76double HypoTestInverter::fgCLAccuracy = 0.005;
77unsigned int HypoTestInverter::fgNToys = 500;
79double HypoTestInverter::fgAbsAccuracy = 0.05;
80double HypoTestInverter::fgRelAccuracy = 0.05;
81std::string HypoTestInverter::fgAlgo =
"logSecant";
83bool HypoTestInverter::fgCloseProof =
false;
87template<
class HypoTestType>
88struct HypoTestWrapper {
90 static void SetToys(HypoTestType *
h,
int toyNull,
int toyAlt) {
h->SetToys(toyNull,toyAlt); }
97void HypoTestInverter::SetCloseProof(
Bool_t flag) {
129 if (!modelSB || ! modelB)
131 assert(modelSB && modelB);
134 <<
"\t\t using as S+B (null) model : "
136 <<
"\t\t using as B (alternate) model : "
137 << modelB->
GetName() <<
"\n" << std::endl;
142 if (!bPdf || !bObs) {
153 if (!poiB || !poiB->
find(scanVariable.
GetName()) ||
156 << scanVariable.
GetName() <<
" not equal to zero "
157 <<
" user must check input model configurations " << endl;
158 if (poiB)
delete poiB;
176 fCalcType(kUndefined),
177 fNBins(0), fXmin(1), fXmax(1),
196 fScannedVariable(scannedVariable),
202 fCalcType(kUndefined),
203 fNBins(0), fXmin(1), fXmax(1),
249 fScannedVariable(scannedVariable),
256 fNBins(0), fXmin(1), fXmax(1),
282 fScannedVariable(scannedVariable),
288 fCalcType(kFrequentist),
289 fNBins(0), fXmin(1), fXmax(1),
314 fScannedVariable(scannedVariable),
320 fCalcType(kAsymptotic),
321 fNBins(0), fXmin(1), fXmax(1),
347 fScannedVariable(scannedVariable),
354 fNBins(0), fXmin(1), fXmax(1),
381 fCalculator0(0), fScannedVariable(0),
394 if (
this == &rhs)
return *
this;
459 TString results_name =
"result_";
462 TString title =
"HypoTestInverter Result For ";
493 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::GetInterval - return an already existing interval " << std::endl;
498 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::GetInterval - run a fixed scan" << std::endl;
501 oocoutE((
TObject*)0,
Eval) <<
"HypoTestInverter::GetInterval - error running a fixed scan " << std::endl;
504 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::GetInterval - run an automatic scan" << std::endl;
505 double limit(0),err(0);
508 oocoutE((
TObject*)0,
Eval) <<
"HypoTestInverter::GetInterval - error running an auto scan " << std::endl;
562 while (clsMidErr >=
fgCLAccuracy && (clsTarget == -1 ||
fabs(clsMid-clsTarget) < 3*clsMidErr) ) {
563 std::unique_ptr<HypoTestResult> more(hc.
GetHypoTest());
568 hcResult->
Append(more.get());
571 if (
fVerbose) std::cout << (
fUseCLs ?
