Support missing chunks in ConsolidateChunks
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 38/100
Research direction
Start from the ConsolidateChunks entry point and reproduce the provided SplitChunks example with one input removed. Compare the desired behavior with xarray.merge; done means missing chunks are filled with NaN instead of raising the current ValueError.
Written by the indexing model from the issue text.
Description
This currently raises an error, but isn't always a programming error:
import xarray
import xarray_beam as xbeam
import numpy as np
ds = xarray.Dataset({"foo": (('x', 'y'), np.zeros((4, 4)))})
key = xbeam.Key({'x': 0, 'y': 0})
([(key, ds)] | xbeam.SplitChunks({'x': 2, 'y': 2}))[:-1] | xbeam.ConsolidateChunks({'x': -1, 'y': -1})
# ValueError: some expected chunks are missing for vars=None shape: [2, 2]
# len(inputs): 3 [while running 'ConsolidateChunks/Consolidate']
It would be nice if we supported missing chunks by filling in values with NaN, like xarray.merge.
- Dominant language
- Python
- Stars
- 170
- Forks
- 15
- Avg merge
- 18h 27m
- Merged PRs (30d)
- 1
Contributor guide
First steps
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