id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
129919128,MDU6SXNzdWUxMjk5MTkxMjg=,735,Implement vnorm for xarray with dask support,15167171,closed,0,,,4,2016-01-30T00:29:40Z,2019-02-28T16:01:39Z,2019-02-28T16:01:39Z,NONE,,,,"Similarly to  #723 I'd like to see if I can port vnorm from Dask into xarray. This a useful quick operation to have handy for linear models. 
Previously as I work around I tried:
`n = da.reduce(np.linalg.norm, ['shapes', 'x'] )`
but it quickly explodes (eats up all ram) for large DaskArrays
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128687346,MDU6SXNzdWUxMjg2ODczNDY=,723,Implement tensordot for xarray with dask support,15167171,closed,0,,,8,2016-01-26T00:57:23Z,2016-03-05T00:47:49Z,2016-03-05T00:47:49Z,NONE,,,,"I've started using X-ray to store responses from convolutional neural nets over different transformations of images (translation(x,y), rotation (radians), etc). So far its been very intuitive storing and transforming results, unfortunately much of my analysis requires the use of tensor dot products, where I can choose arbitrary dimensions over which to make a projection, or perform a correlation.  While dask implements np.tensordot, xray does not.

One can implement a dot product manually by multiplying data arrays then summing over dimensions.

`fitm = (da_response*da_model).sum('imageID').sum('x_translation').max('models')`

but this ends up being very slow, as I imagine when dot products are implemented by numpy or dask, there is a fair amount of optimization going on.

I am relatively new to GitHub, and this project, would you have any advice on the best way to contribute this functionality? tensordot where in you can put in a list of dimension names in two dataarray over which to compute a sum product, using dasks implementation.
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