diff --git a/tutorials/image/imgconv.C b/tutorials/image/imgconv.C
index f5852c063390b3c4a19e06cb2b432eeeeff73c1a..bd8e37e331d685aae2a1245f6569c300e5fe1f7f 100644
--- a/tutorials/image/imgconv.C
+++ b/tutorials/image/imgconv.C
@@ -1,6 +1,6 @@
/// \file
/// \ingroup tutorial_image
-/// \notebook
+/// \notebook -nodraw
/// Open rose512.jpg and save it in the following formats:
/// .png, .gif, .xpm and tiff.
///
diff --git a/tutorials/math/TSVDUnfoldExample.C b/tutorials/math/TSVDUnfoldExample.C
index fed800123c5f1c938e260f7fe641cf2d3d78da25..beb79a2b2d7874db652e25b252606b730ecce108 100644
--- a/tutorials/math/TSVDUnfoldExample.C
+++ b/tutorials/math/TSVDUnfoldExample.C
@@ -48,8 +48,6 @@ Double_t Reconstruct( Double_t xt, TRandom3& R )
void TSVDUnfoldExample()
{
- gROOT->Reset();
- gROOT->SetStyle("Plain");
gStyle->SetOptStat(0);
TRandom3 R;
diff --git a/tutorials/math/mathcoreVectorCollection.C b/tutorials/math/mathcoreVectorCollection.C
index 1df51364635a4d9507fcfae4a16c47f9aa4f3049..4d0b4d2377a47c697d6fca8ab26776705dc2cfe7 100644
--- a/tutorials/math/mathcoreVectorCollection.C
+++ b/tutorials/math/mathcoreVectorCollection.C
@@ -85,7 +85,7 @@ double write(int n) {
return sum;
}
- double read() {
+double read() {
TRandom R;
TStopwatch timer;
diff --git a/tutorials/math/mathcoreVectorIO.C b/tutorials/math/mathcoreVectorIO.C
index dd9d580c42c3621ae008feeb4c53772af388e2fd..fe31d756e2e8a3c2f3b49d36870217c0b1d95d92 100644
--- a/tutorials/math/mathcoreVectorIO.C
+++ b/tutorials/math/mathcoreVectorIO.C
@@ -1,6 +1,6 @@
/// \file
/// \ingroup tutorial_math
-/// \notebook
+/// \notebook -nodraw
/// Example of I/O of a mathcore Lorentz Vectors in a Tree and comparison with a TLorentzVector.
/// A ROOT tree is written and read in both using either a XYZTVector or a TLorentzVector.
///
diff --git a/tutorials/math/testUnfold5a.C b/tutorials/math/testUnfold5a.C
index 5cad9d369e83c64d64b70abb15c14c6a04606016..7c986e601bcdc34948dc539fd1621c259818a79b 100644
--- a/tutorials/math/testUnfold5a.C
+++ b/tutorials/math/testUnfold5a.C
@@ -36,7 +36,6 @@
/// \file
/// \ingroup tutorial_unfold5
/// \notebook -nodraw
-///
/// Version 17.0 example for multi-dimensional unfolding
///
/// \macro_output
diff --git a/tutorials/roofit/rf101_basics.C b/tutorials/roofit/rf101_basics.C
index 4528fd124c5fb65582c00b2c8d9ac0511dce02e9..aa5bb07ce4c39c5c7088409ed134a5200831fe0f 100644
--- a/tutorials/roofit/rf101_basics.C
+++ b/tutorials/roofit/rf101_basics.C
@@ -1,19 +1,18 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #101
///
/// Fitting, plotting, toy data generation on one-dimensional p.d.f
///
/// pdf = gauss(x,m,s)
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -25,62 +24,62 @@ using namespace RooFit ;
void rf101_basics()
{
- // S e t u p m o d e l
- // ---------------------
+ // 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) ;
+ // 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) ;
+ // 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.")) ;
+ // 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
- // ---------------------------------------------------------------------------
+ // 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) ;
+ // Plot gauss in frame (i.e. in x)
+ gauss.plotOn(xframe) ;
- // Change the value of sigma to 3
- sigma.setVal(3) ;
+ // 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)) ;
+ // Plot gauss in frame (i.e. in x) and draw frame on canvas
+ gauss.plotOn(xframe,LineColor(kRed)) ;
- // G e n e r a t e e v e n t s
- // -----------------------------
+ // 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) ;
+ // 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) ;
+ // 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
- // -----------------------------
+ // F i t m o d e l t o d a t a
+ // -----------------------------
- // Fit pdf to data
- gauss.fitTo(*data) ;
+ // Fit pdf to data
+ gauss.fitTo(*data) ;
- // Print values of mean and sigma (that now reflect fitted values and errors)
- mean.Print() ;
- sigma.Print() ;
+ // Print values of mean and sigma (that now reflect fitted values and errors)
+ mean.Print() ;
+ sigma.Print() ;
- // Draw all frames on a canvas
- TCanvas* c = new TCanvas("rf101_basics","rf101_basics",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.6) ; xframe2->Draw() ;
+ // Draw all frames on a canvas
+ TCanvas* c = new TCanvas("rf101_basics","rf101_basics",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.6) ; xframe2->Draw() ;
}
diff --git a/tutorials/roofit/rf102_dataimport.C b/tutorials/roofit/rf102_dataimport.C
index 6f026c9d505afac46f9168e276d3eca4cb207d86..2d5f253cfca95ba5922f0ecd5560b30b2f70d55b 100644
--- a/tutorials/roofit/rf102_dataimport.C
+++ b/tutorials/roofit/rf102_dataimport.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #102
///
/// Importing data from ROOT TTrees and THx histograms
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
@@ -30,114 +28,114 @@ TTree* makeTTree() ;
void rf102_dataimport()
{
- ////////////////////////////////////////////////////////
- // 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 number of events in dataset
- ds.Print() ;
-
- // 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
- TCanvas* c = new TCanvas("rf102_dataimport","rf102_dataimport",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.4) ; frame4->Draw() ;
-
+ ////////////////////////////////////////////////////////
+ // 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 number of events in dataset
+ ds.Print() ;
+
+ // 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
+ TCanvas* c = new TCanvas("rf102_dataimport","rf102_dataimport",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.4) ; frame4->Draw() ;
+
}
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 ;
+ {
+ // 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 ;
}
diff --git a/tutorials/roofit/rf103_interprfuncs.C b/tutorials/roofit/rf103_interprfuncs.C
index d676fdf6a52b64a31b89f23b7840f9dfc69874ba..21560e8040d1b3680462f39bcc66cb1af1fbfb32 100644
--- a/tutorials/roofit/rf103_interprfuncs.C
+++ b/tutorials/roofit/rf103_interprfuncs.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #103
///
/// Interpreted functions and p.d.f.s
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,86 +26,86 @@ using namespace RooFit ;
void rf103_interprfuncs()
{
- /////////////////////////////////////////////////////////
- // G e n e r i c i n t e r p r e t e d p . d . f . //
- /////////////////////////////////////////////////////////
+ /////////////////////////////////////////////////////////
+ // 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) ;
- // 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
+ // -------------------------------------------------------------------------------------------------
- // 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)) ;
- // 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
+ // ---------------------------------------------------------------
- // 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) ;
- // 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) ;
- // 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) ;
- // 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 //
+ // //
+ /////////////////////////////////////////////////////////////////////////////////////////////////////////////////
- /////////////////////////////////////////////////////////////////////////////////////////////////////////////////
- // 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
+ // ------------------------------------------------------------------------------------------------------------
- // 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 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 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) ;
- // 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
+ // ---------------------------------
- // 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) ;
- // 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
+ // -------------------------------------------------------------------
- // 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()) ;
+ r->Print() ;
- // Fit g2 to data from g1
- RooFitResult* r = g2.fitTo(*data2,Save()) ;
- r->Print() ;
+ // Plot data on frame and overlay projection of g2
+ RooPlot* xframe2 = x.frame(Title("Tailored Gaussian pdf")) ;
+ data2->plotOn(xframe2) ;
+ g2.plotOn(xframe2) ;
- // Plot data on frame and overlay projection of g2
- RooPlot* xframe2 = x.frame(Title("Tailored Gaussian pdf")) ;
- data2->plotOn(xframe2) ;
- g2.plotOn(xframe2) ;
-
- // Draw all frames on a canvas
- TCanvas* c = new TCanvas("rf103_interprfuncs","rf103_interprfuncs",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.4) ; xframe2->Draw() ;
+ // Draw all frames on a canvas
+ TCanvas* c = new TCanvas("rf103_interprfuncs","rf103_interprfuncs",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.4) ; xframe2->Draw() ;
}
diff --git a/tutorials/roofit/rf104_classfactory.C b/tutorials/roofit/rf104_classfactory.C
index 3074367a7d1bce7aee5a7dfdb390fea72d62d1da..3d80524e25c3e1c112edebc3d3018dcb775774b3 100644
--- a/tutorials/roofit/rf104_classfactory.C
+++ b/tutorials/roofit/rf104_classfactory.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #104
///
/// The class factory for functions and p.d.f.s
@@ -13,14 +14,11 @@
/// root>.L MyPdfV3.cxx+ // Compile and load created classs
/// root>.x rf104_classfactory.C+ // run compiled code
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,100 +28,94 @@
#include "RooClassFactory.h"
#include "TROOT.h"
-#ifndef __CINT__
-#include "MyPdfV3.h"
-#endif
-
using namespace RooFit ;
void rf104_classfactory()
-{
- // W r i t e c l a s s s k e l e t o n c o d e
- // --------------------------------------------------
-
- // Write skeleton p.d.f class with variable x,a,b
- // To use this class,
- // - Edit the file MyPdfV1.cxx and implement the evaluate() method in terms of x,a and b
- // - Compile and link class with '.x MyPdfV1.cxx+'
- //
- RooClassFactory::makePdf("MyPdfV1","x,A,B") ;
-
-
- // W i t h a d d e d i n i t i a l v a l u e e x p r e s s i o n
- // ---------------------------------------------------------------------
-
- // Write skeleton p.d.f class with variable x,a,b and given formula expression
- // To use this class,
- // - Compile and link class with '.x MyPdfV2.cxx+'
- //
- RooClassFactory::makePdf("MyPdfV2","x,A,B","","A*fabs(x)+pow(x-B,2)") ;
-
-
- // W i t h a d d e d a n a l y t i c a l i n t e g r a l e x p r e s s i o n
- // ---------------------------------------------------------------------------------
-
- // Write skeleton p.d.f class with variable x,a,b, given formula expression _and_
- // given expression for analytical integral over x
- // To use this class,
- // - Compile and link class with '.x MyPdfV3.cxx+'
- //
- RooClassFactory::makePdf("MyPdfV3","x,A,B","","A*fabs(x)+pow(x-B,2)",kTRUE,kFALSE,
- "x:(A/2)*(pow(x.max(rangeName),2)+pow(x.min(rangeName),2))+(1./3)*(pow(x.max(rangeName)-B,3)-pow(x.min(rangeName)-B,3))") ;
-
-
-
- // U s e i n s t a n c e o f c r e a t e d c l a s s
- // ---------------------------------------------------------
-
- // Compile MyPdfV3 class (only when running in CINT)
-#ifdef __CINT__
- gROOT->ProcessLineSync(".x MyPdfV3.cxx+") ;
-#endif
-
- // Creat instance of MyPdfV3 class
- RooRealVar a("a","a",1) ;
- RooRealVar b("b","b",2,-10,10) ;
- RooRealVar y("y","y",-10,10);
- MyPdfV3 pdf("pdf","pdf",y,a,b) ;
-
- // Generate toy data from pdf and plot data and p.d.f on frame
- RooPlot* frame1 = y.frame(Title("Compiled class MyPdfV3")) ;
- RooDataSet* data = pdf.generate(y,1000) ;
- pdf.fitTo(*data) ;
- data->plotOn(frame1) ;
- pdf.plotOn(frame1) ;
-
-
- ///////////////////////////////////////////////////////////////////////
- // C o m p i l e d v e r s i o n o f e x a m p l e r f 1 0 3 //
- ///////////////////////////////////////////////////////////////////////
-
- // Declare observable x
- RooRealVar x("x","x",-20,20) ;
-
- // The RooClassFactory::makePdfInstance() function performs code writing, compiling, linking
- // and object instantiation in one go and can serve as a straight replacement of RooGenericPdf
-
- RooRealVar alpha("alpha","alpha",5,0.1,10) ;
- RooAbsPdf* genpdf = RooClassFactory::makePdfInstance("GenPdf","(1+0.1*fabs(x)+sin(sqrt(fabs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
-
- // Generate a toy dataset from the interpreted p.d.f
- RooDataSet* data2 = genpdf->generate(x,50000) ;
-
- // Fit the interpreted p.d.f to the generated data
- genpdf->fitTo(*data2) ;
-
- // Make a plot of the data and the p.d.f overlaid
- RooPlot* frame2 = x.frame(Title("Compiled version of pdf of rf103")) ;
- data2->plotOn(frame2) ;
- genpdf->plotOn(frame2) ;
-
- // Draw all frames on a canvas
- TCanvas* c = new TCanvas("rf104_classfactory","rf104_classfactory",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+{
+ // W r i t e c l a s s s k e l e t o n c o d e
+ // --------------------------------------------------
+
+ // Write skeleton p.d.f class with variable x,a,b
+ // To use this class,
+ // - Edit the file MyPdfV1.cxx and implement the evaluate() method in terms of x,a and b
+ // - Compile and link class with '.x MyPdfV1.cxx+'
+ //
+ RooClassFactory::makePdf("MyPdfV1","x,A,B") ;
+
+
+ // W i t h a d d e d i n i t i a l v a l u e e x p r e s s i o n
+ // ---------------------------------------------------------------------
+
+ // Write skeleton p.d.f class with variable x,a,b and given formula expression
+ // To use this class,
+ // - Compile and link class with '.x MyPdfV2.cxx+'
+ //
+ RooClassFactory::makePdf("MyPdfV2","x,A,B","","A*fabs(x)+pow(x-B,2)") ;
+
+
+ // W i t h a d d e d a n a l y t i c a l i n t e g r a l e x p r e s s i o n
+ // ---------------------------------------------------------------------------------
+
+ // Write skeleton p.d.f class with variable x,a,b, given formula expression _and_
+ // given expression for analytical integral over x
+ // To use this class,
+ // - Compile and link class with '.x MyPdfV3.cxx+'
+ //
+ RooClassFactory::makePdf("MyPdfV3","x,A,B","","A*fabs(x)+pow(x-B,2)",kTRUE,kFALSE,
+ "x:(A/2)*(pow(x.max(rangeName),2)+pow(x.min(rangeName),2))+(1./3)*(pow(x.max(rangeName)-B,3)-pow(x.min(rangeName)-B,3))") ;
+
+
+
+ // U s e i n s t a n c e o f c r e a t e d c l a s s
+ // ---------------------------------------------------------
+
+ // Compile MyPdfV3 class (only when running in CINT)
+ gROOT->ProcessLineSync(".x MyPdfV3.cxx+") ;
+
+ // Creat instance of MyPdfV3 class
+ RooRealVar a("a","a",1) ;
+ RooRealVar b("b","b",2,-10,10) ;
+ RooRealVar y("y","y",-10,10);
+ MyPdfV3 pdf("pdf","pdf",y,a,b) ;
+
+ // Generate toy data from pdf and plot data and p.d.f on frame
+ RooPlot* frame1 = y.frame(Title("Compiled class MyPdfV3")) ;
+ RooDataSet* data = pdf.generate(y,1000) ;
+ pdf.fitTo(*data) ;
+ data->plotOn(frame1) ;
+ pdf.plotOn(frame1) ;
+
+
+ ///////////////////////////////////////////////////////////////////////
+ // C o m p i l e d v e r s i o n o f e x a m p l e r f 1 0 3 //
+ ///////////////////////////////////////////////////////////////////////
+
+ // Declare observable x
+ RooRealVar x("x","x",-20,20) ;
+
+ // The RooClassFactory::makePdfInstance() function performs code writing, compiling, linking
+ // and object instantiation in one go and can serve as a straight replacement of RooGenericPdf
+
+ RooRealVar alpha("alpha","alpha",5,0.1,10) ;
+ RooAbsPdf* genpdf = RooClassFactory::makePdfInstance("GenPdf","(1+0.1*fabs(x)+sin(sqrt(fabs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
+
+ // Generate a toy dataset from the interpreted p.d.f
+ RooDataSet* data2 = genpdf->generate(x,50000) ;
+
+ // Fit the interpreted p.d.f to the generated data
+ genpdf->fitTo(*data2) ;
+
+ // Make a plot of the data and the p.d.f overlaid
+ RooPlot* frame2 = x.frame(Title("Compiled version of pdf of rf103")) ;
+ data2->plotOn(frame2) ;
+ genpdf->plotOn(frame2) ;
+
+ // Draw all frames on a canvas
+ TCanvas* c = new TCanvas("rf104_classfactory","rf104_classfactory",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf105_funcbinding.C b/tutorials/roofit/rf105_funcbinding.C
index 26a743ca1f5f25710e3526e84a1766c60e3697a0..a67c4e6389ea85d253709caf6af8d2b1abfecfb9 100644
--- a/tutorials/roofit/rf105_funcbinding.C
+++ b/tutorials/roofit/rf105_funcbinding.C
@@ -1,21 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #105
///
/// Demonstration of binding ROOT Math functions as RooFit functions
/// and pdfs
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#else
-// Refer to a class implemented in libRooFit to force its loading
-// via the autoloader.
-class Roo2DKeysPdf;
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -25,10 +20,6 @@ class Roo2DKeysPdf;
#include "TMath.h"
#include "TF1.h"
#include "Math/DistFunc.h"
-#ifndef __CINT__
-#include "RooCFunction1Binding.h"
-#include "RooCFunction3Binding.h"
-#endif
#include "RooTFnBinding.h"
using namespace RooFit;
@@ -37,69 +28,69 @@ using namespace RooFit;
void rf105_funcbinding()
{
- // B i n d T M a t h : : E r f C f u n c t i o n
- // ---------------------------------------------------
+ // 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) ;
- // Bind one-dimensional TMath::Erf function as RooAbsReal function
- RooRealVar x("x","x",-3,3) ;
- RooAbsReal* erf = bindFunction("erf",TMath::Erf,x) ;
+ // Print erf definition
+ erf->Print() ;
- // Print erf definition
- erf->Print() ;
+ // Plot erf on frame
+ RooPlot* frame1 = x.frame(Title("TMath::Erf bound as RooFit function")) ;
+ erf->plotOn(frame1) ;
- // 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
+ // -----------------------------------------------------------------------
- // 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
+ RooRealVar x2("x2","x2",0,0.999) ;
+ 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) ;
- // Bind pdf ROOT::Math::Beta with three variables as RooAbsPdf function
- RooRealVar x2("x2","x2",0,0.999) ;
- 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) ;
+ // Perf beta definition
+ beta->Print() ;
- // Perf beta definition
- beta->Print() ;
+ // Generate some events and fit
+ RooDataSet* data = beta->generate(x2,10000) ;
+ beta->fitTo(*data) ;
- // 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) ;
- // 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
+ // ---------------------------------------------------------------
- // 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 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 an observable
+ RooRealVar x3("x3","x3",0.01,20) ;
- // Create binding of TF1 object to above observable
- RooAbsReal* rfa1 = bindFunction(fa1,x3) ;
+ // Create binding of TF1 object to above observable
+ RooAbsReal* rfa1 = bindFunction(fa1,x3) ;
- // Print rfa1 definition
- rfa1->Print() ;
+ // Print rfa1 definition
+ rfa1->Print() ;
- // Make plot frame in observable, plot TF1 binding function
- RooPlot* frame3 = x3.frame(Title("TF1 bound as RooFit function")) ;
- rfa1->plotOn(frame3) ;
+ // Make plot frame in observable, plot TF1 binding function
+ RooPlot* frame3 = x3.frame(Title("TF1 bound as RooFit function")) ;
+ rfa1->plotOn(frame3) ;
- TCanvas* c = new TCanvas("rf105_funcbinding","rf105_funcbinding",1200,400) ;
- c->Divide(3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
+ TCanvas* c = new TCanvas("rf105_funcbinding","rf105_funcbinding",1200,400) ;
+ c->Divide(3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
diff --git a/tutorials/roofit/rf106_plotdecoration.C b/tutorials/roofit/rf106_plotdecoration.C
index bf8ca336827e08d8f1f7b51a924fdf0b3788f6d2..937f4f9def441e9c6ddce396a1abbfcafd3c78bc 100644
--- a/tutorials/roofit/rf106_plotdecoration.C
+++ b/tutorials/roofit/rf106_plotdecoration.C
@@ -1,18 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #106
///
/// Adding boxes with parameters, statistics to RooPlots.
/// Decorating RooPlots with arrows, text etc...
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
+
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,77 +26,76 @@ using namespace RooFit ;
void rf106_plotdecoration()
{
+ // S e t u p m o d e l
+ // ---------------------
- // S e t u p m o d e l
- // ---------------------
+ // Create observables
+ RooRealVar x("x","x",-10,10) ;
- // Create observables
- RooRealVar x("x","x",-10,10) ;
+ // Create Gaussian
+ RooRealVar sigma("sigma","sigma",1,0.1,10) ;
+ RooRealVar mean("mean","mean",-3,-10,10) ;
+ RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
- // Create Gaussian
- RooRealVar sigma("sigma","sigma",1,0.1,10) ;
- RooRealVar mean("mean","mean",-3,-10,10) ;
- RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
+ // Generate a sample of 1000 events with sigma=3
+ RooDataSet* data = gauss.generate(x,1000) ;
- // Generate a sample of 1000 events with sigma=3
- RooDataSet* data = gauss.generate(x,1000) ;
+ // Fit pdf to data
+ gauss.fitTo(*data) ;
- // Fit pdf to data
- gauss.fitTo(*data) ;
+ // P l o t p . d . f a n d d a t a
+ // -------------------------------------
- // P l o t p . d . f a n d d a t a
- // -------------------------------------
+ // Overlay projection of gauss on data
+ RooPlot* frame = x.frame(Name("xframe"),Title("RooPlot with decorations"),Bins(40)) ;
+ data->plotOn(frame) ;
+ gauss.plotOn(frame) ;
- // Overlay projection of gauss on data
- RooPlot* frame = x.frame(Name("xframe"),Title("RooPlot with decorations"),Bins(40)) ;
- data->plotOn(frame) ;
- gauss.plotOn(frame) ;
+ // A d d b o x w i t h p d f p a r a m e t e r s
+ // -----------------------------------------------------
- // A d d b o x w i t h p d f p a r a m e t e r s
- // -----------------------------------------------------
+ // Left edge of box starts at 55% of Xaxis)
+ gauss.paramOn(frame,Layout(0.55)) ;
- // Left edge of box starts at 55% of Xaxis)
- gauss.paramOn(frame,Layout(0.55)) ;
+ // A d d b o x w i t h d a t a s t a t i s t i c s
+ // -------------------------------------------------------
- // A d d b o x w i t h d a t a s t a t i s t i c s
- // -------------------------------------------------------
+ // X size of box is from 55% to 99% of Xaxis range, top of box is at 80% of Yaxis range)
+ data->statOn(frame,Layout(0.55,0.99,0.8)) ;
- // X size of box is from 55% to 99% of Xaxis range, top of box is at 80% of Yaxis range)
- data->statOn(frame,Layout(0.55,0.99,0.8)) ;
+ // A d d t e x t a n d a r r o w
+ // -----------------------------------
- // A d d t e x t a n d a r r o w
- // -----------------------------------
+ // Add text to frame
+ TText* txt = new TText(2,100,"Signal") ;
+ txt->SetTextSize(0.04) ;
+ txt->SetTextColor(kRed) ;
+ frame->addObject(txt) ;
- // Add text to frame
- TText* txt = new TText(2,100,"Signal") ;
- txt->SetTextSize(0.04) ;
- txt->SetTextColor(kRed) ;
- frame->addObject(txt) ;
+ // Add arrow to frame
+ TArrow* arrow = new TArrow(2,100,-1,50,0.01,"|>") ;
+ arrow->SetLineColor(kRed) ;
+ arrow->SetFillColor(kRed) ;
+ arrow->SetLineWidth(3) ;
+ frame->addObject(arrow) ;
- // Add arrow to frame
- TArrow* arrow = new TArrow(2,100,-1,50,0.01,"|>") ;
- arrow->SetLineColor(kRed) ;
- arrow->SetFillColor(kRed) ;
- arrow->SetLineWidth(3) ;
- frame->addObject(arrow) ;
+ // P e r s i s t f r a m e w i t h a l l d e c o r a t i o n s i n R O O T f i l e
+ // ---------------------------------------------------------------------------------------------
- // P e r s i s t f r a m e w i t h a l l d e c o r a t i o n s i n R O O T f i l e
- // ---------------------------------------------------------------------------------------------
+ TFile f("rf106_plotdecoration.root","RECREATE") ;
+ frame->Write() ;
+ f.Close() ;
- TFile f("rf106_plotdecoration.root","RECREATE") ;
- frame->Write() ;
- f.Close() ;
+ // To read back and plot frame with all decorations in clean root session do
+ // root> TFile f("rf106_plotdecoration.root") ;
+ // root> xframe->Draw() ;
- // To read back and plot frame with all decorations in clean root session do
- // root> TFile f("rf106_plotdecoration.root") ;
- // root> xframe->Draw() ;
+ new TCanvas("rf106_plotdecoration","rf106_plotdecoration",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
- new TCanvas("rf106_plotdecoration","rf106_plotdecoration",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
-
}
diff --git a/tutorials/roofit/rf107_plotstyles.C b/tutorials/roofit/rf107_plotstyles.C
index 1151a564ba5719602cfaf15ee17c6cbd09d58fdd..c589d4fbc23c3bd374b3f0bb496fc9322a862ef2 100644
--- a/tutorials/roofit/rf107_plotstyles.C
+++ b/tutorials/roofit/rf107_plotstyles.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #107
///
/// Demonstration of various plotting styles of data, functions
/// in a RooPlot
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -24,76 +23,76 @@ using namespace RooFit ;
void rf107_plotstyles()
{
- // S e t u p m o d e l
- // ---------------------
+ // S e t u p m o d e l
+ // ---------------------
- // Create observables
- RooRealVar x("x","x",-10,10) ;
+ // Create observables
+ RooRealVar x("x","x",-10,10) ;
- // Create Gaussian
- RooRealVar sigma("sigma","sigma",3,0.1,10) ;
- RooRealVar mean("mean","mean",-3,-10,10) ;
- RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
+ // Create Gaussian
+ RooRealVar sigma("sigma","sigma",3,0.1,10) ;
+ RooRealVar mean("mean","mean",-3,-10,10) ;
+ RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
- // Generate a sample of 100 events with sigma=3
- RooDataSet* data = gauss.generate(x,100) ;
+ // Generate a sample of 100 events with sigma=3
+ RooDataSet* data = gauss.generate(x,100) ;
- // Fit pdf to data
- gauss.fitTo(*data) ;
+ // Fit pdf to data
+ gauss.fitTo(*data) ;
- // M a k e p l o t f r a m e s
- // -------------------------------
+ // M a k e p l o t f r a m e s
+ // -------------------------------
- // Make four plot frames to demonstrate various plotting features
- RooPlot* frame1 = x.frame(Name("xframe"),Title("Red Curve / SumW2 Histo errors"),Bins(20)) ;
- RooPlot* frame2 = x.frame(Name("xframe"),Title("Dashed Curve / No XError bars"),Bins(20)) ;
- RooPlot* frame3 = x.frame(Name("xframe"),Title("Filled Curve / Blue Histo"),Bins(20)) ;
- RooPlot* frame4 = x.frame(Name("xframe"),Title("Partial Range / Filled Bar chart"),Bins(20)) ;
+ // Make four plot frames to demonstrate various plotting features
+ RooPlot* frame1 = x.frame(Name("xframe"),Title("Red Curve / SumW2 Histo errors"),Bins(20)) ;
+ RooPlot* frame2 = x.frame(Name("xframe"),Title("Dashed Curve / No XError bars"),Bins(20)) ;
+ RooPlot* frame3 = x.frame(Name("xframe"),Title("Filled Curve / Blue Histo"),Bins(20)) ;
+ RooPlot* frame4 = x.frame(Name("xframe"),Title("Partial Range / Filled Bar chart"),Bins(20)) ;
- // D a t a p l o t t i n g s t y l e s
- // ---------------------------------------
+ // D a t a p l o t t i n g s t y l e s
+ // ---------------------------------------
- // Use sqrt(sum(weights^2)) error instead of Poisson errors
- data->plotOn(frame1,DataError(RooAbsData::SumW2)) ;
+ // Use sqrt(sum(weights^2)) error instead of Poisson errors
+ data->plotOn(frame1,DataError(RooAbsData::SumW2)) ;
- // Remove horizontal error bars
- data->plotOn(frame2,XErrorSize(0)) ;
+ // Remove horizontal error bars
+ data->plotOn(frame2,XErrorSize(0)) ;
- // Blue markers and error bors
- data->plotOn(frame3,MarkerColor(kBlue),LineColor(kBlue)) ;
+ // Blue markers and error bors
+ data->plotOn(frame3,MarkerColor(kBlue),LineColor(kBlue)) ;
- // Filled bar chart
- data->plotOn(frame4,DrawOption("B"),DataError(RooAbsData::None),XErrorSize(0),FillColor(kGray)) ;
+ // Filled bar chart
+ data->plotOn(frame4,DrawOption("B"),DataError(RooAbsData::None),XErrorSize(0),FillColor(kGray)) ;
- // F u n c t i o n p l o t t i n g s t y l e s
- // -----------------------------------------------
+ // F u n c t i o n p l o t t i n g s t y l e s
+ // -----------------------------------------------
- // Change line color to red
- gauss.plotOn(frame1,LineColor(kRed)) ;
+ // Change line color to red
+ gauss.plotOn(frame1,LineColor(kRed)) ;
- // Change line style to dashed
- gauss.plotOn(frame2,LineStyle(kDashed)) ;
+ // Change line style to dashed
+ gauss.plotOn(frame2,LineStyle(kDashed)) ;
- // Filled shapes in green color
- gauss.plotOn(frame3,DrawOption("F"),FillColor(kOrange),MoveToBack()) ;
+ // Filled shapes in green color
+ gauss.plotOn(frame3,DrawOption("F"),FillColor(kOrange),MoveToBack()) ;
- //
- gauss.plotOn(frame4,Range(-8,3),LineColor(kMagenta)) ;
+ //
+ gauss.plotOn(frame4,Range(-8,3),LineColor(kMagenta)) ;
- TCanvas* c = new TCanvas("rf107_plotstyles","rf107_plotstyles",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.6) ; frame4->Draw() ;
+ TCanvas* c = new TCanvas("rf107_plotstyles","rf107_plotstyles",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.6) ; frame4->Draw() ;
}
diff --git a/tutorials/roofit/rf108_plotbinning.C b/tutorials/roofit/rf108_plotbinning.C
index 4f93deed7ae5483eb47345511a07e46d2f4f91cc..6f48aeaaf2c3767f00a2994f353ea96b82859b2c 100644
--- a/tutorials/roofit/rf108_plotbinning.C
+++ b/tutorials/roofit/rf108_plotbinning.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #108
///
/// Plotting unbinned data with alternate and variable binnings
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
///
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussModel.h"
@@ -29,99 +28,99 @@ using namespace RooFit ;
void rf108_plotbinning()
{
- // S e t u p m o d e l
- // ---------------------
+ // 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.) ;
+ // 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) ;
+ 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 bias1("bias1","bias1",0) ;
- RooRealVar sigma1("sigma1","sigma1",0.1) ;
- RooGaussModel gm1("gm1","gauss model 1",dt,bias1,sigma1) ;
+ // Build a gaussian resolution model
+ 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) ;
+ // 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
- // --------------------------------------------
+ // 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) ;
+ // 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
- // -------------------------------------------------------------------------------
+ // 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
+ // 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) ;
+ // 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 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) ;
+ // 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) ;
+ // 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
+ // 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
- // ------------------------------------------------------------------------------------
+ // 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
+ // Make plot of dt distribution of data asymmetry in 'mixState' with variable binning
- // Create binning object with range (-10,10)
- RooBinning abins(-10,10) ;
+ // 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) ;
+ // 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")) ;
+ // 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 custom binning
- data->plotOn(aframe,Asymmetry(mixState),Binning(abins)) ;
+ // Plot mixState asymmetry of data with specified custom binning
+ data->plotOn(aframe,Asymmetry(mixState),Binning(abins)) ;
- // Plot corresponding property of p.d.f
- bmix.plotOn(aframe,Asymmetry(mixState)) ;
+ // 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) ;
+ // 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)
+ // NB: For asymmetry distributions no density corrects are needed (and are thus not applied)
- // Draw plots on canvas
- TCanvas* c = new TCanvas("rf108_plotbinning","rf108_plotbinning",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; dtframe->GetYaxis()->SetTitleOffset(1.6) ; dtframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; aframe->GetYaxis()->SetTitleOffset(1.6) ; aframe->Draw() ;
+ // Draw plots on canvas
+ TCanvas* c = new TCanvas("rf108_plotbinning","rf108_plotbinning",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; dtframe->GetYaxis()->SetTitleOffset(1.6) ; dtframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; aframe->GetYaxis()->SetTitleOffset(1.6) ; aframe->Draw() ;
}
diff --git a/tutorials/roofit/rf109_chi2residpull.C b/tutorials/roofit/rf109_chi2residpull.C
index 29f62b678ae2f231db7c59062e9444219f40d1e4..539925d5dba93ecc0d2fcb0ff3e4984371a3d530 100644
--- a/tutorials/roofit/rf109_chi2residpull.C
+++ b/tutorials/roofit/rf109_chi2residpull.C
@@ -1,19 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #109
///
/// Calculating chi^2 from histograms and curves in RooPlots,
/// making histogram of residual and pull distributions
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,65 +25,65 @@ using namespace RooFit ;
void rf109_chi2residpull()
{
- // S e t u p m o d e l
- // ---------------------
+ // S e t u p m o d e l
+ // ---------------------
+
+ // Create observables
+ RooRealVar x("x","x",-10,10) ;
- // 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) ;
- // 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) ;
- // Generate a sample of 1000 events with sigma=3
- RooDataSet* data = gauss.generate(x,10000) ;
+ // Change sigma to 3.15
+ sigma=3.15 ;
- // 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
+ // ---------------------------------------------------------------------------
- // 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) ;
- // 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
+ // ------------------------------
- // 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()
+ cout << "chi^2 = " << frame1->chiSquare() << endl ;
- // 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()
- cout << "chi^2 = " << frame1->chiSquare() << endl ;
+ // S h o w r e s i d u a l a n d p u l l d i s t s
+ // -------------------------------------------------------
- // 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
+ RooHist* hresid = frame1->residHist() ;
- // Construct a histogram with the residuals of the data w.r.t. the curve
- RooHist* hresid = frame1->residHist() ;
+ // Construct a histogram with the pulls of the data w.r.t the curve
+ RooHist* hpull = frame1->pullHist() ;
- // Construct a histogram with the pulls of the data w.r.t the curve
- RooHist* hpull = frame1->pullHist() ;
+ // 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 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") ;
- // 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") ;
+ TCanvas* c = new TCanvas("rf109_chi2residpull","rf109_chi2residpull",900,300) ;
+ c->Divide(3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
- TCanvas* c = new TCanvas("rf109_chi2residpull","rf109_chi2residpull",900,300) ;
- c->Divide(3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
-
}
diff --git a/tutorials/roofit/rf110_normintegration.C b/tutorials/roofit/rf110_normintegration.C
index 649c0f0213817b389dd1eff70de4d5cbb3b7beaf..8a23289229105c45e61c63da29fa0251ded4bf19 100644
--- a/tutorials/roofit/rf110_normintegration.C
+++ b/tutorials/roofit/rf110_normintegration.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #110
///
/// Examples on normalization of p.d.f.s,
@@ -7,13 +8,11 @@
/// of cumulative distribution functions from p.d.f.s
/// in one dimension
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
@@ -26,63 +25,63 @@ using namespace RooFit ;
void rf110_normintegration()
{
- // S e t u p m o d e l
- // ---------------------
+ // 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)) ;
+
- // Create observables x,y
- RooRealVar x("x","x",-10,10) ;
+ // 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
+ // --------------------------------------------------------------------------------------------------
- // Create p.d.f. gaussx(x,-2,3)
- RooGaussian gx("gx","gx",x,RooConst(-2),RooConst(3)) ;
+ // Return 'raw' unnormalized value of gx
+ cout << "gx = " << gx.getVal() << endl ;
+ // Return value of gx normalized over x in range [-10,10]
+ RooArgSet nset(x) ;
+ cout << "gx_Norm[x] = " << gx.getVal(&nset) << endl ;
- // 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
- // --------------------------------------------------------------------------------------------------
+ // Create object representing integral over gx
+ // which is used to calculate gx_Norm[x] == gx / gx_Int[x]
+ RooAbsReal* igx = gx.createIntegral(x) ;
+ cout << "gx_Int[x] = " << igx->getVal() << endl ;
- // Return 'raw' unnormalized value of gx
- cout << "gx = " << gx.getVal() << endl ;
-
- // Return value of gx normalized over x in range [-10,10]
- RooArgSet nset(x) ;
- cout << "gx_Norm[x] = " << gx.getVal(&nset) << endl ;
- // Create object representing integral over gx
- // which is used to calculate gx_Norm[x] == gx / gx_Int[x]
- RooAbsReal* igx = gx.createIntegral(x) ;
- cout << "gx_Int[x] = " << igx->getVal() << endl ;
+ // 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) ;
- // 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
- // ----------------------------------------------------------------------------
+ // 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")) ;
+ cout << "gx_Int[x|signal]_Norm[x] = " << igx_sig->getVal() << endl ;
- // 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")) ;
- cout << "gx_Int[x|signal]_Norm[x] = " << igx_sig->getVal() << endl ;
+ // 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
+ // -----------------------------------------------------------------------------------------------------
- // 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) ;
- // 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) ;
+ // Plot cdf of gx versus x
+ RooPlot* frame = x.frame(Title("c.d.f of Gaussian p.d.f")) ;
+ gx_cdf->plotOn(frame) ;
- // Draw plot on canvas
- new TCanvas("rf110_normintegration","rf110_normintegration",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ;
- frame->Draw() ;
+ // Draw plot on canvas
+ new TCanvas("rf110_normintegration","rf110_normintegration",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ;
+ frame->Draw() ;
}
diff --git a/tutorials/roofit/rf111_derivatives.C b/tutorials/roofit/rf111_derivatives.C
index 23f8ff8173f0082f275214f39a23ac92ab727881..c3edfd6d515cc53b086ee9cf2f15bb4a1c54d961 100644
--- a/tutorials/roofit/rf111_derivatives.C
+++ b/tutorials/roofit/rf111_derivatives.C
@@ -1,19 +1,18 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'BASIC FUNCTIONALITY' RooFit tutorial macro #111
///
/// Numerical 1st,2nd and 3rd order derivatives w.r.t. observables and parameters
///
/// pdf = gauss(x,m,s)
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -25,68 +24,68 @@ using namespace RooFit ;
void rf111_derivatives()
{
- // 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) ;
-
-
- // C r e a t e a n d p l o t d e r i v a t i v e s w . r . t . x
- // ----------------------------------------------------------------------
-
- // Derivative of normalized gauss(x) w.r.t. observable x
- RooAbsReal* dgdx = gauss.derivative(x,1) ;
-
- // Second and third derivative of normalized gauss(x) w.r.t. observable x
- RooAbsReal* d2gdx2 = gauss.derivative(x,2) ;
- RooAbsReal* d3gdx3 = gauss.derivative(x,3) ;
-
- // Construct plot frame in 'x'
- RooPlot* xframe = x.frame(Title("d(Gauss)/dx")) ;
-
- // Plot gauss in frame (i.e. in x)
- gauss.plotOn(xframe) ;
-
- // Plot derivatives in same frame
- dgdx->plotOn(xframe,LineColor(kMagenta)) ;
- d2gdx2->plotOn(xframe,LineColor(kRed)) ;
- d3gdx3->plotOn(xframe,LineColor(kOrange)) ;
-
-
- // C r e a t e a n d p l o t d e r i v a t i v e s w . r . t . s i g m a
- // ------------------------------------------------------------------------------
-
- // Derivative of normalized gauss(x) w.r.t. parameter sigma
- RooAbsReal* dgds = gauss.derivative(sigma,1) ;
-
- // Second and third derivative of normalized gauss(x) w.r.t. parameter sigma
- RooAbsReal* d2gds2 = gauss.derivative(sigma,2) ;
- RooAbsReal* d3gds3 = gauss.derivative(sigma,3) ;
-
- // Construct plot frame in 'sigma'
- RooPlot* sframe = sigma.frame(Title("d(Gauss)/d(sigma)"),Range(0.,2.)) ;
-
- // Plot gauss in frame (i.e. in x)
- gauss.plotOn(sframe) ;
-
- // Plot derivatives in same frame
- dgds->plotOn(sframe,LineColor(kMagenta)) ;
- d2gds2->plotOn(sframe,LineColor(kRed)) ;
- d3gds3->plotOn(sframe,LineColor(kOrange)) ;
-
-
-
- // Draw all frames on a canvas
- TCanvas* c = new TCanvas("rf111_derivatives","rf111_derivatives",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; sframe->GetYaxis()->SetTitleOffset(1.6) ; sframe->Draw() ;
-
+ // 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) ;
+
+
+ // C r e a t e a n d p l o t d e r i v a t i v e s w . r . t . x
+ // ----------------------------------------------------------------------
+
+ // Derivative of normalized gauss(x) w.r.t. observable x
+ RooAbsReal* dgdx = gauss.derivative(x,1) ;
+
+ // Second and third derivative of normalized gauss(x) w.r.t. observable x
+ RooAbsReal* d2gdx2 = gauss.derivative(x,2) ;
+ RooAbsReal* d3gdx3 = gauss.derivative(x,3) ;
+
+ // Construct plot frame in 'x'
+ RooPlot* xframe = x.frame(Title("d(Gauss)/dx")) ;
+
+ // Plot gauss in frame (i.e. in x)
+ gauss.plotOn(xframe) ;
+
+ // Plot derivatives in same frame
+ dgdx->plotOn(xframe,LineColor(kMagenta)) ;
+ d2gdx2->plotOn(xframe,LineColor(kRed)) ;
+ d3gdx3->plotOn(xframe,LineColor(kOrange)) ;
+
+
+ // C r e a t e a n d p l o t d e r i v a t i v e s w . r . t . s i g m a
+ // ------------------------------------------------------------------------------
+
+ // Derivative of normalized gauss(x) w.r.t. parameter sigma
+ RooAbsReal* dgds = gauss.derivative(sigma,1) ;
+
+ // Second and third derivative of normalized gauss(x) w.r.t. parameter sigma
+ RooAbsReal* d2gds2 = gauss.derivative(sigma,2) ;
+ RooAbsReal* d3gds3 = gauss.derivative(sigma,3) ;
+
+ // Construct plot frame in 'sigma'
+ RooPlot* sframe = sigma.frame(Title("d(Gauss)/d(sigma)"),Range(0.,2.)) ;
+
+ // Plot gauss in frame (i.e. in x)
+ gauss.plotOn(sframe) ;
+
+ // Plot derivatives in same frame
+ dgds->plotOn(sframe,LineColor(kMagenta)) ;
+ d2gds2->plotOn(sframe,LineColor(kRed)) ;
+ d3gds3->plotOn(sframe,LineColor(kOrange)) ;
+
+
+
+ // Draw all frames on a canvas
+ TCanvas* c = new TCanvas("rf111_derivatives","rf111_derivatives",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; sframe->GetYaxis()->SetTitleOffset(1.6) ; sframe->Draw() ;
+
}
diff --git a/tutorials/roofit/rf201_composite.C b/tutorials/roofit/rf201_composite.C
index 8c73faa0eb44b9f767db9eb6eabe64e2b2b15841..bea4f61baa88d582338b43d80c662bdc6b246f7f 100644
--- a/tutorials/roofit/rf201_composite.C
+++ b/tutorials/roofit/rf201_composite.C
@@ -1,19 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// '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)))
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,99 +25,99 @@ using namespace RooFit ;
void rf201_composite()
{
- // S e t u p c o m p o n e n t p d f s
- // ---------------------------------------
+ // 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) ;
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
- // 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",-0.2,0.,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 //
+ ////////////////////////////////////////////////////
- ////////////////////////////////////////////////////
- // 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 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) ;
- // 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) ;
- // S a m p l e , f i t a n d p l o t m o d e l
- // ---------------------------------------------------
+ // Fit model to data
+ model.fitTo(*data) ;
- // Generate a data sample of 1000 events in x from model
- RooDataSet *data = model.generate(x,1000) ;
+ // Plot data and PDF overlaid
+ RooPlot* xframe = x.frame(Title("Example of composite pdf=(sig1+sig2)+bkg")) ;
+ data->plotOn(xframe) ;
+ model.plotOn(xframe) ;
- // 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 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)) ;
- // Overlay the background+sig2 components of model with a dotted line
- model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted)) ;
+ // Print structure of composite p.d.f.
+ model.Print("t") ;
- // Print structure of composite p.d.f.
- model.Print("t") ;
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
+ // 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 //
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
- ////////////////////////////////////////////////////////////////////////////////////////////////////
- // 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)
+ //
+ RooAddPdf model2("model","g1+g2+a",RooArgList(bkg,sig1,sig2),RooArgList(bkgfrac,sig1frac),kTRUE) ;
- // Construct sum of models on one go using recursive fraction interpretations
- //
- // model2 = bkg + (sig1 + sig2)
- //
- RooAddPdf model2("model","g1+g2+a",RooArgList(bkg,sig1,sig2),RooArgList(bkgfrac,sig1frac),kTRUE) ;
+ // NB: Each coefficient is interpreted as the fraction of the
+ // left-hand component of the i-th recursive sum, i.e.
+ //
+ // sum4 = A + ( B + ( C + D) with fraction fA, fB and fC expands to
+ //
+ // sum4 = fA*A + (1-fA)*(fB*B + (1-fB)*(fC*C + (1-fC)*D))
- // NB: Each coefficient is interpreted as the fraction of the
- // left-hand component of the i-th recursive sum, i.e.
- //
- // sum4 = A + ( B + ( C + D) with fraction fA, fB and fC expands to
- //
- // sum4 = fA*A + (1-fA)*(fB*B + (1-fB)*(fC*C + (1-fC)*D))
+ // P l o t r e c u r s i v e a d d i t i o n m o d e l
+ // ---------------------------------------------------------
+ model2.plotOn(xframe,LineColor(kRed),LineStyle(kDashed)) ;
+ model2.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineColor(kRed),LineStyle(kDashed)) ;
+ model2.Print("t") ;
- // P l o t r e c u r s i v e a d d i t i o n m o d e l
- // ---------------------------------------------------------
- model2.plotOn(xframe,LineColor(kRed),LineStyle(kDashed)) ;
- model2.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineColor(kRed),LineStyle(kDashed)) ;
- model2.Print("t") ;
-
- // Draw the frame on the canvas
- new TCanvas("rf201_composite","rf201_composite",600,600) ;
- gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ // Draw the frame on the canvas
+ new TCanvas("rf201_composite","rf201_composite",600,600) ;
+ gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
}
diff --git a/tutorials/roofit/rf202_extendedmlfit.C b/tutorials/roofit/rf202_extendedmlfit.C
index f32a4c9886323a24eb8809aff306193d4d518464..6dfbb866ff0113b6502106f0ef68b3ab10c5aac9 100644
--- a/tutorials/roofit/rf202_extendedmlfit.C
+++ b/tutorials/roofit/rf202_extendedmlfit.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #202
///
/// Setting up an extended maximum likelihood fit
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,93 +25,93 @@ using namespace RooFit ;
void rf202_extendedmlfit()
{
- // S e t u p c o m p o n e n t p d f s
- // ---------------------------------------
+ // 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) ;
+ // 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) ;
+ // 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",-0.2,0.,1.) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+ RooGaussian sig1("sig1","Signal component 1",x,mean,sigma1) ;
+ RooGaussian sig2("sig2","Signal component 2",x,mean,sigma2) ;
- // 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) ;
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
- /////////////////////
- // M E T H O D 1 //
- /////////////////////
+ // 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) ;
+ /////////////////////
+ // M E T H O D 1 //
+ /////////////////////
- // C o n s t r u c t e x t e n d e d c o m p o s i t e m o d e l
- // -------------------------------------------------------------------
- // Sum the composite signal and background into an extended pdf nsig*sig+nbkg*bkg
- RooRealVar nsig("nsig","number of signal events",500,0.,10000) ;
- RooRealVar nbkg("nbkg","number of background events",500,0,10000) ;
- RooAddPdf model("model","(g1+g2)+a",RooArgList(bkg,sig),RooArgList(nbkg,nsig)) ;
+ // C o n s t r u c t e x t e n d e d c o m p o s i t e m o d e l
+ // -------------------------------------------------------------------
+ // Sum the composite signal and background into an extended pdf nsig*sig+nbkg*bkg
+ RooRealVar nsig("nsig","number of signal events",500,0.,10000) ;
+ RooRealVar nbkg("nbkg","number of background events",500,0,10000) ;
+ RooAddPdf model("model","(g1+g2)+a",RooArgList(bkg,sig),RooArgList(nbkg,nsig)) ;
- // S a m p l e , f i t a n d p l o t e x t e n d e d m o d e l
- // ---------------------------------------------------------------------
- // Generate a data sample of expected number events in x from model
- // = model.expectedEvents() = nsig+nbkg
- RooDataSet *data = model.generate(x) ;
+ // S a m p l e , f i t a n d p l o t e x t e n d e d m o d e l
+ // ---------------------------------------------------------------------
- // Fit model to data, extended ML term automatically included
- model.fitTo(*data) ;
-
- // Plot data and PDF overlaid, use expected number of events for p.d.f projection normalization
- // rather than observed number of events (==data->numEntries())
- RooPlot* xframe = x.frame(Title("extended ML fit example")) ;
- data->plotOn(xframe) ;
- model.plotOn(xframe,Normalization(1.0,RooAbsReal::RelativeExpected)) ;
+ // Generate a data sample of expected number events in x from model
+ // = model.expectedEvents() = nsig+nbkg
+ RooDataSet *data = model.generate(x) ;
+
+ // Fit model to data, extended ML term automatically included
+ model.fitTo(*data) ;
+
+ // Plot data and PDF overlaid, use expected number of events for p.d.f projection normalization
+ // rather than observed number of events (==data->numEntries())
+ RooPlot* xframe = x.frame(Title("extended ML fit example")) ;
+ data->plotOn(xframe) ;
+ model.plotOn(xframe,Normalization(1.0,RooAbsReal::RelativeExpected)) ;
- // Overlay the background component of model with a dashed line
- model.plotOn(xframe,Components(bkg),LineStyle(kDashed),Normalization(1.0,RooAbsReal::RelativeExpected)) ;
+ // Overlay the background component of model with a dashed line
+ model.plotOn(xframe,Components(bkg),LineStyle(kDashed),Normalization(1.0,RooAbsReal::RelativeExpected)) ;
- // Overlay the background+sig2 components of model with a dotted line
- model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted),Normalization(1.0,RooAbsReal::RelativeExpected)) ;
+ // Overlay the background+sig2 components of model with a dotted line
+ model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted),Normalization(1.0,RooAbsReal::RelativeExpected)) ;
- // Print structure of composite p.d.f.
- model.Print("t") ;
+ // Print structure of composite p.d.f.
+ model.Print("t") ;
- /////////////////////
- // M E T H O D 2 //
- /////////////////////
+ /////////////////////
+ // M E T H O D 2 //
+ /////////////////////
- // C o n s t r u c t e x t e n d e d c o m p o n e n t s f i r s t
- // ---------------------------------------------------------------------
+ // C o n s t r u c t e x t e n d e d c o m p o n e n t s f i r s t
+ // ---------------------------------------------------------------------
- // Associated nsig/nbkg as expected number of events with sig/bkg
- RooExtendPdf esig("esig","extended signal p.d.f",sig,nsig) ;
- RooExtendPdf ebkg("ebkg","extended background p.d.f",bkg,nbkg) ;
+ // Associated nsig/nbkg as expected number of events with sig/bkg
+ RooExtendPdf esig("esig","extended signal p.d.f",sig,nsig) ;
+ RooExtendPdf ebkg("ebkg","extended background p.d.f",bkg,nbkg) ;
- // S u m e x t e n d e d c o m p o n e n t s w i t h o u t c o e f s
- // -------------------------------------------------------------------------
+ // S u m e x t e n d e d c o m p o n e n t s w i t h o u t c o e f s
+ // -------------------------------------------------------------------------
- // Construct sum of two extended p.d.f. (no coefficients required)
- RooAddPdf model2("model2","(g1+g2)+a",RooArgList(ebkg,esig)) ;
+ // Construct sum of two extended p.d.f. (no coefficients required)
+ RooAddPdf model2("model2","(g1+g2)+a",RooArgList(ebkg,esig)) ;
- // Draw the frame on the canvas
- new TCanvas("rf202_composite","rf202_composite",600,600) ;
- gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ // Draw the frame on the canvas
+ new TCanvas("rf202_composite","rf202_composite",600,600) ;
+ gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
}
diff --git a/tutorials/roofit/rf203_ranges.C b/tutorials/roofit/rf203_ranges.C
index 89cb4487fae94f3f8287549b6407a13adbc7ee9d..26b95ca5a99dc5d5e8ff41c90b42dd901f523d86 100644
--- a/tutorials/roofit/rf203_ranges.C
+++ b/tutorials/roofit/rf203_ranges.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #203
///
/// Fitting and plotting in sub ranges
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,62 +27,62 @@ using namespace RooFit ;
void rf203_ranges()
{
- // S e t u p m o d e l
- // ---------------------
+ // S e t u p m o d e l
+ // ---------------------
+
+ // Construct observables x
+ RooRealVar x("x","x",-10,10) ;
+
+ // Construct gaussx(x,mx,1)
+ RooRealVar mx("mx","mx",0,-10,10) ;
+ RooGaussian gx("gx","gx",x,mx,RooConst(1)) ;
+
+ // Construct px = 1 (flat in x)
+ RooPolynomial px("px","px",x) ;
- // Construct observables x
- RooRealVar x("x","x",-10,10) ;
-
- // Construct gaussx(x,mx,1)
- RooRealVar mx("mx","mx",0,-10,10) ;
- RooGaussian gx("gx","gx",x,mx,RooConst(1)) ;
+ // Construct model = f*gx + (1-f)px
+ RooRealVar f("f","f",0.,1.) ;
+ RooAddPdf model("model","model",RooArgList(gx,px),f) ;
- // Construct px = 1 (flat in x)
- RooPolynomial px("px","px",x) ;
+ // Generated 10000 events in (x,y) from p.d.f. model
+ RooDataSet* modelData = model.generate(x,10000) ;
- // Construct model = f*gx + (1-f)px
- RooRealVar f("f","f",0.,1.) ;
- RooAddPdf model("model","model",RooArgList(gx,px),f) ;
+ // F i t f u l l r a n g e
+ // ---------------------------
- // Generated 10000 events in (x,y) from p.d.f. model
- RooDataSet* modelData = model.generate(x,10000) ;
+ // Fit p.d.f to all data
+ RooFitResult* r_full = model.fitTo(*modelData,Save(kTRUE)) ;
- // F i t f u l l r a n g e
- // ---------------------------
- // Fit p.d.f to all data
- RooFitResult* r_full = model.fitTo(*modelData,Save(kTRUE)) ;
+ // F i t p a r t i a l r a n g e
+ // ----------------------------------
+ // Define "signal" range in x as [-3,3]
+ x.setRange("signal",-3,3) ;
- // F i t p a r t i a l r a n g e
- // ----------------------------------
+ // Fit p.d.f only to data in "signal" range
+ RooFitResult* r_sig = model.fitTo(*modelData,Save(kTRUE),Range("signal")) ;
- // Define "signal" range in x as [-3,3]
- x.setRange("signal",-3,3) ;
- // Fit p.d.f only to data in "signal" range
- RooFitResult* r_sig = model.fitTo(*modelData,Save(kTRUE),Range("signal")) ;
+ // P l o t / p r i n t r e s u l t s
+ // ---------------------------------------
+ // Make plot frame in x and add data and fitted model
+ RooPlot* frame = x.frame(Title("Fitting a sub range")) ;
+ modelData->plotOn(frame) ;
+ model.plotOn(frame,Range("Full"),LineStyle(kDashed),LineColor(kRed)) ; // Add shape in full ranged dashed
+ model.plotOn(frame) ; // By default only fitted range is shown
- // P l o t / p r i n t r e s u l t s
- // ---------------------------------------
+ // Print fit results
+ cout << "result of fit on all data " << endl ;
+ r_full->Print() ;
+ cout << "result of fit in in signal region (note increased error on signal fraction)" << endl ;
+ r_sig->Print() ;
- // Make plot frame in x and add data and fitted model
- RooPlot* frame = x.frame(Title("Fitting a sub range")) ;
- modelData->plotOn(frame) ;
- model.plotOn(frame,Range("Full"),LineStyle(kDashed),LineColor(kRed)) ; // Add shape in full ranged dashed
- model.plotOn(frame) ; // By default only fitted range is shown
-
- // Print fit results
- cout << "result of fit on all data " << endl ;
- r_full->Print() ;
- cout << "result of fit in in signal region (note increased error on signal fraction)" << endl ;
- r_sig->Print() ;
+ // Draw frame on canvas
+ new TCanvas("rf203_ranges","rf203_ranges",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- // Draw frame on canvas
- new TCanvas("rf203_ranges","rf203_ranges",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ return ;
- return ;
-
}
diff --git a/tutorials/roofit/rf204_extrangefit.C b/tutorials/roofit/rf204_extrangefit.C
index 5f3e22619777b1f96ef400d435cb281306d41ee5..be277c796d2c7b07a1e4a5141b9926697af8232c 100644
--- a/tutorials/roofit/rf204_extrangefit.C
+++ b/tutorials/roofit/rf204_extrangefit.C
@@ -1,19 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #204
///
/// Extended maximum likelihood fit with alternate range definition
/// for observed number of events.
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,59 +28,59 @@ void rf204_extrangefit()
{
- // S e t u p c o m p o n e n t p d f s
- // ---------------------------------------
+ // 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) ;
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
+ RooGaussian sig1("sig1","Signal component 1",x,mean,sigma1) ;
+ RooGaussian sig2("sig2","Signal component 2",x,mean,sigma2) ;
- // 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) ;
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
- 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",-0.2,0.,1.) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+ // 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) ;
- // 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) ;
+ // C o n s t r u c t e x t e n d e d c o m p s wi t h r a n g e s p e c
+ // ------------------------------------------------------------------------------
- // C o n s t r u c t e x t e n d e d c o m p s wi t h r a n g e s p e c
- // ------------------------------------------------------------------------------
+ // Define signal range in which events counts are to be defined
+ x.setRange("signalRange",4,6) ;
- // Define signal range in which events counts are to be defined
- x.setRange("signalRange",4,6) ;
+ // Associated nsig/nbkg as expected number of events with sig/bkg _in_the_range_ "signalRange"
+ RooRealVar nsig("nsig","number of signal events in signalRange",500,0.,10000) ;
+ RooRealVar nbkg("nbkg","number of background events in signalRange",500,0,10000) ;
+ RooExtendPdf esig("esig","extended signal p.d.f",sig,nsig,"signalRange") ;
+ RooExtendPdf ebkg("ebkg","extended background p.d.f",bkg,nbkg,"signalRange") ;
- // Associated nsig/nbkg as expected number of events with sig/bkg _in_the_range_ "signalRange"
- RooRealVar nsig("nsig","number of signal events in signalRange",500,0.,10000) ;
- RooRealVar nbkg("nbkg","number of background events in signalRange",500,0,10000) ;
- RooExtendPdf esig("esig","extended signal p.d.f",sig,nsig,"signalRange") ;
- RooExtendPdf ebkg("ebkg","extended background p.d.f",bkg,nbkg,"signalRange") ;
+ // S u m e x t e n d e d c o m p o n e n t s
+ // ---------------------------------------------
- // S u m e x t e n d e d c o m p o n e n t s
- // ---------------------------------------------
+ // Construct sum of two extended p.d.f. (no coefficients required)
+ RooAddPdf model("model","(g1+g2)+a",RooArgList(ebkg,esig)) ;
- // Construct sum of two extended p.d.f. (no coefficients required)
- RooAddPdf model("model","(g1+g2)+a",RooArgList(ebkg,esig)) ;
-
- // S a m p l e d a t a , f i t m o d e l
- // -------------------------------------------
+ // S a m p l e d a t a , f i t m o d e l
+ // -------------------------------------------
- // Generate 1000 events from model so that nsig,nbkg come out to numbers <<500 in fit
- RooDataSet *data = model.generate(x,1000) ;
+ // Generate 1000 events from model so that nsig,nbkg come out to numbers <<500 in fit
+ RooDataSet *data = model.generate(x,1000) ;
- // Perform unbinned extended ML fit to data
- RooFitResult* r = model.fitTo(*data,Extended(kTRUE),Save()) ;
- r->Print() ;
+ // Perform unbinned extended ML fit to data
+ RooFitResult* r = model.fitTo(*data,Extended(kTRUE),Save()) ;
+ r->Print() ;
}
diff --git a/tutorials/roofit/rf205_compplot.C b/tutorials/roofit/rf205_compplot.C
index f34a87c14507d662abbefab08b955e01d226d220..04a287f3271f01bbe4c1467ed2a139603995700c 100644
--- a/tutorials/roofit/rf205_compplot.C
+++ b/tutorials/roofit/rf205_compplot.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #205
///
/// Options for plotting components of composite p.d.f.s.
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,94 +25,94 @@ using namespace RooFit ;
void rf205_compplot()
{
- // S e t u p c o m p o s i t e p d f
- // --------------------------------------
+ // S e t u p c o m p o s i t e p d f
+ // --------------------------------------
+
+ // 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) ;
+
+ // 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) ;
+
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
+
+ // Build expontential pdf
+ RooRealVar alpha("alpha","alpha",-1) ;
+ RooExponential bkg2("bkg2","Background 2",x,alpha) ;
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
+ // Sum the background components into a composite background p.d.f.
+ RooRealVar bkg1frac("sig1frac","fraction of component 1 in background",0.2,0.,1.) ;
+ RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),sig1frac) ;
- // 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) ;
+ // 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) ;
- // 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) ;
-
- // Build Chebychev polynomial p.d.f.
- RooRealVar a0("a0","a0",0.5,0.,1.) ;
- RooRealVar a1("a1","a1",-0.2,0.,1.) ;
- RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
- // Build expontential pdf
- RooRealVar alpha("alpha","alpha",-1) ;
- RooExponential bkg2("bkg2","Background 2",x,alpha) ;
- // Sum the background components into a composite background p.d.f.
