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- Fancy indexing a Dataset with dask DataArray causes excessive memory usage · 2 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 802101178 | https://github.com/pydata/xarray/issues/5054#issuecomment-802101178 | https://api.github.com/repos/pydata/xarray/issues/5054 | MDEyOklzc3VlQ29tbWVudDgwMjEwMTE3OA== | alimanfoo 703554 | 2021-03-18T16:45:51Z | 2021-03-18T16:58:44Z | CONTRIBUTOR | FWIW my use case actually only needs indexing a single dimension, i.e., something equivalent to the numpy (or dask.array) compress function. This can be hacked for xarray datasets in a fairly straightforward way: ```python def _compress_dataarray(a, indexer, dim): data = a.data try: axis = a.dims.index(dim) except ValueError: v = data else: # rely on array_function to handle dispatching to dask if # data is a dask array v = np.compress(indexer, a.data, axis=axis) if hasattr(v, 'compute_chunk_sizes'): # needed to know dim lengths v.compute_chunk_sizes() return v def compress_dataset(ds, indexer, dim): if isinstance(indexer, str): indexer = ds[indexer].data
``` Given the complexity of fancy indexing in general, I wonder if it's worth contemplating implementing a |
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Fancy indexing a Dataset with dask DataArray causes excessive memory usage 834972299 | |
| 802096873 | https://github.com/pydata/xarray/issues/5054#issuecomment-802096873 | https://api.github.com/repos/pydata/xarray/issues/5054 | MDEyOklzc3VlQ29tbWVudDgwMjA5Njg3Mw== | alimanfoo 703554 | 2021-03-18T16:39:59Z | 2021-03-18T16:39:59Z | CONTRIBUTOR | Thanks @dcherian. |
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Fancy indexing a Dataset with dask DataArray causes excessive memory usage 834972299 |
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