in getAnalyticalIntegral().

bool 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 matchArgs (const RooArgSet &allDeps, RooArgSet &numDeps, const RooArgSet &set) const
Utility function for use in getAnalyticalIntegral().

bool matchArgsByName (const RooArgSet &allArgs, RooArgSet &matchedArgs, const TList &nameList) const
Check if allArgs contains matching elements for each name in nameList.

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 Z, const RooArgSet *params, const RooLinkedList &argList, bool method1) const
Plot function or PDF on frame with support for visualization of the uncertainty encoded in the given fit result fr.

bool plotSanityChecks (RooPlot *frame) const
Utility function for plotOn(), perform general sanity check on frame to ensure safe plotting operations.

bool redirectServersHook (const RooAbsCollection &newServerList, bool mustReplaceAll, bool nameChange, bool isRecursiveStep) override
Function that is called at the end of redirectServers().

virtual void selectNormalization (const RooArgSet *depSet=nullptr, bool force=false)
Interface function to force use of a given set of observables to interpret function value.

virtual void selectNormalizationRange (const char *rangeName=nullptr, bool force=false)
Interface function to force use of a given normalization range to interpret function value.

void setTreeBranchStatus (TTree &t, bool active) override
(De)Activate associated tree branch

void syncCache (const RooArgSet *set=nullptr) override

double 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) 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 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 = 0
Number of errors remaining to print.

Int_t _negCount = 0
Number of negative probabilities remaining to print.

RooAbsReal_norm = nullptr

RooObjCacheManager _normMgr

TString _normRange
Normalization range.

RooArgSet const * _normSet = nullptr
Normalization integral (owned by _normMgr)

double _rawValue = 0

bool _selectComp = false
Component selection flag for RooAbsPdf::plotCompOn.

std::unique_ptr< RooNumGenConfig_specGeneratorConfig
! MC generator configuration specific for this object

Int_t _traceCount = 0
Number of traces remaining to print.

Protected Attributes inherited from RooAbsReal
bool _forceNumInt = false
Force numerical integration if flag set.

TString _label
Plot label for objects value.

RooFit::UniqueId< RooArgSet >::Value_t _lastNormSetId = RooFit::UniqueId<RooArgSet>::nullval
Component selection flag for RooAbsPdf::plotCompOn.

Int_t _plotBins = 100
Number of plot bins.

double _plotMax = 0.0
Maximum of plot range.

double _plotMin = 0.0
Minimum of plot range.

bool _selectComp = true
A buffer for reading values from trees.

std::unique_ptr< RooNumIntConfig_specIntegratorConfig

TString _unit
Unit for objects value.

double _value = 0.0
Cache for current value of object.

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 = std::numeric_limits<std::size_t>::max()
In which workspace do I live, if any.

bool _deleteWatch = false

RooExpensiveObjectCache_eocache {nullptr}
Prohibit server redirects – Debugging tool.

bool _fast = false

bool _isConstant = false
De-duplicated name pointer. This will be equal for all objects with the same name.

bool _localNoInhibitDirty = false
Cached isConstant status.

RooWorkspace_myws = nullptr
Prevent 'AlwaysDirty' mode for this node.

const TNamed_namePtr = nullptr
Pointer to global cache manager for any expensive components created by this object.

OperMode _operMode = Auto

RooArgSet_ownedComponents = nullptr

bool _prohibitServerRedirect = false
Set of owned component.

RooRefArray _proxyList

ProxyListCache _proxyListCache

RefCountList_t _serverList

bool _shapeDirty = true

std::map< std::string, std::string > _stringAttrib

bool _valueDirty = true

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 Int_t _evalErrorCount = 0

static std::map< const RooAbsArg *, std::pair< std::string, std::list< EvalError > > > _evalErrorList

static ErrorLoggingMode _evalErrorMode = RooAbsReal::PrintErrors

static bool _globalSelectComp = false

static bool _hideOffset = true
Offset hiding flag.

Static Protected Attributes inherited from RooAbsArg
static bool _inhibitDirty

static bool _verboseDirty
cache of the list of proxies. Avoids type casting.

Static Protected Attributes inherited from RooPrintable
static Int_t _nameLength

## Private Member Functions

std::unique_ptr< RooDataSetgenerate (RooAbsGenContext &context, const RooArgSet &whatVars, const RooDataSet *prototype, double nEvents, bool verbose, bool randProtoOrder, bool resampleProto, bool skipInit=false, bool extended=false) const
Internal method.

void logBatchComputationErrors (std::span< 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 showConstants=false, const char *label="", double xmin=0.65, double xmax=0.99, double ymax=0.95, const RooCmdArg *formatCmd=nullptr)
Add a text box with the current parameter values and their errors to the frame.

void setActiveNormSet (RooArgSet const *normSet) const
Setter for the _normSet member, which should never be set directly.

bool traceEvalPdf (double value) const
Check that passed value is positive and not 'not-a-number'.

## Private Attributes

RooFit::UniqueId< RooArgSet >::Value_t _normSetId = RooFit::UniqueId<RooArgSet>::nullval
! Unique ID of the currently-active normalization set

## Friends

class RooAbsReal

class RooChi2Var

class RooMCStudy

Protected Types inherited from TObject
enum  { kOnlyPrepStep = (1ULL << ( 3 )) }

Static Protected Member Functions inherited from RooAbsReal
static void globalSelectComp (bool 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]

## ◆ ExtendMode

Enumerator
CanNotBeExtended
CanBeExtended
MustBeExtended

Definition at line 213 of file RooAbsPdf.h.

## ◆ RooAbsPdf() [1/4]

 RooAbsPdf::RooAbsPdf ( )

Default constructor.

Definition at line 230 of file RooAbsPdf.cxx.

## ◆ RooAbsPdf() [2/4]

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

Constructor with name and title only.

Definition at line 235 of file RooAbsPdf.cxx.

## ◆ RooAbsPdf() [3/4]

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

Constructor with name, title, and plot range.

Definition at line 247 of file RooAbsPdf.cxx.

## ◆ ~RooAbsPdf()

 RooAbsPdf::~RooAbsPdf ( )
override

Destructor.

Definition at line 277 of file RooAbsPdf.cxx.

## ◆ RooAbsPdf() [4/4]

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

Copy constructor.

Definition at line 260 of file RooAbsPdf.cxx.

## ◆ analyticalIntegralWN()

 double RooAbsPdf::analyticalIntegralWN ( Int_t code, const RooArgSet * normSet, const char * rangeName = nullptr ) const
overridevirtual

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.

Definition at line 364 of file RooAbsPdf.cxx.

## ◆ autoGenContext()

 RooAbsGenContext * RooAbsPdf::autoGenContext ( const RooArgSet & vars, const RooDataSet * prototype = nullptr, const RooArgSet * auxProto = nullptr, bool verbose = false, bool autoBinned = true, const char * binnedTag = "" ) const
virtual

Reimplemented in RooSimultaneous.

Definition at line 1135 of file RooAbsPdf.cxx.

## ◆ binnedGenContext()

 RooAbsGenContext * RooAbsPdf::binnedGenContext ( const RooArgSet & vars, bool verbose = false ) const
virtual

Return a binned generator context.

Definition at line 1116 of file RooAbsPdf.cxx.

## ◆ canBeExtended()

 bool RooAbsPdf::canBeExtended ( ) const
inline

If true, PDF can provide extended likelihood term.

Definition at line 219 of file RooAbsPdf.h.

## ◆ Class()

 static TClass * RooAbsPdf::Class ( )
static
Returns
TClass describing this class

## ◆ Class_Name()

 static const char * RooAbsPdf::Class_Name ( )
static
Returns
Name of this class

## ◆ Class_Version()

 static constexpr Version_t RooAbsPdf::Class_Version ( )
inlinestaticconstexpr
Returns
Version of this class

Definition at line 352 of file RooAbsPdf.h.

## ◆ compileForNormSet()

 std::unique_ptr< RooAbsArg > RooAbsPdf::compileForNormSet ( RooArgSet const & normSet, RooFit::Detail::CompileContext & ctx ) const
overridevirtual

Reimplemented from RooAbsArg.

Reimplemented in RooAddPdf, RooProdPdf, RooProjectedPdf, RooRealSumPdf, and RooSimultaneous.

Definition at line 2782 of file RooAbsPdf.cxx.

## ◆ createCdf() [1/2]

 RooFit::OwningPtr< 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 2482 of file RooAbsPdf.cxx.

## ◆ createCdf() [2/2]

 RooAbsPdf::createCdf ( const RooArgSet & iset, const RooCmdArg & arg1, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {}, const RooCmdArg & arg6 = {}, const RooCmdArg & arg7 = {}, const RooCmdArg & arg8 = {} )

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 2504 of file RooAbsPdf.cxx.

## ◆ createExpectedEventsFunc()

 std::unique_ptr< RooAbsReal > RooAbsPdf::createExpectedEventsFunc ( const RooArgSet * nset ) const
virtual

Returns an object that represents the expected number of events for a given normalization set, similar to how createIntegral() returns an object that returns the integral.

This is used to build the computation graph for the final likelihood.

Reimplemented in RooNormalizedPdf, RooFixedProdPdf, RooAddPdf, RooExtendedTerm, RooExtendPdf, RooProdPdf, and RooRealSumPdf.

Definition at line 2806 of file RooAbsPdf.cxx.

