issue_comments: 289855515

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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

 
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