Skip to main content

Python package for reading and analysis of OOMMF .odt files.

Project description

oommfodt

Marijan Beg1,2, Ryan A. Pepper2, Thomas Kluyver1, and Hans Fangohr1,2

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

Description Badge
Latest release PyPI version
Anaconda-Server Badge
Build Build Status
Build status
Coverage codecov
Documentation Documentation Status
Badge Binder
Dependecies Requirements Status
License License

About

oommfodt is a Python package that provides:

  • Reading, analysis, and plotting of OOMMF .odt files

  • Conversion of OOMMF .odt files to pandas dataframes and saving to different file types, such as Excel spreadsheet .xls

It is available on all major operating systems (Windows, MacOS, Linux) and requires Python 3.5 or higher.

Installation

We recommend installing oommfodt by using either of the pip or conda package managers.

Python requirements

Before installing oommfodt via pip, please make sure you have Python 3.5 or higher on your system. You can check that by running

python3 --version

If you are on Linux, it is likely that you already have Python installed. However, on MacOS and Windows, this is usually not the case. If you do not have Python 3.5 or higher on your machine, we strongly recommend installing the Anaconda Python distribution. Download Anaconda for your operating system and follow instructions on the download page. Further information about installing Anaconda can be found here.

pip

After installing Anaconda on MacOS or Windows, pip will also be installed. However, on Linux, if you do not already have pip, you can install it with

sudo apt install python3-pip

To install the oommfodt version currently in the Python Package Index repository PyPI on all operating systems run:

python3 -m pip install oommfodt

conda

oommfodt is installed using conda by running

conda install --channel conda-forge oommfodt

For further information on the conda package, dependency, and environment management, please have a look at its documentation.

Updating

If you used pip to install oommfodt, you can update to the latest released version in PyPI by running

python3 -m pip install --upgrade oommfodt

On the other hand, if you used conda for installation, update oommfodt with

conda upgrade oommfodt

Development version

The most recent development version of oommfodt that is not yet released can be installed/updated with

git clone https://github.com/joommf/oommfodt.git
python3 -m pip install --upgrade oommfodt

Note: If you do not have git on your system, it can be installed by following the instructions here.

Binder

oommfodt can be used in the cloud via Binder. This does not require you to have anything installed and no files will be created on your machine. To use oommfodt in the cloud, follow this link.

Documentation

Documentation for oommfodt is available here, where APIs and tutorials (in the form of Jupyter notebooks) are available.

Support

If you require support on installation or usage of oommfodt or if you want to report a problem, you are welcome to raise an issue in our joommf/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 oommfodt in your research, 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. DOI will be available soon

Acknowledgements

oommfodt was developed as a part of OpenDreamKit – Horizon 2020 European Research Infrastructure project (676541).

Project details


Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
oommfodt-0.8.3-py3-none-any.whl (13.9 kB) Copy SHA256 hash SHA256 Wheel py3
oommfodt-0.8.3.tar.gz (12.0 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page