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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at ▲ | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2211106929 | I_kwDOAMm_X86DytBx | 8882 | to_zarr silently loses data when using append_dim, if chunks are different to zarr store | harryC-space-intelligence 140395181 | closed | 0 | 4 | 2024-03-27T15:27:02Z | 2024-03-29T14:35:51Z | 2024-03-29T14:35:51Z | NONE | What happened?When writing a chunked DataArray to an existing zarr store, appending along an existing dimension of the store, I have found that some data are not written if there are multiple array chunks to one zarr chunk. I appreciate it is probably bad practice to have different chunksizes in my DataArray and zarr_store, but I think its a realistic scenario that needs to be caught. This may be related to / the same underlying issue as #8371. Perhaps the checks mentioned in https://github.com/pydata/xarray/issues/8371#issuecomment-1814589157 are somehow getting bypassed? Using zarr's ThreadSynchronizer is the only way I have found to ensure that all the data gets written. What did you expect to happen?I expected that either
Minimal Complete Verifiable Example```Python import xarray as xr import numpy as np from matplotlib import pyplot as plt x_coords = np.arange(10) y_coords = np.arange(10) t_coords = np.array([np.datetime64('2020-01-01').astype('datetime64[ns]')]) data = np.ones((10,10)) for i in range(4): plt.subplot(1,4,i+1)
``` MVCE confirmation
Relevant log outputNo response Anything else we need to know?Output from the plots above: Environment
INSTALLED VERSIONS
------------------
commit: None
python: 3.11.4 | packaged by conda-forge | (main, Jun 10 2023, 18:08:17) [GCC 12.2.0]
python-bits: 64
OS: Linux
OS-release: 5.15.0-1041-azure
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2
xarray: 2024.2.0
pandas: 2.2.1
numpy: 1.26.4
scipy: 1.12.0
netCDF4: 1.6.5
pydap: installed
h5netcdf: 1.3.0
h5py: 3.10.0
Nio: None
zarr: 2.17.1
cftime: 1.6.3
nc_time_axis: 1.4.1
iris: None
bottleneck: 1.3.8
dask: 2024.3.1
distributed: 2024.3.1
matplotlib: 3.8.3
cartopy: 0.22.0
seaborn: 0.13.2
numbagg: None
fsspec: 2024.3.1
cupy: None
pint: 0.23
sparse: 0.15.1
flox: 0.9.5
numpy_groupies: 0.10.2
setuptools: 69.2.0
pip: 24.0
conda: 24.1.2
pytest: 8.1.1
mypy: None
IPython: 8.22.2
sphinx: None
|
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