https://github.com/zincware/symsuite
A python package using machine learning to study symmetry.
https://github.com/zincware/symsuite
group-theory lie-algebra machine-learning symmetry
Last synced: 3 months ago
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A python package using machine learning to study symmetry.
- Host: GitHub
- URL: https://github.com/zincware/symsuite
- Owner: zincware
- License: epl-2.0
- Created: 2020-10-23T06:37:57.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-09-14T09:12:54.000Z (about 3 years ago)
- Last Synced: 2025-03-25T05:02:57.381Z (7 months ago)
- Topics: group-theory, lie-algebra, machine-learning, symmetry
- Language: Python
- Homepage: https://symsuite.readthedocs.io/en/latest/
- Size: 4.36 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
|build| |docs| |madewithpython| |license|
SymSuite
------
A python package to perform symmetry detection and generator extraction on
raw data. Follows the paper by Sven Krippendorf and Marc Syvaeri on
`Detecting symmetries with neural networks `_.
Notes
=====
This project is under heavy development and is therefore not available on PyPi.
I would not expect major API breaks but certainly addition of functionality.
Installation
============
There are several options for installing SymDet
PyPi
****
We host the code on PyPi and so it can be simply installed by:
.. code-block:: bash
pip3 install symdet
Install from source
*******************
**pip installation**
.. code-block:: bash
git clone https://github.com/zincware/SymSuite.git
cd SymDet
pip3 install . --user
**conda installation**
.. code-block:: bash
git clone https://github.com/zincware/SymSuite.git
cd SymDet
conda create -n SymDet python=3.8
conda activate SymDet
pip3 install .
Documentation
*************
There is a live version of the documentation hosted
`here `_. Alternatively you can
build it from source using
.. code-block:: bash
cd Symdet/docs
make html
You can then browse the documentation locally using your favourite browser.
Getting started
===============
As a first step I would suggest looking at the
`examples `_
directory and following along with some tutorials.
From here you may get a better idea of what you can use this package for.
Comments
========
This is a really young project and any comments or contributions would be
welcome. If you see issues in the documentation (particularly if you're a
mathematician) I would always welcome the feedback.
.. badges
.. |build| image:: https://github.com/SamTov/SymDet/actions/workflows/python-package.yml/badge.svg
:alt: Build tests passing
:target: https://github.com/SamTov/SymSuite/blob/readme_badges/
.. |docs| image:: https://readthedocs.org/projects/symdet/badge/?version=latest&style=flat
:alt: Build tests passing
:target: https://SymSuite.readthedocs.io/en/latest/
.. |license| image:: https://img.shields.io/badge/License-EPLv2.0-purple.svg?style=flat
:alt: Project license
:target: https://www.gnu.org/licenses/quick-guide-gplv3.en.html
.. |madewithpython| image:: https://img.shields.io/badge/Made%20With-Python-blue.svg
:alt: Made with python