issue_comments: 1255014363

This data as json

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/7065#issuecomment-1255014363 https://api.github.com/repos/pydata/xarray/issues/7065 1255014363 IC_kwDOAMm_X85Kzf_b 4160723 2022-09-22T13:19:23Z 2022-09-22T13:19:23Z MEMBER

As my latitude and longitude arrays in both datasets have a resolution of 0.1 degrees, wouldn't it make sense to use np.float16 for both arrays?

I don't think so (at least not currently). The numpy arrays are by default converted to pandas.Index objects for each dimension coordinate, and for floats there's only pandas.Float64Index. It looks like it will be depreciated in favor of pandas.NumericIndex that supports more dtypes, but still I don't see support for 16 bits floats.

Regarding your nearest lat/lon point data selection problem, this is something that could probably be better solved using more specific (custom) indexes like the ones available in xoak. Xoak only supports point-wise selection at the moment, though.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   1381955373