Metadata-Version: 2.4
Name: eoshep
Version: 1.0.21.dev1287
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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[![Discord](https://img.shields.io/discord/808999754989961236.svg?label=&logo=discord&logoColor=ffffff&color=7389D8&labelColor=6A7EC2)](https://discord.gg/hyPu7f7K6W)


![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://eos.github.io/doc/use-cases.html#theory-predictions-and-their-uncertainties)
   within the Standard Model or within the Weak Effective Theory;
2. [Bayesian parameter inference](https://eos.github.io/doc/use-cases.html#parameter-inference)
   from both experimental and theoretical constraints; and
3. [Monte Carlo simulation of pseudo events](https://eos.github.io/doc/use-cases.html#pseudo-event-simulation) for flavor processes.

An up-to-date list of publications that use EOS can be found [here](https://eos.github.io/publications/).

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://eos.github.io/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://eos.github.io/doc/installation.html).

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).

Authors and Contributors
------------------------

The main authors are:

- Frederik Beaujean,
- Christoph Bobeth,
- Carolina Bolognani <carolinabolognani@gmail.com>,
- Nico Gubernari <nicogubernari@gmail.com>,
- Florian Herren <florian.s.herren@gmail.com>,
- Matthew J. Kirk <matthew.j.kirk@durham.ac.uk>,
- Meril Reboud <merilreboud@gmail.com>,
- Danny van Dyk <danny.van.dyk@gmail.com>,

with further code contributions by:

- Marzia Bordone,
- Thomas Blake,
- Lorenz Gaertner,
- Elena Graverini,
- Stephan Jahn,
- Ahmet Kokulu,
- Viktor Kuschke,
- Stephan Kürten,
- Philip Lüghausen,
- Bastian Müller,
- Filip Novak,
- Stefanie Reichert,
- Eduardo Romero,
- Rafael Silva Coutinho,
- Ismo Tojiala,
- K. Keri Vos,
- Christian Wacker.

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
