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  • Automatic duck array testing - reductions · 3 ✖

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  • CONTRIBUTOR · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
872438265 https://github.com/pydata/xarray/pull/4972#issuecomment-872438265 https://api.github.com/repos/pydata/xarray/issues/4972 MDEyOklzc3VlQ29tbWVudDg3MjQzODI2NQ== github-actions[bot] 41898282 2021-07-01T17:51:08Z 2021-08-15T13:15:44Z CONTRIBUTOR

Unit Test Results

6 files           6 suites   58m 27s :stopwatch: 16 289 tests 14 528 :heavy_check_mark: 1 753 :zzz:   8 :x: 90 930 runs  82 604 :heavy_check_mark: 8 284 :zzz: 42 :x:

For more details on these failures, see this check.

Results for commit 1d98fec1.

:recycle: This comment has been updated with latest results.

 
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   Automatic duck array testing - reductions 818059250
828924833 https://github.com/pydata/xarray/pull/4972#issuecomment-828924833 https://api.github.com/repos/pydata/xarray/issues/4972 MDEyOklzc3VlQ29tbWVudDgyODkyNDgzMw== Zac-HD 12229877 2021-04-29T04:03:39Z 2021-04-29T04:03:39Z CONTRIBUTOR

is there a way to separate sizes into dims and shape without using draw? I tried dims, shape = sizes.map(lambda s: tuple(zip(*s))), but since the map strategy is not iterable the final unpacking fails.

You've just got to use draw for this.

I'd prefer the verbose repr of st.builds() when debugging strategies, but if I know the strategy is correct and understand what it generates I'd probably prefer the smaller repr of @st.composite.

That's a good way of thinking about it :slightly_smiling_face: 

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   Automatic duck array testing - reductions 818059250
808736180 https://github.com/pydata/xarray/pull/4972#issuecomment-808736180 https://api.github.com/repos/pydata/xarray/issues/4972 MDEyOklzc3VlQ29tbWVudDgwODczNjE4MA== Zac-HD 12229877 2021-03-27T13:51:54Z 2021-03-27T13:51:54Z CONTRIBUTOR

Looking at https://github.com/keewis/xarray/compare/duckarray-tests...duckarray-tests-hypothesis, for high-level feedback:

  • Overall it looks pretty good; though ping me again if/when it's a PR and I'll do line-level feedback on idiom issues
  • A more general test would generate the shapes, and the axes to reduce over - reducing a 1D array over the first dimension is going to miss things
  • You use @st.composite when the .map() method and a lambda would suffice (though the perf gain is small enough that this is mostly a readability issue)
  • I don't see the point of Label, and we advise against mixing "a strategy or a value". We break this rule a few times for backwards-compatibility in our Numpy support, but wouldn't write such an API these days.

And I'm always delighted to see people using Hypothesis to test libraries that I use and love 🥰🤩

 
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   Automatic duck array testing - reductions 818059250

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