issue_comments: 1201147133

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https://github.com/pydata/xarray/issues/6847#issuecomment-1201147133 https://api.github.com/repos/pydata/xarray/issues/6847 1201147133 IC_kwDOAMm_X85HmAz9 8914493 2022-08-01T12:38:12Z 2022-08-01T12:38:12Z NONE

Do you have to go through __array__ (see #6845) or would accessing the underlying array using DataArray.data work for you?

We could also add some properties under the DataArray.cupy namespace for convenience (See https://github.com/xarray-contrib/cupy-xarray)

It'd be good to see a minimal example showcasing the operations you'd like to work. This would also make a great contribution to https://cupy-xarray.readthedocs.io/

Yes, I'll share a workflow example shortly. Ideally I'd like it to be agnostic, rather than CuPy, for example using Numba mapped arrays for arrays which are larger then GPU RAM. I have several which are a lot larger then the 48GB on the RTX8000 GPUs I'm using for this. I have a mix of a dataframe with points of interest, spatial references tables for coordinate transformation (similar to NTv2 grids), and then use interpolation to estimate characteristics from data in a NetCDF file around the local points of interest. At present I have a workaround where I convert the NetCDF file into a dictionary of arrays which is pickled. The image below shows the mapped output of this process on UK rainfall in 2019 (Data source: UK Met Office) 

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