Metadata-Version: 2.4
Name: eoshep
Version: 1.0.21.dev1405
Summary: EOS -- A HEP program for Flavor Observables
Home-page: https://eos.github.io/
Author: Danny van Dyk and others
Author-email: danny.van.dyk@gmail.com
License: GPLv2
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Physics
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Requires-Dist: numpy>=1.13
Requires-Dist: pypandoc
Requires-Dist: pypmc>=1.1.4
Requires-Dist: pyyaml
Requires-Dist: scipy
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Dynamic: author
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![EOS logo](https://eos.github.io/images/github-eos-logo.png)

EOS - A software for Flavor Physics Phenomenology
=================================================

EOS is a software package that addresses several use cases in the field of
high-energy flavor physics:

1. [theory predictions of and uncertainty estimation for flavor observables](https://eoshep.org/doc/user-guide/predictions.html)
   within the Standard Model or within the Weak Effective Theory;
2. [Bayesian parameter inference](https://eoshep.org/doc/user-guide/inference.html)
   from both experimental and theoretical constraints; and
3. [Monte Carlo simulation of pseudo events](https://eoshep.org/doc/user-guide/simulation.html) for flavor processes.

An up-to-date list of publications that use EOS can be found [here](https://collaboration.eoshep.org/publications.html).

EOS is written in C++20 and designed to be used through its Python 3 interface,
ideally within a Jupyter notebook environment.
It depends on as a small set of external software:

- the GNU Scientific Library (libgsl),
- a subset of the BOOST C++ libraries,
- the Python 3 interpreter.

For details on these dependencies we refer to the [online documentation](https://eoshep.org/doc/installation.html#installing-the-dependencies-on-linux).

Installation
------------

EOS supports several methods of installation. For Linux users, the recommended method
is installation via PyPI:
```
pip3 install eoshep
```
Development versions tracking the master branch are also available via PyPi:
```
pip3 install --pre eoshep
```

For instructions on how to build and install EOS on your computer please have a
look at the [online documentation](https://eoshep.org/doc/installation.html).

Attribution
-----------

When you use EOS for a project that results in a publication, please cite [the appropriate / most recent Zenodo release](https://doi.org/10.5281/zenodo.3376590)
*as well as* the publication
```
@article{EOSAuthors:2021xpv,
    author = "van Dyk, Danny and others", collaboration = "EOS Authors",
    title = "{EOS: a software for flavor physics phenomenology}",
    eprint = "2111.15428", archivePrefix = "arXiv", primaryClass = "hep-ph",
    reportNumber = "EOS-2021-04, TUM-HEP 1371/21, P3H-21-094, SI-HEP-2021-32",
    doi = "10.1140/epjc/s10052-022-10177-4",
    journal = "Eur. Phys. J. C", volume = "82", number = "6", pages = "569", year = "2022"
}
```

Contact
-------

If you want to report an error or file a request, please file an issue [here](https://github.com/eos/eos/issues).
For additional information, please contact any of the main authors, e.g. via our [Discord server](https://discord.gg/hyPu7f7K6W).

To approach individual developers, please [look here](https://github.com/eos/eos/wiki/Development-Team).

Acknowledgments
---------------

We would like to extend our thanks to the following people whose input and
support were most helpful in either the development or the maintenance of EOS:

- Gudrun Hiller
- Gino Isidori
- David Leverton
- Thomas Mannel
- Ciaran McCreesh
- Hideki Miyake
- Konstantinos Petridis
- Nicola Serra
- Alexander Shires