"\tCLs = " :
"\tCLsplusb = ") << clsMid <<
" +/- " << clsMidErr << std::endl;
578 "\tCLs = " << hcResult->
CLs() <<
" +/- " << hcResult->
CLsError() <<
"\n" <<
579 "\tCLb = " << hcResult->
CLb() <<
" +/- " << hcResult->
CLbError() <<
"\n" <<
616 if ( nBins==1 && xMin!=xMax ) {
619 if ( xMin==xMax && nBins>1 ) {
625 << xMin <<
") smaller that xMax (" << xMax <<
")\n";
629 if (xMin < fScannedVariable->getMin()) {
632 << xMin << std::endl;
637 << xMax << std::endl;
641 for (
int i=0; i<nBins; i++) {
645 thisX =
exp(
log(xMin) + i*(
log(xMax)-
log(xMin))/(nBins-1) );
647 thisX = xMin + i*(xMax-xMin)/(nBins-1);
653 if ( status==
false ) {
654 std::cout <<
"\t\tLoop interrupted because of failed status\n";
671 if ( rVal < fScannedVariable->getMin() ) {
674 <<
" on the scanned variable rather than " << rVal<<
"\n";
682 <<
" on the scanned variable rather than " << rVal<<
"\n";
697 const_cast<ModelConfig*
>(sbModel)->SetSnapshot(poi);
718 else lastXtested = -999;
723 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::RunOnePoint - Merge with previous result for "
727 prevResult->
Append(result);
732 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::RunOnePoint - replace previous empty result\n";
769 if ((hint != 0) && (*hint >
r->getMin())) {
770 r->setMax(std::min<double>(3.0 * (*hint),
r->getMax()));
771 r->setMin(std::max<double>(0.3 * (*hint),
r->getMin()));
773 <<
" search in interval " <<
r->getMin() <<
" , " <<
r->getMax() << std::endl;
780 typedef std::pair<double,double> CLs_t;
781 double clsTarget =
fSize;
782 CLs_t clsMin(1,0), clsMax(0,0), clsMid(0,0);
783 double rMin =
r->getMin(), rMax =
r->getMax();
784 limit = 0.5*(rMax + rMin);
785 limitErr = 0.5*(rMax - rMin);
788 TF1 expoFit(
"expoFit",
"[0]*exp([1]*(x-[2]))", rMin, rMax);
814 if (
fVerbose > 0) std::cout <<
"Search for upper limit to the limit" << std::endl;
815 for (
int tries = 0; tries < 6; ++tries) {
818 oocoutE((
TObject*)0,
Eval) <<
"HypoTestInverter::RunLimit - Hypotest failed" << std::endl;
822 if (clsMax.first == 0 || clsMax.first + 3 *
fabs(clsMax.second) < clsTarget )
break;
826 <<
" = " << rMax <<
" still get "
827 << (
fUseCLs ?
"CLs" :
"CLsplusb") <<
" = " << clsMax.first << std::endl;
832 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::RunLimit - Search for lower limit to the limit" << std::endl;
842 if (clsMin.first != 1 && clsMin.first - 3 *
fabs(clsMin.second) < clsTarget) {
848 for (
int tries = 0; tries < 6; ++tries) {
851 if (clsMin.first == 1 || clsMin.first - 3 *
fabs(clsMin.second) > clsTarget)
break;
855 <<
" = " << rMin <<
" still get " << (
fUseCLs ?
"CLs" :
"CLsplusb")
856 <<
" = " << clsMin.first << std::endl;
864 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::RunLimit - Now doing proper bracketing & bisection" << std::endl;
869 oocoutW((
TObject*)0,
Eval) <<
"HypoTestInverter::RunLimit - maximum number of toys reached " << std::endl;
875 limit = 0.5*(rMin+rMax); limitErr = 0.5*(rMax-rMin);
876 if (
fgAlgo ==
"logSecant" && clsMax.first != 0) {
877 double logMin =
log(clsMin.first), logMax =
log(clsMax.first), logTarget =
log(clsTarget);
878 limit = rMin + (rMax-rMin) * (logTarget - logMin)/(logMax - logMin);
879 if (clsMax.second != 0 && clsMin.second != 0) {
880 limitErr = hypot((logTarget-logMax) * (clsMin.second/clsMin.first), (logTarget-logMin) * (clsMax.second/clsMax.first));
881 limitErr *= (rMax-rMin)/((logMax-logMin)*(logMax-logMin));
884 r->setError(limitErr);
887 if (limitErr < std::max(absAccuracy, relAccuracy * limit)) {
889 oocoutI((
TObject*)0,
Eval) <<
"HypoTestInverter::RunLimit - reached accuracy " << limitErr
890 <<
" below " << std::max(absAccuracy, relAccuracy * limit) << std::endl;
897 if (!
