Overview

Goals

The goal of this package is to present a single way to work with results from ANY hydrodynamic/oceanographic model – regardless of what type of grid it was computed on. In particular:

• Regular Structured Grids (CF Conventions), with API embedded in Iris and to some degree in xarray

• Unstructured Grids (CF + UGRID Conventions), with nascent API in pyugrid

• Staggered Grids (CF + SGRID Conventions) with nascent API in pysgrid

gridded could/should be useful for other types of gridded data, in particular meteorological models, but so far has motly been developed/tested with oceanographic models.

Why gridded?

gridded has been developed because a number of us need to work with multiple model types, and have found ourselves writing a lot of custom code for each type. In particular, inter-comparison of results is an ugly process. To preserve the integrity of the results, it’s best to NOT interpolate on to a common grid. gridded lets one work with multiple model types with the same API, while preserving the native grid as much as possible.

Other solutions have (so far) built assumptions about the underlying grid type into the code and API, making it difficult to adapt to other grid types. Nevertheless, gridded hopes to learn from the the fabulous work done by other packages, such as:

Iris: http://scitools.org.uk/iris/ and xarray: https://github.com/pydata/xarray

Data standards

gridded seeks to support data standards such as:

• The CF Conventions: http://cfconventions.org/

• UGRID Conventions: http://ugrid-conventions.github.io/ugrid-conventions/

• SGRID Conventions: http://sgrid.github.io/sgrid/

gridded also provided APIs for reading results that do not conform to the conventions, allowing one to work with non-confirming datasets with the same API, as well as providing tools to convert non-confirming files to conforming files (incomplete).