issue_comments

@mcgibbon thanks.

I agree that there is a careful balance when writing test methods between maintainability and readability.

I do think fixtures that can be helpful, but we definitely do over-use them right now with our giant test cases -- those should really be cleaned up and split up. I just haven't had the energy to get around to that.

Appveyor build failed for some reason when trying to set up Miniconda on Windows 32-bit with Python 2.7. The 64-bit build of Python 3.4 passed.

Downloading Miniconda-3.7.3-Windows-x86.exe from http://repo.continuum.io/miniconda/Miniconda-3.7.3-Windows-x86.exe File saved at C:\projects\xray\Miniconda-3.7.3-Windows-x86.exe Installing C:\projects\xray\Miniconda-3.7.3-Windows-x86.exe to C:\Python27-conda32 C:\projects\xray\Miniconda-3.7.3-Windows-x86.exe /S /D=C:\Python27-conda32 Start-Process : This command cannot be run due to the error: The specified executable is not a valid application for this OS platform..

@shoyer I've done the clean-ups you suggested, apart from for-looping tests for the reasons I mentioned in the line note. I hope my "what's new" additions are appropriate.

@mcgibbon That makes sense to me

The idea is that if a dataset has an attribute, it is making a claim about that data. xarray can't guarantee that claims the attributes make about the data remain valid after operating on that data, so it shouldn't retain those attributes unless the user says it can.

I may have ceilometer data that tells me whether a cloud base is detected at any point in time, with an attribute saying that 0 means no cloud detected, another attribute saying that 1 means cloud detected, and another saying that nan means some kind of error. If I resample that data using a mean or median, those attributes are no longer valid.

Or my Dataset may have an attribute saying that it was output by a certain instrument. If I save that Dataset after doing some analysis, it may give the impression to someone reading the netCDF that they're reading unprocessed instrument data, when they aren't.

Or I may want the hourly variance of a dataset, and do dataset.resample('1H', how='var'). In this case, the units are no longer valid.

It may seem like these are edge cases, but it's better to make no claims most of the time than to make bad claims some of the time.

I know I'm late to the party here. Are there many cases where keep_attrs would be expected to be False? I would have thought that it's generally expected that attrs would stick around when doing operations, either as a default or without an option to drop them. (Of course, it's easy to drop them if a user explicitly sets them to {} in a separate operation)

Thanks for diving into this!

I think you can write the tests a little bit more succinctly, but otherwise this looks good. Also, please add a note to the "What's New" giving yourself credit.