Skip to main content

Calculation of indices that describe the Madden-Julian-Oscillation (only OMI by now)

Project description

mjoindices - A python package for calculating the Madden-Julian-Oscillation OMI index



mjoindices is a Python package relevant for users of the atmospheric sciences. It provides functionality to compute an index of the Madden-Julian-Oscillation (MJO), particularly the OLR-based MJO index (OMI).

Whereas the package name has been chosen to indicate that further MJO indices should be included in future, the implementation is currently limited to the OMI algorithm.


If you use this software package, you should currently cite at least the Zenodo DOI. It is planned to publish a peer-reviewed software meta paper in the near-future. Please ask us for the status of this paper, if you use mjoindices in published research. The OMI algorithm itself is described in Kiladis (2014), but please don't forget to also cite the software package, which is an independent development.


mjoindices is written for Python 3 (version > 3.6) and depends on the packages NumPy, Pandas, SciPy, and Matplotlib. It runs on Linux and Windows. Other operating systems have not been tested.


mjoindices is available in the Python Package Index (PyPI). It can be installed using, e.g., pip.

pip3 install mjoindices

It can also be installed from the source, which is available on Zenodo and GitHub. Download the source, move into the directory containing the file and run

python3 install

API documentation

The API documentation is found on GitHub Pages and also in the docs folder of the source.

Getting started / examples

After you have installed mjoindices, you can download an example from the source, which consists of two files:

  • After downloading some data files, which are mentioned and linked in the source documentation of the example, you can run this example to recalculate the original OMI values. The script will save the computed Empirical Orthogonal Functions (EOFs) and the Principal Components (PCs) in two individual files, which can also be configured in the source code. In addition, it will save a few plots into a directory, which can also be configured in the source. These plots show the agreement with the original OMI values (slight deviations are expected due to numerical differences. This will be detailed in the corresponding software meta paper).

    Note that you can use this example also as a template to calculate OMI values with your own OLR data. In order to do that, you have to adapt only two parts of the code, which are also marked in the code documentation.

    Note also that this script may run for one or two hours on common personal computer systems.

  • This script produces more detailed comparison plots and saves them into a directory. The script has to be run before, as the evaluation script is based on the saved results. As for, some file and directory names have to be adapted in the beginning of the code.

Both files are also available as Jupyter notebook files.

Automated testing

After you have installed mjoindices, you can also download unit and integration tests from the source to check your installation using pytest.

  • Download the complete test directory to you local file system.

  • Download some external input and reference data files. The files to be downloaded are described in a separate Readme file.

  • Move into your local test directory and run


Note that the tests may run for a few hours on a common personal computer.

Project details

Download files

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

Files for mjoindices, version 1.0.0
Filename, size File type Python version Upload date Hashes
Filename, size mjoindices-1.0.0-py3-none-any.whl (78.4 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size mjoindices-1.0.0.tar.gz (88.9 kB) File type Source Python version None Upload date Hashes View

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 DigiCert DigiCert EV certificate StatusPage StatusPage Status page