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https://github.com/pydata/xarray/pull/4089#issuecomment-633286352 https://api.github.com/repos/pydata/xarray/issues/4089 633286352 MDEyOklzc3VlQ29tbWVudDYzMzI4NjM1Mg== 56925856 2020-05-24T19:49:30Z 2020-05-24T19:56:24Z CONTRIBUTOR

One problem I came across here is that pandas automatically ignores 'np.nan' values in any corr or cov calculation. This is hard-coded into the package and there's no skipna=False option sadly, so what I've done in the tests is to use the numpy implementation which pandas is built on (see, for example here).

Current tests implemented are (in pseudocode...): - [x] assert_allclose(xr.cov(a, b) / (a.std() * b.std()), xr.corr(a, b)) - [x] assert_allclose(xr.cov(a,a)*(N-1), ((a - a.mean())**2).sum()) - [x] For the example in my previous comment, I now have a loop over all values of (a,x) to reconstruct the covariance / correlation matrix, and check it with an assert_allclose(...). - [x] Add more test arrays, with/without np.nans -- done

@keewis I tried reading the Hypothesis docs and got a bit overwhelmed, so I've stuck with example-based tests for now.

 
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