- RooRealVar bkg1frac("sig1frac","fraction of component 1 in background",0.2,0.,1.) ;
- RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),sig1frac) ;
-
- // 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 e t u p b a s i c p l o t w i t h d a t a a n d f u l l p d f
+ // ------------------------------------------------------------------------------
+ // Generate a data sample of 1000 events in x from model
+ RooDataSet *data = model.generate(x,1000) ;
+ // Plot data and complete PDF overlaid
+ RooPlot* xframe = x.frame(Title("Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")) ;
+ data->plotOn(xframe) ;
+ model.plotOn(xframe) ;
- // S e t u p b a s i c p l o t w i t h d a t a a n d f u l l p d f
- // ------------------------------------------------------------------------------
-
- // Generate a data sample of 1000 events in x from model
- RooDataSet *data = model.generate(x,1000) ;
+ // Clone xframe for use below
+ RooPlot* xframe2 = (RooPlot*) xframe->Clone("xframe2") ;
- // Plot data and complete PDF overlaid
- RooPlot* xframe = x.frame(Title("Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")) ;
- data->plotOn(xframe) ;
- model.plotOn(xframe) ;
- // Clone xframe for use below
- RooPlot* xframe2 = (RooPlot*) xframe->Clone("xframe2") ;
+ // M a k e c o m p o n e n t b y o b j e c t r e f e r e n c e
+ // --------------------------------------------------------------------
+ // Plot single background component specified by object reference
+ model.plotOn(xframe,Components(bkg),LineColor(kRed)) ;
- // M a k e c o m p o n e n t b y o b j e c t r e f e r e n c e
- // --------------------------------------------------------------------
+ // Plot single background component specified by object reference
+ model.plotOn(xframe,Components(bkg2),LineStyle(kDashed),LineColor(kRed)) ;
- // Plot single background component specified by object reference
- model.plotOn(xframe,Components(bkg),LineColor(kRed)) ;
+ // Plot multiple background components specified by object reference
+ // Note that specified components may occur at any level in object tree
+ // (e.g bkg is component of 'model' and 'sig2' is component 'sig')
+ model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted)) ;
- // Plot single background component specified by object reference
- model.plotOn(xframe,Components(bkg2),LineStyle(kDashed),LineColor(kRed)) ;
- // Plot multiple background components specified by object reference
- // Note that specified components may occur at any level in object tree
- // (e.g bkg is component of 'model' and 'sig2' is component 'sig')
- model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted)) ;
+ // M a k e c o m p o n e n t b y n a m e / r e g e x p
+ // ------------------------------------------------------------
+ // Plot single background component specified by name
+ model.plotOn(xframe2,Components("bkg"),LineColor(kCyan)) ;
- // M a k e c o m p o n e n t b y n a m e / r e g e x p
- // ------------------------------------------------------------
+ // Plot multiple background components specified by name
+ model.plotOn(xframe2,Components("bkg1,sig2"),LineStyle(kDotted),LineColor(kCyan)) ;
- // Plot single background component specified by name
- model.plotOn(xframe2,Components("bkg"),LineColor(kCyan)) ;
+ // Plot multiple background components specified by regular expression on name
+ model.plotOn(xframe2,Components("sig*"),LineStyle(kDashed),LineColor(kCyan)) ;
- // Plot multiple background components specified by name
- model.plotOn(xframe2,Components("bkg1,sig2"),LineStyle(kDotted),LineColor(kCyan)) ;
+ // Plot multiple background components specified by multiple regular expressions on name
+ model.plotOn(xframe2,Components("bkg1,sig*"),LineStyle(kDashed),LineColor(kYellow),Invisible()) ;
- // Plot multiple background components specified by regular expression on name
- model.plotOn(xframe2,Components("sig*"),LineStyle(kDashed),LineColor(kCyan)) ;
-
- // Plot multiple background components specified by multiple regular expressions on name
- model.plotOn(xframe2,Components("bkg1,sig*"),LineStyle(kDashed),LineColor(kYellow),Invisible()) ;
-
- // Draw the frame on the canvas
- TCanvas* c = new TCanvas("rf205_compplot","rf205_compplot",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.4) ; xframe2->Draw() ;
+ // Draw the frame on the canvas
+ TCanvas* c = new TCanvas("rf205_compplot","rf205_compplot",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.4) ; xframe2->Draw() ;
}
diff --git a/tutorials/roofit/rf206_treevistools.C b/tutorials/roofit/rf206_treevistools.C
index df7f0af6a406d376b33e5d9f69e994c18432e485..7e211be80a4e3a0f29c023a8ae78ce6aa4d57c95 100644
--- a/tutorials/roofit/rf206_treevistools.C
+++ b/tutorials/roofit/rf206_treevistools.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #206
///
/// Tools for visualization of RooAbsArg expression trees
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,64 +24,64 @@ using namespace RooFit ;
void rf206_treevistools()
{
- // S e t u p c o m p o s i t e p d f
- // --------------------------------------
-
- // 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) ;
-
- // 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) ;
-
- // Build Chebychev polynomial p.d.f.
- RooRealVar a0("a0","a0",0.5,0.,1.) ;
- RooRealVar a1("a1","a1",-0.2,0.,1.) ;
- RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
-
- // Build expontential pdf
- RooRealVar alpha("alpha","alpha",-1) ;
- RooExponential bkg2("bkg2","Background 2",x,alpha) ;
-
- // Sum the background components into a composite background p.d.f.
- RooRealVar bkg1frac("bkg1frac","fraction of component 1 in background",0.2,0.,1.) ;
- RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),bkg1frac) ;
-
- // 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) ;
-
-
- // P r i n t c o m p o s i t e t r e e i n A S C I I
- // -----------------------------------------------------------
-
- // Print tree to stdout
- model.Print("t") ;
-
- // Print tree to file
- model.printCompactTree("","rf206_asciitree.txt") ;
-
-
- // D r a w c o m p o s i t e t r e e g r a p h i c a l l y
- // -------------------------------------------------------------
-
- // Print GraphViz DOT file with representation of tree
- model.graphVizTree("rf206_model.dot") ;
-
- // Make graphic output file with one of the GraphViz tools
- // (freely available from www.graphviz.org)
- //
- // 'Top-to-bottom graph'
- // unix> dot -Tgif -o rf207_model_dot.gif rf207_model.dot
- //
- // 'Spring-model graph'
- // unix> fdp -Tgif -o rf207_model_fdp.gif rf207_model.dot
+ // S e t u p c o m p o s i t e p d f
+ // --------------------------------------
+
+ // 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) ;
+
+ // 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) ;
+
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
+
+ // Build expontential pdf
+ RooRealVar alpha("alpha","alpha",-1) ;
+ RooExponential bkg2("bkg2","Background 2",x,alpha) ;
+
+ // Sum the background components into a composite background p.d.f.
+ RooRealVar bkg1frac("bkg1frac","fraction of component 1 in background",0.2,0.,1.) ;
+ RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),bkg1frac) ;
+
+ // 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) ;
+
+
+ // P r i n t c o m p o s i t e t r e e i n A S C I I
+ // -----------------------------------------------------------
+
+ // Print tree to stdout
+ model.Print("t") ;
+
+ // Print tree to file
+ model.printCompactTree("","rf206_asciitree.txt") ;
+
+
+ // D r a w c o m p o s i t e t r e e g r a p h i c a l l y
+ // -------------------------------------------------------------
+
+ // Print GraphViz DOT file with representation of tree
+ model.graphVizTree("rf206_model.dot") ;
+
+ // Make graphic output file with one of the GraphViz tools
+ // (freely available from www.graphviz.org)
+ //
+ // 'Top-to-bottom graph'
+ // unix> dot -Tgif -o rf207_model_dot.gif rf207_model.dot
+ //
+ // 'Spring-model graph'
+ // unix> fdp -Tgif -o rf207_model_fdp.gif rf207_model.dot
}
diff --git a/tutorials/roofit/rf207_comptools.C b/tutorials/roofit/rf207_comptools.C
index 039a1e13a62f036768336a09d8bd701af267da6a..63ef80109486007562170ef3df279def1d3af2ee 100644
--- a/tutorials/roofit/rf207_comptools.C
+++ b/tutorials/roofit/rf207_comptools.C
@@ -1,17 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #207
///
/// Tools and utilities for manipulation of composite objects
///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,116 +27,116 @@ using namespace RooFit ;
void rf207_comptools()
{
-
- // S e t u p c o m p o s i t e p d f, d a t a s e t
- // --------------------------------------------------------
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
+ // S e t u p c o m p o s i t e p d f, d a t a s e t
+ // --------------------------------------------------------
+
+ // 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 sigma("sigma","width of gaussians",0.5) ;
+ RooGaussian sig("sig","Signal component 1",x,mean,sigma) ;
+
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
+
+ // Build expontential pdf
+ RooRealVar alpha("alpha","alpha",-1) ;
+ RooExponential bkg2("bkg2","Background 2",x,alpha) ;
+
+ // Sum the background components into a composite background p.d.f.
+ RooRealVar bkg1frac("bkg1frac","fraction of component 1 in background",0.2,0.,1.) ;
+ RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),bkg1frac) ;
+
+ // 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) ;
- // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
- RooRealVar mean("mean","mean of gaussians",5) ;
- RooRealVar sigma("sigma","width of gaussians",0.5) ;
- RooGaussian sig("sig","Signal component 1",x,mean,sigma) ;
-
- // Build Chebychev polynomial p.d.f.
- RooRealVar a0("a0","a0",0.5,0.,1.) ;
- RooRealVar a1("a1","a1",-0.2,0.,1.) ;
- RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
- // Build expontential pdf
- RooRealVar alpha("alpha","alpha",-1) ;
- RooExponential bkg2("bkg2","Background 2",x,alpha) ;
- // Sum the background components into a composite background p.d.f.
- RooRealVar bkg1frac("bkg1frac","fraction of component 1 in background",0.2,0.,1.) ;
- RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),bkg1frac) ;
-
- // 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) ;
+ // Create dummy dataset that has more observables than the above pdf
+ RooRealVar y("y","y",-10,10) ;
+ RooDataSet data("data","data",RooArgSet(x,y)) ;
- // Create dummy dataset that has more observables than the above pdf
- RooRealVar y("y","y",-10,10) ;
- RooDataSet data("data","data",RooArgSet(x,y)) ;
+ //////////////////////////////////////////////////////////
+ // B a s i c i n f o r m a t i o n r e q u e s t s //
+ //////////////////////////////////////////////////////////
+ // G e t l i s t o f o b s e r v a b l e s
+ // ---------------------------------------------
- //////////////////////////////////////////////////////////
- // B a s i c i n f o r m a t i o n r e q u e s t s //
- //////////////////////////////////////////////////////////
+ // Get list of observables of pdf in context of a dataset
+ //
+ // Observables are define each context as the variables
+ // shared between a model and a dataset. In this case
+ // that is the variable 'x'
+ RooArgSet* model_obs = model.getObservables(data) ;
+ model_obs->Print("v") ;
- // G e t l i s t o f o b s e r v a b l e s
- // ---------------------------------------------
- // Get list of observables of pdf in context of a dataset
- //
- // Observables are define each context as the variables
- // shared between a model and a dataset. In this case
- // that is the variable 'x'
+ // G e t l i s t o f p a r a m e t e r s
+ // -------------------------------------------
- RooArgSet* model_obs = model.getObservables(data) ;
- model_obs->Print("v") ;
-
+ // Get list of parameters, given list of observables
+ RooArgSet* model_params = model.getParameters(x) ;
+ model_params->Print("v") ;
- // G e t l i s t o f p a r a m e t e r s
- // -------------------------------------------
+ // Get list of parameters, given a dataset
+ // (Gives identical results to operation above)
+ RooArgSet* model_params2 = model.getParameters(data) ;
+ model_params2->Print() ;
- // Get list of parameters, given list of observables
- RooArgSet* model_params = model.getParameters(x) ;
- model_params->Print("v") ;
- // Get list of parameters, given a dataset
- // (Gives identical results to operation above)
- RooArgSet* model_params2 = model.getParameters(data) ;
- model_params2->Print() ;
+ // G e t l i s t o f c o m p o n e n t s
+ // -------------------------------------------
+ // Get list of component objects, including top-level node
+ RooArgSet* model_comps = model.getComponents() ;
+ model_comps->Print("v") ;
- // G e t l i s t o f c o m p o n e n t s
- // -------------------------------------------
- // Get list of component objects, including top-level node
- RooArgSet* model_comps = model.getComponents() ;
- model_comps->Print("v") ;
+ /////////////////////////////////////////////////////////////////////////////////////
+ // M o d i f i c a t i o n s t o s t r u c t u r e o f c o m p o s i t e s //
+ /////////////////////////////////////////////////////////////////////////////////////
- /////////////////////////////////////////////////////////////////////////////////////
- // M o d i f i c a t i o n s t o s t r u c t u r e o f c o m p o s i t e s //
- /////////////////////////////////////////////////////////////////////////////////////
+ // Create a second Gaussian
+ RooRealVar sigma2("sigma2","width of gaussians",1) ;
+ RooGaussian sig2("sig2","Signal component 1",x,mean,sigma2) ;
+ // Create a sum of the original Gaussian plus the new second Gaussian
+ RooRealVar sig1frac("sig1frac","fraction of component 1 in signal",0.8,0.,1.) ;
+ RooAddPdf sigsum("sigsum","sig+sig2",RooArgList(sig,sig2),sig1frac) ;
- // Create a second Gaussian
- RooRealVar sigma2("sigma2","width of gaussians",1) ;
- RooGaussian sig2("sig2","Signal component 1",x,mean,sigma2) ;
+ // Construct a customizer utility to customize model
+ RooCustomizer cust(model,"cust") ;
- // Create a sum of the original Gaussian plus the new second Gaussian
- RooRealVar sig1frac("sig1frac","fraction of component 1 in signal",0.8,0.,1.) ;
- RooAddPdf sigsum("sigsum","sig+sig2",RooArgList(sig,sig2),sig1frac) ;
-
- // Construct a customizer utility to customize model
- RooCustomizer cust(model,"cust") ;
+ // Instruct the customizer to replace node 'sig' with node 'sigsum'
+ cust.replaceArg(sig,sigsum) ;
- // Instruct the customizer to replace node 'sig' with node 'sigsum'
- cust.replaceArg(sig,sigsum) ;
+ // Build a clone of the input pdf according to the above customization
+ // instructions. Each node that requires modified is clone so that the
+ // original pdf remained untouched. The name of each cloned node is that
+ // of the original node suffixed by the name of the customizer object
+ //
+ // The returned head node own all nodes that were cloned as part of
+ // the build process so when cust_clone is deleted so will all other
+ // nodes that were created in the process.
+ RooAbsPdf* cust_clone = (RooAbsPdf*) cust.build(kTRUE) ;
- // Build a clone of the input pdf according to the above customization
- // instructions. Each node that requires modified is clone so that the
- // original pdf remained untouched. The name of each cloned node is that
- // of the original node suffixed by the name of the customizer object
- //
- // The returned head node own all nodes that were cloned as part of
- // the build process so when cust_clone is deleted so will all other
- // nodes that were created in the process.
- RooAbsPdf* cust_clone = (RooAbsPdf*) cust.build(kTRUE) ;
-
- // Print structure of clone of model with sig->sigsum replacement.
- cust_clone->Print("t") ;
+ // Print structure of clone of model with sig->sigsum replacement.
+ cust_clone->Print("t") ;
- delete cust_clone ;
+ delete cust_clone ;
}
diff --git a/tutorials/roofit/rf208_convolution.C b/tutorials/roofit/rf208_convolution.C
index 26008852535f7d641f605fba98f0c5c382478283..9a87e5b4463704498a4efd1bb8f11f3427e2212c 100644
--- a/tutorials/roofit/rf208_convolution.C
+++ b/tutorials/roofit/rf208_convolution.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #208
///
/// One-dimensional numeric convolution
@@ -7,14 +8,12 @@
///
/// pdf = landau(t) (x) gauss(t)
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,53 +29,53 @@ using namespace RooFit ;
void rf208_convolution()
{
- // S e t u p c o m p o n e n t p d f s
- // ---------------------------------------
+ // S e t u p c o m p o n e n t p d f s
+ // ---------------------------------------
+
+ // Construct observable
+ RooRealVar t("t","t",-10,30) ;
- // Construct observable
- RooRealVar t("t","t",-10,30) ;
+ // Construct landau(t,ml,sl) ;
+ RooRealVar ml("ml","mean landau",5.,-20,20) ;
+ RooRealVar sl("sl","sigma landau",1,0.1,10) ;
+ RooLandau landau("lx","lx",t,ml,sl) ;
- // Construct landau(t,ml,sl) ;
- RooRealVar ml("ml","mean landau",5.,-20,20) ;
- RooRealVar sl("sl","sigma landau",1,0.1,10) ;
- RooLandau landau("lx","lx",t,ml,sl) ;
-
- // Construct gauss(t,mg,sg)
- RooRealVar mg("mg","mg",0) ;
- RooRealVar sg("sg","sg",2,0.1,10) ;
- RooGaussian gauss("gauss","gauss",t,mg,sg) ;
+ // Construct gauss(t,mg,sg)
+ RooRealVar mg("mg","mg",0) ;
+ RooRealVar sg("sg","sg",2,0.1,10) ;
+ RooGaussian gauss("gauss","gauss",t,mg,sg) ;
- // C o n s t r u c t c o n v o l u t i o n p d f
- // ---------------------------------------
+ // C o n s t r u c t c o n v o l u t i o n p d f
+ // ---------------------------------------
- // Set #bins to be used for FFT sampling to 10000
- t.setBins(10000,"cache") ;
+ // Set #bins to be used for FFT sampling to 10000
+ t.setBins(10000,"cache") ;
- // Construct landau (x) gauss
- RooFFTConvPdf lxg("lxg","landau (X) gauss",t,landau,gauss) ;
+ // Construct landau (x) gauss
+ RooFFTConvPdf lxg("lxg","landau (X) gauss",t,landau,gauss) ;
- // S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f
- // ----------------------------------------------------------------------
+ // S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f
+ // ----------------------------------------------------------------------
- // Sample 1000 events in x from gxlx
- RooDataSet* data = lxg.generate(t,10000) ;
+ // Sample 1000 events in x from gxlx
+ RooDataSet* data = lxg.generate(t,10000) ;
- // Fit gxlx to data
- lxg.fitTo(*data) ;
+ // Fit gxlx to data
+ lxg.fitTo(*data) ;
- // Plot data, landau pdf, landau (X) gauss pdf
- RooPlot* frame = t.frame(Title("landau (x) gauss convolution")) ;
- data->plotOn(frame) ;
- lxg.plotOn(frame) ;
- landau.plotOn(frame,LineStyle(kDashed)) ;
+ // Plot data, landau pdf, landau (X) gauss pdf
+ RooPlot* frame = t.frame(Title("landau (x) gauss convolution")) ;
+ data->plotOn(frame) ;
+ lxg.plotOn(frame) ;
+ landau.plotOn(frame,LineStyle(kDashed)) ;
- // Draw frame on canvas
- new TCanvas("rf208_convolution","rf208_convolution",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ // Draw frame on canvas
+ new TCanvas("rf208_convolution","rf208_convolution",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}
diff --git a/tutorials/roofit/rf209_anaconv.C b/tutorials/roofit/rf209_anaconv.C
index c3dcb7e7485eb6640af3448fe8f46addb39476c7..df7aa12bbcd879e44fd9b232d7818f5d265ee3f6 100644
--- a/tutorials/roofit/rf209_anaconv.C
+++ b/tutorials/roofit/rf209_anaconv.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #209
///
/// Decay function p.d.fs with optional B physics
@@ -11,13 +12,12 @@
/// pdf2 = decay(t,tau) (x) gauss(t,m,s)
/// pdf3 = decay(t,tau) (x) (f*gauss1(t,m1,s1) + (1-f)*gauss2(t,m1,s1))
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussModel.h"
@@ -34,60 +34,60 @@ using namespace RooFit ;
void rf209_anaconv()
{
- // B - p h y s i c s p d f w i t h t r u t h r e s o l u t i o n
- // ---------------------------------------------------------------------
+ // B - p h y s i c s p d f w i t h t r u t h r e s o l u t i o n
+ // ---------------------------------------------------------------------
- // Variables of decay p.d.f.
- RooRealVar dt("dt","dt",-10,10) ;
- RooRealVar tau("tau","tau",1.548) ;
+ // Variables of decay p.d.f.
+ RooRealVar dt("dt","dt",-10,10) ;
+ RooRealVar tau("tau","tau",1.548) ;
- // Build a truth resolution model (delta function)
- RooTruthModel tm("tm","truth model",dt) ;
+ // Build a truth resolution model (delta function)
+ RooTruthModel tm1("tm","truth model",dt) ;
- // Construct decay(t) (x) delta(t)
- RooDecay decay_tm("decay_tm","decay",dt,tau,tm,RooDecay::DoubleSided) ;
+ // Construct decay(t) (x) delta(t)
+ RooDecay decay_tm("decay_tm","decay",dt,tau,tm1,RooDecay::DoubleSided) ;
- // Plot p.d.f. (dashed)
- RooPlot* frame = dt.frame(Title("Bdecay (x) resolution")) ;
- decay_tm.plotOn(frame,LineStyle(kDashed)) ;
+ // Plot p.d.f. (dashed)
+ RooPlot* frame = dt.frame(Title("Bdecay (x) resolution")) ;
+ decay_tm.plotOn(frame,LineStyle(kDashed)) ;
- // B - p h y s i c s p d f w i t h G a u s s i a n r e s o l u t i o n
- // ----------------------------------------------------------------------------
+ // B - p h y s i c s p d f w i t h G a u s s i a n r e s o l u t i o n
+ // ----------------------------------------------------------------------------
- // Build a gaussian resolution model
- RooRealVar bias1("bias1","bias1",0) ;
- RooRealVar sigma1("sigma1","sigma1",1) ;
- RooGaussModel gm1("gm1","gauss model 1",dt,bias1,sigma1) ;
+ // Build a gaussian resolution model
+ RooRealVar bias1("bias1","bias1",0) ;
+ RooRealVar sigma1("sigma1","sigma1",1) ;
+ RooGaussModel gm1("gm1","gauss model 1",dt,bias1,sigma1) ;
- // Construct decay(t) (x) gauss1(t)
- RooDecay decay_gm1("decay_gm1","decay",dt,tau,gm1,RooDecay::DoubleSided) ;
+ // Construct decay(t) (x) gauss1(t)
+ RooDecay decay_gm1("decay_gm1","decay",dt,tau,gm1,RooDecay::DoubleSided) ;
- // Plot p.d.f.
- decay_gm1.plotOn(frame) ;
+ // Plot p.d.f.
+ decay_gm1.plotOn(frame) ;
- // B - p h y s i c s p d f w i t h d o u b l e G a u s s i a n r e s o l u t i o n
- // ------------------------------------------------------------------------------------------
+ // B - p h y s i c s p d f w i t h d o u b l e G a u s s i a n r e s o l u t i o n
+ // ------------------------------------------------------------------------------------------
- // Build another gaussian resolution model
- RooRealVar bias2("bias2","bias2",0) ;
- RooRealVar sigma2("sigma2","sigma2",5) ;
- RooGaussModel gm2("gm2","gauss model 2",dt,bias2,sigma2) ;
+ // Build another gaussian resolution model
+ RooRealVar bias2("bias2","bias2",0) ;
+ RooRealVar sigma2("sigma2","sigma2",5) ;
+ RooGaussModel gm2("gm2","gauss model 2",dt,bias2,sigma2) ;
- // Build a composite resolution model f*gm1+(1-f)*gm2
- RooRealVar gm1frac("gm1frac","fraction of gm1",0.5) ;
- RooAddModel gmsum("gmsum","sum of gm1 and gm2",RooArgList(gm1,gm2),gm1frac) ;
+ // Build a composite resolution model f*gm1+(1-f)*gm2
+ RooRealVar gm1frac("gm1frac","fraction of gm1",0.5) ;
+ RooAddModel gmsum("gmsum","sum of gm1 and gm2",RooArgList(gm1,gm2),gm1frac) ;
- // Construct decay(t) (x) (f*gm1 + (1-f)*gm2)
- RooDecay decay_gmsum("decay_gmsum","decay",dt,tau,gmsum,RooDecay::DoubleSided) ;
+ // Construct decay(t) (x) (f*gm1 + (1-f)*gm2)
+ RooDecay decay_gmsum("decay_gmsum","decay",dt,tau,gmsum,RooDecay::DoubleSided) ;
- // Plot p.d.f. (red)
- decay_gmsum.plotOn(frame,LineColor(kRed)) ;
+ // Plot p.d.f. (red)
+ decay_gmsum.plotOn(frame,LineColor(kRed)) ;
- // Draw all frames on canvas
- new TCanvas("rf209_anaconv","rf209_anaconv",600, 600);
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+ // Draw all frames on canvas
+ new TCanvas("rf209_anaconv","rf209_anaconv",600, 600);
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
}
diff --git a/tutorials/roofit/rf210_angularconv.C b/tutorials/roofit/rf210_angularconv.C
index ee43a37ad9ee9061659e3dccd07c4d6584fa227c..866a9c66bc1fe86b9d7591383cc44ff4bd079478 100644
--- a/tutorials/roofit/rf210_angularconv.C
+++ b/tutorials/roofit/rf210_angularconv.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #210
///
/// Convolution in cyclical angular observables theta, and
@@ -12,14 +13,12 @@
/// pdf(theta) = T(theta) (x) gauss(theta)
/// pdf(cosTheta) = T(acos(cosTheta)) (x) gauss(acos(cosTheta))
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -36,99 +35,99 @@ using namespace RooFit ;
void rf210_angularconv()
{
- // S e t u p c o m p o n e n t p d f s
- // ---------------------------------------
+ // S e t u p c o m p o n e n t p d f s
+ // ---------------------------------------
+
+ // Define angle psi
+ RooRealVar psi("psi","psi",0,3.14159268) ;
+
+ // Define physics p.d.f T(psi)
+ RooGenericPdf Tpsi("Tpsi","1+sin(2*@0)",psi) ;
- // Define angle psi
- RooRealVar psi("psi","psi",0,3.14159268) ;
+ // Define resolution R(psi)
+ RooRealVar gbias("gbias","gbias",0.2,0.,1) ;
+ RooRealVar greso("greso","greso",0.3,0.1,1.0) ;
+ RooGaussian Rpsi("Rpsi","Rpsi",psi,gbias,greso) ;
- // Define physics p.d.f T(psi)
- RooGenericPdf Tpsi("Tpsi","1+sin(2*@0)",psi) ;
+ // Define cos(psi) and function psif that calculates psi from cos(psi)
+ RooRealVar cpsi("cpsi","cos(psi)",-1,1) ;
+ RooFormulaVar psif("psif","acos(cpsi)",cpsi) ;
- // Define resolution R(psi)
- RooRealVar gbias("gbias","gbias",0.2,0.,1) ;
- RooRealVar greso("greso","greso",0.3,0.1,1.0) ;
- RooGaussian Rpsi("Rpsi","Rpsi",psi,gbias,greso) ;
+ // Define physics p.d.f. also as function of cos(psi): T(psif(cpsi)) = T(cpsi) ;
+ RooGenericPdf Tcpsi("T","1+sin(2*@0)",psif) ;
- // Define cos(psi) and function psif that calculates psi from cos(psi)
- RooRealVar cpsi("cpsi","cos(psi)",-1,1) ;
- RooFormulaVar psif("psif","acos(cpsi)",cpsi) ;
- // Define physics p.d.f. also as function of cos(psi): T(psif(cpsi)) = T(cpsi) ;
- RooGenericPdf Tcpsi("T","1+sin(2*@0)",psif) ;
+ // C o n s t r u c t c o n v o l u t i o n p d f i n p s i
+ // --------------------------------------------------------------
+ // Define convoluted p.d.f. as function of psi: M=[T(x)R](psi) = M(psi)
+ RooFFTConvPdf Mpsi("Mf","Mf",psi,Tpsi,Rpsi) ;
- // C o n s t r u c t c o n v o l u t i o n p d f i n p s i
- // --------------------------------------------------------------
+ // Set the buffer fraction to zero to obtain a true cyclical convolution
+ Mpsi.setBufferFraction(0) ;
- // Define convoluted p.d.f. as function of psi: M=[T(x)R](psi) = M(psi)
- RooFFTConvPdf Mpsi("Mf","Mf",psi,Tpsi,Rpsi) ;
- // Set the buffer fraction to zero to obtain a true cyclical convolution
- Mpsi.setBufferFraction(0) ;
+ // S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f ( p s i )
+ // --------------------------------------------------------------------------------
+ // Generate some events in observable psi
+ RooDataSet* data_psi = Mpsi.generate(psi,10000) ;
- // S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f ( p s i )
- // --------------------------------------------------------------------------------
+ // Fit convoluted model as function of angle psi
+ Mpsi.fitTo(*data_psi) ;
- // Generate some events in observable psi
- RooDataSet* data_psi = Mpsi.generate(psi,10000) ;
+ // Plot cos(psi) frame with Mf(cpsi)
+ RooPlot* frame1 = psi.frame(Title("Cyclical convolution in angle psi")) ;
+ data_psi->plotOn(frame1) ;
+ Mpsi.plotOn(frame1) ;
- // Fit convoluted model as function of angle psi
- Mpsi.fitTo(*data_psi) ;
-
- // Plot cos(psi) frame with Mf(cpsi)
- RooPlot* frame1 = psi.frame(Title("Cyclical convolution in angle psi")) ;
- data_psi->plotOn(frame1) ;
- Mpsi.plotOn(frame1) ;
+ // Overlay comparison to unsmeared physics p.d.f T(psi)
+ Tpsi.plotOn(frame1,LineColor(kRed)) ;
- // Overlay comparison to unsmeared physics p.d.f T(psi)
- Tpsi.plotOn(frame1,LineColor(kRed)) ;
+ // C o n s t r u c t c o n v o l u t i o n p d f i n c o s ( p s i )
+ // --------------------------------------------------------------------------
- // C o n s t r u c t c o n v o l u t i o n p d f i n c o s ( p s i )
- // --------------------------------------------------------------------------
+ // Define convoluted p.d.f. as function of cos(psi): M=[T(x)R](psif(cpsi)) = M(cpsi)
+ //
+ // Need to give both observable psi here (for definition of convolution)
+ // and function psif here (for definition of observables, ultimately in cpsi)
+ RooFFTConvPdf Mcpsi("Mf","Mf",psif,psi,Tpsi,Rpsi) ;
- // Define convoluted p.d.f. as function of cos(psi): M=[T(x)R](psif(cpsi)) = M(cpsi)
- //
- // Need to give both observable psi here (for definition of convolution)
- // and function psif here (for definition of observables, ultimately in cpsi)
- RooFFTConvPdf Mcpsi("Mf","Mf",psif,psi,Tpsi,Rpsi) ;
+ // Set the buffer fraction to zero to obtain a true cyclical convolution
+ Mcpsi.setBufferFraction(0) ;
- // Set the buffer fraction to zero to obtain a true cyclical convolution
- Mcpsi.setBufferFraction(0) ;
+ // S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f ( c o s p s i )
+ // --------------------------------------------------------------------------------
- // S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f ( c o s p s i )
- // --------------------------------------------------------------------------------
+ // Generate some events
+ RooDataSet* data_cpsi = Mcpsi.generate(cpsi,10000) ;
- // Generate some events
- RooDataSet* data_cpsi = Mcpsi.generate(cpsi,10000) ;
+ // set psi constant to exclude to be a parameter of the fit
+ psi.setConstant(true);
- // set psi constant to exclude to be a parameter of the fit
- psi.setConstant(true);
+ // Fit convoluted model as function of cos(psi)
+ Mcpsi.fitTo(*data_cpsi) ;
- // Fit convoluted model as function of cos(psi)
- Mcpsi.fitTo(*data_cpsi) ;
-
- // Plot cos(psi) frame with Mf(cpsi)
- RooPlot* frame2 = cpsi.frame(Title("Same convolution in psi, expressed in cos(psi)")) ;
- data_cpsi->plotOn(frame2) ;
- Mcpsi.plotOn(frame2) ;
+ // Plot cos(psi) frame with Mf(cpsi)
+ RooPlot* frame2 = cpsi.frame(Title("Same convolution in psi, expressed in cos(psi)")) ;
+ data_cpsi->plotOn(frame2) ;
+ Mcpsi.plotOn(frame2) ;
- // Overlay comparison to unsmeared physics p.d.f Tf(cpsi)
- Tcpsi.plotOn(frame2,LineColor(kRed)) ;
+ // Overlay comparison to unsmeared physics p.d.f Tf(cpsi)
+ Tcpsi.plotOn(frame2,LineColor(kRed)) ;
- // Draw frame on canvas
- TCanvas* c = new TCanvas("rf210_angularconv","rf210_angularconv",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ // Draw frame on canvas
+ TCanvas* c = new TCanvas("rf210_angularconv","rf210_angularconv",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf211_paramconv.C b/tutorials/roofit/rf211_paramconv.C
index be2b0d876bd2e66b69a226a7358fb0126a49042d..3c3ca7a24219d3c7480e7333ce85da521ecd1cea 100644
--- a/tutorials/roofit/rf211_paramconv.C
+++ b/tutorials/roofit/rf211_paramconv.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #211
///
/// Working a with a p.d.f. with a convolution operator in terms
@@ -7,14 +8,12 @@
///
/// (require ROOT to be compiled with --enable-fftw3)
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
@@ -30,53 +29,53 @@ using namespace RooFit ;
void rf211_paramconv()
{
- // S e t u p c o m p o n e n t p d f s
- // ---------------------------------------
-
- // Gaussian g(x ; mean,sigma)
- RooRealVar x("x","x",-10,10) ;
- RooRealVar mean("mean","mean",-3,3) ;
- RooRealVar sigma("sigma","sigma",0.5,0.1,10) ;
- RooGaussian modelx("gx","gx",x,mean,sigma) ;
+ // S e t u p c o m p o n e n t p d f s
+ // ---------------------------------------
+
+ // Gaussian g(x ; mean,sigma)
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar mean("mean","mean",-3,3) ;
+ RooRealVar sigma("sigma","sigma",0.5,0.1,10) ;
+ RooGaussian modelx("gx","gx",x,mean,sigma) ;
- // Block function in mean
- RooRealVar a("a","a",2,1,10) ;
- RooGenericPdf model_mean("model_mean","abs(mean)<a",RooArgList(mean,a)) ;
+ // Block function in mean
+ RooRealVar a("a","a",2,1,10) ;
+ RooGenericPdf model_mean("model_mean","abs(mean)<a",RooArgList(mean,a)) ;
- // Convolution in mean parameter model = g(x,mean,sigma) (x) block(mean)
- x.setBins(1000,"cache") ;
- mean.setBins(50,"cache") ;
- RooFFTConvPdf model("model","model",mean,modelx,model_mean) ;
+ // Convolution in mean parameter model = g(x,mean,sigma) (x) block(mean)
+ x.setBins(1000,"cache") ;
+ mean.setBins(50,"cache") ;
+ RooFFTConvPdf model("model","model",mean,modelx,model_mean) ;
- // Configure convolution to construct a 2-D cache in (x,mean)
- // rather than a 1-d cache in mean that needs to be recalculated
- // for each value of x
- model.setCacheObservables(x) ;
- model.setBufferFraction(1.0) ;
+ // Configure convolution to construct a 2-D cache in (x,mean)
+ // rather than a 1-d cache in mean that needs to be recalculated
+ // for each value of x
+ model.setCacheObservables(x) ;
+ model.setBufferFraction(1.0) ;
- // Integrate model over mean projModel = Int model dmean
- RooAbsPdf* projModel = model.createProjection(mean) ;
+ // Integrate model over mean projModel = Int model dmean
+ RooAbsPdf* projModel = model.createProjection(mean) ;
- // Generate 1000 toy events
- RooDataHist* d = projModel->generateBinned(x,1000) ;
+ // Generate 1000 toy events
+ RooDataHist* d = projModel->generateBinned(x,1000) ;
- // Fit p.d.f. to toy data
- projModel->fitTo(*d,Verbose()) ;
+ // Fit p.d.f. to toy data
+ projModel->fitTo(*d,Verbose()) ;
- // Plot data and fitted p.d.f.
- RooPlot* frame = x.frame(Bins(25)) ;
- d->plotOn(frame) ;
- projModel->plotOn(frame) ;
+ // Plot data and fitted p.d.f.
+ RooPlot* frame = x.frame(Bins(25)) ;
+ d->plotOn(frame) ;
+ projModel->plotOn(frame) ;
- // Make 2d histogram of model(x;mean)
- TH1* hh = model.createHistogram("hh",x,Binning(50),YVar(mean,Binning(50)),ConditionalObservables(mean)) ;
- hh->SetTitle("histogram of model(x|mean)") ;
- hh->SetLineColor(kBlue) ;
+ // Make 2d histogram of model(x;mean)
+ TH1* hh = model.createHistogram("hh",x,Binning(50),YVar(mean,Binning(50)),ConditionalObservables(mean)) ;
+ hh->SetTitle("histogram of model(x|mean)") ;
+ hh->SetLineColor(kBlue) ;
- // Draw frame on canvas
- TCanvas* c = new TCanvas("rf211_paramconv","rf211_paramconv",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh->GetZaxis()->SetTitleOffset(2.5) ; hh->Draw("surf") ;
+ // Draw frame on canvas
+ TCanvas* c = new TCanvas("rf211_paramconv","rf211_paramconv",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh->GetZaxis()->SetTitleOffset(2.5) ; hh->Draw("surf") ;
}
diff --git a/tutorials/roofit/rf301_composition.C b/tutorials/roofit/rf301_composition.C
index ad87ae38cef55f2fe03b41b083b57cca08149707..af855396906cc1a568d878db13b64204b2370bf4 100644
--- a/tutorials/roofit/rf301_composition.C
+++ b/tutorials/roofit/rf301_composition.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #301
///
/// Multi-dimensional p.d.f.s through composition, e.g. substituting a
@@ -7,14 +8,12 @@
///
/// pdf = gauss(x,f(y),s) with f(y) = a0 + a1*y
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,51 +28,51 @@ using namespace RooFit ;
void rf301_composition()
{
- // S e t u p c o m p o s e d m o d e l g a u s s ( x , m ( y ) , s )
- // -----------------------------------------------------------------------
+ // S e t u p c o m p o s e d m o d e l g a u s s ( x , m ( y ) , s )
+ // -----------------------------------------------------------------------
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
- // Create function f(y) = a0 + a1*y
- RooRealVar a0("a0","a0",-0.5,-5,5) ;
- RooRealVar a1("a1","a1",-0.5,-1,1) ;
- RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
+ // Create function f(y) = a0 + a1*y
+ RooRealVar a0("a0","a0",-0.5,-5,5) ;
+ RooRealVar a1("a1","a1",-0.5,-1,1) ;
+ RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
- // Creat gauss(x,f(y),s)
- RooRealVar sigma("sigma","width of gaussian",0.5) ;
- RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
+ // Creat gauss(x,f(y),s)
+ RooRealVar sigma("sigma","width of gaussian",0.5) ;
+ RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
- // S a m p l e d a t a , p l o t d a t a a n d p d f o n x a n d y
- // ---------------------------------------------------------------------------------
+ // S a m p l e d a t a , p l o t d a t a a n d p d f o n x a n d y
+ // ---------------------------------------------------------------------------------
- // Generate 10000 events in x and y from model
- RooDataSet *data = model.generate(RooArgSet(x,y),10000) ;
+ // Generate 10000 events in x and y from model
+ RooDataSet *data = model.generate(RooArgSet(x,y),10000) ;
- // Plot x distribution of data and projection of model on x = Int(dy) model(x,y)
- RooPlot* xframe = x.frame() ;
- data->plotOn(xframe) ;
- model.plotOn(xframe) ;
+ // Plot x distribution of data and projection of model on x = Int(dy) model(x,y)
+ RooPlot* xframe = x.frame() ;
+ data->plotOn(xframe) ;
+ model.plotOn(xframe) ;
- // Plot x distribution of data and projection of model on y = Int(dx) model(x,y)
- RooPlot* yframe = y.frame() ;
- data->plotOn(yframe) ;
- model.plotOn(yframe) ;
+ // Plot x distribution of data and projection of model on y = Int(dx) model(x,y)
+ RooPlot* yframe = y.frame() ;
+ data->plotOn(yframe) ;
+ model.plotOn(yframe) ;
- // Make two-dimensional plot in x vs y
- TH1* hh_model = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
- hh_model->SetLineColor(kBlue) ;
+ // Make two-dimensional plot in x vs y
+ TH1* hh_model = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
+ hh_model->SetLineColor(kBlue) ;
- // Make canvas and draw RooPlots
- TCanvas *c = new TCanvas("rf301_composition","rf301_composition",1200, 400);
- c->Divide(3);
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
+ // Make canvas and draw RooPlots
+ TCanvas *c = new TCanvas("rf301_composition","rf301_composition",1200, 400);
+ c->Divide(3);
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
}
diff --git a/tutorials/roofit/rf302_utilfuncs.C b/tutorials/roofit/rf302_utilfuncs.C
index af724d30e7489e0da663770e9aba66f93dd92285..a6143dbebbd3c4e43806c82b0d571f0c8695851e 100644
--- a/tutorials/roofit/rf302_utilfuncs.C
+++ b/tutorials/roofit/rf302_utilfuncs.C
@@ -1,18 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #302
///
/// Utility functions classes available for use in tailoring
/// of composite (multidimensional) pdfs
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -32,82 +31,82 @@ using namespace RooFit ;
void rf302_utilfuncs()
{
- // C r e a t e o b s e r v a b l e s , p a r a m e t e r s
- // -----------------------------------------------------------
+ // C r e a t e o b s e r v a b l e s , p a r a m e t e r s
+ // -----------------------------------------------------------
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
- // Create parameters
- RooRealVar a0("a0","a0",-1.5,-5,5) ;
- RooRealVar a1("a1","a1",-0.5,-1,1) ;
- RooRealVar sigma("sigma","width of gaussian",0.5) ;
+ // Create parameters
+ RooRealVar a0("a0","a0",-1.5,-5,5) ;
+ RooRealVar a1("a1","a1",-0.5,-1,1) ;
+ RooRealVar sigma("sigma","width of gaussian",0.5) ;
- // U s i n g R o o F o r m u l a V a r t o t a i l o r p d f
- // -----------------------------------------------------------------------
+ // U s i n g R o o F o r m u l a V a r t o t a i l o r p d f
+ // -----------------------------------------------------------------------
- // Create interpreted function f(y) = a0 - a1*sqrt(10*abs(y))
- RooFormulaVar fy_1("fy_1","a0-a1*sqrt(10*abs(y))",RooArgSet(y,a0,a1)) ;
+ // Create interpreted function f(y) = a0 - a1*sqrt(10*abs(y))
+ RooFormulaVar fy_1("fy_1","a0-a1*sqrt(10*abs(y))",RooArgSet(y,a0,a1)) ;
- // Create gauss(x,f(y),s)
- RooGaussian model_1("model_1","Gaussian with shifting mean",x,fy_1,sigma) ;
+ // Create gauss(x,f(y),s)
+ RooGaussian model_1("model_1","Gaussian with shifting mean",x,fy_1,sigma) ;
- // U s i n g R o o P o l y V a r t o t a i l o r p d f
- // -----------------------------------------------------------------------
+ // U s i n g R o o P o l y V a r t o t a i l o r p d f
+ // -----------------------------------------------------------------------
- // Create polynomial function f(y) = a0 + a1*y
- RooPolyVar fy_2("fy_2","fy_2",y,RooArgSet(a0,a1)) ;
+ // Create polynomial function f(y) = a0 + a1*y
+ RooPolyVar fy_2("fy_2","fy_2",y,RooArgSet(a0,a1)) ;
- // Create gauss(x,f(y),s)
- RooGaussian model_2("model_2","Gaussian with shifting mean",x,fy_2,sigma) ;
+ // Create gauss(x,f(y),s)
+ RooGaussian model_2("model_2","Gaussian with shifting mean",x,fy_2,sigma) ;
- // U s i n g R o o A d d i t i o n t o t a i l o r p d f
- // -----------------------------------------------------------------------
+ // U s i n g R o o A d d i t i o n t o t a i l o r p d f
+ // -----------------------------------------------------------------------
- // Create sum function f(y) = a0 + y
- RooAddition fy_3("fy_3","a0+y",RooArgSet(a0,y)) ;
+ // Create sum function f(y) = a0 + y
+ RooAddition fy_3("fy_3","a0+y",RooArgSet(a0,y)) ;
- // Create gauss(x,f(y),s)
- RooGaussian model_3("model_3","Gaussian with shifting mean",x,fy_3,sigma) ;
+ // Create gauss(x,f(y),s)
+ RooGaussian model_3("model_3","Gaussian with shifting mean",x,fy_3,sigma) ;
- // U s i n g R o o P r o d u c t t o t a i l o r p d f
- // -----------------------------------------------------------------------
+ // U s i n g R o o P r o d u c t t o t a i l o r p d f
+ // -----------------------------------------------------------------------
- // Create product function f(y) = a1*y
- RooProduct fy_4("fy_4","a1*y",RooArgSet(a1,y)) ;
+ // Create product function f(y) = a1*y
+ RooProduct fy_4("fy_4","a1*y",RooArgSet(a1,y)) ;
- // Create gauss(x,f(y),s)
- RooGaussian model_4("model_4","Gaussian with shifting mean",x,fy_4,sigma) ;
+ // Create gauss(x,f(y),s)
+ RooGaussian model_4("model_4","Gaussian with shifting mean",x,fy_4,sigma) ;
- // P l o t a l l p d f s
- // ----------------------------
+ // P l o t a l l p d f s
+ // ----------------------------
- // Make two-dimensional plots in x vs y
- TH1* hh_model_1 = model_1.createHistogram("hh_model_1",x,Binning(50),YVar(y,Binning(50))) ;
- TH1* hh_model_2 = model_2.createHistogram("hh_model_2",x,Binning(50),YVar(y,Binning(50))) ;
- TH1* hh_model_3 = model_3.createHistogram("hh_model_3",x,Binning(50),YVar(y,Binning(50))) ;
- TH1* hh_model_4 = model_4.createHistogram("hh_model_4",x,Binning(50),YVar(y,Binning(50))) ;
- hh_model_1->SetLineColor(kBlue) ;
- hh_model_2->SetLineColor(kBlue) ;
- hh_model_3->SetLineColor(kBlue) ;
- hh_model_4->SetLineColor(kBlue) ;
+ // Make two-dimensional plots in x vs y
+ TH1* hh_model_1 = model_1.createHistogram("hh_model_1",x,Binning(50),YVar(y,Binning(50))) ;
+ TH1* hh_model_2 = model_2.createHistogram("hh_model_2",x,Binning(50),YVar(y,Binning(50))) ;
+ TH1* hh_model_3 = model_3.createHistogram("hh_model_3",x,Binning(50),YVar(y,Binning(50))) ;
+ TH1* hh_model_4 = model_4.createHistogram("hh_model_4",x,Binning(50),YVar(y,Binning(50))) ;
+ hh_model_1->SetLineColor(kBlue) ;
+ hh_model_2->SetLineColor(kBlue) ;
+ hh_model_3->SetLineColor(kBlue) ;
+ hh_model_4->SetLineColor(kBlue) ;
- // Make canvas and draw RooPlots
- TCanvas *c = new TCanvas("rf302_utilfuncs","rf302_utilfuncs",800, 800);
- c->Divide(2,2);
- c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_model_1->GetZaxis()->SetTitleOffset(2.5) ; hh_model_1->Draw("surf") ;
- c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh_model_2->GetZaxis()->SetTitleOffset(2.5) ; hh_model_2->Draw("surf") ;
- c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model_3->GetZaxis()->SetTitleOffset(2.5) ; hh_model_3->Draw("surf") ;
- c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_model_4->GetZaxis()->SetTitleOffset(2.5) ; hh_model_4->Draw("surf") ;
+ // Make canvas and draw RooPlots
+ TCanvas *c = new TCanvas("rf302_utilfuncs","rf302_utilfuncs",800, 800);
+ c->Divide(2,2);
+ c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_model_1->GetZaxis()->SetTitleOffset(2.5) ; hh_model_1->Draw("surf") ;
+ c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh_model_2->GetZaxis()->SetTitleOffset(2.5) ; hh_model_2->Draw("surf") ;
+ c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model_3->GetZaxis()->SetTitleOffset(2.5) ; hh_model_3->Draw("surf") ;
+ c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_model_4->GetZaxis()->SetTitleOffset(2.5) ; hh_model_4->Draw("surf") ;
}
diff --git a/tutorials/roofit/rf303_conditional.C b/tutorials/roofit/rf303_conditional.C
index b29abd54ec5c0cd1ac1b133f29d4712777c65270..5d4206def8cc5e2f0c18c3401ba74aeb97fc7c6e 100644
--- a/tutorials/roofit/rf303_conditional.C
+++ b/tutorials/roofit/rf303_conditional.C
@@ -1,19 +1,18 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #303
///
/// Use of tailored p.d.f as conditional p.d.fs.s
///
/// pdf = gauss(x,f(y),sx | y ) with f(y) = a0 + a1*y
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
@@ -32,70 +31,71 @@ RooDataSet* makeFakeDataXY() ;
void rf303_conditional()
{
- // S e t u p c o m p o s e d m o d e l g a u s s ( x , m ( y ) , s )
- // -----------------------------------------------------------------------
+ // S e t u p c o m p o s e d m o d e l g a u s s ( x , m ( y ) , s )
+ // -----------------------------------------------------------------------
+
+ // Create observables
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar y("y","y",-10,10) ;
+
+ // Create function f(y) = a0 + a1*y
+ RooRealVar a0("a0","a0",-0.5,-5,5) ;
+ RooRealVar a1("a1","a1",-0.5,-1,1) ;
+ RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
- // Create observables
- RooRealVar x("x","x",-10,10) ;
- RooRealVar y("y","y",-10,10) ;
+ // Creat gauss(x,f(y),s)
+ RooRealVar sigma("sigma","width of gaussian",0.5,0.1,2.0) ;
+ RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
- // Create function f(y) = a0 + a1*y
- RooRealVar a0("a0","a0",-0.5,-5,5) ;
- RooRealVar a1("a1","a1",-0.5,-1,1) ;
- RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
- // Creat gauss(x,f(y),s)
- RooRealVar sigma("sigma","width of gaussian",0.5,0.1,2.0) ;
- RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
+ // Obtain fake external experimental dataset with values for x and y
+ RooDataSet* expDataXY = makeFakeDataXY() ;
- // Obtain fake external experimental dataset with values for x and y
- RooDataSet* expDataXY = makeFakeDataXY() ;
+ // G e n e r a t e d a t a f r o m c o n d i t i o n a l p . d . f m o d e l ( x | y )
+ // ---------------------------------------------------------------------------------------------
+ // Make subset of experimental data with only y values
+ RooDataSet* expDataY= (RooDataSet*) expDataXY->reduce(y) ;
- // G e n e r a t e d a t a f r o m c o n d i t i o n a l p . d . f m o d e l ( x | y )
- // ---------------------------------------------------------------------------------------------
+ // Generate 10000 events in x obtained from _conditional_ model(x|y) with y values taken from experimental data
+ RooDataSet *data = model.generate(x,ProtoData(*expDataY)) ;
+ data->Print() ;
- // Make subset of experimental data with only y values
- RooDataSet* expDataY= (RooDataSet*) expDataXY->reduce(y) ;
- // Generate 10000 events in x obtained from _conditional_ model(x|y) with y values taken from experimental data
- RooDataSet *data = model.generate(x,ProtoData(*expDataY)) ;
- data->Print() ;
+ // F i t c o n d i t i o n a l p . d . f m o d e l ( x | y ) t o d a t a
+ // ---------------------------------------------------------------------------------------------
+ model.fitTo(*expDataXY,ConditionalObservables(y)) ;
- // F i t c o n d i t i o n a l p . d . f m o d e l ( x | y ) t o d a t a
- // ---------------------------------------------------------------------------------------------
- model.fitTo(*expDataXY,ConditionalObservables(y)) ;
-
+ // P r o j e c t c o n d i t i o n a l p . d . f o n x a n d y d i m e n s i o n s
+ // ---------------------------------------------------------------------------------------------
- // P r o j e c t c o n d i t i o n a l p . d . f o n x a n d y d i m e n s i o n s
- // ---------------------------------------------------------------------------------------------
+ // Plot x distribution of data and projection of model on x = 1/Ndata sum(data(y_i)) model(x;y_i)
+ RooPlot* xframe = x.frame() ;
+ expDataXY->plotOn(xframe) ;
+ model.plotOn(xframe,ProjWData(*expDataY)) ;
- // Plot x distribution of data and projection of model on x = 1/Ndata sum(data(y_i)) model(x;y_i)
- RooPlot* xframe = x.frame() ;
- expDataXY->plotOn(xframe) ;
- model.plotOn(xframe,ProjWData(*expDataY)) ;
+ // Speed up (and approximate) projection by using binned clone of data for projection
+ RooAbsData* binnedDataY = expDataY->binnedClone() ;
+ model.plotOn(xframe,ProjWData(*binnedDataY),LineColor(kCyan),LineStyle(kDotted)) ;
- // Speed up (and approximate) projection by using binned clone of data for projection
- RooAbsData* binnedDataY = expDataY->binnedClone() ;
- model.plotOn(xframe,ProjWData(*binnedDataY),LineColor(kCyan),LineStyle(kDotted)) ;
+ // Show effect of projection with too coarse binning
+ ((RooRealVar*)expDataY->get()->find("y"))->setBins(5) ;
+ RooAbsData* binnedDataY2 = expDataY->binnedClone() ;
+ model.plotOn(xframe,ProjWData(*binnedDataY2),LineColor(kRed)) ;
- // Show effect of projection with too coarse binning
- ((RooRealVar*)expDataY->get()->find("y"))->setBins(5) ;
- RooAbsData* binnedDataY2 = expDataY->binnedClone() ;
- model.plotOn(xframe,ProjWData(*binnedDataY2),LineColor(kRed)) ;
+ // Make canvas and draw RooPlots
+ new TCanvas("rf303_conditional","rf303_conditional",600, 460);
+ gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.2) ; xframe->Draw() ;
- // Make canvas and draw RooPlots
- new TCanvas("rf303_conditional","rf303_conditional",600, 460);
- gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.2) ; xframe->Draw() ;
}
@@ -103,23 +103,23 @@ void rf303_conditional()
RooDataSet* makeFakeDataXY()
{
- RooRealVar x("x","x",-10,10) ;
- RooRealVar y("y","y",-10,10) ;
- RooArgSet coord(x,y) ;
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar y("y","y",-10,10) ;
+ RooArgSet coord(x,y) ;
- RooDataSet* d = new RooDataSet("d","d",RooArgSet(x,y)) ;
+ RooDataSet* d = new RooDataSet("d","d",RooArgSet(x,y)) ;
- for (int i=0 ; i<10000 ; i++) {
- Double_t tmpy = gRandom->Gaus(0,10) ;
- Double_t tmpx = gRandom->Gaus(0.5*tmpy,1) ;
- if (fabs(tmpy)<10 && fabs(tmpx)<10) {
+ for (int i=0 ; i<10000 ; i++) {
+ Double_t tmpy = gRandom->Gaus(0,10) ;
+ Double_t tmpx = gRandom->Gaus(0.5*tmpy,1) ;
+ if (fabs(tmpy)<10 && fabs(tmpx)<10) {
x = tmpx ;
y = tmpy ;
d->add(coord) ;
- }
+ }
- }
+ }
- return d ;
+ return d ;
}
diff --git a/tutorials/roofit/rf304_uncorrprod.C b/tutorials/roofit/rf304_uncorrprod.C
index 95df7a5f535c81acfb33e818cc7da5b58f43704e..120250c52f094f015238bb90b4cde5daf9121d95 100644
--- a/tutorials/roofit/rf304_uncorrprod.C
+++ b/tutorials/roofit/rf304_uncorrprod.C
@@ -1,19 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #304
///
/// Simple uncorrelated multi-dimensional p.d.f.s
///
/// pdf = gauss(x,mx,sx) * gauss(y,my,sy)
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,54 +26,54 @@ using namespace RooFit ;
void rf304_uncorrprod()
{
- // C r e a t e c o m p o n e n t p d f s i n x a n d y
- // ----------------------------------------------------------------
+ // C r e a t e c o m p o n e n t p d f s i n x a n d y
+ // ----------------------------------------------------------------
- // Create two p.d.f.s gaussx(x,meanx,sigmax) gaussy(y,meany,sigmay) and its variables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
+ // Create two p.d.f.s gaussx(x,meanx,sigmax) gaussy(y,meany,sigmay) and its variables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
- RooRealVar meanx("mean1","mean of gaussian x",2) ;
- RooRealVar meany("mean2","mean of gaussian y",-2) ;
- RooRealVar sigmax("sigmax","width of gaussian x",1) ;
- RooRealVar sigmay("sigmay","width of gaussian y",5) ;
+ RooRealVar meanx("mean1","mean of gaussian x",2) ;
+ RooRealVar meany("mean2","mean of gaussian y",-2) ;
+ RooRealVar sigmax("sigmax","width of gaussian x",1) ;
+ RooRealVar sigmay("sigmay","width of gaussian y",5) ;
- RooGaussian gaussx("gaussx","gaussian PDF",x,meanx,sigmax) ;
- RooGaussian gaussy("gaussy","gaussian PDF",y,meany,sigmay) ;
+ RooGaussian gaussx("gaussx","gaussian PDF",x,meanx,sigmax) ;
+ RooGaussian gaussy("gaussy","gaussian PDF",y,meany,sigmay) ;
- // C o n s t r u c t u n c o r r e l a t e d p r o d u c t p d f
- // -------------------------------------------------------------------
+ // C o n s t r u c t u n c o r r e l a t e d p r o d u c t p d f
+ // -------------------------------------------------------------------
- // Multiply gaussx and gaussy into a two-dimensional p.d.f. gaussxy
- RooProdPdf gaussxy("gaussxy","gaussx*gaussy",RooArgList(gaussx,gaussy)) ;
+ // Multiply gaussx and gaussy into a two-dimensional p.d.f. gaussxy
+ RooProdPdf gaussxy("gaussxy","gaussx*gaussy",RooArgList(gaussx,gaussy)) ;
- // S a m p l e p d f , p l o t p r o j e c t i o n o n x a n d y
- // ---------------------------------------------------------------------------
+ // S a m p l e p d f , p l o t p r o j e c t i o n o n x a n d y
+ // ---------------------------------------------------------------------------
- // Generate 10000 events in x and y from gaussxy
- RooDataSet *data = gaussxy.generate(RooArgSet(x,y),10000) ;
+ // Generate 10000 events in x and y from gaussxy
+ RooDataSet *data = gaussxy.generate(RooArgSet(x,y),10000) ;
- // Plot x distribution of data and projection of gaussxy on x = Int(dy) gaussxy(x,y)
- RooPlot* xframe = x.frame(Title("X projection of gauss(x)*gauss(y)")) ;
- data->plotOn(xframe) ;
- gaussxy.plotOn(xframe) ;
+ // Plot x distribution of data and projection of gaussxy on x = Int(dy) gaussxy(x,y)
+ RooPlot* xframe = x.frame(Title("X projection of gauss(x)*gauss(y)")) ;
+ data->plotOn(xframe) ;
+ gaussxy.plotOn(xframe) ;
- // Plot x distribution of data and projection of gaussxy on y = Int(dx) gaussxy(x,y)
- RooPlot* yframe = y.frame(Title("Y projection of gauss(x)*gauss(y)")) ;
- data->plotOn(yframe) ;
- gaussxy.plotOn(yframe) ;
+ // Plot x distribution of data and projection of gaussxy on y = Int(dx) gaussxy(x,y)
+ RooPlot* yframe = y.frame(Title("Y projection of gauss(x)*gauss(y)")) ;
+ data->plotOn(yframe) ;
+ gaussxy.plotOn(yframe) ;
- // Make canvas and draw RooPlots
- TCanvas *c = new TCanvas("rf304_uncorrprod","rf304_uncorrprod",800, 400);
- c->Divide(2);
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
+ // Make canvas and draw RooPlots
+ TCanvas *c = new TCanvas("rf304_uncorrprod","rf304_uncorrprod",800, 400);
+ c->Divide(2);
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
}
diff --git a/tutorials/roofit/rf305_condcorrprod.C b/tutorials/roofit/rf305_condcorrprod.C
index 6d3edeefb726415c9baa7e0316b0cd21a6ee8b68..b5d9a82dfe0b7e4cfdd2b467638df311c6eeb205 100644
--- a/tutorials/roofit/rf305_condcorrprod.C
+++ b/tutorials/roofit/rf305_condcorrprod.C
@@ -1,19 +1,18 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #305
///
/// Multi-dimensional p.d.f.s with conditional p.d.fs in product
///
/// pdf = gauss(x,f(y),sx | y ) * gauss(y,ms,sx) with f(y) = a0 + a1*y
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,68 +29,68 @@ using namespace RooFit ;
void rf305_condcorrprod()
{
- // C r e a t e c o n d i t i o n a l p d f g x ( x | y )
- // -----------------------------------------------------------
+ // C r e a t e c o n d i t i o n a l p d f g x ( x | y )
+ // -----------------------------------------------------------
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
- // Create function f(y) = a0 + a1*y
- RooRealVar a0("a0","a0",-0.5,-5,5) ;
- RooRealVar a1("a1","a1",-0.5,-1,1) ;
- RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
+ // Create function f(y) = a0 + a1*y
+ RooRealVar a0("a0","a0",-0.5,-5,5) ;
+ RooRealVar a1("a1","a1",-0.5,-1,1) ;
+ RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
- // Create gaussx(x,f(y),sx)
- RooRealVar sigmax("sigma","width of gaussian",0.5) ;
- RooGaussian gaussx("gaussx","Gaussian in x with shifting mean in y",x,fy,sigmax) ;
+ // Create gaussx(x,f(y),sx)
+ RooRealVar sigmax("sigma","width of gaussian",0.5) ;
+ RooGaussian gaussx("gaussx","Gaussian in x with shifting mean in y",x,fy,sigmax) ;
- // C r e a t e p d f g y ( y )
- // -----------------------------------------------------------
+ // C r e a t e p d f g y ( y )
+ // -----------------------------------------------------------
- // Create gaussy(y,0,5)
- RooGaussian gaussy("gaussy","Gaussian in y",y,RooConst(0),RooConst(3)) ;
+ // Create gaussy(y,0,5)
+ RooGaussian gaussy("gaussy","Gaussian in y",y,RooConst(0),RooConst(3)) ;
- // C r e a t e p r o d u c t g x ( x | y ) * g y ( y )
- // -------------------------------------------------------
+ // C r e a t e p r o d u c t g x ( x | y ) * g y ( y )
+ // -------------------------------------------------------
- // Create gaussx(x,sx|y) * gaussy(y)
- RooProdPdf model("model","gaussx(x|y)*gaussy(y)",gaussy,Conditional(gaussx,x)) ;
+ // Create gaussx(x,sx|y) * gaussy(y)
+ RooProdPdf model("model","gaussx(x|y)*gaussy(y)",gaussy,Conditional(gaussx,x)) ;
- // S a m p l e , f i t a n d p l o t p r o d u c t p d f
- // ---------------------------------------------------------------
+ // S a m p l e , f i t a n d p l o t p r o d u c t p d f
+ // ---------------------------------------------------------------
- // Generate 1000 events in x and y from model
- RooDataSet *data = model.generate(RooArgSet(x,y),10000) ;
+ // Generate 1000 events in x and y from model
+ RooDataSet *data = model.generate(RooArgSet(x,y),10000) ;
- // Plot x distribution of data and projection of model on x = Int(dy) model(x,y)
- RooPlot* xframe = x.frame() ;
- data->plotOn(xframe) ;
- model.plotOn(xframe) ;
+ // Plot x distribution of data and projection of model on x = Int(dy) model(x,y)
+ RooPlot* xframe = x.frame() ;
+ data->plotOn(xframe) ;
+ model.plotOn(xframe) ;
- // Plot x distribution of data and projection of model on y = Int(dx) model(x,y)
- RooPlot* yframe = y.frame() ;
- data->plotOn(yframe) ;
- model.plotOn(yframe) ;
+ // Plot x distribution of data and projection of model on y = Int(dx) model(x,y)
+ RooPlot* yframe = y.frame() ;
+ data->plotOn(yframe) ;
+ model.plotOn(yframe) ;
- // Make two-dimensional plot in x vs y
- TH1* hh_model = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
- hh_model->SetLineColor(kBlue) ;
+ // Make two-dimensional plot in x vs y
+ TH1* hh_model = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
+ hh_model->SetLineColor(kBlue) ;
- // Make canvas and draw RooPlots
- TCanvas *c = new TCanvas("rf305_condcorrprod","rf05_condcorrprod",1200, 400);
- c->Divide(3);
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.6) ; yframe->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
+ // Make canvas and draw RooPlots
+ TCanvas *c = new TCanvas("rf305_condcorrprod","rf05_condcorrprod",1200, 400);
+ c->Divide(3);
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.6) ; yframe->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
}
diff --git a/tutorials/roofit/rf306_condpereventerrors.C b/tutorials/roofit/rf306_condpereventerrors.C
index acfd17458bd17ff98f2579abbeb1b35618647b15..271793f318fb06b3d8cef65900c4b0838db52a8b 100644
--- a/tutorials/roofit/rf306_condpereventerrors.C
+++ b/tutorials/roofit/rf306_condpereventerrors.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #306
///
/// Complete example with use of conditional p.d.f. with per-event errors
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,85 +28,85 @@ using namespace RooFit ;
void rf306_condpereventerrors()
{
- // B - p h y s i c s p d f w i t h p e r - e v e n t G a u s s i a n r e s o l u t i o n
- // ----------------------------------------------------------------------------------------------
+ // B - p h y s i c s p d f w i t h p e r - e v e n t G a u s s i a n r e s o l u t i o n
+ // ----------------------------------------------------------------------------------------------
+
+ // Observables
+ RooRealVar dt("dt","dt",-10,10) ;
+ RooRealVar dterr("dterr","per-event error on dt",0.01,10) ;
+
+ // Build a gaussian resolution model scaled by the per-event error = gauss(dt,bias,sigma*dterr)
+ RooRealVar bias("bias","bias",0,-10,10) ;
+ RooRealVar sigma("sigma","per-event error scale factor",1,0.1,10) ;
+ RooGaussModel gm("gm1","gauss model scaled bt per-event error",dt,bias,sigma,dterr) ;
- // Observables
- RooRealVar dt("dt","dt",-10,10) ;
- RooRealVar dterr("dterr","per-event error on dt",0.01,10) ;
+ // Construct decay(dt) (x) gauss1(dt|dterr)
+ RooRealVar tau("tau","tau",1.548) ;
+ RooDecay decay_gm("decay_gm","decay",dt,tau,gm,RooDecay::DoubleSided) ;
- // Build a gaussian resolution model scaled by the per-event error = gauss(dt,bias,sigma*dterr)
- RooRealVar bias("bias","bias",0,-10,10) ;
- RooRealVar sigma("sigma","per-event error scale factor",1,0.1,10) ;
- RooGaussModel gm("gm1","gauss model scaled bt per-event error",dt,bias,sigma,dterr) ;
- // Construct decay(dt) (x) gauss1(dt|dterr)
- RooRealVar tau("tau","tau",1.548) ;
- RooDecay decay_gm("decay_gm","decay",dt,tau,gm,RooDecay::DoubleSided) ;
+ // C o n s t r u c t f a k e ' e x t e r n a l ' d a t a w i t h p e r - e v e n t e r r o r
+ // ------------------------------------------------------------------------------------------------------
+ // Use landau p.d.f to get somewhat realistic distribution with long tail
+ RooLandau pdfDtErr("pdfDtErr","pdfDtErr",dterr,RooConst(1),RooConst(0.25)) ;
+ RooDataSet* expDataDterr = pdfDtErr.generate(dterr,10000) ;
- // C o n s t r u c t f a k e ' e x t e r n a l ' d a t a w i t h p e r - e v e n t e r r o r
- // ------------------------------------------------------------------------------------------------------
- // Use landau p.d.f to get somewhat realistic distribution with long tail
- RooLandau pdfDtErr("pdfDtErr","pdfDtErr",dterr,RooConst(1),RooConst(0.25)) ;
- RooDataSet* expDataDterr = pdfDtErr.generate(dterr,10000) ;
+ // S a m p l e d a t a f r o m c o n d i t i o n a l d e c a y _ g m ( d t | d t e r r )
+ // ---------------------------------------------------------------------------------------------
+ // Specify external dataset with dterr values to use decay_dm as conditional p.d.f.
