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 can
for coord in set(indexers) and set(self._indexes):
# TODO: better check for PandasIndex
if self.indexes[coord].is_monotonic_decreasing:
obj = obj.isel(coord: slice(None, None, -1))
# TODO: make None the new default
if assume_sorted is None:
# TODO: dims without coordinates are fine too
assume_sorted = all(self.indexes[coord].is_monotonic_increasing for coord in indexers)
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_sortedis 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.https://github.com/pydata/xarray/blob/6df8bd606a8a9a3378c7672c087e08ced00b2e15/xarray/core/dataset.py#L4081
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