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List of Datasets argument to open_boutdataset() #132

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merged 5 commits into from
Sep 4, 2020

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johnomotani
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Allow open_boutdataset() to optionally take a list of existing Datasets instead of a filename-glob. Useful for unit tests which no longer have to create files on disk and then read them. Speeds up the unit tests, which is useful so we can add more tests in other PRs without making Travis time out. A few unit tests do still read and write files, so this functionality is still tested.

As well as reading Datasets from disk, allow list of Datasets to be
passed as the argument to open_boutdataset(). Intended for speeding up
unit tests.
Avoids complicating the Dataset-argument path in open_boutdataset.
Now many tests no longer have to create files on disk, the
tmpdir_factory is not necessary for those tests. Remove references to
it, hoping this speeds up the tests a bit.
@johnomotani johnomotani added the testing Involves writing or running unit, integrated, or regression tests label Sep 4, 2020
@pep8speaks
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pep8speaks commented Sep 4, 2020

Hello @johnomotani! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2020-09-04 00:35:52 UTC

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codecov-commenter commented Sep 4, 2020

Codecov Report

Merging #132 into master will increase coverage by 0.13%.
The diff coverage is 83.33%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master     #132      +/-   ##
==========================================
+ Coverage   71.61%   71.74%   +0.13%     
==========================================
  Files          14       14              
  Lines        1705     1727      +22     
  Branches      363      371       +8     
==========================================
+ Hits         1221     1239      +18     
- Misses        380      383       +3     
- Partials      104      105       +1     
Impacted Files Coverage Δ
xbout/load.py 81.23% <83.33%> (+0.04%) ⬆️

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3 participants