## ◆ createNLL()

template<typename... CmdArgs_t>
 RooAbsPdf::createNLL ( RooAbsData & data, CmdArgs_t const &... cmdArgs )
inline

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.

Parameters
 data Reference to a RooAbsData object representing the dataset. cmdArgs Variadic template arguments representing optional command arguments. You can pass either an arbitrary number of RooCmdArg instances or a single RooLinkedList that points to the RooCmdArg objects.
Returns
An owning pointer to the created RooAbsReal NLL object.
Template Parameters
 CmdArgs_t Template types for optional command arguments. Can either be an arbitrary number of RooCmdArg or a single RooLinkedList.
Note
This front-end function should not be re-implemented in derived PDF types. If you mean to customize the NLL creation routine, you need to override the virtual RooAbsPdf::createNLLImpl() method.

The following named arguments are supported:

Type of CmdArg Effect on NLL
ConditionalObservables(Args_t &&... argsOrArgSet) Do not normalize PDF over listed observables. Arguments can either be multiple RooRealVar or a single RooArgSet containing them.
Extended(bool 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 lo, double 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 istrat) 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 beneficial 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.
EvalBackend(std::string const&) Choose a likelihood evaluation backend:
Backend Description
cpu - default New vectorized evaluation mode, using faster math functions and auto-vectorisation. Since ROOT 6.23, this is the default if EvalBackend() is not passed, succeeding the legacy backend. If all RooAbsArg objects in the model support vectorized evaluation, likelihood computations are 2 to 10 times faster than with the legacy backend
• unless your dataset is so small that the vectorization is not worth it. The relative difference of the single log-likelihoods with respect to the legacy mode is usually better than $$10^{-12}$$, and for fit parameters it's usually better than $$10^{-6}$$. In past ROOT releases, this backend could be activated with the now deprecated BatchMode() option.
cuda Evaluate the likelihood on a GPU that supports CUDA. This backend re-uses code from the cpu backend, but compiled in CUDA kernels. Hence, the results are expected to be identical, modulo some numerical differences that can arise from the different order in which the GPU is summing the log probabilities. This backend can drastically speed up the fit if all RooAbsArg object in the model support it.
legacy The original likelihood evaluation method. Evaluates the PDF for each single data entry at a time before summing the negative log probabilities.
codegen Experimental - Generates and compiles minimal C++ code for the NLL on-the-fly and wraps it in the returned RooAbsReal. Also generates and compiles the code for the gradient using Automatic Differentiation (AD) with Clad. This analytic gradient is passed to the minimizer, which can result in significant speedups for many-parameter fits, even compared to the cpu backend. However, if one of the RooAbsArg objects in the model does not support the code generation, this backend can't be used.
codegen_no_grad Experimental - Same as codegen, but doesn't generate and compile the gradient code and use the regular numerical differentiation instead. This is expected to be slower, but useful for debugging problems with the analytic gradient.
Optimize(bool flag) Activate constant term optimization (on by default)
SplitRange(bool 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 flag) Controls RooFit informational messages in likelihood construction
CloneData(bool flag) Use clone of dataset in NLL (default is true).
Warning
Deprecated option that is ignored. It is up to the implementation of the NLL creation method if the data is cloned or not.
Offset(std::string const& mode) Likelihood offsetting mode. Can be either:
Mode Description
none - default No offsetting.
initial 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.
bin Offset likelihood bin-by-bin with a template histogram model based on the obersved data. This results in per-bin values that are all in the same order of magnitude, which reduces precision loss in the sum, which can drastically improve numeric stability. Furthermore, $$2\cdot \text{NLL}$$ defined like this is approximately chi-square distributed, allowing for goodness-of-fit tests.
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.
RooBinSamplingPdf
ModularL(bool flag) Enable or disable modular likelihoods, which will become the default in a future release. This does not change any user-facing code, but only enables a different likelihood class in the back-end. Note that this should be set to true for parallel minimization of likelihoods! Note that it is currently not recommended to use Modular likelihoods without any parallelization enabled in the minimization, since some features such as offsetting might not yet work in this case.

## PyROOT

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

Definition at line 163 of file RooAbsPdf.h.

## ◆ createNLLImpl()

 std::unique_ptr< RooAbsReal > RooAbsPdf::createNLLImpl ( RooAbsData & data, const RooLinkedList & cmdList )
protectedvirtual

Protected implementation of the NLL creation routine.

This virtual function can be overridden in case you want to change the NLL creation logic for custom PDFs.

Note
Never call this function directly. Instead, call RooAbsPdf::createNLL().

Definition at line 942 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 2447 of file RooAbsPdf.cxx.

## ◆ createScanCdf()

 RooFit::OwningPtr< RooAbsReal > RooAbsPdf::createScanCdf ( const RooArgSet & iset, const RooArgSet & nset, Int_t numScanBins, Int_t intOrder )

Definition at line 2559 of file RooAbsPdf.cxx.

