| Name | flavorpy JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Library for calculations around discrete flavor symmetries in particle physics |
| upload_time | 2024-06-20 16:54:23 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| 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. |
| keywords |
discrete symmetry
flavor model
flavor symmetry
modular flavor symmetry
particle physics
standard model
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# FlavorPy
[](https://doi.org/10.5281/zenodo.11060597)
[](https://pypi.org/project/flavorpy/)
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](https://pypi.org/project/flavorpy/) 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 bringing the two parts together and integrating GAP or SageMath to ConstructTerms.
If you want to contribute, please feel free to contact [Alexander Baur](mailto:alexander.baur@tum.de)
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).
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/).
Raw data
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"description": "# FlavorPy\n\n[](https://doi.org/10.5281/zenodo.11060597)\n[](https://pypi.org/project/flavorpy/)\n\n\nWhat is FlavorPy?\n-----------------\n\n**FlavorPy** is a Python library for calculations around discrete flavor symmetries in particle physics. Currently, it is split into two parts:\n\n* The **constructterms** part allows you to calculate group theoretical tensor products and therefore find the invariant terms in the action.\n\n* 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/).\n\n\nHow to install FlavorPy?\n------------------------\n\nYou can install FlavorPy from [PyPI](https://pypi.org/project/flavorpy/) with pip by running\n\n```bash\n\n pip install flavorpy\n```\n\nAlternatively, you can:\n\n1. Download the files from the [github repository](https://github.com/FlavorPy/FlavorPy/). \n\n2. Open python and load the files with:\n\n```python\n import os\n dir_to_git_folder = \"home/.../FlavorPy/current_version\" # Adjust this to your case !!\n os.chdir(os.path.expanduser(dir_to_git_folder))\n\n import constructterms as ct\n import modelfitting as mf\n```\n\n3. Start using the FlavorPy packages imported as `ct` and `mf`!\n\n\nDocumentation\n-------------\n\nA documentation is hosted on [https://flavorpy.github.io/FlavorPy/](https://flavorpy.github.io/FlavorPy/).\nThis site also contains examples of how to use the code.\n\n\nCurrent development\n-------------------\n\nThe goal of current development is bringing the two parts together and integrating GAP or SageMath to ConstructTerms.\nIf you want to contribute, please feel free to contact [Alexander Baur](mailto:alexander.baur@tum.de)\n\n\nCiting FlavorPy\n---------------\n\nIf FlavorPy contributes to a project that leads to a publication, please acknowledge this fact by citing \n\n[A. Baur, \"FlavorPy\", Zenodo, 2024, doi: 10.5281/zenodo.11060597](https://doi.org/10.5281/zenodo.11060597).\n\nHere is an example of a BibTex entry:\n\n```tex\n @software{FlavorPy,\n author = {Baur, Alexander},\n title = \"{FlavorPy}\",\n year = {2024},\n publisher = {Zenodo},\n version = {v0.1.0},\n doi = {10.5281/zenodo.11060597},\n url = \"\\url{https://doi.org/10.5281/zenodo.11060597}\"\n } \n```\n\nWhen using the NuFit experimental data, please also cite \n\n[I. Esteban, M. C. Gonz\u00e1lez-Garc\u00eda, 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).\n\n\nCredit\n------\n\nThis 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/).\n\n",
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