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stressRooFit_tests.h
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//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #101
//
// Fitting, plotting, toy data generation on one-dimensional p.d.f
//
// pdf = gauss(x,m,s)
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooFormulaVar.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "RooUnitTest.h"
#include "RooHelpers.h"
using namespace RooFit ;
// Elementary operations on a gaussian PDF
class TestBasic101 : public RooUnitTest
{
public:
TestBasic101(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Fitting,plotting & event generation of basic p.d.f",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// S e t u p m o d e l
// ---------------------
// Declare variables x,mean,sigma with associated name, title, initial value and allowed range
RooRealVar x("x","x",-10,10) ;
RooRealVar mean("mean","mean of gaussian",1,-10,10) ;
RooRealVar sigma("sigma","width of gaussian",1,0.1,10) ;
// Build gaussian p.d.f in terms of x,mean and sigma
RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma) ;
// Construct plot frame in 'x'
RooPlot* xframe = x.frame(Title("Gaussian p.d.f.")) ;
// P l o t m o d e l a n d c h a n g e p a r a m e t e r v a l u e s
// ---------------------------------------------------------------------------
// Plot gauss in frame (i.e. in x)
gauss.plotOn(xframe) ;
// Change the value of sigma to 3
sigma.setVal(3) ;
// Plot gauss in frame (i.e. in x) and draw frame on canvas
gauss.plotOn(xframe,LineColor(kRed),Name("another")) ;
// G e n e r a t e e v e n t s
// -----------------------------
// Generate a dataset of 1000 events in x from gauss
RooDataSet* data = gauss.generate(x,10000) ;
// Make a second plot frame in x and draw both the
// data and the p.d.f in the frame
RooPlot* xframe2 = x.frame(Title("Gaussian p.d.f. with data")) ;
data->plotOn(xframe2) ;
gauss.plotOn(xframe2) ;
// F i t m o d e l t o d a t a
// -----------------------------
// Fit pdf to data
gauss.fitTo(*data) ;
// --- Post processing for stressRooFit ---
regPlot(xframe ,"rf101_plot1") ;
regPlot(xframe2,"rf101_plot2") ;
delete data ;
return kTRUE ;
}
} ;
//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #102
//
// Importing data from ROOT TTrees and THx histograms
//
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TTree.h"
#include "TH1D.h"
#include "TRandom.h"
using namespace RooFit ;
class TestBasic102 : public RooUnitTest
{
public:
TestBasic102(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Data import methods",refFile,writeRef,verbose) {} ;
TH1* makeTH1()
{
// Create ROOT TH1 filled with a Gaussian distribution
TH1D* hh = new TH1D("hh","hh",25,-10,10) ;
for (int i=0 ; i<100 ; i++) {
hh->Fill(gRandom->Gaus(0,3)) ;
}
return hh ;
}
TTree* makeTTree()
{
// Create ROOT TTree filled with a Gaussian distribution in x and a uniform distribution in y
TTree* tree = new TTree("tree","tree") ;
Double_t* px = new Double_t ;
Double_t* py = new Double_t ;
tree->Branch("x",px,"x/D") ;
tree->Branch("y",py,"y/D") ;
for (int i=0 ; i<100 ; i++) {
*px = gRandom->Gaus(0,3) ;
*py = gRandom->Uniform()*30 - 15 ;
tree->Fill() ;
}
//delete px ;
//delete py ;
return tree ;
}
Bool_t testCode() {
////////////////////////////////////////////////////////
// I m p o r t i n g R O O T h i s t o g r a m s //
////////////////////////////////////////////////////////
// I m p o r t T H 1 i n t o a R o o D a t a H i s t
// ---------------------------------------------------------
// Create a ROOT TH1 histogram
TH1* hh = makeTH1() ;
// Declare observable x
RooRealVar x("x","x",-10,10) ;
// Create a binned dataset that imports contents of TH1 and associates its contents to observable 'x'
RooDataHist dh("dh","dh",x,Import(*hh)) ;
