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rf306_condpereventerrors.C File Reference
RooFit Tutorials

Detailed Description

'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #306

Complete example with use of conditional p.d.f. with per-event errors

pict1_rf306_condpereventerrors.C.png
Processing /builddir/build/BUILD/root-6.10.00/tutorials/roofit/rf306_condpereventerrors.C...
RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
**********
** 1 **SET PRINT 1
**********
**********
** 2 **SET NOGRAD
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 bias 0.00000e+00 2.00000e+00 -1.00000e+01 1.00000e+01
2 sigma 1.00000e+00 4.50000e-01 1.00000e-01 1.00000e+01
**********
** 3 **SET ERR 0.5
**********
**********
** 4 **SET PRINT 1
**********
**********
** 5 **SET STR 1
**********
NOW USING STRATEGY 1: TRY TO BALANCE SPEED AGAINST RELIABILITY
**********
** 6 **MIGRAD 1000 1
**********
FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
START MIGRAD MINIMIZATION. STRATEGY 1. CONVERGENCE WHEN EDM .LT. 1.00e-03
FCN=23876.4 FROM MIGRAD STATUS=INITIATE 8 CALLS 9 TOTAL
EDM= unknown STRATEGY= 1 NO ERROR MATRIX
EXT PARAMETER CURRENT GUESS STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 bias 0.00000e+00 2.00000e+00 2.01358e-01 -1.70342e+02
2 sigma 1.00000e+00 4.50000e-01 1.63378e-01 8.62474e+01
ERR DEF= 0.5
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=23876.2 FROM MIGRAD STATUS=CONVERGED 30 CALLS 31 TOTAL
EDM=3.03467e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 bias 5.26463e-03 1.72103e-02 1.83632e-04 1.00797e-01
2 sigma 9.87130e-01 2.04183e-02 7.70435e-04 2.52620e-03
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
2.962e-04 -4.402e-06
-4.402e-06 4.169e-04
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.01253 1.000 -0.013
2 0.01253 -0.013 1.000
**********
** 7 **SET ERR 0.5
**********
**********
** 8 **SET PRINT 1
**********
**********
** 9 **HESSE 1000
**********
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=23876.2 FROM HESSE STATUS=OK 12 CALLS 43 TOTAL
EDM=3.12688e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 bias 5.26463e-03 1.72105e-02 3.67263e-05 5.26463e-04
2 sigma 9.87130e-01 2.04260e-02 3.08174e-05 -9.62778e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
2.962e-04 -4.786e-06
-4.786e-06 4.172e-04
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.01361 1.000 -0.014
2 0.01361 -0.014 1.000
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gm1_conv_exp(-abs(@0)/@1)_dt_tau_[decay_gm]_Int[dt,dterr]) using numeric integrator RooIntegrator1D to calculate Int(dterr)
[#1] INFO:Plotting -- RooAbsReal::plotOn(decay_gm) plot on dt averages using data variables (dterr)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(decay_gmDataWgtAvg) constructing data weighted average of function decay_gm_Norm[dt] over 100 data points of (dterr) with a total weight of 10000
.........................................................................................................................................................................................................................
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooGaussModel.h"
#include "RooDecay.h"
#include "RooLandau.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH2D.h"
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
// ----------------------------------------------------------------------------------------------
// 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) ;
// 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) ;
// 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 )
// ---------------------------------------------------------------------
// 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 )
// ---------------------------------------------------------------------
// 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.)) ;
}
// 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)) ;
// 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() ;
}
Author
07/2008 - Wouter Verkerke

Definition in file rf306_condpereventerrors.C.