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https://github.com/pydata/xarray/pull/1750#issuecomment-350033913 https://api.github.com/repos/pydata/xarray/issues/1750 350033913 MDEyOklzc3VlQ29tbWVudDM1MDAzMzkxMw== 1217238 2017-12-07T17:13:49Z 2017-12-07T17:13:49Z MEMBER

I don't know exactly what would go wrong, but I'm pretty sure masked Dask arrays would break xarray in some subtle ways. It would be better to convert them to using unmasked Dask arrays using NaN. On Thu, Dec 7, 2017 at 1:18 AM Duncan Watson-Parris notifications@github.com wrote:

@duncanwp commented on this pull request.

In xarray/convert.py https://github.com/pydata/xarray/pull/1750#discussion_r155465954:

@@ -181,7 +183,9 @@ def from_iris(cube): cell_methods = _iris_cell_methods_to_str(cube.cell_methods) if cell_methods: array_attrs['cell_methods'] = cell_methods - dataarray = DataArray(cube.data, coords=coords, name=name, + + cube_data = ma.filled(cube.core_data(), get_fill_value(cube.dtype)) if hasattr(cube, 'core_data') else cube.data

OK, I hadn't appreciated that dask wasn't a hard requirement. I could leave them as whatever type they're currently stored in - how would xarray cope with a dask masked array?

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