Justus Magin
Sessions
Over the past few years, Discrete Global Grid Systems (DGGS) that subdivide the earth into (roughly) equally sized faces have seen increased popularity. However, their in-memory representation is different from traditional projection-based data, which is either comprises of evenly shaped rectangular grid (aka raster) or discrete geometries (aka vector), and thus requires specialized tooling. In particular, this includes libraries that can work on the numeric cell ids defined by the specific DGGS.
xdggs is a library that provides a unified interface for xarray that allows working with and visualizing a variety of DGGS-indexed data sets.
We illustrate the power and flexibility of a new extension point in Xarray's data model: "custom indexes" that allow Xarray users to neatly handle complex grids, and enables at least one new data model (vector data cubes). We present a whirlwind tour of specific examples to illustrate the power of this feature, and aim to stimulate experimentation during the sprints.
Being able to regrid between various grid types is very important in geoscience research. While the scientific python ecosystem includes numerous geospatial regridding packages, most of them are tailored to only a few specific grid types. Additionally, very few of them are designed to handle regridding of grids that are too big to fit into memory using distributed computation frameworks like dask.
grid-indexing and grid-weights are a set of rust-based libraries that implement regridding between arbitrary grids using a RTree and rely on dask to scale for larger-than-memory grids.