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It would be cool to combine our *_from_delayedrhg_compute_tools.xarray functions with the new xarray combine functions so you could combine based on coords or auto-combine. or just drop the dataarray_ and dataset_ from delayed functions and just provide dataarrays_ and datasets_ functions and point the users to these concat functions.
Workflow would just be:
futures = [ ... ] # flat list of dataarray futures with arbitrary non-overlapping coordinate relationships
da = xr.combine_by_coords(rhgx.dataarrays_from_delayed(futures))
futures = [[...], [...], ...] # nested list of datarrays with hierarchical structures
da = xr.combine_nested(rhgx.dataarrays_from_delayed(futures))
or even, if you want terrible performance and just don't care...
futures = [ ... ] # ordered flat list of dataarray futures with overlapping coordinate relationships
da = functools.reduce(lambda x, y: x.combine_first(y), rhgx.dataarrays_from_delayed(futures))
The text was updated successfully, but these errors were encountered:
Yeah, or just get people to use native xarray and drop the concat functions. Turning futures into lists of dask arrays is already a huge help and then getting people to combine these with xr concat tools is probably the best?
yeah good point so maybe all that's needed is to remove the function that uses concat and add some hints/examples in the docstring of the functions that return a list of dask objects.
Paraphrased from @delgadom in #84:
It would be cool to combine our
*_from_delayed
rhg_compute_tools.xarray
functions with the new xarray combine functions so you could combine based on coords or auto-combine. or just drop the dataarray_ and dataset_ from delayed functions and just provide dataarrays_ and datasets_ functions and point the users to these concat functions.Workflow would just be:
futures = [ ... ] # flat list of dataarray futures with arbitrary non-overlapping coordinate relationships
da = xr.combine_by_coords(rhgx.dataarrays_from_delayed(futures))
futures = [[...], [...], ...] # nested list of datarrays with hierarchical structures
da = xr.combine_nested(rhgx.dataarrays_from_delayed(futures))
or even, if you want terrible performance and just don't care...
futures = [ ... ] # ordered flat list of dataarray futures with overlapping coordinate relationships
da = functools.reduce(lambda x, y: x.combine_first(y), rhgx.dataarrays_from_delayed(futures))
The text was updated successfully, but these errors were encountered: