discretisedfield 0.8.10

Python package for definition, reading, and visualisation of finite difference fields.

discretisedfield

Marijan Beg^1,2, Ryan A. Pepper¹, Thomas Kluyver², and Hans Fangohr^2,1

¹ Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
² European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany

Description	Badge
Releases
Builds
Coverage
Documentation
Binder
Platforms
Downloads
License
DOI

About

discretisedfield is a Python package that provides:

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

• analysis of finite difference fields,

• reading and writing of different file types (OVF, VTK, and HDF5), and

• visualisation using matplotlib and k3d.

It is available on all major operating systems (Windows, MacOS, and Linux) and requires Python 3.6 or higher.

Documentation

APIs and tutorials as Jupyter notebooks are available as a part of documentation.

Installation, testing, and upgrade

We support installation using conda and pip package managers. Instructions can be found in the documentation.

Binder

This package can be used in the cloud via Binder. This does not require to have anything installed and no files will be created on your machine. To access Binder, use the Binder badge in the table above.

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.

License

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

How to cite

If you use this package, please cite it as:

1. 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).

2. M. Beg, R. A. Pepper, T. Kluyver, and H. Fangohr. ubermag/discretisedfield: Python package for the analysis and visualisation of finite difference fields. Zenodo. DOI: 10.5281/zenodo.3539461 (2020).

Acknowledgements

Developed as a part of:

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

• EPSRC Programme grant on Skyrmionics (EP/N032128/1).