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https://github.com/pydata/xarray/issues/2298#issuecomment-406104928 https://api.github.com/repos/pydata/xarray/issues/2298 406104928 MDEyOklzc3VlQ29tbWVudDQwNjEwNDkyOA== 1217238 2018-07-18T23:26:57Z 2018-07-18T23:26:57Z MEMBER

The main practical difference is that it allows us to reliably guarantee that expressions like f(x, y)[i] always get evaluated like f(x[i], y[i]). Dask doesn't have this optimization yet (https://github.com/dask/dask/issues/746), so indexing operations still compute the function f() on each block of an array. This issue provides full context from the xarray side: https://github.com/pydata/xarray/issues/1725

The typical example is spatially referenced imagery, e.g., a 2D satellite photo of the surface of the Earth with 2D latitude/longitude coordinates associated with each point. It would be very expensive to store full latitude and longitude arrays, but fortunately they can usually be computed cheaply from row and column indices.

Ideally, this logic would live outside xarray. But it's important enough to some xarray users (especially geoscience + astronomy) and we have enough related functionality (e.g., for lazy and explicit indexing) that it probably makes sense to add it.

 
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