+ RooDataSet* data = decay_gm.generate(dt,ProtoData(*expDataDterr)) ;
- // S a m p l e d a t a f r o m c o n d i t i o n a l d e c a y _ g m ( d t | d t e r r )
- // ---------------------------------------------------------------------------------------------
- // Specify external dataset with dterr values to use decay_dm as conditional p.d.f.
- RooDataSet* data = decay_gm.generate(dt,ProtoData(*expDataDterr)) ;
-
+ // F i t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
+ // ---------------------------------------------------------------------
- // F i t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
- // ---------------------------------------------------------------------
+ // Specify dterr as conditional observable
+ decay_gm.fitTo(*data,ConditionalObservables(dterr)) ;
- // Specify dterr as conditional observable
- decay_gm.fitTo(*data,ConditionalObservables(dterr)) ;
-
- // P l o t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
- // ---------------------------------------------------------------------
+ // P l o t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
+ // ---------------------------------------------------------------------
- // Make two-dimensional plot of conditional p.d.f in (dt,dterr)
- TH1* hh_decay = decay_gm.createHistogram("hh_decay",dt,Binning(50),YVar(dterr,Binning(50))) ;
- hh_decay->SetLineColor(kBlue) ;
+ // Make two-dimensional plot of conditional p.d.f in (dt,dterr)
+ TH1* hh_decay = decay_gm.createHistogram("hh_decay",dt,Binning(50),YVar(dterr,Binning(50))) ;
+ hh_decay->SetLineColor(kBlue) ;
- // Plot decay_gm(dt|dterr) at various values of dterr
- RooPlot* frame = dt.frame(Title("Slices of decay(dt|dterr) at various dterr")) ;
- for (Int_t ibin=0 ; ibin<100 ; ibin+=20) {
- dterr.setBin(ibin) ;
- decay_gm.plotOn(frame,Normalization(5.)) ;
- }
+ // Plot decay_gm(dt|dterr) at various values of dterr
+ RooPlot* frame = dt.frame(Title("Slices of decay(dt|dterr) at various dterr")) ;
+ for (Int_t ibin=0 ; ibin<100 ; ibin+=20) {
+ dterr.setBin(ibin) ;
+ decay_gm.plotOn(frame,Normalization(5.)) ;
+ }
- // Make projection of data an dt
- RooPlot* frame2 = dt.frame(Title("Projection of decay(dt|dterr) on dt")) ;
- data->plotOn(frame2) ;
+ // Make projection of data an dt
+ RooPlot* frame2 = dt.frame(Title("Projection of decay(dt|dterr) on dt")) ;
+ data->plotOn(frame2) ;
- // Make projection of decay(dt|dterr) on dt.
- //
- // Instead of integrating out dterr, make a weighted average of curves
- // at values dterr_i as given in the external dataset.
- // (The kTRUE argument bins the data before projection to speed up the process)
- decay_gm.plotOn(frame2,ProjWData(*expDataDterr,kTRUE)) ;
+ // Make projection of decay(dt|dterr) on dt.
+ //
+ // Instead of integrating out dterr, make a weighted average of curves
+ // at values dterr_i as given in the external dataset.
+ // (The kTRUE argument bins the data before projection to speed up the process)
+ decay_gm.plotOn(frame2,ProjWData(*expDataDterr,kTRUE)) ;
- // Draw all frames on canvas
- TCanvas* c = new TCanvas("rf306_condpereventerrors","rf306_condperventerrors",1200, 400);
- c->Divide(3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_decay->GetZaxis()->SetTitleOffset(2.5) ; hh_decay->Draw("surf") ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ // Draw all frames on canvas
+ TCanvas* c = new TCanvas("rf306_condpereventerrors","rf306_condperventerrors",1200, 400);
+ c->Divide(3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_decay->GetZaxis()->SetTitleOffset(2.5) ; hh_decay->Draw("surf") ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf307_fullpereventerrors.C b/tutorials/roofit/rf307_fullpereventerrors.C
index 323f54ca361fe734eef46b0f210829af98af008c..9d684b55f1849c2e513c8eecbd79bf33bbb1225a 100644
--- a/tutorials/roofit/rf307_fullpereventerrors.C
+++ b/tutorials/roofit/rf307_fullpereventerrors.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #307
///
/// Complete example with use of full p.d.f. with per-event errors
@@ -10,9 +11,6 @@
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,83 +29,83 @@ using namespace RooFit ;
void rf307_fullpereventerrors()
{
- // B - p h y s i c s p d f w i t h p e r - e v e n t G a u s s i a n r e s o l u t i o n
- // ----------------------------------------------------------------------------------------------
+ // B - p h y s i c s p d f w i t h p e r - e v e n t G a u s s i a n r e s o l u t i o n
+ // ----------------------------------------------------------------------------------------------
- // Observables
- RooRealVar dt("dt","dt",-10,10) ;
- RooRealVar dterr("dterr","per-event error on dt",0.01,10) ;
+ // Observables
+ RooRealVar dt("dt","dt",-10,10) ;
+ RooRealVar dterr("dterr","per-event error on dt",0.01,10) ;
- // Build a gaussian resolution model scaled by the per-event error = gauss(dt,bias,sigma*dterr)
- RooRealVar bias("bias","bias",0,-10,10) ;
- RooRealVar sigma("sigma","per-event error scale factor",1,0.1,10) ;
- RooGaussModel gm("gm1","gauss model scaled bt per-event error",dt,bias,sigma,dterr) ;
+ // Build a gaussian resolution model scaled by the per-event error = gauss(dt,bias,sigma*dterr)
+ RooRealVar bias("bias","bias",0,-10,10) ;
+ RooRealVar sigma("sigma","per-event error scale factor",1,0.1,10) ;
+ RooGaussModel gm("gm1","gauss model scaled bt per-event error",dt,bias,sigma,dterr) ;
- // Construct decay(dt) (x) gauss1(dt|dterr)
- RooRealVar tau("tau","tau",1.548) ;
- RooDecay decay_gm("decay_gm","decay",dt,tau,gm,RooDecay::DoubleSided) ;
+ // Construct decay(dt) (x) gauss1(dt|dterr)
+ RooRealVar tau("tau","tau",1.548) ;
+ RooDecay decay_gm("decay_gm","decay",dt,tau,gm,RooDecay::DoubleSided) ;
- // C o n s t r u c t e m p i r i c a l p d f f o r p e r - e v e n t e r r o r
- // -----------------------------------------------------------------
+ // C o n s t r u c t e m p i r i c a l p d f f o r p e r - e v e n t e r r o r
+ // -----------------------------------------------------------------
- // Use landau p.d.f to get empirical distribution with long tail
- RooLandau pdfDtErr("pdfDtErr","pdfDtErr",dterr,RooConst(1),RooConst(0.25)) ;
- RooDataSet* expDataDterr = pdfDtErr.generate(dterr,10000) ;
+ // Use landau p.d.f to get empirical distribution with long tail
+ RooLandau pdfDtErr("pdfDtErr","pdfDtErr",dterr,RooConst(1),RooConst(0.25)) ;
+ RooDataSet* expDataDterr = pdfDtErr.generate(dterr,10000) ;
- // Construct a histogram pdf to describe the shape of the dtErr distribution
- RooDataHist* expHistDterr = expDataDterr->binnedClone() ;
- RooHistPdf pdfErr("pdfErr","pdfErr",dterr,*expHistDterr) ;
+ // Construct a histogram pdf to describe the shape of the dtErr distribution
+ RooDataHist* expHistDterr = expDataDterr->binnedClone() ;
+ RooHistPdf pdfErr("pdfErr","pdfErr",dterr,*expHistDterr) ;
- // C o n s t r u c t c o n d i t i o n a l p r o d u c t d e c a y _ d m ( d t | d t e r r ) * p d f ( d t e r r )
- // ----------------------------------------------------------------------------------------------------------------------
+ // C o n s t r u c t c o n d i t i o n a l p r o d u c t d e c a y _ d m ( d t | d t e r r ) * p d f ( d t e r r )
+ // ----------------------------------------------------------------------------------------------------------------------
- // Construct production of conditional decay_dm(dt|dterr) with empirical pdfErr(dterr)
- RooProdPdf model("model","model",pdfErr,Conditional(decay_gm,dt)) ;
+ // Construct production of conditional decay_dm(dt|dterr) with empirical pdfErr(dterr)
+ RooProdPdf model("model","model",pdfErr,Conditional(decay_gm,dt)) ;
- // (Alternatively you could also use the landau shape pdfDtErr)
- //RooProdPdf model("model","model",pdfDtErr,Conditional(decay_gm,dt)) ;
+ // (Alternatively you could also use the landau shape pdfDtErr)
+ //RooProdPdf model("model","model",pdfDtErr,Conditional(decay_gm,dt)) ;
-
- // S a m p l e, f i t a n d p l o t p r o d u c t m o d e l
- // ------------------------------------------------------------------
- // Specify external dataset with dterr values to use model_dm as conditional p.d.f.
- RooDataSet* data = model.generate(RooArgSet(dt,dterr),10000) ;
+ // S a m p l e, f i t a n d p l o t p r o d u c t m o d e l
+ // ------------------------------------------------------------------
-
+ // Specify external dataset with dterr values to use model_dm as conditional p.d.f.
+ RooDataSet* data = model.generate(RooArgSet(dt,dterr),10000) ;
- // F i t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
- // ---------------------------------------------------------------------
- // Specify dterr as conditional observable
- model.fitTo(*data) ;
+ // F i t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
+ // ---------------------------------------------------------------------
-
- // P l o t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
- // ---------------------------------------------------------------------
+ // Specify dterr as conditional observable
+ model.fitTo(*data) ;
- // Make two-dimensional plot of conditional p.d.f in (dt,dterr)
- TH1* hh_model = model.createHistogram("hh_model",dt,Binning(50),YVar(dterr,Binning(50))) ;
- hh_model->SetLineColor(kBlue) ;
+ // P l o t c o n d i t i o n a l d e c a y _ d m ( d t | d t e r r )
+ // ---------------------------------------------------------------------
- // Make projection of data an dt
- RooPlot* frame = dt.frame(Title("Projection of model(dt|dterr) on dt")) ;
- data->plotOn(frame) ;
- model.plotOn(frame) ;
+ // Make two-dimensional plot of conditional p.d.f in (dt,dterr)
+ TH1* hh_model = model.createHistogram("hh_model",dt,Binning(50),YVar(dterr,Binning(50))) ;
+ hh_model->SetLineColor(kBlue) ;
- // Draw all frames on canvas
- TCanvas* c = new TCanvas("rf307_fullpereventerrors","rf307_fullperventerrors",800, 400);
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+
+ // Make projection of data an dt
+ RooPlot* frame = dt.frame(Title("Projection of model(dt|dterr) on dt")) ;
+ data->plotOn(frame) ;
+ model.plotOn(frame) ;
+
+
+ // Draw all frames on canvas
+ TCanvas* c = new TCanvas("rf307_fullpereventerrors","rf307_fullperventerrors",800, 400);
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
diff --git a/tutorials/roofit/rf308_normintegration2d.C b/tutorials/roofit/rf308_normintegration2d.C
index 236d2701e799aaa64660902e0ebac0072f62f94c..bdcd8e8419d54ca9213d9a1021b30a8caa4b356a 100644
--- a/tutorials/roofit/rf308_normintegration2d.C
+++ b/tutorials/roofit/rf308_normintegration2d.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #308
///
/// Examples on normalization of p.d.f.s,
@@ -7,13 +8,12 @@
/// of cumulative distribution functions from p.d.f.s
/// in two dimensions
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
@@ -28,78 +28,78 @@ using namespace RooFit ;
void rf308_normintegration2d()
{
- // S e t u p m o d e l
- // ---------------------
+ // S e t u p m o d e l
+ // ---------------------
- // Create observables x,y
- RooRealVar x("x","x",-10,10) ;
- RooRealVar y("y","y",-10,10) ;
+ // Create observables x,y
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar y("y","y",-10,10) ;
- // Create p.d.f. gaussx(x,-2,3), gaussy(y,2,2)
- RooGaussian gx("gx","gx",x,RooConst(-2),RooConst(3)) ;
- RooGaussian gy("gy","gy",y,RooConst(+2),RooConst(2)) ;
+ // Create p.d.f. gaussx(x,-2,3), gaussy(y,2,2)
+ RooGaussian gx("gx","gx",x,RooConst(-2),RooConst(3)) ;
+ RooGaussian gy("gy","gy",y,RooConst(+2),RooConst(2)) ;
- // Create gxy = gx(x)*gy(y)
- RooProdPdf gxy("gxy","gxy",RooArgSet(gx,gy)) ;
+ // Create gxy = gx(x)*gy(y)
+ RooProdPdf gxy("gxy","gxy",RooArgSet(gx,gy)) ;
- // 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
- // --------------------------------------------------------------------------------------------------
+ // 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
- cout << "gxy = " << gxy.getVal() << endl ;
-
- // Return value of gxy normalized over x _and_ y in range [-10,10]
- RooArgSet nset_xy(x,y) ;
- cout << "gx_Norm[x,y] = " << gxy.getVal(&nset_xy) << endl ;
+ // Return 'raw' unnormalized value of gx
+ cout << "gxy = " << gxy.getVal() << endl ;
- // Create object representing integral over gx
- // which is used to calculate gx_Norm[x,y] == gx / gx_Int[x,y]
- RooAbsReal* igxy = gxy.createIntegral(RooArgSet(x,y)) ;
- cout << "gx_Int[x,y] = " << igxy->getVal() << endl ;
+ // Return value of gxy normalized over x _and_ y in range [-10,10]
+ RooArgSet nset_xy(x,y) ;
+ cout << "gx_Norm[x,y] = " << gxy.getVal(&nset_xy) << endl ;
- // NB: it is also possible to do the following
+ // Create object representing integral over gx
+ // which is used to calculate gx_Norm[x,y] == gx / gx_Int[x,y]
+ RooAbsReal* igxy = gxy.createIntegral(RooArgSet(x,y)) ;
+ cout << "gx_Int[x,y] = " << igxy->getVal() << endl ;
- // Return value of gxy normalized over x in range [-10,10] (i.e. treating y as parameter)
- RooArgSet nset_x(x) ;
- cout << "gx_Norm[x] = " << gxy.getVal(&nset_x) << endl ;
+ // NB: it is also possible to do the following
- // Return value of gxy normalized over y in range [-10,10] (i.e. treating x as parameter)
- RooArgSet nset_y(y) ;
- cout << "gx_Norm[y] = " << gxy.getVal(&nset_y) << endl ;
+ // Return value of gxy normalized over x in range [-10,10] (i.e. treating y as parameter)
+ RooArgSet nset_x(x) ;
+ cout << "gx_Norm[x] = " << gxy.getVal(&nset_x) << endl ;
+ // Return value of gxy normalized over y in range [-10,10] (i.e. treating x as parameter)
+ RooArgSet nset_y(y) ;
+ cout << "gx_Norm[y] = " << gxy.getVal(&nset_y) << endl ;
- // 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) ;
- y.setRange("signal",-3,3) ;
-
- // Create an integral of gxy_Norm[x,y] over x and y in range "signal"
- // This is the fraction of of p.d.f. gxy_Norm[x,y] which is in the
- // range named "signal"
- RooAbsReal* igxy_sig = gxy.createIntegral(RooArgSet(x,y),NormSet(RooArgSet(x,y)),Range("signal")) ;
- cout << "gx_Int[x,y|signal]_Norm[x,y] = " << igxy_sig->getVal() << endl ;
+ // 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) ;
+ y.setRange("signal",-3,3) ;
+ // Create an integral of gxy_Norm[x,y] over x and y in range "signal"
+ // This is the fraction of of p.d.f. gxy_Norm[x,y] which is in the
+ // range named "signal"
+ RooAbsReal* igxy_sig = gxy.createIntegral(RooArgSet(x,y),NormSet(RooArgSet(x,y)),Range("signal")) ;
+ cout << "gx_Int[x,y|signal]_Norm[x,y] = " << igxy_sig->getVal() << endl ;
- // 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* gxy_cdf = gxy.createCdf(RooArgSet(x,y)) ;
-
- // Plot cdf of gx versus x
- TH1* hh_cdf = gxy_cdf->createHistogram("hh_cdf",x,Binning(40),YVar(y,Binning(40))) ;
- hh_cdf->SetLineColor(kBlue) ;
- new TCanvas("rf308_normintegration2d","rf308_normintegration2d",600,600) ;
- gPad->SetLeftMargin(0.15) ; hh_cdf->GetZaxis()->SetTitleOffset(1.8) ;
- hh_cdf->Draw("surf") ;
+ // 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* gxy_cdf = gxy.createCdf(RooArgSet(x,y)) ;
+
+ // Plot cdf of gx versus x
+ TH1* hh_cdf = gxy_cdf->createHistogram("hh_cdf",x,Binning(40),YVar(y,Binning(40))) ;
+ hh_cdf->SetLineColor(kBlue) ;
+
+ new TCanvas("rf308_normintegration2d","rf308_normintegration2d",600,600) ;
+ gPad->SetLeftMargin(0.15) ; hh_cdf->GetZaxis()->SetTitleOffset(1.8) ;
+ hh_cdf->Draw("surf") ;
}
diff --git a/tutorials/roofit/rf309_ndimplot.C b/tutorials/roofit/rf309_ndimplot.C
index 3a561cf14d7d3fe6232f905dd0130ac14ba4625d..4dd95ceada56aaa976a6b55a10424c80736612b0 100644
--- a/tutorials/roofit/rf309_ndimplot.C
+++ b/tutorials/roofit/rf309_ndimplot.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #308
///
/// Making 2/3 dimensional plots of p.d.f.s and datasets
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooConstVar.h"
@@ -28,75 +26,75 @@ using namespace RooFit ;
void rf309_ndimplot()
{
- // C r e a t e 2 D m o d e l a n d d a t a s e t
- // -----------------------------------------------------
+ // C r e a t e 2 D m o d e l a n d d a t a s e t
+ // -----------------------------------------------------
+
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
+
+ // Create parameters
+ RooRealVar a0("a0","a0",-3.5,-5,5) ;
+ RooRealVar a1("a1","a1",-1.5,-1,1) ;
+ RooRealVar sigma("sigma","width of gaussian",1.5) ;
+
+ // Create interpreted function f(y) = a0 - a1*sqrt(10*abs(y))
+ RooFormulaVar fy("fy","a0-a1*sqrt(10*abs(y))",RooArgSet(y,a0,a1)) ;
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
+ // Create gauss(x,f(y),s)
+ RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
- // Create parameters
- RooRealVar a0("a0","a0",-3.5,-5,5) ;
- RooRealVar a1("a1","a1",-1.5,-1,1) ;
- RooRealVar sigma("sigma","width of gaussian",1.5) ;
+ // Sample dataset from gauss(x,y)
+ RooDataSet* data = model.generate(RooArgSet(x,y),10000) ;
- // Create interpreted function f(y) = a0 - a1*sqrt(10*abs(y))
- RooFormulaVar fy("fy","a0-a1*sqrt(10*abs(y))",RooArgSet(y,a0,a1)) ;
- // Create gauss(x,f(y),s)
- RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
+ // M a k e 2 D p l o t s o f d a t a a n d m o d e l
+ // -------------------------------------------------------------
- // Sample dataset from gauss(x,y)
- RooDataSet* data = model.generate(RooArgSet(x,y),10000) ;
+ // Create and fill ROOT 2D histogram (20x20 bins) with contents of dataset
+ //TH2D* hh_data = data->createHistogram("hh_data",x,Binning(20),YVar(y,Binning(20))) ;
+ TH1* hh_data = data->createHistogram("x,y",20,20) ;
+ // Create and fill ROOT 2D histogram (50x50 bins) with sampling of pdf
+ //TH2D* hh_pdf = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
+ TH1* hh_pdf = model.createHistogram("x,y",50,50) ;
+ hh_pdf->SetLineColor(kBlue) ;
- // M a k e 2 D p l o t s o f d a t a a n d m o d e l
- // -------------------------------------------------------------
- // Create and fill ROOT 2D histogram (20x20 bins) with contents of dataset
- //TH2D* hh_data = data->createHistogram("hh_data",x,Binning(20),YVar(y,Binning(20))) ;
- TH1* hh_data = data->createHistogram("x,y",20,20) ;
+ // C r e a t e 3 D m o d e l a n d d a t a s e t
+ // -----------------------------------------------------
- // Create and fill ROOT 2D histogram (50x50 bins) with sampling of pdf
- //TH2D* hh_pdf = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
- TH1* hh_pdf = model.createHistogram("x,y",50,50) ;
- hh_pdf->SetLineColor(kBlue) ;
+ // Create observables
+ RooRealVar z("z","z",-5,5) ;
+ RooGaussian gz("gz","gz",z,RooConst(0),RooConst(2)) ;
+ RooProdPdf model3("model3","model3",RooArgSet(model,gz)) ;
- // C r e a t e 3 D m o d e l a n d d a t a s e t
- // -----------------------------------------------------
+ RooDataSet* data3 = model3.generate(RooArgSet(x,y,z),10000) ;
- // Create observables
- RooRealVar z("z","z",-5,5) ;
- RooGaussian gz("gz","gz",z,RooConst(0),RooConst(2)) ;
- RooProdPdf model3("model3","model3",RooArgSet(model,gz)) ;
+ // M a k e 3 D p l o t s o f d a t a a n d m o d e l
+ // -------------------------------------------------------------
- RooDataSet* data3 = model3.generate(RooArgSet(x,y,z),10000) ;
+ // Create and fill ROOT 2D histogram (8x8x8 bins) with contents of dataset
+ TH1* hh_data3 = data3->createHistogram("hh_data3",x,Binning(8),YVar(y,Binning(8)),ZVar(z,Binning(8))) ;
-
- // M a k e 3 D p l o t s o f d a t a a n d m o d e l
- // -------------------------------------------------------------
+ // Create and fill ROOT 2D histogram (20x20x20 bins) with sampling of pdf
+ TH1* hh_pdf3 = model3.createHistogram("hh_model3",x,Binning(20),YVar(y,Binning(20)),ZVar(z,Binning(20))) ;
+ hh_pdf3->SetFillColor(kBlue) ;
- // Create and fill ROOT 2D histogram (8x8x8 bins) with contents of dataset
- TH1* hh_data3 = data3->createHistogram("hh_data3",x,Binning(8),YVar(y,Binning(8)),ZVar(z,Binning(8))) ;
- // Create and fill ROOT 2D histogram (20x20x20 bins) with sampling of pdf
- TH1* hh_pdf3 = model3.createHistogram("hh_model3",x,Binning(20),YVar(y,Binning(20)),ZVar(z,Binning(20))) ;
- hh_pdf3->SetFillColor(kBlue) ;
+ TCanvas* c1 = new TCanvas("rf309_2dimplot","rf309_2dimplot",800,800) ;
+ c1->Divide(2,2) ;
+ c1->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ;
+ c1->cd(2) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.5) ; hh_pdf->Draw("surf") ;
+ c1->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("box") ;
+ c1->cd(4) ; gPad->SetLeftMargin(0.15) ; hh_pdf->GetZaxis()->SetTitleOffset(2.5) ; hh_pdf->Draw("cont3") ;
+ TCanvas* c2 = new TCanvas("rf309_3dimplot","rf309_3dimplot",800,400) ;
+ c2->Divide(2) ;
+ c2->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data3->GetZaxis()->SetTitleOffset(1.4) ; hh_data3->Draw("lego") ;
+ c2->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_pdf3->GetZaxis()->SetTitleOffset(1.4) ; hh_pdf3->Draw("iso") ;
- TCanvas* c1 = new TCanvas("rf309_2dimplot","rf309_2dimplot",800,800) ;
- c1->Divide(2,2) ;
- c1->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ;
- c1->cd(2) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.5) ; hh_pdf->Draw("surf") ;
- c1->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("box") ;
- c1->cd(4) ; gPad->SetLeftMargin(0.15) ; hh_pdf->GetZaxis()->SetTitleOffset(2.5) ; hh_pdf->Draw("cont3") ;
-
- TCanvas* c2 = new TCanvas("rf309_3dimplot","rf309_3dimplot",800,400) ;
- c2->Divide(2) ;
- c2->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data3->GetZaxis()->SetTitleOffset(1.4) ; hh_data3->Draw("lego") ;
- c2->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_pdf3->GetZaxis()->SetTitleOffset(1.4) ; hh_pdf3->Draw("iso") ;
-
}
diff --git a/tutorials/roofit/rf310_sliceplot.C b/tutorials/roofit/rf310_sliceplot.C
index c338af6edb84f54cd96e4f52930e68aabed3bf9d..219666135cdc321bd1aca701b2e05c3eda895930 100644
--- a/tutorials/roofit/rf310_sliceplot.C
+++ b/tutorials/roofit/rf310_sliceplot.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #309
///
/// Projecting p.d.f and data slices in discrete observables
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussModel.h"
@@ -29,75 +26,75 @@ void rf310_sliceplot()
{
- // C r e a t e B d e c a y p d f w it h m i x i n g
- // ----------------------------------------------------------
+ // C r e a t e B d e c a y p d f w it h m i x i n g
+ // ----------------------------------------------------------
+
+ // Decay time observables
+ RooRealVar dt("dt","dt",-20,20) ;
- // Decay time observables
- RooRealVar dt("dt","dt",-20,20) ;
+ // Discrete observables mixState (B0tag==B0reco?) and tagFlav (B0tag==B0(bar)?)
+ RooCategory mixState("mixState","B0/B0bar mixing state") ;
+ RooCategory tagFlav("tagFlav","Flavour of the tagged B0") ;
- // Discrete observables mixState (B0tag==B0reco?) and tagFlav (B0tag==B0(bar)?)
- RooCategory mixState("mixState","B0/B0bar mixing state") ;
- RooCategory tagFlav("tagFlav","Flavour of the tagged B0") ;
+ // Define state labels of discrete observables
+ mixState.defineType("mixed",-1) ;
+ mixState.defineType("unmixed",1) ;
+ tagFlav.defineType("B0",1) ;
+ tagFlav.defineType("B0bar",-1) ;
- // Define state labels of discrete observables
- mixState.defineType("mixed",-1) ;
- mixState.defineType("unmixed",1) ;
- tagFlav.defineType("B0",1) ;
- tagFlav.defineType("B0bar",-1) ;
+ // Model parameters
+ RooRealVar dm("dm","delta m(B)",0.472,0.,1.0) ;
+ RooRealVar tau("tau","B0 decay time",1.547,1.0,2.0) ;
+ RooRealVar w("w","Flavor Mistag rate",0.03,0.0,1.0) ;
+ RooRealVar dw("dw","Flavor Mistag rate difference between B0 and B0bar",0.01) ;
- // Model parameters
- RooRealVar dm("dm","delta m(B)",0.472,0.,1.0) ;
- RooRealVar tau("tau","B0 decay time",1.547,1.0,2.0) ;
- RooRealVar w("w","Flavor Mistag rate",0.03,0.0,1.0) ;
- RooRealVar dw("dw","Flavor Mistag rate difference between B0 and B0bar",0.01) ;
+ // Build a gaussian resolution model
+ RooRealVar bias1("bias1","bias1",0) ;
+ RooRealVar sigma1("sigma1","sigma1",0.01) ;
+ RooGaussModel gm1("gm1","gauss model 1",dt,bias1,sigma1) ;
- // Build a gaussian resolution model
- RooRealVar bias1("bias1","bias1",0) ;
- RooRealVar sigma1("sigma1","sigma1",0.01) ;
- RooGaussModel gm1("gm1","gauss model 1",dt,bias1,sigma1) ;
+ // Construct a decay pdf, smeared with single gaussian resolution model
+ RooBMixDecay bmix_gm1("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,gm1,RooBMixDecay::DoubleSided) ;
- // Construct a decay pdf, smeared with single gaussian resolution model
- RooBMixDecay bmix_gm1("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,gm1,RooBMixDecay::DoubleSided) ;
-
- // Generate BMixing data with above set of event errors
- RooDataSet *data = bmix_gm1.generate(RooArgSet(dt,tagFlav,mixState),20000) ;
+ // Generate BMixing data with above set of event errors
+ RooDataSet *data = bmix_gm1.generate(RooArgSet(dt,tagFlav,mixState),20000) ;
- // P l o t f u l l d e c a y d i s t r i b u t i o n
- // ----------------------------------------------------------
+ // P l o t f u l l d e c a y d i s t r i b u t i o n
+ // ----------------------------------------------------------
- // Create frame, plot data and pdf projection (integrated over tagFlav and mixState)
- RooPlot* frame = dt.frame(Title("Inclusive decay distribution")) ;
- data->plotOn(frame) ;
- bmix_gm1.plotOn(frame) ;
+ // Create frame, plot data and pdf projection (integrated over tagFlav and mixState)
+ RooPlot* frame = dt.frame(Title("Inclusive decay distribution")) ;
+ data->plotOn(frame) ;
+ bmix_gm1.plotOn(frame) ;
- // P l o t d e c a y d i s t r . f o r m i x e d a n d u n m i x e d s l i c e o f m i x S t a t e
- // ------------------------------------------------------------------------------------------------------------------
+ // P l o t d e c a y d i s t r . f o r m i x e d a n d u n m i x e d s l i c e o f m i x S t a t e
+ // ------------------------------------------------------------------------------------------------------------------
- // Create frame, plot data (mixed only)
- RooPlot* frame2 = dt.frame(Title("Decay distribution of mixed events")) ;
- data->plotOn(frame2,Cut("mixState==mixState::mixed")) ;
+ // Create frame, plot data (mixed only)
+ RooPlot* frame2 = dt.frame(Title("Decay distribution of mixed events")) ;
+ data->plotOn(frame2,Cut("mixState==mixState::mixed")) ;
- // Position slice in mixState at "mixed" and plot slice of pdf in mixstate over data (integrated over tagFlav)
- bmix_gm1.plotOn(frame2,Slice(mixState,"mixed")) ;
+ // Position slice in mixState at "mixed" and plot slice of pdf in mixstate over data (integrated over tagFlav)
+ bmix_gm1.plotOn(frame2,Slice(mixState,"mixed")) ;
- // Create frame, plot data (unmixed only)
- RooPlot* frame3 = dt.frame(Title("Decay distribution of unmixed events")) ;
- data->plotOn(frame3,Cut("mixState==mixState::unmixed")) ;
+ // Create frame, plot data (unmixed only)
+ RooPlot* frame3 = dt.frame(Title("Decay distribution of unmixed events")) ;
+ data->plotOn(frame3,Cut("mixState==mixState::unmixed")) ;
- // Position slice in mixState at "unmixed" and plot slice of pdf in mixstate over data (integrated over tagFlav)
- bmix_gm1.plotOn(frame3,Slice(mixState,"unmixed")) ;
+ // Position slice in mixState at "unmixed" and plot slice of pdf in mixstate over data (integrated over tagFlav)
+ bmix_gm1.plotOn(frame3,Slice(mixState,"unmixed")) ;
- TCanvas* c = new TCanvas("rf310_sliceplot","rf310_sliceplot",1200,400) ;
- c->Divide(3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; gPad->SetLogy() ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; gPad->SetLogy() ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; gPad->SetLogy() ; frame3->Draw() ;
+ TCanvas* c = new TCanvas("rf310_sliceplot","rf310_sliceplot",1200,400) ;
+ c->Divide(3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; gPad->SetLogy() ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; gPad->SetLogy() ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; gPad->SetLogy() ; frame3->Draw() ;
}
diff --git a/tutorials/roofit/rf311_rangeplot.C b/tutorials/roofit/rf311_rangeplot.C
index c29d3ec65c0547983e0da059df6afc769dc5ffa2..810541218d2c24ebdac8118c42918f8d53dc15c2 100644
--- a/tutorials/roofit/rf311_rangeplot.C
+++ b/tutorials/roofit/rf311_rangeplot.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #310
///
/// Projecting p.d.f and data ranges in continuous observables
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,68 +27,68 @@ using namespace RooFit ;
void rf311_rangeplot()
{
- // C r e a t e 3 D p d f a n d d a t a
- // -------------------------------------------
+ // C r e a t e 3 D p d f a n d d a t a
+ // -------------------------------------------
+
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
+ RooRealVar z("z","z",-5,5) ;
+
+ // Create signal pdf gauss(x)*gauss(y)*gauss(z)
+ RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
+ RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
+ RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
+ RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
- RooRealVar z("z","z",-5,5) ;
+ // Create background pdf poly(x)*poly(y)*poly(z)
+ RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
+ RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
+ RooPolynomial pz("pz","pz",z) ;
+ RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
- // Create signal pdf gauss(x)*gauss(y)*gauss(z)
- RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
- RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
- RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
- RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
+ // Create composite pdf sig+bkg
+ RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
+ RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
- // Create background pdf poly(x)*poly(y)*poly(z)
- RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
- RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
- RooPolynomial pz("pz","pz",z) ;
- RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
+ RooDataSet* data = model.generate(RooArgSet(x,y,z),20000) ;
- // Create composite pdf sig+bkg
- RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
- RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
- RooDataSet* data = model.generate(RooArgSet(x,y,z),20000) ;
+ // P r o j e c t p d f a n d d a t a o n x
+ // -------------------------------------------------
+ // Make plain projection of data and pdf on x observable
+ RooPlot* frame = x.frame(Title("Projection of 3D data and pdf on X"),Bins(40)) ;
+ data->plotOn(frame) ;
+ model.plotOn(frame) ;
- // P r o j e c t p d f a n d d a t a o n x
- // -------------------------------------------------
- // Make plain projection of data and pdf on x observable
- RooPlot* frame = x.frame(Title("Projection of 3D data and pdf on X"),Bins(40)) ;
- data->plotOn(frame) ;
- model.plotOn(frame) ;
-
+ // P r o j e c t p d f a n d d a t a o n x i n s i g n a l r a n g e
+ // ----------------------------------------------------------------------------------
- // P r o j e c t p d f a n d d a t a o n x i n s i g n a l r a n g e
- // ----------------------------------------------------------------------------------
-
- // Define signal region in y and z observables
- y.setRange("sigRegion",-1,1) ;
- z.setRange("sigRegion",-1,1) ;
+ // Define signal region in y and z observables
+ y.setRange("sigRegion",-1,1) ;
+ z.setRange("sigRegion",-1,1) ;
- // Make plot frame
- RooPlot* frame2 = x.frame(Title("Same projection on X in signal range of (Y,Z)"),Bins(40)) ;
+ // Make plot frame
+ RooPlot* frame2 = x.frame(Title("Same projection on X in signal range of (Y,Z)"),Bins(40)) ;
- // Plot subset of data in which all observables are inside "sigRegion"
- // For observables that do not have an explicit "sigRegion" range defined (e.g. observable)
- // an implicit definition is used that is identical to the full range (i.e. [-5,5] for x)
- data->plotOn(frame2,CutRange("sigRegion")) ;
+ // Plot subset of data in which all observables are inside "sigRegion"
+ // For observables that do not have an explicit "sigRegion" range defined (e.g. observable)
+ // an implicit definition is used that is identical to the full range (i.e. [-5,5] for x)
+ data->plotOn(frame2,CutRange("sigRegion")) ;
- // Project model on x, integrating projected observables (y,z) only in "sigRegion"
- model.plotOn(frame2,ProjectionRange("sigRegion")) ;
+ // Project model on x, integrating projected observables (y,z) only in "sigRegion"
+ model.plotOn(frame2,ProjectionRange("sigRegion")) ;
- TCanvas* c = new TCanvas("rf311_rangeplot","rf310_rangeplot",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ TCanvas* c = new TCanvas("rf311_rangeplot","rf310_rangeplot",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf312_multirangefit.C b/tutorials/roofit/rf312_multirangefit.C
index c68f3333a8f06869ceae827217bf44d911fce37f..47667ccd8cdccddbcb433324d6b72dbe2d1dd272 100644
--- a/tutorials/roofit/rf312_multirangefit.C
+++ b/tutorials/roofit/rf312_multirangefit.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #312
///
/// Performing fits in multiple (disjoint) ranges in one or more dimensions
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,91 +27,91 @@ using namespace RooFit ;
void rf312_multirangefit()
{
- // C r e a t e 2 D p d f a n d d a t a
- // -------------------------------------------
+ // C r e a t e 2 D p d f a n d d a t a
+ // -------------------------------------------
+
+ // Define observables x,y
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar y("y","y",-10,10) ;
- // Define observables x,y
- RooRealVar x("x","x",-10,10) ;
- RooRealVar y("y","y",-10,10) ;
+ // Construct the signal pdf gauss(x)*gauss(y)
+ RooRealVar mx("mx","mx",1,-10,10) ;
+ RooRealVar my("my","my",1,-10,10) ;
- // Construct the signal pdf gauss(x)*gauss(y)
- RooRealVar mx("mx","mx",1,-10,10) ;
- RooRealVar my("my","my",1,-10,10) ;
+ RooGaussian gx("gx","gx",x,mx,RooConst(1)) ;
+ RooGaussian gy("gy","gy",y,my,RooConst(1)) ;
- RooGaussian gx("gx","gx",x,mx,RooConst(1)) ;
- RooGaussian gy("gy","gy",y,my,RooConst(1)) ;
+ RooProdPdf sig("sig","sig",gx,gy) ;
- RooProdPdf sig("sig","sig",gx,gy) ;
+ // Construct the background pdf (flat in x,y)
+ RooPolynomial px("px","px",x) ;
+ RooPolynomial py("py","py",y) ;
+ RooProdPdf bkg("bkg","bkg",px,py) ;
- // Construct the background pdf (flat in x,y)
- RooPolynomial px("px","px",x) ;
- RooPolynomial py("py","py",y) ;
- RooProdPdf bkg("bkg","bkg",px,py) ;
+ // Construct the composite model sig+bkg
+ RooRealVar f("f","f",0.,1.) ;
+ RooAddPdf model("model","model",RooArgList(sig,bkg),f) ;
- // Construct the composite model sig+bkg
- RooRealVar f("f","f",0.,1.) ;
- RooAddPdf model("model","model",RooArgList(sig,bkg),f) ;
+ // Sample 10000 events in (x,y) from the model
+ RooDataSet* modelData = model.generate(RooArgSet(x,y),10000) ;
- // Sample 10000 events in (x,y) from the model
- RooDataSet* modelData = model.generate(RooArgSet(x,y),10000) ;
+ // D e f i n e s i g n a l a n d s i d e b a n d r e g i o n s
+ // -------------------------------------------------------------------
- // D e f i n e s i g n a l a n d s i d e b a n d r e g i o n s
- // -------------------------------------------------------------------
+ // Construct the SideBand1,SideBand2,Signal regions
+ //
+ // |
+ // +-------------+-----------+
+ // | | |
+ // | Side | Sig |
+ // | Band1 | nal |
+ // | | |
+ // --+-------------+-----------+--
+ // | |
+ // | Side |
+ // | Band2 |
+ // | |
+ // +-------------+-----------+
+ // |
- // Construct the SideBand1,SideBand2,Signal regions
- //
- // |
- // +-------------+-----------+
- // | | |
- // | Side | Sig |
- // | Band1 | nal |
- // | | |
- // --+-------------+-----------+--
- // | |
- // | Side |
- // | Band2 |
- // | |
- // +-------------+-----------+
- // |
+ x.setRange("SB1",-10,+10) ;
+ y.setRange("SB1",-10,0) ;
- x.setRange("SB1",-10,+10) ;
- y.setRange("SB1",-10,0) ;
+ x.setRange("SB2",-10,0) ;
+ y.setRange("SB2",0,+10) ;
- x.setRange("SB2",-10,0) ;
- y.setRange("SB2",0,+10) ;
+ x.setRange("SIG",0,+10) ;
+ y.setRange("SIG",0,+10) ;
- x.setRange("SIG",0,+10) ;
- y.setRange("SIG",0,+10) ;
+ x.setRange("FULL",-10,+10) ;
+ y.setRange("FULL",-10,+10) ;
- x.setRange("FULL",-10,+10) ;
- y.setRange("FULL",-10,+10) ;
+ // P e r f o r m f i t s i n i n d i v i d u a l s i d e b a n d r e g i o n s
+ // -------------------------------------------------------------------------------------
- // P e r f o r m f i t s i n i n d i v i d u a l s i d e b a n d r e g i o n s
- // -------------------------------------------------------------------------------------
+ // Perform fit in SideBand1 region (RooAddPdf coefficients will be interpreted in full range)
+ RooFitResult* r_sb1 = model.fitTo(*modelData,Range("SB1"),Save()) ;
- // Perform fit in SideBand1 region (RooAddPdf coefficients will be interpreted in full range)
- RooFitResult* r_sb1 = model.fitTo(*modelData,Range("SB1"),Save()) ;
+ // Perform fit in SideBand2 region (RooAddPdf coefficients will be interpreted in full range)
+ RooFitResult* r_sb2 = model.fitTo(*modelData,Range("SB2"),Save()) ;
- // Perform fit in SideBand2 region (RooAddPdf coefficients will be interpreted in full range)
- RooFitResult* r_sb2 = model.fitTo(*modelData,Range("SB2"),Save()) ;
+ // P e r f o r m f i t s i n j o i n t s i d e b a n d r e g i o n s
+ // -----------------------------------------------------------------------------
- // P e r f o r m f i t s i n j o i n t s i d e b a n d r e g i o n s
- // -----------------------------------------------------------------------------
+ // Now perform fit to joint 'L-shaped' sideband region 'SB1|SB2'
+ // (RooAddPdf coefficients will be interpreted in full range)
+ RooFitResult* r_sb12 = model.fitTo(*modelData,Range("SB1,SB2"),Save()) ;
- // Now perform fit to joint 'L-shaped' sideband region 'SB1|SB2'
- // (RooAddPdf coefficients will be interpreted in full range)
- RooFitResult* r_sb12 = model.fitTo(*modelData,Range("SB1,SB2"),Save()) ;
+ // Print results for comparison
+ r_sb1->Print() ;
+ r_sb2->Print() ;
+ r_sb12->Print() ;
- // Print results for comparison
- r_sb1->Print() ;
- r_sb2->Print() ;
- r_sb12->Print() ;
-
}
diff --git a/tutorials/roofit/rf313_paramranges.C b/tutorials/roofit/rf313_paramranges.C
index ff6c44d211efa663958f2423916e7119de473829..a08d4b5f18a951bcc9b399c36a691f2ef711c273 100644
--- a/tutorials/roofit/rf313_paramranges.C
+++ b/tutorials/roofit/rf313_paramranges.C
@@ -1,19 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #313
///
/// Working with parameterized ranges to define non-rectangular regions
/// for fitting and integration
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,56 +27,56 @@ using namespace RooFit ;
void rf313_paramranges()
{
- // C r e a t e 3 D p d f
- // -------------------------
+ // C r e a t e 3 D p d f
+ // -------------------------
- // Define observable (x,y,z)
- RooRealVar x("x","x",0,10) ;
- RooRealVar y("y","y",0,10) ;
- RooRealVar z("z","z",0,10) ;
+ // Define observable (x,y,z)
+ RooRealVar x("x","x",0,10) ;
+ RooRealVar y("y","y",0,10) ;
+ RooRealVar z("z","z",0,10) ;
- // Define 3 dimensional pdf
- RooRealVar z0("z0","z0",-0.1,1) ;
- RooPolynomial px("px","px",x,RooConst(0)) ;
- RooPolynomial py("py","py",y,RooConst(0)) ;
- RooPolynomial pz("pz","pz",z,z0) ;
- RooProdPdf pxyz("pxyz","pxyz",RooArgSet(px,py,pz)) ;
+ // Define 3 dimensional pdf
+ RooRealVar z0("z0","z0",-0.1,1) ;
+ RooPolynomial px("px","px",x,RooConst(0)) ;
+ RooPolynomial py("py","py",y,RooConst(0)) ;
+ RooPolynomial pz("pz","pz",z,z0) ;
+ RooProdPdf pxyz("pxyz","pxyz",RooArgSet(px,py,pz)) ;
- // D e f i n e d n o n - r e c t a n g u l a r r e g i o n R i n ( x , y , z )
- // -------------------------------------------------------------------------------------
+ // D e f i n e d n o n - r e c t a n g u l a r r e g i o n R i n ( x , y , z )
+ // -------------------------------------------------------------------------------------
- //
- // R = Z[0 - 0.1*Y^2] * Y[0.1*X - 0.9*X] * X[0 - 10]
- //
+ //
+ // R = Z[0 - 0.1*Y^2] * Y[0.1*X - 0.9*X] * X[0 - 10]
+ //
- // Construct range parameterized in "R" in y [ 0.1*x, 0.9*x ]
- RooFormulaVar ylo("ylo","0.1*x",x) ;
- RooFormulaVar yhi("yhi","0.9*x",x) ;
- y.setRange("R",ylo,yhi) ;
+ // Construct range parameterized in "R" in y [ 0.1*x, 0.9*x ]
+ RooFormulaVar ylo("ylo","0.1*x",x) ;
+ RooFormulaVar yhi("yhi","0.9*x",x) ;
+ y.setRange("R",ylo,yhi) ;
- // Construct parameterized ranged "R" in z [ 0, 0.1*y^2 ]
- RooFormulaVar zlo("zlo","0.0*y",y) ;
- RooFormulaVar zhi("zhi","0.1*y*y",y) ;
- z.setRange("R",zlo,zhi) ;
+ // Construct parameterized ranged "R" in z [ 0, 0.1*y^2 ]
+ RooFormulaVar zlo("zlo","0.0*y",y) ;
+ RooFormulaVar zhi("zhi","0.1*y*y",y) ;
+ z.setRange("R",zlo,zhi) ;
- // C a l c u l a t e i n t e g r a l o f n o r m a l i z e d p d f i n R
- // ----------------------------------------------------------------------------------
+ // C a l c u l a t e i n t e g r a l o f n o r m a l i z e d p d f i n R
+ // ----------------------------------------------------------------------------------
- // Create integral over normalized pdf model over x,y,z in "R" region
- RooAbsReal* intPdf = pxyz.createIntegral(RooArgSet(x,y,z),RooArgSet(x,y,z),"R") ;
+ // Create integral over normalized pdf model over x,y,z in "R" region
+ RooAbsReal* intPdf = pxyz.createIntegral(RooArgSet(x,y,z),RooArgSet(x,y,z),"R") ;
- // Plot value of integral as function of pdf parameter z0
- RooPlot* frame = z0.frame(Title("Integral of pxyz over x,y,z in region R")) ;
- intPdf->plotOn(frame) ;
+ // Plot value of integral as function of pdf parameter z0
+ RooPlot* frame = z0.frame(Title("Integral of pxyz over x,y,z in region R")) ;
+ intPdf->plotOn(frame) ;
- new TCanvas("rf313_paramranges","rf313_paramranges",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+ new TCanvas("rf313_paramranges","rf313_paramranges",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
- return ;
+ return ;
}
diff --git a/tutorials/roofit/rf314_paramfitrange.C b/tutorials/roofit/rf314_paramfitrange.C
index ad57da7d8c2d8a5c39c5c0065fcfd66f22530373..c02682ff37c59a9d77431a88ae5fd193d3492975 100644
--- a/tutorials/roofit/rf314_paramfitrange.C
+++ b/tutorials/roofit/rf314_paramfitrange.C
@@ -1,5 +1,6 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #314
///
/// Working with parameterized ranges in a fit. This an example of a
@@ -9,13 +10,12 @@
///
/// where t and tmin are both observables in the dataset
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -32,58 +32,58 @@ using namespace RooFit ;
void rf314_paramfitrange()
{
- // D e f i n e o b s e r v a b l e s a n d d e c a y p d f
- // ---------------------------------------------------------------
+ // D e f i n e o b s e r v a b l e s a n d d e c a y p d f
+ // ---------------------------------------------------------------
- // Declare observables
- RooRealVar t("t","t",0,5) ;
- RooRealVar tmin("tmin","tmin",0,0,5) ;
+ // Declare observables
+ RooRealVar t("t","t",0,5) ;
+ RooRealVar tmin("tmin","tmin",0,0,5) ;
- // Make parameterized range in t : [tmin,5]
- t.setRange(tmin,RooConst(t.getMax())) ;
+ // Make parameterized range in t : [tmin,5]
+ t.setRange(tmin,RooConst(t.getMax())) ;
- // Make pdf
- RooRealVar tau("tau","tau",-1.54,-10,-0.1) ;
- RooExponential model("model","model",t,tau) ;
+ // Make pdf
+ RooRealVar tau("tau","tau",-1.54,-10,-0.1) ;
+ RooExponential model("model","model",t,tau) ;
- // C r e a t e i n p u t d a t a
- // ------------------------------------
+ // C r e a t e i n p u t d a t a
+ // ------------------------------------
- // Generate complete dataset without acceptance cuts (for reference)
- RooDataSet* dall = model.generate(t,10000) ;
+ // Generate complete dataset without acceptance cuts (for reference)
+ RooDataSet* dall = model.generate(t,10000) ;
- // Generate a (fake) prototype dataset for acceptance limit values
- RooDataSet* tmp = RooGaussian("gmin","gmin",tmin,RooConst(0),RooConst(0.5)).generate(tmin,5000) ;
+ // Generate a (fake) prototype dataset for acceptance limit values
+ RooDataSet* tmp = RooGaussian("gmin","gmin",tmin,RooConst(0),RooConst(0.5)).generate(tmin,5000) ;
- // Generate dataset with t values that observe (t>tmin)
- RooDataSet* dacc = model.generate(t,ProtoData(*tmp)) ;
+ // Generate dataset with t values that observe (t>tmin)
+ RooDataSet* dacc = model.generate(t,ProtoData(*tmp)) ;
- // F i t p d f t o d a t a i n a c c e p t a n c e r e g i o n
- // -----------------------------------------------------------------------
+ // F i t p d f t o d a t a i n a c c e p t a n c e r e g i o n
+ // -----------------------------------------------------------------------
- RooFitResult* r = model.fitTo(*dacc,Save()) ;
+ RooFitResult* r = model.fitTo(*dacc,Save()) ;
-
- // P l o t f i t t e d p d f o n f u l l a n d a c c e p t e d d a t a
- // ---------------------------------------------------------------------------------
- // Make plot frame, add datasets and overlay model
- RooPlot* frame = t.frame(Title("Fit to data with per-event acceptance")) ;
- dall->plotOn(frame,MarkerColor(kRed),LineColor(kRed)) ;
- model.plotOn(frame) ;
- dacc->plotOn(frame) ;
+ // P l o t f i t t e d p d f o n f u l l a n d a c c e p t e d d a t a
+ // ---------------------------------------------------------------------------------
- // Print fit results to demonstrate absence of bias
- r->Print("v") ;
+ // Make plot frame, add datasets and overlay model
+ RooPlot* frame = t.frame(Title("Fit to data with per-event acceptance")) ;
+ dall->plotOn(frame,MarkerColor(kRed),LineColor(kRed)) ;
+ model.plotOn(frame) ;
+ dacc->plotOn(frame) ;
+ // Print fit results to demonstrate absence of bias
+ r->Print("v") ;
- new TCanvas("rf314_paramranges","rf314_paramranges",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
- return ;
+ new TCanvas("rf314_paramranges","rf314_paramranges",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+
+ return ;
}
diff --git a/tutorials/roofit/rf315_projectpdf.C b/tutorials/roofit/rf315_projectpdf.C
index ca4948362316fa3e76c46d12d4bf2b11353348f2..c90a8066c11d4e6b58a45c15fdfb92f961dfdff1 100644
--- a/tutorials/roofit/rf315_projectpdf.C
+++ b/tutorials/roofit/rf315_projectpdf.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #315
///
/// Marginizalization of multi-dimensional p.d.f.s through integration
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
@@ -29,65 +27,65 @@ using namespace RooFit ;
void rf315_projectpdf()
{
- // C r e a t e p d f m ( x , y ) = g x ( x | y ) * g ( y )
- // --------------------------------------------------------------
+ // C r e a t e p d f m ( x , y ) = g x ( x | y ) * g ( y )
+ // --------------------------------------------------------------
+
+ // Increase default precision of numeric integration
+ // as this exercise has high sensitivity to numeric integration precision
+ RooAbsPdf::defaultIntegratorConfig()->setEpsRel(1e-8) ;
+ RooAbsPdf::defaultIntegratorConfig()->setEpsAbs(1e-8) ;
+
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-2,2) ;
- // Increase default precision of numeric integration
- // as this exercise has high sensitivity to numeric integration precision
- RooAbsPdf::defaultIntegratorConfig()->setEpsRel(1e-8) ;
- RooAbsPdf::defaultIntegratorConfig()->setEpsAbs(1e-8) ;
+ // Create function f(y) = a0 + a1*y
+ RooRealVar a0("a0","a0",0) ;
+ RooRealVar a1("a1","a1",-1.5,-3,1) ;
+ RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-2,2) ;
+ // Create gaussx(x,f(y),sx)
+ RooRealVar sigmax("sigmax","width of gaussian",0.5) ;
+ RooGaussian gaussx("gaussx","Gaussian in x with shifting mean in y",x,fy,sigmax) ;
- // Create function f(y) = a0 + a1*y
- RooRealVar a0("a0","a0",0) ;
- RooRealVar a1("a1","a1",-1.5,-3,1) ;
- RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
+ // Create gaussy(y,0,2)
+ RooGaussian gaussy("gaussy","Gaussian in y",y,RooConst(0),RooConst(2)) ;
- // Create gaussx(x,f(y),sx)
- RooRealVar sigmax("sigmax","width of gaussian",0.5) ;
- RooGaussian gaussx("gaussx","Gaussian in x with shifting mean in y",x,fy,sigmax) ;
+ // Create gaussx(x,sx|y) * gaussy(y)
+ RooProdPdf model("model","gaussx(x|y)*gaussy(y)",gaussy,Conditional(gaussx,x)) ;
- // Create gaussy(y,0,2)
- RooGaussian gaussy("gaussy","Gaussian in y",y,RooConst(0),RooConst(2)) ;
- // Create gaussx(x,sx|y) * gaussy(y)
- RooProdPdf model("model","gaussx(x|y)*gaussy(y)",gaussy,Conditional(gaussx,x)) ;
-
+ // M a r g i n a l i z e m ( x , y ) t o m ( x )
+ // ----------------------------------------------------
- // M a r g i n a l i z e m ( x , y ) t o m ( x )
- // ----------------------------------------------------
+ // modelx(x) = Int model(x,y) dy
+ RooAbsPdf* modelx = model.createProjection(y) ;
- // modelx(x) = Int model(x,y) dy
- RooAbsPdf* modelx = model.createProjection(y) ;
+ // U s e m a r g i n a l i z e d p . d . f . a s r e g u l a r 1 - D p . d . f .
+ // ------------------------------------------------------------------------------------------
- // U s e m a r g i n a l i z e d p . d . f . a s r e g u l a r 1 - D p . d . f .
- // ------------------------------------------------------------------------------------------
+ // Sample 1000 events from modelx
+ RooAbsData* data = modelx->generateBinned(x,1000) ;
- // Sample 1000 events from modelx
- RooAbsData* data = modelx->generateBinned(x,1000) ;
+ // Fit modelx to toy data
+ modelx->fitTo(*data,Verbose()) ;
- // Fit modelx to toy data
- modelx->fitTo(*data,Verbose()) ;
+ // Plot modelx over data
+ RooPlot* frame = x.frame(40) ;
+ data->plotOn(frame) ;
+ modelx->plotOn(frame) ;
- // Plot modelx over data
- RooPlot* frame = x.frame(40) ;
- data->plotOn(frame) ;
- modelx->plotOn(frame) ;
+ // Make 2D histogram of model(x,y)
+ TH1* hh = model.createHistogram("x,y") ;
+ hh->SetLineColor(kBlue) ;
- // Make 2D histogram of model(x,y)
- TH1* hh = model.createHistogram("x,y") ;
- hh->SetLineColor(kBlue) ;
+ TCanvas* c = new TCanvas("rf315_projectpdf","rf315_projectpdf",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh->GetZaxis()->SetTitleOffset(2.5) ; hh->Draw("surf") ;
- TCanvas* c = new TCanvas("rf315_projectpdf","rf315_projectpdf",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh->GetZaxis()->SetTitleOffset(2.5) ; hh->Draw("surf") ;
-
}
diff --git a/tutorials/roofit/rf316_llratioplot.C b/tutorials/roofit/rf316_llratioplot.C
index fc9da67c4a7ae81d7bab1febc3f349835d944597..442c613b6588f4ff3e42aff90882bd35a190c9e1 100644
--- a/tutorials/roofit/rf316_llratioplot.C
+++ b/tutorials/roofit/rf316_llratioplot.C
@@ -1,19 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #316
///
/// Using the likelihood ratio techique to construct a signal enhanced
/// one-dimensional projection of a multi-dimensional p.d.f.
