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assume_sorted is False, so for vectorized interpolation across multiple dimensions, we end up lexsorting the coordinates all the time. For some reason, this can be quite slow with dask.
obj=self# sort by slicing if we canforcoordinset(indexers) andset(self._indexes):
# TODO: better check for PandasIndexifself.indexes[coord].is_monotonic_decreasing:
obj=obj.isel(coord: slice(None, None, -1))
# TODO: make None the new defaultifassume_sortedisNone:
# TODO: dims without coordinates are fine tooassume_sorted=all(self.indexes[coord].is_monotonic_increasingforcoordinindexers)
I'll add a reproducible example later, but the problem I've been playing gets much faster for graph construction:
What is your issue?
assume_sorted
is False, so for vectorized interpolation across multiple dimensions, we end up lexsorting the coordinates all the time. For some reason, this can be quite slow with dask.xarray/xarray/core/dataset.py
Line 4081 in 6df8bd6
Instead we should be able to do
I'll add a reproducible example later, but the problem I've been playing gets much faster for graph construction:
xref #6799
cc @mpiannucci @Illviljan
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