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rf405_realtocatfuncs.py
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rf405_realtocatfuncs.py
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#####################################
#
# 'DATA AND CATEGORIES' ROOT.RooFit tutorial macro #405
#
# Demonstration of real-.discrete mapping functions
#
#
#
# 07/2008 - Wouter Verkerke
#
# /
import ROOT
def rf405_realtocatfuncs():
# D e f i n e p d f i n x , a m p l e d a t a s e t i n x
# ------------------------------------------------------------------------
# Define a dummy PDF in x
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
data = a.generate(ROOT.RooArgSet(x), 10000)
# C r e a t e a t h r e s h o l d r e a l - > c a t f u n c t i o n
# --------------------------------------------------------------------------
# A ROOT.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")
# U s e t h r e s h o l d f u n c t i o n t o p l o t d a t a r e g i o n s
# -------------------------------------------------------------------------------------
# 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(ROOT.RooFit.Title(
"Demo of threshold and binning mapping functions"))
data.plotOn(xframe)
# Use calculated category to select sideband data
data.plotOn(xframe, ROOT.RooFit.Cut("xRegion==xRegion::SideBand"),
ROOT.RooFit.MarkerColor(ROOT.kRed), ROOT.RooFit.LineColor(ROOT.kRed))
# C r e a t e a b i n n i n g r e a l - > c a t f u n c t i o n
# ----------------------------------------------------------------------
# A ROOT.RooBinningCategory is a category function that maps bins of a (named) binning definition
# in a real-valued input observable observables to state names. ROOT.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")
# U s e b i n n i n g f u n c t i o n f o r t a b u l a t i o n a n d p l o t t i n g
# -----------------------------------------------------------------------------------------------
# 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(ROOT.RooFit.CutRange(
"alt"), ROOT.RooFit.EventRange(0, 5000))
dataSel.plotOn(xframe, ROOT.RooFit.MarkerColor(ROOT.kGreen),
ROOT.RooFit.LineColor(ROOT.kGreen))
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")
if __name__ == "__main__":
rf405_realtocatfuncs()