issue_comments

For nearest-neighbor style resampling, we already have support for 1-dimensional resampling in .reindex()/.sel(). It would feel pretty natural to add support for N-dimensional resampling, too, if those lookups can use an index of some sort (i.e., a KDTree) to do things efficiently.

@mraspaud - What functionality are you interested in bringing over? I've been watching pyresample for a while and would love to see our two packages leverage each other's functionality (where possible).

@mraspaud of pyresample expressed interest to me offline about bringing some of pyresample's resampling features into xarray -- welcome to the conversation!

Awesome work @JiaweiZhuang! This could be a great way forward for this important need. I think lots of us would be keen to contribute to your project. I encourage you to add tests and docs...that will help other contributors feel comfortable getting involved.

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?

It's pretty standard to follow Python's PEP8 with NumPy-style docstrings.

I generally like the Google style guide, too, but it leans towards being overly strict -- sometimes it's OK to be more relaxed (e.g., it's rules for valid import statements). Also, the public facing version has gotten out of date (I think there are plans to update it).

@rabernat - I've more or less settled that this belongs in a separate package that uses xarray's accessor features.

I've setup a little project (xmap) to get this started that I hope we can garner some volunteers to help push forward (nudge @godfrey4000 and @forman): https://github.com/jhamman/xmap

There will likely be some overlap between features that xmap and xarray. In those cases, we can try to push relevant features toward xarray.

@godfrey4000: lots of us in the climate community would like xarray-backed regridding. It is a hard problem, however. It wold be great if you wanted to work on it.

At a recent workshop, a group of xarray users developed a draft design document for a regridding package. https://aospy.hackpad.com/Regridding-Design-Document-tENJARIeg83

Your comments on this would be very welcome. An open question is whether the regridding belongs within xarray or in a standalone package (which would of course have xarray as a dependency).

I feel like the biggest application of the multi-dimensional groupby will be with "conservative resampling" and coarse-graining, where you want to make sure to conserve certain integrals (e.g. total heat content) while changing coordinates.

pyresample will be more useful for fine-graining and interpolating missing data.

@forman - no progress on my end and no plans to work on this in the next 6 months. If your team is interested in working on this, we can discuss the api further and I'm happy to provide input and guidance along the way. One option is to wrap pyresample and possibly fill in some of the missing pieces in their api. I'd like to see a user interface that looks something like this:

python da.resample_like(other, kind='bilinear', dims=('lat', 'lon'))

As @rabernat mentions, for some remapping/resampling, the multi-dimensional groupby will help with some of these applications.

I'd be interested in helping build a wrapper / api that supports the following types of regridding operations. I don't think pyresample handles all of these: 1. Bilinear interpolation 2. Bicubic interpolation 3. Distance-weighted average remapping 4. Nearest neighbor remapping 5. Conservative (area) remapping 6. Largest area fraction remapping

I would also like to see some better support for 2D coordinate variables. These are specifically outlined in the cf-conventions in section: 5.2. Two-Dimensional Latitude, Longitude, Coordinate Variables. Maybe we can open an additional issue for that...

@forman I am starting to suspect that this might be possible to implement through #818

Indeed, SciPy's ndimage.interpolation.zoom would probably be more appropriate (and faster) for regular grids.

Xray currently doesn't have any built-in support for handling projected data, but basic selection and regridding (from explicit arrays of 2D coordinates) seems in scope for the project.

Pyresample is probably overkill for that case. Aggregating / decimating regular lat-lon grids could probably be done much more simply. For example

python N = 10 fac = 2 x = np.arange(N, dtype=np.float64) np.add.reduceat(x, np.arange(0,N,fac)) / fac

This gives array([ 0.5, 2.5, 4.5, 6.5, 8.5])

This type of resampling has the advantage of preserving certain integral invariants, as opposed to the nearest neighbor resampling in the example above. (Imagine if there had been lots of spatial variance below the 5 degree scale in that example--it would have been aliased horribly. That was only avoided because the original field was very smooth.) It is also very fast.

Pyresample seems best for complicated transformations from one map projection to another. I'm not sure I fully understand how xray handles grids where the coordinates are themselves 2d fields, as in this example.