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  • max-sixty · 5 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
464282626 https://github.com/pydata/xarray/pull/2766#issuecomment-464282626 https://api.github.com/repos/pydata/xarray/issues/2766 MDEyOklzc3VlQ29tbWVudDQ2NDI4MjYyNg== max-sixty 5635139 2019-02-16T03:52:01Z 2019-02-16T03:52:01Z MEMBER

OK, thanks. Being able to call np.pinv(da, dim='x') would be awesome, but ack the downsides you outlined in the PEP 

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   ENH - Adding Pseudo-Inverse to computation.py 409618015
464254541 https://github.com/pydata/xarray/pull/2766#issuecomment-464254541 https://api.github.com/repos/pydata/xarray/issues/2766 MDEyOklzc3VlQ29tbWVudDQ2NDI1NDU0MQ== max-sixty 5635139 2019-02-16T00:01:36Z 2019-02-16T00:01:36Z MEMBER

Yes, np.pinv will raise if passed a dim kwargs. This was an intentional choice: http://www.numpy.org/neps/nep-0018-array-function-protocol.html#support-for-implementation-specific-arguments

Thanks.

With the current state, am I correct in thinking we still need wrappers for any numpy.linalg function that requires a dim kwarg? (I guess we could use the axis kwarg instead, but that might be confusing things)

 
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   ENH - Adding Pseudo-Inverse to computation.py 409618015
464234058 https://github.com/pydata/xarray/pull/2766#issuecomment-464234058 https://api.github.com/repos/pydata/xarray/issues/2766 MDEyOklzc3VlQ29tbWVudDQ2NDIzNDA1OA== max-sixty 5635139 2019-02-15T22:48:33Z 2019-02-15T22:48:33Z MEMBER

No, that would give an error message about passing in a bad argument name. We'll need our own wrappers to switch axis to dim.

I understand we'd need to define __array_function__ on xarray objects managed the conversion.

Conditional on that, could someone call np.pinv(da, dim=['x', 'y']? And then DataArray.__array_function__ would receive the dim kwarg.

Or would np.pinv raise if passed a dim kwarg? (if so, we'd still need to declare n wrappers, IIUC?)

 
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   ENH - Adding Pseudo-Inverse to computation.py 409618015
464183807 https://github.com/pydata/xarray/pull/2766#issuecomment-464183807 https://api.github.com/repos/pydata/xarray/issues/2766 MDEyOklzc3VlQ29tbWVudDQ2NDE4MzgwNw== max-sixty 5635139 2019-02-15T20:11:38Z 2019-02-15T20:11:38Z MEMBER

Note that with NEP-18 we'll be able to directly everything in numpy.linalg on xarray objects, without requiring the use of a separate wrapper.

It would be awesome for those numpy functions to just work! Could we start on this now, making it conditional on numpy 1.16 installed?

Reading the NEP, I think you could literally pass dim=['x','y'] to the numpy function, and it'll pass that onto xarray. Lmk if I'm mistaken.

Speaking for @erbian (my colleague) , no rush to merge this - we thought it would be helpful but have it internally. Even better if we can get a general solution.

 
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   ENH - Adding Pseudo-Inverse to computation.py 409618015
463719035 https://github.com/pydata/xarray/pull/2766#issuecomment-463719035 https://api.github.com/repos/pydata/xarray/issues/2766 MDEyOklzc3VlQ29tbWVudDQ2MzcxOTAzNQ== max-sixty 5635139 2019-02-14T17:36:36Z 2019-02-14T17:36:36Z MEMBER

Unfortunately this requires numpy 1.14. That's a year old - too soon to bump the minimum version?

 
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   ENH - Adding Pseudo-Inverse to computation.py 409618015

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