issues

Full log here: https://dev.azure.com/xarray/xarray/_build/results?buildId=3023&view=logs&jobId=2280efed-fda1-53bd-9213-1fa8ec9b4fa8&j=2280efed-fda1-53bd-9213-1fa8ec9b4fa8&t=175181ee-1928-5a6b-f537-168f7a8b7c2d

Here are three of the errors:

/usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/sparse/_coo/umath.py:739: SystemError _ test_variable_method[obj.where(*(), **{'cond': <xarray.Variable (x: 10, y: 5)>\n<COO: shape=(10, 5), dtype=bool, nnz=3, fill_value=False>})-True] _ TypeError: expected dtype object, got 'numpy.dtype[uint64]'

`` def _match_coo(*args, **kwargs): """ Matches the coordinates for any number of input :obj:COO` arrays. Equivalent to "sparse" broadcasting for all arrays.

    Parameters
    ----------
    args : Tuple[COO]
        The input :obj:`COO` arrays.
    return_midx : bool
        Whether to return matched indices or matched arrays. Matching
        only supported for two arrays. ``False`` by default.
    cache : dict
        Cache of things already matched. No cache by default.

    Returns
    -------
    matched_idx : List[ndarray]
        The indices of matched elements in the original arrays. Only returned if
        ``return_midx`` is ``True``.
    matched_arrays : List[COO]
        The expanded, matched :obj:`COO` objects. Only returned if
        ``return_midx`` is ``False``.
    """
    from .core import COO
    from .common import linear_loc

    cache = kwargs.pop("cache", None)
    return_midx = kwargs.pop("return_midx", False)
    broadcast_shape = kwargs.pop("broadcast_shape", None)

    if kwargs:
        linear = [idx[s] for idx, s in zip(linear, sorted_idx)]

      matched_idx = _match_arrays(*linear)

E SystemError: CPUDispatcher(<function _match_arrays at 0x7f66b6272af0>) returned a result with an error set

```

``` ____ testdask_token ______

@requires_dask
def test_dask_token():
    import dask

    s = sparse.COO.from_numpy(np.array([0, 0, 1, 2]))

    # https://github.com/pydata/sparse/issues/300
    s.__dask_tokenize__ = lambda: dask.base.normalize_token(s.__dict__)

    a = DataArray(s)
    t1 = dask.base.tokenize(a)
    t2 = dask.base.tokenize(a)
    t3 = dask.base.tokenize(a + 1)
    assert t1 == t2
    assert t3 != t2
    assert isinstance(a.data, sparse.COO)

    ac = a.chunk(2)
    t4 = dask.base.tokenize(ac)
    t5 = dask.base.tokenize(ac + 1)
    assert t4 != t5

  assert isinstance(ac.data._meta, sparse.COO)

E AssertionError: assert False E + where False = isinstance(array([], dtype=int64), <class 'sparse._coo.core.COO'>) E + where array([], dtype=int64) = dask.array<xarray-\<this-array>, shape=(4,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray>._meta E + where dask.array<xarray-\<this-array>, shape=(4,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray> = <xarray.DataArray (dim_0: 4)>\ndask.array<xarray-\<this-array>, shape=(4,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray>\nDimensions without coordinates: dim_0.data E + and <class 'sparse._coo.core.COO'> = sparse.COO ```

From @keewis comment in #4138

I just noticed that the rackcdn.org repository doesn't have matplotlib>=3.2.0, so since about late February we don't test against matplotlib upstream anymore.