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,94 +28,94 @@ using namespace RooFit ;
void rf316_llratioplot()
{
- // C r e a t e 3 D p d f a n d d a t a
- // -------------------------------------------
+ // C r e a t e 3 D p d f a n d d a t a
+ // -------------------------------------------
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
- RooRealVar z("z","z",-5,5) ;
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
+ RooRealVar z("z","z",-5,5) ;
- // Create signal pdf gauss(x)*gauss(y)*gauss(z)
- RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
- RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
- RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
- RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
+ // Create signal pdf gauss(x)*gauss(y)*gauss(z)
+ RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
+ RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
+ RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
+ RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
- // Create background pdf poly(x)*poly(y)*poly(z)
- RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
- RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
- RooPolynomial pz("pz","pz",z) ;
- RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
+ // Create background pdf poly(x)*poly(y)*poly(z)
+ RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
+ RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
+ RooPolynomial pz("pz","pz",z) ;
+ RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
- // Create composite pdf sig+bkg
- RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
- RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
+ // Create composite pdf sig+bkg
+ RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
+ RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
- RooDataSet* data = model.generate(RooArgSet(x,y,z),20000) ;
+ RooDataSet* data = model.generate(RooArgSet(x,y,z),20000) ;
- // P r o j e c t p d f a n d d a t a o n x
- // -------------------------------------------------
+ // P r o j e c t p d f a n d d a t a o n x
+ // -------------------------------------------------
+
+ // Make plain projection of data and pdf on x observable
+ RooPlot* frame = x.frame(Title("Projection of 3D data and pdf on X"),Bins(40)) ;
+ data->plotOn(frame) ;
+ model.plotOn(frame) ;
- // Make plain projection of data and pdf on x observable
- RooPlot* frame = x.frame(Title("Projection of 3D data and pdf on X"),Bins(40)) ;
- data->plotOn(frame) ;
- model.plotOn(frame) ;
-
- // D e f i n e p r o j e c t e d s i g n a l l i k e l i h o o d r a t i o
- // ----------------------------------------------------------------------------------
+ // D e f i n e p r o j e c t e d s i g n a l l i k e l i h o o d r a t i o
+ // ----------------------------------------------------------------------------------
- // Calculate projection of signal and total likelihood on (y,z) observables
- // i.e. integrate signal and composite model over x
- RooAbsPdf* sigyz = sig.createProjection(x) ;
- RooAbsPdf* totyz = model.createProjection(x) ;
+ // Calculate projection of signal and total likelihood on (y,z) observables
+ // i.e. integrate signal and composite model over x
+ RooAbsPdf* sigyz = sig.createProjection(x) ;
+ RooAbsPdf* totyz = model.createProjection(x) ;
- // Construct the log of the signal / signal+background probability
- RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
+ // Construct the log of the signal / signal+background probability
+ RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
- // P l o t d a t a w i t h a L L r a t i o c u t
- // -------------------------------------------------------
+ // P l o t d a t a w i t h a L L r a t i o c u t
+ // -------------------------------------------------------
- // Calculate the llratio value for each event in the dataset
- data->addColumn(llratio_func) ;
+ // Calculate the llratio value for each event in the dataset
+ data->addColumn(llratio_func) ;
- // Extract the subset of data with large signal likelihood
- RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
+ // Extract the subset of data with large signal likelihood
+ RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
- // Make plot frame
- RooPlot* frame2 = x.frame(Title("Same projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
+ // Make plot frame
+ RooPlot* frame2 = x.frame(Title("Same projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
- // Plot select data on frame
- dataSel->plotOn(frame2) ;
+ // Plot select data on frame
+ dataSel->plotOn(frame2) ;
- // M a k e M C p r o j e c t i o n o f p d f w i t h s a m e L L r a t i o c u t
- // ---------------------------------------------------------------------------------------------
+ // M a k e M C p r o j e c t i o n o f p d f w i t h s a m e L L r a t i o c u t
+ // ---------------------------------------------------------------------------------------------
- // Generate large number of events for MC integration of pdf projection
- RooDataSet* mcprojData = model.generate(RooArgSet(x,y,z),10000) ;
+ // Generate large number of events for MC integration of pdf projection
+ RooDataSet* mcprojData = model.generate(RooArgSet(x,y,z),10000) ;
- // Calculate LL ratio for each generated event and select MC events with llratio)0.7
- mcprojData->addColumn(llratio_func) ;
- RooDataSet* mcprojDataSel = (RooDataSet*) mcprojData->reduce(Cut("llratio>0.7")) ;
-
- // Project model on x, integrating projected observables (y,z) with Monte Carlo technique
- // on set of events with the same llratio cut as was applied to data
- model.plotOn(frame2,ProjWData(*mcprojDataSel)) ;
+ // Calculate LL ratio for each generated event and select MC events with llratio)0.7
+ mcprojData->addColumn(llratio_func) ;
+ RooDataSet* mcprojDataSel = (RooDataSet*) mcprojData->reduce(Cut("llratio>0.7")) ;
+
+ // Project model on x, integrating projected observables (y,z) with Monte Carlo technique
+ // on set of events with the same llratio cut as was applied to data
+ model.plotOn(frame2,ProjWData(*mcprojDataSel)) ;
- TCanvas* c = new TCanvas("rf316_llratioplot","rf316_llratioplot",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ TCanvas* c = new TCanvas("rf316_llratioplot","rf316_llratioplot",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
diff --git a/tutorials/roofit/rf401_importttreethx.C b/tutorials/roofit/rf401_importttreethx.C
index dff07ee35f5a45b1970b73956b2d2b670a789d49..31604d132395c7e9ccc3cdf53c4ba84e5f7a8bb9 100644
--- a/tutorials/roofit/rf401_importttreethx.C
+++ b/tutorials/roofit/rf401_importttreethx.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'DATA AND CATEGORIES' RooFit tutorial macro #401
///
/// Overview of advanced option for importing data from ROOT TTree and THx histograms
/// Basic import options are demonstrated in rf102_dataimport.C
///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
@@ -37,88 +35,88 @@ TTree* makeTTree() ;
void rf401_importttreethx()
{
- // I m p o r t m u l t i p l e T H 1 i n t o a R o o D a t a H i s t
- // --------------------------------------------------------------------------
+ // I m p o r t m u l t i p l e T H 1 i n t o a R o o D a t a H i s t
+ // --------------------------------------------------------------------------
- // Create thee ROOT TH1 histograms
- TH1* hh_1 = makeTH1("hh1",0,3) ;
- TH1* hh_2 = makeTH1("hh2",-3,1) ;
- TH1* hh_3 = makeTH1("hh3",+3,4) ;
+ // Create thee ROOT TH1 histograms
+ TH1* hh_1 = makeTH1("hh1",0,3) ;
+ TH1* hh_2 = makeTH1("hh2",-3,1) ;
+ TH1* hh_3 = makeTH1("hh3",+3,4) ;
- // Declare observable x
- RooRealVar x("x","x",-10,10) ;
+ // Declare observable x
+ RooRealVar x("x","x",-10,10) ;
- // Create category observable c that serves as index for the ROOT histograms
- RooCategory c("c","c") ;
- c.defineType("SampleA") ;
- c.defineType("SampleB") ;
- c.defineType("SampleC") ;
+ // Create category observable c that serves as index for the ROOT histograms
+ RooCategory c("c","c") ;
+ c.defineType("SampleA") ;
+ c.defineType("SampleB") ;
+ c.defineType("SampleC") ;
- // Create a binned dataset that imports contents of all TH1 mapped by index category c
- RooDataHist* dh = new RooDataHist("dh","dh",x,Index(c),Import("SampleA",*hh_1),Import("SampleB",*hh_2),Import("SampleC",*hh_3)) ;
- dh->Print() ;
+ // Create a binned dataset that imports contents of all TH1 mapped by index category c
+ RooDataHist* dh = new RooDataHist("dh","dh",x,Index(c),Import("SampleA",*hh_1),Import("SampleB",*hh_2),Import("SampleC",*hh_3)) ;
+ dh->Print() ;
- // Alternative constructor form for importing multiple histograms
- map<string,TH1*> hmap ;
- hmap["SampleA"] = hh_1 ;
- hmap["SampleB"] = hh_2 ;
- hmap["SampleC"] = hh_3 ;
- RooDataHist* dh2 = new RooDataHist("dh","dh",x,c,hmap) ;
- dh2->Print() ;
+ // Alternative constructor form for importing multiple histograms
+ map<string,TH1*> hmap ;
+ hmap["SampleA"] = hh_1 ;
+ hmap["SampleB"] = hh_2 ;
+ hmap["SampleC"] = hh_3 ;
+ RooDataHist* dh2 = new RooDataHist("dh","dh",x,c,hmap) ;
+ dh2->Print() ;
-
+
- // I m p o r t i n g a T T r e e i n t o a R o o D a t a S e t w i t h c u t s
- // -----------------------------------------------------------------------------------------
+ // I m p o r t i n g a T T r e e i n t o a R o o D a t a S e t w i t h c u t s
+ // -----------------------------------------------------------------------------------------
- TTree* tree = makeTTree() ;
+ TTree* tree = makeTTree() ;
- // Define observables y,z
- RooRealVar y("y","y",-10,10) ;
- RooRealVar z("z","z",-10,10) ;
+ // Define observables y,z
+ RooRealVar y("y","y",-10,10) ;
+ RooRealVar z("z","z",-10,10) ;
- // Import only observables (y,z)
- RooDataSet ds("ds","ds",RooArgSet(x,y),Import(*tree)) ;
- ds.Print() ;
+ // Import only observables (y,z)
+ RooDataSet ds("ds","ds",RooArgSet(x,y),Import(*tree)) ;
+ ds.Print() ;
- // Import observables (x,y,z) but only event for which (y+z<0) is true
- RooDataSet ds2("ds2","ds2",RooArgSet(x,y,z),Import(*tree),Cut("y+z<0")) ;
- ds2.Print() ;
+ // Import observables (x,y,z) but only event for which (y+z<0) is true
+ RooDataSet ds2("ds2","ds2",RooArgSet(x,y,z),Import(*tree),Cut("y+z<0")) ;
+ ds2.Print() ;
- // I m p o r t i n g i n t e g e r T T r e e b r a n c h e s
- // ---------------------------------------------------------------
+ // I m p o r t i n g i n t e g e r T T r e e b r a n c h e s
+ // ---------------------------------------------------------------
- // Import integer tree branch as RooRealVar
- RooRealVar i("i","i",0,5) ;
- RooDataSet ds3("ds3","ds3",RooArgSet(i,x),Import(*tree)) ;
- ds3.Print() ;
+ // Import integer tree branch as RooRealVar
+ RooRealVar i("i","i",0,5) ;
+ RooDataSet ds3("ds3","ds3",RooArgSet(i,x),Import(*tree)) ;
+ ds3.Print() ;
- // Define category i
- RooCategory icat("i","i") ;
- icat.defineType("State0",0) ;
- icat.defineType("State1",1) ;
+ // Define category i
+ RooCategory icat("i","i") ;
+ icat.defineType("State0",0) ;
+ icat.defineType("State1",1) ;
- // Import integer tree branch as RooCategory (only events with i==0 and i==1
- // will be imported as those are the only defined states)
- RooDataSet ds4("ds4","ds4",RooArgSet(icat,x),Import(*tree)) ;
- ds4.Print() ;
+ // Import integer tree branch as RooCategory (only events with i==0 and i==1
+ // will be imported as those are the only defined states)
+ RooDataSet ds4("ds4","ds4",RooArgSet(icat,x),Import(*tree)) ;
+ ds4.Print() ;
- // I m p o r t m u l t i p l e R o o D a t a S e t s i n t o a R o o D a t a S e t
- // ----------------------------------------------------------------------------------------
+ // I m p o r t m u l t i p l e R o o D a t a S e t s i n t o a R o o D a t a S e t
+ // ----------------------------------------------------------------------------------------
- // Create three RooDataSets in (y,z)
- RooDataSet* dsA = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"z<-5") ;
- RooDataSet* dsB = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"abs(z)<5") ;
- RooDataSet* dsC = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"z>5") ;
+ // Create three RooDataSets in (y,z)
+ RooDataSet* dsA = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"z<-5") ;
+ RooDataSet* dsB = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"abs(z)<5") ;
+ RooDataSet* dsC = (RooDataSet*) ds2.reduce(RooArgSet(x,y),"z>5") ;
- // Create a dataset that imports contents of all the above datasets mapped by index category c
- RooDataSet* dsABC = new RooDataSet("dsABC","dsABC",RooArgSet(x,y),Index(c),Import("SampleA",*dsA),Import("SampleB",*dsB),Import("SampleC",*dsC)) ;
+ // Create a dataset that imports contents of all the above datasets mapped by index category c
+ RooDataSet* dsABC = new RooDataSet("dsABC","dsABC",RooArgSet(x,y),Index(c),Import("SampleA",*dsA),Import("SampleB",*dsB),Import("SampleC",*dsC)) ;
- dsABC->Print() ;
+ dsABC->Print() ;
}
diff --git a/tutorials/roofit/rf402_datahandling.C b/tutorials/roofit/rf402_datahandling.C
index 5011c2e38d61e4625d42bb6ee0dc905fab50b4ed..adccdcc1328f2ad0cf1b2b10f720aa2c3979caf8 100644
--- a/tutorials/roofit/rf402_datahandling.C
+++ b/tutorials/roofit/rf402_datahandling.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'DATA AND CATEGORIES' RooFit tutorial macro #402
///
/// Tools for manipulation of (un)binned datasets
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
@@ -31,150 +29,150 @@ using namespace RooFit ;
void rf402_datahandling()
{
- // Binned (RooDataHist) and unbinned datasets (RooDataSet) share
- // many properties and inherit from a common abstract base class
- // (RooAbsData), that provides an interface for all operations
- // that can be performed regardless of the data format
+ // Binned (RooDataHist) and unbinned datasets (RooDataSet) share
+ // many properties and inherit from a common abstract base class
+ // (RooAbsData), that provides an interface for all operations
+ // that can be performed regardless of the data format
+
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar y("y","y", 0, 40) ;
+ RooCategory c("c","c") ;
+ c.defineType("Plus",+1) ;
+ c.defineType("Minus",-1) ;
+
- RooRealVar x("x","x",-10,10) ;
- RooRealVar y("y","y", 0, 40) ;
- RooCategory c("c","c") ;
- c.defineType("Plus",+1) ;
- c.defineType("Minus",-1) ;
+ // B a s i c O p e r a t i o n s o n u n b i n n e d d a t a s e t s
+ // --------------------------------------------------------------
+ // RooDataSet is an unbinned dataset (a collection of points in N-dimensional space)
+ RooDataSet d("d","d",RooArgSet(x,y,c)) ;
+
+ // Unlike RooAbsArgs (RooAbsPdf,RooFormulaVar,....) datasets are not attached to
+ // the variables they are constructed from. Instead they are attached to an internal
+ // clone of the supplied set of arguments
+
+ // Fill d with dummy values
+ Int_t i ;
+ for (i=0 ; i<1000 ; i++) {
+ x = i/50 - 10 ;
+ y = sqrt(1.0*i) ;
+ c.setLabel((i%2)?"Plus":"Minus") ;
- // B a s i c O p e r a t i o n s o n u n b i n n e d d a t a s e t s
- // --------------------------------------------------------------
+ // We must explicitly refer to x,y,c here to pass the values because
+ // d is not linked to them (as explained above)
+ d.add(RooArgSet(x,y,c)) ;
+ }
+ d.Print("v") ;
+ cout << endl ;
- // RooDataSet is an unbinned dataset (a collection of points in N-dimensional space)
- RooDataSet d("d","d",RooArgSet(x,y,c)) ;
-
- // Unlike RooAbsArgs (RooAbsPdf,RooFormulaVar,....) datasets are not attached to
- // the variables they are constructed from. Instead they are attached to an internal
- // clone of the supplied set of arguments
-
- // Fill d with dummy values
- Int_t i ;
- for (i=0 ; i<1000 ; i++) {
- x = i/50 - 10 ;
- y = sqrt(1.0*i) ;
- c.setLabel((i%2)?"Plus":"Minus") ;
+ // The get() function returns a pointer to the internal copy of the RooArgSet(x,y,c)
+ // supplied in the constructor
+ const RooArgSet* row = d.get() ;
+ row->Print("v") ;
+ cout << endl ;
- // We must explicitly refer to x,y,c here to pass the values because
- // d is not linked to them (as explained above)
- d.add(RooArgSet(x,y,c)) ;
- }
- d.Print("v") ;
- cout << endl ;
+ // Get with an argument loads a specific data point in row and returns
+ // a pointer to row argset. get() always returns the same pointer, unless
+ // an invalid row number is specified. In that case a null ptr is returned
+ d.get(900)->Print("v") ;
+ cout << endl ;
- // The get() function returns a pointer to the internal copy of the RooArgSet(x,y,c)
- // supplied in the constructor
- const RooArgSet* row = d.get() ;
- row->Print("v") ;
- cout << endl ;
- // Get with an argument loads a specific data point in row and returns
- // a pointer to row argset. get() always returns the same pointer, unless
- // an invalid row number is specified. In that case a null ptr is returned
- d.get(900)->Print("v") ;
- cout << endl ;
+ // R e d u c i n g , A p p e n d i n g a n d M e r g i n g
+ // -------------------------------------------------------------
+ // The reduce() function returns a new dataset which is a subset of the original
+ cout << endl << ">> d1 has only columns x,c" << endl ;
+ RooDataSet* d1 = (RooDataSet*) d.reduce(RooArgSet(x,c)) ;
+ d1->Print("v") ;
- // R e d u c i n g , A p p e n d i n g a n d M e r g i n g
- // -------------------------------------------------------------
+ cout << endl << ">> d2 has only column y" << endl ;
+ RooDataSet* d2 = (RooDataSet*) d.reduce(RooArgSet(y)) ;
+ d2->Print("v") ;
- // The reduce() function returns a new dataset which is a subset of the original
- cout << endl << ">> d1 has only columns x,c" << endl ;
- RooDataSet* d1 = (RooDataSet*) d.reduce(RooArgSet(x,c)) ;
- d1->Print("v") ;
+ cout << endl << ">> d3 has only the points with y>5.17" << endl ;
+ RooDataSet* d3 = (RooDataSet*) d.reduce("y>5.17") ;
+ d3->Print("v") ;
- cout << endl << ">> d2 has only column y" << endl ;
- RooDataSet* d2 = (RooDataSet*) d.reduce(RooArgSet(y)) ;
- d2->Print("v") ;
+ cout << endl << ">> d4 has only columns x,c for data points with y>5.17" << endl ;
+ RooDataSet* d4 = (RooDataSet*) d.reduce(RooArgSet(x,c),"y>5.17") ;
+ d4->Print("v") ;
- cout << endl << ">> d3 has only the points with y>5.17" << endl ;
- RooDataSet* d3 = (RooDataSet*) d.reduce("y>5.17") ;
- d3->Print("v") ;
+ // The merge() function adds two data set column-wise
+ cout << endl << ">> merge d2(y) with d1(x,c) to form d1(x,c,y)" << endl ;
+ d1->merge(d2) ;
+ d1->Print("v") ;
- cout << endl << ">> d4 has only columns x,c for data points with y>5.17" << endl ;
- RooDataSet* d4 = (RooDataSet*) d.reduce(RooArgSet(x,c),"y>5.17") ;
- d4->Print("v") ;
+ // The append() function addes two datasets row-wise
+ cout << endl << ">> append data points of d3 to d1" << endl ;
+ d1->append(*d3) ;
+ d1->Print("v") ;
- // The merge() function adds two data set column-wise
- cout << endl << ">> merge d2(y) with d1(x,c) to form d1(x,c,y)" << endl ;
- d1->merge(d2) ;
- d1->Print("v") ;
- // The append() function addes two datasets row-wise
- cout << endl << ">> append data points of d3 to d1" << endl ;
- d1->append(*d3) ;
- d1->Print("v") ;
+ // O p e r a t i o n s o n b i n n e d d a t a s e t s
+ // ---------------------------------------------------------
+ // A binned dataset can be constructed empty, from an unbinned dataset, or
+ // from a ROOT native histogram (TH1,2,3)
- // O p e r a t i o n s o n b i n n e d d a t a s e t s
- // ---------------------------------------------------------
+ cout << ">> construct dh (binned) from d(unbinned) but only take the x and y dimensions," << endl
+ << ">> the category 'c' will be projected in the filling process" << endl ;
+
+ // The binning of real variables (like x,y) is done using their fit range
+ // 'get/setRange()' and number of specified fit bins 'get/setBins()'.
+ // Category dimensions of binned datasets get one bin per defined category state
+ x.setBins(10) ;
+ y.setBins(10) ;
+ RooDataHist dh("dh","binned version of d",RooArgSet(x,y),d) ;
+ dh.Print("v") ;
- // A binned dataset can be constructed empty, from an unbinned dataset, or
- // from a ROOT native histogram (TH1,2,3)
+ RooPlot* yframe = y.frame(Bins(10),Title("Operations on binned datasets")) ;
+ dh.plotOn(yframe) ; // plot projection of 2D binned data on y
- cout << ">> construct dh (binned) from d(unbinned) but only take the x and y dimensions," << endl
- << ">> the category 'c' will be projected in the filling process" << endl ;
-
- // The binning of real variables (like x,y) is done using their fit range
- // 'get/setRange()' and number of specified fit bins 'get/setBins()'.
- // Category dimensions of binned datasets get one bin per defined category state
- x.setBins(10) ;
- y.setBins(10) ;
- RooDataHist dh("dh","binned version of d",RooArgSet(x,y),d) ;
- dh.Print("v") ;
+ // Examine the statistics of a binned dataset
+ cout << ">> number of bins in dh : " << dh.numEntries() << endl ;
+ cout << ">> sum of weights in dh : " << dh.sum(kFALSE) << endl ;
+ cout << ">> integral over histogram: " << dh.sum(kTRUE) << endl ; // accounts for bin volume
- RooPlot* yframe = y.frame(Bins(10),Title("Operations on binned datasets")) ;
- dh.plotOn(yframe) ; // plot projection of 2D binned data on y
+ // Locate a bin from a set of coordinates and retrieve its properties
+ x = 0.3 ; y = 20.5 ;
+ cout << ">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) " << endl ;
+ cout << " bin center:" << endl ;
+ dh.get(RooArgSet(x,y))->Print("v") ; // load bin center coordinates in internal buffer
+ cout << " weight = " << dh.weight() << endl ; // return weight of last loaded coordinates
- // Examine the statistics of a binned dataset
- cout << ">> number of bins in dh : " << dh.numEntries() << endl ;
- cout << ">> sum of weights in dh : " << dh.sum(kFALSE) << endl ;
- cout << ">> integral over histogram: " << dh.sum(kTRUE) << endl ; // accounts for bin volume
+ // Reduce the 2-dimensional binned dataset to a 1-dimensional binned dataset
+ //
+ // All reduce() methods are interfaced in RooAbsData. All reduction techniques
+ // demonstrated on unbinned datasets can be applied to binned datasets as well.
+ cout << ">> Creating 1-dimensional projection on y of dh for bins with x>0" << endl ;
+ RooDataHist* dh2 = (RooDataHist*) dh.reduce(y,"x>0") ;
+ dh2->Print("v") ;
- // Locate a bin from a set of coordinates and retrieve its properties
- x = 0.3 ; y = 20.5 ;
- cout << ">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) " << endl ;
- cout << " bin center:" << endl ;
- dh.get(RooArgSet(x,y))->Print("v") ; // load bin center coordinates in internal buffer
- cout << " weight = " << dh.weight() << endl ; // return weight of last loaded coordinates
+ // Add dh2 to yframe and redraw
+ dh2->plotOn(yframe,LineColor(kRed),MarkerColor(kRed)) ;
- // Reduce the 2-dimensional binned dataset to a 1-dimensional binned dataset
- //
- // All reduce() methods are interfaced in RooAbsData. All reduction techniques
- // demonstrated on unbinned datasets can be applied to binned datasets as well.
- cout << ">> Creating 1-dimensional projection on y of dh for bins with x>0" << endl ;
- RooDataHist* dh2 = (RooDataHist*) dh.reduce(y,"x>0") ;
- dh2->Print("v") ;
-
- // Add dh2 to yframe and redraw
- dh2->plotOn(yframe,LineColor(kRed),MarkerColor(kRed)) ;
+ // S a v i n g a n d l o a d i n g f r o m f i l e
+ // -------------------------------------------------------
- // S a v i n g a n d l o a d i n g f r o m f i l e
- // -------------------------------------------------------
+ // Datasets can be persisted with ROOT I/O
+ cout << endl << ">> Persisting d via ROOT I/O" << endl ;
+ TFile f("rf402_datahandling.root","RECREATE") ;
+ d.Write() ;
+ f.ls() ;
- // Datasets can be persisted with ROOT I/O
- cout << endl << ">> Persisting d via ROOT I/O" << endl ;
- TFile f("rf402_datahandling.root","RECREATE") ;
- d.Write() ;
- f.ls() ;
+ // To read back in future session:
+ // > TFile f("rf402_datahandling.root") ;
+ // > RooDataSet* d = (RooDataSet*) f.FindObject("d") ;
- // To read back in future session:
- // > TFile f("rf402_datahandling.root") ;
- // > RooDataSet* d = (RooDataSet*) f.FindObject("d") ;
-
- new TCanvas("rf402_datahandling","rf402_datahandling",600,600) ;
- gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
+ new TCanvas("rf402_datahandling","rf402_datahandling",600,600) ;
+ gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
}
diff --git a/tutorials/roofit/rf403_weightedevts.C b/tutorials/roofit/rf403_weightedevts.C
index 6d8eac98f8574268e321897a52899613f6952056..03959dbd0d16902cf3d8e89da4ff0a31a54229de 100644
--- a/tutorials/roofit/rf403_weightedevts.C
+++ b/tutorials/roofit/rf403_weightedevts.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'DATA AND CATEGORIES' RooFit tutorial macro #403
///
/// Using weights in unbinned datasets
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
@@ -32,151 +30,151 @@ using namespace RooFit ;
void rf403_weightedevts()
{
- // C r e a t e o b s e r v a b l e a n d u n w e i g h t e d d a t a s e t
- // -------------------------------------------------------------------------------
+ // C r e a t e o b s e r v a b l e a n d u n w e i g h t e d d a t a s e t
+ // -------------------------------------------------------------------------------
- // Declare observable
- RooRealVar x("x","x",-10,10) ;
- x.setBins(40) ;
+ // Declare observable
+ RooRealVar x("x","x",-10,10) ;
+ x.setBins(40) ;
- // Construction a uniform pdf
- RooPolynomial p0("px","px",x) ;
+ // Construction a uniform pdf
+ RooPolynomial p0("px","px",x) ;
- // Sample 1000 events from pdf
- RooDataSet* data = p0.generate(x,1000) ;
+ // Sample 1000 events from pdf
+ RooDataSet* data = p0.generate(x,1000) ;
-
+
- // C a l c u l a t e w e i g h t a n d m a k e d a t a s e t w e i g h t e d
- // -----------------------------------------------------------------------------------
+ // C a l c u l a t e w e i g h t a n d m a k e d a t a s e t w e i g h t e d
+ // -----------------------------------------------------------------------------------
- // Construct formula to calculate (fake) weight for events
- RooFormulaVar wFunc("w","event weight","(x*x+10)",x) ;
+ // Construct formula to calculate (fake) weight for events
+ RooFormulaVar wFunc("w","event weight","(x*x+10)",x) ;
- // Add column with variable w to previously generated dataset
- RooRealVar* w = (RooRealVar*) data->addColumn(wFunc) ;
+ // Add column with variable w to previously generated dataset
+ RooRealVar* w = (RooRealVar*) data->addColumn(wFunc) ;
- // Dataset d is now a dataset with two observable (x,w) with 1000 entries
- data->Print() ;
+ // Dataset d is now a dataset with two observable (x,w) with 1000 entries
+ data->Print() ;
- // Instruct dataset wdata in interpret w as event weight rather than as observable
- RooDataSet wdata(data->GetName(),data->GetTitle(),data,*data->get(),0,w->GetName()) ;
+ // Instruct dataset wdata in interpret w as event weight rather than as observable
+ RooDataSet wdata(data->GetName(),data->GetTitle(),data,*data->get(),0,w->GetName()) ;
- // Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K
- wdata.Print() ;
+ // Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K
+ wdata.Print() ;
- // U n b i n n e d M L f i t t o w e i g h t e d d a t a
- // ---------------------------------------------------------------
+ // U n b i n n e d M L f i t t o w e i g h t e d d a t a
+ // ---------------------------------------------------------------
- // Construction quadratic polynomial pdf for fitting
- RooRealVar a0("a0","a0",1) ;
- RooRealVar a1("a1","a1",0,-1,1) ;
- RooRealVar a2("a2","a2",1,0,10) ;
- RooPolynomial p2("p2","p2",x,RooArgList(a0,a1,a2),0) ;
+ // Construction quadratic polynomial pdf for fitting
+ RooRealVar a0("a0","a0",1) ;
+ RooRealVar a1("a1","a1",0,-1,1) ;
+ RooRealVar a2("a2","a2",1,0,10) ;
+ RooPolynomial p2("p2","p2",x,RooArgList(a0,a1,a2),0) ;
- // Fit quadratic polynomial to weighted data
+ // Fit quadratic polynomial to weighted data
- // NOTE: A plain Maximum likelihood fit to weighted data does in general
- // NOT result in correct error estimates, unless individual
- // event weights represent Poisson statistics themselves.
- //
- // Fit with 'wrong' errors
- RooFitResult* r_ml_wgt = p2.fitTo(wdata,Save()) ;
-
- // A first order correction to estimated parameter errors in an
- // (unbinned) ML fit can be obtained by calculating the
- // covariance matrix as
- //
- // V' = V C-1 V
- //
- // where V is the covariance matrix calculated from a fit
- // to -logL = - sum [ w_i log f(x_i) ] and C is the covariance
- // matrix calculated from -logL' = -sum [ w_i^2 log f(x_i) ]
- // (i.e. the weights are applied squared)
- //
- // A fit in this mode can be performed as follows:
+ // NOTE: A plain Maximum likelihood fit to weighted data does in general
+ // NOT result in correct error estimates, unless individual
+ // event weights represent Poisson statistics themselves.
+ //
+ // Fit with 'wrong' errors
+ RooFitResult* r_ml_wgt = p2.fitTo(wdata,Save()) ;
+
+ // A first order correction to estimated parameter errors in an
+ // (unbinned) ML fit can be obtained by calculating the
+ // covariance matrix as
+ //
+ // V' = V C-1 V
+ //
+ // where V is the covariance matrix calculated from a fit
+ // to -logL = - sum [ w_i log f(x_i) ] and C is the covariance
+ // matrix calculated from -logL' = -sum [ w_i^2 log f(x_i) ]
+ // (i.e. the weights are applied squared)
+ //
+ // A fit in this mode can be performed as follows:
- RooFitResult* r_ml_wgt_corr = p2.fitTo(wdata,Save(),SumW2Error(kTRUE)) ;
+ RooFitResult* r_ml_wgt_corr = p2.fitTo(wdata,Save(),SumW2Error(kTRUE)) ;
- // P l o t w e i g h e d d a t a a n d f i t r e s u l t
- // ---------------------------------------------------------------
+ // P l o t w e i g h e d d a t a a n d f i t r e s u l t
+ // ---------------------------------------------------------------
- // Construct plot frame
- RooPlot* frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data")) ;
+ // Construct plot frame
+ RooPlot* frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data")) ;
- // Plot data using sum-of-weights-squared error rather than Poisson errors
- wdata.plotOn(frame,DataError(RooAbsData::SumW2)) ;
+ // Plot data using sum-of-weights-squared error rather than Poisson errors
+ wdata.plotOn(frame,DataError(RooAbsData::SumW2)) ;
- // Overlay result of 2nd order polynomial fit to weighted data
- p2.plotOn(frame) ;
+ // Overlay result of 2nd order polynomial fit to weighted data
+ p2.plotOn(frame) ;
- // M L F i t o f p d f t o e q u i v a l e n t u n w e i g h t e d d a t a s e t
- // -----------------------------------------------------------------------------------------
-
- // Construct a pdf with the same shape as p0 after weighting
- RooGenericPdf genPdf("genPdf","x*x+10",x) ;
+ // ML Fit of pdf to equivalent unweighted dataset
+ // -----------------------------------------------------------------------------------------
+
+ // Construct a pdf with the same shape as p0 after weighting
+ RooGenericPdf genPdf("genPdf","x*x+10",x) ;
- // Sample a dataset with the same number of events as data
- RooDataSet* data2 = genPdf.generate(x,1000) ;
+ // Sample a dataset with the same number of events as data
+ RooDataSet* data2 = genPdf.generate(x,1000) ;
- // Sample a dataset with the same number of weights as data
- RooDataSet* data3 = genPdf.generate(x,43000) ;
+ // Sample a dataset with the same number of weights as data
+ RooDataSet* data3 = genPdf.generate(x,43000) ;
- // Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison
- RooFitResult* r_ml_unw10 = p2.fitTo(*data2,Save()) ;
- RooFitResult* r_ml_unw43 = p2.fitTo(*data3,Save()) ;
+ // Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison
+ RooFitResult* r_ml_unw10 = p2.fitTo(*data2,Save()) ;
+ RooFitResult* r_ml_unw43 = p2.fitTo(*data3,Save()) ;
- // C h i 2 f i t o f p d f t o b i n n e d w e i g h t e d d a t a s e t
- // ------------------------------------------------------------------------------------
+ // C h i 2 f i t o f p d f t o b i n n e d w e i g h t e d d a t a s e t
+ // ------------------------------------------------------------------------------------
- // Construct binned clone of unbinned weighted dataset
- RooDataHist* binnedData = wdata.binnedClone() ;
- binnedData->Print("v") ;
+ // Construct binned clone of unbinned weighted dataset
+ RooDataHist* binnedData = wdata.binnedClone() ;
+ binnedData->Print("v") ;
- // Perform chi2 fit to binned weighted dataset using sum-of-weights errors
- //
- // NB: Within the usual approximations of a chi2 fit, a chi2 fit to weighted
- // data using sum-of-weights-squared errors does give correct error
- // estimates
- RooChi2Var chi2("chi2","chi2",p2,*binnedData,DataError(RooAbsData::SumW2)) ;
- RooMinuit m(chi2) ;
- m.migrad() ;
- m.hesse() ;
+ // Perform chi2 fit to binned weighted dataset using sum-of-weights errors
+ //
+ // NB: Within the usual approximations of a chi2 fit, a chi2 fit to weighted
+ // data using sum-of-weights-squared errors does give correct error
+ // estimates
+ RooChi2Var chi2("chi2","chi2",p2,*binnedData,DataError(RooAbsData::SumW2)) ;
+ RooMinuit m(chi2) ;
+ m.migrad() ;
+ m.hesse() ;
- // Plot chi^2 fit result on frame as well
- RooFitResult* r_chi2_wgt = m.save() ;
- p2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
+ // Plot chi^2 fit result on frame as well
+ RooFitResult* r_chi2_wgt = m.save() ;
+ p2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
- // C o m p a r e f i t r e s u l t s o f c h i 2 , M L f i t s t o ( u n ) w e i g h t e d d a t a
- // ---------------------------------------------------------------------------------------------------------------
+ // C o m p a r e f i t r e s u l t s o f c h i 2 , M L f i t s t o ( u n ) w e i g h t e d d a t a
+ // ---------------------------------------------------------------------------------------------------------------
- // Note that ML fit on 1Kevt of weighted data is closer to result of ML fit on 43Kevt of unweighted data
- // than to 1Kevt of unweighted data, whereas the reference chi^2 fit with SumW2 error gives a result closer to
- // that of an unbinned ML fit to 1Kevt of unweighted data.
+ // Note that ML fit on 1Kevt of weighted data is closer to result of ML fit on 43Kevt of unweighted data
+ // than to 1Kevt of unweighted data, whereas the reference chi^2 fit with SumW2 error gives a result closer to
+ // that of an unbinned ML fit to 1Kevt of unweighted data.
- cout << "==> ML Fit results on 1K unweighted events" << endl ;
- r_ml_unw10->Print() ;
- cout << "==> ML Fit results on 43K unweighted events" << endl ;
- r_ml_unw43->Print() ;
- cout << "==> ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
- r_ml_wgt->Print() ;
- cout << "==> Corrected ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
- r_ml_wgt_corr->Print() ;
- cout << "==> Chi2 Fit results on 1K weighted events with a summed weight of 43K" << endl ;
- r_chi2_wgt->Print() ;
+ cout << "==> ML Fit results on 1K unweighted events" << endl ;
+ r_ml_unw10->Print() ;
+ cout << "==> ML Fit results on 43K unweighted events" << endl ;
+ r_ml_unw43->Print() ;
+ cout << "==> ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
+ r_ml_wgt->Print() ;
+ cout << "==> Corrected ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
+ r_ml_wgt_corr->Print() ;
+ cout << "==> Chi2 Fit results on 1K weighted events with a summed weight of 43K" << endl ;
+ r_chi2_wgt->Print() ;
- new TCanvas("rf403_weightedevts","rf403_weightedevts",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.8) ; frame->Draw() ;
+ new TCanvas("rf403_weightedevts","rf403_weightedevts",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.8) ; frame->Draw() ;
}
diff --git a/tutorials/roofit/rf404_categories.C b/tutorials/roofit/rf404_categories.C
index 5b3632f6767d0f955f017850623dbae14e153414..48f7fa580a1724af891c162296e8545247d2c7d5 100644
--- a/tutorials/roofit/rf404_categories.C
+++ b/tutorials/roofit/rf404_categories.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'DATA AND CATEGORIES' RooFit tutorial macro #404
///
/// Working with RooCategory objects to describe discrete variables
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooPolynomial.h"
@@ -29,80 +26,80 @@ using namespace RooFit ;
void rf404_categories()
{
- // C o n s t r u c t a c a t e g o r y w i t h l a b e l s
- // ----------------------------------------------------------------
+ // C o n s t r u c t a c a t e g o r y w i t h l a b e l s
+ // ----------------------------------------------------------------
- // Define a category with labels only
- RooCategory tagCat("tagCat","Tagging category") ;
- tagCat.defineType("Lepton") ;
- tagCat.defineType("Kaon") ;
- tagCat.defineType("NetTagger-1") ;
- tagCat.defineType("NetTagger-2") ;
- tagCat.Print() ;
+ // Define a category with labels only
+ RooCategory tagCat("tagCat","Tagging category") ;
+ tagCat.defineType("Lepton") ;
+ tagCat.defineType("Kaon") ;
+ tagCat.defineType("NetTagger-1") ;
+ tagCat.defineType("NetTagger-2") ;
+ tagCat.Print() ;
- // C o n s t r u c t a c a t e g o r y w i t h l a b e l s a n d i n d e c e s
- // ----------------------------------------------------------------------------------------
+ // C o n s t r u c t a c a t e g o r y w i t h l a b e l s a n d i n d e c e s
+ // ----------------------------------------------------------------------------------------
- // Define a category with explicitly numbered states
- RooCategory b0flav("b0flav","B0 flavour eigenstate") ;
- b0flav.defineType("B0",-1) ;
- b0flav.defineType("B0bar",1) ;
- b0flav.Print() ;
+ // Define a category with explicitly numbered states
+ RooCategory b0flav("b0flav","B0 flavour eigenstate") ;
+ b0flav.defineType("B0",-1) ;
+ b0flav.defineType("B0bar",1) ;
+ b0flav.Print() ;
- // G e n e r a t e d u m m y d a t a f o r t a b u l a t i o n d e m o
- // ----------------------------------------------------------------------------
-
- // Generate a dummy dataset
- RooRealVar x("x","x",0,10) ;
- RooDataSet *data = RooPolynomial("p","p",x).generate(RooArgSet(x,b0flav,tagCat),10000) ;
+ // G e n e r a t e d u m m y d a t a f o r t a b u l a t i o n d e m o
+ // ----------------------------------------------------------------------------
+
+ // Generate a dummy dataset
+ RooRealVar x("x","x",0,10) ;
+ RooDataSet *data = RooPolynomial("p","p",x).generate(RooArgSet(x,b0flav,tagCat),10000) ;
- // P r i n t t a b l e s o f c a t e g o r y c o n t e n t s o f d a t a s e t s
- // ------------------------------------------------------------------------------------------
+ // P r i n t t a b l e s o f c a t e g o r y c o n t e n t s o f d a t a s e t s
+ // ------------------------------------------------------------------------------------------
- // Tables are equivalent of plots for categories
- Roo1DTable* btable = data->table(b0flav) ;
- btable->Print() ;
- btable->Print("v") ;
+ // Tables are equivalent of plots for categories
+ Roo1DTable* btable = data->table(b0flav) ;
+ btable->Print() ;
+ btable->Print("v") ;
- // Create table for subset of events matching cut expression
- Roo1DTable* ttable = data->table(tagCat,"x>8.23") ;
- ttable->Print() ;
- ttable->Print("v") ;
+ // Create table for subset of events matching cut expression
+ Roo1DTable* ttable = data->table(tagCat,"x>8.23") ;
+ ttable->Print() ;
+ ttable->Print("v") ;
- // Create table for all (tagCat x b0flav) state combinations
- Roo1DTable* bttable = data->table(RooArgSet(tagCat,b0flav)) ;
- bttable->Print("v") ;
+ // Create table for all (tagCat x b0flav) state combinations
+ Roo1DTable* bttable = data->table(RooArgSet(tagCat,b0flav)) ;
+ bttable->Print("v") ;
- // Retrieve number of events from table
- // Number can be non-integer if source dataset has weighed events
- Double_t nb0 = btable->get("B0") ;
- cout << "Number of events with B0 flavor is " << nb0 << endl ;
+ // Retrieve number of events from table
+ // Number can be non-integer if source dataset has weighed events
+ Double_t nb0 = btable->get("B0") ;
+ cout << "Number of events with B0 flavor is " << nb0 << endl ;
- // Retrieve fraction of events with "Lepton" tag
- Double_t fracLep = ttable->getFrac("Lepton") ;
- cout << "Fraction of events tagged with Lepton tag is " << fracLep << endl ;
-
+ // Retrieve fraction of events with "Lepton" tag
+ Double_t fracLep = ttable->getFrac("Lepton") ;
+ cout << "Fraction of events tagged with Lepton tag is " << fracLep << endl ;
+
- // D e f i n i n g r a n g e s f o r p l o t t i n g , f i t t i n g o n c a t e g o r i e s
- // ------------------------------------------------------------------------------------------------------
+ // D e f i n i n g r a n g e s f o r p l o t t i n g , f i t t i n g o n c a t e g o r i e s
+ // ------------------------------------------------------------------------------------------------------
- // Define named range as comma separated list of labels
- tagCat.setRange("good","Lepton,Kaon") ;
+ // Define named range as comma separated list of labels
+ tagCat.setRange("good","Lepton,Kaon") ;
- // Or add state names one by one
- tagCat.addToRange("soso","NetTagger-1") ;
- tagCat.addToRange("soso","NetTagger-2") ;
-
- // Use category range in dataset reduction specification
- RooDataSet* goodData = (RooDataSet*) data->reduce(CutRange("good")) ;
- goodData->table(tagCat)->Print("v") ;
+ // Or add state names one by one
+ tagCat.addToRange("soso","NetTagger-1") ;
+ tagCat.addToRange("soso","NetTagger-2") ;
+
+ // Use category range in dataset reduction specification
+ RooDataSet* goodData = (RooDataSet*) data->reduce(CutRange("good")) ;
+ goodData->table(tagCat)->Print("v") ;
}
diff --git a/tutorials/roofit/rf405_realtocatfuncs.C b/tutorials/roofit/rf405_realtocatfuncs.C
index 5af4ee7b37de3a846755c7bbed206d3278b1a7ca..2cd0f30f8f2c750f005dcf45b81546513dc9b0d4 100644
--- a/tutorials/roofit/rf405_realtocatfuncs.C
+++ b/tutorials/roofit/rf405_realtocatfuncs.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'DATA AND CATEGORIES' RooFit tutorial macro #405
///
/// Demonstration of real-->discrete mapping functions
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,93 +29,93 @@ using namespace RooFit ;
void rf405_realtocatfuncs()
{
- // D e f i n e p d f i n x , s a m p l e d a t a s e t i n x
- // ------------------------------------------------------------------------
+ // D e f i n e p d f i n x , s a m p l e d a t a s e t i n x
+ // ------------------------------------------------------------------------
+
+ // Define a dummy PDF in x
+ RooRealVar x("x","x",0,10) ;
+ RooArgusBG a("a","argus(x)",x,RooConst(10),RooConst(-1)) ;
- // Define a dummy PDF in x
- RooRealVar x("x","x",0,10) ;
- RooArgusBG a("a","argus(x)",x,RooConst(10),RooConst(-1)) ;
+ // Generate a dummy dataset
+ RooDataSet *data = a.generate(x,10000) ;
- // Generate a dummy dataset
- RooDataSet *data = a.generate(x,10000) ;
+ // C r e a t e a t h r e s h o l d r e a l - > c a t f u n c t i o n
+ // --------------------------------------------------------------------------
- // C r e a t e a t h r e s h o l d r e a l - > c a t f u n c t i o n
- // --------------------------------------------------------------------------
+ // A RooThresholdCategory is a category function that maps regions in a real-valued
+ // input observable observables to state names. At construction time a 'default'
+ // state name must be specified to which all values of x are mapped that are not
+ // otherwise assigned
+ RooThresholdCategory xRegion("xRegion","region of x",x,"Background") ;
- // A RooThresholdCategory is a category function that maps regions in a real-valued
- // input observable observables to state names. At construction time a 'default'
- // state name must be specified to which all values of x are mapped that are not
- // otherwise assigned
- RooThresholdCategory xRegion("xRegion","region of x",x,"Background") ;
+ // Specify thresholds and state assignments one-by-one.
+ // Each statement specifies that all values _below_ the given value
+ // (and above any lower specified threshold) are mapped to the
+ // category state with the given name
+ //
+ // Background | SideBand | Signal | SideBand | Background
+ // 4.23 5.23 8.23 9.23
+ xRegion.addThreshold(4.23,"Background") ;
+ xRegion.addThreshold(5.23,"SideBand") ;
+ xRegion.addThreshold(8.23,"Signal") ;
+ xRegion.addThreshold(9.23,"SideBand") ;
- // Specify thresholds and state assignments one-by-one.
- // Each statement specifies that all values _below_ the given value
- // (and above any lower specified threshold) are mapped to the
- // category state with the given name
- //
- // Background | SideBand | Signal | SideBand | Background
- // 4.23 5.23 8.23 9.23
- xRegion.addThreshold(4.23,"Background") ;
- xRegion.addThreshold(5.23,"SideBand") ;
- xRegion.addThreshold(8.23,"Signal") ;
- xRegion.addThreshold(9.23,"SideBand") ;
+ // U s e t h r e s h o l d f u n c t i o n t o p l o t d a t a r e g i o n s
+ // -------------------------------------------------------------------------------------
- // U s e t h r e s h o l d f u n c t i o n t o p l o t d a t a r e g i o n s
- // -------------------------------------------------------------------------------------
+ // Add values of threshold function to dataset so that it can be used as observable
+ data->addColumn(xRegion) ;
- // Add values of threshold function to dataset so that it can be used as observable
- data->addColumn(xRegion) ;
+ // Make plot of data in x
+ RooPlot* xframe = x.frame(Title("Demo of threshold and binning mapping functions")) ;
+ data->plotOn(xframe) ;
- // Make plot of data in x
- RooPlot* xframe = x.frame(Title("Demo of threshold and binning mapping functions")) ;
- data->plotOn(xframe) ;
+ // Use calculated category to select sideband data
+ data->plotOn(xframe,Cut("xRegion==xRegion::SideBand"),MarkerColor(kRed),LineColor(kRed)) ;
- // Use calculated category to select sideband data
- data->plotOn(xframe,Cut("xRegion==xRegion::SideBand"),MarkerColor(kRed),LineColor(kRed)) ;
+ // C r e a t e a b i n n i n g r e a l - > c a t f u n c t i o n
+ // ----------------------------------------------------------------------
- // C r e a t e a b i n n i n g r e a l - > c a t f u n c t i o n
- // ----------------------------------------------------------------------
+ // A RooBinningCategory is a category function that maps bins of a (named) binning definition
+ // in a real-valued input observable observables to state names. The state names are automatically
+ // constructed from the variable name, the binning name and the bin number. If no binning name
+ // is specified the default binning is mapped
- // A RooBinningCategory is a category function that maps bins of a (named) binning definition
- // in a real-valued input observable observables to state names. The state names are automatically
- // constructed from the variable name, the binning name and the bin number. If no binning name
- // is specified the default binning is mapped
+ x.setBins(10,"coarse") ;
+ RooBinningCategory xBins("xBins","coarse bins in x",x,"coarse") ;
- x.setBins(10,"coarse") ;
- RooBinningCategory xBins("xBins","coarse bins in x",x,"coarse") ;
+ // U s e b i n n i n g f u n c t i o n f o r t a b u l a t i o n a n d p l o t t i n g
+ // -----------------------------------------------------------------------------------------------
- // U s e b i n n i n g f u n c t i o n f o r t a b u l a t i o n a n d p l o t t i n g
- // -----------------------------------------------------------------------------------------------
+ // Print table of xBins state multiplicity. Note that xBins does not need to be an observable in data
+ // it can be a function of observables in data as well
+ Roo1DTable* xbtable = data->table(xBins) ;
+ xbtable->Print("v") ;
- // Print table of xBins state multiplicity. Note that xBins does not need to be an observable in data
- // it can be a function of observables in data as well
- Roo1DTable* xbtable = data->table(xBins) ;
- xbtable->Print("v") ;
+ // Add values of xBins function to dataset so that it can be used as observable
+ RooCategory* xb = (RooCategory*) data->addColumn(xBins) ;
- // Add values of xBins function to dataset so that it can be used as observable
- RooCategory* xb = (RooCategory*) data->addColumn(xBins) ;
+ // Define range "alt" as including bins 1,3,5,7,9
+ xb->setRange("alt","x_coarse_bin1,x_coarse_bin3,x_coarse_bin5,x_coarse_bin7,x_coarse_bin9") ;
- // Define range "alt" as including bins 1,3,5,7,9
- xb->setRange("alt","x_coarse_bin1,x_coarse_bin3,x_coarse_bin5,x_coarse_bin7,x_coarse_bin9") ;
-
- // Construct subset of data matching range "alt" but only for the first 5000 events and plot it on the frame
- RooDataSet* dataSel = (RooDataSet*) data->reduce(CutRange("alt"),EventRange(0,5000)) ;
- dataSel->plotOn(xframe,MarkerColor(kGreen),LineColor(kGreen)) ;
+ // Construct subset of data matching range "alt" but only for the first 5000 events and plot it on the frame
+ RooDataSet* dataSel = (RooDataSet*) data->reduce(CutRange("alt"),EventRange(0,5000)) ;
+ dataSel->plotOn(xframe,MarkerColor(kGreen),LineColor(kGreen)) ;
- new TCanvas("rf405_realtocatfuncs","rf405_realtocatfuncs",600,600) ;
- xframe->SetMinimum(0.01) ;
- gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ new TCanvas("rf405_realtocatfuncs","rf405_realtocatfuncs",600,600) ;
+ xframe->SetMinimum(0.01) ;
+ gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
}
diff --git a/tutorials/roofit/rf406_cattocatfuncs.C b/tutorials/roofit/rf406_cattocatfuncs.C
index 7471365279da2e2e0368be6514dfbc84a3f03f08..54bcdc78036f9d156fa91d7cf9963a11e17c2672 100644
--- a/tutorials/roofit/rf406_cattocatfuncs.C
+++ b/tutorials/roofit/rf406_cattocatfuncs.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'DATA AND CATEGORIES' RooFit tutorial macro #406
///
/// Demonstration of discrete-->discrete (invertable) functions
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooPolynomial.h"
@@ -29,74 +26,74 @@ using namespace RooFit ;
void rf406_cattocatfuncs()
{
- // C o n s t r u c t t w o c a t e g o r i e s
- // ----------------------------------------------
+ // C o n s t r u c t t w o c a t e g o r i e s
+ // ----------------------------------------------
+
+ // Define a category with labels only
+ RooCategory tagCat("tagCat","Tagging category") ;
+ tagCat.defineType("Lepton") ;
+ tagCat.defineType("Kaon") ;
+ tagCat.defineType("NetTagger-1") ;
+ tagCat.defineType("NetTagger-2") ;
+ tagCat.Print() ;
- // Define a category with labels only
- RooCategory tagCat("tagCat","Tagging category") ;
- tagCat.defineType("Lepton") ;
- tagCat.defineType("Kaon") ;
- tagCat.defineType("NetTagger-1") ;
- tagCat.defineType("NetTagger-2") ;
- tagCat.Print() ;
+ // Define a category with explicitly numbered states
+ RooCategory b0flav("b0flav","B0 flavour eigenstate") ;
+ b0flav.defineType("B0",-1) ;
+ b0flav.defineType("B0bar",1) ;
+ b0flav.Print() ;
- // Define a category with explicitly numbered states
- RooCategory b0flav("b0flav","B0 flavour eigenstate") ;
- b0flav.defineType("B0",-1) ;
- b0flav.defineType("B0bar",1) ;
- b0flav.Print() ;
+ // Construct a dummy dataset with random values of tagCat and b0flav
+ RooRealVar x("x","x",0,10) ;
+ RooPolynomial p("p","p",x) ;
+ RooDataSet* data = p.generate(RooArgSet(x,b0flav,tagCat),10000) ;
- // Construct a dummy dataset with random values of tagCat and b0flav
- RooRealVar x("x","x",0,10) ;
- RooPolynomial p("p","p",x) ;
- RooDataSet* data = p.generate(RooArgSet(x,b0flav,tagCat),10000) ;
+ // C r e a t e a c a t - > c a t m a p p i n g c a t e g o r y
+ // ---------------------------------------------------------------------
- // C r e a t e a c a t - > c a t m a p p i n g c a t e g o r y
- // ---------------------------------------------------------------------
+ // A RooMappedCategory is category->category mapping function based on string expression
+ // The constructor takes an input category an a default state name to which unassigned
+ // states are mapped
+ RooMappedCategory tcatType("tcatType","tagCat type",tagCat,"Cut based") ;
- // A RooMappedCategory is category->category mapping function based on string expression
- // The constructor takes an input category an a default state name to which unassigned
- // states are mapped
- RooMappedCategory tcatType("tcatType","tagCat type",tagCat,"Cut based") ;
+ // Enter fully specified state mappings
+ tcatType.map("Lepton","Cut based") ;
+ tcatType.map("Kaon","Cut based") ;
- // Enter fully specified state mappings
- tcatType.map("Lepton","Cut based") ;
- tcatType.map("Kaon","Cut based") ;
+ // Enter a wilcard expression mapping
+ tcatType.map("NetTagger*","Neural Network") ;
- // Enter a wilcard expression mapping
- tcatType.map("NetTagger*","Neural Network") ;
+ // Make a table of the mapped category state multiplicit in data
+ Roo1DTable* mtable = data->table(tcatType) ;
+ mtable->Print("v") ;
- // Make a table of the mapped category state multiplicit in data
- Roo1DTable* mtable = data->table(tcatType) ;
- mtable->Print("v") ;
+ // C r e a t e a c a t X c a t p r o d u c t c a t e g o r y
+ // ----------------------------------------------------------------------
- // C r e a t e a c a t X c a t p r o d u c t c a t e g o r y
- // ----------------------------------------------------------------------
+ // A SUPER-category is 'product' of _lvalue_ categories. The state names of a super
+ // category is a composite of the state labels of the input categories
+ RooSuperCategory b0Xtcat("b0Xtcat","b0flav X tagCat",RooArgSet(b0flav,tagCat)) ;
- // A SUPER-category is 'product' of _lvalue_ categories. The state names of a super
- // category is a composite of the state labels of the input categories
- RooSuperCategory b0Xtcat("b0Xtcat","b0flav X tagCat",RooArgSet(b0flav,tagCat)) ;
+ // Make a table of the product category state multiplicity in data
+ Roo1DTable* stable = data->table(b0Xtcat) ;
+ stable->Print("v") ;
- // Make a table of the product category state multiplicity in data
- Roo1DTable* stable = data->table(b0Xtcat) ;
- stable->Print("v") ;
+ // Since the super category is an lvalue, assignment is explicitly possible
+ b0Xtcat.setLabel("{B0bar;Lepton}") ;
- // Since the super category is an lvalue, assignment is explicitly possible
- b0Xtcat.setLabel("{B0bar;Lepton}") ;
+ // A MULTI-category is a 'product' of any category (function). The state names of a super
+ // category is a composite of the state labels of the input categories
+ RooMultiCategory b0Xttype("b0Xttype","b0flav X tagType",RooArgSet(b0flav,tcatType)) ;
- // A MULTI-category is a 'product' of any category (function). The state names of a super
- // category is a composite of the state labels of the input categories
- RooMultiCategory b0Xttype("b0Xttype","b0flav X tagType",RooArgSet(b0flav,tcatType)) ;
-
- // Make a table of the product category state multiplicity in data
- Roo1DTable* xtable = data->table(b0Xttype) ;
- xtable->Print("v") ;
+ // Make a table of the product category state multiplicity in data
+ Roo1DTable* xtable = data->table(b0Xttype) ;
+ xtable->Print("v") ;
}
diff --git a/tutorials/roofit/rf407_latextables.C b/tutorials/roofit/rf407_latextables.C
index 3e334ab77b3c61949f684d34b1f7d7ffdc77cab7..72457d4893896a9441a155461e78fa4261068781 100644
--- a/tutorials/roofit/rf407_latextables.C
+++ b/tutorials/roofit/rf407_latextables.C
@@ -1,18 +1,14 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'DATA AND CATEGORIES' RooFit tutorial macro #407
///
/// Latex printing of lists and sets of RooArgSets
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,75 +24,75 @@ using namespace RooFit ;
void rf407_latextables()
{
- // S e t u p c o m p o s i t e p d f
- // --------------------------------------
+ // S e t u p c o m p o s i t e p d f
+ // --------------------------------------
+
+ // 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) ;
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
+ // 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) ;
- // 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",-0.2,0.,1.) ;
+ RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
- // 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) ;
-
- // Build Chebychev polynomial p.d.f.
- RooRealVar a0("a0","a0",0.5,0.,1.) ;
- RooRealVar a1("a1","a1",-0.2,0.,1.) ;
- RooChebychev bkg1("bkg1","Background 1",x,RooArgSet(a0,a1)) ;
+ // Build expontential pdf
+ RooRealVar alpha("alpha","alpha",-1) ;
+ RooExponential bkg2("bkg2","Background 2",x,alpha) ;
- // Build expontential pdf
- RooRealVar alpha("alpha","alpha",-1) ;
- RooExponential bkg2("bkg2","Background 2",x,alpha) ;
+ // Sum the background components into a composite background p.d.f.
+ RooRealVar bkg1frac("sig1frac","fraction of component 1 in background",0.2,0.,1.) ;
+ RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),sig1frac) ;
- // Sum the background components into a composite background p.d.f.
- RooRealVar bkg1frac("sig1frac","fraction of component 1 in background",0.2,0.,1.) ;
- RooAddPdf bkg("bkg","Signal",RooArgList(bkg1,bkg2),sig1frac) ;
-
- // 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) ;
+ // 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) ;
- // M a k e l i s t o f p a r a m e t e r s b e f o r e a n d a f t e r f i t
- // ----------------------------------------------------------------------------------------
+ // M a k e l i s t o f p a r a m e t e r s b e f o r e a n d a f t e r f i t
+ // ----------------------------------------------------------------------------------------
- // Make list of model parameters
- RooArgSet* params = model.getParameters(x) ;
+ // Make list of model parameters
+ RooArgSet* params = model.getParameters(x) ;
- // Save snapshot of prefit parameters
- RooArgSet* initParams = (RooArgSet*) params->snapshot() ;
+ // Save snapshot of prefit parameters
+ RooArgSet* initParams = (RooArgSet*) params->snapshot() ;
- // Do fit to data, to obtain error estimates on parameters
- RooDataSet* data = model.generate(x,1000) ;
- model.fitTo(*data) ;
+ // Do fit to data, to obtain error estimates on parameters
+ RooDataSet* data = model.generate(x,1000) ;
+ model.fitTo(*data) ;
- // P r i n t l a t ex t a b l e o f p a r a m e t e r s o f p d f
- // --------------------------------------------------------------------------
+ // P r i n t l a t ex t a b l e o f p a r a m e t e r s o f p d f
+ // --------------------------------------------------------------------------
- // Print parameter list in LaTeX for (one column with names, one column with values)
- params->printLatex() ;
+ // Print parameter list in LaTeX for (one column with names, one column with values)
+ params->printLatex() ;
- // Print parameter list in LaTeX for (names values|names values)
- params->printLatex(Columns(2)) ;
+ // Print parameter list in LaTeX for (names values|names values)
+ params->printLatex(Columns(2)) ;
- // Print two parameter lists side by side (name values initvalues)
- params->printLatex(Sibling(*initParams)) ;
+ // Print two parameter lists side by side (name values initvalues)
+ params->printLatex(Sibling(*initParams)) ;
- // Print two parameter lists side by side (name values initvalues|name values initvalues)
- params->printLatex(Sibling(*initParams),Columns(2)) ;
+ // Print two parameter lists side by side (name values initvalues|name values initvalues)
+ params->printLatex(Sibling(*initParams),Columns(2)) ;
- // Write LaTex table to file
- params->printLatex(Sibling(*initParams),OutputFile("rf407_latextables.tex")) ;
+ // Write LaTex table to file
+ params->printLatex(Sibling(*initParams),OutputFile("rf407_latextables.tex")) ;
}
diff --git a/tutorials/roofit/rf501_simultaneouspdf.C b/tutorials/roofit/rf501_simultaneouspdf.C
index 3f77a2f0ea941bb54cc7481869403aa8d7e972c8..d6dd9a8e0e9869ced94da7d5ba4cee0cfd63e0a1 100644
--- a/tutorials/roofit/rf501_simultaneouspdf.C
+++ b/tutorials/roofit/rf501_simultaneouspdf.C
@@ -1,19 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #501
///
/// Using simultaneous p.d.f.s to describe simultaneous fits to multiple
/// datasets
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,117 +28,117 @@ using namespace RooFit ;
void rf501_simultaneouspdf()
{
- // C r e a t e m o d e l f o r p h y s i c s s a m p l e
- // -------------------------------------------------------------
+ // C r e a t e m o d e l f o r p h y s i c s s a m p l e
+ // -------------------------------------------------------------
- // Create observables
- RooRealVar x("x","x",-8,8) ;
+ // Create observables
+ RooRealVar x("x","x",-8,8) ;
- // Construct signal pdf
- RooRealVar mean("mean","mean",0,-8,8) ;
- RooRealVar sigma("sigma","sigma",0.3,0.1,10) ;
- RooGaussian gx("gx","gx",x,mean,sigma) ;
+ // Construct signal pdf
+ RooRealVar mean("mean","mean",0,-8,8) ;
+ RooRealVar sigma("sigma","sigma",0.3,0.1,10) ;
+ RooGaussian gx("gx","gx",x,mean,sigma) ;
- // Construct background pdf
- RooRealVar a0("a0","a0",-0.1,-1,1) ;
- RooRealVar a1("a1","a1",0.004,-1,1) ;
- RooChebychev px("px","px",x,RooArgSet(a0,a1)) ;
+ // Construct background pdf
+ RooRealVar a0("a0","a0",-0.1,-1,1) ;
+ RooRealVar a1("a1","a1",0.004,-1,1) ;
+ RooChebychev px("px","px",x,RooArgSet(a0,a1)) ;
- // Construct composite pdf
- RooRealVar f("f","f",0.2,0.,1.) ;
- RooAddPdf model("model","model",RooArgList(gx,px),f) ;
+ // Construct composite pdf
+ RooRealVar f("f","f",0.2,0.,1.) ;
+ RooAddPdf model("model","model",RooArgList(gx,px),f) ;
- // C r e a t e m o d e l f o r c o n t r o l s a m p l e
- // --------------------------------------------------------------
+ // C r e a t e m o d e l f o r c o n t r o l s a m p l e
+ // --------------------------------------------------------------
- // Construct signal pdf.
- // NOTE that sigma is shared with the signal sample model
- RooRealVar mean_ctl("mean_ctl","mean_ctl",-3,-8,8) ;
- RooGaussian gx_ctl("gx_ctl","gx_ctl",x,mean_ctl,sigma) ;
+ // Construct signal pdf.
