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- Unexpected chunking behavior when using `xr.align` with `join='outer'` · 1 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 704530619 | https://github.com/pydata/xarray/issues/4112#issuecomment-704530619 | https://api.github.com/repos/pydata/xarray/issues/4112 | MDEyOklzc3VlQ29tbWVudDcwNDUzMDYxOQ== | jbusecke 14314623 | 2020-10-06T20:20:34Z | 2020-10-06T20:20:34Z | CONTRIBUTOR | Just tried this with the newest dask version and can confirm that I do not get huge chunks anymore IF i specify short_time = xr.cftime_range('2000', periods=12) long_time = xr.cftime_range('2000', periods=120) data_short = np.random.rand(len(short_time)) data_long = np.random.rand(len(long_time)) n=1000 a = xr.DataArray(data_short, dims=['time'], coords={'time':short_time}).expand_dims(a=n, b=n).chunk({'time':3}) b = xr.DataArray(data_long, dims=['time'], coords={'time':long_time}).expand_dims(a=n, b=n).chunk({'time':3}) a,b = xr.align(a,b, join = 'outer')
with the defaults, I still get one giant chunk.
Ill try this soon in a real world scenario described above. Just wanted to report back here. |
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Unexpected chunking behavior when using `xr.align` with `join='outer'` 627600168 |
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