issue_comments

OK, in it goes. Thanks @ocefpaf !

:+1: for filter_by_attrs , and I find it good to have such convenience functions in addition to the general ones like https://github.com/pydata/xarray/issues/916 : newcomers will find their way through xarray more easily

Anyone else have an opinion on the name? I'd like to merge this shortly before releasing v0.8.0 tomorrow.

Assuming we do add the filter method, maybe filter_by_attrs?

It's nice to use names with the same prefix because they appear next to each other in auto-complete.

I'm OK with the specialized get_variables_by_attributes. It doesn't need to be mutually exclusive with the more generic option. I do think the name is a mouthful, though :).

@ocefpaf Good point. It probably is indeed better to write be able to write something generic like ds.filter(lambda x: x.attrs['standard_name'] == 'convective_precipitation_flux') rather the specialized to attributes ds.get_variables_by_attributes(standard_name='convective_precipitation_flux').

Another option would be simply renaming this to filter_attrs, which is more succinct.

Agreed -- this is almost ready. Please add to the API docs (api.rst) and do the docstring fixes.

This looks good to me. Do we want to add this to the api page in the docs?

@ocefpaf We can squash on merge now in the web interface, so that makes things easier.

The appveyor build failure looks unrelated to this change -- something about conda dependencies.

Well, if you guys are mostly excited about using this for coordinate variables, another consistent choice would be to return a list of matching DataArrays. But if we want to return a Dataset, we should only do data variables, because it's weird to lose all the describing coordinates when you match, e.g., standard_name="air_temperature".

On Thu, May 5, 2016 at 7:05 AM, Kyle Wilcox notifications@github.com wrote:

@ocefpaf https://github.com/ocefpaf I agree that xarray model consistent should take precedence. I also use this method to (mostly) pull out coordinate variables and can continue to use netCDF4.Dataset to do that.

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An important design question: should this filter both data variables and coordinates or only data variables? My thought is that it's only worth filtering data variables -- filtering out unmatched coordinates is not very useful.