+ // NOTE that sigma is shared with the signal sample model
+ RooRealVar mean_ctl("mean_ctl","mean_ctl",-3,-8,8) ;
+ RooGaussian gx_ctl("gx_ctl","gx_ctl",x,mean_ctl,sigma) ;
- // Construct the background pdf
- RooRealVar a0_ctl("a0_ctl","a0_ctl",-0.1,-1,1) ;
- RooRealVar a1_ctl("a1_ctl","a1_ctl",0.5,-0.1,1) ;
- RooChebychev px_ctl("px_ctl","px_ctl",x,RooArgSet(a0_ctl,a1_ctl)) ;
+ // Construct the background pdf
+ RooRealVar a0_ctl("a0_ctl","a0_ctl",-0.1,-1,1) ;
+ RooRealVar a1_ctl("a1_ctl","a1_ctl",0.5,-0.1,1) ;
+ RooChebychev px_ctl("px_ctl","px_ctl",x,RooArgSet(a0_ctl,a1_ctl)) ;
- // Construct the composite model
- RooRealVar f_ctl("f_ctl","f_ctl",0.5,0.,1.) ;
- RooAddPdf model_ctl("model_ctl","model_ctl",RooArgList(gx_ctl,px_ctl),f_ctl) ;
+ // Construct the composite model
+ RooRealVar f_ctl("f_ctl","f_ctl",0.5,0.,1.) ;
+ RooAddPdf model_ctl("model_ctl","model_ctl",RooArgList(gx_ctl,px_ctl),f_ctl) ;
- // G e n e r a t e e v e n t s f o r b o t h s a m p l e s
- // ---------------------------------------------------------------
+ // G e n e r a t e e v e n t s f o r b o t h s a m p l e s
+ // ---------------------------------------------------------------
- // Generate 1000 events in x and y from model
- RooDataSet *data = model.generate(RooArgSet(x),100) ;
- RooDataSet *data_ctl = model_ctl.generate(RooArgSet(x),2000) ;
+ // Generate 1000 events in x and y from model
+ RooDataSet *data = model.generate(RooArgSet(x),100) ;
+ RooDataSet *data_ctl = model_ctl.generate(RooArgSet(x),2000) ;
- // C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s
- // ---------------------------------------------------------------------------
+ // C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s
+ // ---------------------------------------------------------------------------
- // Define category to distinguish physics and control samples events
- RooCategory sample("sample","sample") ;
- sample.defineType("physics") ;
- sample.defineType("control") ;
+ // Define category to distinguish physics and control samples events
+ RooCategory sample("sample","sample") ;
+ sample.defineType("physics") ;
+ sample.defineType("control") ;
- // Construct combined dataset in (x,sample)
- RooDataSet combData("combData","combined data",x,Index(sample),Import("physics",*data),Import("control",*data_ctl)) ;
+ // Construct combined dataset in (x,sample)
+ RooDataSet combData("combData","combined data",x,Index(sample),Import("physics",*data),Import("control",*data_ctl)) ;
- // C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e )
- // -----------------------------------------------------------------------------------
+ // C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e )
+ // -----------------------------------------------------------------------------------
- // Construct a simultaneous pdf using category sample as index
- RooSimultaneous simPdf("simPdf","simultaneous pdf",sample) ;
+ // Construct a simultaneous pdf using category sample as index
+ RooSimultaneous simPdf("simPdf","simultaneous pdf",sample) ;
- // Associate model with the physics state and model_ctl with the control state
- simPdf.addPdf(model,"physics") ;
- simPdf.addPdf(model_ctl,"control") ;
+ // Associate model with the physics state and model_ctl with the control state
+ simPdf.addPdf(model,"physics") ;
+ simPdf.addPdf(model_ctl,"control") ;
- // P e r f o r m a s i m u l t a n e o u s f i t
- // ---------------------------------------------------
+ // P e r f o r m a s i m u l t a n e o u s f i t
+ // ---------------------------------------------------
- // Perform simultaneous fit of model to data and model_ctl to data_ctl
- simPdf.fitTo(combData) ;
+ // Perform simultaneous fit of model to data and model_ctl to data_ctl
+ simPdf.fitTo(combData) ;
- // P l o t m o d e l s l i c e s o n d a t a s l i c e s
- // ----------------------------------------------------------------
+ // P l o t m o d e l s l i c e s o n d a t a s l i c e s
+ // ----------------------------------------------------------------
- // Make a frame for the physics sample
- RooPlot* frame1 = x.frame(Bins(30),Title("Physics sample")) ;
+ // Make a frame for the physics sample
+ RooPlot* frame1 = x.frame(Bins(30),Title("Physics sample")) ;
- // Plot all data tagged as physics sample
- combData.plotOn(frame1,Cut("sample==sample::physics")) ;
+ // Plot all data tagged as physics sample
+ combData.plotOn(frame1,Cut("sample==sample::physics")) ;
- // Plot "physics" slice of simultaneous pdf.
- // NBL You _must_ project the sample index category with data using ProjWData
- // as a RooSimultaneous makes no prediction on the shape in the index category
- // and can thus not be integrated
- simPdf.plotOn(frame1,Slice(sample,"physics"),ProjWData(sample,combData)) ;
- simPdf.plotOn(frame1,Slice(sample,"physics"),Components("px"),ProjWData(sample,combData),LineStyle(kDashed)) ;
+ // Plot "physics" slice of simultaneous pdf.
+ // NBL You _must_ project the sample index category with data using ProjWData
+ // as a RooSimultaneous makes no prediction on the shape in the index category
+ // and can thus not be integrated
+ simPdf.plotOn(frame1,Slice(sample,"physics"),ProjWData(sample,combData)) ;
+ simPdf.plotOn(frame1,Slice(sample,"physics"),Components("px"),ProjWData(sample,combData),LineStyle(kDashed)) ;
- // The same plot for the control sample slice
- RooPlot* frame2 = x.frame(Bins(30),Title("Control sample")) ;
- combData.plotOn(frame2,Cut("sample==sample::control")) ;
- simPdf.plotOn(frame2,Slice(sample,"control"),ProjWData(sample,combData)) ;
- simPdf.plotOn(frame2,Slice(sample,"control"),Components("px_ctl"),ProjWData(sample,combData),LineStyle(kDashed)) ;
+ // The same plot for the control sample slice
+ RooPlot* frame2 = x.frame(Bins(30),Title("Control sample")) ;
+ combData.plotOn(frame2,Cut("sample==sample::control")) ;
+ simPdf.plotOn(frame2,Slice(sample,"control"),ProjWData(sample,combData)) ;
+ simPdf.plotOn(frame2,Slice(sample,"control"),Components("px_ctl"),ProjWData(sample,combData),LineStyle(kDashed)) ;
- TCanvas* c = new TCanvas("rf501_simultaneouspdf","rf403_simultaneouspdf",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ TCanvas* c = new TCanvas("rf501_simultaneouspdf","rf403_simultaneouspdf",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf502_wspacewrite.C b/tutorials/roofit/rf502_wspacewrite.C
index ae54638b3e5cb15b347a2b697383bb43b085e711..81490386bb6567de2185f8e5987604b51d3414ac 100644
--- a/tutorials/roofit/rf502_wspacewrite.C
+++ b/tutorials/roofit/rf502_wspacewrite.C
@@ -1,10 +1,11 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #502
///
/// Creating and writing a workspace
///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
@@ -30,63 +31,63 @@ using namespace RooFit ;
void rf502_wspacewrite()
{
- // C r e a t e m o d e l a n d d a t a s e t
- // -----------------------------------------------
+ // C r e a t e m o d e l a n d d a t a s e t
+ // -----------------------------------------------
+
+ // Declare observable x
+ RooRealVar x("x","x",0,10) ;
- // 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",5,0,10) ;
+ RooRealVar sigma1("sigma1","width of gaussians",0.5) ;
+ RooRealVar sigma2("sigma2","width of gaussians",1) ;
- // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
- RooRealVar mean("mean","mean of gaussians",5,0,10) ;
- 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) ;
- 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",-0.2,0.,1.) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+ // Build Chebychev polynomial p.d.f.
+ RooRealVar a0("a0","a0",0.5,0.,1.) ;
+ RooRealVar a1("a1","a1",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
- // 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) ;
+ // 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) ;
- // 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) ;
+ // 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) ;
- // Generate a data sample of 1000 events in x from model
- RooDataSet *data = model.generate(x,1000) ;
+ // Generate a data sample of 1000 events in x from model
+ RooDataSet *data = model.generate(x,1000) ;
- // C r e a t e w o r k s p a c e , i m p o r t d a t a a n d m o d e l
- // -----------------------------------------------------------------------------
+ // C r e a t e w o r k s p a c e , i m p o r t d a t a a n d m o d e l
+ // -----------------------------------------------------------------------------
- // Create a new empty workspace
- RooWorkspace *w = new RooWorkspace("w","workspace") ;
+ // Create a new empty workspace
+ RooWorkspace *w = new RooWorkspace("w","workspace") ;
- // Import model and all its components into the workspace
- w->import(model) ;
+ // Import model and all its components into the workspace
+ w->import(model) ;
- // Import data into the workspace
- w->import(*data) ;
+ // Import data into the workspace
+ w->import(*data) ;
- // Print workspace contents
- w->Print() ;
+ // Print workspace contents
+ w->Print() ;
- // S a v e w o r k s p a c e i n f i l e
- // -------------------------------------------
+ // S a v e w o r k s p a c e i n f i l e
+ // -------------------------------------------
- // Save the workspace into a ROOT file
- w->writeToFile("rf502_workspace.root") ;
+ // Save the workspace into a ROOT file
+ w->writeToFile("rf502_workspace.root") ;
- // Workspace will remain in memory after macro finishes
- gDirectory->Add(w) ;
+ // Workspace will remain in memory after macro finishes
+ gDirectory->Add(w) ;
}
diff --git a/tutorials/roofit/rf503_wspaceread.C b/tutorials/roofit/rf503_wspaceread.C
index 67e830d65590485dd7d3235dda7a2c73f4c86e0a..64bdcc1a6a9fc5fa4fa15fef01633f378de72dda 100644
--- a/tutorials/roofit/rf503_wspaceread.C
+++ b/tutorials/roofit/rf503_wspaceread.C
@@ -1,19 +1,18 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #503
///
/// Reading and using a workspace
///
/// --> The input file for this macro is generated by rf502_wspaceread.C
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,51 +30,51 @@ using namespace RooFit ;
void rf503_wspaceread()
{
- // R e a d w o r k s p a c e f r o m f i l e
- // -----------------------------------------------
+ // R e a d w o r k s p a c e f r o m f i l e
+ // -----------------------------------------------
- // Open input file with workspace (generated by rf14_wspacewrite)
- TFile *f = new TFile("rf502_workspace.root") ;
+ // Open input file with workspace (generated by rf14_wspacewrite)
+ TFile *f = new TFile("rf502_workspace.root") ;
- // Retrieve workspace from file
- RooWorkspace* w = (RooWorkspace*) f->Get("w") ;
+ // Retrieve workspace from file
+ RooWorkspace* w = (RooWorkspace*) f->Get("w") ;
- // R e t r i e v e p d f , d a t a f r o m w o r k s p a c e
- // -----------------------------------------------------------------
+ // R e t r i e v e p d f , d a t a f r o m w o r k s p a c e
+ // -----------------------------------------------------------------
- // Retrieve x,model and data from workspace
- RooRealVar* x = w->var("x") ;
- RooAbsPdf* model = w->pdf("model") ;
- RooAbsData* data = w->data("modelData") ;
+ // Retrieve x,model and data from workspace
+ RooRealVar* x = w->var("x") ;
+ RooAbsPdf* model = w->pdf("model") ;
+ RooAbsData* data = w->data("modelData") ;
- // Print structure of composite p.d.f.
- model->Print("t") ;
+ // Print structure of composite p.d.f.
+ model->Print("t") ;
- // F i t m o d e l t o d a t a , p l o t m o d e l
- // ---------------------------------------------------------
+ // F i t m o d e l t o d a t a , p l o t m o d e l
+ // ---------------------------------------------------------
- // Fit model to data
- model->fitTo(*data) ;
-
- // Plot data and PDF overlaid
- RooPlot* xframe = x->frame(Title("Model and data read from workspace")) ;
- data->plotOn(xframe) ;
- model->plotOn(xframe) ;
+ // Fit model to data
+ model->fitTo(*data) ;
+
+ // Plot data and PDF overlaid
+ RooPlot* xframe = x->frame(Title("Model and data read from workspace")) ;
+ 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 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("bkg,sig2"),LineStyle(kDotted)) ;
+ // Overlay the background+sig2 components of model with a dotted line
+ model->plotOn(xframe,Components("bkg,sig2"),LineStyle(kDotted)) ;
- // Draw the frame on the canvas
- new TCanvas("rf503_wspaceread","rf503_wspaceread",600,600) ;
- gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
+ // Draw the frame on the canvas
+ new TCanvas("rf503_wspaceread","rf503_wspaceread",600,600) ;
+ gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
}
diff --git a/tutorials/roofit/rf504_simwstool.C b/tutorials/roofit/rf504_simwstool.C
index 37a0681e77d643755838acd81b4ff30faf83ba2c..2a1a3b32263976f4f81cad3c2c625c6678753a36 100644
--- a/tutorials/roofit/rf504_simwstool.C
+++ b/tutorials/roofit/rf504_simwstool.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #504
///
/// Using RooSimWSTool to construct a simultaneous p.d.f that is built
/// of variations of an input p.d.f
///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooCategory.h"
#include "RooDataSet.h"
@@ -33,82 +31,82 @@ using namespace RooFit ;
void rf504_simwstool()
{
- // C r e a t e m a s t e r p d f
- // ---------------------------------
-
- // Construct gauss(x,m,s)
- RooRealVar x("x","x",-10,10) ;
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",1,-10,10) ;
- RooGaussian gauss("g","g",x,m,s) ;
+ // C r e a t e m a s t e r p d f
+ // ---------------------------------
+
+ // Construct gauss(x,m,s)
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",1,-10,10) ;
+ RooGaussian gauss("g","g",x,m,s) ;
- // Construct poly(x,p0)
- RooRealVar p0("p0","p0",0.01,0.,1.) ;
- RooPolynomial poly("p","p",x,p0) ;
+ // Construct poly(x,p0)
+ RooRealVar p0("p0","p0",0.01,0.,1.) ;
+ RooPolynomial poly("p","p",x,p0) ;
- // Construct model = f*gauss(x) + (1-f)*poly(x)
- RooRealVar f("f","f",0.5,0.,1.) ;
- RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
+ // Construct model = f*gauss(x) + (1-f)*poly(x)
+ RooRealVar f("f","f",0.5,0.,1.) ;
+ RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
- // C r e a t e c a t e g o r y o b s e r v a b l e s f o r s p l i t t i n g
- // ----------------------------------------------------------------------------------
+ // C r e a t e c a t e g o r y o b s e r v a b l e s f o r s p l i t t i n g
+ // ----------------------------------------------------------------------------------
- // Define two categories that can be used for splitting
- RooCategory c("c","c") ;
- c.defineType("run1") ;
- c.defineType("run2") ;
+ // Define two categories that can be used for splitting
+ RooCategory c("c","c") ;
+ c.defineType("run1") ;
+ c.defineType("run2") ;
- RooCategory d("d","d") ;
- d.defineType("foo") ;
- d.defineType("bar") ;
+ RooCategory d("d","d") ;
+ d.defineType("foo") ;
+ d.defineType("bar") ;
- // S e t u p S i m W S T o o l
- // -----------------------------
+ // S e t u p S i m W S T o o l
+ // -----------------------------
- // Import ingredients in a workspace
- RooWorkspace w("w","w") ;
- w.import(RooArgSet(model,c,d)) ;
+ // Import ingredients in a workspace
+ RooWorkspace w("w","w") ;
+ w.import(RooArgSet(model,c,d)) ;
- // Make Sim builder tool
- RooSimWSTool sct(w) ;
+ // Make Sim builder tool
+ RooSimWSTool sct(w) ;
- // B u i l d a s i m u l t a n e o u s m o d e l w i t h o n e s p l i t
- // ---------------------------------------------------------------------------------
+ // B u i l d a s i m u l t a n e o u s m o d e l w i t h o n e s p l i t
+ // ---------------------------------------------------------------------------------
- // Construct a simultaneous p.d.f with the following form
- //
- // model_run1(x) = f*gauss_run1(x,m_run1,s) + (1-f)*poly
- // model_run2(x) = f*gauss_run2(x,m_run2,s) + (1-f)*poly
- // simpdf(x,c) = model_run1(x) if c=="run1"
- // = model_run2(x) if c=="run2"
- //
- // Returned p.d.f is owned by the workspace
- RooSimultaneous* model_sim = sct.build("model_sim","model",SplitParam("m","c")) ;
+ // Construct a simultaneous p.d.f with the following form
+ //
+ // model_run1(x) = f*gauss_run1(x,m_run1,s) + (1-f)*poly
+ // model_run2(x) = f*gauss_run2(x,m_run2,s) + (1-f)*poly
+ // simpdf(x,c) = model_run1(x) if c=="run1"
+ // = model_run2(x) if c=="run2"
+ //
+ // Returned p.d.f is owned by the workspace
+ RooSimultaneous* model_sim = sct.build("model_sim","model",SplitParam("m","c")) ;
- // Print tree structure of model
- model_sim->Print("t") ;
+ // Print tree structure of model
+ model_sim->Print("t") ;
- // Adjust model_sim parameters in workspace
- w.var("m_run1")->setVal(-3) ;
- w.var("m_run2")->setVal(+3) ;
+ // Adjust model_sim parameters in workspace
+ w.var("m_run1")->setVal(-3) ;
+ w.var("m_run2")->setVal(+3) ;
- // Print contents of workspace
- w.Print("v") ;
+ // Print contents of workspace
+ w.Print("v") ;
- // B u i l d a s i m u l t a n e o u s m o d e l w i t h p r o d u c t s p l i t
- // -----------------------------------------------------------------------------------------
+ // B u i l d a s i m u l t a n e o u s m o d e l w i t h p r o d u c t s p l i t
+ // -----------------------------------------------------------------------------------------
- // Build another simultaneous p.d.f using a composite split in states c X d
- RooSimultaneous* model_sim2 = sct.build("model_sim2","model",SplitParam("p0","c,d")) ;
+ // Build another simultaneous p.d.f using a composite split in states c X d
+ RooSimultaneous* model_sim2 = sct.build("model_sim2","model",SplitParam("p0","c,d")) ;
- // Print tree structure of this model
- model_sim2->Print("t") ;
+ // Print tree structure of this model
+ model_sim2->Print("t") ;
}
diff --git a/tutorials/roofit/rf505_asciicfg.C b/tutorials/roofit/rf505_asciicfg.C
index 50e2f2d1eee545a7019f8539e016f4ab3803f397..9e149a0a6956673202873f34c6f166710ab74c1f 100644
--- a/tutorials/roofit/rf505_asciicfg.C
+++ b/tutorials/roofit/rf505_asciicfg.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #505
///
/// Reading and writing ASCII configuration files
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,68 +24,72 @@ using namespace RooFit ;
void rf505_asciicfg()
{
- // C r e a t e p d f
- // ------------------
-
- // Construct gauss(x,m,s)
- RooRealVar x("x","x",-10,10) ;
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",1,-10,10) ;
- RooGaussian gauss("g","g",x,m,s) ;
+ // C r e a t e p d f
+ // ------------------
+
+ // Construct gauss(x,m,s)
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",1,-10,10) ;
+ RooGaussian gauss("g","g",x,m,s) ;
- // Construct poly(x,p0)
- RooRealVar p0("p0","p0",0.01,0.,1.) ;
- RooPolynomial poly("p","p",x,p0) ;
+ // Construct poly(x,p0)
+ RooRealVar p0("p0","p0",0.01,0.,1.) ;
+ RooPolynomial poly("p","p",x,p0) ;
- // Construct model = f*gauss(x) + (1-f)*poly(x)
- RooRealVar f("f","f",0.5,0.,1.) ;
- RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
+ // Construct model = f*gauss(x) + (1-f)*poly(x)
+ RooRealVar f("f","f",0.5,0.,1.) ;
+ RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
- // F i t m o d e l t o t o y d a t a
- // -----------------------------------------
+ // F i t m o d e l t o t o y d a t a
+ // -----------------------------------------
- RooDataSet* d = model.generate(x,1000) ;
- model.fitTo(*d) ;
+ RooDataSet* d = model.generate(x,1000) ;
+ model.fitTo(*d) ;
- // W r i t e p a r a m e t e r s t o a s c i i f i l e
- // -----------------------------------------------------------
+ // W r i t e p a r a m e t e r s t o a s c i i f i l e
+ // -----------------------------------------------------------
- // Obtain set of parameters
- RooArgSet* params = model.getParameters(x) ;
+ // Obtain set of parameters
+ RooArgSet* params = model.getParameters(x) ;
- // Write parameters to file
- params->writeToFile("rf505_asciicfg_example.txt") ;
+ // Write parameters to file
+ params->writeToFile("rf505_asciicfg_example.txt") ;
+ TString dir1 = gROOT->GetTutorialsDir() ;
+ dir1.Append("/roofit/rf505_asciicfg.txt") ;
+ TString dir2 = gROOT->GetTutorialsDir() ;
+ dir2.Append("/roofit/rf505_asciicfg_example.txt") ;
+ // R e a d p a r a m e t e r s f r o m a s c i i f i l e
+ // ----------------------------------------------------------------
+
+ // Read parameters from file
+ params->readFromFile(dir2) ;
+ params->Print("v") ;
- // R e a d p a r a m e t e r s f r o m a s c i i f i l e
- // ----------------------------------------------------------------
-
- // Read parameters from file
- params->readFromFile("rf505_asciicfg_example.txt") ;
- params->Print("v") ;
+ // Read parameters from section 'Section2' of file
+ params->readFromFile(dir1,0,"Section2") ;
+ params->Print("v") ;
- // Read parameters from section 'Section2' of file
- params->readFromFile("rf505_asciicfg.txt",0,"Section2") ;
- params->Print("v") ;
+ // Read parameters from section 'Section3' of file. Mark all
+ // variables that were processed with the "READ" attribute
+ params->readFromFile(dir1,"READ","Section3") ;
- // Read parameters from section 'Section3' of file. Mark all
- // variables that were processed with the "READ" attribute
- params->readFromFile("rf505_asciicfg.txt","READ","Section3") ;
+ // Print the list of parameters that were not read from Section3
+ cout << "The following parameters of the were _not_ read from Section3: "
+ << (*params->selectByAttrib("READ",kFALSE)) << endl ;
- // Print the list of parameters that were not read from Section3
- cout << "The following parameters of the were _not_ read from Section3: "
- << (*params->selectByAttrib("READ",kFALSE)) << endl ;
+ // Read parameters from section 'Section4' of file, which contains
+ // 'include file' statement of rf505_asciicfg_example.txt
+ // so that we effective read the same
- // Read parameters from section 'Section4' of file, which contains
- // 'include file' statement of rf505_asciicfg_example.txt
- // so that we effective read the same
- params->readFromFile("rf505_asciicfg.txt",0,"Section4") ;
- params->Print("v") ;
+ params->readFromFile(dir1,0,"Section4") ;
+ params->Print("v") ;
diff --git a/tutorials/roofit/rf506_msgservice.C b/tutorials/roofit/rf506_msgservice.C
index 0b4a0e29516445e58fcf7d79532a80ff0160a483..4e48290f4b44b4656e98f6b0ecbf9c4fe6b7d3fd 100644
--- a/tutorials/roofit/rf506_msgservice.C
+++ b/tutorials/roofit/rf506_msgservice.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #506
///
/// Tuning and customizing the RooFit message logging facility
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,98 +26,98 @@ using namespace RooFit ;
void rf506_msgservice()
{
- // C r e a t e p d f
- // --------------------
+ // C r e a t e p d f
+ // --------------------
- // Construct gauss(x,m,s)
- RooRealVar x("x","x",-10,10) ;
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",1,-10,10) ;
- RooGaussian gauss("g","g",x,m,s) ;
+ // Construct gauss(x,m,s)
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",1,-10,10) ;
+ RooGaussian gauss("g","g",x,m,s) ;
- // Construct poly(x,p0)
- RooRealVar p0("p0","p0",0.01,0.,1.) ;
- RooPolynomial poly("p","p",x,p0) ;
+ // Construct poly(x,p0)
+ RooRealVar p0("p0","p0",0.01,0.,1.) ;
+ RooPolynomial poly("p","p",x,p0) ;
- // Construct model = f*gauss(x) + (1-f)*poly(x)
- RooRealVar f("f","f",0.5,0.,1.) ;
- RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
+ // Construct model = f*gauss(x) + (1-f)*poly(x)
+ RooRealVar f("f","f",0.5,0.,1.) ;
+ RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
- RooDataSet* data = model.generate(x,10) ;
+ RooDataSet* data = model.generate(x,10) ;
- // P r i n t c o n f i g u r a t i o n o f m e s s a g e s e r v i c e
- // ---------------------------------------------------------------------------
+ // P r i n t c o n f i g u r a t i o n o f m e s s a g e s e r v i c e
+ // ---------------------------------------------------------------------------
- // Print streams configuration
- RooMsgService::instance().Print() ;
- cout << endl ;
+ // Print streams configuration
+ RooMsgService::instance().Print() ;
+ cout << endl ;
- // A d d i n g I n t e g r a t i o n t o p i c t o e x i s t i n g I N F O s t r e a m
- // -----------------------------------------------------------------------------------------------
+ // A d d i n g I n t e g r a t i o n t o p i c t o e x i s t i n g I N F O s t r e a m
+ // -----------------------------------------------------------------------------------------------
- // Print streams configuration
- RooMsgService::instance().Print() ;
- cout << endl ;
+ // Print streams configuration
+ RooMsgService::instance().Print() ;
+ cout << endl ;
- // Add Integration topic to existing INFO stream
- RooMsgService::instance().getStream(1).addTopic(Integration) ;
+ // Add Integration topic to existing INFO stream
+ RooMsgService::instance().getStream(1).addTopic(Integration) ;
- // Construct integral over gauss to demonstrate new message stream
- RooAbsReal* igauss = gauss.createIntegral(x) ;
- igauss->Print() ;
+ // Construct integral over gauss to demonstrate new message stream
+ RooAbsReal* igauss = gauss.createIntegral(x) ;
+ igauss->Print() ;
- // Print streams configuration in verbose, which also shows inactive streams
- cout << endl ;
- RooMsgService::instance().Print() ;
- cout << endl ;
+ // Print streams configuration in verbose, which also shows inactive streams
+ cout << endl ;
+ RooMsgService::instance().Print() ;
+ cout << endl ;
- // Remove stream
- RooMsgService::instance().getStream(1).removeTopic(Integration) ;
+ // Remove stream
+ RooMsgService::instance().getStream(1).removeTopic(Integration) ;
- // E x a m p l e s o f p d f v a l u e t r a c i n g s t r e a m
- // -----------------------------------------------------------------------
-
- // Show DEBUG level message on function tracing, trace RooGaussian only
- RooMsgService::instance().addStream(DEBUG,Topic(Tracing),ClassName("RooGaussian")) ;
+ // E x a m p l e s o f p d f v a l u e t r a c i n g s t r e a m
+ // -----------------------------------------------------------------------
+
+ // Show DEBUG level message on function tracing, trace RooGaussian only
+ RooMsgService::instance().addStream(DEBUG,Topic(Tracing),ClassName("RooGaussian")) ;
- // Perform a fit to generate some tracing messages
- model.fitTo(*data,Verbose(kTRUE)) ;
+ // Perform a fit to generate some tracing messages
+ model.fitTo(*data,Verbose(kTRUE)) ;
- // Reset message service to default stream configuration
- RooMsgService::instance().reset() ;
+ // Reset message service to default stream configuration
+ RooMsgService::instance().reset() ;
- // Show DEBUG level message on function tracing on all objects, redirect output to file
- RooMsgService::instance().addStream(DEBUG,Topic(Tracing),OutputFile("rf506_debug.log")) ;
+ // Show DEBUG level message on function tracing on all objects, redirect output to file
+ RooMsgService::instance().addStream(DEBUG,Topic(Tracing),OutputFile("rf506_debug.log")) ;
- // Perform a fit to generate some tracing messages
- model.fitTo(*data,Verbose(kTRUE)) ;
+ // Perform a fit to generate some tracing messages
+ model.fitTo(*data,Verbose(kTRUE)) ;
- // Reset message service to default stream configuration
- RooMsgService::instance().reset() ;
+ // Reset message service to default stream configuration
+ RooMsgService::instance().reset() ;
- // E x a m p l e o f a n o t h e r d e b u g g i n g s t r e a m
- // ---------------------------------------------------------------------
+ // E x a m p l e o f a n o t h e r d e b u g g i n g s t r e a m
+ // ---------------------------------------------------------------------
- // Show DEBUG level messages on client/server link state management
- RooMsgService::instance().addStream(DEBUG,Topic(LinkStateMgmt)) ;
- RooMsgService::instance().Print("v") ;
+ // Show DEBUG level messages on client/server link state management
+ RooMsgService::instance().addStream(DEBUG,Topic(LinkStateMgmt)) ;
+ RooMsgService::instance().Print("v") ;
- // Clone composite pdf g to trigger some link state management activity
- RooAbsArg* gprime = gauss.cloneTree() ;
- gprime->Print() ;
+ // Clone composite pdf g to trigger some link state management activity
+ RooAbsArg* gprime = gauss.cloneTree() ;
+ gprime->Print() ;
- // Reset message service to default stream configuration
- RooMsgService::instance().reset() ;
+ // Reset message service to default stream configuration
+ RooMsgService::instance().reset() ;
diff --git a/tutorials/roofit/rf507_debugtools.C b/tutorials/roofit/rf507_debugtools.C
index beb5372203733455c7c3ee9324715e735a90d4a4..428da29a1105d1a99da1767ace7ffdbe0693a2d8 100644
--- a/tutorials/roofit/rf507_debugtools.C
+++ b/tutorials/roofit/rf507_debugtools.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #507
///
/// RooFit memory tracing debug tool
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,39 +25,39 @@ using namespace RooFit ;
void rf507_debugtools()
{
- // Activate RooFit memory tracing
- RooTrace::active(kTRUE) ;
+ // Activate RooFit memory tracing
+ RooTrace::active(kTRUE) ;
- // Construct gauss(x,m,s)
- RooRealVar x("x","x",-10,10) ;
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",1,-10,10) ;
- RooGaussian gauss("g","g",x,m,s) ;
+ // Construct gauss(x,m,s)
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",1,-10,10) ;
+ RooGaussian gauss("g","g",x,m,s) ;
- // Show dump of all RooFit object in memory
- RooTrace::dump() ;
+ // Show dump of all RooFit object in memory
+ RooTrace::dump() ;
- // Activate verbose mode
- RooTrace::verbose(kTRUE) ;
+ // Activate verbose mode
+ RooTrace::verbose(kTRUE) ;
- // Construct poly(x,p0)
- RooRealVar p0("p0","p0",0.01,0.,1.) ;
- RooPolynomial poly("p","p",x,p0) ;
+ // Construct poly(x,p0)
+ RooRealVar p0("p0","p0",0.01,0.,1.) ;
+ RooPolynomial poly("p","p",x,p0) ;
- // Put marker in trace list for future reference
- RooTrace::mark() ;
+ // Put marker in trace list for future reference
+ RooTrace::mark() ;
- // Construct model = f*gauss(x) + (1-f)*poly(x)
- RooRealVar f("f","f",0.5,0.,1.) ;
- RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
+ // Construct model = f*gauss(x) + (1-f)*poly(x)
+ RooRealVar f("f","f",0.5,0.,1.) ;
+ RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
- // Show object added to memory since marker
- RooTrace::printObjectCounts() ;
+ // Show object added to memory since marker
+ RooTrace::printObjectCounts() ;
- // Since verbose mode is still on, you will see messages
- // pertaining to destructor calls of all RooFit objects
- // made in this macro
- //
- // A call to RooTrace::dump() at the end of this macro
- // should show that there a no RooFit object left in memory
+ // Since verbose mode is still on, you will see messages
+ // pertaining to destructor calls of all RooFit objects
+ // made in this macro
+ //
+ // A call to RooTrace::dump() at the end of this macro
+ // should show that there a no RooFit object left in memory
}
diff --git a/tutorials/roofit/rf508_listsetmanip.C b/tutorials/roofit/rf508_listsetmanip.C
index d30d18b535f29b608f92b1632012b11c1d75de64..db2eef4680b3c578aa421fe41aa2ef94448aa02c 100644
--- a/tutorials/roofit/rf508_listsetmanip.C
+++ b/tutorials/roofit/rf508_listsetmanip.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #508
///
/// RooArgSet and RooArgList tools and tricks
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,138 +26,138 @@ using namespace RooFit ;
void rf508_listsetmanip()
{
- // C r e a t e d u m m y o b j e c t s
- // ---------------------------------------
+ // C r e a t e d u m m y o b j e c t s
+ // ---------------------------------------
+
+ // Create some variables
+ RooRealVar a("a","a",1,-10,10) ;
+ RooRealVar b("b","b",2,-10,10) ;
+ RooRealVar c("c","c",3,-10,10) ;
+ RooRealVar d("d","d",4,-10,10) ;
+ RooRealVar x("x","x",0,-10,10) ;
+ c.setError(0.5) ;
+ a.setConstant() ;
+ b.setConstant() ;
- // Create some variables
- RooRealVar a("a","a",1,-10,10) ;
- RooRealVar b("b","b",2,-10,10) ;
- RooRealVar c("c","c",3,-10,10) ;
- RooRealVar d("d","d",4,-10,10) ;
- RooRealVar x("x","x",0,-10,10) ;
- c.setError(0.5) ;
- a.setConstant() ;
- b.setConstant() ;
+ // Create a category
+ RooCategory e("e","e") ;
+ e.defineType("sig") ;
+ e.defineType("bkg") ;
- // Create a category
- RooCategory e("e","e") ;
- e.defineType("sig") ;
- e.defineType("bkg") ;
+ // Create a pdf
+ RooGaussian g("g","g",x,a,b) ;
- // Create a pdf
- RooGaussian g("g","g",x,a,b) ;
+ // C r e a t i n g , f i l l i n g R o o A r g S e t s
+ // -------------------------------------------------------
- // C r e a t i n g , f i l l i n g R o o A r g S e t s
- // -------------------------------------------------------
+ // A RooArgSet is a set of RooAbsArg objects. Each object in the set must have
+ // a unique name
- // A RooArgSet is a set of RooAbsArg objects. Each object in the set must have
- // a unique name
+ // Set constructors exists with up to 9 initial arguments
+ RooArgSet s(a,b) ;
- // Set constructors exists with up to 9 initial arguments
- RooArgSet s(a,b) ;
+ // At any time objects can be added with add()
+ s.add(e) ;
- // At any time objects can be added with add()
- s.add(e) ;
+ // Add up to 9 additional arguments in one call
+ s.add(RooArgSet(c,d)) ;
- // Add up to 9 additional arguments in one call
- s.add(RooArgSet(c,d)) ;
+ // Sets can contain any type of RooAbsArg, also pdf and functions
+ s.add(g) ;
- // Sets can contain any type of RooAbsArg, also pdf and functions
- s.add(g) ;
+ // Remove element d
+ s.remove(d) ;
- // Remove element d
- s.remove(d) ;
+ // A c c e s s i n g R o o A r g S e t c o n t e n t s
+ // -------------------------------------------------------
- // A c c e s s i n g R o o A r g S e t c o n t e n t s
- // -------------------------------------------------------
-
- // You can look up objects by name
- RooAbsArg* aptr = s.find("a") ;
+ // You can look up objects by name
+ RooAbsArg* aptr = s.find("a") ;
- // Construct a subset by name
- RooArgSet* subset1 = (RooArgSet*) s.selectByName("a,b,c") ;
+ // Construct a subset by name
+ RooArgSet* subset1 = (RooArgSet*) s.selectByName("a,b,c") ;
- // Construct asubset by attribute
- RooArgSet* subset2 = (RooArgSet*) s.selectByAttrib("Constant",kTRUE) ;
+ // Construct asubset by attribute
+ RooArgSet* subset2 = (RooArgSet*) s.selectByAttrib("Constant",kTRUE) ;
- // Construct the subset of overlapping contents with another set
- RooArgSet s1(a,b,c) ;
- RooArgSet s2(c,d,e) ;
- RooArgSet* subset3 = (RooArgSet*) s1.selectCommon(s2) ;
+ // Construct the subset of overlapping contents with another set
+ RooArgSet s1(a,b,c) ;
+ RooArgSet s2(c,d,e) ;
+ RooArgSet* subset3 = (RooArgSet*) s1.selectCommon(s2) ;
- // O w n i n g R o o A r g S e t s
- // ---------------------------------
+ // O w n i n g R o o A r g S e t s
+ // ---------------------------------
- // Create a RooArgSet that owns its components
- // A set either owns all of its components or none,
- // so once addOwned() is used, add() can no longer be
- // used and will result in an error message
+ // Create a RooArgSet that owns its components
+ // A set either owns all of its components or none,
+ // so once addOwned() is used, add() can no longer be
+ // used and will result in an error message
- RooRealVar* ac = (RooRealVar*) a.clone("a") ;
- RooRealVar* bc = (RooRealVar*) b.clone("b") ;
- RooRealVar* cc = (RooRealVar*) c.clone("c") ;
+ RooRealVar* ac = (RooRealVar*) a.clone("a") ;
+ RooRealVar* bc = (RooRealVar*) b.clone("b") ;
+ RooRealVar* cc = (RooRealVar*) c.clone("c") ;
- RooArgSet s3 ;
- s3.addOwned(RooArgSet(*ac,*bc,*cc)) ;
+ RooArgSet s3 ;
+ s3.addOwned(RooArgSet(*ac,*bc,*cc)) ;
- // Another possibility is to add an owned clone
- // of an object instead of the original
- s3.addClone(RooArgSet(d,e,g)) ;
+ // Another possibility is to add an owned clone
+ // of an object instead of the original
+ s3.addClone(RooArgSet(d,e,g)) ;
- // A clone of a owning set is non-owning and its
- // contents is owned by the originating owning set
- RooArgSet* sclone = (RooArgSet*) s3.Clone("sclone") ;
+ // A clone of a owning set is non-owning and its
+ // contents is owned by the originating owning set
+ RooArgSet* sclone = (RooArgSet*) s3.Clone("sclone") ;
- // To make a clone of a set and its contents use
- // the snapshot method
- RooArgSet* sclone2 = (RooArgSet*) s3.snapshot() ;
+ // To make a clone of a set and its contents use
+ // the snapshot method
+ RooArgSet* sclone2 = (RooArgSet*) s3.snapshot() ;
- // If a set contains function objects, only the head node
- // is cloned in a snapshot. To make a snapshot of all
- // servers of a function object do as follows. The result
- // of a RooArgSet snapshot with deepCloning option is a set
- // of cloned objects, and all their clone (recursive) server
- // dependencies, that together form a self-consistent
- // set that is free of external dependencies
+ // If a set contains function objects, only the head node
+ // is cloned in a snapshot. To make a snapshot of all
+ // servers of a function object do as follows. The result
+ // of a RooArgSet snapshot with deepCloning option is a set
+ // of cloned objects, and all their clone (recursive) server
+ // dependencies, that together form a self-consistent
+ // set that is free of external dependencies
- RooArgSet* sclone3 = (RooArgSet*) s3.snapshot(kTRUE) ;
+ RooArgSet* sclone3 = (RooArgSet*) s3.snapshot(kTRUE) ;
- // S e t p r i n t i n g
- // ------------------------
+ // S e t p r i n t i n g
+ // ------------------------
- // Inline printing only show list of names of contained objects
- cout << "sclone = " << (*sclone) << endl ;
+ // Inline printing only show list of names of contained objects
+ cout << "sclone = " << (*sclone) << endl ;
- // Plain print shows the same, prefixed by name of the set
- sclone->Print() ;
+ // Plain print shows the same, prefixed by name of the set
+ sclone->Print() ;
- // Standard printing shows one line for each item with the items name, class name and value
- sclone->Print("s") ;
+ // Standard printing shows one line for each item with the items name, class name and value
+ sclone->Print("s") ;
- // Verbose printing adds each items arguments, address and 'extras' as defined by the object
- sclone->Print("v") ;
+ // Verbose printing adds each items arguments, address and 'extras' as defined by the object
+ sclone->Print("v") ;
- // U s i n g R o o A r g L i s t s
- // ---------------------------------
+ // U s i n g R o o A r g L i s t s
+ // ---------------------------------
- // List constructors exists with up to 9 initial arguments
- RooArgList l(a,b,c,d) ;
+ // List constructors exists with up to 9 initial arguments
+ RooArgList l(a,b,c,d) ;
- // Lists have an explicit order and allow multiple arguments with the same name
- l.add(RooArgList(a,b,c,d)) ;
+ // Lists have an explicit order and allow multiple arguments with the same name
+ l.add(RooArgList(a,b,c,d)) ;
- // Access by index is provided
- RooAbsArg* arg4 = l.at(4) ;
+ // Access by index is provided
+ RooAbsArg* arg4 = l.at(4) ;
}
diff --git a/tutorials/roofit/rf509_wsinteractive.C b/tutorials/roofit/rf509_wsinteractive.C
index c4dda6b2296881c8b7440632f8d6ba834fd81031..9b15fde7121e9cc83581372a3b480fda979ae65f 100644
--- a/tutorials/roofit/rf509_wsinteractive.C
+++ b/tutorials/roofit/rf509_wsinteractive.C
@@ -9,13 +9,12 @@
/// *** NB: This macro exploits a feature native to CINT and _cannot_ be compiled ***
/// *********************************************************************************
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -35,65 +34,65 @@ void fillWorkspace(RooWorkspace& w) ;
void rf509_wsinteractive()
{
- // C r e a t e a n d f i l l w o r k s p a c e
- // ------------------------------------------------
+ // C r e a t e a n d f i l l w o r k s p a c e
+ // ------------------------------------------------
- // Create a workspace named 'w'
- // With CINT w could exports its contents to
- // a same-name C++ namespace in CINT 'namespace w'.
- // but this does not work anymore in CLING.
- // so this tutorial is an example on how to
- // change the code
- RooWorkspace* w = new RooWorkspace("w",kTRUE) ;
+ // Create a workspace named 'w'
+ // With CINT w could exports its contents to
+ // a same-name C++ namespace in CINT 'namespace w'.
+ // but this does not work anymore in CLING.
+ // so this tutorial is an example on how to
+ // change the code
+ RooWorkspace* w = new RooWorkspace("w",kTRUE) ;
- // Fill workspace with p.d.f. and data in a separate function
- fillWorkspace(*w) ;
+ // Fill workspace with p.d.f. and data in a separate function
+ fillWorkspace(*w) ;
- // Print workspace contents
- w->Print() ;
+ // Print workspace contents
+ w->Print() ;
- // this does not work anymore with CLING
- // use normal workspace functionality
+ // this does not work anymore with CLING
+ // use normal workspace functionality
-
- // U s e w o r k s p a c e c o n t e n t s
- // ----------------------------------------------
+
+ // U s e w o r k s p a c e c o n t e n t s
+ // ----------------------------------------------
- // Old syntax to use the name space prefix operator to access the workspace contents
- //
- //RooDataSet* d = w::model.generate(w::x,1000) ;
- //RooFitResult* r = w::model.fitTo(*d) ;
+ // Old syntax to use the name space prefix operator to access the workspace contents
+ //
+ //RooDataSet* d = w::model.generate(w::x,1000) ;
+ //RooFitResult* r = w::model.fitTo(*d) ;
- // use normal workspace methods
- RooAbsPdf * model = w->pdf("model");
- RooRealVar * x = w->var("x");
+ // use normal workspace methods
+ RooAbsPdf * model = w->pdf("model");
+ RooRealVar * x = w->var("x");
- RooDataSet* d = model->generate(*x,1000) ;
- RooFitResult* r = model->fitTo(*d) ;
+ RooDataSet* d = model->generate(*x,1000) ;
+ RooFitResult* r = model->fitTo(*d) ;
- // old syntax to access the variable x
- // RooPlot* frame = w::x.frame() ;
+ // old syntax to access the variable x
+ // RooPlot* frame = w::x.frame() ;
- RooPlot* frame = x->frame() ;
- d->plotOn(frame) ;
+ RooPlot* frame = x->frame() ;
+ d->plotOn(frame) ;
- // OLD syntax to ommit x::
- // NB: The 'w::' prefix can be omitted if namespace w is imported in local namespace
- // in the usual C++ way
- //
- // using namespace w;
- // model.plotOn(frame) ;
- // model.plotOn(frame,Components(bkg),LineStyle(kDashed)) ;
+ // OLD syntax to ommit x::
+ // NB: The 'w::' prefix can be omitted if namespace w is imported in local namespace
+ // in the usual C++ way
+ //
+ // using namespace w;
+ // model.plotOn(frame) ;
+ // model.plotOn(frame,Components(bkg),LineStyle(kDashed)) ;
- // new correct syntax
- RooAbsPdf *bkg = w->pdf("bkg");
- model->plotOn(frame);
- model->plotOn(frame,Components(*bkg),LineStyle(kDashed)) ;
+ // new correct syntax
+ RooAbsPdf *bkg = w->pdf("bkg");
+ model->plotOn(frame);
+ model->plotOn(frame,Components(*bkg),LineStyle(kDashed)) ;
- // Draw the frame on the canvas
- new TCanvas("rf509_wsinteractive","rf509_wsinteractive",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ // Draw the frame on the canvas
+ new TCanvas("rf509_wsinteractive","rf509_wsinteractive",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}
@@ -103,33 +102,33 @@ void rf509_wsinteractive()
void fillWorkspace(RooWorkspace& w)
{
- // C r e a t e p d f a n d f i l l w o r k s p a c e
- // --------------------------------------------------------
-
- // 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",5,0,10) ;
- 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",-0.2,0.,1.) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
-
- // 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) ;
-
- // 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) ;
-
- w.import(model) ;
+ // C r e a t e p d f a n d f i l l w o r k s p a c e
+ // --------------------------------------------------------
+
+ // 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",5,0,10) ;
+ 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",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+
+ // 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) ;
+
+ // 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) ;
+
+ w.import(model) ;
}
diff --git a/tutorials/roofit/rf510_wsnamedsets.C b/tutorials/roofit/rf510_wsnamedsets.C
index 716337ca4f71d745fcac7f1bdde6ee24c46f3195..5cb75d7d254e0a90993f49966aa70ecb9652f8e0 100644
--- a/tutorials/roofit/rf510_wsnamedsets.C
+++ b/tutorials/roofit/rf510_wsnamedsets.C
@@ -1,17 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #510
///
/// Working with named parameter sets and parameter snapshots in
/// workspaces
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,45 +31,45 @@ void fillWorkspace(RooWorkspace& w) ;
void rf510_wsnamedsets()
{
- // C r e a t e m o d e l a n d d a t a s e t
- // -----------------------------------------------
+ // C r e a t e m o d e l a n d d a t a s e t
+ // -----------------------------------------------
- RooWorkspace* w = new RooWorkspace("w") ;
- fillWorkspace(*w) ;
+ RooWorkspace* w = new RooWorkspace("w") ;
+ fillWorkspace(*w) ;
- // Exploit convention encoded in named set "parameters" and "observables"
- // to use workspace contents w/o need for introspected
- RooAbsPdf* model = w->pdf("model") ;
+ // Exploit convention encoded in named set "parameters" and "observables"
+ // to use workspace contents w/o need for introspected
+ RooAbsPdf* model = w->pdf("model") ;
- // Generate data from p.d.f. in given observables
- RooDataSet* data = model->generate(*w->set("observables"),1000) ;
+ // Generate data from p.d.f. in given observables
+ RooDataSet* data = model->generate(*w->set("observables"),1000) ;
- // Fit model to data
- model->fitTo(*data) ;
-
- // Plot fitted model and data on frame of first (only) observable
- RooPlot* frame = ((RooRealVar*)w->set("observables")->first())->frame() ;
- data->plotOn(frame) ;
- model->plotOn(frame) ;
+ // Fit model to data
+ model->fitTo(*data) ;
+
+ // Plot fitted model and data on frame of first (only) observable
+ RooPlot* frame = ((RooRealVar*)w->set("observables")->first())->frame() ;
+ data->plotOn(frame) ;
+ model->plotOn(frame) ;
- // Overlay plot with model with reference parameters as stored in snapshots
- w->loadSnapshot("reference_fit") ;
- model->plotOn(frame,LineColor(kRed)) ;
- w->loadSnapshot("reference_fit_bkgonly") ;
- model->plotOn(frame,LineColor(kRed),LineStyle(kDashed)) ;
+ // Overlay plot with model with reference parameters as stored in snapshots
+ w->loadSnapshot("reference_fit") ;
+ model->plotOn(frame,LineColor(kRed)) ;
+ w->loadSnapshot("reference_fit_bkgonly") ;
+ model->plotOn(frame,LineColor(kRed),LineStyle(kDashed)) ;
- // Draw the frame on the canvas
- new TCanvas("rf510_wsnamedsets","rf503_wsnamedsets",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ // Draw the frame on the canvas
+ new TCanvas("rf510_wsnamedsets","rf503_wsnamedsets",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- // Print workspace contents
- w->Print() ;
+ // Print workspace contents
+ w->Print() ;
- // Workspace will remain in memory after macro finishes
- gDirectory->Add(w) ;
+ // Workspace will remain in memory after macro finishes
+ gDirectory->Add(w) ;
}
@@ -77,83 +77,83 @@ void rf510_wsnamedsets()
void fillWorkspace(RooWorkspace& w)
{
- // C r e a t e m o d e l
- // -----------------------
-
- // 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",5,0,10) ;
- 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",-0.2,0.,1.) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
-
- // 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) ;
-
- // 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) ;
-
- // Import model into p.d.f.
- w.import(model) ;
-
-
- // E n c o d e d e f i n i t i o n o f p a r a m e t e r s i n w o r k s p a c e
- // ---------------------------------------------------------------------------------------
-
-
- // Define named sets "parameters" and "observables", which list which variables should be considered
- // parameters and observables by the users convention
- //
- // Variables appearing in sets _must_ live in the workspace already, or the autoImport flag
- // of defineSet must be set to import them on the fly. Named sets contain only references
- // to the original variables, therefore the value of observables in named sets already
- // reflect their 'current' value
- RooArgSet* params = (RooArgSet*) model.getParameters(x) ;
- w.defineSet("parameters",*params) ;
- w.defineSet("observables",x) ;
-
-
- // E n c o d e r e f e r e n c e v a l u e f o r p a r a m e t e r s i n w o r k s p a c e
- // ---------------------------------------------------------------------------------------------------
-
-
- // Define a parameter 'snapshot' in the p.d.f.
- // Unlike a named set, a parameter snapshot stores an independent set of values for
- // a given set of variables in the workspace. The values can be stored and reloaded
- // into the workspace variable objects using the loadSnapshot() and saveSnapshot()
- // methods. A snapshot saves the value of each variable, any errors that are stored
- // with it as well as the 'Constant' flag that is used in fits to determine if a
- // parameter is kept fixed or not.
-
- // Do a dummy fit to a (supposedly) reference dataset here and store the results
- // of that fit into a snapshot
- RooDataSet* refData = model.generate(x,10000) ;
- model.fitTo(*refData,PrintLevel(-1)) ;
-
- // The kTRUE flag imports the values of the objects in (*params) into the workspace
- // If not set, the present values of the workspace parameters objects are stored
- w.saveSnapshot("reference_fit",*params,kTRUE) ;
-
- // Make another fit with the signal componentforced to zero
- // and save those parameters too
-
- bkgfrac.setVal(1) ;
- bkgfrac.setConstant(kTRUE) ;
- bkgfrac.removeError() ;
- model.fitTo(*refData,PrintLevel(-1)) ;
-
- w.saveSnapshot("reference_fit_bkgonly",*params,kTRUE) ;
-
+ // C r e a t e m o d e l
+ // -----------------------
+
+ // 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",5,0,10) ;
+ 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",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+
+ // 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) ;
+
+ // 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) ;
+
+ // Import model into p.d.f.
+ w.import(model) ;
+
+
+ // E n c o d e d e f i n i t i o n o f p a r a m e t e r s i n w o r k s p a c e
+ // ---------------------------------------------------------------------------------------
+
+
+ // Define named sets "parameters" and "observables", which list which variables should be considered
+ // parameters and observables by the users convention
+ //
+ // Variables appearing in sets _must_ live in the workspace already, or the autoImport flag
+ // of defineSet must be set to import them on the fly. Named sets contain only references
+ // to the original variables, therefore the value of observables in named sets already
+ // reflect their 'current' value
+ RooArgSet* params = (RooArgSet*) model.getParameters(x) ;
+ w.defineSet("parameters",*params) ;
+ w.defineSet("observables",x) ;
+
+
+ // E n c o d e r e f e r e n c e v a l u e f o r p a r a m e t e r s i n w o r k s p a c e
+ // ---------------------------------------------------------------------------------------------------
+
+
+ // Define a parameter 'snapshot' in the p.d.f.
+ // Unlike a named set, a parameter snapshot stores an independent set of values for
+ // a given set of variables in the workspace. The values can be stored and reloaded
+ // into the workspace variable objects using the loadSnapshot() and saveSnapshot()
+ // methods. A snapshot saves the value of each variable, any errors that are stored
+ // with it as well as the 'Constant' flag that is used in fits to determine if a
+ // parameter is kept fixed or not.
+
+ // Do a dummy fit to a (supposedly) reference dataset here and store the results
+ // of that fit into a snapshot
+ RooDataSet* refData = model.generate(x,10000) ;
+ model.fitTo(*refData,PrintLevel(-1)) ;
+
+ // The kTRUE flag imports the values of the objects in (*params) into the workspace
+ // If not set, the present values of the workspace parameters objects are stored
+ w.saveSnapshot("reference_fit",*params,kTRUE) ;
+
+ // Make another fit with the signal componentforced to zero
+ // and save those parameters too
+
+ bkgfrac.setVal(1) ;
+ bkgfrac.setConstant(kTRUE) ;
+ bkgfrac.removeError() ;
+ model.fitTo(*refData,PrintLevel(-1)) ;
+
+ w.saveSnapshot("reference_fit_bkgonly",*params,kTRUE) ;
+
}
diff --git a/tutorials/roofit/rf511_wsfactory_basic.C b/tutorials/roofit/rf511_wsfactory_basic.C
index a057626e394980f347416433c994f90f2194ef13..593ec6ca9ad5d4f1199163dcffb4ab3947ced4dc 100644
--- a/tutorials/roofit/rf511_wsfactory_basic.C
+++ b/tutorials/roofit/rf511_wsfactory_basic.C
@@ -1,18 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #511
///
/// Basic use of the 'object factory' associated with a workspace
/// to rapidly build p.d.f.s functions and their parameter components
///
-///
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
+
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,64 +27,64 @@ using namespace RooFit ;
void rf511_wsfactory_basic(Bool_t compact=kFALSE)
{
- RooWorkspace* w = new RooWorkspace("w") ;
+ RooWorkspace* w = new RooWorkspace("w") ;
- // C r e a t i n g a n d a d d i n g b a s i c p . d . f . s
- // ----------------------------------------------------------------
+ // C r e a t i n g a n d a d d i n g b a s i c p . d . f . s
+ // ----------------------------------------------------------------
- // Remake example p.d.f. of tutorial rs502_wspacewrite.C:
- //
- // Basic p.d.f. construction: ClassName::ObjectName(constructor arguments)
- // Variable construction : VarName[x,xlo,xhi], VarName[xlo,xhi], VarName[x]
- // P.d.f. addition : SUM::ObjectName(coef1*pdf1,...coefM*pdfM,pdfN)
- //
+ // Remake example p.d.f. of tutorial rs502_wspacewrite.C:
+ //
+ // Basic p.d.f. construction: ClassName::ObjectName(constructor arguments)
+ // Variable construction : VarName[x,xlo,xhi], VarName[xlo,xhi], VarName[x]
+ // P.d.f. addition : SUM::ObjectName(coef1*pdf1,...coefM*pdfM,pdfN)
+ //
- if (!compact) {
+ if (!compact) {
- // Use object factory to build p.d.f. of tutorial rs502_wspacewrite
- w->factory("Gaussian::sig1(x[-10,10],mean[5,0,10],0.5)") ;
- w->factory("Gaussian::sig2(x,mean,1)") ;
- w->factory("Chebychev::bkg(x,{a0[0.5,0.,1],a1[-0.2,0.,1.]})") ;
- w->factory("SUM::sig(sig1frac[0.8,0.,1.]*sig1,sig2)") ;
- w->factory("SUM::model(bkgfrac[0.5,0.,1.]*bkg,sig)") ;
+ // Use object factory to build p.d.f. of tutorial rs502_wspacewrite
+ w->factory("Gaussian::sig1(x[-10,10],mean[5,0,10],0.5)") ;
+ w->factory("Gaussian::sig2(x,mean,1)") ;
+ w->factory("Chebychev::bkg(x,{a0[0.5,0.,1],a1[-0.2,0.,1.]})") ;
+ w->factory("SUM::sig(sig1frac[0.8,0.,1.]*sig1,sig2)") ;
+ w->factory("SUM::model(bkgfrac[0.5,0.,1.]*bkg,sig)") ;
- } else {
+ } else {
- // Use object factory to build p.d.f. of tutorial rs502_wspacewrite but
- // - Contracted to a single line recursive expression,
- // - Omitting explicit names for components that are not referred to explicitly later
+ // Use object factory to build p.d.f. of tutorial rs502_wspacewrite but
+ // - Contracted to a single line recursive expression,
+ // - Omitting explicit names for components that are not referred to explicitly later
- w->factory("SUM::model(bkgfrac[0.5,0.,1.]*Chebychev::bkg(x[-10,10],{a0[0.5,0.,1],a1[-0.2,0.,1.]}),"
- "SUM(sig1frac[0.8,0.,1.]*Gaussian(x,mean[5,0,10],0.5), Gaussian(x,mean,1)))") ;
- }
+ w->factory("SUM::model(bkgfrac[0.5,0.,1.]*Chebychev::bkg(x[-10,10],{a0[0.5,0.,1],a1[-0.2,0.,1.]}),"
+ "SUM(sig1frac[0.8,0.,1.]*Gaussian(x,mean[5,0,10],0.5), Gaussian(x,mean,1)))") ;
+ }
- // A d v a n c e d p . d . f . c o n s t r u c t o r a r g u m e n t s
- // ----------------------------------------------------------------
- //
- // P.d.f. constructor arguments may by any type of RooAbsArg, but also
- //
- // Double_t --> converted to RooConst(...)
- // {a,b,c} --> converted to RooArgSet() or RooArgList() depending on required ctor arg
- // dataset name --> convered to RooAbsData reference for any dataset residing in the workspace
- // enum --> Any enum label that belongs to an enum defined in the (base) class
+ // A d v a n c e d p . d . f . c o n s t r u c t o r a r g u m e n t s
+ // ----------------------------------------------------------------
+ //
+ // P.d.f. constructor arguments may by any type of RooAbsArg, but also
+ //
+ // Double_t --> converted to RooConst(...)
+ // {a,b,c} --> converted to RooArgSet() or RooArgList() depending on required ctor arg
+ // dataset name --> convered to RooAbsData reference for any dataset residing in the workspace
+ // enum --> Any enum label that belongs to an enum defined in the (base) class
- // Make a dummy dataset p.d.f. 'model' and import it in the workspace
- RooDataSet* data = w->pdf("model")->generate(*w->var("x"),1000) ;
- w->import(*data,Rename("data")) ;
+ // Make a dummy dataset p.d.f. 'model' and import it in the workspace
+ RooDataSet* data = w->pdf("model")->generate(*w->var("x"),1000) ;
+ w->import(*data,Rename("data")) ;
- // Construct a KEYS p.d.f. passing a dataset name and an enum type defining the
- // mirroring strategy
- w->factory("KeysPdf::k(x,data,NoMirror,0.2)") ;
+ // Construct a KEYS p.d.f. passing a dataset name and an enum type defining the
+ // mirroring strategy
+ w->factory("KeysPdf::k(x,data,NoMirror,0.2)") ;
- // Print workspace contents
- w->Print() ;
+ // Print workspace contents
+ w->Print() ;
- // Make workspace visible on command line
- gDirectory->Add(w) ;
+ // Make workspace visible on command line
+ gDirectory->Add(w) ;
}
diff --git a/tutorials/roofit/rf512_wsfactory_oper.C b/tutorials/roofit/rf512_wsfactory_oper.C
index 6ac5b6ac738ff7c70ddaf9de309922737a623852..c164cca5c787cf00f541cea44a03f19142af77ef 100644
--- a/tutorials/roofit/rf512_wsfactory_oper.C
+++ b/tutorials/roofit/rf512_wsfactory_oper.C
@@ -1,17 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #512
///
/// Illustration of operator expressions and expression-based
/// basic p.d.f.s in the workspace factory syntax
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,74 +27,74 @@ using namespace RooFit ;
void rf512_wsfactory_oper()
{
- RooWorkspace* w = new RooWorkspace("w") ;
+ RooWorkspace* w = new RooWorkspace("w") ;
- // You can define typedefs for even shorter construction semantics
- w->factory("$Typedef(Gaussian,Gaus)") ;
- w->factory("$Typedef(Chebychev,Cheby)") ;
+ // You can define typedefs for even shorter construction semantics
+ w->factory("$Typedef(Gaussian,Gaus)") ;
+ w->factory("$Typedef(Chebychev,Cheby)") ;
- // O p e r a t o r p . d . f . e x a m p l e s
- // ------------------------------------------------
+ // O p e r a t o r p . d . f . e x a m p l e s
+ // ------------------------------------------------
- // PDF addition is done with SUM (coef1*pdf1,pdf2)
- w->factory("SUM::summodel( f[0,1]*Gaussian::gx(x[-10,10],m[0],1.0), Chebychev::ch(x,{0.1,0.2,-0.3}) )") ;
-
+ // PDF addition is done with SUM (coef1*pdf1,pdf2)
+ w->factory("SUM::summodel( f[0,1]*Gaussian::gx(x[-10,10],m[0],1.0), Chebychev::ch(x,{0.1,0.2,-0.3}) )") ;
+
- // Extended PDF addition is done with SUM (yield1*pdf1,yield2*pdf2)
- w->factory("SUM::extsummodel( Nsig[0,1000]*gx, Nbkg[0,1000]*ch )") ;
-
+ // Extended PDF addition is done with SUM (yield1*pdf1,yield2*pdf2)
+ w->factory("SUM::extsummodel( Nsig[0,1000]*gx, Nbkg[0,1000]*ch )") ;
+
- // PDF multiplication is done with PROD ( pdf1, pdf2 )
- w->factory("PROD::gxz( gx, Gaussian::gz(z[-10,10],0,1) )") ;
+ // PDF multiplication is done with PROD ( pdf1, pdf2 )
+ w->factory("PROD::gxz( gx, Gaussian::gz(z[-10,10],0,1) )") ;
- // Conditional p.d.f multiplication is done with PROD ( pdf1|obs, pdf2 )
- w->factory("Gaussian::gy( y[-10,10], x, 1.0 )") ;
- w->factory("PROD::gxycond( gy|x, gx )") ;
+ // Conditional p.d.f multiplication is done with PROD ( pdf1|obs, pdf2 )
+ w->factory("Gaussian::gy( y[-10,10], x, 1.0 )") ;
+ w->factory("PROD::gxycond( gy|x, gx )") ;
- // Convolution (numeric/ fft) is done with NCONV/FCONV (obs,pdf1,pdf2)
- w->factory("FCONV::lxg( x, Gaussian::g(x,mg[0],1), Landau::lc(x,0,1) )") ;
+ // Convolution (numeric/ fft) is done with NCONV/FCONV (obs,pdf1,pdf2)
+ w->factory("FCONV::lxg( x, Gaussian::g(x,mg[0],1), Landau::lc(x,0,1) )") ;
- // Simultaneous p.d.f.s are constructed with SIMUL( index, state1=pdf1, state2=pdf2,...)
- w->factory("SIMUL::smodel( c[A=0,B=1], A=Gaussian::gs(x,m,s[1]), B=Landau::ls(x,0,1) )") ;
+ // Simultaneous p.d.f.s are constructed with SIMUL( index, state1=pdf1, state2=pdf2,...)
+ w->factory("SIMUL::smodel( c[A=0,B=1], A=Gaussian::gs(x,m,s[1]), B=Landau::ls(x,0,1) )") ;
- // O p e r a t o r f u n c t i o n e x a m p l e s
- // ---------------------------------------------------
-
- // Function multiplication is done with prod (func1, func2,...)
- w->factory("prod::uv(u[10],v[10])") ;
+ // O p e r a t o r f u n c t i o n e x a m p l e s
+ // ---------------------------------------------------
+
+ // Function multiplication is done with prod (func1, func2,...)
+ w->factory("prod::uv(u[10],v[10])") ;
- // Function addition is done with sum(func1,func2)
- w->factory("sum::uv2(u,v)") ;
+ // Function addition is done with sum(func1,func2)
+ w->factory("sum::uv2(u,v)") ;
- // I n t e r p r e t e d a n d c o m p i l e d e x p r e s s i o n b a s e d p . d . f . s .
- // ---------------------------------------------------------------------------------------------------
+ // I n t e r p r e t e d a n d c o m p i l e d e x p r e s s i o n b a s e d p . d . f . s .
+ // ---------------------------------------------------------------------------------------------------
- // Create a RooGenericPdf interpreted p.d.f. You can use single quotes to pass the expression string argument
- w->factory("EXPR::G('x*x+1',x)") ;
+ // Create a RooGenericPdf interpreted p.d.f. You can use single quotes to pass the expression string argument
+ w->factory("EXPR::G('x*x+1',x)") ;
- // Create a custom compiled p.d.f similar to the above interpreted p.d.f.
