{ "info": { "author": "xgcm Developers", "author_email": "rpa@ldeo.columbia.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering" ], "description": "xgcm: General Circulation Model Postprocessing with xarray\n==========================================================\n\n|pypi| |conda forge| |Build Status| |codecov| |docs| |DOI| |license|\n\n**xgcm** is a python package for working with the datasets produced by numerical\n`General Circulation Models `_\n(GCMs) and similar gridded datasets that are amenable to\n`finite volume `_ analysis.\nIn these datasets, different variables are located at different positions with\nrespect to a volume or area element (e.g. cell center, cell face, etc.)\nxgcm solves the problem of how to interpolate and difference these variables\nfrom one position to another.\n\nxgcm consumes and produces xarray_ data structures, which are coordinate and\nmetadata-rich representations of multidimensional array data. xarray is ideal\nfor analyzing GCM data in many ways, providing convenient indexing and grouping,\ncoordinate-aware data transformations, and (via dask_) parallel,\nout-of-core array computation. On top of this, xgcm adds an understanding of\nthe finite volume `Arakawa Grids`_ commonly used in ocean and atmospheric\nmodels and differential and integral operators suited to these grids.\n\nxgcm was motivated by the rapid growth in the numerical resolution of\nocean, atmosphere, and climate models. While highly parallel supercomputers can\nnow easily generate tera- and petascale datasets, common post-processing\nworkflows struggle with these volumes. Furthermore, we believe that a flexible,\nevolving, open-source, python-based framework for GCM analysis will enhance\nthe productivity of the field as a whole, accelerating the rate of discovery in\nclimate science. xgcm is part of the Pangeo_ initiative.\n\nFor more information, including installation instructions, read the full\n`xgcm documentation`_.\n\n.. _Pangeo: http://pangeo-data.github.io\n.. _dask: http://dask.pydata.org\n.. _xarray: http://xarray.pydata.org\n.. _Arakawa Grids: https://en.wikipedia.org/wiki/Arakawa_grid\n.. _xgcm documentation: https://xgcm.readthedocs.io/\n\n.. |conda forge| image:: https://anaconda.org/conda-forge/xgcm/badges/version.svg\n :target: https://anaconda.org/conda-forge/xgcm\n.. |DOI| image:: https://zenodo.org/badge/41581350.svg\n :target: https://zenodo.org/badge/latestdoi/41581350\n.. |Build Status| image:: https://travis-ci.org/xgcm/xgcm.svg?branch=master\n :target: https://travis-ci.org/xgcm/xgcm\n :alt: travis-ci build status\n.. |codecov| 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