// P l o t a n d f i t a R o o D a t a H i s t
// ---------------------------------------------------
// Make plot of binned dataset showing Poisson error bars (RooFit default)
RooPlot* frame = x.frame(Title("Imported TH1 with Poisson error bars")) ;
dh.plotOn(frame) ;
// Fit a Gaussian p.d.f to the data
RooRealVar mean("mean","mean",0,-10,10) ;
RooRealVar sigma("sigma","sigma",3,0.1,10) ;
RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
gauss.fitTo(dh) ;
gauss.plotOn(frame) ;
// P l o t a n d f i t a R o o D a t a H i s t w i t h i n t e r n a l e r r o r s
// ---------------------------------------------------------------------------------------------
// If histogram has custom error (i.e. its contents is does not originate from a Poisson process
// but e.g. is a sum of weighted events) you can data with symmetric 'sum-of-weights' error instead
// (same error bars as shown by ROOT)
RooPlot* frame2 = x.frame(Title("Imported TH1 with internal errors")) ;
dh.plotOn(frame2,DataError(RooAbsData::SumW2)) ;
gauss.plotOn(frame2) ;
// Please note that error bars shown (Poisson or SumW2) are for visualization only, the are NOT used
// in a maximum likelihood fit
//
// A (binned) ML fit will ALWAYS assume the Poisson error interpretation of data (the mathematical definition
// of likelihood does not take any external definition of errors). Data with non-unit weights can only be correctly
// fitted with a chi^2 fit (see rf602_chi2fit.C)
////////////////////////////////////////////////
// I m p o r t i n g R O O T T T r e e s //
////////////////////////////////////////////////
// I m p o r t T T r e e i n t o a R o o D a t a S e t
// -----------------------------------------------------------
TTree* tree = makeTTree() ;
// Define 2nd observable y
RooRealVar y("y","y",-10,10) ;
// Construct unbinned dataset importing tree branches x and y matching between branches and RooRealVars
// is done by name of the branch/RRV
//
// Note that ONLY entries for which x,y have values within their allowed ranges as defined in
// RooRealVar x and y are imported. Since the y values in the import tree are in the range [-15,15]
// and RRV y defines a range [-10,10] this means that the RooDataSet below will have less entries than the TTree 'tree'
RooDataSet ds("ds","ds",RooArgSet(x,y),Import(*tree)) ;
// P l o t d a t a s e t w i t h m u l t i p l e b i n n i n g c h o i c e s
// ------------------------------------------------------------------------------------
// Print unbinned dataset with default frame binning (100 bins)
RooPlot* frame3 = y.frame(Title("Unbinned data shown in default frame binning")) ;
ds.plotOn(frame3) ;
// Print unbinned dataset with custom binning choice (20 bins)
RooPlot* frame4 = y.frame(Title("Unbinned data shown with custom binning")) ;
ds.plotOn(frame4,Binning(20)) ;
// Draw all frames on a canvas
regPlot(frame ,"rf102_plot1") ;
regPlot(frame2,"rf102_plot2") ;
regPlot(frame3,"rf102_plot3") ;
regPlot(frame4,"rf102_plot4") ;
delete hh ;
delete tree ;
return kTRUE ;
}
} ;
//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #103
//
// Interpreted functions and p.d.f.s
//
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooConstVar.h"
#include "RooPlot.h"
#include "RooFitResult.h"
#include "RooGenericPdf.h"
using namespace RooFit ;
class TestBasic103 : public RooUnitTest
{
public:
TestBasic103(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Interpreted expression p.d.f.",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
/////////////////////////////////////////////////////////
// G e n e r i c i n t e r p r e t e d p . d . f . //
/////////////////////////////////////////////////////////
// Declare observable x
RooRealVar x("x","x",-20,20) ;
// C o n s t r u c t g e n e r i c p d f f r o m i n t e r p r e t e d e x p r e s s i o n
