Exercise 2: Getting started with PyROOT

Instructions

  • Learn differences between c++ and python based ROOT
  • Untar Ex2.tar
  • Read in root file: toy_sigbkg.root
  • Get tree: TreeS
  • Loop over entries and read in two variables var1 and var2
  • Make a 2D correlation plot of var1 and var2
  • Get the covariance of the the variables

Open a new python file for instance Ex2.py. Execute python Ex2.py.
As for every python program, you need to import the needed modules. Since the ROOT library is huge, it is advisable to specify the needed classes:

In [1]:
import os,sys
from ROOT import TH1D,TH2D,TFile,TTree,TCanvas

Welcome to ROOTaaS 6.06/04

Now read in the root file and tree.
Remember: No need to specify types in python (dynamic type) and no semicolons are need:

In [2]:
fFile = TFile("toy_sigbkg.root", "READ")
fTree = fFile.Get("TreeS")

Similarly, we can define a canvas and a 2D histogram:

In [3]:
fCanvas = TCanvas("c", "c", 600, 600)
fHist   = TH2D("var1var2", "", 20, -6, 6, 20, -6, 6)

Now, like in the C++ version, get the number of events in the tree (GetEntries()) and loop over the tree like this
for i in range(0, nEntries): in order to fill the histogram.
In contrast to C++ ROOT, you don't need to link the branches to variables, they are directly available via tree.var

In [4]:
nEntries = fTree.GetEntries()

for i in range(0, nEntries):
    fTree.GetEntry(i)
    fHist.Fill(fTree.var1, fTree.var2)
fHist.Print()

TH1.Print Name  = var1var2, Entries= 6000, Total sum= 6000

You can actually do this even faster in PyROOT, just loop over the object TTree:

In [5]:
fHist.Reset();
for i in fTree:
    fHist.Fill(fTree.var1, fTree.var2)
fHist.Print()

TH1.Print Name  = var1var2, Entries= 6000, Total sum= 6000

Wow, that is really much easier!
Ok, almost done, we can now draw the histogram and print it to a file:

In [6]:
fHist.Draw("COLZ")
fCanvas.Draw()
fCanvas.Print("toy_sigbkg_corr.eps")

Info in <TCanvas::Print>: eps file toy_sigbkg_corr.eps has been created

And finally we can get the covariance of the two variables, which quantifies how strongly the are correlated:

In [7]:
print fHist.GetCovariance()

0.385349730897