RunOnePoint(limit,
true, clsTarget) )
return false;
900 if (clsMid.second == -1) {
901 std::cerr <<
"Hypotest failed" << std::endl;
906 if (
fabs(clsMid.first-clsTarget) >= 2*clsMid.second) {
907 if ((clsMid.first>clsTarget) == (clsMax.first>clsTarget)) {
908 rMax = limit; clsMax = clsMid;
910 rMin = limit; clsMin = clsMid;
913 if (
fVerbose > 0) std::cout <<
"Trying to move the interval edges closer" << std::endl;
914 double rMinBound = rMin, rMaxBound = rMax;
916 while (clsMin.second == 0 ||
fabs(rMin-limit) > std::max(absAccuracy, relAccuracy * limit)) {
917 rMin = 0.5*(rMin+limit);
918 if (!
RunOnePoint(rMin,
true, clsTarget) )
return false;
921 if (
fabs(clsMin.first-clsTarget) <= 2*clsMin.second)
break;
924 while (clsMax.second == 0 ||
fabs(rMax-limit) > std::max(absAccuracy, relAccuracy * limit)) {
925 rMax = 0.5*(rMax+limit);
927 if (!
RunOnePoint(rMax,
true,clsTarget) )
return false;
929 if (
fabs(clsMax.first-clsTarget) <= 2*clsMax.second)
break;
932 expoFit.
SetRange(rMinBound,rMaxBound);
940 std::cout <<
"Limit: " <<
r->
GetName() <<
" < " << limit <<
" +/- " << limitErr <<
" [" << rMin <<
", " << rMax <<
"]\n";
946 double rMinBound, rMaxBound; expoFit.
GetRange(rMinBound, rMaxBound);
947 limitErr = std::max(
fabs(rMinBound-limit),
fabs(rMaxBound-limit));
954 for (
int j = 0; j <
fLimitPlot->GetN(); ++j) {
957 for (
int i = 0, imax = 8; i <= imax; ++i, ++npoints) {
967 if (limitErr < std::max(absAccuracy, relAccuracy * limit))
break;
972 if (!
RunOnePoint(rTry,
true,clsTarget) )
return false;
984 double xmin =
r->getMin(),
xmax =
r->getMax();
985 for (
int j = 0; j <
fLimitPlot->GetN(); ++j) {
991 fLimitPlot->GetYaxis()->SetRangeUser(0.5*clsTarget, 1.5*clsTarget);
993 expoFit.
Draw(
"SAME");
1004 <<
"\tLimit: " <<
r->
GetName() <<
" < " << limit <<
" +/- " << limitErr <<
" @ " << (1-
fSize) * 100 <<
"% CL\n";
1035 TList * clsDist = 0;
1036 TList * clsbDist = 0;
1062 TList * clsDist = 0;
1063 TList * clsbDist = 0;
1091 if (!bModel || ! sbModel)
return 0;
1099 <<
" assume is for POI = 0" << std::endl;
1104 paramPoint = *poibkg;
1108 if (!toymcSampler) {
1127 bool storePValues = clsDist || clsbDist || clbDist;
1128 if (
fNBins <=0 && storePValues) {
1129 oocoutW((
TObject*)0,
InputArguments) <<
"HypoTestInverter::RebuildDistribution - cannot return p values distribution with the auto scan" << std::endl;
1130 storePValues =
false;
1143 if (nToys <= 0) nToys = 100;
1145 std::vector<std::vector<double> > CLs_values(nPoints);
1146 std::vector<std::vector<double> > CLsb_values(nPoints);
1147 std::vector<std::vector<double> > CLb_values(nPoints);
1150 for (
int i = 0; i < nPoints; ++i) {
1151 CLs_values[i].reserve(nToys);
1152 CLb_values[i].reserve(nToys);
1153 CLsb_values[i].reserve(nToys);
1157 std::vector<double> limit_values; limit_values.reserve(nToys);
1160 << nToys << std::endl;
