Rf 1 0 7_Plotstyles

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

Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Saturday, November 28, 2020 at 10:37 AM.

In [1]:
import ROOT
Welcome to JupyROOT 6.23/01

Set up model

Create observables

In [2]:
x = ROOT.RooRealVar("x", "x", -10, 10)
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

Create Gaussian

In [3]:
sigma = ROOT.RooRealVar("sigma", "sigma", 3, 0.1, 10)
mean = ROOT.RooRealVar("mean", "mean", -3, -10, 10)
gauss = ROOT.RooGaussian("gauss", "gauss", x, mean, sigma)

Generate a sample of 100 events with sigma=3

In [4]:
data = gauss.generate(ROOT.RooArgSet(x), 100)

Fit pdf to data

In [5]:
gauss.fitTo(data)
Out[5]:
<cppyy.gbl.RooFitResult object at 0x(nil)>
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
 **********
 **    1 **SET PRINT           1
 **********
 **********
 **    2 **SET NOGRAD
 **********
 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
 **********
 **    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=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
 **********
 **    7 **SET ERR         0.5
 **********
 **********
 **    8 **SET PRINT           1
 **********
 **********
 **    9 **HESSE        1000
 **********
 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:Minization -- RooMinimizer::optimizeConst: deactivating const optimization

Make plot frames

Make four plot frames to demonstrate various plotting features

In [6]:
frame1 = x.frame(ROOT.RooFit.Name("xframe"), ROOT.RooFit.Title(
    "Red Curve / SumW2 Histo errors"), ROOT.RooFit.Bins(20))
frame2 = x.frame(ROOT.RooFit.Name("xframe"), ROOT.RooFit.Title(
    "Dashed Curve / No XError bars"), ROOT.RooFit.Bins(20))
frame3 = x.frame(ROOT.RooFit.Name("xframe"), ROOT.RooFit.Title(
    "Filled Curve / Blue Histo"), ROOT.RooFit.Bins(20))
frame4 = x.frame(ROOT.RooFit.Name("xframe"), ROOT.RooFit.Title(
    "Partial Range / Filled Bar chart"), ROOT.RooFit.Bins(20))

Data plotting styles

Use sqrt(sum(weights^2)) error instead of Poisson errors

In [7]:
data.plotOn(frame1, ROOT.RooFit.DataError(ROOT.RooAbsData.SumW2))
Out[7]:
<cppyy.gbl.RooPlot object at 0x812e080>

Remove horizontal error bars

In [8]:
data.plotOn(frame2, ROOT.RooFit.XErrorSize(0))
Out[8]:
<cppyy.gbl.RooPlot object at 0x81f2d30>

Blue markers and error bors

In [9]:
data.plotOn(frame3, ROOT.RooFit.MarkerColor(
    ROOT.kBlue), ROOT.RooFit.LineColor(ROOT.kBlue))
Out[9]:
<cppyy.gbl.RooPlot object at 0x81f31c0>

Filled bar chart

In [10]:
data.plotOn(
    frame4,
    ROOT.RooFit.DrawOption("B"),
    ROOT.RooFit.DataError(
        ROOT.RooAbsData.ErrorType(2)),
    ROOT.RooFit.XErrorSize(0),
    ROOT.RooFit.FillColor(
        ROOT.kGray))
Out[10]:
<cppyy.gbl.RooPlot object at 0x69247c0>

Function plotting styles

Change line color to red

In [11]:
gauss.plotOn(frame1, ROOT.RooFit.LineColor(ROOT.kRed))
Out[11]:
<cppyy.gbl.RooPlot object at 0x812e080>

Change line style to dashed

In [12]:
gauss.plotOn(frame2, ROOT.RooFit.LineStyle(ROOT.kDashed))
Out[12]:
<cppyy.gbl.RooPlot object at 0x81f2d30>

Filled shapes in green color

In [13]:
gauss.plotOn(frame3, ROOT.RooFit.DrawOption("F"),
             ROOT.RooFit.FillColor(ROOT.kOrange), ROOT.RooFit.MoveToBack())
Out[13]:
<cppyy.gbl.RooPlot object at 0x81f31c0>
In [14]:
gauss.plotOn(frame4, ROOT.RooFit.Range(-8, 3),
             ROOT.RooFit.LineColor(ROOT.kMagenta))

c = ROOT.TCanvas("rf107_plotstyles", "rf107_plotstyles", 800, 800)
c.Divide(2, 2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.6)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.6)
frame2.Draw()
c.cd(3)
ROOT.gPad.SetLeftMargin(0.15)
frame3.GetYaxis().SetTitleOffset(1.6)
frame3.Draw()
c.cd(4)
ROOT.gPad.SetLeftMargin(0.15)
frame4.GetYaxis().SetTitleOffset(1.6)
frame4.Draw()

c.SaveAs("rf107_plotstyles.png")
[#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]
Info in <TCanvas::Print>: png file rf107_plotstyles.png has been created

Draw all canvases

In [15]:
from ROOT import gROOT 
gROOT.GetListOfCanvases().Draw()