issues: 1284094480
This data as json
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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1284094480 | I_kwDOAMm_X85MiboQ | 6722 | Avoid loading any data for reprs | 2448579 | closed | 0 | 5 | 2022-06-24T19:04:30Z | 2022-10-28T16:23:20Z | 2022-10-28T16:23:20Z | MEMBER | What happened?For "small" datasets, we load in to memory when displaying the repr. For cloud backed datasets with large number of "small" variables, this can use a lot of time sequentially loading O(100) variables just for a repr. What did you expect to happen?Fast reprs! Minimal Complete Verifiable ExampleThis dataset has 48 "small" variables ```Python import xarray as xr dc1 = xr.open_dataset('s3://its-live-data/datacubes/v02/N40E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y4750000.zarr', engine= 'zarr', storage_options = {'anon':True}) dc1.repr_html() ``` On MVCE confirmation
Relevant log outputNo response Anything else we need to know?No response Environment
INSTALLED VERSIONS
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commit: None
python: 3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:43:32) [Clang 12.0.1 ]
python-bits: 64
OS: Darwin
OS-release: 21.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: None
libnetcdf: None
xarray: 2022.3.0
pandas: 1.4.2
numpy: 1.22.4
scipy: 1.8.1
netCDF4: None
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.11.3
cftime: None
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.10
cfgrib: None
iris: None
bottleneck: None
dask: 2022.05.2
distributed: None
matplotlib: 3.5.2
cartopy: 0.20.2
seaborn: 0.11.2
numbagg: None
fsspec: 2022.5.0
cupy: None
pint: None
sparse: None
setuptools: 62.3.2
pip: 22.1.2
conda: None
pytest: None
IPython: 8.4.0
sphinx: 4.5.0
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completed | 13221727 | issue |