- // The code required to make this p.d.f. is automatically embedded in the workspace
- w->factory("CEXPR::GC('x*x+a',{x,a[1]})") ;
+ // Create a custom compiled p.d.f similar to the above interpreted p.d.f.
+ // The code required to make this p.d.f. is automatically embedded in the workspace
+ w->factory("CEXPR::GC('x*x+a',{x,a[1]})") ;
- // Compiled and interpreted functions (rather than p.d.f.s) can be made with the lower case
- // 'expr' and 'cexpr' types
+ // Compiled and interpreted functions (rather than p.d.f.s) can be made with the lower case
+ // 'expr' and 'cexpr' types
- // Print workspace contents
- w->Print() ;
+ // Print workspace contents
+ w->Print() ;
- // Make workspace visible on command line
- gDirectory->Add(w) ;
+ // Make workspace visible on command line
+ gDirectory->Add(w) ;
}
diff --git a/tutorials/roofit/rf513_wsfactory_tools.C b/tutorials/roofit/rf513_wsfactory_tools.C
index 81bbba293732d25b78268b20c4626395d9cc174c..c53b5b9f3d99b723d0c28b030e81d4e32c3983fe 100644
--- a/tutorials/roofit/rf513_wsfactory_tools.C
+++ b/tutorials/roofit/rf513_wsfactory_tools.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #513
///
/// Illustration use of RooCustomizer and RooSimWSTool interface
/// in factory workspace tool in a complex standalone B physics example
///
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,68 +26,68 @@ using namespace RooFit ;
void rf513_wsfactory_tools()
{
- RooWorkspace* w = new RooWorkspace("w") ;
-
-
- // B u i l d a c o m p l e x e x a m p l e p . d . f .
- // -----------------------------------------------------------
-
- // Make signal model for CPV: A bmixing decay function in t (convoluted with a triple Gaussian resolution model)
- // times a Gaussian function the reconstructed mass
- w->factory("PROD::sig( BMixDecay::sig_t( dt[-20,20], mixState[mixed=1,unmix=-1], tagFlav[B0=1,B0bar=-1], "
- "tau[1.54], dm[0.472], w[0.05], dw[0],"
- "AddModel::gm({GaussModel(dt,biasC[-10,10],sigmaC[0.1,3],dterr[0.01,0.2]),"
- "GaussModel(dt,0,sigmaT[3,10]),"
- "GaussModel(dt,0,20)},{fracC[0,1],fracT[0,1]}),"
- "DoubleSided ),"
- "Gaussian::sig_m( mes[5.20,5.30], mB0[5.20,5.30], sigmB0[0.01,0.05] ))") ;
-
- // Make background component: A plain decay function in t times an Argus function in the reconstructed mass
- w->factory("PROD::bkg( Decay::bkg_t( dt, tau, gm, DoubleSided),"
- "ArgusBG::bkg_m( mes, 5.291, k[-100,-10]))") ;
-
- // Make composite model from the signal and background component
- w->factory("SUM::model( Nsig[5000,0,10000]*sig, NBkg[500,0,10000]*bkg )") ;
-
-
- // E x a m p l e o f R o o S i m W S T o o l i n t e r f a c e
- // ------------------------------------------------------------------
-
- // Introduce a flavour tagging category tagCat as observable with 4 states corresponding
- // to 4 flavour tagging techniques with different performance that require different
- // parameterizations of the fit model
- //
- // RooSimWSTool operation:
- // - Make 4 clones of model (for each tagCat) state, that will gain an individual
- // copy of parameters w,dw and biasC. The other parameters remain common
- // - Make a simultaneous p.d.f. of the 4 clones assigning each to the appropriate
- // state of the tagCat index category
-
- // RooSimWSTool is interfaced as meta-type SIMCLONE in the factory. The $SplitParam()
- // argument maps to the SplitParam() named argument in the RooSimWSTool constructor
- w->factory("SIMCLONE::model_sim( model, $SplitParam({w,dw,biasC},tagCat[Lep,Kao,NT1,NT2]))") ;
-
-
- // E x a m p l e o f R o o C u s t o m i z e r i n t e r f a c e
- // -------------------------------------------------------------------
- //
- // Class RooCustomizer makes clones of existing p.d.f.s with certain prescribed
- // modifications (branch of leaf node replacements)
- //
- // Here we take our model (the original before RooSimWSTool modifications)
- // and request that the parameter w (the mistag rate) is replaced with
- // an expression-based function that calculates w in terms of the Dilution
- // parameter D that is defined as D = 1-2*w
-
- // Make a clone model_D of original 'model' replacing 'w' with 'expr('0.5-D/2',D[0,1])'
- w->factory("EDIT::model_D(model, w=expr('0.5-D/2',D[0,1]) )") ;
-
-
- // Print workspace contents
- w->Print() ;
-
- // Make workspace visible on command line
- gDirectory->Add(w) ;
+ RooWorkspace* w = new RooWorkspace("w") ;
+
+
+ // B u i l d a c o m p l e x e x a m p l e p . d . f .
+ // -----------------------------------------------------------
+
+ // Make signal model for CPV: A bmixing decay function in t (convoluted with a triple Gaussian resolution model)
+ // times a Gaussian function the reconstructed mass
+ w->factory("PROD::sig( BMixDecay::sig_t( dt[-20,20], mixState[mixed=1,unmix=-1], tagFlav[B0=1,B0bar=-1], "
+ "tau[1.54], dm[0.472], w[0.05], dw[0],"
+ "AddModel::gm({GaussModel(dt,biasC[-10,10],sigmaC[0.1,3],dterr[0.01,0.2]),"
+ "GaussModel(dt,0,sigmaT[3,10]),"
+ "GaussModel(dt,0,20)},{fracC[0,1],fracT[0,1]}),"
+ "DoubleSided ),"
+ "Gaussian::sig_m( mes[5.20,5.30], mB0[5.20,5.30], sigmB0[0.01,0.05] ))") ;
+
+ // Make background component: A plain decay function in t times an Argus function in the reconstructed mass
+ w->factory("PROD::bkg( Decay::bkg_t( dt, tau, gm, DoubleSided),"
+ "ArgusBG::bkg_m( mes, 5.291, k[-100,-10]))") ;
+
+ // Make composite model from the signal and background component
+ w->factory("SUM::model( Nsig[5000,0,10000]*sig, NBkg[500,0,10000]*bkg )") ;
+
+
+ // E x a m p l e o f R o o S i m W S T o o l i n t e r f a c e
+ // ------------------------------------------------------------------
+
+ // Introduce a flavour tagging category tagCat as observable with 4 states corresponding
+ // to 4 flavour tagging techniques with different performance that require different
+ // parameterizations of the fit model
+ //
+ // RooSimWSTool operation:
+ // - Make 4 clones of model (for each tagCat) state, that will gain an individual
+ // copy of parameters w,dw and biasC. The other parameters remain common
+ // - Make a simultaneous p.d.f. of the 4 clones assigning each to the appropriate
+ // state of the tagCat index category
+
+ // RooSimWSTool is interfaced as meta-type SIMCLONE in the factory. The $SplitParam()
+ // argument maps to the SplitParam() named argument in the RooSimWSTool constructor
+ w->factory("SIMCLONE::model_sim( model, $SplitParam({w,dw,biasC},tagCat[Lep,Kao,NT1,NT2]))") ;
+
+
+ // E x a m p l e o f R o o C u s t o m i z e r i n t e r f a c e
+ // -------------------------------------------------------------------
+ //
+ // Class RooCustomizer makes clones of existing p.d.f.s with certain prescribed
+ // modifications (branch of leaf node replacements)
+ //
+ // Here we take our model (the original before RooSimWSTool modifications)
+ // and request that the parameter w (the mistag rate) is replaced with
+ // an expression-based function that calculates w in terms of the Dilution
+ // parameter D that is defined as D = 1-2*w
+
+ // Make a clone model_D of original 'model' replacing 'w' with 'expr('0.5-D/2',D[0,1])'
+ w->factory("EDIT::model_D(model, w=expr('0.5-D/2',D[0,1]) )") ;
+
+
+ // Print workspace contents
+ w->Print() ;
+
+ // Make workspace visible on command line
+ gDirectory->Add(w) ;
}
diff --git a/tutorials/roofit/rf601_intminuit.C b/tutorials/roofit/rf601_intminuit.C
index c534d29cef9bd7955c20cbdb30feabb4173355bf..a89d9d29e2f0b5b8e05d5b01341f62f1aaa30a7c 100644
--- a/tutorials/roofit/rf601_intminuit.C
+++ b/tutorials/roofit/rf601_intminuit.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #601
///
/// Interactive minimization with MINUIT
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,106 +28,106 @@ using namespace RooFit ;
void rf601_intminuit()
{
- // S e t u p p d f a n d l i k e l i h o o d
- // -----------------------------------------------
+ // S e t u p p d f a n d l i k e l i h o o d
+ // -----------------------------------------------
- // Observable
- RooRealVar x("x","x",-20,20) ;
+ // Observable
+ RooRealVar x("x","x",-20,20) ;
- // Model (intentional strong correlations)
- RooRealVar mean("mean","mean of g1 and g2",0) ;
- RooRealVar sigma_g1("sigma_g1","width of g1",3) ;
- RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
+ // Model (intentional strong correlations)
+ RooRealVar mean("mean","mean of g1 and g2",0) ;
+ RooRealVar sigma_g1("sigma_g1","width of g1",3) ;
+ RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
- RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,6.0) ;
- RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
+ RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,6.0) ;
+ RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
- RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
- RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
+ RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
+ RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
- // Generate 1000 events
- RooDataSet* data = model.generate(x,1000) ;
-
- // Construct unbinned likelihood of model w.r.t. data
- RooAbsReal* nll = model.createNLL(*data) ;
+ // Generate 1000 events
+ RooDataSet* data = model.generate(x,1000) ;
+
+ // Construct unbinned likelihood of model w.r.t. data
+ RooAbsReal* nll = model.createNLL(*data) ;
- // I n t e r a c t i v e m i n i m i z a t i o n , e r r o r a n a l y s i s
- // -------------------------------------------------------------------------------
+ // I n t e r a c t i v e m i n i m i z a t i o n , e r r o r a n a l y s i s
+ // -------------------------------------------------------------------------------
- // Create MINUIT interface object
- RooMinuit m(*nll) ;
+ // Create MINUIT interface object
+ RooMinuit m(*nll) ;
- // Activate verbose logging of MINUIT parameter space stepping
- m.setVerbose(kTRUE) ;
+ // Activate verbose logging of MINUIT parameter space stepping
+ m.setVerbose(kTRUE) ;
- // Call MIGRAD to minimize the likelihood
- m.migrad() ;
+ // Call MIGRAD to minimize the likelihood
+ m.migrad() ;
- // Print values of all parameters, that reflect values (and error estimates)
- // that are back propagated from MINUIT
- model.getParameters(x)->Print("s") ;
+ // Print values of all parameters, that reflect values (and error estimates)
+ // that are back propagated from MINUIT
+ model.getParameters(x)->Print("s") ;
- // Disable verbose logging
- m.setVerbose(kFALSE) ;
+ // Disable verbose logging
+ m.setVerbose(kFALSE) ;
- // Run HESSE to calculate errors from d2L/dp2
- m.hesse() ;
+ // Run HESSE to calculate errors from d2L/dp2
+ m.hesse() ;
- // Print value (and error) of sigma_g2 parameter, that reflects
- // value and error back propagated from MINUIT
- sigma_g2.Print() ;
+ // Print value (and error) of sigma_g2 parameter, that reflects
+ // value and error back propagated from MINUIT
+ sigma_g2.Print() ;
- // Run MINOS on sigma_g2 parameter only
- m.minos(sigma_g2) ;
+ // Run MINOS on sigma_g2 parameter only
+ m.minos(sigma_g2) ;
- // Print value (and error) of sigma_g2 parameter, that reflects
- // value and error back propagated from MINUIT
- sigma_g2.Print() ;
+ // Print value (and error) of sigma_g2 parameter, that reflects
+ // value and error back propagated from MINUIT
+ sigma_g2.Print() ;
- // S a v i n g r e s u l t s , c o n t o u r p l o t s
- // ---------------------------------------------------------
+ // S a v i n g r e s u l t s , c o n t o u r p l o t s
+ // ---------------------------------------------------------
- // Save a snapshot of the fit result. This object contains the initial
- // fit parameters, the final fit parameters, the complete correlation
- // matrix, the EDM, the minimized FCN , the last MINUIT status code and
- // the number of times the RooFit function object has indicated evaluation
- // problems (e.g. zero probabilities during likelihood evaluation)
- RooFitResult* r = m.save() ;
+ // Save a snapshot of the fit result. This object contains the initial
+ // fit parameters, the final fit parameters, the complete correlation
+ // matrix, the EDM, the minimized FCN , the last MINUIT status code and
+ // the number of times the RooFit function object has indicated evaluation
+ // problems (e.g. zero probabilities during likelihood evaluation)
+ RooFitResult* r = m.save() ;
- // Make contour plot of mx vs sx at 1,2,3 sigma
- RooPlot* frame = m.contour(frac,sigma_g2,1,2,3) ;
- frame->SetTitle("RooMinuit contour plot") ;
+ // Make contour plot of mx vs sx at 1,2,3 sigma
+ RooPlot* frame = m.contour(frac,sigma_g2,1,2,3) ;
+ frame->SetTitle("RooMinuit contour plot") ;
- // Print the fit result snapshot
- r->Print("v") ;
+ // Print the fit result snapshot
+ r->Print("v") ;
- // C h a n g e p a r a m e t e r v a l u e s , f l o a t i n g
- // -----------------------------------------------------------------
+ // C h a n g e p a r a m e t e r v a l u e s , f l o a t i n g
+ // -----------------------------------------------------------------
- // At any moment you can manually change the value of a (constant)
- // parameter
- mean = 0.3 ;
-
- // Rerun MIGRAD,HESSE
- m.migrad() ;
- m.hesse() ;
- frac.Print() ;
+ // At any moment you can manually change the value of a (constant)
+ // parameter
+ mean = 0.3 ;
+
+ // Rerun MIGRAD,HESSE
+ m.migrad() ;
+ m.hesse() ;
+ frac.Print() ;
- // Now fix sigma_g2
- sigma_g2.setConstant(kTRUE) ;
+ // Now fix sigma_g2
+ sigma_g2.setConstant(kTRUE) ;
- // Rerun MIGRAD,HESSE
- m.migrad() ;
- m.hesse() ;
- frac.Print() ;
+ // Rerun MIGRAD,HESSE
+ m.migrad() ;
+ m.hesse() ;
+ frac.Print() ;
- new TCanvas("rf601_intminuit","rf601_intminuit",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ new TCanvas("rf601_intminuit","rf601_intminuit",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}
diff --git a/tutorials/roofit/rf602_chi2fit.C b/tutorials/roofit/rf602_chi2fit.C
index 48991f541f45b04668b473fdf7ba6b96667f2aa3..0bd45faa8bcf0958e57d3aa9dfce5d5f909c57af 100644
--- a/tutorials/roofit/rf602_chi2fit.C
+++ b/tutorials/roofit/rf602_chi2fit.C
@@ -1,18 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #602
///
/// Setting up a chi^2 fit to a binned dataset
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,57 +27,57 @@ using namespace RooFit ;
void rf602_chi2fit()
{
- // S e t u p m o d e l
- // ---------------------
-
- // 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",-0.2,0.,1.) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
-
- // 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) ;
-
- // 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) ;
-
-
- // C r e a t e b i n n e d d a t a s e t
- // -----------------------------------------
-
- RooDataSet* d = model.generate(x,10000) ;
- RooDataHist* dh = d->binnedClone() ;
-
- // Construct a chi^2 of the data and the model.
- // When a p.d.f. is used in a chi^2 fit, the probability density scaled
- // by the number of events in the dataset to obtain the fit function
- // If model is an extended p.d.f, the expected number events is used
- // instead of the observed number of events.
- model.chi2FitTo(*dh) ;
-
- // NB: It is also possible to fit a RooAbsReal function to a RooDataHist
- // using chi2FitTo().
-
- // Note that entries with zero bins are _not_ allowed
- // for a proper chi^2 calculation and will give error
- // messages
- RooDataSet* dsmall = (RooDataSet*) d->reduce(EventRange(1,100)) ;
- RooDataHist* dhsmall = dsmall->binnedClone() ;
- RooChi2Var chi2_lowstat("chi2_lowstat","chi2",model,*dhsmall) ;
- cout << chi2_lowstat.getVal() << endl ;
+ // S e t u p m o d e l
+ // ---------------------
+
+ // 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",-0.2,0.,1.) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+
+ // 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) ;
+
+ // 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) ;
+
+
+ // C r e a t e b i n n e d d a t a s e t
+ // -----------------------------------------
+
+ RooDataSet* d = model.generate(x,10000) ;
+ RooDataHist* dh = d->binnedClone() ;
+
+ // Construct a chi^2 of the data and the model.
+ // When a p.d.f. is used in a chi^2 fit, the probability density scaled
+ // by the number of events in the dataset to obtain the fit function
+ // If model is an extended p.d.f, the expected number events is used
+ // instead of the observed number of events.
+ model.chi2FitTo(*dh) ;
+
+ // NB: It is also possible to fit a RooAbsReal function to a RooDataHist
+ // using chi2FitTo().
+
+ // Note that entries with zero bins are _not_ allowed
+ // for a proper chi^2 calculation and will give error
+ // messages
+ RooDataSet* dsmall = (RooDataSet*) d->reduce(EventRange(1,100)) ;
+ RooDataHist* dhsmall = dsmall->binnedClone() ;
+ RooChi2Var chi2_lowstat("chi2_lowstat","chi2",model,*dhsmall) ;
+ cout << chi2_lowstat.getVal() << endl ;
}
diff --git a/tutorials/roofit/rf603_multicpu.C b/tutorials/roofit/rf603_multicpu.C
index aac29edb2393267bca1157f9099b2fb8a45fdb74..3c3cb7b1927733700fa096947d6764488631bdf5 100644
--- a/tutorials/roofit/rf603_multicpu.C
+++ b/tutorials/roofit/rf603_multicpu.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #603
///
/// Setting up a multi-core parallelized unbinned maximum likelihood fit
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,74 +27,74 @@ using namespace RooFit ;
void rf603_multicpu()
{
- // C r e a t e 3 D p d f a n d d a t a
- // -------------------------------------------
+ // C r e a t e 3 D p d f a n d d a t a
+ // -------------------------------------------
- // Create observables
- RooRealVar x("x","x",-5,5) ;
- RooRealVar y("y","y",-5,5) ;
- RooRealVar z("z","z",-5,5) ;
+ // Create observables
+ RooRealVar x("x","x",-5,5) ;
+ RooRealVar y("y","y",-5,5) ;
+ RooRealVar z("z","z",-5,5) ;
- // Create signal pdf gauss(x)*gauss(y)*gauss(z)
- RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
- RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
- RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
- RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
+ // Create signal pdf gauss(x)*gauss(y)*gauss(z)
+ RooGaussian gx("gx","gx",x,RooConst(0),RooConst(1)) ;
+ RooGaussian gy("gy","gy",y,RooConst(0),RooConst(1)) ;
+ RooGaussian gz("gz","gz",z,RooConst(0),RooConst(1)) ;
+ RooProdPdf sig("sig","sig",RooArgSet(gx,gy,gz)) ;
- // Create background pdf poly(x)*poly(y)*poly(z)
- RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
- RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
- RooPolynomial pz("pz","pz",z) ;
- RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
+ // Create background pdf poly(x)*poly(y)*poly(z)
+ RooPolynomial px("px","px",x,RooArgSet(RooConst(-0.1),RooConst(0.004))) ;
+ RooPolynomial py("py","py",y,RooArgSet(RooConst(0.1),RooConst(-0.004))) ;
+ RooPolynomial pz("pz","pz",z) ;
+ RooProdPdf bkg("bkg","bkg",RooArgSet(px,py,pz)) ;
- // Create composite pdf sig+bkg
- RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
- RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
+ // Create composite pdf sig+bkg
+ RooRealVar fsig("fsig","signal fraction",0.1,0.,1.) ;
+ RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
- // Generate large dataset
- RooDataSet* data = model.generate(RooArgSet(x,y,z),200000) ;
+ // Generate large dataset
+ RooDataSet* data = model.generate(RooArgSet(x,y,z),200000) ;
- // P a r a l l e l f i t t i n g
- // -------------------------------
+ // P a r a l l e l f i t t i n g
+ // -------------------------------
- // In parallel mode the likelihood calculation is split in N pieces,
- // that are calculated in parallel and added a posteriori before passing
- // it back to MINUIT.
+ // In parallel mode the likelihood calculation is split in N pieces,
+ // that are calculated in parallel and added a posteriori before passing
+ // it back to MINUIT.
- // Use four processes and time results both in wall time and CPU time
- model.fitTo(*data,NumCPU(4),Timer(kTRUE)) ;
+ // Use four processes and time results both in wall time and CPU time
+ model.fitTo(*data,NumCPU(4),Timer(kTRUE)) ;
- // P a r a l l e l M C p r o j e c t i o n s
- // ----------------------------------------------
+ // P a r a l l e l M C p r o j e c t i o n s
+ // ----------------------------------------------
- // Construct signal, total likelihood projection on (y,z) observables and likelihood ratio
- RooAbsPdf* sigyz = sig.createProjection(x) ;
- RooAbsPdf* totyz = model.createProjection(x) ;
- RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
+ // Construct signal, total likelihood projection on (y,z) observables and likelihood ratio
+ RooAbsPdf* sigyz = sig.createProjection(x) ;
+ RooAbsPdf* totyz = model.createProjection(x) ;
+ RooFormulaVar llratio_func("llratio","log10(@0)-log10(@1)",RooArgList(*sigyz,*totyz)) ;
- // Calculate likelihood ratio for each event, define subset of events with high signal likelihood
- data->addColumn(llratio_func) ;
- RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
+ // Calculate likelihood ratio for each event, define subset of events with high signal likelihood
+ data->addColumn(llratio_func) ;
+ RooDataSet* dataSel = (RooDataSet*) data->reduce(Cut("llratio>0.7")) ;
- // Make plot frame and plot data
- RooPlot* frame = x.frame(Title("Projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
- dataSel->plotOn(frame) ;
+ // Make plot frame and plot data
+ RooPlot* frame = x.frame(Title("Projection on X with LLratio(y,z)>0.7"),Bins(40)) ;
+ dataSel->plotOn(frame) ;
- // Perform parallel projection using MC integration of pdf using given input dataSet.
- // In this mode the data-weighted average of the pdf is calculated by splitting the
- // input dataset in N equal pieces and calculating in parallel the weighted average
- // one each subset. The N results of those calculations are then weighted into the
- // final result
-
- // Use four processes
- model.plotOn(frame,ProjWData(*dataSel),NumCPU(4)) ;
+ // Perform parallel projection using MC integration of pdf using given input dataSet.
+ // In this mode the data-weighted average of the pdf is calculated by splitting the
+ // input dataset in N equal pieces and calculating in parallel the weighted average
+ // one each subset. The N results of those calculations are then weighted into the
+ // final result
+
+ // Use four processes
+ model.plotOn(frame,ProjWData(*dataSel),NumCPU(4)) ;
- new TCanvas("rf603_multicpu","rf603_multicpu",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+ new TCanvas("rf603_multicpu","rf603_multicpu",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
}
diff --git a/tutorials/roofit/rf604_constraints.C b/tutorials/roofit/rf604_constraints.C
index 1ae63422c2f148961e801892a1bd8782229c070d..186a54a69de297cfe55285290aa965d100ff28b7 100644
--- a/tutorials/roofit/rf604_constraints.C
+++ b/tutorials/roofit/rf604_constraints.C
@@ -1,17 +1,15 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #604
///
/// Fitting with constraints
///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,70 +28,70 @@ using namespace RooFit ;
void rf604_constraints()
{
- // C r e a t e m o d e l a n d d a t a s e t
- // ----------------------------------------------
+ // C r e a t e m o d e l a n d d a t a s e t
+ // ----------------------------------------------
- // Construct a Gaussian p.d.f
- RooRealVar x("x","x",-10,10) ;
+ // Construct a Gaussian p.d.f
+ RooRealVar x("x","x",-10,10) ;
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",2,0.1,10) ;
- RooGaussian gauss("gauss","gauss(x,m,s)",x,m,s) ;
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",2,0.1,10) ;
+ RooGaussian gauss("gauss","gauss(x,m,s)",x,m,s) ;
- // Construct a flat p.d.f (polynomial of 0th order)
- RooPolynomial poly("poly","poly(x)",x) ;
+ // Construct a flat p.d.f (polynomial of 0th order)
+ RooPolynomial poly("poly","poly(x)",x) ;
- // Construct model = f*gauss + (1-f)*poly
- RooRealVar f("f","f",0.5,0.,1.) ;
- RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
+ // Construct model = f*gauss + (1-f)*poly
+ RooRealVar f("f","f",0.5,0.,1.) ;
+ RooAddPdf model("model","model",RooArgSet(gauss,poly),f) ;
- // Generate small dataset for use in fitting below
- RooDataSet* d = model.generate(x,50) ;
+ // Generate small dataset for use in fitting below
+ RooDataSet* d = model.generate(x,50) ;
- // C r e a t e c o n s t r a i n t p d f
- // -----------------------------------------
+ // C r e a t e c o n s t r a i n t p d f
+ // -----------------------------------------
- // Construct Gaussian constraint p.d.f on parameter f at 0.8 with resolution of 0.1
- RooGaussian fconstraint("fconstraint","fconstraint",f,RooConst(0.8),RooConst(0.1)) ;
+ // Construct Gaussian constraint p.d.f on parameter f at 0.8 with resolution of 0.1
+ RooGaussian fconstraint("fconstraint","fconstraint",f,RooConst(0.8),RooConst(0.1)) ;
- // M E T H O D 1 - A d d i n t e r n a l c o n s t r a i n t t o m o d e l
- // -------------------------------------------------------------------------------------
+ // M E T H O D 1 - A d d i n t e r n a l c o n s t r a i n t t o m o d e l
+ // -------------------------------------------------------------------------------------
- // Multiply constraint term with regular p.d.f using RooProdPdf
- // Specify in fitTo() that internal constraints on parameter f should be used
+ // Multiply constraint term with regular p.d.f using RooProdPdf
+ // Specify in fitTo() that internal constraints on parameter f should be used
- // Multiply constraint with p.d.f
- RooProdPdf modelc("modelc","model with constraint",RooArgSet(model,fconstraint)) ;
-
- // Fit model (without use of constraint term)
- RooFitResult* r1 = model.fitTo(*d,Save()) ;
+ // Multiply constraint with p.d.f
+ RooProdPdf modelc("modelc","model with constraint",RooArgSet(model,fconstraint)) ;
+
+ // Fit model (without use of constraint term)
+ RooFitResult* r1 = model.fitTo(*d,Save()) ;
- // Fit modelc with constraint term on parameter f
- RooFitResult* r2 = modelc.fitTo(*d,Constrain(f),Save()) ;
+ // Fit modelc with constraint term on parameter f
+ RooFitResult* r2 = modelc.fitTo(*d,Constrain(f),Save()) ;
- // M E T H O D 2 - S p e c i f y e x t e r n a l c o n s t r a i n t w h e n f i t t i n g
- // -------------------------------------------------------------------------------------------------------
+ // M E T H O D 2 - S p e c i f y e x t e r n a l c o n s t r a i n t w h e n f i t t i n g
+ // -------------------------------------------------------------------------------------------------------
- // Construct another Gaussian constraint p.d.f on parameter f at 0.8 with resolution of 0.1
- RooGaussian fconstext("fconstext","fconstext",f,RooConst(0.2),RooConst(0.1)) ;
+ // Construct another Gaussian constraint p.d.f on parameter f at 0.8 with resolution of 0.1
+ RooGaussian fconstext("fconstext","fconstext",f,RooConst(0.2),RooConst(0.1)) ;
- // Fit with external constraint
- RooFitResult* r3 = model.fitTo(*d,ExternalConstraints(fconstext),Save()) ;
+ // Fit with external constraint
+ RooFitResult* r3 = model.fitTo(*d,ExternalConstraints(fconstext),Save()) ;
- // Print the fit results
- cout << "fit result without constraint (data generated at f=0.5)" << endl ;
- r1->Print("v") ;
- cout << "fit result with internal constraint (data generated at f=0.5, constraint is f=0.8+/-0.2)" << endl ;
- r2->Print("v") ;
- cout << "fit result with (another) external constraint (data generated at f=0.5, constraint is f=0.2+/-0.1)" << endl ;
- r3->Print("v") ;
+ // Print the fit results
+ cout << "fit result without constraint (data generated at f=0.5)" << endl ;
+ r1->Print("v") ;
+ cout << "fit result with internal constraint (data generated at f=0.5, constraint is f=0.8+/-0.2)" << endl ;
+ r2->Print("v") ;
+ cout << "fit result with (another) external constraint (data generated at f=0.5, constraint is f=0.2+/-0.1)" << endl ;
+ r3->Print("v") ;
}
diff --git a/tutorials/roofit/rf605_profilell.C b/tutorials/roofit/rf605_profilell.C
index 4d88e3109c6f53c50674fdcae2323af2e9431dc5..78b3ceaa8d1bd7e688af4bb0b6346177afd4a0d2 100644
--- a/tutorials/roofit/rf605_profilell.C
+++ b/tutorials/roofit/rf605_profilell.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #605
///
/// Working with the profile likelihood estimator
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,88 +25,88 @@ using namespace RooFit ;
void rf605_profilell()
{
- // C r e a t e m o d e l a n d d a t a s e t
- // -----------------------------------------------
+ // C r e a t e m o d e l a n d d a t a s e t
+ // -----------------------------------------------
- // Observable
- RooRealVar x("x","x",-20,20) ;
+ // Observable
+ RooRealVar x("x","x",-20,20) ;
- // Model (intentional strong correlations)
- RooRealVar mean("mean","mean of g1 and g2",0,-10,10) ;
- RooRealVar sigma_g1("sigma_g1","width of g1",3) ;
- RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
+ // Model (intentional strong correlations)
+ RooRealVar mean("mean","mean of g1 and g2",0,-10,10) ;
+ RooRealVar sigma_g1("sigma_g1","width of g1",3) ;
+ RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
- RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,6.0) ;
- RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
+ RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,6.0) ;
+ RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
- RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
- RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
+ RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
+ RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
- // Generate 1000 events
- RooDataSet* data = model.generate(x,1000) ;
-
+ // Generate 1000 events
+ RooDataSet* data = model.generate(x,1000) ;
+
- // C o n s t r u c t p l a i n l i k e l i h o o d
- // ---------------------------------------------------
+ // C o n s t r u c t p l a i n l i k e l i h o o d
+ // ---------------------------------------------------
- // Construct unbinned likelihood
- RooAbsReal* nll = model.createNLL(*data,NumCPU(2)) ;
+ // Construct unbinned likelihood
+ RooAbsReal* nll = model.createNLL(*data,NumCPU(2)) ;
- // Minimize likelihood w.r.t all parameters before making plots
- RooMinuit(*nll).migrad() ;
+ // Minimize likelihood w.r.t all parameters before making plots
+ RooMinuit(*nll).migrad() ;
- // Plot likelihood scan frac
- RooPlot* frame1 = frac.frame(Bins(10),Range(0.01,0.95),Title("LL and profileLL in frac")) ;
- nll->plotOn(frame1,ShiftToZero()) ;
+ // Plot likelihood scan frac
+ RooPlot* frame1 = frac.frame(Bins(10),Range(0.01,0.95),Title("LL and profileLL in frac")) ;
+ nll->plotOn(frame1,ShiftToZero()) ;
- // Plot likelihood scan in sigma_g2
- RooPlot* frame2 = sigma_g2.frame(Bins(10),Range(3.3,5.0),Title("LL and profileLL in sigma_g2")) ;
- nll->plotOn(frame2,ShiftToZero()) ;
+ // Plot likelihood scan in sigma_g2
+ RooPlot* frame2 = sigma_g2.frame(Bins(10),Range(3.3,5.0),Title("LL and profileLL in sigma_g2")) ;
+ nll->plotOn(frame2,ShiftToZero()) ;
- // C o n s t r u c t p r o f i l e l i k e l i h o o d i n f r a c
- // -----------------------------------------------------------------------
+ // C o n s t r u c t p r o f i l e l i k e l i h o o d i n f r a c
+ // -----------------------------------------------------------------------
- // The profile likelihood estimator on nll for frac will minimize nll w.r.t
- // all floating parameters except frac for each evaluation
+ // The profile likelihood estimator on nll for frac will minimize nll w.r.t
+ // all floating parameters except frac for each evaluation
- RooAbsReal* pll_frac = nll->createProfile(frac) ;
+ RooAbsReal* pll_frac = nll->createProfile(frac) ;
- // Plot the profile likelihood in frac
- pll_frac->plotOn(frame1,LineColor(kRed)) ;
+ // Plot the profile likelihood in frac
+ pll_frac->plotOn(frame1,LineColor(kRed)) ;
- // Adjust frame maximum for visual clarity
- frame1->SetMinimum(0) ;
- frame1->SetMaximum(3) ;
+ // Adjust frame maximum for visual clarity
+ frame1->SetMinimum(0) ;
+ frame1->SetMaximum(3) ;
- // C o n s t r u c t p r o f i l e l i k e l i h o o d i n s i g m a _ g 2
- // -------------------------------------------------------------------------------
+ // C o n s t r u c t p r o f i l e l i k e l i h o o d i n s i g m a _ g 2
+ // -------------------------------------------------------------------------------
- // The profile likelihood estimator on nll for sigma_g2 will minimize nll
- // w.r.t all floating parameters except sigma_g2 for each evaluation
- RooAbsReal* pll_sigmag2 = nll->createProfile(sigma_g2) ;
+ // The profile likelihood estimator on nll for sigma_g2 will minimize nll
+ // w.r.t all floating parameters except sigma_g2 for each evaluation
+ RooAbsReal* pll_sigmag2 = nll->createProfile(sigma_g2) ;
- // Plot the profile likelihood in sigma_g2
- pll_sigmag2->plotOn(frame2,LineColor(kRed)) ;
+ // Plot the profile likelihood in sigma_g2
+ pll_sigmag2->plotOn(frame2,LineColor(kRed)) ;
- // Adjust frame maximum for visual clarity
- frame2->SetMinimum(0) ;
- frame2->SetMaximum(3) ;
+ // Adjust frame maximum for visual clarity
+ frame2->SetMinimum(0) ;
+ frame2->SetMaximum(3) ;
- // Make canvas and draw RooPlots
- TCanvas *c = new TCanvas("rf605_profilell","rf605_profilell",800, 400);
- c->Divide(2);
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ // Make canvas and draw RooPlots
+ TCanvas *c = new TCanvas("rf605_profilell","rf605_profilell",800, 400);
+ c->Divide(2);
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
- delete pll_frac ;
- delete pll_sigmag2 ;
- delete nll ;
+ delete pll_frac ;
+ delete pll_sigmag2 ;
+ delete nll ;
}
diff --git a/tutorials/roofit/rf606_nllerrorhandling.C b/tutorials/roofit/rf606_nllerrorhandling.C
index 1c11a19966e0a15fdd2713e9c3be496d79579901..ac2fc7bcb35e51c0119213bda568738e0b900923 100644
--- a/tutorials/roofit/rf606_nllerrorhandling.C
+++ b/tutorials/roofit/rf606_nllerrorhandling.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #606
///
/// Understanding and customizing error handling in likelihood evaluations
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooArgusBG.h"
@@ -25,84 +23,84 @@ using namespace RooFit ;
void rf606_nllerrorhandling()
{
- // C r e a t e m o d e l a n d d a t a s e t
- // ----------------------------------------------
+ // C r e a t e m o d e l a n d d a t a s e t
+ // ----------------------------------------------
- // Observable
- RooRealVar m("m","m",5.20,5.30) ;
+ // Observable
+ RooRealVar m("m","m",5.20,5.30) ;
- // Parameters
- RooRealVar m0("m0","m0",5.291,5.20,5.30) ;
- RooRealVar k("k","k",-30,-50,-10) ;
+ // Parameters
+ RooRealVar m0("m0","m0",5.291,5.20,5.30) ;
+ RooRealVar k("k","k",-30,-50,-10) ;
- // Pdf
- RooArgusBG argus("argus","argus",m,m0,k) ;
+ // Pdf
+ RooArgusBG argus("argus","argus",m,m0,k) ;
- // Sample 1000 events in m from argus
- RooDataSet* data = argus.generate(m,1000) ;
+ // Sample 1000 events in m from argus
+ RooDataSet* data = argus.generate(m,1000) ;
- // P l o t m o d e l a n d d a t a
- // --------------------------------------
+ // P l o t m o d e l a n d d a t a
+ // --------------------------------------
- RooPlot* frame1 = m.frame(Bins(40),Title("Argus model and data")) ;
- data->plotOn(frame1) ;
- argus.plotOn(frame1) ;
+ RooPlot* frame1 = m.frame(Bins(40),Title("Argus model and data")) ;
+ data->plotOn(frame1) ;
+ argus.plotOn(frame1) ;
- // F i t m o d e l t o d a t a
- // ---------------------------------
+ // F i t m o d e l t o d a t a
+ // ---------------------------------
- // The ARGUS background shape has a sharp kinematic cutoff at m=m0
- // and is prone to evaluation errors if the cutoff parameter m0
- // is floated: when the pdf cutoff value is lower than that in data
- // events with m>m0 will have zero probability
+ // The ARGUS background shape has a sharp kinematic cutoff at m=m0
+ // and is prone to evaluation errors if the cutoff parameter m0
+ // is floated: when the pdf cutoff value is lower than that in data
+ // events with m>m0 will have zero probability
- // Perform unbinned ML fit. Print detailed error messages for up to
- // 10 events per likelihood evaluation. The default error handling strategy
- // is to return a very high value of the likelihood to MINUIT if errors occur,
- // which will force MINUIT to retreat from the problematic area
+ // Perform unbinned ML fit. Print detailed error messages for up to
+ // 10 events per likelihood evaluation. The default error handling strategy
+ // is to return a very high value of the likelihood to MINUIT if errors occur,
+ // which will force MINUIT to retreat from the problematic area
- argus.fitTo(*data,PrintEvalErrors(10)) ;
+ argus.fitTo(*data,PrintEvalErrors(10)) ;
- // Peform another fit. In this configuration only the number of errors per
- // likelihood evaluation is shown, if it is greater than zero. The
- // EvalErrorWall(kFALSE) arguments disables the default error handling strategy
- // and will cause the actual (problematic) value of the likelihood to be passed
- // to MINUIT.
- //
- // NB: Use of this option is NOT recommended as default strategt as broken -log(L) values
- // can often be lower than 'good' ones because offending events are removed.
- // This may effectively create a false minimum in problem areas. This is clearly
- // illustrated in the second plot
+ // Peform another fit. In this configuration only the number of errors per
+ // likelihood evaluation is shown, if it is greater than zero. The
+ // EvalErrorWall(kFALSE) arguments disables the default error handling strategy
+ // and will cause the actual (problematic) value of the likelihood to be passed
+ // to MINUIT.
+ //
+ // NB: Use of this option is NOT recommended as default strategt as broken -log(L) values
+ // can often be lower than 'good' ones because offending events are removed.
+ // This may effectively create a false minimum in problem areas. This is clearly
+ // illustrated in the second plot
- m0.setError(0.1) ;
- argus.fitTo(*data,PrintEvalErrors(0),EvalErrorWall(kFALSE)) ;
+ m0.setError(0.1) ;
+ argus.fitTo(*data,PrintEvalErrors(0),EvalErrorWall(kFALSE)) ;
- // P l o t l i k e l i h o o d a s f u n c t i o n o f m 0
- // ------------------------------------------------------------------
+ // P l o t l i k e l i h o o d a s f u n c t i o n o f m 0
+ // ------------------------------------------------------------------
- // Construct likelihood function of model and data
- RooNLLVar nll("nll","nll",argus,*data) ;
+ // Construct likelihood function of model and data
+ RooNLLVar nll("nll","nll",argus,*data) ;
- // Plot likelihood in m0 in range that includes problematic values
- // In this configuration no messages are printed for likelihood evaluation errors,
- // but if an likelihood value evaluates with error, the corresponding value
- // on the curve will be set to the value given in EvalErrorValue().
+ // Plot likelihood in m0 in range that includes problematic values
+ // In this configuration no messages are printed for likelihood evaluation errors,
+ // but if an likelihood value evaluates with error, the corresponding value
+ // on the curve will be set to the value given in EvalErrorValue().
- RooPlot* frame2 = m0.frame(Range(5.288,5.293),Title("-log(L) scan vs m0, problematic regions masked")) ;
- nll.plotOn(frame2,PrintEvalErrors(-1),ShiftToZero(),EvalErrorValue(nll.getVal()+10),LineColor(kRed)) ;
- frame2->SetMaximum(15) ;
- frame2->SetMinimum(0) ;
+ RooPlot* frame2 = m0.frame(Range(5.288,5.293),Title("-log(L) scan vs m0, problematic regions masked")) ;
+ nll.plotOn(frame2,PrintEvalErrors(-1),ShiftToZero(),EvalErrorValue(nll.getVal()+10),LineColor(kRed)) ;
+ frame2->SetMaximum(15) ;
+ frame2->SetMinimum(0) ;
- TCanvas* c = new TCanvas("rf606_nllerrorhandling","rf606_nllerrorhandling",1200,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ TCanvas* c = new TCanvas("rf606_nllerrorhandling","rf606_nllerrorhandling",1200,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf607_fitresult.C b/tutorials/roofit/rf607_fitresult.C
index 3d34f2a59d9f96b305cfb7095fecb29a24ae7872..ff70dd4fcc83d0ce1c6907c3f74e873e3c922d2f 100644
--- a/tutorials/roofit/rf607_fitresult.C
+++ b/tutorials/roofit/rf607_fitresult.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #607
///
/// Demonstration of options of the RooFitResult class
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -33,115 +31,115 @@ using namespace RooFit ;
void rf607_fitresult()
{
- // C r e a t e p d f , d a t a
- // --------------------------------
+ // C r e a t e p d f , d a t a
+ // --------------------------------
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
+ // 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,-10,10) ;
- RooRealVar sigma1("sigma1","width of gaussians",0.5,0.1,10) ;
- RooRealVar sigma2("sigma2","width of gaussians",1,0.1,10) ;
+ // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
+ RooRealVar mean("mean","mean of gaussians",5,-10,10) ;
+ RooRealVar sigma1("sigma1","width of gaussians",0.5,0.1,10) ;
+ RooRealVar sigma2("sigma2","width of gaussians",1,0.1,10) ;
- 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) ;
- RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
+ 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) ;
+ RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;
- // 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) ;
+ // 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) ;
- // 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) ;
+ // 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) ;
- // Generate 1000 events
- RooDataSet* data = model.generate(x,1000) ;
+ // Generate 1000 events
+ RooDataSet* data = model.generate(x,1000) ;
- // F i t p d f t o d a t a , s a v e f i t r e s u l t
- // -------------------------------------------------------------
+ // F i t p d f t o d a t a , s a v e f i t r e s u l t
+ // -------------------------------------------------------------
- // Perform fit and save result
- RooFitResult* r = model.fitTo(*data,Save()) ;
+ // Perform fit and save result
+ RooFitResult* r = model.fitTo(*data,Save()) ;
- // P r i n t f i t r e s u l t s
- // ---------------------------------
+ // P r i n t f i t r e s u l t s
+ // ---------------------------------
- // Summary printing: Basic info plus final values of floating fit parameters
- r->Print() ;
+ // Summary printing: Basic info plus final values of floating fit parameters
+ r->Print() ;
- // Verbose printing: Basic info, values of constant parameters, initial and
- // final values of floating parameters, global correlations
- r->Print("v") ;
+ // Verbose printing: Basic info, values of constant parameters, initial and
+ // final values of floating parameters, global correlations
+ r->Print("v") ;
- // V i s u a l i z e c o r r e l a t i o n m a t r i x
- // -------------------------------------------------------
+ // V i s u a l i z e c o r r e l a t i o n m a t r i x
+ // -------------------------------------------------------
- // Construct 2D color plot of correlation matrix
- gStyle->SetOptStat(0) ;
- gStyle->SetPalette(1) ;
- TH2* hcorr = r->correlationHist() ;
+ // Construct 2D color plot of correlation matrix
+ gStyle->SetOptStat(0) ;
+ gStyle->SetPalette(1) ;
+ TH2* hcorr = r->correlationHist() ;
- // Visualize ellipse corresponding to single correlation matrix element
- RooPlot* frame = new RooPlot(sigma1,sig1frac,0.45,0.60,0.65,0.90) ;
- frame->SetTitle("Covariance between sigma1 and sig1frac") ;
- r->plotOn(frame,sigma1,sig1frac,"ME12ABHV") ;
+ // Visualize ellipse corresponding to single correlation matrix element
+ RooPlot* frame = new RooPlot(sigma1,sig1frac,0.45,0.60,0.65,0.90) ;
+ frame->SetTitle("Covariance between sigma1 and sig1frac") ;
+ r->plotOn(frame,sigma1,sig1frac,"ME12ABHV") ;
- // A c c e s s f i t r e s u l t i n f o r m a t i o n
- // ---------------------------------------------------------
+ // A c c e s s f i t r e s u l t i n f o r m a t i o n
+ // ---------------------------------------------------------
- // Access basic information
- cout << "EDM = " << r->edm() << endl ;
- cout << "-log(L) at minimum = " << r->minNll() << endl ;
+ // Access basic information
+ cout << "EDM = " << r->edm() << endl ;
+ cout << "-log(L) at minimum = " << r->minNll() << endl ;
- // Access list of final fit parameter values
- cout << "final value of floating parameters" << endl ;
- r->floatParsFinal().Print("s") ;
+ // Access list of final fit parameter values
+ cout << "final value of floating parameters" << endl ;
+ r->floatParsFinal().Print("s") ;
- // Access correlation matrix elements
- cout << "correlation between sig1frac and a0 is " << r->correlation(sig1frac,a0) << endl ;
- cout << "correlation between bkgfrac and mean is " << r->correlation("bkgfrac","mean") << endl ;
+ // Access correlation matrix elements
+ cout << "correlation between sig1frac and a0 is " << r->correlation(sig1frac,a0) << endl ;
+ cout << "correlation between bkgfrac and mean is " << r->correlation("bkgfrac","mean") << endl ;
- // Extract covariance and correlation matrix as TMatrixDSym
- const TMatrixDSym& cor = r->correlationMatrix() ;
- const TMatrixDSym& cov = r->covarianceMatrix() ;
+ // Extract covariance and correlation matrix as TMatrixDSym
+ const TMatrixDSym& cor = r->correlationMatrix() ;
+ const TMatrixDSym& cov = r->covarianceMatrix() ;
- // Print correlation, covariance matrix
- cout << "correlation matrix" << endl ;
- cor.Print() ;
- cout << "covariance matrix" << endl ;
- cov.Print() ;
-
+ // Print correlation, covariance matrix
+ cout << "correlation matrix" << endl ;
+ cor.Print() ;
+ cout << "covariance matrix" << endl ;
+ cov.Print() ;
+
- // P e r s i s t f i t r e s u l t i n r o o t f i l e
- // -------------------------------------------------------------
-
- // Open new ROOT file save save result
- TFile f("rf607_fitresult.root","RECREATE") ;
- r->Write("rf607") ;
- f.Close() ;
+ // P e r s i s t f i t r e s u l t i n r o o t f i l e
+ // -------------------------------------------------------------
+
+ // Open new ROOT file save save result
+ TFile f("rf607_fitresult.root","RECREATE") ;
+ r->Write("rf607") ;
+ f.Close() ;
- // In a clean ROOT session retrieve the persisted fit result as follows:
- // RooFitResult* r = gDirectory->Get("rf607") ;
-
+ // In a clean ROOT session retrieve the persisted fit result as follows:
+ // RooFitResult* r = gDirectory->Get("rf607") ;
+
- TCanvas* c = new TCanvas("rf607_fitresult","rf607_fitresult",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; hcorr->GetYaxis()->SetTitleOffset(1.4) ; hcorr->Draw("colz") ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
+ TCanvas* c = new TCanvas("rf607_fitresult","rf607_fitresult",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; hcorr->GetYaxis()->SetTitleOffset(1.4) ; hcorr->Draw("colz") ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
}
diff --git a/tutorials/roofit/rf608_fitresultaspdf.C b/tutorials/roofit/rf608_fitresultaspdf.C
index 18927aea3dece082120ea07e650a9f9e983c18d2..1bd5209b96c8ea9d50c6eb3530e75069673ca1b7 100644
--- a/tutorials/roofit/rf608_fitresultaspdf.C
+++ b/tutorials/roofit/rf608_fitresultaspdf.C
@@ -1,18 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #608
///
/// Representing the parabolic approximation of the fit as
/// a multi-variate Gaussian on the parameters of the fitted p.d.f.
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -33,89 +32,89 @@ using namespace RooFit ;
void rf608_fitresultaspdf()
{
- // C r e a t e m o d e l a n d d a t a s e t
- // -----------------------------------------------
+ // C r e a t e m o d e l a n d d a t a s e t
+ // -----------------------------------------------
- // Observable
- RooRealVar x("x","x",-20,20) ;
+ // Observable
+ RooRealVar x("x","x",-20,20) ;
- // Model (intentional strong correlations)
- RooRealVar mean("mean","mean of g1 and g2",0,-1,1) ;
- RooRealVar sigma_g1("sigma_g1","width of g1",2) ;
- RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
+ // Model (intentional strong correlations)
+ RooRealVar mean("mean","mean of g1 and g2",0,-1,1) ;
+ RooRealVar sigma_g1("sigma_g1","width of g1",2) ;
+ RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
- RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,5.0) ;
- RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
+ RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,5.0) ;
+ RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
- RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
- RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
+ RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
+ RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
- // Generate 1000 events
- RooDataSet* data = model.generate(x,1000) ;
+ // Generate 1000 events
+ RooDataSet* data = model.generate(x,1000) ;
- // F i t m o d e l t o d a t a
- // ----------------------------------
+ // F i t m o d e l t o d a t a
+ // ----------------------------------
- RooFitResult* r = model.fitTo(*data,Save()) ;
+ RooFitResult* r = model.fitTo(*data,Save()) ;
- // C r e a t e M V G a u s s i a n p d f o f f i t t e d p a r a m e t e r s
- // ------------------------------------------------------------------------------------
+ // C r e a t e M V G a u s s i a n p d f o f f i t t e d p a r a m e t e r s
+ // ------------------------------------------------------------------------------------
- RooAbsPdf* parabPdf = r->createHessePdf(RooArgSet(frac,mean,sigma_g2)) ;
+ RooAbsPdf* parabPdf = r->createHessePdf(RooArgSet(frac,mean,sigma_g2)) ;
- // S o m e e x e c e r c i s e s w i t h t h e p a r a m e t e r p d f
- // -----------------------------------------------------------------------------
+ // S o m e e x e c e r c i s e s w i t h t h e p a r a m e t e r p d f
+ // -----------------------------------------------------------------------------
- // Generate 100K points in the parameter space, sampled from the MVGaussian p.d.f.
- RooDataSet* d = parabPdf->generate(RooArgSet(mean,sigma_g2,frac),100000) ;
+ // Generate 100K points in the parameter space, sampled from the MVGaussian p.d.f.
+ RooDataSet* d = parabPdf->generate(RooArgSet(mean,sigma_g2,frac),100000) ;
- // Sample a 3-D histogram of the p.d.f. to be visualized as an error ellipsoid using the GLISO draw option
- TH3* hh_3d = (TH3*) parabPdf->createHistogram("mean,sigma_g2,frac",25,25,25) ;
- hh_3d->SetFillColor(kBlue) ;
+ // Sample a 3-D histogram of the p.d.f. to be visualized as an error ellipsoid using the GLISO draw option
+ TH3* hh_3d = (TH3*) parabPdf->createHistogram("mean,sigma_g2,frac",25,25,25) ;
+ hh_3d->SetFillColor(kBlue) ;
- // Project 3D parameter p.d.f. down to 3 permutations of two-dimensional p.d.f.s
- // The integrations corresponding to these projections are performed analytically
- // by the MV Gaussian p.d.f.
- RooAbsPdf* pdf_sigmag2_frac = parabPdf->createProjection(mean) ;
- RooAbsPdf* pdf_mean_frac = parabPdf->createProjection(sigma_g2) ;
- RooAbsPdf* pdf_mean_sigmag2 = parabPdf->createProjection(frac) ;
+ // Project 3D parameter p.d.f. down to 3 permutations of two-dimensional p.d.f.s
+ // The integrations corresponding to these projections are performed analytically
+ // by the MV Gaussian p.d.f.
+ RooAbsPdf* pdf_sigmag2_frac = parabPdf->createProjection(mean) ;
+ RooAbsPdf* pdf_mean_frac = parabPdf->createProjection(sigma_g2) ;
+ RooAbsPdf* pdf_mean_sigmag2 = parabPdf->createProjection(frac) ;
- // Make 2D plots of the 3 two-dimensional p.d.f. projections
- TH2* hh_sigmag2_frac = (TH2*) pdf_sigmag2_frac->createHistogram("sigma_g2,frac",50,50) ;
- TH2* hh_mean_frac = (TH2*) pdf_mean_frac->createHistogram("mean,frac",50,50) ;
- TH2* hh_mean_sigmag2 = (TH2*) pdf_mean_sigmag2->createHistogram("mean,sigma_g2",50,50) ;
- hh_mean_frac->SetLineColor(kBlue) ;
- hh_sigmag2_frac->SetLineColor(kBlue) ;
- hh_mean_sigmag2->SetLineColor(kBlue) ;
+ // Make 2D plots of the 3 two-dimensional p.d.f. projections
+ TH2* hh_sigmag2_frac = (TH2*) pdf_sigmag2_frac->createHistogram("sigma_g2,frac",50,50) ;
+ TH2* hh_mean_frac = (TH2*) pdf_mean_frac->createHistogram("mean,frac",50,50) ;
+ TH2* hh_mean_sigmag2 = (TH2*) pdf_mean_sigmag2->createHistogram("mean,sigma_g2",50,50) ;
+ hh_mean_frac->SetLineColor(kBlue) ;
+ hh_sigmag2_frac->SetLineColor(kBlue) ;
+ hh_mean_sigmag2->SetLineColor(kBlue) ;
- // Draw the 'sigar'
- gStyle->SetCanvasPreferGL(true);
- gStyle->SetPalette(1) ;
- new TCanvas("rf608_fitresultaspdf_1","rf608_fitresultaspdf_1",600,600) ;
- hh_3d->Draw("gliso") ;
+ // Draw the 'sigar'
+ gStyle->SetCanvasPreferGL(true);
+ gStyle->SetPalette(1) ;
+ new TCanvas("rf608_fitresultaspdf_1","rf608_fitresultaspdf_1",600,600) ;
+ hh_3d->Draw("gliso") ;
- // Draw the 2D projections of the 3D p.d.f.
- TCanvas* c2 = new TCanvas("rf608_fitresultaspdf_2","rf608_fitresultaspdf_2",900,600) ;
- c2->Divide(3,2) ;
- c2->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_mean_sigmag2->GetZaxis()->SetTitleOffset(1.4) ; hh_mean_sigmag2->Draw("surf3") ;
- c2->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_sigmag2_frac->GetZaxis()->SetTitleOffset(1.4) ; hh_sigmag2_frac->Draw("surf3") ;
- c2->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_mean_frac->GetZaxis()->SetTitleOffset(1.4) ; hh_mean_frac->Draw("surf3") ;
+ // Draw the 2D projections of the 3D p.d.f.
+ TCanvas* c2 = new TCanvas("rf608_fitresultaspdf_2","rf608_fitresultaspdf_2",900,600) ;
+ c2->Divide(3,2) ;
+ c2->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_mean_sigmag2->GetZaxis()->SetTitleOffset(1.4) ; hh_mean_sigmag2->Draw("surf3") ;
+ c2->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_sigmag2_frac->GetZaxis()->SetTitleOffset(1.4) ; hh_sigmag2_frac->Draw("surf3") ;
+ c2->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_mean_frac->GetZaxis()->SetTitleOffset(1.4) ; hh_mean_frac->Draw("surf3") ;
- // Draw the distributions of parameter points sampled from the p.d.f.
- TH1* tmp1 = d->createHistogram("mean,sigma_g2",50,50) ;
- TH1* tmp2 = d->createHistogram("sigma_g2,frac",50,50) ;
- TH1* tmp3 = d->createHistogram("mean,frac",50,50) ;
+ // Draw the distributions of parameter points sampled from the p.d.f.
+ TH1* tmp1 = d->createHistogram("mean,sigma_g2",50,50) ;
+ TH1* tmp2 = d->createHistogram("sigma_g2,frac",50,50) ;
+ TH1* tmp3 = d->createHistogram("mean,frac",50,50) ;
- c2->cd(4) ; gPad->SetLeftMargin(0.15) ; tmp1->GetZaxis()->SetTitleOffset(1.4) ; tmp1->Draw("lego3") ;
- c2->cd(5) ; gPad->SetLeftMargin(0.15) ; tmp2->GetZaxis()->SetTitleOffset(1.4) ; tmp2->Draw("lego3") ;
- c2->cd(6) ; gPad->SetLeftMargin(0.15) ; tmp3->GetZaxis()->SetTitleOffset(1.4) ; tmp3->Draw("lego3") ;
+ c2->cd(4) ; gPad->SetLeftMargin(0.15) ; tmp1->GetZaxis()->SetTitleOffset(1.4) ; tmp1->Draw("lego3") ;
+ c2->cd(5) ; gPad->SetLeftMargin(0.15) ; tmp2->GetZaxis()->SetTitleOffset(1.4) ; tmp2->Draw("lego3") ;
+ c2->cd(6) ; gPad->SetLeftMargin(0.15) ; tmp3->GetZaxis()->SetTitleOffset(1.4) ; tmp3->Draw("lego3") ;
}
diff --git a/tutorials/roofit/rf609_xychi2fit.C b/tutorials/roofit/rf609_xychi2fit.C
index e690a3fcd3126209deef3a8cc49ebe6e5b6c3eba..4cf56e145ea4c46d19a5b0572afd0c95cb354308 100644
--- a/tutorials/roofit/rf609_xychi2fit.C
+++ b/tutorials/roofit/rf609_xychi2fit.C
@@ -1,19 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #609
///
/// Setting up a chi^2 fit to an unbinned dataset with X,Y,err(Y)
/// values (and optionally err(X) values)
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooPolyVar.h"
@@ -30,65 +28,65 @@ using namespace RooFit ;
void rf609_xychi2fit()
{
- // C r e a t e d a t a s e t w i t h X a n d Y v a l u e s
- // -------------------------------------------------------------------
+ // C r e a t e d a t a s e t w i t h X a n d Y v a l u e s
+ // -------------------------------------------------------------------
- // Make weighted XY dataset with asymmetric errors stored
- // The StoreError() argument is essential as it makes
- // the dataset store the error in addition to the values
- // of the observables. If errors on one or more observables
- // are asymmetric, one can store the asymmetric error
- // using the StoreAsymError() argument
+ // Make weighted XY dataset with asymmetric errors stored
+ // The StoreError() argument is essential as it makes
+ // the dataset store the error in addition to the values
+ // of the observables. If errors on one or more observables
+ // are asymmetric, one can store the asymmetric error
+ // using the StoreAsymError() argument
- RooRealVar x("x","x",-11,11) ;
- RooRealVar y("y","y",-10,200) ;
- RooDataSet dxy("dxy","dxy",RooArgSet(x,y),StoreError(RooArgSet(x,y))) ;
+ RooRealVar x("x","x",-11,11) ;
+ RooRealVar y("y","y",-10,200) ;
+ RooDataSet dxy("dxy","dxy",RooArgSet(x,y),StoreError(RooArgSet(x,y))) ;
- // Fill an example dataset with X,err(X),Y,err(Y) values
- for (int i=0 ; i<=10 ; i++) {
+ // Fill an example dataset with X,err(X),Y,err(Y) values
+ for (int i=0 ; i<=10 ; i++) {
- // Set X value and error
- x = -10 + 2*i;
- x.setError( i<5 ? 0.5/1. : 1.0/1. ) ;
-
- // Set Y value and error
- y = x.getVal() * x.getVal() + 4*fabs(gRandom->Gaus()) ;
- y.setError(sqrt(y.getVal())) ;
+ // Set X value and error
+ x = -10 + 2*i;
+ x.setError( i<5 ? 0.5/1. : 1.0/1. ) ;
+
+ // Set Y value and error
+ y = x.getVal() * x.getVal() + 4*fabs(gRandom->Gaus()) ;
+ y.setError(sqrt(y.getVal())) ;
- dxy.add(RooArgSet(x,y)) ;
- }
+ dxy.add(RooArgSet(x,y)) ;
+ }
- // P e r f o r m c h i 2 f i t t o X + / - d x a n d Y + / - d Y v a l u e s
- // ---------------------------------------------------------------------------------------
+ // P e r f o r m c h i 2 f i t t o X + / - d x a n d Y + / - d Y v a l u e s
+ // ---------------------------------------------------------------------------------------
- // Make fit function
- RooRealVar a("a","a",0.0,-10,10) ;
- RooRealVar b("b","b",0.0,-100,100) ;
- RooPolyVar f("f","f",x,RooArgList(b,a,RooConst(1))) ;
+ // Make fit function
+ RooRealVar a("a","a",0.0,-10,10) ;
+ RooRealVar b("b","b",0.0,-100,100) ;
+ RooPolyVar f("f","f",x,RooArgList(b,a,RooConst(1))) ;
- // Plot dataset in X-Y interpretation
- RooPlot* frame = x.frame(Title("Chi^2 fit of function set of (X#pmdX,Y#pmdY) values")) ;
- dxy.plotOnXY(frame,YVar(y)) ;
+ // Plot dataset in X-Y interpretation
+ RooPlot* frame = x.frame(Title("Chi^2 fit of function set of (X#pmdX,Y#pmdY) values")) ;
+ dxy.plotOnXY(frame,YVar(y)) ;
- // Fit chi^2 using X and Y errors
- f.chi2FitTo(dxy,YVar(y)) ;
+ // Fit chi^2 using X and Y errors
+ f.chi2FitTo(dxy,YVar(y)) ;
- // Overlay fitted function
- f.plotOn(frame) ;
-
- // Alternative: fit chi^2 integrating f(x) over ranges defined by X errors, rather
- // than taking point at center of bin
- f.chi2FitTo(dxy,YVar(y),Integrate(kTRUE)) ;
+ // Overlay fitted function
+ f.plotOn(frame) ;
+
+ // Alternative: fit chi^2 integrating f(x) over ranges defined by X errors, rather
+ // than taking point at center of bin
+ f.chi2FitTo(dxy,YVar(y),Integrate(kTRUE)) ;
- // Overlay alternate fit result
- f.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
+ // Overlay alternate fit result
+ f.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
- // Draw the plot on a canvas
- new TCanvas("rf609_xychi2fit","rf609_xychi2fit",600,600) ;
- gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
-
+ // Draw the plot on a canvas
+ new TCanvas("rf609_xychi2fit","rf609_xychi2fit",600,600) ;
+ gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+
}
diff --git a/tutorials/roofit/rf610_visualerror.C b/tutorials/roofit/rf610_visualerror.C
index df8b761e4699ac611062a0db64ff3e2209ad4831..f22977b2a1783e3beaccab70a50c1eddaeb5da9a 100644
--- a/tutorials/roofit/rf610_visualerror.C
+++ b/tutorials/roofit/rf610_visualerror.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #610
///
/// Visualization of errors from a covariance matrix
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
@@ -27,135 +25,135 @@ using namespace RooFit ;
void rf610_visualerror()
{
- // S e t u p e x a m p l e f i t
- // ---------------------------------------
-
- // Create sum of two Gaussians p.d.f. with factory
- RooRealVar x("x","x",-10,10) ;
-
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",2,1,50) ;
- RooGaussian sig("sig","sig",x,m,s) ;
-
- RooRealVar m2("m2","m2",-1,-10,10) ;
- RooRealVar s2("s2","s2",6,1,50) ;
- RooGaussian bkg("bkg","bkg",x,m2,s2) ;
-
- RooRealVar fsig("fsig","fsig",0.33,0,1) ;
- RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
-
- // Create binned dataset
- x.setBins(25) ;
- RooAbsData* d = model.generateBinned(x,1000) ;
-
- // Perform fit and save fit result
- RooFitResult* r = model.fitTo(*d,Save()) ;
-
-
- // V i s u a l i z e f i t e r r o r
- // -------------------------------------
-
- // Make plot frame
- RooPlot* frame = x.frame(Bins(40),Title("P.d.f with visualized 1-sigma error band")) ;
- d->plotOn(frame) ;
-
- // Visualize 1-sigma error encoded in fit result 'r' as orange band using linear error propagation
- // This results in an error band that is by construction symmetric
- //
- // The linear error is calculated as
- // error(x) = Z* F_a(x) * Corr(a,a') F_a'(x)
- //
- // where F_a(x) = [ f(x,a+da) - f(x,a-da) ] / 2,
- //
- // with f(x) = the plotted curve
- // 'da' = error taken from the fit result
- // Corr(a,a') = the correlation matrix from the fit result
- // Z = requested significance 'Z sigma band'
- //
- // The linear method is fast (required 2*N evaluations of the curve, where N is the number of parameters),
- // but may not be accurate in the presence of strong correlations (~>0.9) and at Z>2 due to linear and
- // Gaussian approximations made
- //
- model.plotOn(frame,VisualizeError(*r,1),FillColor(kOrange)) ;
-
-
- // Calculate error using sampling method and visualize as dashed red line.
