xmitgcm: Read MITgcm mds binary files into xarray

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xmitgcm is a python package for reading MITgcm_ binary MDS files into xarray_ data structures. By storing data in dask_ arrays, xmitgcm enables parallel, out-of-core_ analysis of MITgcm output data.

Links

• HTML documentation: https://xmitgcm.readthedocs.org
• Issue tracker: https://github.com/MITgcm/xmitgcm/issues
• Source code: https://github.com/MITgcm/xmitgcm

Installation

Requirements ^^^^^^^^^^^^

xmitgcm is compatible with python >=3.7. It requires xarray_ (>= version 0.14.1) and dask_ (>= version 1.0). These packages are most reliably installed via the conda <https://conda.pydata.org/docs/>_ environment management system, which is part of the Anaconda_ python distribution. Assuming you have conda available on your system, the dependencies can be installed with the command::

conda install xarray dask

If you are using earlier versions of these packages, you should update before installing xmitgcm.

Installation via pip ^^^^^^^^^^^^^^^^^^^^

If you just want to use xmitgcm, the easiest way is to install via pip::

pip install xmitgcm

This will automatically install the latest release from pypi <https://pypi.python.org/pypi>_.

Installation from github ^^^^^^^^^^^^^^^^^^^^^^^^

xmitgcm is under active development. To obtain the latest development version, you may clone the source repository <https://github.com/MITgcm/xmitgcm>_ and install it::

git clone https://github.com/MITgcm/xmitgcm.git
cd xmitgcm
python setup.py install

Users are encouraged to fork <https://help.github.com/articles/fork-a-repo/>_ xmitgcm and submit issues_ and pull requests_.

Quick Start

First make sure you understand what an xarray_ Dataset object is. Then find some MITgcm MDS data. If you don't have any data of your own, you can download the xmitgcm test repositories <https://figshare.com/articles/xmitgcm_test_datasets/4033530>_ To download the some test data, run the shell commands::

$ curl -L -J -O https://ndownloader.figshare.com/files/6494718
$ tar -xvzf global_oce_latlon.tar.gz

This will create a directory called global_oce_latlon which we will use for the rest of these examples. If you have your own data, replace this with the path to your mitgcm files.

To open MITgcm MDS data as an xarray.Dataset, do the following in python::

from xmitgcm import open_mdsdataset
data_dir = './global_oce_latlon'
ds = open_mdsdataset(data_dir)

data_dir, should be the path (absolute or relative) to an MITgcm run directory. xmitgcm will automatically scan this directory and try to determine the file prefixes and iteration numbers to read. In some configurations, the open_mdsdataset function may work without further keyword arguments. In most cases, you will have to specify further details.

Consult the online documentation <https://xmitgcm.readthedocs.org>_ for more details.

.. |DOI| image:: https://zenodo.org/badge/70649781.svg :target: https://zenodo.org/badge/latestdoi/70649781 .. |Build Status| image:: https://travis-ci.org/MITgcm/xmitgcm.svg?branch=master :target: https://travis-ci.org/MITgcm/xmitgcm :alt: travis-ci build status .. |codecov| image:: https://codecov.io/github/MITgcm/xmitgcm/coverage.svg?branch=master :target: https://codecov.io/github/MITgcm/xmitgcm?branch=master :alt: code coverage .. |pypi| image:: https://badge.fury.io/py/xmitgcm.svg :target: https://badge.fury.io/py/xmitgcm :alt: pypi package .. |docs| image:: https://readthedocs.org/projects/xmitgcm/badge/?version=stable :target: https://xmitgcm.readthedocs.org/en/stable/?badge=stable :alt: documentation status

.. _dask: https://dask.pydata.org .. _xarray: https://xarray.pydata.org .. _Comodo: https://pycomodo.forge.imag.fr/norm.html .. _issues: https://github.com/MITgcm/xmitgcm/issues .. _pull requests: https://github.com/MITgcm/xmitgcm/pulls .. _MITgcm: http://mitgcm.org/public/r2_manual/latest/online_documents/node277.html .. _out-of-core: https://en.wikipedia.org/wiki/Out-of-core_algorithm .. _Anaconda: https://www.continuum.io/downloads