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  • keewis · 3 ✖

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  • xr.cov() and xr.corr() · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
633922774 https://github.com/pydata/xarray/pull/4089#issuecomment-633922774 https://api.github.com/repos/pydata/xarray/issues/4089 MDEyOklzc3VlQ29tbWVudDYzMzkyMjc3NA== keewis 14808389 2020-05-26T09:43:29Z 2020-05-26T09:43:29Z MEMBER

thanks. Do you want to put in a PR fixing that?

 
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   xr.cov() and xr.corr() 623751213
633310925 https://github.com/pydata/xarray/pull/4089#issuecomment-633310925 https://api.github.com/repos/pydata/xarray/issues/4089 MDEyOklzc3VlQ29tbWVudDYzMzMxMDkyNQ== keewis 14808389 2020-05-24T22:38:54Z 2020-05-24T22:38:54Z MEMBER

no worries about hypothesis, that's something we can add in a new PR.

Also, I don't think there is a hypothesis.extra.xarray module, yet. Any comments on that, @Zac-HD?

 
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   xr.cov() and xr.corr() 623751213
633216248 https://github.com/pydata/xarray/pull/4089#issuecomment-633216248 https://api.github.com/repos/pydata/xarray/issues/4089 MDEyOklzc3VlQ29tbWVudDYzMzIxNjI0OA== keewis 14808389 2020-05-24T11:22:42Z 2020-05-24T12:09:51Z MEMBER

If you want to test individual values without reimplementing the function in the tests (which is what I suspect comparing with the result of np.cov would require), that might be the only way.

If not, you could also check properties of covariance / correlation matrices, e.g. that assert_allclose(xr.cov(a, b) / (a.std() * b.std()), xr.corr(a, b)) (I'm not sure if I remember that formula correctly) or that the diagonal of the auto-covariance matrix is the same as the variance of the array (with a 1D vector, not sure about more dimensions). If you decide to test using properties, you could also extend our small collection of tests using hypothesis (see #1846).

 
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   xr.cov() and xr.corr() 623751213

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