ROOT   Reference Guide
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rf104_classfactory.py File Reference

## Detailed Description

Basic functionality: the class factory for functions and pdfs

NOTE: This demo uses code that is generated by the macro, which can be compiled on the fly (set to MyPdfV3 below). To use MyPdfV1 or MyPdfV2, adjust lines below accordingly.

import ROOT
# Write class skeleton code
# --------------------------------------------------
# Write skeleton pdf class with variable x,a,b
# To use this class,
# - Edit the file MyPdfV1.cxx and implement the evaluate() method in terms of x,a and b
# - Compile and link class with '.x MyPdfV1.cxx+'
#
ROOT.RooClassFactory.makePdf("MyPdfV1", "x,A,B")
# With added initial value expression
# ---------------------------------------------------------------------
# Write skeleton pdf class with variable x,a,b and given formula expression
# To use this class,
# - Compile and link class with '.x MyPdfV2.cxx+'
#
ROOT.RooClassFactory.makePdf("MyPdfV2", "x,A,B", "", "A*fabs(x)+pow(x-B,2)")
# With added analytical integral expression
# ---------------------------------------------------------------------------------
# Write skeleton pdf class with variable x,a,b, given formula expression _and_
# given expression for analytical integral over x
# To use this class,
# - Compile and link class with '.x MyPdfV3.cxx+'
#
ROOT.RooClassFactory.makePdf(
"MyPdfV3",
"x,A,B",
"",
"A*fabs(x)+pow(x-B,2)",
True,
False,
"x:(A/2)*(pow(x.max(rangeName),2)+pow(x.min(rangeName),2))+(1./3)*(pow(x.max(rangeName)-B,3)-pow(x.min(rangeName)-B,3))",
)
# Use instance of created class
# ---------------------------------------------------------
# Compile MyPdfV3 class
ROOT.gROOT.ProcessLineSync(".x MyPdfV3.cxx+")
# Creat instance of MyPdfV3 class
a = ROOT.RooRealVar("a", "a", 1)
b = ROOT.RooRealVar("b", "b", 2, -10, 10)
y = ROOT.RooRealVar("y", "y", -10, 10)
pdf = ROOT.MyPdfV3("pdf", "pdf", y, a, b)
# Generate toy data from pdf and plot data and pdf on frame
frame1 = y.frame(Title="Compiled class MyPdfV3")
data = pdf.generate({y}, 1000)
pdf.fitTo(data, PrintLevel=-1)
data.plotOn(frame1)
pdf.plotOn(frame1)
# /
# C o m p i l e d v e r s i o n o f e x a m p l e r f 1 0 3 #
# /
# Declare observable x
x = ROOT.RooRealVar("x", "x", -20, 20)
# The ROOT.RooClassFactory.makePdfInstance() function performs code writing, compiling, linking
# and object instantiation in one go and can serve as a straight
# replacement of ROOT.RooGenericPdf
alpha = ROOT.RooRealVar("alpha", "alpha", 5, 0.1, 10)
genpdf = ROOT.RooClassFactory.makePdfInstance("GenPdf", "(1+0.1*fabs(x)+sin(sqrt(fabs(x*alpha+0.1))))", [x, alpha])
# Generate a toy dataset from the interpreted pdf
data2 = genpdf.generate({x}, 50000)
# Fit the interpreted pdf to the generated data
genpdf.fitTo(data2, PrintLevel=-1)
# Make a plot of the data and the pdf overlaid
frame2 = x.frame(Title="Compiled version of pdf of rf103")
data2.plotOn(frame2)
genpdf.plotOn(frame2)
# Draw all frames on a canvas
c = ROOT.TCanvas("rf104_classfactory", "rf104_classfactory", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.4)
frame2.Draw()
c.SaveAs("rf104_classfactory.png")
(MyPdfV3) An instance of MyPdfV3.
[#1] INFO:Fitting -- RooAbsPdf::fitTo(pdf_over_pdf_Int[y]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx2
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_pdf_over_pdf_Int[y]_pdfData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GenPdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GenPdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:Fitting -- RooAbsPdf::fitTo(GenPdf_over_GenPdf_Int[x]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_GenPdf_over_GenPdf_Int[x]_GenPdfData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GenPdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GenPdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
Date
February 2018

Definition in file rf104_classfactory.py.