Skip to main content

OOMMF calculator.

Project description

oommfc

Marijan Beg1,2, Martin Lang2, Ryan A. Pepper3, Thomas Kluyver4, Samuel Holt2,5, Swapneel Amit Pathak2,6, and Hans Fangohr2,6,7

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 Research Software Group, University of Birmingham, Birmingham B15 2TT, UK
4 European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany
5 Department of Physics, University of Warwick, Coventry CV4 7AL, UK
6 Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany
7 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

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

  • An Object Oriented MicroMagnetic Framework OOMMF calculator for computational magnetism models defined with micromagneticmodel.

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, Ryan A. Pepper, Thomas Kluyver, Samuel Holt, Swapneel Amit Pathak, and Hans Fangohr. oommfc: OOMMF calculator. DOI: 10.5281/zenodo.3539461 (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

oommfc-0.64.0.tar.gz (38.0 kB view details)

Uploaded Source

Built Distribution

oommfc-0.64.0-py3-none-any.whl (75.2 kB view details)

Uploaded Python 3

File details

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

File metadata

  • Download URL: oommfc-0.64.0.tar.gz
  • Upload date:
  • Size: 38.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for oommfc-0.64.0.tar.gz
Algorithm Hash digest
SHA256 c64c963d62b7758b2bad5a43dc6b0a12d2112f64c3a2d93d5635ddaa5032f9f9
MD5 761392f822a8409c67a62d3a6257a220
BLAKE2b-256 18c80108a7ed48b61bd258e8c13bcbc079f1e04a12de8434d8958199b37fa95f

See more details on using hashes here.

File details

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

File metadata

  • Download URL: oommfc-0.64.0-py3-none-any.whl
  • Upload date:
  • Size: 75.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for oommfc-0.64.0-py3-none-any.whl
Algorithm Hash digest
SHA256 23165d1c4cfb163874a1d5039ea27cb364a6263bb86775c51a5d2eac4e468ea9
MD5 47e0ca242748999b95e2fa6b96f60b1c
BLAKE2b-256 1ca6d4a399815eefb206207ba457d3eaf07fa0f1da92f18d1b6acf55efb7780e

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