Skip to main content

Python-based domain-specific language for computational magnetism

Project description

micromagneticmodel

Marijan Beg1,2, Martin Lang2, Samuel Holt2,3, Swapneel Amit Pathak2,4, and Hans Fangohr2,4,5

1 Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK
2 Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
3 Department of Physics, University of Warwick, Coventry CV4 7AL, UK
4 Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany
5 Center for Free-Electron Laser Science, Luruper Chaussee 149, 22761 Hamburg, Germany

Description Badge
Tests Build status
Linting pre-commit.ci status
Code style: black
Releases PyPI version
Anaconda-Server Badge
Coverage codecov
Documentation Documentation
YouTube YouTube
Binder Binder
Platforms Platforms
Downloads Downloads
License License
DOI DOI

About

micromagneticmodel is a Python package, integrated with Jupyter, providing:

  • Domain-specific language for computational magnetism.

It is available on Windows, MacOS, and Linux. It requires Python 3.8+.

Documentation

APIs and tutorials are available in the documentation. To access the documentation, use the badge in the table above.

Installation, testing, and upgrade

We recommend installation using conda package manager. Instructions can be found in the documentation.

Binder

This package can be used in the cloud via Binder. To access Binder, use the badge in the table above.

YouTube

YouTube video tutorials are available on the Ubermag channel.

Support

If you require support, have questions, want to report a bug, or want to suggest an improvement, please raise an issue in ubermag/help repository.

Contributions

All contributions are welcome, however small they are. If you would like to contribute, please fork the repository and create a pull request. If you are not sure how to contribute, please contact us by raising an issue in ubermag/help repository, and we are going to help you get started and assist you on the way.

Contributors:

License

Licensed under the BSD 3-Clause "New" or "Revised" License. For details, please refer to the LICENSE file.

How to cite

  1. M. Beg, M. Lang, and H. Fangohr. Ubermag: Towards more effective micromagnetic workflows. IEEE Transactions on Magnetics 58, 7300205 (2022).

  2. M. Beg, R. A. Pepper, and H. Fangohr. User interfaces for computational science: A domain specific language for OOMMF embedded in Python. AIP Advances 7, 56025 (2017).

  3. Marijan Beg, Martin Lang, Samuel Holt, Swapneel Amit Pathak, and Hans Fangohr. micromagneticmodel: Python-based domain-specific language for computational magnetism DOI: 10.5281/zenodo.3539479 (2023).

Acknowledgements

  • OpenDreamKit – Horizon 2020 European Research Infrastructure project (676541)

  • EPSRC Programme Grant on Skyrmionics (EP/N032128/1)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

micromagneticmodel-0.64.0.tar.gz (37.0 kB view details)

Uploaded Source

Built Distribution

micromagneticmodel-0.64.0-py3-none-any.whl (57.0 kB view details)

Uploaded Python 3

File details

Details for the file micromagneticmodel-0.64.0.tar.gz.

File metadata

  • Download URL: micromagneticmodel-0.64.0.tar.gz
  • Upload date:
  • Size: 37.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.9

File hashes

Hashes for micromagneticmodel-0.64.0.tar.gz
Algorithm Hash digest
SHA256 6e181ab1d7f70ba6510a3e74c36c7cafb551f45a0955345e92a48d890ea2bb7a
MD5 9598dfae2b7135c95289f164bdd21323
BLAKE2b-256 cf0c58bb0099c299e5327b2b9baa383f81d19ee6c309b5afa37286ea02298400

See more details on using hashes here.

File details

Details for the file micromagneticmodel-0.64.0-py3-none-any.whl.

File metadata

File hashes

Hashes for micromagneticmodel-0.64.0-py3-none-any.whl
Algorithm Hash digest
SHA256 35ea981185960c5b011defa77c33f7dc06473556f7fba72cd03a70737bc099e3
MD5 3f9fe89a1a1f2ff1473b7d8e5c337ec6
BLAKE2b-256 39448649d0481e07369b8317162fa0a697f8ab7c11e6c8dd8fc6720ebb0da0a9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page