## ◆ DeclFileName()

 static const char * RooAbsPdf::DeclFileName ( )
inlinestatic
Returns
Name of the file containing the class declaration

Definition at line 352 of file RooAbsPdf.h.

## ◆ defaultGeneratorConfig()

 RooNumGenConfig * RooAbsPdf::defaultGeneratorConfig ( )
static

Returns the default numeric MC generator configuration for all RooAbsReals.

Definition at line 2600 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.

expectedEvents(const RooArgSet*) const

Definition at line 233 of file RooAbsPdf.h.

## ◆ expectedEvents() [2/2]

 double 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

Definition at line 2397 of file RooAbsPdf.cxx.

## ◆ extendedTerm() [1/3]

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

Definition at line 731 of file RooAbsPdf.cxx.

## ◆ extendedTerm() [2/3]

 double RooAbsPdf::extendedTerm ( double sumEntries, RooArgSet const * nset, double sumEntriesW2 = 0.0, bool doOffset = false ) 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] observed The number of observed events. [in] nset The normalization set when asking the pdf for the expected number of events. [in] observedSumW2 The number of observed events when weighting with squared weights. If non-zero, the weight-squared error correction is applied to the extended term. [in] doOffset Offset the extended term by a counterterm where the expected number of events equals the observed number of events. This constant shift results in a term closer to zero that is approximately chi-square distributed. It is useful to do this also when summing multiple NLL terms to avoid numeric precision loss that happens if you sum multiple terms of different orders of magnitude.

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 entries
• $$W_\mathrm{observed} = \sum w_{i} \cdot \sum w_{i} / \sum w_{i}^2$$ : 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, bool doOffset), which takes a dataset to extract $$N_\mathrm{observed}$$ and the normalization set.

Definition at line 726 of file RooAbsPdf.cxx.

## ◆ extendedTerm() [3/3]

 double RooAbsPdf::extendedTerm ( RooAbsData const & data, bool weightSquared, bool doOffset = false ) 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, RooArgSet const *, double, bool) const, 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] data The RooAbsData to retrieve the set of observables and number of expected events. [in] weightSquared If set to true, the extended term will be scaled by the ratio of squared event weights over event weights: $$\sum w_{i}^2 / \sum w_{i}$$. Intended to be used by fits with the SumW2Error() option that can be passed to RooAbsPdf::fitTo() (see the documentation of said function to learn more about the interpretation of fits with squared weights). [in] doOffset See RooAbsPdf::extendedTerm(double, RooArgSet const*, double, bool) const.

Definition at line 794 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.

Definition at line 217 of file RooAbsPdf.h.

## ◆ fitTo()

template<typename... CmdArgs_t>
 RooAbsPdf::fitTo ( RooAbsData & data, CmdArgs_t const &... cmdArgs )
inline

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
 data Reference to a RooAbsData object representing the dataset. cmdArgs Variadic template arguments representing optional command arguments. You can pass either an arbitrary number of RooCmdArg instances or a single RooLinkedList that points to the RooCmdArg objects.
Returns
An owning pointer to the created RooAbsReal NLL object.
RooFitResult with fit status and parameters if option Save() is used, nullptr otherwise. The user takes ownership of the fit result.
Template Parameters
 CmdArgs_t Template types for optional command arguments. Can either be an arbitrary number of RooCmdArg or a single RooLinkedList.
Note
This front-end function should not be re-implemented in derived PDF types. If you mean to customize the likelihood fitting routine, you need to override the virtual RooAbsPdf::fitToImpl() method.

The following named arguments are supported:

Type of CmdArg Options to control construction of -log(L)
All command arguments that can also be passed to the NLL creation method.
RooAbsPdf::createNLL()
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
GSLMultiMin conjugatefr, conjugatepr, bfgs, bfgs2, steepestdescent
GSLSimAn -

InitialHesse(bool flag) Flag controls if HESSE before MIGRAD as well, off by default
Optimize(bool flag) Activate constant term optimization of test statistic during minimization (on by default)
Hesse(bool flag) Flag controls if HESSE is run after MIGRAD, on by default
Minos(bool 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 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)
MaxCalls(int n) Change maximum number of likelihood function calls from MINUIT (if n <= 0, the default of 500 * #parameters is used)
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..

