html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2281#issuecomment-508078893,https://api.github.com/repos/pydata/xarray/issues/2281,508078893,MDEyOklzc3VlQ29tbWVudDUwODA3ODg5Mw==,1828519,2019-07-03T12:49:20Z,2019-07-03T12:49:20Z,CONTRIBUTOR,"@kmuehlbauer Thanks for the ping. I don't have time to read this whole thread, but based on your comment I have a few things I'd like to point out. First, the [pykdtree package](https://github.com/storpipfugl/pykdtree) is a good alternative to the scipy kdtree implementation. It has been shown to be much faster and uses openmp for parallel processing. Second, the [pyresample library](https://pyresample.readthedocs.io/en/latest/) is my main way of resampled geolocated data. We use it in Satpy for [resampling](https://satpy.readthedocs.io/en/latest/resample.html), but right now we haven't finalized the interfaces so things are kind of spread between satpy and pyresample as far as easy xarray handling. Pyresample uses SwathDefinition and AreaDefinition objects to define the geolocation of the data. In Satpy the same KDTree is used for every in-memory gridding, but we also allow a `cache_dir` which will save the indexes for every `(source, target)` area pair used in the resampling.

I'm hoping to sit down and get some geoxarray stuff implemented during SciPy next week, but usually get distracted by all the talks so no promises. I'd like geoxarray to provide a low level interface for getting and converting CRS and geolocation information on xarray objects and leave resampling and other tasks to libraries like pyresample and [rioxarray](https://github.com/corteva/rioxarray).","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,340486433