issue_comments: 850307092

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https://github.com/pydata/xarray/issues/5376#issuecomment-850307092 https://api.github.com/repos/pydata/xarray/issues/5376 850307092 MDEyOklzc3VlQ29tbWVudDg1MDMwNzA5Mg== 4160723 2021-05-28T10:04:52Z 2021-05-28T10:04:52Z MEMBER

I think there's certainly something to be won just by having a data structure which says these arrays/datasets represent a multiscale series.

I agree, but I'm wondering whether the multiscale series couldn't be also viewed as something that can be abstracted away, i.e., the original dataset (level 0) is the "real" dataset while all other levels are some derived datasets that are convenient for some specific applications (e.g., visualization) but not very useful for general use.

Having a single xarray.Dataset with a custom index (+ custom Dataset extension) taking care of all the multiscale stuff may have benefits too. For example, it would be pretty straightforward reusing a tool like https://github.com/xarray-contrib/xpublish to interactively (pre)fetch data to web-based clients (via some custom API endpoints). More generally, I guess it's easier to integrate with some existing tools built on top of Xarray vs. adding support for a new data structure.

Some related questions (out of curiosity):

  • Are there cases in practice where on-demand downsampling computation would be preferred over pre-computing and storing all pyramid levels for the full dataset? I admit it's probably a very naive question since most workflows on the client side would likely start by loading the top level (lowest resolution) dataset at full extent, which would require pre-computing the whole thing?
  • Are there cases where it makes sense to pre-compute all the the pyramid levels in-memory (could be, e.g., chunked dask arrays persisted on a distributed cluster) without the need to store them?
 
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