SumW2Error(bool 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 flag) Flag controls if verbose output is printed (NLL, parameter changes during fit).
Timer(bool 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 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.
Parallelize(Int_t nWorkers) Control global parallelization settings. Arguments 1 and above enable the use of RooFit's parallel minimization backend and uses the number given as the number of workers to use in the parallelization. -1 also enables RooFit's parallel minimization backend, and sets the number of workers to the number of available processes. 0 disables this feature. In case parallelization is requested, this option implies ModularL(true) in the internal call to the NLL creation method.
ParallelGradientOptions(bool enable=true, int orderStrategy=0, int chainFactor=1) Experimental - Control gradient parallelization settings. The first argument only disables or enables gradient parallelization, this is on by default. The second argument determines the internal partial derivative calculation ordering strategy. The third argument determines the number of partial derivatives that are executed per task package on each worker.
ParallelDescentOptions(bool enable=false, int splitStrategy=0, int numSplits=4) Experimental - Control settings related to the parallelization of likelihoods outside of the gradient calculation but in the minimization, most prominently in the linesearch step. The first argument this disables or enables likelihood parallelization. The second argument determines whether to split the task batches per event or per likelihood component. And the third argument how many events or respectively components to include in each batch.
TimingAnalysis(bool flag) Experimental - Log timings. This feature logs timings with NewStyle likelihoods on multiple processes simultaneously and outputs the timings at the end of a run to json log files, which can be analyzed with the RooFit::MultiProcess::HeatmapAnalyzer. Only works with simultaneous likelihoods.

## 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 157 of file RooAbsPdf.h.

## ◆ fitToImpl()

 std::unique_ptr< RooFitResult > RooAbsPdf::fitToImpl ( RooAbsData & data, const RooLinkedList & cmdList )
protectedvirtual

Protected implementation of the likelihood fitting routine.

This virtual function can be overridden in case you want to change the likelihood fitting logic for custom PDFs.

Note
Never call this function directly. Instead, call RooAbsPdf::fitTo().

Definition at line 1071 of file RooAbsPdf.cxx.

## ◆ genContext()

 RooAbsGenContext * RooAbsPdf::genContext ( const RooArgSet & vars, const RooDataSet * prototype = nullptr, const RooArgSet * auxProto = nullptr, bool verbose = false ) 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

Definition at line 1126 of file RooAbsPdf.cxx.

## ◆ generate() [1/6]

 RooAbsPdf::generate ( const RooArgSet & whatVars, const RooCmdArg & arg1 = {}, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {}, const RooCmdArg & arg6 = {} )

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] whatVars Choose variables in which to generate events. Variables not listed here will remain constant and not be used for event generation. [in] arg1,arg2,arg3,arg4,arg5,arg6 Optional 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 flag) Print informational messages during event generation
NumEvents(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 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 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 1209 of file RooAbsPdf.cxx.

## ◆ generate() [2/6]

 RooFit::OwningPtr< RooDataSet > RooAbsPdf::generate ( const RooArgSet & whatVars, const RooDataSet & prototype, Int_t nEvents = 0, bool verbose = false, bool randProtoOrder = false, bool resampleProto = false ) 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] whatVars Generate for these variables. [in] prototype Use 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] nEvents Number of events to generate. Defaults to 0, which means number of event in prototype dataset. [in] verbose Show which generator strategies are being used. [in] randProtoOrder Randomise order of retrieval of events from proto dataset. [in] resampleProto Resample 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 1468 of file RooAbsPdf.cxx.

## ◆ generate() [3/6]

 RooFit::OwningPtr< RooDataSet > RooAbsPdf::generate ( const RooArgSet & whatVars, double nEvents = 0, bool verbose = false, bool autoBinned = true, const char * binnedTag = "", bool expectedData = false, bool extended = false ) const

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

Parameters
 [in] whatVars Generate 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] nEvents Generate the specified number of events or else try to use expectedEvents() if nEvents <= 0 (default). [in] verbose Show which generator strategies are being used. [in] autoBinned If original distribution is binned, return bin centers and randomise weights instead of generating single events. [in] binnedTag [in] expectedData Call setExpectedData on the genContext. [in] extended Randomise 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 1384 of file RooAbsPdf.cxx.

## ◆ generate() [4/6]

 RooFit::OwningPtr< RooDataSet > RooAbsPdf::generate ( const RooArgSet & whatVars, Int_t nEvents, const RooCmdArg & arg1, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {} )
inline
Parameters
 [in] whatVars Set of observables to generate for each event according to this model. [in] nEvents How many events to generate arg1,arg2,arg3,arg4,arg5 Optional command arguments.

Definition at line 57 of file RooAbsPdf.h.

## ◆ generate() [5/6]

 RooFit::OwningPtr< 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 1347 of file RooAbsPdf.cxx.

## ◆ generate() [6/6]

 std::unique_ptr< RooDataSet > RooAbsPdf::generate ( RooAbsGenContext & context, const RooArgSet & whatVars, const RooDataSet * prototype, double nEvents, bool verbose, bool randProtoOrder, bool resampleProto, bool skipInit = false, bool extended = false ) const
private

Internal method.

