issue_comments: 177039255

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https://github.com/pydata/xarray/issues/735#issuecomment-177039255 https://api.github.com/repos/pydata/xarray/issues/735 177039255 MDEyOklzc3VlQ29tbWVudDE3NzAzOTI1NQ== 15167171 2016-01-30T01:18:08Z 2016-01-30T01:26:34Z NONE

Note I mis-typed vecnorm instead of vnorm previously.

I would be OK adding a norm method, although I don't think there's a super strong need for it -- usually I've been happy writing expressions like (x * 2).sum(['shapes', 'x']) * 0.5 instead.

When using large dask arrays that operation tended to fill up ram. I'm not sure why, but it made the dask objects very large, I don't have a good understanding of how those graphs are constructed so I was unable to track down the problem.

I tried out da.std(['shapes', 'x']) and it worked like a charm, but it felt a little silly rescaling it to get the norm.

You would either need to implement this all in xarray, or preferably write da.linalg.norm in dask and use that take. Take a look at the scipy source code for this function -- I suspect you could port this almost directly to dask.

dask has a vnorm, http://dask.pydata.org/en/latest/array-api.html?highlight=norm#dask.array.core.Array.vnorm which I am currently trying to make use of, using the very helpful: ops._dask_or_eager_func('vnorm', n_array_args=1) unfortunately numpy calls their norm norm as opposed to vnorm so I think I'll need to put in a switch depending on the array type before hitting _dask_or_eager_func

 
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