Basic Operations

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• How can I read common nuclear physics data file formats?

Objectives

• Import a Python library to read ROOT data files.

• Plot simple graphs from ROOT data files.

Reading ROOT files with python

The majority of nuclear, hadronic, and particle physics data files are in the ROOT file format. At some point or other, reading a ROOT file is a requirement in any analysis chain. In this episode we explore how to read ROOT files in the Jupyter notebook using (only) python tools.

We will be using the package uproot to read ROOT files. Since uproot is a quickly evolving package, we refer to the documentations for the latest.

PyROOT vs. uproot

There is a python interface included in most ROOT installation: PyROOT. This package solves a different problem than what we are aiming for here. PyROOT provides bindings from python to the ROOT C++ functions. It allows therefore not only the reading of ROOT files but access to any ROOT function.

uproot only aims to enable the reading of ROOT files in python, and assumes that there are sufficient tools available within the python ecosystem to do the rest of the analysis.

Let’s start by opening a new python 3 notebook and reading a ROOT file using uproot. We will use the notebook “read-root-files.ipynb” that you uploaded in the previous episode of this lesson.

Load a ROOT file using uproot, convert into pandas and plot using matplotlib

The notebook “read-root-files.ipynb” reads in a ROOT file located at

/lustre/expphy/cache/hallc/qweak/rootfiles/pass5b/QwPass5b_18110.000.trees.root

As you may surmise, this is one of the ROOT files of the Qweak experiments. It contains about 5 minutes of data (from a 2-year long experiment).

On the Jupyter server we cannot use the convenient shortcut /cache/ to access the MSS cache storage. Instead we use the full path /lustre/expphy/cache/.

1. Execute the entire notebook and observe the results
2. The same file contains another tree, Mps_Tree, with slightly differently named branches. Modify the notebook to load this different tree in the same input ROOT file.
3. Use uproot’s function keys() to list the names of the branches in this different tree. Find the ones that are most likely equivalent, i.e. also beam current monitor (BCM) outputs.
4. Recreate the correlation plots for the MPS quantities.
5. What could explain the feature in the lag plot?
6. Can you remove the offending entries from the pandas dataframe?

Key Points

• Use the uproot library to read ROOT data files in Python.

• Use arrays() to access fields in ROOT data files as numpy arrays.

• Use the pyplot library from matplotlib for creating simple visualizations.