- //
- // In this method a number of curves is calculated with variations of the parameter values, as sampled
- // from a multi-variate Gaussian p.d.f. that is constructed from the fit results covariance matrix.
- // The error(x) is determined by calculating a central interval that capture N% of the variations
- // for each valye of x, where N% is controlled by Z (i.e. Z=1 gives N=68%). The number of sampling curves
- // is chosen to be such that at least 100 curves are expected to be outside the N% interval, and is minimally
- // 100 (e.g. Z=1->Ncurve=356, Z=2->Ncurve=2156)) 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
- model.plotOn(frame,VisualizeError(*r,1,kFALSE),DrawOption("L"),LineWidth(2),LineColor(kRed)) ;
-
- // Perform the same type of error visualization on the background component only.
- // The VisualizeError() option can generally applied to _any_ kind of plot (components, asymmetries, efficiencies etc..)
- model.plotOn(frame,VisualizeError(*r,1),FillColor(kOrange),Components("bkg")) ;
- model.plotOn(frame,VisualizeError(*r,1,kFALSE),DrawOption("L"),LineWidth(2),LineColor(kRed),Components("bkg"),LineStyle(kDashed)) ;
-
- // Overlay central value
- model.plotOn(frame) ;
- model.plotOn(frame,Components("bkg"),LineStyle(kDashed)) ;
- d->plotOn(frame) ;
- frame->SetMinimum(0) ;
-
-
- // V i s u a l i z e p a r t i a l f i t e r r o r
- // ------------------------------------------------------
-
- // Make plot frame
- RooPlot* frame2 = x.frame(Bins(40),Title("Visualization of 2-sigma partial error from (m,m2)")) ;
-
- // Visualize partial error. For partial error visualization the covariance matrix is first reduced as follows
- // ___ -1
- // Vred = V22 = V11 - V12 * V22 * V21
- //
- // Where V11,V12,V21,V22 represent a block decomposition of the covariance matrix into observables that
- // are propagated (labeled by index '1') and that are not propagated (labeled by index '2'), and V22bar
- // is the Shur complement of V22, calculated as shown above
- //
- // (Note that Vred is _not_ a simple sub-matrix of V)
-
- // Propagate partial error due to shape parameters (m,m2) using linear and sampling method
- model.plotOn(frame2,VisualizeError(*r,RooArgSet(m,m2),2),FillColor(kCyan)) ;
- model.plotOn(frame2,Components("bkg"),VisualizeError(*r,RooArgSet(m,m2),2),FillColor(kCyan)) ;
-
- model.plotOn(frame2) ;
- model.plotOn(frame2,Components("bkg"),LineStyle(kDashed)) ;
- frame2->SetMinimum(0) ;
-
-
- // Make plot frame
- RooPlot* frame3 = x.frame(Bins(40),Title("Visualization of 2-sigma partial error from (s,s2)")) ;
-
- // Propagate partial error due to yield parameter using linear and sampling method
- model.plotOn(frame3,VisualizeError(*r,RooArgSet(s,s2),2),FillColor(kGreen)) ;
- model.plotOn(frame3,Components("bkg"),VisualizeError(*r,RooArgSet(s,s2),2),FillColor(kGreen)) ;
-
- model.plotOn(frame3) ;
- model.plotOn(frame3,Components("bkg"),LineStyle(kDashed)) ;
- frame3->SetMinimum(0) ;
-
-
- // Make plot frame
- RooPlot* frame4 = x.frame(Bins(40),Title("Visualization of 2-sigma partial error from fsig")) ;
-
- // Propagate partial error due to yield parameter using linear and sampling method
- model.plotOn(frame4,VisualizeError(*r,RooArgSet(fsig),2),FillColor(kMagenta)) ;
- model.plotOn(frame4,Components("bkg"),VisualizeError(*r,RooArgSet(fsig),2),FillColor(kMagenta)) ;
-
- model.plotOn(frame4) ;
- model.plotOn(frame4,Components("bkg"),LineStyle(kDashed)) ;
- frame4->SetMinimum(0) ;
-
-
-
- TCanvas* c = new TCanvas("rf610_visualerror","rf610_visualerror",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.6) ; frame4->Draw() ;
+ // S e t u p e x a m p l e f i t
+ // ---------------------------------------
+
+ // Create sum of two Gaussians p.d.f. with factory
+ RooRealVar x("x","x",-10,10) ;
+
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",2,1,50) ;
+ RooGaussian sig("sig","sig",x,m,s) ;
+
+ RooRealVar m2("m2","m2",-1,-10,10) ;
+ RooRealVar s2("s2","s2",6,1,50) ;
+ RooGaussian bkg("bkg","bkg",x,m2,s2) ;
+
+ RooRealVar fsig("fsig","fsig",0.33,0,1) ;
+ RooAddPdf model("model","model",RooArgList(sig,bkg),fsig) ;
+
+ // Create binned dataset
+ x.setBins(25) ;
+ RooAbsData* d = model.generateBinned(x,1000) ;
+
+ // Perform fit and save fit result
+ RooFitResult* r = model.fitTo(*d,Save()) ;
+
+
+ // V i s u a l i z e f i t e r r o r
+ // -------------------------------------
+
+ // Make plot frame
+ RooPlot* frame = x.frame(Bins(40),Title("P.d.f with visualized 1-sigma error band")) ;
+ d->plotOn(frame) ;
+
+ // Visualize 1-sigma error encoded in fit result 'r' as orange band using linear error propagation
+ // This results in an error band that is by construction symmetric
+ //
+ // The linear error is calculated as
+ // error(x) = Z* F_a(x) * Corr(a,a') F_a'(x)
+ //
+ // where F_a(x) = [ f(x,a+da) - f(x,a-da) ] / 2,
+ //
+ // with f(x) = the plotted curve
+ // 'da' = error taken from the fit result
+ // Corr(a,a') = the correlation matrix from the fit result
+ // Z = requested significance 'Z sigma band'
+ //
+ // The linear method is fast (required 2*N evaluations of the curve, where N is the number of parameters),
+ // but may not be accurate in the presence of strong correlations (~>0.9) and at Z>2 due to linear and
+ // Gaussian approximations made
+ //
+ model.plotOn(frame,VisualizeError(*r,1),FillColor(kOrange)) ;
+
+
+ // Calculate error using sampling method and visualize as dashed red line.
+ //
+ // In this method a number of curves is calculated with variations of the parameter values, as sampled
+ // from a multi-variate Gaussian p.d.f. that is constructed from the fit results covariance matrix.
+ // The error(x) is determined by calculating a central interval that capture N% of the variations
+ // for each valye of x, where N% is controlled by Z (i.e. Z=1 gives N=68%). The number of sampling curves
+ // is chosen to be such that at least 100 curves are expected to be outside the N% interval, and is minimally
+ // 100 (e.g. Z=1->Ncurve=356, Z=2->Ncurve=2156)) 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
+ model.plotOn(frame,VisualizeError(*r,1,kFALSE),DrawOption("L"),LineWidth(2),LineColor(kRed)) ;
+
+ // Perform the same type of error visualization on the background component only.
+ // The VisualizeError() option can generally applied to _any_ kind of plot (components, asymmetries, efficiencies etc..)
+ model.plotOn(frame,VisualizeError(*r,1),FillColor(kOrange),Components("bkg")) ;
+ model.plotOn(frame,VisualizeError(*r,1,kFALSE),DrawOption("L"),LineWidth(2),LineColor(kRed),Components("bkg"),LineStyle(kDashed)) ;
+
+ // Overlay central value
+ model.plotOn(frame) ;
+ model.plotOn(frame,Components("bkg"),LineStyle(kDashed)) ;
+ d->plotOn(frame) ;
+ frame->SetMinimum(0) ;
+
+
+ // V i s u a l i z e p a r t i a l f i t e r r o r
+ // ------------------------------------------------------
+
+ // Make plot frame
+ RooPlot* frame2 = x.frame(Bins(40),Title("Visualization of 2-sigma partial error from (m,m2)")) ;
+
+ // Visualize partial error. For partial error visualization the covariance matrix is first reduced as follows
+ // ___ -1
+ // Vred = V22 = V11 - V12 * V22 * V21
+ //
+ // Where V11,V12,V21,V22 represent a block decomposition of the covariance matrix into observables that
+ // are propagated (labeled by index '1') and that are not propagated (labeled by index '2'), and V22bar
+ // is the Shur complement of V22, calculated as shown above
+ //
+ // (Note that Vred is _not_ a simple sub-matrix of V)
+
+ // Propagate partial error due to shape parameters (m,m2) using linear and sampling method
+ model.plotOn(frame2,VisualizeError(*r,RooArgSet(m,m2),2),FillColor(kCyan)) ;
+ model.plotOn(frame2,Components("bkg"),VisualizeError(*r,RooArgSet(m,m2),2),FillColor(kCyan)) ;
+
+ model.plotOn(frame2) ;
+ model.plotOn(frame2,Components("bkg"),LineStyle(kDashed)) ;
+ frame2->SetMinimum(0) ;
+
+
+ // Make plot frame
+ RooPlot* frame3 = x.frame(Bins(40),Title("Visualization of 2-sigma partial error from (s,s2)")) ;
+
+ // Propagate partial error due to yield parameter using linear and sampling method
+ model.plotOn(frame3,VisualizeError(*r,RooArgSet(s,s2),2),FillColor(kGreen)) ;
+ model.plotOn(frame3,Components("bkg"),VisualizeError(*r,RooArgSet(s,s2),2),FillColor(kGreen)) ;
+
+ model.plotOn(frame3) ;
+ model.plotOn(frame3,Components("bkg"),LineStyle(kDashed)) ;
+ frame3->SetMinimum(0) ;
+
+
+ // Make plot frame
+ RooPlot* frame4 = x.frame(Bins(40),Title("Visualization of 2-sigma partial error from fsig")) ;
+
+ // Propagate partial error due to yield parameter using linear and sampling method
+ model.plotOn(frame4,VisualizeError(*r,RooArgSet(fsig),2),FillColor(kMagenta)) ;
+ model.plotOn(frame4,Components("bkg"),VisualizeError(*r,RooArgSet(fsig),2),FillColor(kMagenta)) ;
+
+ model.plotOn(frame4) ;
+ model.plotOn(frame4,Components("bkg"),LineStyle(kDashed)) ;
+ frame4->SetMinimum(0) ;
+
+
+
+ TCanvas* c = new TCanvas("rf610_visualerror","rf610_visualerror",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.6) ; frame4->Draw() ;
}
diff --git a/tutorials/roofit/rf701_efficiencyfit.C b/tutorials/roofit/rf701_efficiencyfit.C
index 22d952a59f55a51cc23ffd13d4f479b4845f11fd..e976c2abb1ceb77413841812ab5e564b3552e640 100644
--- a/tutorials/roofit/rf701_efficiencyfit.C
+++ b/tutorials/roofit/rf701_efficiencyfit.C
@@ -1,18 +1,18 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'SPECIAL PDFS' RooFit tutorial macro #701
///
/// Unbinned maximum likelihood fit of an efficiency eff(x) function to
/// a dataset D(x,cut), where cut is a category encoding a selection, of which
/// the efficiency as function of x should be described by eff(x)
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,74 +30,74 @@ using namespace RooFit ;
void rf701_efficiencyfit()
{
- // C o n s t r u c t e f f i c i e n c y f u n c t i o n e ( x )
- // -------------------------------------------------------------------
+ // C o n s t r u c t e f f i c i e n c y f u n c t i o n e ( x )
+ // -------------------------------------------------------------------
- // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
- RooRealVar x("x","x",-10,10) ;
+ // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
+ RooRealVar x("x","x",-10,10) ;
- // Efficiency function eff(x;a,b)
- RooRealVar a("a","a",0.4,0,1) ;
- RooRealVar b("b","b",5) ;
- RooRealVar c("c","c",-1,-10,10) ;
- RooFormulaVar effFunc("effFunc","(1-a)+a*cos((x-c)/b)",RooArgList(a,b,c,x)) ;
+ // Efficiency function eff(x;a,b)
+ RooRealVar a("a","a",0.4,0,1) ;
+ RooRealVar b("b","b",5) ;
+ RooRealVar c("c","c",-1,-10,10) ;
+ RooFormulaVar effFunc("effFunc","(1-a)+a*cos((x-c)/b)",RooArgList(a,b,c,x)) ;
- // C o n s t r u c t c o n d i t i o n a l e f f i c i e n c y p d f E ( c u t | x )
- // ------------------------------------------------------------------------------------------
+ // C o n s t r u c t c o n d i t i o n a l e f f i c i e n c y p d f E ( c u t | x )
+ // ------------------------------------------------------------------------------------------
- // Acceptance state cut (1 or 0)
- RooCategory cut("cut","cutr") ;
- cut.defineType("accept",1) ;
- cut.defineType("reject",0) ;
+ // Acceptance state cut (1 or 0)
+ RooCategory cut("cut","cutr") ;
+ cut.defineType("accept",1) ;
+ cut.defineType("reject",0) ;
- // Construct efficiency p.d.f eff(cut|x)
- RooEfficiency effPdf("effPdf","effPdf",effFunc,cut,"accept") ;
+ // Construct efficiency p.d.f eff(cut|x)
+ RooEfficiency effPdf("effPdf","effPdf",effFunc,cut,"accept") ;
- // G e n e r a t e d a t a ( x , c u t ) f r o m a t o y m o d e l
- // -----------------------------------------------------------------------------
+ // G e n e r a t e d a t a ( x , c u t ) f r o m a t o y m o d e l
+ // -----------------------------------------------------------------------------
- // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x)
- // (These are _only_ needed to generate some toy MC here to be used later)
- RooPolynomial shapePdf("shapePdf","shapePdf",x,RooConst(-0.095)) ;
- RooProdPdf model("model","model",shapePdf,Conditional(effPdf,cut)) ;
+ // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x)
+ // (These are _only_ needed to generate some toy MC here to be used later)
+ RooPolynomial shapePdf("shapePdf","shapePdf",x,RooConst(-0.095)) ;
+ RooProdPdf model("model","model",shapePdf,Conditional(effPdf,cut)) ;
- // Generate some toy data from model
- RooDataSet* data = model.generate(RooArgSet(x,cut),10000) ;
+ // Generate some toy data from model
+ RooDataSet* data = model.generate(RooArgSet(x,cut),10000) ;
- // F i t c o n d i t i o n a l e f f i c i e n c y p d f t o d a t a
- // --------------------------------------------------------------------------
+ // F i t c o n d i t i o n a l e f f i c i e n c y p d f t o d a t a
+ // --------------------------------------------------------------------------
- // Fit conditional efficiency p.d.f to data
- effPdf.fitTo(*data,ConditionalObservables(x)) ;
+ // Fit conditional efficiency p.d.f to data
+ effPdf.fitTo(*data,ConditionalObservables(x)) ;
- // P l o t f i t t e d , d a t a e f f i c i e n c y
- // --------------------------------------------------------
+ // P l o t f i t t e d , d a t a e f f i c i e n c y
+ // --------------------------------------------------------
- // Plot distribution of all events and accepted fraction of events on frame
- RooPlot* frame1 = x.frame(Bins(20),Title("Data (all, accepted)")) ;
- data->plotOn(frame1) ;
- data->plotOn(frame1,Cut("cut==cut::accept"),MarkerColor(kRed),LineColor(kRed)) ;
+ // Plot distribution of all events and accepted fraction of events on frame
+ RooPlot* frame1 = x.frame(Bins(20),Title("Data (all, accepted)")) ;
+ data->plotOn(frame1) ;
+ data->plotOn(frame1,Cut("cut==cut::accept"),MarkerColor(kRed),LineColor(kRed)) ;
- // Plot accept/reject efficiency on data overlay fitted efficiency curve
- RooPlot* frame2 = x.frame(Bins(20),Title("Fitted efficiency")) ;
- data->plotOn(frame2,Efficiency(cut)) ; // needs ROOT version >= 5.21
- effFunc.plotOn(frame2,LineColor(kRed)) ;
+ // Plot accept/reject efficiency on data overlay fitted efficiency curve
+ RooPlot* frame2 = x.frame(Bins(20),Title("Fitted efficiency")) ;
+ data->plotOn(frame2,Efficiency(cut)) ; // needs ROOT version >= 5.21
+ effFunc.plotOn(frame2,LineColor(kRed)) ;
- // Draw all frames on a canvas
- TCanvas* ca = new TCanvas("rf701_efficiency","rf701_efficiency",800,400) ;
- ca->Divide(2) ;
- ca->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
- ca->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
-
+ // Draw all frames on a canvas
+ TCanvas* ca = new TCanvas("rf701_efficiency","rf701_efficiency",800,400) ;
+ ca->Divide(2) ;
+ ca->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
+ ca->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+
}
diff --git a/tutorials/roofit/rf702_efficiencyfit_2D.C b/tutorials/roofit/rf702_efficiencyfit_2D.C
index eab0562c3f795731aa500a0530a6854cd8fff1fd..0b32bdf21c35ab1b625e6efc5788236a2c177676 100644
--- a/tutorials/roofit/rf702_efficiencyfit_2D.C
+++ b/tutorials/roofit/rf702_efficiencyfit_2D.C
@@ -1,17 +1,17 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'SPECIAL PDFS' RooFit tutorial macro #702
///
/// Unbinned maximum likelihood fit of an efficiency eff(x) function to
/// a dataset D(x,cut), where cut is a category encoding a selection whose
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author //
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,86 +30,86 @@ using namespace RooFit ;
void rf702_efficiencyfit_2D(Bool_t flat=kFALSE)
{
- // C o n s t r u c t e f f i c i e n c y f u n c t i o n e ( x , y )
- // -----------------------------------------------------------------------
+ // C o n s t r u c t e f f i c i e n c y f u n c t i o n e ( x , y )
+ // -----------------------------------------------------------------------
- // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
- RooRealVar x("x","x",-10,10) ;
- RooRealVar y("y","y",-10,10) ;
+ // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
+ RooRealVar x("x","x",-10,10) ;
+ RooRealVar y("y","y",-10,10) ;
- // Efficiency function eff(x;a,b)
- RooRealVar ax("ax","ay",0.6,0,1) ;
- RooRealVar bx("bx","by",5) ;
- RooRealVar cx("cx","cy",-1,-10,10) ;
+ // Efficiency function eff(x;a,b)
+ RooRealVar ax("ax","ay",0.6,0,1) ;
+ RooRealVar bx("bx","by",5) ;
+ RooRealVar cx("cx","cy",-1,-10,10) ;
- RooRealVar ay("ay","ay",0.2,0,1) ;
- RooRealVar by("by","by",5) ;
- RooRealVar cy("cy","cy",-1,-10,10) ;
+ RooRealVar ay("ay","ay",0.2,0,1) ;
+ RooRealVar by("by","by",5) ;
+ RooRealVar cy("cy","cy",-1,-10,10) ;
- RooFormulaVar effFunc("effFunc","((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))",RooArgList(ax,bx,cx,x,ay,by,cy,y)) ;
+ RooFormulaVar effFunc("effFunc","((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))",RooArgList(ax,bx,cx,x,ay,by,cy,y)) ;
- // Acceptance state cut (1 or 0)
- RooCategory cut("cut","cutr") ;
- cut.defineType("accept",1) ;
- cut.defineType("reject",0) ;
+ // Acceptance state cut (1 or 0)
+ RooCategory cut("cut","cutr") ;
+ cut.defineType("accept",1) ;
+ cut.defineType("reject",0) ;
- // C o n s t r u c t c o n d i t i o n a l e f f i c i e n c y p d f E ( c u t | x , y )
- // ---------------------------------------------------------------------------------------------
+ // C o n s t r u c t c o n d i t i o n a l e f f i c i e n c y p d f E ( c u t | x , y )
+ // ---------------------------------------------------------------------------------------------
- // Construct efficiency p.d.f eff(cut|x)
- RooEfficiency effPdf("effPdf","effPdf",effFunc,cut,"accept") ;
+ // Construct efficiency p.d.f eff(cut|x)
+ RooEfficiency effPdf("effPdf","effPdf",effFunc,cut,"accept") ;
- // G e n e r a t e d a t a ( x , y , c u t ) f r o m a t o y m o d e l
- // -------------------------------------------------------------------------------
+ // G e n e r a t e d a t a ( x , y , c u t ) f r o m a t o y m o d e l
+ // -------------------------------------------------------------------------------
- // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x)
- // (These are _only_ needed to generate some toy MC here to be used later)
- RooPolynomial shapePdfX("shapePdfX","shapePdfX",x,RooConst(flat?0:-0.095)) ;
- RooPolynomial shapePdfY("shapePdfY","shapePdfY",y,RooConst(flat?0:+0.095)) ;
- RooProdPdf shapePdf("shapePdf","shapePdf",RooArgSet(shapePdfX,shapePdfY)) ;
- RooProdPdf model("model","model",shapePdf,Conditional(effPdf,cut)) ;
+ // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x)
+ // (These are _only_ needed to generate some toy MC here to be used later)
+ RooPolynomial shapePdfX("shapePdfX","shapePdfX",x,RooConst(flat?0:-0.095)) ;
+ RooPolynomial shapePdfY("shapePdfY","shapePdfY",y,RooConst(flat?0:+0.095)) ;
+ RooProdPdf shapePdf("shapePdf","shapePdf",RooArgSet(shapePdfX,shapePdfY)) ;
+ RooProdPdf model("model","model",shapePdf,Conditional(effPdf,cut)) ;
- // Generate some toy data from model
- RooDataSet* data = model.generate(RooArgSet(x,y,cut),10000) ;
+ // Generate some toy data from model
+ RooDataSet* data = model.generate(RooArgSet(x,y,cut),10000) ;
- // F i t c o n d i t i o n a l e f f i c i e n c y p d f t o d a t a
- // --------------------------------------------------------------------------
+ // F i t c o n d i t i o n a l e f f i c i e n c y p d f t o d a t a
+ // --------------------------------------------------------------------------
- // Fit conditional efficiency p.d.f to data
- effPdf.fitTo(*data,ConditionalObservables(RooArgSet(x,y))) ;
+ // Fit conditional efficiency p.d.f to data
+ effPdf.fitTo(*data,ConditionalObservables(RooArgSet(x,y))) ;
- // P l o t f i t t e d , d a t a e f f i c i e n c y
- // --------------------------------------------------------
+ // P l o t f i t t e d , d a t a e f f i c i e n c y
+ // --------------------------------------------------------
- // Make 2D histograms of all data, selected data and efficiency function
- TH1* hh_data_all = data->createHistogram("hh_data_all",x,Binning(8),YVar(y,Binning(8))) ;
- TH1* hh_data_sel = data->createHistogram("hh_data_sel",x,Binning(8),YVar(y,Binning(8)),Cut("cut==cut::accept")) ;
- TH1* hh_eff = effFunc.createHistogram("hh_eff",x,Binning(50),YVar(y,Binning(50))) ;
+ // Make 2D histograms of all data, selected data and efficiency function
+ TH1* hh_data_all = data->createHistogram("hh_data_all",x,Binning(8),YVar(y,Binning(8))) ;
+ TH1* hh_data_sel = data->createHistogram("hh_data_sel",x,Binning(8),YVar(y,Binning(8)),Cut("cut==cut::accept")) ;
+ TH1* hh_eff = effFunc.createHistogram("hh_eff",x,Binning(50),YVar(y,Binning(50))) ;
- // Some adjustsment for good visualization
- hh_data_all->SetMinimum(0) ;
- hh_data_sel->SetMinimum(0) ;
- hh_eff->SetMinimum(0) ;
- hh_eff->SetLineColor(kBlue) ;
+ // Some adjustsment for good visualization
+ hh_data_all->SetMinimum(0) ;
+ hh_data_sel->SetMinimum(0) ;
+ hh_eff->SetMinimum(0) ;
+ hh_eff->SetLineColor(kBlue) ;
- // Draw all frames on a canvas
- TCanvas* ca = new TCanvas("rf702_efficiency_2D","rf702_efficiency_2D",1200,400) ;
- ca->Divide(3) ;
- ca->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data_all->GetZaxis()->SetTitleOffset(1.8) ; hh_data_all->Draw("lego") ;
- ca->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_data_sel->GetZaxis()->SetTitleOffset(1.8) ; hh_data_sel->Draw("lego") ;
- ca->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_eff->GetZaxis()->SetTitleOffset(1.8) ; hh_eff->Draw("surf") ;
+ // Draw all frames on a canvas
+ TCanvas* ca = new TCanvas("rf702_efficiency_2D","rf702_efficiency_2D",1200,400) ;
+ ca->Divide(3) ;
+ ca->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data_all->GetZaxis()->SetTitleOffset(1.8) ; hh_data_all->Draw("lego") ;
+ ca->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_data_sel->GetZaxis()->SetTitleOffset(1.8) ; hh_data_sel->Draw("lego") ;
+ ca->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_eff->GetZaxis()->SetTitleOffset(1.8) ; hh_eff->Draw("surf") ;
- return ;
+ return ;
}
diff --git a/tutorials/roofit/rf703_effpdfprod.C b/tutorials/roofit/rf703_effpdfprod.C
index 93d83845b063c24f673a03c113d73fe385735d9f..a3bb6af796d1521492cb5a096efb3eefa7b8cfbe 100644
--- a/tutorials/roofit/rf703_effpdfprod.C
+++ b/tutorials/roofit/rf703_effpdfprod.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'SPECIAL PDFS' RooFit tutorial macro #703
///
/// Using a product of an (acceptance) efficiency and a p.d.f as p.d.f.
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -28,67 +26,67 @@ using namespace RooFit ;
void rf703_effpdfprod()
{
- // D e f i n e o b s e r v a b l e s a n d d e c a y p d f
- // ---------------------------------------------------------------
+ // D e f i n e o b s e r v a b l e s a n d d e c a y p d f
+ // ---------------------------------------------------------------
- // Declare observables
- RooRealVar t("t","t",0,5) ;
+ // Declare observables
+ RooRealVar t("t","t",0,5) ;
- // Make pdf
- RooRealVar tau("tau","tau",-1.54,-4,-0.1) ;
- RooExponential model("model","model",t,tau) ;
+ // Make pdf
+ RooRealVar tau("tau","tau",-1.54,-4,-0.1) ;
+ RooExponential model("model","model",t,tau) ;
- // D e f i n e e f f i c i e n c y f u n c t i o n
- // ---------------------------------------------------
-
- // Use error function to simulate turn-on slope
- RooFormulaVar eff("eff","0.5*(TMath::Erf((t-1)/0.5)+1)",t) ;
+ // D e f i n e e f f i c i e n c y f u n c t i o n
+ // ---------------------------------------------------
+
+ // Use error function to simulate turn-on slope
+ RooFormulaVar eff("eff","0.5*(TMath::Erf((t-1)/0.5)+1)",t) ;
- // D e f i n e d e c a y p d f w i t h e f f i c i e n c y
- // ---------------------------------------------------------------
+ // D e f i n e d e c a y p d f w i t h e f f i c i e n c y
+ // ---------------------------------------------------------------
- // Multiply pdf(t) with efficiency in t
- RooEffProd modelEff("modelEff","model with efficiency",model,eff) ;
+ // Multiply pdf(t) with efficiency in t
+ RooEffProd modelEff("modelEff","model with efficiency",model,eff) ;
-
+
- // P l o t e f f i c i e n c y , p d f
- // ----------------------------------------
+ // P l o t e f f i c i e n c y , p d f
+ // ----------------------------------------
- RooPlot* frame1 = t.frame(Title("Efficiency")) ;
- eff.plotOn(frame1,LineColor(kRed)) ;
+ RooPlot* frame1 = t.frame(Title("Efficiency")) ;
+ eff.plotOn(frame1,LineColor(kRed)) ;
- RooPlot* frame2 = t.frame(Title("Pdf with and without efficiency")) ;
+ RooPlot* frame2 = t.frame(Title("Pdf with and without efficiency")) ;
- model.plotOn(frame2,LineStyle(kDashed)) ;
- modelEff.plotOn(frame2) ;
+ model.plotOn(frame2,LineStyle(kDashed)) ;
+ modelEff.plotOn(frame2) ;
- // G e n e r a t e t o y d a t a , f i t m o d e l E f f t o d a t a
- // ------------------------------------------------------------------------------
+ // G e n e r a t e t o y d a t a , f i t m o d e l E f f t o d a t a
+ // ------------------------------------------------------------------------------
- // Generate events. If the input pdf has an internal generator, the internal generator
- // is used and an accept/reject sampling on the efficiency is applied.
- RooDataSet* data = modelEff.generate(t,10000) ;
+ // Generate events. If the input pdf has an internal generator, the internal generator
+ // is used and an accept/reject sampling on the efficiency is applied.
+ RooDataSet* data = modelEff.generate(t,10000) ;
- // Fit pdf. The normalization integral is calculated numerically.
- modelEff.fitTo(*data) ;
+ // Fit pdf. The normalization integral is calculated numerically.
+ modelEff.fitTo(*data) ;
- // Plot generated data and overlay fitted pdf
- RooPlot* frame3 = t.frame(Title("Fitted pdf with efficiency")) ;
- data->plotOn(frame3) ;
- modelEff.plotOn(frame3) ;
+ // Plot generated data and overlay fitted pdf
+ RooPlot* frame3 = t.frame(Title("Fitted pdf with efficiency")) ;
+ data->plotOn(frame3) ;
+ modelEff.plotOn(frame3) ;
-
- TCanvas* c = new TCanvas("rf703_effpdfprod","rf703_effpdfprod",1200,400) ;
- c->Divide(3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
+
+ TCanvas* c = new TCanvas("rf703_effpdfprod","rf703_effpdfprod",1200,400) ;
+ c->Divide(3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
diff --git a/tutorials/roofit/rf704_amplitudefit.C b/tutorials/roofit/rf704_amplitudefit.C
index de15e294ee642544351cfa4b9ffbc5f449131c45..01b69f74d8f97da5cce38760514ce485e66d06a2 100644
--- a/tutorials/roofit/rf704_amplitudefit.C
+++ b/tutorials/roofit/rf704_amplitudefit.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'SPECIAL PDFS' RooFit tutorial macro #704
///
/// Using a p.d.f defined by a sum of real-valued amplitude components
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,84 +29,84 @@ using namespace RooFit ;
void rf704_amplitudefit()
{
- // S e t u p 2 D a m p l i t u d e f u n c t i o n s
- // -------------------------------------------------------
-
- // Observables
- RooRealVar t("t","time",-1.,15.);
- RooRealVar cosa("cosa","cos(alpha)",-1.,1.);
-
- // Use RooTruthModel to obtain compiled implementation of sinh/cosh modulated decay functions
- RooRealVar tau("tau","#tau",1.5);
- RooRealVar deltaGamma("deltaGamma","deltaGamma", 0.3);
- RooTruthModel tm("tm","tm",t) ;
- RooFormulaVar coshGBasis("coshGBasis","exp(-@0/ @1)*cosh(@0*@2/2)",RooArgList(t,tau,deltaGamma));
- RooFormulaVar sinhGBasis("sinhGBasis","exp(-@0/ @1)*sinh(@0*@2/2)",RooArgList(t,tau,deltaGamma));
- RooAbsReal* coshGConv = tm.convolution(&coshGBasis,&t);
- RooAbsReal* sinhGConv = tm.convolution(&sinhGBasis,&t);
-
- // Construct polynomial amplitudes in cos(a)
- RooPolyVar poly1("poly1","poly1",cosa,RooArgList(RooConst(0.5),RooConst(0.2),RooConst(0.2)),0);
- RooPolyVar poly2("poly2","poly2",cosa,RooArgList(RooConst(1),RooConst(-0.2),RooConst(3)),0);
-
- // Construct 2D amplitude as uncorrelated product of amp(t)*amp(cosa)
- RooProduct ampl1("ampl1","amplitude 1",RooArgSet(poly1,*coshGConv));
- RooProduct ampl2("ampl2","amplitude 2",RooArgSet(poly2,*sinhGConv));
-
-
-
- // C o n s t r u c t a m p l i t u d e s u m p d f
- // -----------------------------------------------------
-
- // Amplitude strengths
- RooRealVar f1("f1","f1",1,0,2) ;
- RooRealVar f2("f2","f2",0.5,0,2) ;
-
- // Construct pdf
- RooRealSumPdf pdf("pdf","pdf",RooArgList(ampl1,ampl2),RooArgList(f1,f2)) ;
-
- // Generate some toy data from pdf
- RooDataSet* data = pdf.generate(RooArgSet(t,cosa),10000);
-
- // Fit pdf to toy data with only amplitude strength floating
- pdf.fitTo(*data) ;
-
-
-
- // P l o t a m p l i t u d e s u m p d f
- // -------------------------------------------
-
- // Make 2D plots of amplitudes
- TH1* hh_cos = ampl1.createHistogram("hh_cos",t,Binning(50),YVar(cosa,Binning(50))) ;
- TH1* hh_sin = ampl2.createHistogram("hh_sin",t,Binning(50),YVar(cosa,Binning(50))) ;
- hh_cos->SetLineColor(kBlue) ;
- hh_sin->SetLineColor(kRed) ;
-
-
- // Make projection on t, plot data, pdf and its components
- // Note component projections may be larger than sum because amplitudes can be negative
- RooPlot* frame1 = t.frame();
- data->plotOn(frame1);
- pdf.plotOn(frame1);
- pdf.plotOn(frame1,Components(ampl1),LineStyle(kDashed));
- pdf.plotOn(frame1,Components(ampl2),LineStyle(kDashed),LineColor(kRed));
-
- // Make projection on cosa, plot data, pdf and its components
- // Note that components projection may be larger than sum because amplitudes can be negative
- RooPlot* frame2 = cosa.frame();
- data->plotOn(frame2);
- pdf.plotOn(frame2);
- pdf.plotOn(frame2,Components(ampl1),LineStyle(kDashed));
- pdf.plotOn(frame2,Components(ampl2),LineStyle(kDashed),LineColor(kRed));
-
-
-
- TCanvas* c = new TCanvas("rf704_amplitudefit","rf704_amplitudefit",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw();
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw();
- c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_cos->GetZaxis()->SetTitleOffset(2.3) ; hh_cos->Draw("surf") ;
- c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_sin->GetZaxis()->SetTitleOffset(2.3) ; hh_sin->Draw("surf") ;
+ // S e t u p 2 D a m p l i t u d e f u n c t i o n s
+ // -------------------------------------------------------
+
+ // Observables
+ RooRealVar t("t","time",-1.,15.);
+ RooRealVar cosa("cosa","cos(alpha)",-1.,1.);
+
+ // Use RooTruthModel to obtain compiled implementation of sinh/cosh modulated decay functions
+ RooRealVar tau("tau","#tau",1.5);
+ RooRealVar deltaGamma("deltaGamma","deltaGamma", 0.3);
+ RooTruthModel tm("tm","tm",t) ;
+ RooFormulaVar coshGBasis("coshGBasis","exp(-@0/ @1)*cosh(@0*@2/2)",RooArgList(t,tau,deltaGamma));
+ RooFormulaVar sinhGBasis("sinhGBasis","exp(-@0/ @1)*sinh(@0*@2/2)",RooArgList(t,tau,deltaGamma));
+ RooAbsReal* coshGConv = tm.convolution(&coshGBasis,&t);
+ RooAbsReal* sinhGConv = tm.convolution(&sinhGBasis,&t);
+
+ // Construct polynomial amplitudes in cos(a)
+ RooPolyVar poly1("poly1","poly1",cosa,RooArgList(RooConst(0.5),RooConst(0.2),RooConst(0.2)),0);
+ RooPolyVar poly2("poly2","poly2",cosa,RooArgList(RooConst(1),RooConst(-0.2),RooConst(3)),0);
+
+ // Construct 2D amplitude as uncorrelated product of amp(t)*amp(cosa)
+ RooProduct ampl1("ampl1","amplitude 1",RooArgSet(poly1,*coshGConv));
+ RooProduct ampl2("ampl2","amplitude 2",RooArgSet(poly2,*sinhGConv));
+
+
+
+ // C o n s t r u c t a m p l i t u d e s u m p d f
+ // -----------------------------------------------------
+
+ // Amplitude strengths
+ RooRealVar f1("f1","f1",1,0,2) ;
+ RooRealVar f2("f2","f2",0.5,0,2) ;
+
+ // Construct pdf
+ RooRealSumPdf pdf("pdf","pdf",RooArgList(ampl1,ampl2),RooArgList(f1,f2)) ;
+
+ // Generate some toy data from pdf
+ RooDataSet* data = pdf.generate(RooArgSet(t,cosa),10000);
+
+ // Fit pdf to toy data with only amplitude strength floating
+ pdf.fitTo(*data) ;
+
+
+
+ // P l o t a m p l i t u d e s u m p d f
+ // -------------------------------------------
+
+ // Make 2D plots of amplitudes
+ TH1* hh_cos = ampl1.createHistogram("hh_cos",t,Binning(50),YVar(cosa,Binning(50))) ;
+ TH1* hh_sin = ampl2.createHistogram("hh_sin",t,Binning(50),YVar(cosa,Binning(50))) ;
+ hh_cos->SetLineColor(kBlue) ;
+ hh_sin->SetLineColor(kRed) ;
+
+
+ // Make projection on t, plot data, pdf and its components
+ // Note component projections may be larger than sum because amplitudes can be negative
+ RooPlot* frame1 = t.frame();
+ data->plotOn(frame1);
+ pdf.plotOn(frame1);
+ pdf.plotOn(frame1,Components(ampl1),LineStyle(kDashed));
+ pdf.plotOn(frame1,Components(ampl2),LineStyle(kDashed),LineColor(kRed));
+
+ // Make projection on cosa, plot data, pdf and its components
+ // Note that components projection may be larger than sum because amplitudes can be negative
+ RooPlot* frame2 = cosa.frame();
+ data->plotOn(frame2);
+ pdf.plotOn(frame2);
+ pdf.plotOn(frame2,Components(ampl1),LineStyle(kDashed));
+ pdf.plotOn(frame2,Components(ampl2),LineStyle(kDashed),LineColor(kRed));
+
+
+
+ TCanvas* c = new TCanvas("rf704_amplitudefit","rf704_amplitudefit",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw();
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw();
+ c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_cos->GetZaxis()->SetTitleOffset(2.3) ; hh_cos->Draw("surf") ;
+ c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_sin->GetZaxis()->SetTitleOffset(2.3) ; hh_sin->Draw("surf") ;
}
diff --git a/tutorials/roofit/rf705_linearmorph.C b/tutorials/roofit/rf705_linearmorph.C
index bfce92473cd15290c2db06b118cbba2ce352e956..f6497ff21d546a00f3bcf087c29b48b3f6469bbf 100644
--- a/tutorials/roofit/rf705_linearmorph.C
+++ b/tutorials/roofit/rf705_linearmorph.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'SPECIAL PDFS' RooFit tutorial macro #705
///
/// Linear interpolation between p.d.f shapes using the 'Alex Read' algorithm
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,112 +27,112 @@ using namespace RooFit ;
void rf705_linearmorph()
{
- // C r e a t e e n d p o i n t p d f s h a p e s
- // ------------------------------------------------------
+ // C r e a t e e n d p o i n t p d f s h a p e s
+ // ------------------------------------------------------
- // Observable
- RooRealVar x("x","x",-20,20) ;
+ // Observable
+ RooRealVar x("x","x",-20,20) ;
- // Lower end point shape: a Gaussian
- RooRealVar g1mean("g1mean","g1mean",-10) ;
- RooGaussian g1("g1","g1",x,g1mean,RooConst(2)) ;
+ // Lower end point shape: a Gaussian
+ RooRealVar g1mean("g1mean","g1mean",-10) ;
+ RooGaussian g1("g1","g1",x,g1mean,RooConst(2)) ;
- // Upper end point shape: a Polynomial
- RooPolynomial g2("g2","g2",x,RooArgSet(RooConst(-0.03),RooConst(-0.001))) ;
+ // Upper end point shape: a Polynomial
+ RooPolynomial g2("g2","g2",x,RooArgSet(RooConst(-0.03),RooConst(-0.001))) ;
-
- // C r e a t e i n t e r p o l a t i n g p d f
- // -----------------------------------------------
+
+ // C r e a t e i n t e r p o l a t i n g p d f
+ // -----------------------------------------------
- // Create interpolation variable
- RooRealVar alpha("alpha","alpha",0,1.0) ;
+ // Create interpolation variable
+ RooRealVar alpha("alpha","alpha",0,1.0) ;
- // Specify sampling density on observable and interpolation variable
- x.setBins(1000,"cache") ;
- alpha.setBins(50,"cache") ;
+ // Specify sampling density on observable and interpolation variable
+ x.setBins(1000,"cache") ;
+ alpha.setBins(50,"cache") ;
- // Construct interpolating pdf in (x,a) represent g1(x) at a=a_min
- // and g2(x) at a=a_max
- RooIntegralMorph lmorph("lmorph","lmorph",g1,g2,x,alpha) ;
+ // Construct interpolating pdf in (x,a) represent g1(x) at a=a_min
+ // and g2(x) at a=a_max
+ RooIntegralMorph lmorph("lmorph","lmorph",g1,g2,x,alpha) ;
- // P l o t i n t e r p o l a t i n g p d f a t v a r i o u s a l p h a
- // -----------------------------------------------------------------------------
+ // P l o t i n t e r p o l a t i n g p d f a t v a r i o u s a l p h a
+ // -----------------------------------------------------------------------------
- // Show end points as blue curves
- RooPlot* frame1 = x.frame() ;
- g1.plotOn(frame1) ;
- g2.plotOn(frame1) ;
+ // Show end points as blue curves
+ RooPlot* frame1 = x.frame() ;
+ g1.plotOn(frame1) ;
+ g2.plotOn(frame1) ;
- // Show interpolated shapes in red
- alpha.setVal(0.125) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.25) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.375) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.50) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.625) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.75) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.875) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
- alpha.setVal(0.95) ;
- lmorph.plotOn(frame1,LineColor(kRed)) ;
+ // Show interpolated shapes in red
+ alpha.setVal(0.125) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.25) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.375) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.50) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.625) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.75) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.875) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
+ alpha.setVal(0.95) ;
+ lmorph.plotOn(frame1,LineColor(kRed)) ;
- // S h o w 2 D d i s t r i b u t i o n o f p d f ( x , a l p h a )
- // -----------------------------------------------------------------------
-
- // Create 2D histogram
- TH1* hh = lmorph.createHistogram("hh",x,Binning(40),YVar(alpha,Binning(40))) ;
- hh->SetLineColor(kBlue) ;
+ // S h o w 2 D d i s t r i b u t i o n o f p d f ( x , a l p h a )
+ // -----------------------------------------------------------------------
+
+ // Create 2D histogram
+ TH1* hh = lmorph.createHistogram("hh",x,Binning(40),YVar(alpha,Binning(40))) ;
+ hh->SetLineColor(kBlue) ;
- // F i t p d f t o d a t a s e t w i t h a l p h a = 0 . 8
- // -----------------------------------------------------------------
+ // F i t p d f t o d a t a s e t w i t h a l p h a = 0 . 8
+ // -----------------------------------------------------------------
- // Generate a toy dataset at alpha = 0.8
- alpha=0.8 ;
- RooDataSet* data = lmorph.generate(x,1000) ;
+ // Generate a toy dataset at alpha = 0.8
+ alpha=0.8 ;
+ RooDataSet* data = lmorph.generate(x,1000) ;
- // Fit pdf to toy data
- lmorph.setCacheAlpha(kTRUE) ;
- lmorph.fitTo(*data,Verbose(kTRUE)) ;
+ // Fit pdf to toy data
+ lmorph.setCacheAlpha(kTRUE) ;
+ lmorph.fitTo(*data,Verbose(kTRUE)) ;
- // Plot fitted pdf and data overlaid
- RooPlot* frame2 = x.frame(Bins(100)) ;
- data->plotOn(frame2) ;
- lmorph.plotOn(frame2) ;
+ // Plot fitted pdf and data overlaid
+ RooPlot* frame2 = x.frame(Bins(100)) ;
+ data->plotOn(frame2) ;
+ lmorph.plotOn(frame2) ;
- // S c a n - l o g ( L ) v s a l p h a
- // -----------------------------------------
+ // S c a n - l o g ( L ) v s a l p h a
+ // -----------------------------------------
- // Show scan -log(L) of dataset w.r.t alpha
- RooPlot* frame3 = alpha.frame(Bins(100),Range(0.1,0.9)) ;
-
- // Make 2D pdf of histogram
- RooNLLVar nll("nll","nll",lmorph,*data) ;
- nll.plotOn(frame3,ShiftToZero()) ;
+ // Show scan -log(L) of dataset w.r.t alpha
+ RooPlot* frame3 = alpha.frame(Bins(100),Range(0.1,0.9)) ;
+
+ // Make 2D pdf of histogram
+ RooNLLVar nll("nll","nll",lmorph,*data) ;
+ nll.plotOn(frame3,ShiftToZero()) ;
- lmorph.setCacheAlpha(kFALSE) ;
+ lmorph.setCacheAlpha(kFALSE) ;
- TCanvas* c = new TCanvas("rf705_linearmorph","rf705_linearmorph",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh->GetZaxis()->SetTitleOffset(2.5) ; hh->Draw("surf") ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
-
-
- return ;
+ TCanvas* c = new TCanvas("rf705_linearmorph","rf705_linearmorph",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.20) ; hh->GetZaxis()->SetTitleOffset(2.5) ; hh->Draw("surf") ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+
+
+ return ;
}
diff --git a/tutorials/roofit/rf706_histpdf.C b/tutorials/roofit/rf706_histpdf.C
index 2b6a700cd7db53ebc46183d7948c6a057fa263ea..a758700ada941449f4df3a302717e08cf184847a 100644
--- a/tutorials/roofit/rf706_histpdf.C
+++ b/tutorials/roofit/rf706_histpdf.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'SPECIAL PDFS' RooFit tutorial macro #706
///
/// Histogram based p.d.f.s and functions
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -27,56 +25,56 @@ using namespace RooFit ;
void rf706_histpdf()
{
- // C r e a t e p d f f o r s a m p l i n g
- // ---------------------------------------------
+ // C r e a t e p d f f o r s a m p l i n g
+ // ---------------------------------------------
- RooRealVar x("x","x",0,20) ;
- RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;
+ RooRealVar x("x","x",0,20) ;
+ RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;
- // C r e a t e l o w s t a t s h i s t o g r a m
- // ---------------------------------------------------
+ // C r e a t e l o w s t a t s h i s t o g r a m
+ // ---------------------------------------------------
- // Sample 500 events from p
- x.setBins(20) ;
- RooDataSet* data1 = p.generate(x,500) ;
-
- // Create a binned dataset with 20 bins and 500 events
- RooDataHist* hist1 = data1->binnedClone() ;
+ // Sample 500 events from p
+ x.setBins(20) ;
+ RooDataSet* data1 = p.generate(x,500) ;
+
+ // Create a binned dataset with 20 bins and 500 events
+ RooDataHist* hist1 = data1->binnedClone() ;
- // Represent data in dh as pdf in x
- RooHistPdf histpdf1("histpdf1","histpdf1",x,*hist1,0) ;
+ // Represent data in dh as pdf in x
+ RooHistPdf histpdf1("histpdf1","histpdf1",x,*hist1,0) ;
- // Plot unbinned data and histogram pdf overlaid
- RooPlot* frame1 = x.frame(Title("Low statistics histogram pdf"),Bins(100)) ;
- data1->plotOn(frame1) ;
- histpdf1.plotOn(frame1) ;
-
+ // Plot unbinned data and histogram pdf overlaid
+ RooPlot* frame1 = x.frame(Title("Low statistics histogram pdf"),Bins(100)) ;
+ data1->plotOn(frame1) ;
+ histpdf1.plotOn(frame1) ;
+
- // C r e a t e h i g h s t a t s h i s t o g r a m
- // -----------------------------------------------------
+ // C r e a t e h i g h s t a t s h i s t o g r a m
+ // -----------------------------------------------------
- // Sample 100000 events from p
- x.setBins(10) ;
- RooDataSet* data2 = p.generate(x,100000) ;
+ // Sample 100000 events from p
+ x.setBins(10) ;
+ RooDataSet* data2 = p.generate(x,100000) ;
- // Create a binned dataset with 10 bins and 100K events
- RooDataHist* hist2 = data2->binnedClone() ;
+ // Create a binned dataset with 10 bins and 100K events
+ RooDataHist* hist2 = data2->binnedClone() ;
- // Represent data in dh as pdf in x, apply 2nd order interpolation
- RooHistPdf histpdf2("histpdf2","histpdf2",x,*hist2,2) ;
+ // Represent data in dh as pdf in x, apply 2nd order interpolation
+ RooHistPdf histpdf2("histpdf2","histpdf2",x,*hist2,2) ;
- // Plot unbinned data and histogram pdf overlaid
- RooPlot* frame2 = x.frame(Title("High stats histogram pdf with interpolation"),Bins(100)) ;
- data2->plotOn(frame2) ;
- histpdf2.plotOn(frame2) ;
+ // Plot unbinned data and histogram pdf overlaid
+ RooPlot* frame2 = x.frame(Title("High stats histogram pdf with interpolation"),Bins(100)) ;
+ data2->plotOn(frame2) ;
+ histpdf2.plotOn(frame2) ;
- TCanvas* c = new TCanvas("rf706_histpdf","rf706_histpdf",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;
+ TCanvas* c = new TCanvas("rf706_histpdf","rf706_histpdf",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf707_kernelestimation.C b/tutorials/roofit/rf707_kernelestimation.C
index ac9227230038c241d4a7e2422d202cf47b501a26..c128181e105b75024eaf5c8e3a54e37680953855 100644
--- a/tutorials/roofit/rf707_kernelestimation.C
+++ b/tutorials/roofit/rf707_kernelestimation.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook
/// 'SPECIAL PDFS' RooFit tutorial macro #707
///
/// Using non-parametric (multi-dimensional) kernel estimation p.d.f.s
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,87 +28,86 @@ using namespace RooFit ;
void rf707_kernelestimation()
{
- // C r e a t e l o w s t a t s 1 - D d a t a s e t
- // -------------------------------------------------------
+ // C r e a t e l o w s t a t s 1 - D d a t a s e t
+ // -------------------------------------------------------
- // Create a toy pdf for sampling
- RooRealVar x("x","x",0,20) ;
- RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;
+ // Create a toy pdf for sampling
+ RooRealVar x("x","x",0,20) ;
+ RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;
- // Sample 500 events from p
- RooDataSet* data1 = p.generate(x,200) ;
+ // Sample 500 events from p
+ RooDataSet* data1 = p.generate(x,200) ;
- // C r e a t e 1 - D k e r n e l e s t i m a t i o n p d f
- // ---------------------------------------------------------------
+ // C r e a t e 1 - D k e r n e l e s t i m a t i o n p d f
+ // ---------------------------------------------------------------
- // Create adaptive kernel estimation pdf. In this configuration the input data
- // is mirrored over the boundaries to minimize edge effects in distribution
- // that do not fall to zero towards the edges
- RooKeysPdf kest1("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth) ;
+ // Create adaptive kernel estimation pdf. In this configuration the input data
+ // is mirrored over the boundaries to minimize edge effects in distribution
+ // that do not fall to zero towards the edges
+ RooKeysPdf kest1("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth) ;
- // An adaptive kernel estimation pdf on the same data without mirroring option
- // for comparison
- RooKeysPdf kest2("kest2","kest2",x,*data1,RooKeysPdf::NoMirror) ;
+ // An adaptive kernel estimation pdf on the same data without mirroring option
+ // for comparison
+ RooKeysPdf kest2("kest2","kest2",x,*data1,RooKeysPdf::NoMirror) ;
- // Adaptive kernel estimation pdf with increased bandwidth scale factor
- // (promotes smoothness over detail preservation)
- RooKeysPdf kest3("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth,2) ;
+ // Adaptive kernel estimation pdf with increased bandwidth scale factor
+ // (promotes smoothness over detail preservation)
+ RooKeysPdf kest3("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth,2) ;
- // Plot kernel estimation pdfs with and without mirroring over data
- RooPlot* frame = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ;
- data1->plotOn(frame) ;
- kest1.plotOn(frame) ;
- kest2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
+ // Plot kernel estimation pdfs with and without mirroring over data
+ RooPlot* frame = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ;
+ data1->plotOn(frame) ;
+ kest1.plotOn(frame) ;
+ kest2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
- // Plot kernel estimation pdfs with regular and increased bandwidth
- RooPlot* frame2 = x.frame(Title("Adaptive kernel estimation pdf with regular, increased bandwidth")) ;
- kest1.plotOn(frame2) ;
- kest3.plotOn(frame2,LineColor(kMagenta)) ;
+ // Plot kernel estimation pdfs with regular and increased bandwidth
+ RooPlot* frame2 = x.frame(Title("Adaptive kernel estimation pdf with regular, increased bandwidth")) ;
+ kest1.plotOn(frame2) ;
+ kest3.plotOn(frame2,LineColor(kMagenta)) ;
- // C r e a t e l o w s t a t s 2 - D d a t a s e t
- // -------------------------------------------------------
+ // C r e a t e l o w s t a t s 2 - D d a t a s e t
+ // -------------------------------------------------------
- // Construct a 2D toy pdf for sampleing
- RooRealVar y("y","y",0,20) ;
- RooPolynomial py("py","py",y,RooArgList(RooConst(0.01),RooConst(0.01),RooConst(-0.0004))) ;
- RooProdPdf pxy("pxy","pxy",RooArgSet(p,py)) ;
- RooDataSet* data2 = pxy.generate(RooArgSet(x,y),1000) ;
+ // Construct a 2D toy pdf for sampleing
+ RooRealVar y("y","y",0,20) ;
+ RooPolynomial py("py","py",y,RooArgList(RooConst(0.01),RooConst(0.01),RooConst(-0.0004))) ;
+ RooProdPdf pxy("pxy","pxy",RooArgSet(p,py)) ;
+ RooDataSet* data2 = pxy.generate(RooArgSet(x,y),1000) ;
- // C r e a t e 2 - D k e r n e l e s t i m a t i o n p d f
- // ---------------------------------------------------------------
+ // C r e a t e 2 - D k e r n e l e s t i m a t i o n p d f
+ // ---------------------------------------------------------------
- // Create 2D adaptive kernel estimation pdf with mirroring
- RooNDKeysPdf kest4("kest4","kest4",RooArgSet(x,y),*data2,"am") ;
+ // Create 2D adaptive kernel estimation pdf with mirroring
+ RooNDKeysPdf kest4("kest4","kest4",RooArgSet(x,y),*data2,"am") ;
- // Create 2D adaptive kernel estimation pdf with mirroring and double bandwidth
- RooNDKeysPdf kest5("kest5","kest5",RooArgSet(x,y),*data2,"am",2) ;
+ // Create 2D adaptive kernel estimation pdf with mirroring and double bandwidth
+ RooNDKeysPdf kest5("kest5","kest5",RooArgSet(x,y),*data2,"am",2) ;
- // Create a histogram of the data
- TH1* hh_data = data2->createHistogram("hh_data",x,Binning(10),YVar(y,Binning(10))) ;
+ // Create a histogram of the data
+ TH1* hh_data = data2->createHistogram("hh_data",x,Binning(10),YVar(y,Binning(10))) ;
- // Create histogram of the 2d kernel estimation pdfs
- TH1* hh_pdf = kest4.createHistogram("hh_pdf",x,Binning(25),YVar(y,Binning(25))) ;
- TH1* hh_pdf2 = kest5.createHistogram("hh_pdf2",x,Binning(25),YVar(y,Binning(25))) ;
- hh_pdf->SetLineColor(kBlue) ;
- hh_pdf2->SetLineColor(kMagenta) ;
-
+ // Create histogram of the 2d kernel estimation pdfs
+ TH1* hh_pdf = kest4.createHistogram("hh_pdf",x,Binning(25),YVar(y,Binning(25))) ;
+ TH1* hh_pdf2 = kest5.createHistogram("hh_pdf2",x,Binning(25),YVar(y,Binning(25))) ;
+ hh_pdf->SetLineColor(kBlue) ;
+ hh_pdf2->SetLineColor(kMagenta) ;
+
- TCanvas* c = new TCanvas("rf707_kernelestimation","rf707_kernelestimation",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ;
- c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.4) ; hh_pdf->Draw("surf") ; hh_pdf2->Draw("surfsame") ;
-
+ TCanvas* c = new TCanvas("rf707_kernelestimation","rf707_kernelestimation",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ;
+ c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.4) ; hh_pdf->Draw("surf") ; hh_pdf2->Draw("surfsame") ;
}
diff --git a/tutorials/roofit/rf708_bphysics.C b/tutorials/roofit/rf708_bphysics.C
index 3ca4ac9e50da16b5b2a123ecbbae15fe95db8c33..e6e358aa551a987aa101912411532b27582a8b5f 100644
--- a/tutorials/roofit/rf708_bphysics.C
+++ b/tutorials/roofit/rf708_bphysics.C
@@ -1,18 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'SPECIAL PDFS' RooFit tutorial macro #708
///
/// Special decay pdf for B physics with mixing and/or CP violation
///
-///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -30,185 +28,185 @@ using namespace RooFit ;
void rf708_bphysics()
{
- ////////////////////////////////////////////////////
- // B - D e c a y w i t h m i x i n g //
- ////////////////////////////////////////////////////
+ ////////////////////////////////////////////////////
+ // B - D e c a y w i t h m i x i n g //
+ ////////////////////////////////////////////////////
- // C o n s t r u c t p d f
- // -------------------------
-
- // Observable
- RooRealVar dt("dt","dt",-10,10) ;
- dt.setBins(40) ;
+ // C o n s t r u c t p d f
+ // -------------------------
+
+ // Observable
+ RooRealVar dt("dt","dt",-10,10) ;
+ dt.setBins(40) ;
- // Parameters
- RooRealVar dm("dm","delta m(B0)",0.472) ;
- RooRealVar tau("tau","tau (B0)",1.547) ;
- RooRealVar w("w","flavour mistag rate",0.1) ;
- RooRealVar dw("dw","delta mistag rate for B0/B0bar",0.1) ;
+ // Parameters
+ RooRealVar dm("dm","delta m(B0)",0.472) ;
+ RooRealVar tau("tau","tau (B0)",1.547) ;
+ RooRealVar w("w","flavour mistag rate",0.1) ;
+ RooRealVar dw("dw","delta mistag rate for B0/B0bar",0.1) ;
- RooCategory mixState("mixState","B0/B0bar mixing state") ;
- mixState.defineType("mixed",-1) ;
- mixState.defineType("unmixed",1) ;
+ 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) ;
+ RooCategory tagFlav("tagFlav","Flavour of the tagged B0") ;
+ tagFlav.defineType("B0",1) ;
+ tagFlav.defineType("B0bar",-1) ;
- // Use delta function resolution model
- RooTruthModel tm("tm","truth model",dt) ;
+ // Use delta function resolution model
+ RooTruthModel tm("tm","truth model",dt) ;
- // Construct Bdecay with mixing
- RooBMixDecay bmix("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,tm,RooBMixDecay::DoubleSided) ;
+ // Construct Bdecay with mixing
+ RooBMixDecay bmix("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,tm,RooBMixDecay::DoubleSided) ;
- // P l o t p d f i n v a r i o u s s l i c e s
- // ---------------------------------------------------
+ // P l o t p d f i n v a r i o u s s l i c e s
+ // ---------------------------------------------------
- // Generate some data
- RooDataSet* data = bmix.generate(RooArgSet(dt,mixState,tagFlav),10000) ;
+ // Generate some data
+ RooDataSet* data = bmix.generate(RooArgSet(dt,mixState,tagFlav),10000) ;
- // Plot B0 and B0bar tagged data separately
- // For all plots below B0 and B0 tagged data will look somewhat differently
- // if the flavor tagging mistag rate for B0 and B0 is different (i.e. dw!=0)
- RooPlot* frame1 = dt.frame(Title("B decay distribution with mixing (B0/B0bar)")) ;
+ // Plot B0 and B0bar tagged data separately
+ // For all plots below B0 and B0 tagged data will look somewhat differently
+ // if the flavor tagging mistag rate for B0 and B0 is different (i.e. dw!=0)
+ RooPlot* frame1 = dt.frame(Title("B decay distribution with mixing (B0/B0bar)")) ;
- data->plotOn(frame1,Cut("tagFlav==tagFlav::B0")) ;
- bmix.plotOn(frame1,Slice(tagFlav,"B0")) ;
+ data->plotOn(frame1,Cut("tagFlav==tagFlav::B0")) ;
+ bmix.plotOn(frame1,Slice(tagFlav,"B0")) ;
- data->plotOn(frame1,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
- bmix.plotOn(frame1,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
+ data->plotOn(frame1,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
+ bmix.plotOn(frame1,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
- // Plot mixed slice for B0 and B0bar tagged data separately
- RooPlot* frame2 = dt.frame(Title("B decay distribution of mixed events (B0/B0bar)")) ;
+ // Plot mixed slice for B0 and B0bar tagged data separately
+ RooPlot* frame2 = dt.frame(Title("B decay distribution of mixed events (B0/B0bar)")) ;
- data->plotOn(frame2,Cut("mixState==mixState::mixed&&tagFlav==tagFlav::B0")) ;
- bmix.plotOn(frame2,Slice(tagFlav,"B0"),Slice(mixState,"mixed")) ;
+ data->plotOn(frame2,Cut("mixState==mixState::mixed&&tagFlav==tagFlav::B0")) ;
+ bmix.plotOn(frame2,Slice(tagFlav,"B0"),Slice(mixState,"mixed")) ;
- data->plotOn(frame2,Cut("mixState==mixState::mixed&&tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
- bmix.plotOn(frame2,Slice(tagFlav,"B0bar"),Slice(mixState,"mixed"),LineColor(kCyan)) ;
+ data->plotOn(frame2,Cut("mixState==mixState::mixed&&tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
+ bmix.plotOn(frame2,Slice(tagFlav,"B0bar"),Slice(mixState,"mixed"),LineColor(kCyan)) ;
- // Plot unmixed slice for B0 and B0bar tagged data separately
- RooPlot* frame3 = dt.frame(Title("B decay distribution of unmixed events (B0/B0bar)")) ;
+ // Plot unmixed slice for B0 and B0bar tagged data separately
+ RooPlot* frame3 = dt.frame(Title("B decay distribution of unmixed events (B0/B0bar)")) ;
- data->plotOn(frame3,Cut("mixState==mixState::unmixed&&tagFlav==tagFlav::B0")) ;
- bmix.plotOn(frame3,Slice(tagFlav,"B0"),Slice(mixState,"unmixed")) ;
+ data->plotOn(frame3,Cut("mixState==mixState::unmixed&&tagFlav==tagFlav::B0")) ;
+ bmix.plotOn(frame3,Slice(tagFlav,"B0"),Slice(mixState,"unmixed")) ;
- data->plotOn(frame3,Cut("mixState==mixState::unmixed&&tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
- bmix.plotOn(frame3,Slice(tagFlav,"B0bar"),Slice(mixState,"unmixed"),LineColor(kCyan)) ;
+ data->plotOn(frame3,Cut("mixState==mixState::unmixed&&tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
+ bmix.plotOn(frame3,Slice(tagFlav,"B0bar"),Slice(mixState,"unmixed"),LineColor(kCyan)) ;
- ///////////////////////////////////////////////////////
- // B - D e c a y w i t h C P v i o l a t i o n //
- ///////////////////////////////////////////////////////
+ ///////////////////////////////////////////////////////
+ // B - D e c a y w i t h C P v i o l a t i o n //
+ ///////////////////////////////////////////////////////
- // C o n s t r u c t p d f
- // -------------------------
-
- // Additional parameters needed for B decay with CPV
- RooRealVar CPeigen("CPeigen","CP eigen value",-1) ;
- RooRealVar absLambda("absLambda","|lambda|",1,0,2) ;
- RooRealVar argLambda("absLambda","|lambda|",0.7,-1,1) ;
- RooRealVar effR("effR","B0/B0bar reco efficiency ratio",1) ;
+ // C o n s t r u c t p d f
+ // -------------------------
+
+ // Additional parameters needed for B decay with CPV
+ RooRealVar CPeigen("CPeigen","CP eigen value",-1) ;
+ RooRealVar absLambda("absLambda","|lambda|",1,0,2) ;
+ RooRealVar argLambda("absLambda","|lambda|",0.7,-1,1) ;
+ RooRealVar effR("effR","B0/B0bar reco efficiency ratio",1) ;
- // Construct Bdecay with CP violation
- RooBCPEffDecay bcp("bcp","bcp", dt, tagFlav, tau, dm, w, CPeigen, absLambda, argLambda, effR, dw, tm, RooBCPEffDecay::DoubleSided) ;
-
+ // Construct Bdecay with CP violation
+ RooBCPEffDecay bcp("bcp","bcp", dt, tagFlav, tau, dm, w, CPeigen, absLambda, argLambda, effR, dw, tm, RooBCPEffDecay::DoubleSided) ;
+
- // P l o t s c e n a r i o 1 - s i n ( 2 b ) = 0 . 7 , | l | = 1
- // ---------------------------------------------------------------------------
+ // P l o t s c e n a r i o 1 - s i n ( 2 b ) = 0 . 7 , | l | = 1
+ // ---------------------------------------------------------------------------
- // Generate some data
- RooDataSet* data2 = bcp.generate(RooArgSet(dt,tagFlav),10000) ;
+ // Generate some data
+ RooDataSet* data2 = bcp.generate(RooArgSet(dt,tagFlav),10000) ;
- // Plot B0 and B0bar tagged data separately
- RooPlot* frame4 = dt.frame(Title("B decay distribution with CPV(|l|=1,Im(l)=0.7) (B0/B0bar)")) ;
+ // Plot B0 and B0bar tagged data separately
+ RooPlot* frame4 = dt.frame(Title("B decay distribution with CPV(|l|=1,Im(l)=0.7) (B0/B0bar)")) ;
- data2->plotOn(frame4,Cut("tagFlav==tagFlav::B0")) ;
- bcp.plotOn(frame4,Slice(tagFlav,"B0")) ;
+ data2->plotOn(frame4,Cut("tagFlav==tagFlav::B0")) ;
+ bcp.plotOn(frame4,Slice(tagFlav,"B0")) ;
- data2->plotOn(frame4,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
- bcp.plotOn(frame4,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
+ data2->plotOn(frame4,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
+ bcp.plotOn(frame4,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
- // P l o t s c e n a r i o 2 - s i n ( 2 b ) = 0 . 7 , | l | = 0 . 7
- // -------------------------------------------------------------------------------
+ // P l o t s c e n a r i o 2 - s i n ( 2 b ) = 0 . 7 , | l | = 0 . 7
+ // -------------------------------------------------------------------------------
- absLambda=0.7 ;
+ absLambda=0.7 ;
- // Generate some data
- RooDataSet* data3 = bcp.generate(RooArgSet(dt,tagFlav),10000) ;
+ // Generate some data
+ RooDataSet* data3 = bcp.generate(RooArgSet(dt,tagFlav),10000) ;
- // Plot B0 and B0bar tagged data separately (sin2b = 0.7 plus direct CPV |l|=0.5)
- RooPlot* frame5 = dt.frame(Title("B decay distribution with CPV(|l|=0.7,Im(l)=0.7) (B0/B0bar)")) ;
+ // Plot B0 and B0bar tagged data separately (sin2b = 0.7 plus direct CPV |l|=0.5)
+ RooPlot* frame5 = dt.frame(Title("B decay distribution with CPV(|l|=0.7,Im(l)=0.7) (B0/B0bar)")) ;
- data3->plotOn(frame5,Cut("tagFlav==tagFlav::B0")) ;
- bcp.plotOn(frame5,Slice(tagFlav,"B0")) ;
+ data3->plotOn(frame5,Cut("tagFlav==tagFlav::B0")) ;
+ bcp.plotOn(frame5,Slice(tagFlav,"B0")) ;
- data3->plotOn(frame5,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
- bcp.plotOn(frame5,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
+ data3->plotOn(frame5,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
+ bcp.plotOn(frame5,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
- //////////////////////////////////////////////////////////////////////////////////
- // G e n e r i c B d e c a y w i t h u s e r c o e f f i c i e n t s //
- //////////////////////////////////////////////////////////////////////////////////
+ //////////////////////////////////////////////////////////////////////////////////
+ // G e n e r i c B d e c a y w i t h u s e r c o e f f i c i e n t s //
+ //////////////////////////////////////////////////////////////////////////////////
- // C o n s t r u c t p d f
- // -------------------------
-
- // Model parameters
- RooRealVar DGbG("DGbG","DGamma/GammaAvg",0.5,-1,1);
- RooRealVar Adir("Adir","-[1-abs(l)**2]/[1+abs(l)**2]",0);
- RooRealVar Amix("Amix","2Im(l)/[1+abs(l)**2]",0.7);
- RooRealVar Adel("Adel","2Re(l)/[1+abs(l)**2]",0.7);
-
- // Derived input parameters for pdf
- RooFormulaVar DG("DG","Delta Gamma","@1/@0",RooArgList(tau,DGbG));
-
- // Construct coefficient functions for sin,cos,sinh modulations of decay distribution
- RooFormulaVar fsin("fsin","fsin","@0*@1*(1-2*@2)",RooArgList(Amix,tagFlav,w));
- RooFormulaVar fcos("fcos","fcos","@0*@1*(1-2*@2)",RooArgList(Adir,tagFlav,w));
- RooFormulaVar fsinh("fsinh","fsinh","@0",RooArgList(Adel));
-
- // Construct generic B decay pdf using above user coefficients
- RooBDecay bcpg("bcpg","bcpg",dt,tau,DG,RooConst(1),fsinh,fcos,fsin,dm,tm,RooBDecay::DoubleSided);
-
-
-
- // P l o t - I m ( l ) = 0 . 7 , R e ( l ) = 0 . 7 | l | = 1, d G / G = 0 . 5
- // -------------------------------------------------------------------------------------
-
- // Generate some data
- RooDataSet* data4 = bcpg.generate(RooArgSet(dt,tagFlav),10000) ;
-
- // Plot B0 and B0bar tagged data separately
- RooPlot* frame6 = dt.frame(Title("B decay distribution with CPV(Im(l)=0.7,Re(l)=0.7,|l|=1,dG/G=0.5) (B0/B0bar)")) ;
-
- data4->plotOn(frame6,Cut("tagFlav==tagFlav::B0")) ;
- bcpg.plotOn(frame6,Slice(tagFlav,"B0")) ;
-
- data4->plotOn(frame6,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
- bcpg.plotOn(frame6,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
-
-
-
-
- TCanvas* c = new TCanvas("rf708_bphysics","rf708_bphysics",1200,800) ;
- c->Divide(3,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.6) ; frame4->Draw() ;
- c->cd(5) ; gPad->SetLeftMargin(0.15) ; frame5->GetYaxis()->SetTitleOffset(1.6) ; frame5->Draw() ;
- c->cd(6) ; gPad->SetLeftMargin(0.15) ; frame6->GetYaxis()->SetTitleOffset(1.6) ; frame6->Draw() ;
-
+ // C o n s t r u c t p d f
+ // -------------------------
+
+ // Model parameters
+ RooRealVar DGbG("DGbG","DGamma/GammaAvg",0.5,-1,1);
+ RooRealVar Adir("Adir","-[1-abs(l)**2]/[1+abs(l)**2]",0);
+ RooRealVar Amix("Amix","2Im(l)/[1+abs(l)**2]",0.7);
+ RooRealVar Adel("Adel","2Re(l)/[1+abs(l)**2]",0.7);
+
+ // Derived input parameters for pdf
+ RooFormulaVar DG("DG","Delta Gamma","@1/@0",RooArgList(tau,DGbG));
+
+ // Construct coefficient functions for sin,cos,sinh modulations of decay distribution
+ RooFormulaVar fsin("fsin","fsin","@0*@1*(1-2*@2)",RooArgList(Amix,tagFlav,w));
+ RooFormulaVar fcos("fcos","fcos","@0*@1*(1-2*@2)",RooArgList(Adir,tagFlav,w));
+ RooFormulaVar fsinh("fsinh","fsinh","@0",RooArgList(Adel));
+
+ // Construct generic B decay pdf using above user coefficients
+ RooBDecay bcpg("bcpg","bcpg",dt,tau,DG,RooConst(1),fsinh,fcos,fsin,dm,tm,RooBDecay::DoubleSided);
+
+
+
+ // P l o t - I m ( l ) = 0 . 7 , R e ( l ) = 0 . 7 | l | = 1, d G / G = 0 . 5
+ // -------------------------------------------------------------------------------------
+
+ // Generate some data
+ RooDataSet* data4 = bcpg.generate(RooArgSet(dt,tagFlav),10000) ;
+
+ // Plot B0 and B0bar tagged data separately
+ RooPlot* frame6 = dt.frame(Title("B decay distribution with CPV(Im(l)=0.7,Re(l)=0.7,|l|=1,dG/G=0.5) (B0/B0bar)")) ;
+
+ data4->plotOn(frame6,Cut("tagFlav==tagFlav::B0")) ;
+ bcpg.plotOn(frame6,Slice(tagFlav,"B0")) ;
+
+ data4->plotOn(frame6,Cut("tagFlav==tagFlav::B0bar"),MarkerColor(kCyan)) ;
+ bcpg.plotOn(frame6,Slice(tagFlav,"B0bar"),LineColor(kCyan)) ;
+
+
+
+
+ TCanvas* c = new TCanvas("rf708_bphysics","rf708_bphysics",1200,800) ;
+ c->Divide(3,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.6) ; frame4->Draw() ;
+ c->cd(5) ; gPad->SetLeftMargin(0.15) ; frame5->GetYaxis()->SetTitleOffset(1.6) ; frame5->Draw() ;
+ c->cd(6) ; gPad->SetLeftMargin(0.15) ; frame6->GetYaxis()->SetTitleOffset(1.6) ; frame6->Draw() ;
+
}
diff --git a/tutorials/roofit/rf801_mcstudy.C b/tutorials/roofit/rf801_mcstudy.C
index 4b8f35253724f575a4db9662c6d83de1e55a744e..a46e6b00a4626c4e9284c501e835bf6749a0dd42 100644
--- a/tutorials/roofit/rf801_mcstudy.C
+++ b/tutorials/roofit/rf801_mcstudy.C
@@ -1,16 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'VALIDATION AND MC STUDIES' RooFit tutorial macro #801
///
/// A Toy Monte Carlo study that perform cycles of
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author //
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,105 +31,105 @@ using namespace RooFit ;
void rf801_mcstudy()
{
- // C r e a t e m o d e l
- // -----------------------
-
- // Declare observable x
- RooRealVar x("x","x",0,10) ;
- x.setBins(40) ;
-
- // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
- RooRealVar mean("mean","mean of gaussians",5,0,10) ;
- 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)) ;
-
- // 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) ;
-
- // Sum the composite signal and background
- RooRealVar nbkg("nbkg","number of background events,",150,0,1000) ;
- RooRealVar nsig("nsig","number of signal events",150,0,1000) ;
- RooAddPdf model("model","g1+g2+a",RooArgList(bkg,sig),RooArgList(nbkg,nsig)) ;
-
-
-
- // C r e a t e m a n a g e r
- // ---------------------------
-
- // Instantiate RooMCStudy manager on model with x as observable and given choice of fit options
- //
- // The Silence() option kills all messages below the PROGRESS level, leaving only a single message
- // per sample executed, and any error message that occur during fitting
- //
- // The Extended() option has two effects:
- // 1) The extended ML term is included in the likelihood and
- // 2) A poisson fluctuation is introduced on the number of generated events
- //
- // The FitOptions() given here are passed to the fitting stage of each toy experiment.