Definition at line 1412 of file RooAbsPdf.cxx.

## ◆ generateBinned() [1/3]

 RooAbsPdf::generateBinned ( const RooArgSet & whatVars, const RooCmdArg & arg1 = {}, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {}, const RooCmdArg & arg6 = {} ) const
virtual

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

Parameters
 [in] whatVars Choose variables in which to generate events. Variables not listed here will remain constant and not be used for event generation [in] arg1,arg2,arg3,arg4,arg5,arg6 Optional 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 flag) Print informational messages during event generation
NumEvents(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 1597 of file RooAbsPdf.cxx.

## ◆ generateBinned() [2/3]

 RooFit::OwningPtr< RooDataHist > RooAbsPdf::generateBinned ( const RooArgSet & whatVars, double nEvents, bool expectedData = false, bool extended = false ) const
virtual

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

Parameters
 [in] whatVars Variables that values should be generated for. [in] nEvents How many events to generate. If nEvents <=0, use the value returned by expectedEvents() as target. [in] expectedData If set to true (false by default), the returned histogram returns the 'expected' data sample, i.e. no statistical fluctuations are present. [in] extended For 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);
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 1679 of file RooAbsPdf.cxx.

## ◆ generateBinned() [3/3]

 virtual RooFit::OwningPtr< RooDataHist > RooAbsPdf::generateBinned ( const RooArgSet & whatVars, double nEvents, const RooCmdArg & arg1, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {} ) const
inlinevirtual
Parameters
 [in] whatVars set [in] nEvents How many events to generate arg1,arg2,arg3,arg4,arg5 ordered arguments

Definition at line 110 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.

Definition at line 1543 of file RooAbsPdf.cxx.

## ◆ generateSimGlobal()

 RooFit::OwningPtr< 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 1798 of file RooAbsPdf.cxx.

## ◆ getAllConstraints()

 RooArgSet * RooAbsPdf::getAllConstraints ( const RooArgSet & observables, RooArgSet & constrainedParams, bool stripDisconnected = true, bool removeConstraintsFromPdf = false ) 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.

Definition at line 2576 of file RooAbsPdf.cxx.

## ◆ getConstraints()

 virtual RooArgSet * RooAbsPdf::getConstraints ( const RooArgSet & , RooArgSet & , bool , bool = false ) const
inlinevirtual

Reimplemented in RooProdPdf.

Definition at line 169 of file RooAbsPdf.h.

## ◆ getGenerator()

 Int_t RooAbsPdf::getGenerator ( const RooArgSet & directVars, RooArgSet & generateVars, bool staticInitOK = true ) 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 implementation of this method returns zero. Subclasses will usually implement this method using the matchArgs() methods to advertise the algorithms they provide.

Definition at line 1521 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 2638 of file RooAbsPdf.cxx.

## ◆ getLogProbabilities()

 void RooAbsPdf::getLogProbabilities ( std::span< const double > pdfValues, double * output ) const

Definition at line 672 of file RooAbsPdf.cxx.

## ◆ getLogVal()

 double RooAbsPdf::getLogVal ( const RooArgSet * set = nullptr ) 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 RooExpPoly, and RooHistConstraint.

Definition at line 621 of file RooAbsPdf.cxx.

## ◆ getNorm() [1/2]

 double 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
 nset Set of variables to normalise over.

Definition at line 196 of file RooAbsPdf.h.

## ◆ getNorm() [2/2]

 double RooAbsPdf::getNorm ( const RooArgSet * nset = nullptr ) 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
 nset Set of variables to normalise over.

Reimplemented in RooResolutionModel.

Definition at line 419 of file RooAbsPdf.cxx.

## ◆ getNormIntegral()

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

Definition at line 256 of file RooAbsPdf.h.

## ◆ getNormObj()

 const RooAbsReal * RooAbsPdf::getNormObj ( const RooArgSet * set, const RooArgSet * iset, const TNamed * rangeName = nullptr ) 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 443 of file RooAbsPdf.cxx.

## ◆ getValV()

 double RooAbsPdf::getValV ( const RooArgSet * nset = nullptr ) const
overridevirtual

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, RooMomentMorph, and RooAddPdf.

Definition at line 319 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.

Definition at line 1531 of file RooAbsPdf.cxx.

## ◆ IsA()

 TClass * RooAbsPdf::IsA ( ) const
inlineoverridevirtual

## ◆ isActiveNormSet()

 bool RooAbsPdf::isActiveNormSet ( RooArgSet const * normSet ) const
inlineprotected

Checks if normSet is the currently active normalization set of this PDF, meaning is exactly the same object as the one the _normSet member points to (or both are nullptr).

Definition at line 300 of file RooAbsPdf.h.

## ◆ isDirectGenSafe()

 bool 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, RooBinSamplingPdf, and RooProdPdf.

