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  • dcherian · 2 ✖

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  • Add defaults during concat 508 · 2 ✖

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586346637 https://github.com/pydata/xarray/pull/3545#issuecomment-586346637 https://api.github.com/repos/pydata/xarray/issues/3545 MDEyOklzc3VlQ29tbWVudDU4NjM0NjYzNw== dcherian 2448579 2020-02-14T15:52:21Z 2020-02-14T15:52:33Z MEMBER

I am now wondering if we can use align or reindex to do the filling for us.

Example: goal is concat along 'x' with result dataset having x=[1,2,3,4] 1. Loop through datasets and assign coordinate values as appropriate. 2. Break datasets up into mappings collected = {"variable": [var1_at_x=1, var2_at_x=2, var4_at_x=4]} -> there's some stuff in merge.py that could be reused for this 3. concatenate these lists to get a new mapping concatenated = {"variable": [var_at_x=[1,2,4]]} 4. apply reindexed = {concatenated[var].reindex(x=[1,2,3,4], fill_value=...) for var in concatenated} 5. create dataset Dataset(reindexed)

Step 1 would be where we deal with all the edge cases mentioned in @shoyer's comment viz

For example:

Pre-existing vs non-pre-existing dimension Pre-existing dimensions of different sizes Missing data variables vs coordinates vs indexed coordinates

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  Add defaults during concat 508 524043729
577737475 https://github.com/pydata/xarray/pull/3545#issuecomment-577737475 https://api.github.com/repos/pydata/xarray/issues/3545 MDEyOklzc3VlQ29tbWVudDU3NzczNzQ3NQ== dcherian 2448579 2020-01-23T15:41:33Z 2020-01-23T15:41:33Z MEMBER

the most time consuming part is the expand_dims for every dataset, which accounts for roughly 80% overall concat runtime.

Hmmm... maybe we need a short-circuit version of expand_dims?

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  Add defaults during concat 508 524043729

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