alexander-held / pyhep-resources

Python libraries of interest to particle physicists. This is meant for educational purposes.

Python Libraries of Interest to Particle Physics

Python libraries of interest to particle physicists. This is meant to be a living document. Therefore, if you have suggestions, click the edit button then make a pull request with your proposed change(s). This is meant to focus on Python resources for new-ish users; if you just want to look for different libraries for a particular purpose, please visit the Python section of the awesome-hep list.

You are more than welcome to join the HSF/PyHEP Gitter channel and contribute to the informal discussions there. The channel HSF/PyHEP-newcomers Gitter channel specifically targets, well, newcomers.

See the HSF PyHEP Working Group page for a full list of Gitter channels and resources.

New to Python

If you are new to Python, the following contains general information on using Python in Science.

Name	Use
Software Carpentry Python Lesson	Lesson aimed at people who have never used Python before.
Scipy tutorials	You'll want the beginner courses, the intermediate/advanced courses are actually quite advanced. Setup instructions are linked on the page, videos are here
Dive into Python 3	Very useful for learning python, though it's a bit old and doesn't cover any of the scientific python stuff you really need.
Code academy
Many EdX and Coursera courses	Often introductory CS courses which can teach other useful skills (algorithms and datastructures)
The python docs
Level Up Your Python	Advanced topics: Debuggers, static typing, logging, decorators, generators and more as interactive Jupyter book

Otherwise, just google python + description of problem, usually answer is on stackoverflow.

Youtube channels with talks / tutorials:

Pycon, e.g.:

• https://www.youtube.com/channel/UCrJhliKNQ8g0qoE_zvL8eVg (2017)
• https://www.youtube.com/channel/UCwTD5zJbsQGJN75MwbykYNw (2016)
• https://www.youtube.com/channel/UCgxzjK6GuOHVKR_08TT4hJQ (2015)

Some more advanced talks of interested:

• Ned Batchelder, "Facts and Myths about Python names and values"
• David Baumgold, "Advanced Git"
• David Beazley, "Discovering python"
• Thomas Ballinger, "Finding closure with closures"

Getting Python

Name	Use
Conda	Anaconda packages most scientific Python libraries while also living purely in user space. Therefore, you don't need special permissions to setup. Anaconda is a metapackage of 100 or so scientific Python packages for Conda.
pipenv	Very slick all-in-one combination of virtual environments and package installation, can manage Python installs too.
ripa	ripa solves the packaging issue by letting you install packages (or requirements.txt) where priority given to conda channels, otherwise fetches from PyPI. (Works but rough)

Scientific Python Stack

The packages that are used in Physics and/or data science within Python grew somewhat organically before forming the current ecosystem. Detailed information on what exists can be found here, but we will summarize here.

Name	Use
jupyter notebook	Main one way of doing interactive and/or exploratory analysis.
numpy	Array and matrix operations (including math operations) at C speeds.
pandas	A very elegant way to work with tabular data (i.e. ntuples) with in memory calculations. Especially good at time series.
xarray	Extension of pandas to N-Dim structures.
h5py	Simple numpy to HDF5 bindings (backend for Keras saved models).
scipy	Various scientific routines like minimization.
matplotlib	Main Python plotting library. Start from matplotlib gallery then adapt to your application.
scikit-learn	Very easy to use machine learning routines with great examples.

Visualisation:

Name	Use
matplotlib	Main Python plotting library. Start from matplotlib gallery then adapt to your application.
seaborn	Easier to use plotting library with some statistical routines. Builds on matplotlib, but annoying to customize.
vegascope	View Vega/Vega-Lite plots in your web browser from local or remote Python processes.

Machine learning:

Name	Use
scikit-learn	Popular package. Very easy to use ML routines, with great examples.
tensorflow	By Google, for deep neural networks and more.
pytorch	Deep learning framework for fast, flexible experimentation with dynamic computational graphs.
keras	Higher level neural network interfaces.

General information through talks that may be useful on PyData (various conferences each year):

• https://www.youtube.com/user/PyDataTV and other per-conference channels
• Scipy conferences
• Enthought
• Continuum Analytics

Data manipulation

Name	Use
boost-histogram	Python bindings for the C++14 Boost::Histogram library.
hist	Analyst-friendly front-end for boost-histogram.

Statistical analysis and fitting

Name	Use
iminuit	Jupyter-friendly Python frontend to the MINUIT2 C++ library.
uncertainties	Calculations with numbers with uncertainties. It also yields the derivatives of any expression.
pyhf	Pure python implementation of HistFactory specification with auto-diff enabled backends in tensorflow, pytorch, and MXNet.
hepstats	HEP statistics tools and utilities.
zfit	Scalable pythonic fitting.