Definition at line 1556 of file RooAbsPdf.cxx.

## ◆ logBatchComputationErrors()

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

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

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

Definition at line 655 of file RooAbsPdf.cxx.

## ◆ mustBeExtended()

 bool RooAbsPdf::mustBeExtended ( ) const
inline

If true PDF must provide extended likelihood term.

Definition at line 223 of file RooAbsPdf.h.

## ◆ normalizeWithNaNPacking()

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

Definition at line 282 of file RooAbsPdf.cxx.

## ◆ normRange()

 const char * RooAbsPdf::normRange ( ) const
inline

Definition at line 251 of file RooAbsPdf.h.

## ◆ paramOn() [1/2]

 RooPlot * RooAbsPdf::paramOn ( RooPlot * frame, const RooArgSet & params, bool showConstants = false, const char * label = "", double xmin = 0.65, double xmax = 0.99, double ymax = 0.95, const RooCmdArg * formatCmd = nullptr )
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 2339 of file RooAbsPdf.cxx.

## ◆ paramOn() [2/2]

 RooAbsPdf::paramOn ( RooPlot * frame, const RooCmdArg & arg1 = {}, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {}, const RooCmdArg & arg6 = {}, const RooCmdArg & arg7 = {}, const RooCmdArg & arg8 = {} )
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 flag) Also display constant parameters
Format(const char* what,...) Parameter formatting options.
Parameter Format
const char* what Controls what is shown. "N" adds name (alternatively, "T" adds the title), "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 xmin, double xmax, double 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:2442

## 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 2284 of file RooAbsPdf.cxx.

## ◆ plotOn() [1/3]

 RooAbsPdf::plotOn ( RooPlot * frame, const RooCmdArg & arg1 = {}, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {}, const RooCmdArg & arg6 = {}, const RooCmdArg & arg7 = {}, const RooCmdArg & arg8 = {}, const RooCmdArg & arg9 = {}, const RooCmdArg & arg10 = {} ) const
inlineoverridevirtual

## 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 124 of file RooAbsPdf.h.

## ◆ plotOn() [2/3]

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

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
Option_t Option_t option
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void on

Reimplemented from RooAbsReal.

Reimplemented in RooSimultaneous.

Definition at line 2220 of file RooAbsPdf.cxx.

## ◆ plotOn() [3/3]

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

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 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 indices -1,0 and +1.
ShiftToZero(bool 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 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 flag) Add curve to frame, but do not display. Useful in combination AddTo()
VisualizeError(const RooFitResult& fitres, double Z=1, bool linearMethod=true) 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 Z=1, bool linearMethod=true) 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 1936 of file RooAbsPdf.cxx.

## ◆ prepareMultiGen()

 RooAbsPdf::prepareMultiGen ( const RooArgSet & whatVars, const RooCmdArg & arg1 = {}, const RooCmdArg & arg2 = {}, const RooCmdArg & arg3 = {}, const RooCmdArg & arg4 = {}, const RooCmdArg & arg5 = {}, const RooCmdArg & arg6 = {} )

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] whatVars Choose variables in which to generate events. Variables not listed here will remain constant and not be used for event generation. [in] arg1,arg2,arg3,arg4,arg5,arg6 Optional 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 flag) Print informational messages during event generation
NumEvents(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 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 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 1298 of file RooAbsPdf.cxx.

## ◆ printMultiline()

 void RooAbsPdf::printMultiline ( std::ostream & os, Int_t contents, bool verbose = false, TString indent = "" ) const
overridevirtual

Print multi line detailed information of this RooAbsPdf.

Reimplemented from RooAbsArg.

Reimplemented in RooGenericPdf, and RooResolutionModel.

Definition at line 1099 of file RooAbsPdf.cxx.

## ◆ printValue()

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

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

Reimplemented from RooPrintable.

Definition at line 1080 of file RooAbsPdf.cxx.

## ◆ randomizeProtoOrder()

 Int_t * RooAbsPdf::randomizeProtoOrder ( Int_t nProto, Int_t nGen, bool resample = false ) const
protected

Return lookup table with randomized order for nProto prototype events.

Definition at line 1484 of file RooAbsPdf.cxx.

## ◆ redirectServersHook()

 bool RooAbsPdf::redirectServersHook ( const RooAbsCollection & newServerList, bool mustReplaceAll, bool nameChange, bool isRecursiveStep )
overrideprotectedvirtual

The cache manager.

Hook function intercepting redirectServer calls.

Discard current normalization object if any server is redirected

Reimplemented from RooAbsArg.

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

Definition at line 2762 of file RooAbsPdf.cxx.

## ◆ 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'.

Definition at line 587 of file RooAbsPdf.cxx.

## ◆ selfNormalized()

 virtual bool 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.

