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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 |
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1358960570 | I_kwDOAMm_X85RABe6 | 6976 | dataset.sel inconsistent results when argument is a list or a slice. | JamiePringle 12818667 | open | 0 | 5 | 2022-09-01T14:40:34Z | 2022-09-02T14:13:27Z | NONE | What happened?I am not sure if what I report is a bug; however, it is certainly not what I expect from a careful reading of the documentation, and I wonder if it is leading to some issues I describe below. I am working with a large dataset produced by merging the output of runs made by different MPI processes. There are two coordinates, ("trajectory","obs"). All of the "obs" in the dataset are in order, but the "trajectory" coordinate is not in order. I made a smaller dataset that illustrates the issue below by reducing the number of "obs" from 250 to 2; this dataset can be found at http://oxbow.sr.unh.edu/data/smallExample.zarr.zip . This dataset looks like:
If I want an ordered set of trajectories, say trajectories [1,2,3,4,5,6,7,8,9,10], and I do this with But if I use the slice operator to specify what I want, I get something very different: I have had all sorts of issues with the full dataset, including .to_zarr(dataset, compute=False) failing to return a delayedObject because it used all the memory, What did you expect to happen?See above for full descriptions Minimal Complete Verifiable Example```Python get data from http://oxbow.sr.unh.edu/data/smallExample.zarr.zipdataIn=xr.open_zarr('smallExample.zarr') print(dataIn.sel(trajectory=arange(1,11))) print(dataIn.sel(trajectory=slice(1,11))) ``` MVCE confirmation
Relevant log outputNo response Anything else we need to know?No response Environment
INSTALLED VERSIONS
------------------
commit: None
python: 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:04:59) [GCC 10.3.0]
python-bits: 64
OS: Linux
OS-release: 5.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.8.1
xarray: 2022.6.0
pandas: 1.4.3
numpy: 1.23.2
scipy: 1.9.0
netCDF4: 1.6.0
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.12.0
cftime: 1.6.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2022.8.1
distributed: 2022.8.1
matplotlib: 3.5.3
cartopy: 0.20.3
seaborn: None
numbagg: None
fsspec: 2022.7.1
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 65.2.0
pip: 22.2.2
conda: None
pytest: 7.1.2
IPython: 8.4.0
sphinx: None
|
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