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- ljstrnadiii · 3 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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1527606751 | https://github.com/pydata/xarray/issues/7015#issuecomment-1527606751 | https://api.github.com/repos/pydata/xarray/issues/7015 | IC_kwDOAMm_X85bDW3f | ljstrnadiii 3171991 | 2023-04-28T13:55:34Z | 2023-04-28T13:55:34Z | NONE | @jdldeauna how did you resolve this issue? I am seeing similar issues, but only encounter this when writing to zarr with a dask cluster. |
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ArrayNotFoundError when saving xarray Dataset as zarr 1368186791 | |
1297218347 | https://github.com/pydata/xarray/issues/7234#issuecomment-1297218347 | https://api.github.com/repos/pydata/xarray/issues/7234 | IC_kwDOAMm_X85NUfsr | ljstrnadiii 3171991 | 2022-10-31T15:00:06Z | 2022-10-31T15:09:42Z | NONE | Yeah, I was afraid of that lol. This must also be a pandas issue, too, since they recommend a similar way of extending pandas dataframes. I couldn't find a similar pandas issue/pr and was a bit surprised by that. My temp solution is to use type narrowing:
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Inspecting arguments with accessors 1427457128 | |
1215233142 | https://github.com/pydata/xarray/issues/6916#issuecomment-1215233142 | https://api.github.com/repos/pydata/xarray/issues/6916 | IC_kwDOAMm_X85Ibvx2 | ljstrnadiii 3171991 | 2022-08-15T15:59:29Z | 2022-08-15T16:03:41Z | NONE | @dcherian sure thing! Use-case:Sometimes I map functions over the chunks by passing slices around and read the dataset from zarr in the function, then slice on the subset and apply some function instead of map_blocks because I always struggle with that function and often write to zarr and don't return anything. So, I find myself passing store, group and the dataset itself (dask will complain if I try to pass the dataset around and ask to scatter--my guess is that the meta data is large enough to trigger that recommendation). ``` def iter_dset_chunks(dset: xr.Dataset): # these correspond to the start/stop of the underlying zarr chunks x_starts = np.cumsum([0] + list(dset.chunks["x"])[:-1]) x_start_step = zip(x_starts, dset.chunksizes["x"]) y_starts = np.cumsum([0] + list(dset.chunks["y"])[:-1]) y_start_step = zip(y_starts, dset.chunksizes["y"]) chunk_slices = list(product(x_start_step, y_start_step))
def compute_write(store, group, x_slice, y_slice): dset = xr.open_zarr(store=store, group=group).sel(x=x_slice, y=y_slice) # some longer running operation result = big_op(dset) result.to_zarr(...) def map_compute_write_v1(dset, store, group): slices = iter_dset_chunks(dset) for x_slice, y_slice in slices: f = client.submit(compute_write, store, group, x_slice, y_slice) ... def map_compute_write_v2(dset):
slices = iter_dset_chunks(dset)
store = dset.encoding['source']['store']
group = dset.encoding['source']['group']
for x_slice, y_slice in slices:
f = client.submit(compute_write, store, group, x_slice, y_slice)
...
I also assume that all datasets are zarr backed, but if I didn't I would need to know how to read again given the dataset's attributes. |
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Given zarr-backed Xarray determine store and group 1339129609 |
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