// -------------------------------------------------------------------------------------------------
// To construct a proper p.d.f, the formula expression is explicitly normalized internally by dividing
// it by a numeric integral of the expresssion over x in the range [-20,20]
//
RooRealVar alpha("alpha","alpha",5,0.1,10) ;
RooGenericPdf genpdf("genpdf","genpdf","(1+0.1*abs(x)+sin(sqrt(abs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
// S a m p l e , f i t a n d p l o t g e n e r i c p d f
// ---------------------------------------------------------------
// Generate a toy dataset from the interpreted p.d.f
RooDataSet* data = genpdf.generate(x,10000) ;
// Fit the interpreted p.d.f to the generated data
genpdf.fitTo(*data) ;
// Make a plot of the data and the p.d.f overlaid
RooPlot* xframe = x.frame(Title("Interpreted expression pdf")) ;
data->plotOn(xframe) ;
genpdf.plotOn(xframe) ;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// S t a n d a r d p . d . f a d j u s t w i t h i n t e r p r e t e d h e l p e r f u n c t i o n //
// //
// Make a gauss(x,sqrt(mean2),sigma) from a standard RooGaussian //
// //
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// C o n s t r u c t s t a n d a r d p d f w i t h f o r m u l a r e p l a c i n g p a r a m e t e r
// ------------------------------------------------------------------------------------------------------------
// Construct parameter mean2 and sigma
RooRealVar mean2("mean2","mean^2",10,0,200) ;
RooRealVar sigma("sigma","sigma",3,0.1,10) ;
// Construct interpreted function mean = sqrt(mean^2)
RooFormulaVar mean("mean","mean","sqrt(mean2)",mean2) ;
// Construct a gaussian g2(x,sqrt(mean2),sigma) ;
RooGaussian g2("g2","h2",x,mean,sigma) ;
// G e n e r a t e t o y d a t a
// ---------------------------------
// Construct a separate gaussian g1(x,10,3) to generate a toy Gaussian dataset with mean 10 and width 3
RooGaussian g1("g1","g1",x,RooConst(10),RooConst(3)) ;
RooDataSet* data2 = g1.generate(x,1000) ;
// F i t a n d p l o t t a i l o r e d s t a n d a r d p d f
// -------------------------------------------------------------------
// Fit g2 to data from g1
RooFitResult* r = g2.fitTo(*data2,Save()) ;
// Plot data on frame and overlay projection of g2
RooPlot* xframe2 = x.frame(Title("Tailored Gaussian pdf")) ;
data2->plotOn(xframe2) ;
g2.plotOn(xframe2) ;
regPlot(xframe,"rf103_plot1") ;
regPlot(xframe2,"rf103_plot2") ;
regResult(r,"rf103_fit1") ;
delete data ;
delete data2 ;
return kTRUE ;
}
} ;
//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #105
//
// Demonstration of binding ROOT Math functions as RooFit functions
// and pdfs
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TMath.h"
#include "TF1.h"
#include "Math/DistFunc.h"
#include "RooCFunction1Binding.h"
#include "RooCFunction3Binding.h"
#include "RooTFnBinding.h"
using namespace RooFit ;
class TestBasic105 : public RooUnitTest
{
public:
TestBasic105(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("C++ function binding operator p.d.f",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// B i n d T M a t h : : E r f C f u n c t i o n
// ---------------------------------------------------
// Bind one-dimensional TMath::Erf function as RooAbsReal function
RooRealVar x("x","x",-3,3) ;
RooAbsReal* erf = bindFunction("erf",TMath::Erf,x) ;
// Plot erf on frame
RooPlot* frame1 = x.frame(Title("TMath::Erf bound as RooFit function")) ;
erf->plotOn(frame1) ;
// B i n d R O O T : : M a t h : : b e t a _ p d f C f u n c t i o n
// -----------------------------------------------------------------------
// Bind pdf ROOT::Math::Beta with three variables as RooAbsPdf function
// exclude x=0 and x=1 points from range since beta_pdf diverges at x=0 and x=1
// when a < 1 and/or b <1