Definition at line 208 of file RooAbsPdf.h.

## ◆ setActiveNormSet()

 void RooAbsPdf::setActiveNormSet ( RooArgSet const * normSet ) const
inlineprivate

Setter for the _normSet member, which should never be set directly.

Definition at line 285 of file RooAbsPdf.h.

## ◆ setGeneratorConfig() [1/2]

 void RooAbsPdf::setGeneratorConfig ( )

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

Definition at line 2662 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 2651 of file RooAbsPdf.cxx.

## ◆ setNormRange()

 void RooAbsPdf::setNormRange ( const char * rangeName )

Definition at line 2720 of file RooAbsPdf.cxx.

## ◆ setNormRangeOverride()

 void RooAbsPdf::setNormRangeOverride ( const char * rangeName )

Definition at line 2740 of file RooAbsPdf.cxx.

## ◆ setTraceCounter()

 void RooAbsPdf::setTraceCounter ( Int_t value, bool allNodes = false )

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

Definition at line 599 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 2610 of file RooAbsPdf.cxx.

## ◆ specialGeneratorConfig() [2/2]

 RooNumGenConfig * RooAbsPdf::specialGeneratorConfig ( bool createOnTheFly )

Returns the specialized integrator configuration for this RooAbsReal.

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

Definition at line 2623 of file RooAbsPdf.cxx.

## ◆ Streamer()

 void RooAbsPdf::Streamer ( TBuffer & R__b )
overridevirtual

## ◆ StreamerNVirtual()

 void RooAbsPdf::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )
inline

Definition at line 352 of file RooAbsPdf.h.

## ◆ syncNormalization()

 bool RooAbsPdf::syncNormalization ( const RooArgSet * nset, bool adjustProxies = true ) 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 482 of file RooAbsPdf.cxx.

## ◆ traceEvalPdf()

 bool RooAbsPdf::traceEvalPdf ( double 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 384 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 2417 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 2407 of file RooAbsPdf.cxx.

## ◆ RooAbsReal

 friend class RooAbsReal
friend

Definition at line 349 of file RooAbsPdf.h.

## ◆ RooChi2Var

 friend class RooChi2Var
friend

Definition at line 350 of file RooAbsPdf.h.

## ◆ RooMCStudy

 friend class RooMCStudy
friend

Definition at line 308 of file RooAbsPdf.h.

## ◆ _errorCount

 Int_t RooAbsPdf::_errorCount = 0
mutableprotected

Number of errors remaining to print.

Definition at line 335 of file RooAbsPdf.h.

## ◆ _negCount

 Int_t RooAbsPdf::_negCount = 0
mutableprotected

Number of negative probabilities remaining to print.

Definition at line 337 of file RooAbsPdf.h.

## ◆ _norm

 RooAbsReal* RooAbsPdf::_norm = nullptr
mutableprotected

Definition at line 320 of file RooAbsPdf.h.

## ◆ _normMgr

 RooObjCacheManager RooAbsPdf::_normMgr
mutableprotected

Definition at line 330 of file RooAbsPdf.h.

## ◆ _normRange

 TString RooAbsPdf::_normRange
protected

Normalization range.

Definition at line 343 of file RooAbsPdf.h.

## ◆ _normRangeOverride

 TString RooAbsPdf::_normRangeOverride
staticprotected

Definition at line 344 of file RooAbsPdf.h.

## ◆ _normSet

 RooArgSet const* RooAbsPdf::_normSet = nullptr
mutableprotected

Normalization integral (owned by _normMgr)

Definition at line 321 of file RooAbsPdf.h.

## ◆ _normSetId

 RooFit::UniqueId::Value_t RooAbsPdf::_normSetId = RooFit::UniqueId::nullval
mutableprivate

! Unique ID of the currently-active normalization set

Definition at line 347 of file RooAbsPdf.h.

## ◆ _rawValue

 double RooAbsPdf::_rawValue = 0
mutableprotected

Definition at line 319 of file RooAbsPdf.h.

## ◆ _selectComp

 bool RooAbsPdf::_selectComp = false
protected

Component selection flag for RooAbsPdf::plotCompOn.

Definition at line 339 of file RooAbsPdf.h.

## ◆ _specGeneratorConfig

 std::unique_ptr RooAbsPdf::_specGeneratorConfig
protected

! MC generator configuration specific for this object

Definition at line 341 of file RooAbsPdf.h.

## ◆ _traceCount

 Int_t RooAbsPdf::_traceCount = 0
mutableprotected

Number of traces remaining to print.

Definition at line 336 of file RooAbsPdf.h.

## ◆ _verboseEval

 Int_t RooAbsPdf::_verboseEval = 0
staticprotected

Definition at line 315 of file RooAbsPdf.h.

Libraries for RooAbsPdf:

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