- // If Save() is specified, the fit result of each experiment is saved by the manager
- //
- // A Binned() option is added in this example to bin the data between generation and fitting
- // to speed up the study at the expemse of some precision
-
- RooMCStudy* mcstudy = new RooMCStudy(model,x,Binned(kTRUE),Silence(),Extended(),
- FitOptions(Save(kTRUE),PrintEvalErrors(0))) ;
-
-
- // G e n e r a t e a n d f i t e v e n t s
- // ---------------------------------------------
-
- // Generate and fit 1000 samples of Poisson(nExpected) events
- mcstudy->generateAndFit(1000) ;
-
-
-
- // E x p l o r e r e s u l t s o f s t u d y
- // ------------------------------------------------
-
- // Make plots of the distributions of mean, the error on mean and the pull of mean
- RooPlot* frame1 = mcstudy->plotParam(mean,Bins(40)) ;
- RooPlot* frame2 = mcstudy->plotError(mean,Bins(40)) ;
- RooPlot* frame3 = mcstudy->plotPull(mean,Bins(40),FitGauss(kTRUE)) ;
-
- // Plot distribution of minimized likelihood
- RooPlot* frame4 = mcstudy->plotNLL(Bins(40)) ;
-
- // Make some histograms from the parameter dataset
- TH1* hh_cor_a0_s1f = mcstudy->fitParDataSet().createHistogram("hh",a1,YVar(sig1frac)) ;
- TH1* hh_cor_a0_a1 = mcstudy->fitParDataSet().createHistogram("hh",a0,YVar(a1)) ;
-
- // Access some of the saved fit results from individual toys
- TH2* corrHist000 = mcstudy->fitResult(0)->correlationHist("c000") ;
- TH2* corrHist127 = mcstudy->fitResult(127)->correlationHist("c127") ;
- TH2* corrHist953 = mcstudy->fitResult(953)->correlationHist("c953") ;
-
-
-
- // Draw all plots on a canvas
- gStyle->SetPalette(1) ;
- gStyle->SetOptStat(0) ;
- TCanvas* c = new TCanvas("rf801_mcstudy","rf801_mcstudy",900,900) ;
- c->Divide(3,3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.4) ; frame4->Draw() ;
- c->cd(5) ; gPad->SetLeftMargin(0.15) ; hh_cor_a0_s1f->GetYaxis()->SetTitleOffset(1.4) ; hh_cor_a0_s1f->Draw("box") ;
- c->cd(6) ; gPad->SetLeftMargin(0.15) ; hh_cor_a0_a1->GetYaxis()->SetTitleOffset(1.4) ; hh_cor_a0_a1->Draw("box") ;
- c->cd(7) ; gPad->SetLeftMargin(0.15) ; corrHist000->GetYaxis()->SetTitleOffset(1.4) ; corrHist000->Draw("colz") ;
- c->cd(8) ; gPad->SetLeftMargin(0.15) ; corrHist127->GetYaxis()->SetTitleOffset(1.4) ; corrHist127->Draw("colz") ;
- c->cd(9) ; gPad->SetLeftMargin(0.15) ; corrHist953->GetYaxis()->SetTitleOffset(1.4) ; corrHist953->Draw("colz") ;
-
- // Make RooMCStudy object available on command line after
- // macro finishes
- gDirectory->Add(mcstudy) ;
+ // C r e a t e m o d e l
+ // -----------------------
+
+ // Declare observable x
+ RooRealVar x("x","x",0,10) ;
+ x.setBins(40) ;
+
+ // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
+ RooRealVar mean("mean","mean of gaussians",5,0,10) ;
+ 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)) ;
+
+ // 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) ;
+
+ // Sum the composite signal and background
+ RooRealVar nbkg("nbkg","number of background events,",150,0,1000) ;
+ RooRealVar nsig("nsig","number of signal events",150,0,1000) ;
+ RooAddPdf model("model","g1+g2+a",RooArgList(bkg,sig),RooArgList(nbkg,nsig)) ;
+
+
+
+ // C r e a t e m a n a g e r
+ // ---------------------------
+
+ // Instantiate RooMCStudy manager on model with x as observable and given choice of fit options
+ //
+ // The Silence() option kills all messages below the PROGRESS level, leaving only a single message
+ // per sample executed, and any error message that occur during fitting
+ //
+ // The Extended() option has two effects:
+ // 1) The extended ML term is included in the likelihood and
+ // 2) A poisson fluctuation is introduced on the number of generated events
+ //
+ // The FitOptions() given here are passed to the fitting stage of each toy experiment.
+ // If Save() is specified, the fit result of each experiment is saved by the manager
+ //
+ // A Binned() option is added in this example to bin the data between generation and fitting
+ // to speed up the study at the expemse of some precision
+
+ RooMCStudy* mcstudy = new RooMCStudy(model,x,Binned(kTRUE),Silence(),Extended(),
+ FitOptions(Save(kTRUE),PrintEvalErrors(0))) ;
+
+
+ // G e n e r a t e a n d f i t e v e n t s
+ // ---------------------------------------------
+
+ // Generate and fit 1000 samples of Poisson(nExpected) events
+ mcstudy->generateAndFit(1000) ;
+
+
+
+ // E x p l o r e r e s u l t s o f s t u d y
+ // ------------------------------------------------
+
+ // Make plots of the distributions of mean, the error on mean and the pull of mean
+ RooPlot* frame1 = mcstudy->plotParam(mean,Bins(40)) ;
+ RooPlot* frame2 = mcstudy->plotError(mean,Bins(40)) ;
+ RooPlot* frame3 = mcstudy->plotPull(mean,Bins(40),FitGauss(kTRUE)) ;
+
+ // Plot distribution of minimized likelihood
+ RooPlot* frame4 = mcstudy->plotNLL(Bins(40)) ;
+
+ // Make some histograms from the parameter dataset
+ TH1* hh_cor_a0_s1f = mcstudy->fitParDataSet().createHistogram("hh",a1,YVar(sig1frac)) ;
+ TH1* hh_cor_a0_a1 = mcstudy->fitParDataSet().createHistogram("hh",a0,YVar(a1)) ;
+
+ // Access some of the saved fit results from individual toys
+ TH2* corrHist000 = mcstudy->fitResult(0)->correlationHist("c000") ;
+ TH2* corrHist127 = mcstudy->fitResult(127)->correlationHist("c127") ;
+ TH2* corrHist953 = mcstudy->fitResult(953)->correlationHist("c953") ;
+
+
+
+ // Draw all plots on a canvas
+ gStyle->SetPalette(1) ;
+ gStyle->SetOptStat(0) ;
+ TCanvas* c = new TCanvas("rf801_mcstudy","rf801_mcstudy",900,900) ;
+ c->Divide(3,3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.4) ; frame3->Draw() ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; frame4->GetYaxis()->SetTitleOffset(1.4) ; frame4->Draw() ;
+ c->cd(5) ; gPad->SetLeftMargin(0.15) ; hh_cor_a0_s1f->GetYaxis()->SetTitleOffset(1.4) ; hh_cor_a0_s1f->Draw("box") ;
+ c->cd(6) ; gPad->SetLeftMargin(0.15) ; hh_cor_a0_a1->GetYaxis()->SetTitleOffset(1.4) ; hh_cor_a0_a1->Draw("box") ;
+ c->cd(7) ; gPad->SetLeftMargin(0.15) ; corrHist000->GetYaxis()->SetTitleOffset(1.4) ; corrHist000->Draw("colz") ;
+ c->cd(8) ; gPad->SetLeftMargin(0.15) ; corrHist127->GetYaxis()->SetTitleOffset(1.4) ; corrHist127->Draw("colz") ;
+ c->cd(9) ; gPad->SetLeftMargin(0.15) ; corrHist953->GetYaxis()->SetTitleOffset(1.4) ; corrHist953->Draw("colz") ;
+
+ // Make RooMCStudy object available on command line after
+ // macro finishes
+ gDirectory->Add(mcstudy) ;
}
diff --git a/tutorials/roofit/rf802_mcstudy_addons.C b/tutorials/roofit/rf802_mcstudy_addons.C
index 26f8287d4e6cad357012ab5f16192d8d7da7535b..0ac3e0db8990630ff09f2fdbc1be2b514d5f0f7b 100644
--- a/tutorials/roofit/rf802_mcstudy_addons.C
+++ b/tutorials/roofit/rf802_mcstudy_addons.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'VALIDATION AND MC STUDIES' RooFit tutorial macro #802
///
/// RooMCStudy: using separate fit and generator models, using the chi^2 calculator model
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -32,76 +31,76 @@ using namespace RooFit ;
void rf802_mcstudy_addons()
{
- // C r e a t e m o d e l
- // -----------------------
+ // C r e a t e m o d e l
+ // -----------------------
- // Observables, parameters
- RooRealVar x("x","x",-10,10) ;
- x.setBins(10) ;
- RooRealVar mean("mean","mean of gaussian",0) ;
- RooRealVar sigma("sigma","width of gaussian",5,1,10) ;
+ // Observables, parameters
+ RooRealVar x("x","x",-10,10) ;
+ x.setBins(10) ;
+ RooRealVar mean("mean","mean of gaussian",0) ;
+ RooRealVar sigma("sigma","width of gaussian",5,1,10) ;
- // Create Gaussian pdf
- RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma) ;
+ // Create Gaussian pdf
+ RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma) ;
- // C r e a t e m a n a g e r w i t h c h i ^ 2 a d d - o n m o d u l e
- // ----------------------------------------------------------------------------
+ // C r e a t e m a n a g e r w i t h c h i ^ 2 a d d - o n m o d u l e
+ // ----------------------------------------------------------------------------
- // Create study manager for binned likelihood fits of a Gaussian pdf in 10 bins
- RooMCStudy* mcs = new RooMCStudy(gauss,x,Silence(),Binned()) ;
+ // Create study manager for binned likelihood fits of a Gaussian pdf in 10 bins
+ RooMCStudy* mcs = new RooMCStudy(gauss,x,Silence(),Binned()) ;
- // Add chi^2 calculator module to mcs
- RooChi2MCSModule chi2mod ;
- mcs->addModule(chi2mod) ;
+ // Add chi^2 calculator module to mcs
+ RooChi2MCSModule chi2mod ;
+ mcs->addModule(chi2mod) ;
- // Generate 1000 samples of 1000 events
- mcs->generateAndFit(2000,1000) ;
-
- // Fill histograms with distributions chi2 and prob(chi2,ndf) that
- // are calculated by RooChiMCSModule
- TH1* hist_chi2 = mcs->fitParDataSet().createHistogram("chi2") ;
- TH1* hist_prob = mcs->fitParDataSet().createHistogram("prob") ;
+ // Generate 1000 samples of 1000 events
+ mcs->generateAndFit(2000,1000) ;
+
+ // Fill histograms with distributions chi2 and prob(chi2,ndf) that
+ // are calculated by RooChiMCSModule
+ TH1* hist_chi2 = mcs->fitParDataSet().createHistogram("chi2") ;
+ TH1* hist_prob = mcs->fitParDataSet().createHistogram("prob") ;
- // C r e a t e m a n a g e r w i t h s e p a r a t e f i t m o d e l
- // ----------------------------------------------------------------------------
+ // C r e a t e m a n a g e r w i t h s e p a r a t e f i t m o d e l
+ // ----------------------------------------------------------------------------
- // Create alternate pdf with shifted mean
- RooRealVar mean2("mean2","mean of gaussian 2",0.5) ;
- RooGaussian gauss2("gauss2","gaussian PDF2",x,mean2,sigma) ;
+ // Create alternate pdf with shifted mean
+ RooRealVar mean2("mean2","mean of gaussian 2",0.5) ;
+ RooGaussian gauss2("gauss2","gaussian PDF2",x,mean2,sigma) ;
- // Create study manager with separate generation and fit model. This configuration
- // is set up to generate bad fits as the fit and generator model have different means
- // and the mean parameter is not floating in the fit
- RooMCStudy* mcs2 = new RooMCStudy(gauss2,x,FitModel(gauss),Silence(),Binned()) ;
+ // Create study manager with separate generation and fit model. This configuration
+ // is set up to generate bad fits as the fit and generator model have different means
+ // and the mean parameter is not floating in the fit
+ RooMCStudy* mcs2 = new RooMCStudy(gauss2,x,FitModel(gauss),Silence(),Binned()) ;
- // Add chi^2 calculator module to mcs
- RooChi2MCSModule chi2mod2 ;
- mcs2->addModule(chi2mod2) ;
+ // Add chi^2 calculator module to mcs
+ RooChi2MCSModule chi2mod2 ;
+ mcs2->addModule(chi2mod2) ;
- // Generate 1000 samples of 1000 events
- mcs2->generateAndFit(2000,1000) ;
-
- // Fill histograms with distributions chi2 and prob(chi2,ndf) that
- // are calculated by RooChiMCSModule
- TH1* hist2_chi2 = mcs2->fitParDataSet().createHistogram("chi2") ;
- TH1* hist2_prob = mcs2->fitParDataSet().createHistogram("prob") ;
- hist2_chi2->SetLineColor(kRed) ;
- hist2_prob->SetLineColor(kRed) ;
+ // Generate 1000 samples of 1000 events
+ mcs2->generateAndFit(2000,1000) ;
+
+ // Fill histograms with distributions chi2 and prob(chi2,ndf) that
+ // are calculated by RooChiMCSModule
+ TH1* hist2_chi2 = mcs2->fitParDataSet().createHistogram("chi2") ;
+ TH1* hist2_prob = mcs2->fitParDataSet().createHistogram("prob") ;
+ hist2_chi2->SetLineColor(kRed) ;
+ hist2_prob->SetLineColor(kRed) ;
-
+
- TCanvas* c = new TCanvas("rf802_mcstudy_addons","rf802_mcstudy_addons",800,400) ;
- c->Divide(2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; hist_chi2->GetYaxis()->SetTitleOffset(1.4) ; hist_chi2->Draw() ; hist2_chi2->Draw("esame") ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; hist_prob->GetYaxis()->SetTitleOffset(1.4) ; hist_prob->Draw() ; hist2_prob->Draw("esame") ;
+ TCanvas* c = new TCanvas("rf802_mcstudy_addons","rf802_mcstudy_addons",800,400) ;
+ c->Divide(2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; hist_chi2->GetYaxis()->SetTitleOffset(1.4) ; hist_chi2->Draw() ; hist2_chi2->Draw("esame") ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; hist_prob->GetYaxis()->SetTitleOffset(1.4) ; hist_prob->Draw() ; hist2_prob->Draw("esame") ;
-
- // Make RooMCStudy object available on command line after
- // macro finishes
- gDirectory->Add(mcs) ;
+
+ // Make RooMCStudy object available on command line after
+ // macro finishes
+ gDirectory->Add(mcs) ;
}
diff --git a/tutorials/roofit/rf803_mcstudy_addons2.C b/tutorials/roofit/rf803_mcstudy_addons2.C
index afa31bb3d605ac8840a21ec54b40195ff130d1aa..7de024e4944c24c27e4b9e449da40f39b301c3ad 100644
--- a/tutorials/roofit/rf803_mcstudy_addons2.C
+++ b/tutorials/roofit/rf803_mcstudy_addons2.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'VALIDATION AND MC STUDIES' RooFit tutorial macro #803
///
/// RooMCStudy: Using the randomizer and profile likelihood add-on models
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -32,97 +31,97 @@ using namespace RooFit ;
void rf803_mcstudy_addons2()
{
- // C r e a t e m o d e l
- // -----------------------
+ // C r e a t e m o d e l
+ // -----------------------
- // Simulation of signal and background of top quark decaying into
- // 3 jets with background
+ // Simulation of signal and background of top quark decaying into
+ // 3 jets with background
- // Observable
- RooRealVar mjjj("mjjj","m(3jet) (GeV)",100,85.,350.) ;
+ // Observable
+ RooRealVar mjjj("mjjj","m(3jet) (GeV)",100,85.,350.) ;
- // Signal component (Gaussian)
- RooRealVar mtop("mtop","m(top)",162) ;
- RooRealVar wtop("wtop","m(top) resolution",15.2) ;
- RooGaussian sig("sig","top signal",mjjj,mtop,wtop) ;
+ // Signal component (Gaussian)
+ RooRealVar mtop("mtop","m(top)",162) ;
+ RooRealVar wtop("wtop","m(top) resolution",15.2) ;
+ RooGaussian sig("sig","top signal",mjjj,mtop,wtop) ;
- // Background component (Chebychev)
- RooRealVar c0("c0","Chebychev coefficient 0",-0.846,-1.,1.) ;
- RooRealVar c1("c1","Chebychev coefficient 1", 0.112,-1.,1.) ;
- RooRealVar c2("c2","Chebychev coefficient 2", 0.076,-1.,1.) ;
- RooChebychev bkg("bkg","combinatorial background",mjjj,RooArgList(c0,c1,c2)) ;
+ // Background component (Chebychev)
+ RooRealVar c0("c0","Chebychev coefficient 0",-0.846,-1.,1.) ;
+ RooRealVar c1("c1","Chebychev coefficient 1", 0.112,-1.,1.) ;
+ RooRealVar c2("c2","Chebychev coefficient 2", 0.076,-1.,1.) ;
+ RooChebychev bkg("bkg","combinatorial background",mjjj,RooArgList(c0,c1,c2)) ;
- // Composite model
- RooRealVar nsig("nsig","number of signal events",53,0,1e3) ;
- RooRealVar nbkg("nbkg","number of background events",103,0,5e3) ;
- RooAddPdf model("model","model",RooArgList(sig,bkg),RooArgList(nsig,nbkg)) ;
+ // Composite model
+ RooRealVar nsig("nsig","number of signal events",53,0,1e3) ;
+ RooRealVar nbkg("nbkg","number of background events",103,0,5e3) ;
+ RooAddPdf model("model","model",RooArgList(sig,bkg),RooArgList(nsig,nbkg)) ;
- // C r e a t e m a n a g e r
- // ---------------------------
+ // C r e a t e m a n a g e r
+ // ---------------------------
- // Configure manager to perform binned extended likelihood fits (Binned(),Extended()) on data generated
- // with a Poisson fluctuation on Nobs (Extended())
- RooMCStudy* mcs = new RooMCStudy(model,mjjj,Binned(),Silence(),Extended(kTRUE),
- FitOptions(Extended(kTRUE),PrintEvalErrors(-1))) ;
+ // Configure manager to perform binned extended likelihood fits (Binned(),Extended()) on data generated
+ // with a Poisson fluctuation on Nobs (Extended())
+ RooMCStudy* mcs = new RooMCStudy(model,mjjj,Binned(),Silence(),Extended(kTRUE),
+ FitOptions(Extended(kTRUE),PrintEvalErrors(-1))) ;
- // C u s t o m i z e m a n a g e r
- // ---------------------------------
+ // C u s t o m i z e m a n a g e r
+ // ---------------------------------
- // Add module that randomizes the summed value of nsig+nbkg
- // sampling from a uniform distribution between 0 and 1000
- //
- // In general one can randomize a single parameter, or a
- // sum of N parameters, using either a uniform or a Gaussian
- // distribution. Multiple randomization can be executed
- // by a single randomizer module
-
- RooRandomizeParamMCSModule randModule ;
- randModule.sampleSumUniform(RooArgSet(nsig,nbkg),50,500) ;
- mcs->addModule(randModule) ;
+ // Add module that randomizes the summed value of nsig+nbkg
+ // sampling from a uniform distribution between 0 and 1000
+ //
+ // In general one can randomize a single parameter, or a
+ // sum of N parameters, using either a uniform or a Gaussian
+ // distribution. Multiple randomization can be executed
+ // by a single randomizer module
+
+ RooRandomizeParamMCSModule randModule ;
+ randModule.sampleSumUniform(RooArgSet(nsig,nbkg),50,500) ;
+ mcs->addModule(randModule) ;
- // Add profile likelihood calculation of significance. Redo each
- // fit while keeping parameter nsig fixed to zero. For each toy,
- // the difference in -log(L) of both fits is stored, as well
- // a simple significance interpretation of the delta(-logL)
- // using Dnll = 0.5 sigma^2
+ // Add profile likelihood calculation of significance. Redo each
+ // fit while keeping parameter nsig fixed to zero. For each toy,
+ // the difference in -log(L) of both fits is stored, as well
+ // a simple significance interpretation of the delta(-logL)
+ // using Dnll = 0.5 sigma^2
- RooDLLSignificanceMCSModule sigModule(nsig,0) ;
- mcs->addModule(sigModule) ;
+ RooDLLSignificanceMCSModule sigModule(nsig,0) ;
+ mcs->addModule(sigModule) ;
- // R u n m a n a g e r , m a k e p l o t s
- // ---------------------------------------------
+ // R u n m a n a g e r , m a k e p l o t s
+ // ---------------------------------------------
- // Run 1000 experiments. This configuration will generate a fair number
- // of (harmless) MINUIT warnings due to the instability of the Chebychev polynomial fit
- // at low statistics.
- mcs->generateAndFit(500) ;
+ // Run 1000 experiments. This configuration will generate a fair number
+ // of (harmless) MINUIT warnings due to the instability of the Chebychev polynomial fit
+ // at low statistics.
+ mcs->generateAndFit(500) ;
- // Make some plots
- TH1* dll_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,dll_nullhypo_nsig",-40,-40) ;
- TH1* z_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,significance_nullhypo_nsig",-40,-40) ;
- TH1* errnsig_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,nsigerr",-40,-40) ;
- TH1* errnsig_vs_nsig = mcs->fitParDataSet().createHistogram("nsig,nsigerr",-40,-40) ;
+ // Make some plots
+ TH1* dll_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,dll_nullhypo_nsig",-40,-40) ;
+ TH1* z_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,significance_nullhypo_nsig",-40,-40) ;
+ TH1* errnsig_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,nsigerr",-40,-40) ;
+ TH1* errnsig_vs_nsig = mcs->fitParDataSet().createHistogram("nsig,nsigerr",-40,-40) ;
- // Draw plots on canvas
- TCanvas* c = new TCanvas("rf803_mcstudy_addons2","rf802_mcstudy_addons2",800,800) ;
- c->Divide(2,2) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; dll_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; dll_vs_ngen->Draw("box") ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; z_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; z_vs_ngen->Draw("box") ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; errnsig_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; errnsig_vs_ngen->Draw("box") ;
- c->cd(4) ; gPad->SetLeftMargin(0.15) ; errnsig_vs_nsig->GetYaxis()->SetTitleOffset(1.6) ; errnsig_vs_nsig->Draw("box") ;
+ // Draw plots on canvas
+ TCanvas* c = new TCanvas("rf803_mcstudy_addons2","rf802_mcstudy_addons2",800,800) ;
+ c->Divide(2,2) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; dll_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; dll_vs_ngen->Draw("box") ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; z_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; z_vs_ngen->Draw("box") ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; errnsig_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; errnsig_vs_ngen->Draw("box") ;
+ c->cd(4) ; gPad->SetLeftMargin(0.15) ; errnsig_vs_nsig->GetYaxis()->SetTitleOffset(1.6) ; errnsig_vs_nsig->Draw("box") ;
-
- // Make RooMCStudy object available on command line after
- // macro finishes
- gDirectory->Add(mcs) ;
+
+ // Make RooMCStudy object available on command line after
+ // macro finishes
+ gDirectory->Add(mcs) ;
}
diff --git a/tutorials/roofit/rf804_mcstudy_constr.C b/tutorials/roofit/rf804_mcstudy_constr.C
index 64278a68b5196a04a85c268961393fec2509b4f0..d2541a76dedbd27feb3d894353e418b737dc59d4 100644
--- a/tutorials/roofit/rf804_mcstudy_constr.C
+++ b/tutorials/roofit/rf804_mcstudy_constr.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'VALIDATION AND MC STUDIES' RooFit tutorial macro #804
///
/// Using RooMCStudy on models with constrains
///
-///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -29,61 +28,61 @@ using namespace RooFit ;
void rf804_mcstudy_constr()
{
- // C r e a t e m o d e l w i t h p a r a m e t e r c o n s t r a i n t
- // ---------------------------------------------------------------------------
+ // C r e a t e m o d e l w i t h p a r a m e t e r c o n s t r a i n t
+ // ---------------------------------------------------------------------------
- // Observable
- RooRealVar x("x","x",-10,10) ;
+ // Observable
+ RooRealVar x("x","x",-10,10) ;
- // Signal component
- RooRealVar m("m","m",0,-10,10) ;
- RooRealVar s("s","s",2,0.1,10) ;
- RooGaussian g("g","g",x,m,s) ;
+ // Signal component
+ RooRealVar m("m","m",0,-10,10) ;
+ RooRealVar s("s","s",2,0.1,10) ;
+ RooGaussian g("g","g",x,m,s) ;
- // Background component
- RooPolynomial p("p","p",x) ;
+ // Background component
+ RooPolynomial p("p","p",x) ;
- // Composite model
- RooRealVar f("f","f",0.4,0.,1.) ;
- RooAddPdf sum("sum","sum",RooArgSet(g,p),f) ;
+ // Composite model
+ RooRealVar f("f","f",0.4,0.,1.) ;
+ RooAddPdf sum("sum","sum",RooArgSet(g,p),f) ;
- // Construct constraint on parameter f
- RooGaussian fconstraint("fconstraint","fconstraint",f,RooConst(0.7),RooConst(0.1)) ;
+ // Construct constraint on parameter f
+ RooGaussian fconstraint("fconstraint","fconstraint",f,RooConst(0.7),RooConst(0.1)) ;
- // Multiply constraint with p.d.f
- RooProdPdf sumc("sumc","sum with constraint",RooArgSet(sum,fconstraint)) ;
+ // Multiply constraint with p.d.f
+ RooProdPdf sumc("sumc","sum with constraint",RooArgSet(sum,fconstraint)) ;
- // S e t u p t o y s t u d y w i t h m o d e l
- // ---------------------------------------------------
+ // S e t u p t o y s t u d y w i t h m o d e l
+ // ---------------------------------------------------
- // Perform toy study with internal constraint on f
- RooMCStudy mcs(sumc,x,Constrain(f),Silence(),Binned(),FitOptions(PrintLevel(-1))) ;
+ // Perform toy study with internal constraint on f
+ RooMCStudy mcs(sumc,x,Constrain(f),Silence(),Binned(),FitOptions(PrintLevel(-1))) ;
- // Run 500 toys of 2000 events.
- // Before each toy is generated, a value for the f is sampled from the constraint pdf and
- // that value is used for the generation of that toy.
- mcs.generateAndFit(500,2000) ;
+ // Run 500 toys of 2000 events.
+ // Before each toy is generated, a value for the f is sampled from the constraint pdf and
+ // that value is used for the generation of that toy.
+ mcs.generateAndFit(500,2000) ;
- // Make plot of distribution of generated value of f parameter
- TH1* h_f_gen = mcs.fitParDataSet().createHistogram("f_gen",-40) ;
+ // Make plot of distribution of generated value of f parameter
+ TH1* h_f_gen = mcs.fitParDataSet().createHistogram("f_gen",-40) ;
- // Make plot of distribution of fitted value of f parameter
- RooPlot* frame1 = mcs.plotParam(f,Bins(40)) ;
- frame1->SetTitle("Distribution of fitted f values") ;
+ // Make plot of distribution of fitted value of f parameter
+ RooPlot* frame1 = mcs.plotParam(f,Bins(40)) ;
+ frame1->SetTitle("Distribution of fitted f values") ;
- // Make plot of pull distribution on f
- RooPlot* frame2 = mcs.plotPull(f,Bins(40),FitGauss()) ;
- frame1->SetTitle("Distribution of f pull values") ;
+ // Make plot of pull distribution on f
+ RooPlot* frame2 = mcs.plotPull(f,Bins(40),FitGauss()) ;
+ frame1->SetTitle("Distribution of f pull values") ;
- TCanvas* c = new TCanvas("rf804_mcstudy_constr","rf804_mcstudy_constr",1200,400) ;
- c->Divide(3) ;
- c->cd(1) ; gPad->SetLeftMargin(0.15) ; h_f_gen->GetYaxis()->SetTitleOffset(1.4) ; h_f_gen->Draw() ;
- c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
- c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
+ TCanvas* c = new TCanvas("rf804_mcstudy_constr","rf804_mcstudy_constr",1200,400) ;
+ c->Divide(3) ;
+ c->cd(1) ; gPad->SetLeftMargin(0.15) ; h_f_gen->GetYaxis()->SetTitleOffset(1.4) ; h_f_gen->Draw() ;
+ c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
+ c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
diff --git a/tutorials/roofit/rf901_numintconfig.C b/tutorials/roofit/rf901_numintconfig.C
index b56a609a3dcc656681e9988e3df9fcf71d23aae4..5e17adf53cf9f950b9f49fc99e36a3ce527d658f 100644
--- a/tutorials/roofit/rf901_numintconfig.C
+++ b/tutorials/roofit/rf901_numintconfig.C
@@ -1,19 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'NUMERIC ALGORITHM TUNING' RooFit tutorial macro #901
///
/// Configuration and customization of how numeric (partial) integrals
/// are executed
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
@@ -31,97 +28,97 @@ using namespace RooFit ;
void rf901_numintconfig()
{
- // 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
- // ----------------------------------------------------------------------------
+ // 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
+ // ----------------------------------------------------------------------------
- // Print current global default configuration for numeric integration strategies
- RooAbsReal::defaultIntegratorConfig()->Print("v") ;
+ // Print current global default configuration for numeric integration strategies
+ RooAbsReal::defaultIntegratorConfig()->Print("v") ;
- // 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) ;
+ // 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)) ;
-
+ // 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)) ;
+
- // Activate debug-level messages for topic integration to be able to follow actions below
- RooMsgService::instance().addStream(DEBUG,Topic(Integration)) ;
+ // Activate debug-level messages for topic integration to be able to follow actions below
+ RooMsgService::instance().addStream(DEBUG,Topic(Integration)) ;
- // Calculate integral over landau with default choice of numeric integrator
- RooAbsReal* intLandau = landau.createIntegral(x) ;
- Double_t val = intLandau->getVal() ;
- cout << " [1] int_dx landau(x) = " << setprecision(15) << val << endl ;
+ // Calculate integral over landau with default choice of numeric integrator
+ RooAbsReal* intLandau = landau.createIntegral(x) ;
+ Double_t val = intLandau->getVal() ;
+ cout << " [1] int_dx landau(x) = " << setprecision(15) << val << endl ;
- // 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
- // -----------------------------------------------------------
-
+ // 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()) ;
- customConfig.method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
+ // Construct a custom configuration which uses the adaptive Gauss-Kronrod technique
+ // for closed 1D integrals
+ RooNumIntConfig customConfig(*RooAbsReal::defaultIntegratorConfig()) ;
+ customConfig.method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
- // Calculate integral over landau with custom integral specification
- RooAbsReal* intLandau2 = landau.createIntegral(x,NumIntConfig(customConfig)) ;
- Double_t val2 = intLandau2->getVal() ;
- cout << " [2] int_dx landau(x) = " << val2 << endl ;
+ // Calculate integral over landau with custom integral specification
+ RooAbsReal* intLandau2 = landau.createIntegral(x,NumIntConfig(customConfig)) ;
+ Double_t val2 = intLandau2->getVal() ;
+ cout << " [2] int_dx landau(x) = " << val2 << endl ;
- // 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
- // -------------------------------------------------------------------------------------
-
+ // 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) ;
+ // 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() ;
- cout << " [3] int_dx landau(x) = " << val3 << endl ;
-
+ // Calculate integral over landau custom numeric integrator specified as object default
+ RooAbsReal* intLandau3 = landau.createIntegral(x) ;
+ Double_t val3 = intLandau3->getVal() ;
+ cout << " [3] int_dx landau(x) = " << val3 << endl ;
+
- // Another possibility: Change global default for 1D numeric integration strategy on finite domains
- RooAbsReal::defaultIntegratorConfig()->method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
+ // Another possibility: Change global default for 1D numeric integration strategy on finite domains
+ RooAbsReal::defaultIntegratorConfig()->method1D().setLabel("RooAdaptiveGaussKronrodIntegrator1D") ;
- // A d j u s t i n g p a r a m e t e r s o f a s p e c i f i c t e c h n i q u e
- // ---------------------------------------------------------------------------------------
+ // A d j u s t i n g p a r a m e t e r s o f a s p e c i f i c t e c h n i q u e
+ // ---------------------------------------------------------------------------------------
- // Adjust maximum number of steps of RooIntegrator1D in the global default configuration
- RooAbsReal::defaultIntegratorConfig()->getConfigSection("RooIntegrator1D").setRealValue("maxSteps",30) ;
+ // Adjust maximum number of steps of RooIntegrator1D in the global default configuration
+ RooAbsReal::defaultIntegratorConfig()->getConfigSection("RooIntegrator1D").setRealValue("maxSteps",30) ;
-
- // Example of how to change the parameters of a numeric integrator
- // (Each config section is a RooArgSet with RooRealVars holding real-valued parameters
- // and RooCategories holding parameters with a finite set of options)
- customConfig.getConfigSection("RooAdaptiveGaussKronrodIntegrator1D").setRealValue("maxSeg",50) ;
- customConfig.getConfigSection("RooAdaptiveGaussKronrodIntegrator1D").setCatLabel("method","15Points") ;
+
+ // Example of how to change the parameters of a numeric integrator
+ // (Each config section is a RooArgSet with RooRealVars holding real-valued parameters
+ // and RooCategories holding parameters with a finite set of options)
+ customConfig.getConfigSection("RooAdaptiveGaussKronrodIntegrator1D").setRealValue("maxSeg",50) ;
+ customConfig.getConfigSection("RooAdaptiveGaussKronrodIntegrator1D").setCatLabel("method","15Points") ;
- // Example of how to print set of possible values for "method" category
- customConfig.getConfigSection("RooAdaptiveGaussKronrodIntegrator1D").find("method")->Print("v") ;
+ // Example of how to print set of possible values for "method" category
+ customConfig.getConfigSection("RooAdaptiveGaussKronrodIntegrator1D").find("method")->Print("v") ;
}
diff --git a/tutorials/roofit/rf902_numgenconfig.C b/tutorials/roofit/rf902_numgenconfig.C
index 451d600c872a6f059c60b882ee2f52d921dcfece..315a98a9afa76dff3f60131ff0fa2fade6074e31 100644
--- a/tutorials/roofit/rf902_numgenconfig.C
+++ b/tutorials/roofit/rf902_numgenconfig.C
@@ -1,19 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -nodraw
/// 'NUMERIC ALGORITHM TUNING' RooFit tutorial macro #902
///
/// Configuration and customization of how MC sampling algorithms
/// on specific p.d.f.s are executed
///
-///
-///
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooConstVar.h"
@@ -30,49 +27,49 @@ using namespace RooFit ;
void rf902_numgenconfig()
{
- // A d j u s t g l o b a l MC s a m p l i n g s t r a t e g y
- // ------------------------------------------------------------------
+ // A d j u s t g l o b a l MC s a m p l i n g s t r a t e g y
+ // ------------------------------------------------------------------
- // Example p.d.f. for use below
- RooRealVar x("x","x",0,10) ;
- RooChebychev model("model","model",x,RooArgList(RooConst(0),RooConst(0.5),RooConst(-0.1))) ;
+ // Example p.d.f. for use below
+ RooRealVar x("x","x",0,10) ;
+ RooChebychev model("model","model",x,RooArgList(RooConst(0),RooConst(0.5),RooConst(-0.1))) ;
- // Change global strategy for 1D sampling problems without conditional observable
- // (1st kFALSE) and without discrete observable (2nd kFALSE) from RooFoamGenerator,
- // ( an interface to the TFoam MC generator with adaptive subdivisioning strategy ) to RooAcceptReject,
- // a plain accept/reject sampling algorithm [ RooFit default before ROOT 5.23/04 ]
- RooAbsPdf::defaultGeneratorConfig()->method1D(kFALSE,kFALSE).setLabel("RooAcceptReject") ;
+ // Change global strategy for 1D sampling problems without conditional observable
+ // (1st kFALSE) and without discrete observable (2nd kFALSE) from RooFoamGenerator,
+ // ( an interface to the TFoam MC generator with adaptive subdivisioning strategy ) to RooAcceptReject,
+ // a plain accept/reject sampling algorithm [ RooFit default before ROOT 5.23/04 ]
+ RooAbsPdf::defaultGeneratorConfig()->method1D(kFALSE,kFALSE).setLabel("RooAcceptReject") ;
- // Generate 10Kevt using RooAcceptReject
- RooDataSet* data_ar = model.generate(x,10000,Verbose(kTRUE)) ;
- data_ar->Print() ;
+ // Generate 10Kevt using RooAcceptReject
+ RooDataSet* data_ar = model.generate(x,10000,Verbose(kTRUE)) ;
+ data_ar->Print() ;
- // 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 MC sampling configuration as default for object 'model'
- // The kTRUE argument will install a clone of the default configuration as specialized configuration
- // for this model if none existed so far
- model.specialGeneratorConfig(kTRUE)->method1D(kFALSE,kFALSE).setLabel("RooFoamGenerator") ;
+ // 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 MC sampling configuration as default for object 'model'
+ // The kTRUE argument will install a clone of the default configuration as specialized configuration
+ // for this model if none existed so far
+ model.specialGeneratorConfig(kTRUE)->method1D(kFALSE,kFALSE).setLabel("RooFoamGenerator") ;
- // A d j u s t i n g p a r a m e t e r s o f a s p e c i f i c t e c h n i q u e
- // ---------------------------------------------------------------------------------------
+ // A d j u s t i n g p a r a m e t e r s o f a s p e c i f i c t e c h n i q u e
+ // ---------------------------------------------------------------------------------------
- // Adjust maximum number of steps of RooIntegrator1D in the global default configuration
- RooAbsPdf::defaultGeneratorConfig()->getConfigSection("RooAcceptReject").setRealValue("nTrial1D",2000) ;
+ // Adjust maximum number of steps of RooIntegrator1D in the global default configuration
+ RooAbsPdf::defaultGeneratorConfig()->getConfigSection("RooAcceptReject").setRealValue("nTrial1D",2000) ;
-
- // Example of how to change the parameters of a numeric integrator
- // (Each config section is a RooArgSet with RooRealVars holding real-valued parameters
- // and RooCategories holding parameters with a finite set of options)
- model.specialGeneratorConfig()->getConfigSection("RooFoamGenerator").setRealValue("chatLevel",1) ;
+
+ // Example of how to change the parameters of a numeric integrator
+ // (Each config section is a RooArgSet with RooRealVars holding real-valued parameters
+ // and RooCategories holding parameters with a finite set of options)
+ model.specialGeneratorConfig()->getConfigSection("RooFoamGenerator").setRealValue("chatLevel",1) ;
- // Generate 10Kevt using RooFoamGenerator (FOAM verbosity increased with above chatLevel adjustment for illustration purposes)
- RooDataSet* data_foam = model.generate(x,10000,Verbose()) ;
- data_foam->Print() ;
+ // Generate 10Kevt using RooFoamGenerator (FOAM verbosity increased with above chatLevel adjustment for illustration purposes)
+ RooDataSet* data_foam = model.generate(x,10000,Verbose()) ;
+ data_foam->Print() ;
}
diff --git a/tutorials/roofit/rf903_numintcache.C b/tutorials/roofit/rf903_numintcache.C
index 0b90fe87dc44eb392b1ee935c15b9e8d35722ee2..883258126fb0ebb52ad9cac42a4b7b83a9bbb436 100644
--- a/tutorials/roofit/rf903_numintcache.C
+++ b/tutorials/roofit/rf903_numintcache.C
@@ -1,17 +1,16 @@
/// \file
/// \ingroup tutorial_roofit
+/// \notebook -js
/// 'NUMERIC ALGORITHM TUNING' RooFit tutorial macro #903
///
/// Caching of slow numeric integrals and parameterizations of slow
/// numeric integrals
///
+/// \macro_image
+/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
-
-#ifndef __CINT__
-#include "RooGlobalFunc.h"
-#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
@@ -31,105 +30,104 @@ RooWorkspace* getWorkspace(Int_t mode) ;
void rf903_numintcache(Int_t mode=0)
{
- // Mode = 0 : Run plain fit (slow)
- // Mode = 1 : Generate workspace with precalculated integral and store it on file (prepare for accelerated running)
- // Mode = 2 : Run fit from previously stored workspace including cached integrals (fast, requires run in mode=1 first)
+ // Mode = 0 : Run plain fit (slow)
+ // Mode = 1 : Generate workspace with precalculated integral and store it on file (prepare for accelerated running)
+ // Mode = 2 : Run fit from previously stored workspace including cached integrals (fast, requires run in mode=1 first)
- // C r e a t e , s a v e o r l o a d w o r k s p a c e w i t h p . d . f .
- // -----------------------------------------------------------------------------------
+ // C r e a t e , s a v e o r l o a d w o r k s p a c e w i t h p . d . f .
+ // -----------------------------------------------------------------------------------
- // Make/load workspace, exit here in mode 1
- RooWorkspace* w = getWorkspace(mode) ;
- if (mode==1) {
+ // Make/load workspace, exit here in mode 1
+ RooWorkspace* w = getWorkspace(mode) ;
+ if (mode==1) {
- // Show workspace that was created
- w->Print() ;
+ // Show workspace that was created
+ w->Print() ;
- // Show plot of cached integral values
- RooDataHist* hhcache = (RooDataHist*) w->expensiveObjectCache().getObj(1) ;
- if (hhcache) {
+ // Show plot of cached integral values
+ RooDataHist* hhcache = (RooDataHist*) w->expensiveObjectCache().getObj(1) ;
+ if (hhcache) {
- new TCanvas("rf903_numintcache","rf903_numintcache",600,600) ;
- hhcache->createHistogram("a")->Draw() ;
-
- }
- else {
- Error("rf903_numintcache","Cached histogram is not existing in workspace");
- }
- return ;
- }
+ new TCanvas("rf903_numintcache","rf903_numintcache",600,600) ;
+ hhcache->createHistogram("a")->Draw() ;
+
+ }
+ else {
+ Error("rf903_numintcache","Cached histogram is not existing in workspace");
+ }
+ return ;
+ }
- // U s e p . d . f . f r o m w o r k s p a c e f o r g e n e r a t i o n a n d f i t t i n g
- // -----------------------------------------------------------------------------------
+ // U s e p . d . f . f r o m w o r k s p a c e f o r g e n e r a t i o n a n d f i t t i n g
+ // -----------------------------------------------------------------------------------
- // This is always slow (need to find maximum function value empirically in 3D space)
- RooDataSet* d = w->pdf("model")->generate(RooArgSet(*w->var("x"),*w->var("y"),*w->var("z")),1000) ;
+ // This is always slow (need to find maximum function value empirically in 3D space)
+ RooDataSet* d = w->pdf("model")->generate(RooArgSet(*w->var("x"),*w->var("y"),*w->var("z")),1000) ;
- // This is slow in mode 0, but fast in mode 1
- w->pdf("model")->fitTo(*d,Verbose(kTRUE),Timer(kTRUE)) ;
+ // This is slow in mode 0, but fast in mode 1
+ w->pdf("model")->fitTo(*d,Verbose(kTRUE),Timer(kTRUE)) ;
- // Projection on x (always slow as 2D integral over Y,Z at fitted value of a is not cached)
- RooPlot* framex = w->var("x")->frame(Title("Projection of 3D model on X")) ;
- d->plotOn(framex) ;
- w->pdf("model")->plotOn(framex) ;
+ // Projection on x (always slow as 2D integral over Y,Z at fitted value of a is not cached)
+ RooPlot* framex = w->var("x")->frame(Title("Projection of 3D model on X")) ;
+ d->plotOn(framex) ;
+ w->pdf("model")->plotOn(framex) ;
- // Draw x projection on canvas
- new TCanvas("rf903_numintcache","rf903_numintcache",600,600) ;
- framex->Draw() ;
+ // Draw x projection on canvas
+ new TCanvas("rf903_numintcache","rf903_numintcache",600,600) ;
+ framex->Draw() ;
- // Make workspace available on command line after macro finishes
- gDirectory->Add(w) ;
+ // Make workspace available on command line after macro finishes
+ gDirectory->Add(w) ;
- return ;
+ return ;
-
}
RooWorkspace* getWorkspace(Int_t mode)
{
- // C r e a t e , s a v e o r l o a d w o r k s p a c e w i t h p . d . f .
- // -----------------------------------------------------------------------------------
- //
- // Mode = 0 : Create workspace for plain running (no integral caching)
- // Mode = 1 : Generate workspace with precalculated integral and store it on file
- // Mode = 2 : Load previously stored workspace from file
-
- RooWorkspace* w(0) ;
-
- if (mode!=2) {
-
- // Create empty workspace workspace
- w = new RooWorkspace("w",1) ;
-
- // Make a difficult to normalize p.d.f. in 3 dimensions that is integrated numerically.
- w->factory("EXPR::model('1/((x-a)*(x-a)+0.01)+1/((y-a)*(y-a)+0.01)+1/((z-a)*(z-a)+0.01)',x[-1,1],y[-1,1],z[-1,1],a[-5,5])") ;
- }
-
- if (mode==1) {
-
- // Instruct model to precalculate normalization integral that integrate at least
- // two dimensions numerically. In this specific case the integral value for
- // all values of parameter 'a' are stored in a histogram and available for use
- // in subsequent fitting and plotting operations (interpolation is applied)
-
- // w->pdf("model")->setNormValueCaching(3) ;
- w->pdf("model")->setStringAttribute("CACHEPARMINT","x:y:z");
-
- // Evaluate p.d.f. once to trigger filling of cache
- RooArgSet normSet(*w->var("x"),*w->var("y"),*w->var("z")) ;
- w->pdf("model")->getVal(&normSet) ;
- w->writeToFile("rf903_numintcache.root") ;
-
- }
-
- if (mode==2) {
- // Load preexisting workspace from file in mode==2
- TFile* f = new TFile("rf903_numintcache.root") ;
- w = (RooWorkspace*) f->Get("w") ;
- }
-
- // Return created or loaded workspace
- return w ;
+ // C r e a t e , s a v e o r l o a d w o r k s p a c e w i t h p . d . f .
+ // -----------------------------------------------------------------------------------
+ //
+ // Mode = 0 : Create workspace for plain running (no integral caching)
+ // Mode = 1 : Generate workspace with precalculated integral and store it on file
+ // Mode = 2 : Load previously stored workspace from file
+
+ RooWorkspace* w(0) ;
+
+ if (mode!=2) {
+
+ // Create empty workspace workspace
+ w = new RooWorkspace("w",1) ;
+
+ // Make a difficult to normalize p.d.f. in 3 dimensions that is integrated numerically.
+ w->factory("EXPR::model('1/((x-a)*(x-a)+0.01)+1/((y-a)*(y-a)+0.01)+1/((z-a)*(z-a)+0.01)',x[-1,1],y[-1,1],z[-1,1],a[-5,5])") ;
+ }
+
+ if (mode==1) {
+
+ // Instruct model to precalculate normalization integral that integrate at least
+ // two dimensions numerically. In this specific case the integral value for
+ // all values of parameter 'a' are stored in a histogram and available for use
+ // in subsequent fitting and plotting operations (interpolation is applied)
+
+ // w->pdf("model")->setNormValueCaching(3) ;
+ w->pdf("model")->setStringAttribute("CACHEPARMINT","x:y:z");
+
+ // Evaluate p.d.f. once to trigger filling of cache
+ RooArgSet normSet(*w->var("x"),*w->var("y"),*w->var("z")) ;
+ w->pdf("model")->getVal(&normSet) ;
+ w->writeToFile("rf903_numintcache.root") ;
+
+ }
+
+ if (mode==2) {
+ // Load preexisting workspace from file in mode==2
+ TFile* f = new TFile("rf903_numintcache.root") ;
+ w = (RooWorkspace*) f->Get("w") ;
+ }
+
+ // Return created or loaded workspace
+ return w ;
}
diff --git a/tutorials/tree/h1analysisProxy.C b/tutorials/tree/h1analysisProxy.C
index 43d8f7f5ba93a5e01555443261bcfa109f45d46c..008b4b63fe61606ca834e6d195bcdb663c399c8c 100644
--- a/tutorials/tree/h1analysisProxy.C
+++ b/tutorials/tree/h1analysisProxy.C
@@ -1,6 +1,5 @@
/// \file
/// \ingroup tutorial_tree
-/// \notebook -header
/// Example of analysis class for the H1 data using code generated by MakeProxy.
///
/// This file uses 4 large data sets from the H1 collaboration at DESY Hamburg.
diff --git a/tutorials/tree/printSizes.C b/tutorials/tree/printSizes.C
index afbec583c49c521e24aad17f678c867a9c632614..7ba5ffee5c7f46eba586d1a445280ccc4648ab69 100644
--- a/tutorials/tree/printSizes.C
+++ b/tutorials/tree/printSizes.C
@@ -1,6 +1,6 @@
/// \file
/// \ingroup tutorial_tree
-/// \notebook
+/// \notebook -nodraw
/// This macro can be used to get aggregate information on the size
/// take on disk or in memory by the various branches in a TTree.
///
@@ -47,6 +47,9 @@
Long64_t GetTotalSize(TBranch * b, bool ondisk, bool inclusive);
Long64_t GetBasketSize(TBranch * b, bool ondisk, bool inclusive);
+void printSizes() {
+}
+
Long64_t GetBasketSize(TObjArray * branches, bool ondisk, bool inclusive) {
Long64_t result = 0;
size_t n = branches->GetEntries();
@@ -166,4 +169,3 @@ void printTreeSummary(TTree *t)
}
}
-void printSizes() {}
diff --git a/tutorials/tree/staff.C b/tutorials/tree/staff.C
index 0c29a7a055337988c92ff1a846f603ee430fda39..6ff6c2b034d97234603657a8940430f19c2f1141 100644
--- a/tutorials/tree/staff.C
+++ b/tutorials/tree/staff.C
@@ -1,6 +1,6 @@
/// \file
/// \ingroup tutorial_tree
-/// \notebook \nodraw
+/// \notebook -nodraw
/// Create a plot of the data in cernstaff.root
/// To create cernstaff.root, execute tutorial $ROOTSYS/tutorials/tree/cernbuild.C
/// \macro_image