issue_comments: 656119415

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https://github.com/pydata/xarray/issues/4208#issuecomment-656119415 https://api.github.com/repos/pydata/xarray/issues/4208 656119415 MDEyOklzc3VlQ29tbWVudDY1NjExOTQxNQ== 3460034 2020-07-09T13:13:43Z 2020-07-09T13:13:43Z CONTRIBUTOR

I think there are already enough headaches with __iter__ being always defined and confusing libraries such as pandas (hgrecco/pint#1128). I don't see why pint should be explicitly aware of dask (except in unit tests)? It should only deal with generic NEP18-compatible libraries (numpy, dask, sparse, cupy, etc.).

Since Pint wraps Dask, in order to leverage Dask Array functionality on Pint Quantities, we need to have the Dask collection interface available. In a sense, Pint needs special handling for Dask like xarray Variables do since they both can be upcast types of Dask Array. Implicitly passing through attributes (how Pint handles special methods/attributes of downcast types in general) from the wrapped Dask Array is not sufficient, however, because the finalizers have to rewrap with Quantity (see https://github.com/hgrecco/pint/pull/1129/files#diff-d9924213798d0fc092b8cff13928d747R1947-R1950), hence the explicit awareness of Dask being needed in Pint.

We should ask the dask team to formalize what defines a "dask-array-like", like they already did with dask collections, and implement their definition in xarray.

Done! See https://github.com/dask/dask/issues/6385.

 
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