Python package for the analysis and visualisation of finite-difference fields.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for fangohr from gravatar.com fangohr Avatar for lang-m from gravatar.com lang-m Avatar for marijanbeg from gravatar.com marijanbeg Avatar for samjrholt from gravatar.com samjrholt Avatar for swapneelap from gravatar.com swapneelap

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: BSD License (Copyright 2022, Marijan Beg and Hans Fangohr Redistribution and use in source and binary forms, with...)
  • Author: Marijan Beg, Martin Lang, Samuel Holt, Swapneel Amit Pathak, Ryan A. Pepper, Hans Fangohr
  • Requires: Python >=3.8
Classifiers

Project description

discretisedfield

Marijan Beg1,2, Martin Lang2, Samuel Holt2,3, Swapneel Amit Pathak2,4, Ryan A. Pepper5, and Hans Fangohr2,4,6

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 Research Software Group, University of Birmingham, Birmingham B15 2TT, UK
6 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

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

  • definition of finite-difference regions, meshes, lines, and fields,

  • analysis of finite-difference fields,

  • visualisation using matplotlib and k3d, and

  • manipulation of different file types (OVF, VTK, and HDF5).

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, Ryan A. Pepper, and Hans Fangohr. discretisedfield: Python package for the analysis and visualisation of finite-difference fields. 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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for fangohr from gravatar.com fangohr Avatar for lang-m from gravatar.com lang-m Avatar for marijanbeg from gravatar.com marijanbeg Avatar for samjrholt from gravatar.com samjrholt Avatar for swapneelap from gravatar.com swapneelap

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: BSD License (Copyright 2022, Marijan Beg and Hans Fangohr Redistribution and use in source and binary forms, with...)
  • Author: Marijan Beg, Martin Lang, Samuel Holt, Swapneel Amit Pathak, Ryan A. Pepper, Hans Fangohr
  • Requires: Python >=3.8
Classifiers

Release history Release notifications | RSS feed

0.91.0

0.90.1

0.90.0

0.65.1

0.65.0

This version

0.64.0

0.63.0

0.62.1

0.62.0

0.61.0

0.60.0

0.11.1

0.11

0.10

0.9

0.8.15

0.8.14

0.8.13

0.8.12

0.8.11

0.8.10

0.8.9

0.8.8

0.8.7

0.8.6

0.8.5

0.8.4

0.8.3

0.8.2

0.8.1

0.8

0.7.6

0.7.5

0.7.4

0.7.2

0.7.1

0.7

0.6

0.5.6

0.5.4.2

0.5.4.1

0.5.4

0.5.3

0.5.2

0.5.1

0.5.0

Download files

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

Source Distribution

discretisedfield-0.64.0.tar.gz (2.3 MB view details)

Uploaded Source

Built Distribution

discretisedfield-0.64.0-py3-none-any.whl (2.3 MB view details)

Uploaded Python 3

File details

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

File metadata

  • Download URL: discretisedfield-0.64.0.tar.gz
  • Upload date:
  • Size: 2.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for discretisedfield-0.64.0.tar.gz
Algorithm Hash digest
SHA256 f3b609c1c96d5d398481b4bb97e3375501f5404ed460531ddb1b317df58afc36
MD5 24e27b7de4a45f7cff43d73ad9ea93f7
BLAKE2b-256 28b20a2f077457cf0271cd96cdd3b5e6d06bc2183f025e1d8a2a3f2e4fdb650a

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for discretisedfield-0.64.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ff63603cdc8fc3d73da8a424a8776090f3e11daa7eeab072770d956ab9f9116a
MD5 0488dcf6b0b87f5ed7ae6f9d9503d580
BLAKE2b-256 93cd03044f4bfbedf363b79c1a9c6b5ae570c5c14a8e80e27b904e45d4c7559d

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