1170 assert(bModel->
GetPdf() );
1179 <<
" - the resulting limits will not be stored" << std::endl;
1182 TH1D * hL =
new TH1D(
"lowerLimitDist",
"Rebuilt lower limit distribution",100,1.,0.);
1183 TH1D * hU =
new TH1D(
"upperLimitDist",
"Rebuilt upper limit distribution",100,1.,0.);
1184 TH1D * hN =
new TH1D(
"nObs",
"Observed events",100,1.,0.);
1187 std::vector<TH1*> hCLb;
1188 std::vector<TH1*> hCLsb;
1189 std::vector<TH1*> hCLs;
1191 for (
int i = 0; i < nPoints; ++i) {
1192 hCLb.push_back(
new TH1D(
TString::Format(
"CLbDist_bin%d",i),
"CLb distribution",100,1.,0.));
1193 hCLs.push_back(
new TH1D(
TString::Format(
"ClsDist_bin%d",i),
"CLs distribution",100,1.,0.));
1194 hCLsb.push_back(
new TH1D(
TString::Format(
"CLsbDist_bin%d",i),
"CLs+b distribution",100,1.,0.));
1200 for (
int itoy = 0; itoy < nToys; ++itoy) {
1202 oocoutP((
TObject*)0,
Eval) <<
"\nHypoTestInverter - RebuildDistributions - running toy # " << itoy <<
" / "
1203 << nToys << std::endl;
1206 printf(
"\n\nshnapshot of s+b model \n");
1210 if (itoy> 0) *allParams = saveParams;
1226 for (
int i = 0; i < genObs.
getSize(); ++i) {
1228 if (
x) nObs +=
x->getVal();
1243 if (
r == 0)
continue;
1245 double value = (isUpper) ?
r->UpperLimit() :
r->LowerLimit();
1246 limit_values.push_back( value );
1247 hU->
Fill(
r->UpperLimit() );
1248 hL->
Fill(
r->LowerLimit() );
1251 std::cout <<
"The computed upper limit for toy #" << itoy <<
" is " << value << std::endl;
1254 if (itoy%10 == 0 || itoy == nToys-1) {
1260 if (!storePValues)
continue;
1265 else if (nPoints >
r->ArraySize()) {
1270 for (
int ipoint = 0; ipoint < nPoints; ++ipoint) {
1273 CLs_values[ipoint].push_back( hr->
CLs() );
1274 CLsb_values[ipoint].push_back( hr->
CLsplusb() );
1275 CLb_values[ipoint].push_back( hr->
CLb() );
1276 hCLs[ipoint]->Fill( hr->
CLs() );
1277 hCLb[ipoint]->Fill( hr->
CLb() );
1278 hCLsb[ipoint]->Fill( hr->
CLsplusb() );
1286 if (itoy%10 == 0 || itoy == nToys-1) {
1287 for (
int ipoint = 0; ipoint < nPoints; ++ipoint) {
1301 if (clsDist) clsDist->
SetOwner(
true);
1302 if (clbDist) clbDist->
SetOwner(
true);
1303 if (clsbDist) clsbDist->
SetOwner(
true);
1307 for (
int ipoint = 0; ipoint < nPoints; ++ipoint) {
1330 for (
int i = 0; i < nPoints && storePValues; ++i) {
1337 const char * disName = (isUpper) ?
"upperLimit_dist" :
"lowerLimit_dist";
RooArgSet * getParameters(const RooAbsData *data, Bool_t stripDisconnected=kTRUE) const
Create a list of leaf nodes in the arg tree starting with ourself as top node that don't match any of...
RooArgSet * getVariables(Bool_t stripDisconnected=kTRUE) const
Return RooArgSet with all variables (tree leaf nodes of expresssion tree)
RooAbsCollection * selectCommon(const RooAbsCollection &refColl) const
Create a subset of the current collection, consisting only of those elements that are contained as we...