RooRealVar x2("x2","x2",0.001,0.999) ;
RooRealVar a("a","a",5,0,10) ;
RooRealVar b("b","b",2,0,10) ;
RooAbsPdf* beta = bindPdf("beta",ROOT::Math::beta_pdf,x2,a,b) ;
// Generate some events and fit
RooDataSet* data = beta->generate(x2,10000) ;
beta->fitTo(*data) ;
// Plot data and pdf on frame
RooPlot* frame2 = x2.frame(Title("ROOT::Math::Beta bound as RooFit pdf")) ;
data->plotOn(frame2) ;
beta->plotOn(frame2) ;
// B i n d R O O T T F 1 a s R o o F i t f u n c t i o n
// ---------------------------------------------------------------
// Create a ROOT TF1 function
TF1 *fa1 = new TF1("fa1","sin(x)/x",0,10);
// Create an observable
RooRealVar x3("x3","x3",0.01,20) ;
// Create binding of TF1 object to above observable
RooAbsReal* rfa1 = bindFunction(fa1,x3) ;
// Make plot frame in observable, plot TF1 binding function
RooPlot* frame3 = x3.frame(Title("TF1 bound as RooFit function")) ;
rfa1->plotOn(frame3) ;
regPlot(frame1,"rf105_plot1") ;
regPlot(frame2,"rf105_plot2") ;
regPlot(frame3,"rf105_plot3") ;
delete erf ;
delete beta ;
delete fa1 ;
delete rfa1 ;
delete data ;
return kTRUE ;
}
} ;
/////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #108
//
// Plotting unbinned data with alternate and variable binnings
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussModel.h"
#include "RooDecay.h"
#include "RooBMixDecay.h"
#include "RooCategory.h"
#include "RooBinning.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TH1.h"
using namespace RooFit ;
class TestBasic108 : public RooUnitTest
{
public:
TestBasic108(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Non-standard binning in counting and asymmetry plots",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// S e t u p m o d e l
// ---------------------
// Build a B decay p.d.f with mixing
RooRealVar dt("dt","dt",-20,20) ;
RooRealVar dm("dm","dm",0.472) ;
RooRealVar tau("tau","tau",1.547) ;
RooRealVar w("w","mistag rate",0.1) ;
RooRealVar dw("dw","delta mistag rate",0.) ;
RooCategory mixState("mixState","B0/B0bar mixing state") ;
mixState.defineType("mixed",-1) ;
mixState.defineType("unmixed",1) ;
RooCategory tagFlav("tagFlav","Flavour of the tagged B0") ;
tagFlav.defineType("B0",1) ;
tagFlav.defineType("B0bar",-1) ;
// Build a gaussian resolution model
RooRealVar dterr("dterr","dterr",0.1,1.0) ;
RooRealVar bias1("bias1","bias1",0) ;
RooRealVar sigma1("sigma1","sigma1",0.1) ;
RooGaussModel gm1("gm1","gauss model 1",dt,bias1,sigma1) ;
// Construct Bdecay (x) gauss
RooBMixDecay bmix("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,gm1,RooBMixDecay::DoubleSided) ;
// S a m p l e d a t a f r o m m o d e l
// --------------------------------------------
// Sample 2000 events in (dt,mixState,tagFlav) from bmix
RooDataSet *data = bmix.generate(RooArgSet(dt,mixState,tagFlav),2000) ;
// S h o w d t d i s t r i b u t i o n w i t h c u s t o m b i n n i n g
// -------------------------------------------------------------------------------
// Make plot of dt distribution of data in range (-15,15) with fine binning for dt>0 and coarse binning for dt<0
// Create binning object with range (-15,15)
RooBinning tbins(-15,15) ;
// Add 60 bins with uniform spacing in range (-15,0)
tbins.addUniform(60,-15,0) ;
// Add 15 bins with uniform spacing in range (0,15)
tbins.addUniform(15,0,15) ;
// Make plot with specified binning
RooPlot* dtframe = dt.frame(Range(-15,15),Title("dt distribution with custom binning")) ;
data->plotOn(dtframe,Binning(tbins)) ;
bmix.plotOn(dtframe) ;
// NB: Note that bin density for each bin is adjusted to that of default frame binning as shown
// in Y axis label (100 bins --> Events/0.4*Xaxis-dim) so that all bins represent a consistent density distribution
// S h o w m i x s t a t e a s y m m e t r y w i t h c u s t o m b i n n i n g