Particle Physics packages

Name	Use
DecayLanguage	Describe, manipulate and display particle decays.
hepstats	HEP statistics tools and utilities.
hepunits	Units and constants in the HEP system of units.
Particle	PDG particle data and identification codes.
numpythia	Interface between PYTHIA and NumPy.
pylhe	Interface to read Les Houches Event (LHE) files.
pyjet	Interface between FastJet and NumPy.
vector	Arrays of 2D, 3D, and Lorentz vectors.

ROOT and interoperability with ROOT

For many particle physics experiments, a lot of data is stored within ROOT files. This means at very least one must have the ability to read ROOT files. ROOT also serves as a tool suite designed to solve many computational problems encountered in HEP, which means that one may want to access some of this tool suite. The following packages below are worth knowing for these situations:

Package name	Use	Pro	Con	Further information
ostap	User-friendly & more intuitive interface to(Py)ROOT	Many decorations to ROOT classes	Requires C++ code compilation
uproot	Native Python ROOT I/O	Easy to install, fast, no dependence on C++ ROOT	Although it can read all ROOT files, can only write ROOT files with specific objects.
Conda-Forge ROOT	Using ROOT within Anaconda	Full-featured ROOT and PyROOT on Linux and macOS
PyROOT	Official ROOT Python bindings	Good support and many examples	Raw C++ wrapping results in weird Python code (improved in 6.22)
rootpy	Pythonic ROOT access	More logical for people who know Python	Abandoned, mostly replaced by ROOT 6.22+ new bindings or the uproot family.	Repository
alphatwirl	Summerizing ROOT data into categorical data as Pandas' data frames	Small output size. Easy one-function interface with qtwirl	Not for data type conversion
pyhf	statistical analysis / fitting	pure python implementation of HistFactory specification with auto-diff enabled backends in tensorflow, pytorch, and MXNet	not yet interoperable with ROOT-based RooFit models

Jupyter extensions

Jupyter has a wide ecosystem of extensions that can be used to extend the functionality. Some useful extensions for HEP data analysis are summarised here.

Name	Use
nbdime	Simplifies diffing and merging of jupyter notebooks that are stored in version control.
jupytext	Splits notebooks into a .ipynb and .py file for easier version control and to allow them to be run as scripts idependently of jupyter.

Speeding up code

Often, it is not needed anymore to write C++/C routines that get wrapped since there are other ways to speed up your Python code. Namely:

Name	Use
numba	Tight loops are often the slow part of Python code, where this JIT compiles them!
Pythran	Whole scripts
numpy	Expressing your code as array options means you get native-C speeds per sub-expression.
jax	Allows compiling and running NumPy operations on accelerated hardware such as GPU's. Also offers a JIT compiler (less sophisticated than Numba), automatic analytic differentiation (gradient) of Python functions and efficient vectorization. Was developed with the aim of being able to develop efficient machine learning algorithms "from scratch" with NumPy-like code.
NumExpr	Single pass "mapper" operations (one input → one output).

Binding C/C++ to Python

Before you read this, realize that this is for existing C++ code. If you want to write new C/C++ code for speed, see section above.

Python entered into the particle physics ecosystem since it was useful as a 'glue lanaguage'. This means that you can get multiple softwares in different languages to work with one another. Given the large ecosystem of Python packages in the last decade, this is less common now. However, the situation still does arise that you want to call some existing C/C++ code from Python.

Please be aware that wrapping C code is significantly easier than wrapping C++ code due to details of how function names get garbled in libraries within C++; but the tools below can make wrapping C++ easy as well.

At present, the best summary of how to bind code in HEP applications comes from Henry Schreiner in a 2018 PyHEP talk.

Package name	Use	Pro	Con	Further information
pybind11	Wrapping existing C++ codes	Small elegant package, simple build.	Young but gaining populatiry quickly.	Henry's slides
Cython	Wrapping C++ code	Widely used, freely mixing Python and C++.	Mix of C and Python is a new language, incomplete coverage of C++.
SWIG	Wrapping C++ code	Widely used, multiple languages.	Have to write wrapper file, harder to customize, and development is slow/dated.
Boost::Python	Wrapping C++ code	Widely used.	Giant dependency since Boost does many other things, uses "jam" to build.

Miscellaneous

Packages that do not easily fit in any of the above topics.

Name	Use
comparxiv	Compare 2 versions of an arXiv preprint with a single command

Tutorials

See tutorials here and other resources collected by IML HEP-ML Resources.

Experimental codes

Stealing code from other physicists is its own sign of flattery. Codes that are abandoned more than two years will get struck through.:

Name	Collaboration	Use	Further information	Date added to list