ubermagtable 0.1.9

Python package for manipulating OOMMF and mumax3 tabular data.

ubermagtable

Marijan Beg^1,2, Vanessa Nehruji¹, Sergii Mamedov², 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

ubermagtable is a Python package that provides:

• Conversion of scalar data files (OOMMF .odt and mumax3 .txt) to pandas.DataFrames
• Merging of multiple OOMMF .odt files into a single pandas.DataFrame

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

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, V. Nehruji, S. Mamedov, R. A. Pepper, T. Kluyver, and H. Fangohr. ubermag/ubermagtable: Python package for manipulating tabular data. Zenodo. DOI: 10.5281/zenodo.3539492 (2020).

Acknowledgements

Developed as a part of:

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

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