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
conda
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 (2022).

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-0.63.1.tar.gz (35.5 kB view details)

Uploaded Source

Built Distribution

micromagneticmodel-0.63.1-py3-none-any.whl (56.0 kB view details)

Uploaded Python 3

File details

Details for the file micromagneticmodel-0.63.1.tar.gz.

File metadata

  • Download URL: micromagneticmodel-0.63.1.tar.gz
  • Upload date:
  • Size: 35.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for micromagneticmodel-0.63.1.tar.gz
Algorithm Hash digest
SHA256 365060dc854b5d800ff4de70d2e318fc9d3785af270469df02fd43412bad02b9
MD5 54b56ead60e180fa1d33e39520e09c95
BLAKE2b-256 771fb71bd4784c41badf2078893f4d0cb996c6b1168c1afa1869a7f68d99a60f

See more details on using hashes here.

File details

Details for the file micromagneticmodel-0.63.1-py3-none-any.whl.

File metadata

File hashes

Hashes for micromagneticmodel-0.63.1-py3-none-any.whl
Algorithm Hash digest
SHA256 89d14757ccb03635a7d3ec1d41a6d5b95504c96ad45718a44bb832b74d259f81
MD5 5159448487e609ef1a0f9b411b5799bf
BLAKE2b-256 ff9fa0301ba9a6f0cc787c46820a8c06fd9183f8d9a1ed8d3dca9a59391308cc

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