rf107_plotstyles.C File Reference

Detailed Description

Basic functionality: various plotting styles of data, functions in a RooPlot

 
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
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 **    1 **SET PRINT           1
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 **    2 **SET NOGRAD
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 PARAMETER DEFINITIONS:
    NO.   NAME         VALUE      STEP SIZE      LIMITS
     1 mean        -3.00000e+00  2.00000e+00   -1.00000e+01  1.00000e+01
     2 sigma        3.00000e+00  9.90000e-01    1.00000e-01  1.00000e+01
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 **    3 **SET ERR         0.5
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 **    4 **SET PRINT           1
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 **    5 **SET STR           1
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 NOW USING STRATEGY  1: TRY TO BALANCE SPEED AGAINST RELIABILITY
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 **    6 **MIGRAD        1000           1
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 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=244.778 FROM MIGRAD    STATUS=INITIATE        6 CALLS           7 TOTAL
                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       
  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  mean        -3.00000e+00   2.00000e+00   2.11716e-01   7.88402e+00
   2  sigma        3.00000e+00   9.90000e-01   2.22742e-01   8.68850e+00
                               ERR DEF= 0.5
 MIGRAD MINIMIZATION HAS CONVERGED.
 MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=244.648 FROM MIGRAD    STATUS=CONVERGED      27 CALLS          28 TOTAL
                     EDM=6.12289e-07    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                   STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  mean        -3.06106e+00   3.00167e-01   3.38614e-04  -1.01280e-02
   2  sigma        2.89572e+00   2.28664e-01   5.51106e-04   1.31676e-02
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5
  9.013e-02 -8.498e-03 
 -8.498e-03  5.233e-02 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2
        1  0.12374   1.000 -0.124
        2  0.12374  -0.124  1.000
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 **    7 **SET ERR         0.5
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 **    8 **SET PRINT           1
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 **    9 **HESSE        1000
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 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=244.648 FROM HESSE     STATUS=OK             10 CALLS          38 TOTAL
                     EDM=6.13161e-07    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                INTERNAL      INTERNAL  
  NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE   
   1  mean        -3.06106e+00   3.00196e-01   6.77227e-05  -3.11100e-01
   2  sigma        2.89572e+00   2.28685e-01   1.10221e-04  -4.50268e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5
  9.015e-02 -8.552e-03 
 -8.552e-03  5.234e-02 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2
        1  0.12449   1.000 -0.124
        2  0.12449  -0.124  1.000
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:Plotting -- RooAbsPdf::plotOn(gauss) only plotting range [-8,3], curve is normalized to data in given range
[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'plotRange' created with bounds [-8,3]

 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit;
 
void rf107_plotstyles()
{
 
   // S e t u p   m o d e l
   // ---------------------
 
   // Create observables
   RooRealVar x("x", "x", -10, 10);
 
   // Create Gaussian
   RooRealVar sigma("sigma", "sigma", 3, 0.1, 10);
   RooRealVar mean("mean", "mean", -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);
 
   // Fit pdf to data
   gauss.fitTo(*data);
 
   // 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));
 
   // 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));
 
   // Remove horizontal error bars
   data->plotOn(frame2, XErrorSize(0));
 
   // 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));
 
   // 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 style to dashed
   gauss.plotOn(frame2, LineStyle(kDashed));
 
   // Filled shapes in green color
   gauss.plotOn(frame3, DrawOption("F"), FillColor(kOrange), MoveToBack());
 
   //
   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();
}

Date

July 2008

Author

Wouter Verkerke

Definition in file rf107_plotstyles.C.