// ------------------------------------------------------------------------------------
// Make plot of dt distribution of data asymmetry in 'mixState' with variable binning
// Create binning object with range (-10,10)
RooBinning abins(-10,10) ;
// Add boundaries at 0, (-1,1), (-2,2), (-3,3), (-4,4) and (-6,6)
abins.addBoundary(0) ;
abins.addBoundaryPair(1) ;
abins.addBoundaryPair(2) ;
abins.addBoundaryPair(3) ;
abins.addBoundaryPair(4) ;
abins.addBoundaryPair(6) ;
// Create plot frame in dt
RooPlot* aframe = dt.frame(Range(-10,10),Title("mixState asymmetry distribution with custom binning")) ;
// Plot mixState asymmetry of data with specified customg binning
data->plotOn(aframe,Asymmetry(mixState),Binning(abins)) ;
// Plot corresponding property of p.d.f
bmix.plotOn(aframe,Asymmetry(mixState)) ;
// Adjust vertical range of plot to sensible values for an asymmetry
aframe->SetMinimum(-1.1) ;
aframe->SetMaximum(1.1) ;
// NB: For asymmetry distributions no density corrects are needed (and are thus not applied)
regPlot(dtframe,"rf108_plot1") ;
regPlot(aframe,"rf108_plot2") ;
delete data ;
return kTRUE ;
}
} ;
//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #109
//
// Calculating chi^2 from histograms and curves in RooPlots,
// making histogram of residual and pull distributions
//
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "RooHist.h"
using namespace RooFit ;
class TestBasic109 : public RooUnitTest
{
public:
TestBasic109(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Calculation of chi^2 and residuals in plots",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// S e t u p m o d e l
// ---------------------
// Create observables
RooRealVar x("x","x",-10,10) ;
// Create Gaussian
RooRealVar sigma("sigma","sigma",3,0.1,10) ;
RooRealVar mean("mean","mean",0,-10,10) ;
RooGaussian gauss("gauss","gauss",x,RooConst(0),sigma) ;
// Generate a sample of 1000 events with sigma=3
RooDataSet* data = gauss.generate(x,10000) ;
// Change sigma to 3.15
sigma=3.15 ;
// P l o t d a t a a n d s l i g h t l y d i s t o r t e d m o d e l
// ---------------------------------------------------------------------------
// Overlay projection of gauss with sigma=3.15 on data with sigma=3.0
RooPlot* frame1 = x.frame(Title("Data with distorted Gaussian pdf"),Bins(40)) ;
data->plotOn(frame1,DataError(RooAbsData::SumW2)) ;
gauss.plotOn(frame1) ;
// C a l c u l a t e c h i ^ 2
// ------------------------------
// Show the chi^2 of the curve w.r.t. the histogram
// If multiple curves or datasets live in the frame you can specify
// the name of the relevant curve and/or dataset in chiSquare()
regValue(frame1->chiSquare(),"rf109_chi2") ;
// S h o w r e s i d u a l a n d p u l l d i s t s
// -------------------------------------------------------
// Construct a histogram with the residuals of the data w.r.t. the curve
// we set `useAverage` to false for this test because this was done for the reference histogram
RooHist* hresid = frame1->residHist(nullptr, nullptr, false, false) ;
// Construct a histogram with the pulls of the data w.r.t the curve
// we set `useAverage` to false for this test because this was done for the reference histogram
RooHist* hpull = frame1->pullHist(nullptr, nullptr, false) ;
// Create a new frame to draw the residual distribution and add the distribution to the frame
RooPlot* frame2 = x.frame(Title("Residual Distribution")) ;
frame2->addPlotable(hresid,"P") ;
// Create a new frame to draw the pull distribution and add the distribution to the frame
RooPlot* frame3 = x.frame(Title("Pull Distribution")) ;
frame3->addPlotable(hpull,"P") ;
regPlot(frame1,"rf109_plot1") ;
regPlot(frame2,"rf109_plot2") ;
regPlot(frame3,"rf109_plot3") ;
delete data ;
//delete hresid ;
//delete hpull ;
return kTRUE ;
}
} ;
