issue_comments

Thanks for the review. I'll merge when tests pass.

OK great! Looks good to me!

On Tue, Jan 21, 2020 at 9:32 AM Deepak Cherian notifications@github.com wrote:

@dcherian commented on this pull request.

In xarray/tests/test_sparse.py https://github.com/pydata/xarray/pull/3660#discussion_r369142143:

@@ -873,3 +873,16 @@ def test_dask_token(): t5 = dask.base.tokenize(ac + 1) assert t4 != t5 assert isinstance(ac.data._meta, sparse.COO) + + +@requires_dask

There's a importorskip("sparse") on top so I think this is fine

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/pydata/xarray/pull/3660?email_source=notifications&email_token=AAJJFVVGGV56U62XNSJLVNTQ64WURA5CNFSM4KCEN2J2YY3PNVWWK3TUL52HS4DFWFIHK3DMKJSXC5LFON2FEZLWNFSXPKTDN5WW2ZLOORPWSZGOCSQHUMA#discussion_r369142143, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAJJFVVWVJ2KZUF3NYURBM3Q64WURANCNFSM4KCEN2JQ .

ready for review and merge.

Ah found it: https://github.com/dask/dask/pull/5334. Missed it when I looked through the changelog earlier.

This looks like an dask bug that we fixed a somewhat recently (6 months ago?).

I wouldn’t worry about it too much about it. Supporting old versions of dependencies is a best effort, not a mandate. I would just skip it on old versions of dask and move on.

Does anyone have thoughts on what might be happening here?

min-all-deps fails with this weird error:

```

def test_append_with_new_variable(self):

    ds, ds_to_append, ds_with_new_var = create_append_test_data()

    # check append mode for new variable
    with self.create_zarr_target() as store_target:
        xr.concat([ds, ds_to_append], dim="time").to_zarr(store_target, mode="w")
        ds_with_new_var.to_zarr(store_target, mode="a")
        combined = xr.concat([ds, ds_to_append], dim="time")
        combined["new_var"] = ds_with_new_var["new_var"]

      assert_identical(combined, xr.open_zarr(store_target))

xarray/tests/test_backends.py:1876:

xarray/core/dataset.py:1354: in identical other, "identical" xarray/core/dataset.py:1302: in _all_compat self._variables, other._variables, compat=compat xarray/core/utils.py:338: in dict_equiv if k not in second or not compat(first[k], second[k]): xarray/core/dataset.py:1299: in compat return getattr(x, compat_str)(y) xarray/core/variable.py:1670: in identical other, equiv=equiv xarray/core/variable.py:1647: in equals self._data is other._data or equiv(self.data, other.data) xarray/core/duck_array_ops.py:224: in array_equiv flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) /usr/share/miniconda/envs/xarray-tests/lib/python3.6/site-packages/dask/array/core.py:1739: in eq return elemwise(operator.eq, self, other) /usr/share/miniconda/envs/xarray-tests/lib/python3.6/site-packages/dask/array/core.py:3749: in elemwise *blockwise_kwargs /usr/share/miniconda/envs/xarray-tests/lib/python3.6/site-packages/dask/array/blockwise.py:145: in blockwise chunkss, arrays = unify_chunks(args) /usr/share/miniconda/envs/xarray-tests/lib/python3.6/site-packages/dask/array/core.py:3020: in unify_chunks (asanyarray(a) if ind is not None else a, ind) for a, ind in partition(2, args) /usr/share/miniconda/envs/xarray-tests/lib/python3.6/site-packages/dask/array/core.py:3020: in <listcomp> (asanyarray(a) if ind is not None else a, ind) for a, ind in partition(2, args)

a = array(['2019-01-01T00:00:00.000000000', '2019-01-02T00:00:00.000000000', '2019-01-03T00:00:00.000000000', '2019-01-04T00:00:00.000000000', '2019-01-05T00:00:00.000000000'], dtype='datetime64[ns]')

def asanyarray(a):
    """Convert the input to a dask array.

    Subclasses of ``np.ndarray`` will be passed through as chunks unchanged.

    Parameters
    ----------
    a : array-like
        Input data, in any form that can be converted to a dask array.

    Returns
    -------
    out : dask array
        Dask array interpretation of a.

    Examples
    --------
    >>> import dask.array as da
    >>> import numpy as np
    >>> x = np.arange(3)
    >>> da.asanyarray(x)
    dask.array<array, shape=(3,), dtype=int64, chunksize=(3,)>

    >>> y = [[1, 2, 3], [4, 5, 6]]
    >>> da.asanyarray(y)
    dask.array<array, shape=(2, 3), dtype=int64, chunksize=(2, 3)>
    """
    if isinstance(a, Array):
        return a
    elif hasattr(a, "to_dask_array"):
        return a.to_dask_array()

  elif hasattr(a, "data") and type(a).__module__.startswith("xarray."):

E ValueError: cannot include dtype 'M' in a buffer

```

The test failed because meta was not valid for dask==1.2

Our minimum versions policy says we can bump dask to 2.1.0. dask 1.2 (2019-04-13) 2.1 (2019-07-08) <

So I've bumped the min dask version and added meta as a kwarg to apply_ufunc.