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I've wrapped ESMF/ESMPy by xarray: https://github.com/JiaweiZhuang/xESMF

It supports remapping between arbitrary quadrilateral grids, using ESMF's regridding algorithms including bilinear, conservative, nearest neighbour, etc... See this notebook for an example.

The package is still preliminary but it already works. See "Issues & Plans" in the main page for more details.

I am thinking about the API design for xESMF (JiaweiZhuang/xESMF#9). Any comments are welcome 😃

@ocefpaf Any plan for Python3-compatible ESMPy? I only see Python2.7 here: https://github.com/conda-forge/esmpy-feedstock

@ocefpaf Awesome, good to know that hurdle has already been leaped :)

If ESMF is the way to go, then some effort needs to be made to build conda recipes and other infrastructure for distributing and building the platform. It's a heavy dependency to haul around.

@rabernat Thanks for the suggestion! I'll add tests&docs when time allows.

If you want to look into details: The package contains the two layers (explained in the "Design Idea" section). The first layer has nothing to do with xarray, but just provides a convenient way (only with numpy) to access a useful subset of ESMPy functions. This layer is important because ESMPy's API is too complicated, but once it is done it doesn't need to be changed too often. The second layer wraps the first layer using xarray. Most of the crafts will be added to the second layer.

As a temporary workaround, I've added another notebook for using the low-level wrapper, for interested developers.

Super cool, thanks!

@PeterDSteinberg please have a look at module gridtools.resampling of repo https://github.com/CAB-LAB/gridtools. There are various up- and downsampling methods, which can deal with NaNs, and which are fast as C thanks to JIT through Numba. We use this package successfully in two projects.

Regridding is of interest to NASA and other clients of ours. It is important to them to be able to do broadcast operations between rasters that differ in resolution or are otherwise offset. We'll follow the XMap repo mentioned above ( @jhamman ) and see about building on that style. Our clients and open source tools like datashader for viz and elm for ML could use XMap and benefit from coordinate transformations and regridding. We have a meeting internally to discuss approaches to the coordinates' metadata and resampling / regridding and I'll be in touch further soon about how we can help here (see also the issues on this experimental earthio repo).

I'm ready to start working on this project. I already have a prototype regridding class that I developed as part of another project. Working on that, I discovered these points: - regridding takes a long time because the lattices can be huge - the design should accomodate parallel processing on a cluster - data needs to be normalized first (deal with missing values, etc.) - the user will want choices

Some of these choices are: - the destination lattice - the interpolation algorithm - subset of the dimension space   As the first step in a strategy to achieve this with a sequence of realizable goals, I plan to implement a regridding of just the latitude and longitude dimensions.

Is there a style guide that I can/should follow? Something like this: https://google.github.io/styleguide/pyguide.html? Does it or something else define naming conventions?

I have an immediate need in this area. My objective is to create a tool that will enable arithmetic on variables defined on lattices whose points don't coincide. Through my attempts thus far, it has become clear that I need data structures that incorporate spacial indexing and lattice indexing.

Since I have to tackle this issue to proceed, I thought I should follow the thinking discussed in this forum, so that it may be useful to others.

@jhamman: any progress on this? Our team would be happy to contribute as we have similar requirements in our project.