doc/v626/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 = {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({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 = {x}

igx_sig = gx.createIntegral(xset, NormSet=xset, 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({x})


# Plot cdf of gx versus x

frame = x.frame(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")