Metadata-Version: 2.3
Name: flavorpy
Version: 0.1.0
Summary: Library for calculations around discrete flavor symmetries in particle physics
Project-URL: Homepage, https://flavorpy.github.io/FlavorPy
Project-URL: Repository, https://github.com/FlavorPy/FlavorPy
Author-email: Alexander Baur <alexander.baur@tum.de>
License: BSD 3-Clause License
        
        Copyright (c) 2024, Alexander Baur*⁺
        
        * Fakultät für Physik, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
        ⁺ Instituto de Física, Universidad Nacional Autónoma de México, POB 20-364, Cd.Mx. 01000, México
        
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        1. Redistributions of source code must retain the above copyright notice, this
           list of conditions and the following disclaimer.
        
        2. Redistributions in binary form must reproduce the above copyright notice,
           this list of conditions and the following disclaimer in the documentation
           and/or other materials provided with the distribution.
        
        3. Neither the name of the copyright holder nor the names of its
           contributors may be used to endorse or promote products derived from
           this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        
        
        Some experimental values provided within this code are from NuFit.
        If you are using these results, refer to JHEP 09 (2020) 178, arXiv:2007.14792 
        as well as www.nu-fit.org.
License-File: LICENSE
Keywords: discrete symmetry,flavor model,flavor symmetry,modular flavor symmetry,particle physics,standard model
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.8
Requires-Dist: lmfit
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Description-Content-Type: text/markdown

# FlavorPy

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11060597.svg)](https://doi.org/10.5281/zenodo.11060597)


What is FlavorPy?
-----------------

**FlavorPy** is a Python library for calculations around discrete flavor symmetries in particle physics. Currently, it is split into two parts:

* The **constructterms** part allows you to calculate group theoretical tensor products and therefore find the invariant terms in the action.

* The **modelfitting** part is concerned with fitting a model to experimental data. More specifically flavor observables, i.e. masses and mixing, for given mass matrices with an associated parameter space can be compared and fitted to experimental data. The minimization heavily relies on [lmfit](https://lmfit.github.io/lmfit-py/).


How to install FlavorPy?
------------------------

You can install FlavorPy from PyPI with pip by running

```bash

   pip install flavorpy
```

Alternatively, you can:

1. Download the files from the [github repository](https://github.com/FlavorPy/FlavorPy/). 

2. Open python and load the files with:

```python
    import os
    dir_to_git_folder = "home/.../FlavorPy/current_version"  # Adjust this to your case !!
    os.chdir(os.path.expanduser(dir_to_git_folder))

    import constructterms as ct
    import modelfitting as mf
```

3. Start using the FlavorPy packages imported as `ct` and `mf`!


Documentation
-------------

A documentation is hosted on [https://flavorpy.github.io/FlavorPy/](https://flavorpy.github.io/FlavorPy/).
This site also contains examples of how to use the code.


Current development
-------------------

The goal of current development is to bring the two parts together, integrate GAP, have quark models, and extend the modelfitting with a MCMC method to study the vicinity of minima.
If you want to contribute, please feel free to contact [Alexander Baur](mailto:alexander.baur@tum.de)


Credit
------

This package uses experimental data obtained by NuFit published in [JHEP 09 (2020) 178](http://dx.doi.org/10.1007/JHEP09(2020)178), [arXiv:2007.14792](http://arxiv.org/abs/2007.14792), and their website [www.nu-fit.org](http://www.nu-fit.org/).

Citing FlavorPy
---------------

If FlavorPy contributes to a project that leads to a publication, please acknowledge this fact by citing 

[A. Baur, "FlavorPy", Zenodo, 2024, doi: 10.5281/zenodo.11060597](https://doi.org/10.5281/zenodo.11060597).

Here is an example of a BibTex entry:

```tex
    @software{FlavorPy,
      author        = {Baur, Alexander},
      title         = "{FlavorPy}",
      year          = {2024},
      publisher     = {Zenodo},
      version       = {v0.1.0},
      doi           = {10.5281/zenodo.11060597},
      url           = "\url{https://doi.org/10.5281/zenodo.11060597}"
    } 
```

When using the NuFit experimental data, please also cite 

[I. Esteban, M. C. González-García, M. Maltoni, T. Schwetz, and A. Zhou, The fate of hints: updated global analysis of three-flavor neutrino oscillations, JHEP 09 (2020), 178, arXiv:2007.14792 [hep-ph], https://www.nu-fit.org](https://link.springer.com/article/10.1007/JHEP09(2020)178).