RooAbsArg * first() const
virtual void Print(Option_t *options=0) const
This method must be overridden when a class wants to print itself.
RooAbsArg * find(const char *name) const
Find object with given name in list.
RooAbsData is the common abstract base class for binned and unbinned datasets.
virtual const RooArgSet * get() const
virtual Double_t sumEntries() const =0
virtual Int_t numEntries() const
Bool_t canBeExtended() const
virtual Double_t getMax(const char *name=0) const
Get maximum of currently defined range.
virtual Double_t getMin(const char *name=0) const
Get miniminum of currently defined range.
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
RooArgList is a container object that can hold multiple RooAbsArg objects.
RooArgSet is a container object that can hold multiple RooAbsArg objects.
RooArgSet * snapshot(bool deepCopy=true) const
Use RooAbsCollection::snapshot(), but return as RooArgSet.
virtual Bool_t add(const RooAbsCollection &col, Bool_t silent=kFALSE)
Add a collection of arguments to this collection by calling add() for each element in the source coll...
static Double_t uniform(TRandom *generator=randomGenerator())
Return a number uniformly distributed from (0,1)
RooRealVar represents a fundamental (non-derived) real valued object.
virtual void setVal(Double_t value)
Set value of variable to 'value'.
Hypothesis Test Calculator based on the asymptotic formulae for the profile likelihood ratio.
Does a frequentist hypothesis test.
Same purpose as HybridCalculatorOriginal, but different implementation.
Common base class for the Hypothesis Test Calculators.
const ModelConfig * GetNullModel(void) const
const ModelConfig * GetAlternateModel(void) const
TestStatSampler * GetTestStatSampler(void) const
Returns instance of TestStatSampler.
virtual HypoTestResult * GetHypoTest() const
inherited methods from HypoTestCalculator interface
virtual void SetData(RooAbsData &data)
Set the DataSet.
Class to plot an HypoTestInverterResult, result of the HypoTestInverter calculator.
TGraphErrors * MakePlot(Option_t *opt="")
return a TGraphErrors with the obtained observed p-values resultinf from the scan By default (Option ...
HypoTestInverterResult class holds the array of hypothesis test results and compute a confidence inte...
SamplingDistribution * GetLowerLimitDistribution() const
get expected lower limit distributions implemented using interpolation The size for the sampling dist...
HypoTestResult * GetResult(int index) const
return a pointer to the i^th result object
double fLowerLimitError
interpolation option (linear or spline)
int ArraySize() const
number of entries in the results array
virtual void SetConfidenceLevel(Double_t cl)
set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)
std::vector< double > fXValues
number of points used to build expected p-values
void UseCLs(bool on=true)
flag to switch between using CLsb (default) or CLs as confidence level
double GetXValue(int index) const
function to return the value of the parameter of interest for the i^th entry in the results
double GetLastYError() const
TList fExpPValues
list of HypoTestResult for each point
double GetLastYValue() const
SamplingDistribution * GetUpperLimitDistribution() const
get expected upper limit distributions implemented using interpolation
HypoTestInverter class for performing an hypothesis test inversion by scanning the hypothesis test re...
virtual void SetData(RooAbsData &)
Set the DataSet ( add to the the workspace if not already there ?)
static void CheckInputModels(const HypoTestCalculatorGeneric &hc, const RooRealVar &scanVar)
check the model given the given hypotestcalculator
RooRealVar * fScannedVariable
pointer to the generic hypotest calculator used
virtual ~HypoTestInverter()
destructor (delete the HypoTestInverterResult)
static double fgCLAccuracy
std::unique_ptr< HypoTestCalculatorGeneric > fHC
SamplingDistribution * RebuildDistributions(bool isUpper=true, int nToys=100, TList *clsDist=0, TList *clsbDist=0, TList *clbDist=0, const char *outputfile="HypoTestInverterRebuiltDist.root")
rebuild the sampling distributions by generating some toys and find for each of them a new upper limi...
