issue_comments

Thanks!

We have a bit of a circular imports mess :). For now, just import them at the top of the to_netcdf method.

I have a couple inline comments that I think you missed?

I have a few final nits, but looking very nice!

The easiest fix would be to add a decorator to skip tests on Windows, like the existing decorators that we use: https://github.com/pydata/xarray/blob/master/xarray/test/init.py#L73

I'm not sure off hand how to detect if we're running on Windows but it can't be hard.

To add a close method, I would copy these lines from dataset.py (or better use, move them to the common base class BaseDataObject in common.py): https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L251-L262

Then, you just need to define _file_obj = None in DataArray.__init__ and set it when appropriate.

I would add these tests to test_backends, probably a new test class. The best test is probably a round-trip test where we save and load a DataArray and verify that the result is identical to what we started with. On Sun, Aug 28, 2016 at 2:20 PM Robin Wilson notifications@github.com wrote:

Thanks @shoyer https://github.com/shoyer - I wasn't actually aware that DataArrays can have a name before they're put into a Dataset (that's one of the things I love about contributing to OSS: you always learn things about the software that you never knew before!). I'll deal with the design issues ASAP.

How would you suggest testing this? I wasn't sure where to put the tests, and what exactly to test? Presumably something about the equivalence between this and saving after manually converting to a dataset, but I wasn't really sure how best to go about it.

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I'm also a little concerned about the design here, because it always saves/restores the DataArray with the name 'data', which could override the original name. Ideally this method should save/restore a DataArray as faithfully as possible.

Instead, maybe we should only override the original name if it's None, or the same name as one of it's coordinates (which would otherwise result in an invalid NetCDF file).

Then we could restore by looking for a Dataset with single data variable regardless of the name, e.g.,

if len(dataset.data_vars) != 1: raise ValueError data_array, = dataset.data_vars.values()

This is great, but it needs tests and documentation :).