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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
996352280 PR_kwDOAMm_X84rv1Fo 5794 Single matplotlib import Illviljan 14371165 closed 0     7 2021-09-14T19:15:12Z 2022-08-12T09:06:30Z 2021-10-24T09:54:28Z MEMBER   0 pydata/xarray/pulls/5794

Reduce number of imports inside functions. I think it helps making the code easier to read as well, as now you know that plt is always available.

  • [x] Tests added
  • [x] Passes pre-commit run --all-files
  • [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

Seems to not be a major difference in initial imports from (my small sample of) repeated tests:

This branch: ```python %timeit -n1 -r1 import xarray

3.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.83 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.87 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.77 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.91 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.8 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

np.mean([3.81, 3.83, 3.87, 3.7, 3.77, 3.91, 3.8]) Out[3]: 3.812857142857143 ```

Main: ```python %timeit -n1 -r1 import xarray

3.93 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.64 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.76 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.79 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.68 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

np.mean([3.93, 3.69, 3.64, 3.76, 3.79, 3.81, 3.68]) Out[4]: 3.7571428571428567 ```

 
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     xarray 13221727 pull
996475523 PR_kwDOAMm_X84rwPey 5796 Add asv benchmark jobs to CI Illviljan 14371165 closed 0     16 2021-09-14T22:00:49Z 2022-08-12T09:01:15Z 2021-10-24T10:08:02Z MEMBER   0 pydata/xarray/pulls/5796

Workflow based on the version from scikit-image. Modfied to have asv.conf.json inside a subdirectory and triggers every push if the PR has the run-benchmark label.

Notes: * https://github.com/scikit-image/scikit-image doesn't have the same benchmark folder setup, for example config file is in root directory, other folder names. * https://github.com/numpy/numpy has same folder name as sckit-image. config file is in the folder however.

References: * https://labs.quansight.org/blog/2021/08/github-actions-benchmarks/ * https://github.com/scikit-image/scikit-image/pull/5424 * https://github.com/jaimergp/scikit-image/pull/1

Tests checked:

  • [x] interp
  • [x] pandas
  • [x] repr
  • [x] combine
  • [x] datarray_missing
  • [x] dataset_io - Skipped too difficult to understand. Some of the tests are possibly broken.
  • [x] indexing
  • [x] reindexing
  • [x] rolling - Some division by 0 prints left.
  • [x] unstacking

TODO: * self.setup_cache

  • [x] Related to #4648
  • [x] Tests added
  • [x] Passes pre-commit run --all-files
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
 
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     xarray 13221727 pull

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