HypoTestInverterResult * fResults
HypoTestInverter()
default constructor (doesn't do anything)
bool SetTestStatistic(TestStatistic &stat)
set the test statistic to use
virtual Double_t ConfidenceLevel() const
Get the Confidence level for the test.
SamplingDistribution * GetUpperLimitDistribution(bool rebuild=false, int nToys=100)
get the distribution of lower limit if rebuild = false (default) it will re-use the results of the sc...
static unsigned int fgNToys
void Clear()
delete contained result and graph
TestStatistic * GetTestStatistic() const
return the test statistic which is or will be used by the class
virtual HypoTestInverterResult * GetInterval() const
Run a fixed scan or the automatic scan depending on the configuration Return if needed a copy of the ...
static std::string fgAlgo
bool RunLimit(double &limit, double &limitErr, double absTol=0, double relTol=0, const double *hint=0) const
run an automatic scan until the desired accuracy is reached Start by default from the full interval (...
std::unique_ptr< TGraphErrors > fLimitPlot
bool RunFixedScan(int nBins, double xMin, double xMax, bool scanLog=false) const
Run a Fixed scan in npoints between min and max.
bool RunOnePoint(double thisX, bool adaptive=false, double clTarget=-1) const
run only one point at the given POI value
ECalculatorType fCalcType
SamplingDistribution * GetLowerLimitDistribution(bool rebuild=false, int nToys=100)
get the distribution of lower limit if rebuild = false (default) it will re-use the results of the sc...
HypoTestInverter & operator=(const HypoTestInverter &rhs)
assignment operator NOTE: this class does not copy the contained result and the HypoTestCalculator,...
HypoTestCalculatorGeneric * fCalculator0
int fTotalToysRun
plot of limits
static double fgRelAccuracy
void CreateResults() const
create a new HypoTestInverterResult to hold all computed results
static double fgAbsAccuracy
static RooRealVar * GetVariableToScan(const HypoTestCalculatorGeneric &hc)
get the variable to scan try first with null model if not go to alternate model
HypoTestResult * Eval(HypoTestCalculatorGeneric &hc, bool adaptive, double clsTarget) const
Run the Hypothesis test at a previous configured point (internal function called by RunOnePoint)
HypoTestResult is a base class for results from hypothesis tests.
void SetBackgroundAsAlt(Bool_t l=kTRUE)
Double_t CLbError() const
The error on the "confidence level" of the null hypothesis.
Bool_t GetPValueIsRightTail(void) const
Double_t GetTestStatisticData(void) const
virtual void Append(const HypoTestResult *other)
add values from another HypoTestResult
virtual Double_t CLb() const
Convert NullPValue into a "confidence level".
virtual Double_t CLsplusb() const
Convert AlternatePValue into a "confidence level".
virtual Double_t AlternatePValue() const
Return p-value for alternate hypothesis.
virtual Double_t NullPValue() const
Return p-value for null hypothesis.
void SetTestStatisticData(const Double_t tsd)
Double_t CLsplusbError() const
The error on the "confidence level" of the alternative hypothesis.
virtual Double_t CLs() const
is simply (not a method, but a quantity)
SamplingDistribution * GetNullDistribution(void) const
Double_t CLsError() const
The error on the ratio .
SamplingDistribution * GetAltDistribution(void) const
IntervalCalculator is an interface class for a tools which produce RooStats ConfIntervals.
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
const RooArgSet * GetGlobalObservables() const
get RooArgSet for global observables (return NULL if not existing)
const RooArgSet * GetParametersOfInterest() const
get RooArgSet containing the parameter of interest (return NULL if not existing)
const RooArgSet * GetNuisanceParameters() const
get RooArgSet containing the nuisance parameters (return NULL if not existing)
const RooArgSet * GetObservables() const
get RooArgSet for observables (return NULL if not existing)
const RooArgSet * GetSnapshot() const
get RooArgSet for parameters for a particular hypothesis (return NULL if not existing)
RooAbsPdf * GetPdf() const
get model PDF (return NULL if pdf has not been specified or does not exist)
ProfileLikelihoodTestStat is an implementation of the TestStatistic interface that calculates the pro...
static void CloseProof(Option_t *option="s")
close all proof connections
This class simply holds a sampling distribution of some test statistic.
