Scikit-HEP project - welcome!
The Scikit-HEP project is a community-driven and community-oriented project with the aim of providing Particle Physics at large with an ecosystem for data analysis in Python. Read more →
Basics:
Manipulate arrays of complex data structures as easily as Numpy.
Units and constants in the HEP system of units.
Data manipulation and interoperability:
Easy conversions between different styles of expressions.
Interface between ROOT and NumPy.
Module for conveniently loading/saving ROOT files as pandas DataFrames.
Minimalist ROOT I/O in pure Python and Numpy.
Pythonic behaviours for non-I/O related ROOT classes.
Histogramming:
Convert between histogram representations
Python bindings for the C++14 Boost::Histogram library.
Hist is a analyst friendly front-end for boost-histogram, designed for Python 3.6+.
Versatile, high-performance histogram toolkit for Numpy.
Particles and decays:
Describe and convert particle decays between digital representations.
PDG particle data and identification codes.
Fitting:
GPU/OpenMP fitting in Python and C++.
MINUIT from Python - Fitting like a boss.
Cost function builder. For fitting distributions.
Scalable Pythonic fitting
Statistics:
Statistics tools and utilities.
pure-Python implementation of HistFactory models.
Interface to HEP libraries:
Interface between Pythia and NumPy.
Next generation Python bindings for HepMC3.
Interface between FastJet and NumPy.
Lightweight Python interface to read Les Houches Event (LHE) files.
Machine Learning:
Collection of tools and algorithms to enable conversion of HEP ML to mass usage model.
Visualization:
Plotting and styling helpers for matplotlib.
View Vega/Vega-Lite plots in your web browser from local or remote Python processes.
Miscellaneous:
A utility for building all PyPI supported binary wheels on all CI systems. See our guide for instructions.
CERN’s ROOT on Conda-Forge.
Toolset of interfaces and tools for Particle Physics. To become a metapackage.
Common package to provide example files (e.g., ROOT) for testing and developing packages against.
In some cases, the packages provide a bridge between different technologies and/or popular packages from the Python scientific software stack.