Data and categories: demonstration of real-discrete mapping functions

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 11:07 AM.

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

Define pdf in x, sample dataset in x

Define a dummy PDF in x

In [2]:
x = ROOT.RooRealVar("x", "x", 0, 10)
a = ROOT.RooArgusBG("a", "argus(x)", x, ROOT.RooFit.RooConst(10), ROOT.RooFit.RooConst(-1))

Generate a dummy dataset

In [3]:
data = a.generate({x}, 10000)

Create a threshold real -> cat function

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

In [4]:
xRegion = ROOT.RooThresholdCategory("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

In [5]:
xRegion.addThreshold(4.23, "Background")
xRegion.addThreshold(5.23, "SideBand")
xRegion.addThreshold(8.23, "Signal")
xRegion.addThreshold(9.23, "SideBand")

Use threshold function to plot data regions

Add values of threshold function to dataset so that it can be used as observable

In [6]:
<cppyy.gbl.RooCategory object at 0x9e42640>

Make plot of data in x

In [7]:
xframe = x.frame(Title="Demo of threshold and binning mapping functions")
<cppyy.gbl.RooPlot object at 0x9efcab0>

Use calculated category to select sideband data

In [8]:
data.plotOn(xframe, Cut="xRegion==xRegion::SideBand", MarkerColor="r", LineColor="r")
<cppyy.gbl.RooPlot object at 0x9efcab0>
[#1] INFO:Plotting -- RooTreeData::plotOn: plotting 2748 events out of 10000 total events

Create a binning real -> cat function

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, binning name and the bin number. If no binning name is specified the default binning is mapped

In [9]:
x.setBins(10, "coarse")
xBins = ROOT.RooBinningCategory("xBins", "coarse bins in x", x, "coarse")

Use binning function for tabulation and plotting

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

In [10]:
xbtable = data.table(xBins)
  Table xBins : aData
  | x_coarse_bin0 |  105 |
  | x_coarse_bin1 |  329 |
  | x_coarse_bin2 |  499 |
  | x_coarse_bin3 |  739 |
  | x_coarse_bin4 |  934 |
  | x_coarse_bin5 | 1218 |
  | x_coarse_bin6 | 1450 |
  | x_coarse_bin7 | 1675 |
  | x_coarse_bin8 | 1767 |
  | x_coarse_bin9 | 1284 |

Add values of xBins function to dataset so that it can be used as observable

In [11]:
xb = data.addColumn(xBins)

Define range "alt" as including bins 1,3,5,7,9

In [12]:
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

In [13]:
dataSel = data.reduce(CutRange="alt", EventRange=(0, 5000))
dataSel.plotOn(xframe, MarkerColor="g", LineColor="g")

c = ROOT.TCanvas("rf405_realtocatfuncs", "rf405_realtocatfuncs", 600, 600)

[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 2627 will supercede previous event count of 10000 for normalization of PDF projections
Info in <TCanvas::Print>: png file rf405_realtocatfuncs.png has been created

Draw all canvases

In [14]:
from ROOT import gROOT