/////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #110
//
// Examples on normalization of p.d.f.s,
// integration of p.d.fs, construction
// of cumulative distribution functions from p.d.f.s
// in one dimension
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooGaussian.h"
#include "RooAbsReal.h"
#include "RooPlot.h"
#include "TCanvas.h"
using namespace RooFit ;
class TestBasic110 : public RooUnitTest
{
public:
TestBasic110(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Normalization of p.d.f.s in 1D",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// S e t u p m o d e l
// ---------------------
// Create observables x,y
RooRealVar x("x","x",-10,10) ;
// Create p.d.f. gaussx(x,-2,3)
RooGaussian gx("gx","gx",x,RooConst(-2),RooConst(3)) ;
// R e t r i e v e r a w & n o r m a l i z e d v a l u e s o f R o o F i t p . d . f . s
// --------------------------------------------------------------------------------------------------
// Return 'raw' unnormalized value of gx
regValue(gx.getVal(),"rf110_gx") ;
// Return value of gx normalized over x in range [-10,10]
RooArgSet nset(x) ;
regValue(gx.getVal(&nset),"rf110_gx_Norm[x]") ;
// Create object representing integral over gx
// which is used to calculate gx_Norm[x] == gx / gx_Int[x]
RooAbsReal* igx = gx.createIntegral(x) ;
regValue(igx->getVal(),"rf110_gx_Int[x]") ;
// I n t e g r a t e n o r m a l i z e d p d f o v e r s u b r a n g e
// ----------------------------------------------------------------------------
// Define a range named "signal" in x from -5,5
x.setRange("signal",-5,5) ;
// Create an integral of gx_Norm[x] over x in range "signal"
// This is the fraction of of p.d.f. gx_Norm[x] which is in the
// range named "signal"
RooAbsReal* igx_sig = gx.createIntegral(x,NormSet(x),Range("signal")) ;
regValue(igx_sig->getVal(),"rf110_gx_Int[x|signal]_Norm[x]") ;
// C o n s t r u c t c u m u l a t i v e d i s t r i b u t i o n f u n c t i o n f r o m p d f
// -----------------------------------------------------------------------------------------------------
// Create the cumulative distribution function of gx
// i.e. calculate Int[-10,x] gx(x') dx'
RooAbsReal* gx_cdf = gx.createCdf(x) ;
// Plot cdf of gx versus x
RooPlot* frame = x.frame(Title("c.d.f of Gaussian p.d.f")) ;
gx_cdf->plotOn(frame) ;
regPlot(frame,"rf110_plot1") ;
delete igx ;
delete igx_sig ;
delete gx_cdf ;
return kTRUE ;
}
} ;
//////////////////////////////////////////////////////////////////////////
//
// 'BASIC FUNCTIONALITY' RooFit tutorial macro #111
//
// Configuration and customization of how numeric (partial) integrals
// are executed
//
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "RooNumIntConfig.h"
#include "RooLandau.h"
#include "RooArgSet.h"
#include <iomanip>
using namespace RooFit ;
class TestBasic111 : public RooUnitTest
{
public:
TestBasic111(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Numeric integration configuration",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// A d j u s t g l o b a l 1 D i n t e g r a t i o n p r e c i s i o n
// ----------------------------------------------------------------------------
// Example: Change global precision for 1D integrals from 1e-7 to 1e-6
//
// The relative epsilon (change as fraction of current best integral estimate) and
// absolute epsilon (absolute change w.r.t last best integral estimate) can be specified
// separately. For most p.d.f integrals the relative change criterium is the most important,
// however for certain non-p.d.f functions that integrate out to zero a separate absolute
// change criterium is necessary to declare convergence of the integral
//
// NB: This change is for illustration only. In general the precision should be at least 1e-7
// for normalization integrals for MINUIT to succeed.
