micromagneticmodel

Marijan Beg^1,2, Martin Lang², Samuel Holt^2,3, Swapneel Amit Pathak^2,4, and Hans Fangohr^2,4,5

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

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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

M. Beg, M. Lang, and H. Fangohr. Ubermag: Towards more effective micromagnetic workflows. IEEE Transactions on Magnetics 58, 7300205 (2022).
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).
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 (2025).

Acknowledgements

OpenDreamKit – Horizon 2020 European Research Infrastructure project (676541)
EPSRC Programme Grant on Skyrmionics (EP/N032128/1)

Metadata

Release files for micromagneticmodel 0.65.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for micromagneticmodel 0.65.0
File	Size	Uploaded
micromagneticmodel-0.65.0.tar.gz	36.4 kB	Jun 1, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for micromagneticmodel 0.65.0
File	Interpreter	ABI	Platform	Reset
micromagneticmodel-0.65.0-py3-none-any.whl 56.6 kB Jun 1, 2025	Python 3	none	any	Details

Total release size: 93.0 kB

Release files / micromagneticmodel-0.65.0.tar.gz

Download URL	micromagneticmodel-0.65.0.tar.gz
Size	36.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`e3839f05f9c6de917bc0ad8dd7057865db7e3bffb72eb39bab41ae28bdb16a60`
BLAKE2b-256 checksum How to use checksums	`7e0ae861cf2a3499981f28f20094fcf0568b903fb7acfde1a397a52acc5a45c2`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/ubermag/micromagneticmodel
Publishing commit: github.com/ubermag/micromagneticmodel/tree/a51f2d3aeb50c71393aca03f23baca7e90f88068

Workflow

Publishing configuration: github.com/ubermag/micromagneticmodel/blob/a51f2d3aeb50c71393aca03f23baca7e90f88068/.github/workflows/publish.yml
Publishing logs: github.com/ubermag/micromagneticmodel/actions/runs/15377178366/attempts/1

Artifact

Subject: micromagneticmodel-0.65.0.tar.gz
SHA-256 checksum: e3839f05f9c6de917bc0ad8dd7057865db7e3bffb72eb39bab41ae28bdb16a60 Verifying attestations

Release files / micromagneticmodel-0.65.0-py3-none-any.whl

Download URL	micromagneticmodel-0.65.0-py3-none-any.whl
Size	56.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d9304f0f56415c6525450411878dfc09600fefe3b8c95b9ba6a7a4de22d7e764`
BLAKE2b-256 checksum How to use checksums	`d589c38e6e254c0b8ca756c9d68020aa6708cf756c3c76e47676e3f213911e84`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/ubermag/micromagneticmodel
Publishing commit: github.com/ubermag/micromagneticmodel/tree/a51f2d3aeb50c71393aca03f23baca7e90f88068

Workflow

Publishing configuration: github.com/ubermag/micromagneticmodel/blob/a51f2d3aeb50c71393aca03f23baca7e90f88068/.github/workflows/publish.yml
Publishing logs: github.com/ubermag/micromagneticmodel/actions/runs/15377178366/attempts/1

Artifact

Subject: micromagneticmodel-0.65.0-py3-none-any.whl
SHA-256 checksum: d9304f0f56415c6525450411878dfc09600fefe3b8c95b9ba6a7a4de22d7e764 Verifying attestations