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..
future dev note: The doc site makes a copy of this README and inserts it into
the main intro page. Since the main intro page includes a top-level section
(underlined by `=), we start sections here with a second-level section (-).
Columnar Object Framework For Effective Analysis ------------------------------------------------
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Basic tools and wrappers for enabling not-too-alien syntax when running columnar Collider HEP analysis.
coffea is a prototype package for pulling together all the typical needs
of a high-energy collider physics (HEP) experiment analysis using the scientific
Python ecosystem. It makes use of uproot _
and awkward-array _ to provide an
array-based syntax for manipulating HEP event data in an efficient and numpythonic
way. There are sub-packages that implement histogramming, plotting, and look-up
table functionalities that are needed to convey scientific insight, apply transformations
to data, and correct for discrepancies in Monte Carlo simulations compared to data.
coffea also supplies facilities for horizontally scaling an analysis in order to reduce
time-to-insight in a way that is largely independent of the resource the analysis
is being executed on. By making use of modern big-data technologies like
parsl _, Dask _ ,
and TaskVine _,
it is possible with coffea to scale a HEP analysis from a testing
on a laptop to: a large multi-core server, computing clusters, and super-computers without
the need to alter or otherwise adapt the analysis code itself.
coffea is a HEP community project collaborating with iris-hep _
and is currently a prototype. We welcome input to improve its quality as we progress towards
a sensible refactorization into the scientific python ecosystem and a first release. Please
feel free to contribute at our GitHub repo _!
Installation ^^^^^^^^^^^^
Install coffea like any other Python package:
.. code-block:: bash
pip install coffea
or similar for your environment tooling (e.g. uv pip install coffea or pixi add coffea).
For more details, see the Installing coffea _ section of the documentation.
Strict dependencies ^^^^^^^^^^^^^^^^^^^
- Python
__ (3.10+)
- numpy
__ (1.22+); - uproot
__ for interacting with ROOT files and handling their data transparently; - awkward-array
__ to manipulate complex-structured columnar data, such as jagged arrays; - numba
__ just-in-time compilation of python functions; - scipy
__ for many statistical functions; - matplotlib
__ as a plotting backend; - and other utility packages, as enumerated in
pyproject.toml.
Citation
^^^^^^^^
If you would like to cite this code in your work, you can use the zenodo DOI indicated in CITATION.cff, or the latest DOI
- "N. Smith et al 2020 EPJ Web Conf. 245 06012"
- "L. Gray et al 2023 J. Phys.: Conf. Ser. 2438 012033"