rf405_realtocatfuncs.py

## \ingroup tutorial_roofit_main

## \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")