doc/v624/rf110__normintegration_8py.html

from __future__ import print_function

import ROOT


# Set up model

# ---------------------


# Create observables x,y

x = ROOT.RooRealVar("x", "x", -10, 10)


# Create pdf gaussx(x,-2,3)

gx = ROOT.RooGaussian(

    "gx", "gx", x, ROOT.RooFit.RooConst(-2), ROOT.RooFit.RooConst(3))


# Retrieve raw & normalized values of RooFit pdfs

# --------------------------------------------------------------------------------------------------


# Return 'raw' unnormalized value of gx

print("gx = ", gx.getVal())


# Return value of gx normalized over x in range [-10,10]

nset = ROOT.RooArgSet(x)

print("gx_Norm[x] = ", gx.getVal(nset))


# Create object representing integral over gx

# which is used to calculate  gx_Norm[x] == gx / gx_Int[x]

igx = gx.createIntegral(ROOT.RooArgSet(x))

print("gx_Int[x] = ", igx.getVal())


# Integrate normalized pdf over subrange

# ----------------------------------------------------------------------------


# Define a range named "signal" in x from -5,5

x.setRange("signal", -5, 5)


# Create an integral of gx_Norm[x] over x in range "signal"

# ROOT.This is the fraction of of pdf gx_Norm[x] which is in the

# range named "signal"

xset = ROOT.RooArgSet(x)

igx_sig = gx.createIntegral(xset, ROOT.RooFit.NormSet(xset), ROOT.RooFit.Range("signal"))

print("gx_Int[x|signal]_Norm[x] = ", igx_sig.getVal())


# Construct cumulative distribution function from pdf

# -----------------------------------------------------------------------------------------------------


# Create the cumulative distribution function of gx

# i.e. calculate Int[-10,x] gx(x') dx'

gx_cdf = gx.createCdf(ROOT.RooArgSet(x))


# Plot cdf of gx versus x

frame = x.frame(ROOT.RooFit.Title("cdf of Gaussian pdf"))

gx_cdf.plotOn(frame)


# Draw plot on canvas

c = ROOT.TCanvas("rf110_normintegration",

                 "rf110_normintegration", 600, 600)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.6)

frame.Draw()


c.SaveAs("rf110_normintegration.png")