issue_comments

OK, I figured it out. We set dask.set_options(get=dask.get) in xarray/tests/__init__.py, which is convenient for debugging (warning: changing global state!). Naturally, that means dask ignores the value of __dask_scheduler__ attribute.

I don't really have better ideas for how to test this, but it is reassuring that __dask_scheduler__ is the only attribute where we could reasonably expect to encounter this issue and its implementation is basically trivial. I suppose we could make one basic sanity test where we test each xarray data structure using the default scheduler. Possibly it would also make sense to change the scheduler explicitly (e.g., via a helper function for equality checks) rather than overloading it for all tests.

I can remove all the mock related code from test_dask.py entirely and test_dataarray_with_dask_coords still passes.

any suggestions on comprehensive ways to test things?

I still don't understand what actually went wrong here. It looks like we have test coverage for calling compute on an xarray.DataArray (see test_dataarray_with_dask_coords), but even though the exact example from that test fails at the repl the test passes when called with pytest: ``` In [5]: import xarray as xr

In [6]: import numpy as np

In [7]: import dask

In [8]: import dask.array as da

In [9]: import toolz ...: x = xr.Variable('x', da.arange(8, chunks=(4,))) ...: y = xr.Variable('y', da.arange(8, chunks=(4,)) * 2) ...: data = da.random.random((8, 8), chunks=(4, 4)) + 1 ...: array = xr.DataArray(data, dims=['x', 'y']) ...: array.coords['xx'] = x ...: array.coords['yy'] = y ...: ...: assert dict(array.dask_graph()) == toolz.merge(data.dask_graph(), ...: x.dask_graph(), ...: y.dask_graph()) ...: ...: (array2,) = dask.compute(array) ...:

ValueError Traceback (most recent call last) <ipython-input-9-4aaee405bed6> in <module>() 11 y.dask_graph()) 12 ---> 13 (array2,) = dask.compute(array)

~/conda/envs/xarray-py36/lib/python3.6/site-packages/dask/base.py in compute(args, kwargs) 334 results_iter = iter(results) 335 return tuple(a if f is None else f(next(results_iter), a) --> 336 for f, a in postcomputes) 337 338

~/conda/envs/xarray-py36/lib/python3.6/site-packages/dask/base.py in <genexpr>(.0) 334 results_iter = iter(results) 335 return tuple(a if f is None else f(next(results_iter), *a) --> 336 for f, a in postcomputes) 337 338

~/dev/xarray/xarray/core/dataarray.py in _dask_finalize(results, func, args, name) 607 @staticmethod 608 def _dask_finalize(results, func, args, name): --> 609 ds = func(results, *args) 610 variable = ds._variables.pop(_THIS_ARRAY) 611 coords = ds._variables

~/dev/xarray/xarray/core/dataset.py in _dask_postcompute(results, info, args) 551 func, args2 = v 552 r = results2.pop() --> 553 result = func(r, args2) 554 else: 555 result = v

~/dev/xarray/xarray/core/variable.py in _dask_finalize(results, array_func, array_args, dims, attrs, encoding) 389 results = {k: v for k, v in results.items() if k[0] == name} # cull 390 data = array_func(results, *array_args) --> 391 return Variable(dims, data, attrs=attrs, encoding=encoding) 392 393 @property

~/dev/xarray/xarray/core/variable.py in init(self, dims, data, attrs, encoding, fastpath) 267 """ 268 self._data = as_compatible_data(data, fastpath=fastpath) --> 269 self._dims = self._parse_dimensions(dims) 270 self._attrs = None 271 self._encoding = None

~/dev/xarray/xarray/core/variable.py in _parse_dimensions(self, dims) 431 raise ValueError('dimensions %s must have the same length as the ' 432 'number of data dimensions, ndim=%s' --> 433 % (dims, self.ndim)) 434 return dims 435

ValueError: dimensions ('x',) must have the same length as the number of data dimensions, ndim=0 ```

Any objection to having the .compute methods point to dask.compute if the dask.version is appropriate?

Yes, this seems like a small win.

I don't think you're doing anything wrong. This looks like a bug related to @mrocklin's recent addition of dask duck methods for xarray.

Here are the values being passed to xarray.Dataset._dask_postcompute: results = ('mean_agg-aggregate-21a3ca7e382bdeee43ea06bee1ce3feb', 0) info = [(True, <this-array>, (<function Variable._dask_finalize at 0x118156158>, (<function finalize at 0x118350730>, (), ('y',), None, None)))] args = (set(), {'y': 2}, None, None, None)

It seems like something is going wrong on the dask side: results should be the computed numpy array, not an uncomputed dask key.