Int_t GetSize() const
size of samples
TestStatSampler is an interface class for a tools which produce RooStats SamplingDistributions.
virtual TestStatistic * GetTestStatistic() const =0
virtual void SetTestStatistic(TestStatistic *testStatistic)=0
TestStatistic is an interface class to provide a facility for construction test statistics distributi...
ToyMCSampler is an implementation of the TestStatSampler interface.
virtual void SetObservables(const RooArgSet &o)
virtual void SetPdf(RooAbsPdf &pdf)
virtual RooAbsData * GenerateToyData(RooArgSet ¶mPoint, RooAbsPdf &pdf) const
virtual void SetGlobalObservables(const RooArgSet &o)
virtual void SetNEventsPerToy(const Int_t nevents)
virtual void SetNuisanceParameters(const RooArgSet &np)
virtual void SetParametersForTestStat(const RooArgSet &nullpoi)
virtual void SetLineStyle(Style_t lstyle)
Set the line style.
virtual void SetLineWidth(Width_t lwidth)
Set the line width.
virtual void SetLineColor(Color_t lcolor)
Set the line color.
virtual void SetOwner(Bool_t enable=kTRUE)
Set whether this collection is the owner (enable==true) of its content.
virtual Double_t GetParError(Int_t ipar) const
Return value of parameter number ipar.
virtual void SetRange(Double_t xmin, Double_t xmax)
Initialize the upper and lower bounds to draw the function.
virtual void GetRange(Double_t *xmin, Double_t *xmax) const
Return range of a generic N-D function.
virtual void Draw(Option_t *option="")
Draw this function with its current attributes.
virtual void SetParameter(Int_t param, Double_t value)
virtual void FixParameter(Int_t ipar, Double_t value)
Fix the value of a parameter The specified value will be used in a fit operation.
virtual Double_t GetParameter(Int_t ipar) const
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
virtual void Close(Option_t *option="")
Close a file.
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseGeneralPurpose, Int_t netopt=0)
Create / open a file.
A TGraphErrors is a TGraph with error bars.
1-D histogram with a double per channel (see TH1 documentation)}
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
virtual void SetBuffer(Int_t buffersize, Option_t *option="")
Set the maximum number of entries to be kept in the buffer.
virtual TLine * DrawLine(Double_t x1, Double_t y1, Double_t x2, Double_t y2)
Draw this line with new coordinates.
virtual void Add(TObject *obj)
virtual TObject * Remove(TObject *obj)
Remove object from the list.
virtual void SetTitle(const char *title="")
Set the title of the TNamed.
virtual void SetName(const char *name)
Set the name of the TNamed.
virtual TObject * Clone(const char *newname="") const
Make a clone of an object using the Streamer facility.
virtual const char * GetName() const
Returns name of object.
Mother of all ROOT objects.
virtual Int_t Write(const char *name=0, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.
virtual const char * GetName() const
Returns name of object.
@ kOverwrite
overwrite existing object with same name
virtual void Draw(Option_t *option="")
Default Draw method for all objects.
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
Py_ssize_t ArraySize(const std::string &name)
Extract size from an array type, if available.
VecExpr< UnaryOp< Fabs< T >, VecExpr< A, T, D >, T >, T, D > fabs(const VecExpr< A, T, D > &rhs)
Namespace for the RooStats classes.
void PrintListContent(const RooArgList &l, std::ostream &os=std::cout)
Bool_t AreEqualRel(Double_t af, Double_t bf, Double_t relPrec)
Bool_t AreEqualAbs(Double_t af, Double_t bf, Double_t epsilon)