issue_comments

@hrishikeshac in case you come back to see this: thank you for taking it so far; your code was helpful to eventually getting this feature in. And we'd of course appreciate any additional contributions.

@r-beer great—you were right to start your own PR

Yeah those are pretty normal. Your suggestions look right: you can keep both on both sets and eliminate any duplicate imports in the first,

(FYI I edited your comment so it displayed properly, seems you need a line break after <details>)

Yes 100%! Let me know if that doesn't work!

Great @r-beer , we can be helpful in getting you up & running

Given this branch has diverged from master, I would make your own fork and merge in master; looks like you'll have some minor conflicts. (more details in our contributing.rst docs, or post here if confused). You can then open up your own draft PR.

Re the tests: pytest should print a list of the tests that failed and their stack traces, do you not see anything?

I agree that the case for DataArray.dot is questionable. It sort of makes sense because numpy and pandas both have it as a method, but the @ operator is a really a cleaner way to express this now that we're Python 3 only. (Speaking of which, why don't we support @ in xarray yet? :).)

On Mon, Jan 7, 2019 at 1:43 AM Keisuke Fujii notifications@github.com wrote:

@max-sixty https://github.com/max-sixty I am not sure whether DataArray.dot is a right choice. But I am wondering for cov case, it sounds like to compute a covariance of the DataArray itself rather than the cross covariance with another DataArray.

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@max-sixty I am not sure whether DataArray.dot is a right choice. But I am wondering for cov case, it sounds like to compute a covariance of the DataArray itself rather than the cross covariance with another DataArray.

Probably a function like xr.cov(x, y) is better than method?

We should be more concerned with correctness than consistency - but is having DataArray.dot consistent with DataArray.cov? If not, what's the salient difference?

I also think making this a function is probably a good idea, even though it's different from pandas.

One question: how should these functions align their arguments? Recall that xarray does an inner join for arithmetic (though there's an option to control this), and an outer join in most other cases. It's not entirely obvious to me what the right choice is here (or if it really even matters).

I agree with @fujisoup

I am a little worrying that users could misunderstand cov is for (auto-)covariance rather than cross-covariance, which we are implementing here. Probably a function like xr.cov(x, y) is better than method?