Using ROOT with Python

Overview

Teaching: min
Exercises: min

Questions

How can I use ROOT with Python?

Objectives

Learn about the basics of ROOT with Python

Python

ROOT provides the Python bindings called PyROOT. PyROOT is not just ROOT from Python, but a full-featured interface to call C++ libraries in a pythonic way. This lets you import the ROOT module from Python and makes all features dynamically available. Let’s rewrite the C++ example from above and put the code in the file myScript.py!

import ROOT

rfile = ROOT.TFile.Open('https://root.cern/files/tmva_class_example.root')
for key in rfile.GetListOfKeys():
    name = key.GetName()
    entries = rfile.Get(name).GetEntries()
    print('{} : {}'.format(name, entries))

Calling the Python script works as expected:

$ python myScript.py
TreeS : 6000
TreeB : 6000

But PyROOT can do much more for you than simply providing access to C++ libraries from Python. You can also inject efficient C++ code into your Python program to speed up potentially slow parts of your program!

import ROOT

ROOT.gInterpreter.Declare('''
int my_heavy_computation(std::string x) {
    // heavy computation goes here
    return x.length();
}
''')

# Functions and object made available via the interpreter are accessible from
# the ROOT module
y = ROOT.my_heavy_computation("the ultimate answer to life and everything")
print(y) # Guess the result!

Basic histogramming, fitting and plotting

The following script uses basic features from ROOT, which are used commonly in day-to-day work with ROOT. You can investigate the typical workflow to create histograms with TH1F, fit a function to the data with TF1 and produce an accurate visualization with TCanvas and others. Below, you can see the output of the fit to the data with the measured parameters.

import ROOT
import numpy as np

# Make global style changes
ROOT.gStyle.SetOptStat(0) # Disable the statistics box
ROOT.gStyle.SetTextFont(42)

# Create a canvas
c = ROOT.TCanvas('c', 'my canvas', 800, 600)

# Create a histogram with some dummy data and draw it
data = np.random.randn(1000).astype(np.float32)
h = ROOT.TH1F('h', ';Gaussian process; N_{Events}', 30, -3, 3)
for x in data: h.Fill(x)
h.Draw('E')

# Fit a Gaussian function to the data
f = ROOT.TF1('f', '[0] * exp(-0.5 * ((x - [1]) / [2])**2)')
f.SetParameters(100, 0, 1)
h.Fit(f)

# Let's add some CMS style headline
label = ROOT.TLatex()
label.SetNDC(True)
label.SetTextSize(0.040)
label.DrawLatex(0.10, 0.92, '#bf{CMS Dummy Data}')
label.DrawLatex(0.58, 0.92, '#sqrt{s} = 13 TeV, L_{int} = 100 fb^{-1}')

# Save as png file and show interactively
c.SaveAs('dummy_data.png')
c.Draw()

 FCN=30.2937 FROM MIGRAD    STATUS=CONVERGED      67 CALLS          68 TOTAL
                     EDM=1.34686e-08    STRATEGY= 1      ERROR MATRIX ACCURATE
  EXT PARAMETER                                   STEP         FIRST
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE
   1  p0           8.09397e+01   3.19887e+00   7.10307e-03  -3.40988e-05
   2  p1          -3.46483e-03   3.10501e-02   8.47265e-05  -2.30742e-03
   3  p2           9.56532e-01   2.24141e-02   4.97399e-05   2.58872e-03
Info in <TCanvas::Print>: file dummy_data.png has been created

Try it by yourself!

Run the example code by yourself! In case the execution ends without displaying the plot on screen, you can run the script in interpreted mode with python -i your_script.py. That will keep the process alive after the plot is displayed.

A guide to such advanced features of PyROOT can be found in the official manual at https://root.cern/manual/python. Feel free to investigate!

Interoperability with NumPy arrays

There are many reasons, for example machine learning applications, to want to export your data in Python to NumPy arrays. This is easily possible with ROOT and is part of RDataFrame. The code snippets below show you how to do this conversion and how to move the data to typical tools in the Python ecosystem, e.g., numpy and pandas.

numpy and pandas

Have you installed numpy and pandas or are you on a system which has them available? Normally, you can just run pip install --user numpy pandas to install missing packages! Another option is searching in your system package manager, they are typically available on all platforms.

Convert data in ROOT files to numpy arrays

The conversion feature is attached to the class RDataFrame. We will not introduce you here to this way to process data with ROOT because the following section is dedicated to RDataFrame. For now, just keep in mind that you call AsNumpy! The data is returned as a dictionary of one-dimensional numpy arrays.

