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rf405_realtocatfuncs.py
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1## \file
2## \ingroup tutorial_roofit
3## \notebook
4## Data and categories: demonstration of real-discrete mapping functions
5##
6## \macro_code
7##
8## \date February 2018
9## \authors Clemens Lange, Wouter Verkerke (C++ version)
10
11import ROOT
12
13
14# Define pdf in x, sample dataset in x
15# ------------------------------------------------------------------------
16
17# Define a dummy PDF in x
18x = ROOT.RooRealVar("x", "x", 0, 10)
19a = ROOT.RooArgusBG("a", "argus(x)", x, ROOT.RooFit.RooConst(
20 10), ROOT.RooFit.RooConst(-1))
21
22# Generate a dummy dataset
23data = a.generate(ROOT.RooArgSet(x), 10000)
24
25# Create a threshold real -> cat function
26# --------------------------------------------------------------------------
27
28# A RooThresholdCategory is a category function that maps regions in a real-valued
29# input observable observables to state names. At construction time a 'default'
30# state name must be specified to which all values of x are mapped that are not
31# otherwise assigned
32xRegion = ROOT.RooThresholdCategory(
33 "xRegion", "region of x", x, "Background")
34
35# Specify thresholds and state assignments one-by-one.
36# Each statement specifies that all values _below_ the given value
37# (and above any lower specified threshold) are mapped to the
38# category state with the given name
39#
40# Background | SideBand | Signal | SideBand | Background
41# 4.23 5.23 8.23 9.23
46
47# Use threshold function to plot data regions
48# ----------------------------------------------
49
50# Add values of threshold function to dataset so that it can be used as
51# observable
53
54# Make plot of data in x
55xframe = x.frame(ROOT.RooFit.Title(
56 "Demo of threshold and binning mapping functions"))
57data.plotOn(xframe)
58
59# Use calculated category to select sideband data
60data.plotOn(
61 xframe,
62 ROOT.RooFit.Cut("xRegion==xRegion::SideBand"),
63 ROOT.RooFit.MarkerColor(
64 ROOT.kRed),
65 ROOT.RooFit.LineColor(
66 ROOT.kRed))
67
68# Create a binning real -> cat function
69# ----------------------------------------------------------------------
70
71# A RooBinningCategory is a category function that maps bins of a (named) binning definition
72# in a real-valued input observable observables to state names. The state names are automatically
73# constructed from the variable name, binning name and the bin number. If no binning name
74# is specified the default binning is mapped
75
76x.setBins(10, "coarse")
77xBins = ROOT.RooBinningCategory("xBins", "coarse bins in x", x, "coarse")
78
79# Use binning function for tabulation and plotting
80# -----------------------------------------------------------------------------------------------
81
82# Print table of xBins state multiplicity. Note that xBins does not need to be an observable in data
83# it can be a function of observables in data as well
84xbtable = data.table(xBins)
85xbtable.Print("v")
86
87# Add values of xBins function to dataset so that it can be used as
88# observable
90
91# Define range "alt" as including bins 1,3,5,7,9
92xb.setRange(
93 "alt",
94 "x_coarse_bin1,x_coarse_bin3,x_coarse_bin5,x_coarse_bin7,x_coarse_bin9")
95
96# Construct subset of data matching range "alt" but only for the first
97# 5000 events and plot it on the frame
98dataSel = data.reduce(ROOT.RooFit.CutRange(
99 "alt"), ROOT.RooFit.EventRange(0, 5000))
100dataSel.plotOn(xframe, ROOT.RooFit.MarkerColor(ROOT.kGreen),
101 ROOT.RooFit.LineColor(ROOT.kGreen))
102
103c = ROOT.TCanvas("rf405_realtocatfuncs", "rf405_realtocatfuncs", 600, 600)
104xframe.SetMinimum(0.01)