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xgcm

General Circulation Model Postprocessing with xarray

  • 0.8.1
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xgcm: General Circulation Model Postprocessing with xarray

|pypi| |conda forge| |conda-forge| |Build Status| |codecov| |docs| |DOI| |license| |Code style| |pre-commit|

Binder Examples

========= ============== ============================================================================ Link Provider Description ========= ============== ============================================================================ |Binder| mybinder.org Basic self-contained example |PBinder| Pangeo Binder More complex examples integrated with other Pangeo tools (dask, zarr, etc.) ========= ============== ============================================================================

Description

xgcm is a python package for working with the datasets produced by numerical General Circulation Models <https://en.wikipedia.org/wiki/General_circulation_model>_ (GCMs) and similar gridded datasets that are amenable to finite volume <https://en.wikipedia.org/wiki/Finite_volume_method>_ analysis. In these datasets, different variables are located at different positions with respect to a volume or area element (e.g. cell center, cell face, etc.) xgcm solves the problem of how to interpolate and difference these variables from one position to another.

xgcm consumes and produces xarray_ data structures, which are coordinate and metadata-rich representations of multidimensional array data. xarray is ideal for analyzing GCM data in many ways, providing convenient indexing and grouping, coordinate-aware data transformations, and (via dask_) parallel, out-of-core array computation. On top of this, xgcm adds an understanding of the finite volume Arakawa Grids_ commonly used in ocean and atmospheric models and differential and integral operators suited to these grids.

xgcm was motivated by the rapid growth in the numerical resolution of ocean, atmosphere, and climate models. While highly parallel supercomputers can now easily generate tera- and petascale datasets, common post-processing workflows struggle with these volumes. Furthermore, we believe that a flexible, evolving, open-source, python-based framework for GCM analysis will enhance the productivity of the field as a whole, accelerating the rate of discovery in climate science. xgcm is part of the Pangeo_ initiative.

Getting Started

To learn how to install and use xgcm for your dataset, visit the xgcm documentation_.

.. _Pangeo: http://pangeo.io .. _dask: http://dask.pydata.org .. _xarray: http://xarray.pydata.org .. _Arakawa Grids: https://en.wikipedia.org/wiki/Arakawa_grids .. _xgcm documentation: https://xgcm.readthedocs.io/

.. |conda forge| image:: https://img.shields.io/conda/vn/conda-forge/xgcm :target: https://anaconda.org/conda-forge/xgcm .. |DOI| image:: https://zenodo.org/badge/41581350.svg :target: https://zenodo.org/badge/latestdoi/41581350 .. |Build Status| image:: https://img.shields.io/github/workflow/status/xgcm/xgcm/CI?logo=github :target: https://github.com/xgcm/xgcm/actions :alt: GitHub Workflow CI Status .. |codecov| image:: https://codecov.io/github/xgcm/xgcm/coverage.svg?branch=master :target: https://codecov.io/github/xgcm/xgcm?branch=master :alt: code coverage .. |pypi| image:: https://badge.fury.io/py/xgcm.svg :target: https://badge.fury.io/py/xgcm :alt: pypi package .. |docs| image:: http://readthedocs.org/projects/xgcm/badge/?version=latest :target: http://xgcm.readthedocs.org/en/stable/?badge=latest :alt: documentation status .. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg :target: https://github.com/xgcm/xgcm :alt: license .. |Code style| image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/python/black :alt: Code style .. |Binder| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/xgcm/xgcm/master?filepath=doc%2Fexample_mitgcm.ipynb .. |PBinder| image:: https://binder.pangeo.io/badge_logo.svg :target: https://binder.pangeo.io/v2/gh/pangeo-data/pangeo-ocean-examples/master .. |conda-forge| image:: https://img.shields.io/conda/dn/conda-forge/xgcm?label=conda-forge :target: https://anaconda.org/conda-forge/xgcm .. |pre-commit| image:: https://results.pre-commit.ci/badge/github/xgcm/xgcm/master.svg :target: https://results.pre-commit.ci/latest/github/xgcm/xgcm/master :alt: pre-commit.ci status

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