# Read out the data as a dictionary of numpy arrays
import ROOT
df = ROOT.RDataFrame('TreeS', 'https://root.cern/files/tmva_class_example.root')
columns = ['var1', 'var2', 'var3', 'var4']
data = df.AsNumpy(columns)
print('var1: {}'.format(data['var1']))

var1: [-1.1436108   2.1434433  -0.44391322 ...  0.37746507 -2.072639 -0.09141494]

Move the data to numpy or pandas

The data can be passed naturally to any method in the Python ecosystem which processes numpy arrays. Below is an example that computes the mean of each column.

# Apply numpy methods
import numpy as np
print('Means: {}'.format([np.mean(data[c]).item() for c in columns]))

Means: [0.18244409561157227, 0.28425973653793335, 0.3789360225200653, 0.7712161540985107]

Another interesting usecase is moving the dataset directly to a pandas dataframe. You can use the output of AsNumpy directly as input to its constructor.

# Convert to a pandas dataframe
import pandas
pdf = pandas.DataFrame(data)
print(pdf)

          var1      var2      var3      var4
  -1.143611 -0.822373 -0.495426 -0.629427
   2.143443 -0.018923  0.267030  1.267493
  -0.443913  0.486827  0.139535  0.611483
   0.281100 -0.347094 -0.240525  0.347208
   0.604006  0.151232  0.964091  1.227711
...        ...       ...       ...       ...
-0.040650 -0.154212 -0.097715  0.440331
0.099931 -1.183759  0.034616  0.644502
0.377465 -0.030945  1.166082  0.728614
-2.072639 -0.635586 -0.747371 -1.285679
-0.091415  0.221271  0.569032  1.386130

[6000 rows x 4 columns]

Try it by yourself!

The statements are very short, you can just copy paste them into the Python prompt. Feel free to investigate what you can do with AsNumpy! Further information can be found here.

ROOT in Jupyter notebooks

ROOT provides a deep integration with Jupyter notebooks. You can start a Jupyter notebook server including ROOT features with the following command:

root --notebook

Alternatively, you can go to https://swan.cern.ch, which provides Jupyter notebooks integrated with CERN’s cloud storage as a web service. Note that you may have to visit https://cernbox.cern.ch first at least once with your user account to create your CERNBox space!

Python kernel

Jupyter is often use to edit Python code interactively. By creating a new notebook with a Python kernel, you will see something similar to the screenshot below and you can work interactively with Python in the browser!

C++ kernel

ROOT provides a Jupyter C++ kernel, which behaves similarly to the Python kernel but for C++! Similar to the ROOT prompt, you can work interactively with C++ in the notebook. Just select the C++ kernel in the drop-down menu!

JSROOT

Another feature of ROOT is the %jsroot on magic, which enables ROOT’s JavaScript integration! This allows you to interact with the visualization such as you are used to it from the interactive graphics in the Python prompt.

Because it’s JavaScript, we can also embed these plots easily in any website. You can find an interactive version of the plot from the top of this section at the bottom of the page. For example, you can zoom in, add grid lines or get detailed information about the data points, right here!

Try it by yourself!

Either run Jupyter locally via root --notebook or go to https://swan.cern.ch to try ROOT in a Jupyter notebook!

More useful features

ROOT is made for HEP analysis and contains many other features that are useful in typical tasks, for example:

TEfficiency, to handle histograms representing efficiencies and their uncertainties
THStack, to stack histograms
TRatioPlot, to create ratio plots with the histograms on the top and the ratio with the correct uncertainties on the bottom
And many more!

Try using ROOT with interactive C++, compiled C++ and Python!

Make yourself familiar with the different ways you can run an analysis with ROOT!

Key Points

ROOT can be used with python

PyROOT lets you use C++ from Python but offers many more advanced features to speed up your analysis in Python. Details about the dynamic Python bindings provided by PyROOT can be found on https://root.cern/manual/python.

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Using ROOT with Python

Overview

Python

Basic histogramming, fitting and plotting

Try it by yourself!

Interoperability with NumPy arrays

numpy and pandas

Convert data in ROOT files to numpy arrays

Move the data to numpy or pandas

Try it by yourself!

ROOT in Jupyter notebooks

Python kernel

C++ kernel

JSROOT

Try it by yourself!

More useful features

Try using ROOT with interactive C++, compiled C++ and Python!

Key Points

previous episode

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