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/1332#issuecomment-289855515,https://api.github.com/repos/pydata/xarray/issues/1332,289855515,MDEyOklzc3VlQ29tbWVudDI4OTg1NTUxNQ==,6200806,2017-03-28T18:06:41Z,2017-03-28T18:06:41Z,CONTRIBUTOR,">I'm not sure we want to wrap np.gradient.  It seems like other approaches like @rabernat 's xgcm would be more appropriate as a superset of xarray.

Certainly grid-aware differencing and integral operators are preferred when the grid information is known and available, but I'm not sure that therefore a more naive version akin to np.gradient would not be useful.  It's quite likely that there are xarray users (e.g. in non climate/weather/ocean-related fields) wherein a 'c' grid is meaningless to them, yet they still would appreciate being able to easily compute derivatives via xarray operations.

But then we're back to the valid questions raised before re: what is the appropriate scope of xarray functionality, c.f. https://github.com/pydata/xarray/issues/1288#issuecomment-283062107 and subsequent in that thread","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,217385961