ROOT analysis without ROOT

2018 February 2

The high energy physics community has used ROOT since the late 90's. It's a very large and monolithic set of libraries packaged up with a C++ interpreter called Cling. ROOT's strength (in my opinion) is its ability to serialize C++ objects to disk in binary format (you can read all about it here). This is perfect for HEP. We have classes for events as a whole, classes for hits in the detector, classes for whole reconstructed particles, etc. ROOT is great for storing this in an intuitive way, for example: particles live in containers owned by an event, hits live in a container owned by a track, whole reconstructed particles have an "Element Link" (a class to act as a pointer on disk) to a track associated with it, etc.

The problem

ROOT is a monolithic beast. It's a lot to carry around if all one needs to do is look at a few numbers stored in a ROOT file. It takes a while to build the entire library (and the packaged interpreter). The ROOT team distributes some binaries, and some package managers provide binaries or a way to build locally (e.g. the Arch User's Repository)... but for beginners and quick tasks that's not always a great solution1.

Then, to actually look at one's data a C++ "macro" has be be written (not a compiler preprocessor macro, this is something that is meant to be processed by ROOT's C++ interpreter, cling); or, one writes a proper executable, compile it, link it, and run it. This C++ code can be verbose and full of boilerplate (especially for reading ROOT files, where one has to connect C++ variables to ROOT "branches", one line at a time2).

The old solution

If a ROOT build was aware of a python installation during the build process, one can end up with PyROOT - ROOT's builtin python bindings. PyROOT basically allows writing C++ style code in python to talk to ROOT objects. That's not even the old solution I'm about to mention. root-numpy is what I'd consider the old solution -- it's a python library accelerated with Cython which turns the C style arrays stored in ROOT files into numpy arrays. It can also be installed with pip. Unfortunately, it requires a ROOT installation (because it requires import ROOT).

The solution

Now enter uproot. This awesome new library is pure Python and does not require a ROOT installation. We can interact with ROOT files is as easy as:

$ pip install uproot
$ python
>>> import uproot
>>> file ="myfile.root")

uproot has knowledge of ROOT's binary format implemented completely in python. No ROOT installation required.

In action

A few days ago I needed to throw together a quick histogram to explain a task to a colleague. The task required just a bit of information about some hits along a track. Given the structure of our data format stored in ROOT files, I would need to do something like this cascade of data retrieval (in kind of pseudo C++ code, this is very similar to ATLAS code, but with a few made up function names):

// some histogram object that we're going to fill with data
ns::Histogram fooHistogram(20, 0.0, 100.0);

for (const auto& event : eventContainer()) {
  // grab particle container
  const ns::ParticleContainer* particleContainer = event->getParticleContainer();
  // loop over particles
  for (const auto& particle : particleContainer) {
    // get link to track and make sure valid
    auto trackLink = getAssociatedTrackLink(particle);
    if (!trackLink.isValid()) {
    // dereference link to get actual object (the track pointer)
    const ns::Track* track = *trackLink;
    // get link to hit container and make sure valid
    auto hitContainerLink = getAssociatedHitsLink(track);
    if (!hitContainerLink.isValid()) {
    const ns::HitContainer* hitContainer = *hitContainerLink;
    // loop over container
    for (const auto& hit : hitContainer) {
      // get dynamically set properties of the hit and finally use them
      float hitFoo = hit->getAuxiliaryData<float>("foo");
      int hitBar = hit->getAuxiliaryData<int>("bar");
      if (hitBar == 42) {

fooHistogram.Draw(/* some options */);

In python, with uproot, if I know the naming convention for the hit container, I can simply write:

import uproot
import matplotlib.pyplot as plt

datatree ="myfile.root")["data"]
bar = datatree.array("")
foo = datatree.array("")
selected_foo = foo[bar == 42]

plt.hist(selected_foo, bins=20)

The python code is very simple and to the point, it's fast because the binary format is being read directly into numpy arrays3.

There is absolutely a place for the C++ code. If I wanted to apply a complex set of requirements to select different objects above the hit level (but based on hit properties), I need this structure. If we had a perfectly columnar data format (each event as a row in a table and a column for every feature), the hit information would be duplicated in multiple places because a low level hit may be associated with multiple higher level objects. Given our many petabytes of data, this is not feasible. This is where the "links" come in (the pointers on disk that tell a track where the associated hits are).

In this simple case, I didn't care about selecting hits based on any other information except another (simply) accessible hit property.

To wrap up: it's nice to have (a) an isolated python library for accessing data stored in ROOT and (b) options for selecting tools to analyze data.


Update summer 2019: ROOT is now available as a conda-forge package, providing a very easy installation method.


As of ROOT version 6.14 (released June 2018) there is a new feature allowing tree analysis using functional chains with the RDataFrame class.


In some special situations (e.g. reading a column of std::vector objects into a jagged array) the implementation is accelerated with numba (if installed).