rf107_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 Sunday, November 27, 2022 at 11:06 AM.

In [1]:
import ROOT
Welcome to JupyROOT 6.27/01

Set up model

Create observables

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

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({x}, 100)

Fit pdf to data

In [5]:
gauss.fitTo(data)
Out[5]:
<cppyy.gbl.RooFitResult object at 0x(nil)>
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: 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:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization

Make plot frames

Make four plot frames to demonstrate various plotting features

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

Data plotting styles

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

In [7]:
data.plotOn(frame1, DataError="SumW2")
Out[7]:
<cppyy.gbl.RooPlot object at 0xa19df00>

Remove horizontal error bars

In [8]:
data.plotOn(frame2, XErrorSize=0)
Out[8]:
<cppyy.gbl.RooPlot object at 0x9ffaca0>

Blue markers and error bors

In [9]:
data.plotOn(frame3, MarkerColor="b", LineColor="b")
Out[9]:
<cppyy.gbl.RooPlot object at 0x8ea8f20>

Filled bar chart

In [10]:
data.plotOn(frame4, DrawOption="B", DataError=None, XErrorSize=0, FillColor="kGray")
Out[10]:
<cppyy.gbl.RooPlot object at 0xa1c00c0>

Function plotting styles

Change line color to red

In [11]:
gauss.plotOn(frame1, LineColor="r")
Out[11]:
<cppyy.gbl.RooPlot object at 0xa19df00>

Change line style to dashed

In [12]:
gauss.plotOn(frame2, LineStyle="--")
Out[12]:
<cppyy.gbl.RooPlot object at 0x9ffaca0>

Filled shapes in green color

In [13]:
gauss.plotOn(frame3, MoveToBack=True, DrawOption="F", FillColor="kOrange")
Out[13]:
<cppyy.gbl.RooPlot object at 0x8ea8f20>
In [14]:
gauss.plotOn(frame4, Range=(-8, 3), LineColor="m")

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()