rf405_realtocatfuncs.py

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## \file
## \ingroup tutorial_roofit
## \notebook
## Data and categories: demonstration of real-discrete mapping functions
##
## \macro_image
## \macro_code
## \macro_output
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)
 
import ROOT
 
 
# Define pdf in x, sample dataset in x
# ------------------------------------------------------------------------
 
# Define a dummy PDF in x
x = ROOT.RooRealVar("x", "x", 0, 10)
a = ROOT.RooArgusBG("a", "argus(x)", x, 10.0, -1.0)
 
# Generate a dummy dataset
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
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
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
data.addColumn(xRegion)
 
# Make plot of data in x
xframe = x.frame(Title="Demo of threshold and binning mapping functions")
data.plotOn(xframe)
 
# Use calculated category to select sideband data
data.plotOn(xframe, Cut="xRegion==xRegion::SideBand", MarkerColor="r", LineColor="r")
 
# 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
 
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
xbtable = data.table(xBins)
xbtable.Print("v")
 
# Add values of xBins function to dataset so that it can be used as
# observable
xb = data.addColumn(xBins)
 
# Define range "alt" as including bins 1,3,5,7,9
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
dataSel = data.reduce(CutRange="alt", EventRange=(0, 5000))
dataSel.plotOn(xframe, MarkerColor="g", LineColor="g")
 
c = ROOT.TCanvas("rf405_realtocatfuncs", "rf405_realtocatfuncs", 600, 600)
xframe.SetMinimum(0.01)
ROOT.gPad.SetLeftMargin(0.15)
xframe.GetYaxis().SetTitleOffset(1.4)
xframe.Draw()
 
c.SaveAs("rf405_realtocatfuncs.png")