//
RooAbsReal::defaultIntegratorConfig()->setEpsAbs(1e-6) ;
RooAbsReal::defaultIntegratorConfig()->setEpsRel(1e-6) ;
// N u m e r i c i n t e g r a t i o n o f l a n d a u p d f
// ------------------------------------------------------------------
// Construct p.d.f without support for analytical integrator for demonstration purposes
RooRealVar x("x","x",-10,10) ;
RooLandau landau("landau","landau",x,RooConst(0),RooConst(0.1)) ;
// Calculate integral over landau with default choice of numeric integrator
RooAbsReal* intLandau = landau.createIntegral(x) ;
Double_t val = intLandau->getVal() ;
regValue(val,"rf111_val1") ;
// S a m e w i t h c u s t o m c o n f i g u r a t i o n
// -----------------------------------------------------------
// Construct a custom configuration which uses the adaptive Gauss-Kronrod technique
// for closed 1D integrals
RooNumIntConfig customConfig(*RooAbsReal::defaultIntegratorConfig()) ;
#ifdef R__HAS_MATHMORE
customConfig.method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
#endif
// Calculate integral over landau with custom integral specification
RooAbsReal* intLandau2 = landau.createIntegral(x,NumIntConfig(customConfig)) ;
Double_t val2 = intLandau2->getVal() ;
regValue(val2,"rf111_val2") ;
// A d j u s t i n g d e f a u l t c o n f i g f o r a s p e c i f i c p d f
// -------------------------------------------------------------------------------------
// Another possibility: associate custom numeric integration configuration as default for object 'landau'
landau.setIntegratorConfig(customConfig) ;
// Calculate integral over landau custom numeric integrator specified as object default
RooAbsReal* intLandau3 = landau.createIntegral(x) ;
Double_t val3 = intLandau3->getVal() ;
regValue(val3,"rf111_val3") ;
delete intLandau ;
delete intLandau2 ;
delete intLandau3 ;
return kTRUE ;
}
} ;
/////////////////////////////////////////////////////////////////////////
//
// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #201
//
// Composite p.d.f with signal and background component
//
// pdf = f_bkg * bkg(x,a0,a1) + (1-fbkg) * (f_sig1 * sig1(x,m,s1 + (1-f_sig1) * sig2(x,m,s2)))
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "TCanvas.h"
#include "RooPlot.h"
using namespace RooFit ;
class TestBasic201 : public RooUnitTest
{
public:
TestBasic201(TFile* refFile, Bool_t writeRef, Int_t verbose) : RooUnitTest("Addition operator p.d.f.",refFile,writeRef,verbose) {} ;
Bool_t testCode() {
// S e t u p c o m p o n e n t p d f s
// ---------------------------------------
// Declare observable x
RooRealVar x("x","x",0,10) ;
// Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
RooRealVar mean("mean","mean of gaussians",5) ;
RooRealVar sigma1("sigma1","width of gaussians",0.5) ;
RooRealVar sigma2("sigma2","width of gaussians",1) ;
RooGaussian sig1("sig1","Signal component 1",x,mean,sigma1) ;
RooGaussian sig2("sig2","Signal component 2",x,mean,sigma2) ;
// Build Chebychev polynomial p.d.f.
RooRealVar a0("a0","a0",0.5,0.,1.) ;
RooRealVar a1("a1","a1",-0.2,-1.,1.) ;
RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
////////////////////////////////////////////////////
// M E T H O D 1 - T w o R o o A d d P d f s //
////////////////////////////////////////////////////
// A d d s i g n a l c o m p o n e n t s
// ------------------------------------------
// Sum the signal components into a composite signal p.d.f.
RooRealVar sig1frac("sig1frac","fraction of component 1 in signal",0.8,0.,1.) ;
RooAddPdf sig("sig","Signal",RooArgList(sig1,sig2),sig1frac) ;
// A d d s i g n a l a n d b a c k g r o u n d
// ------------------------------------------------
// Sum the composite signal and background
RooRealVar bkgfrac("bkgfrac","fraction of background",0.5,0.,1.) ;
RooAddPdf model("model","g1+g2+a",RooArgList(bkg,sig),bkgfrac) ;
// S a m p l e , f i t a n d p l o t m o d e l
// ---------------------------------------------------
// Generate a data sample of 1000 events in x from model
RooDataSet *data = model.generate(x,1000) ;
// Fit model to data
model.fitTo(*data) ;
// Plot data and PDF overlaid
RooPlot* xframe = x.frame(Title("Example of composite pdf=(sig1+sig2)+bkg")) ;
data->plotOn(xframe) ;
model.plotOn(xframe) ;
// Overlay the background component of model with a dashed line
model.plotOn(xframe,Components(bkg),LineStyle(kDashed)) ;
// Overlay the background+sig2 components of model with a dotted line
model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted)) ;
////////////////////////////////////////////////////////////////////////////////////////////////////
// M E T H O D 2 - O n e R o o A d d P d f w i t h r e c u r s i v e f r a c t i o n s //
////////////////////////////////////////////////////////////////////////////////////////////////////
// Construct sum of models on one go using recursive fraction interpretations
//
// model2 = bkg + (sig1 + sig2)