SigmaEpsilon.Math - A Python Library for Applied Mathematics in Physical Sciences

SigmaEpsilon.Math is a Python library that provides tools to formulate and solve problems related to all kinds of scientific disciplines. It is a part of the SigmaEpsilon ecosystem, which is designed mainly to solve problems related to computational solid mechanics, but if something is general enough, it ends up here. A good example is the included vector and tensor algebra modules, or the various optimizers, which are applicable in a much broader context than they were originally designed for.

Documentation

The documentation is hosted on ReadTheDocs. You can find examples there.

Installation

The library can be installed from PyPI. For the best performance, install it together with Numba:

pip install "sigmaepsilon.math[numba]"  # recommended, with Numba
pip install sigmaepsilon.math           # without Numba

Performance: Numba is optional, but strongly recommended. It compiles the performance-critical parts of the library to machine code; without it, they run as plain Python, which can be orders of magnitude slower. Some parts of the library, like the optimizers, don't use Numba at all.

For more details, including what Numba does and which parts of the library benefit from it, see the installation guide in the documentation.

Changes and versioning

See the changelog, for the most notable changes between releases.

The project adheres to semantic versioning.

How to contribute?

Contributions are currently expected in any the following ways:

• finding bugs If you run into trouble when using the library and you think it is a bug, feel free to raise an issue.
• feedback All kinds of ideas are welcome. For instance if you feel like something is still shady (after reading the user guide), we want to know. Be gentle though, the development of the library is financially not supported yet.
• feature requests Tell us what you think is missing (with realistic expectations).
• examples If you've done something with the library and you think that it would make for a good example, get in touch with the developers and we will happily inlude it in the documention.
• sharing is caring If you like the library, share it with your friends or colleagues so they can like it too.

In all cases, read the contributing guidelines before you do anything.

Acknowledgements

Although sigmaepsilon.math heavily builds on NumPy, Scipy, Numba and Awkward and it also has functionality related to networkx and other third party libraries. Whithout these libraries the concept of writing performant, yet elegant Python code would be much more difficult.

A lot of the packages mentioned on this document here and the introduction have a citable research paper. If you use them in your work through sigmaepsilon.mesh, take a moment to check out their documentations and cite their papers.

Also, funding of these libraries is partly based on the size of the community they are able to support. If what you are doing strongly relies on these libraries, don't forget to press the ⭐ button to show your support.

License

This package is licensed under the MIT license.