with #7855 and the recent change to pint we're finally down to just two test failures (and a whole lot of warnings):

xarray/tests/test_dataarray.py::TestDataArray::test_to_and_from_cdms2_sgrid: ValueError: operands could not be broadcast together with shapes (3,) (4,)
xarray/tests/test_ufuncs.py::test_unary: AssertionError: assert (<class 'int'> is <class 'numpy.float64'> or 1.0 == 0.9999999999999999)

The first one looks like cdms2 is incompatible with a change in numpy>=1.25. Not sure if we can do anything about that, especially since there's a big warning on the cdms2 repo stating that the package is going to be retired / archived by the end of this year... I guess we should start thinking about removing cdms2 support?

The second looks like a precision issue, which we should be able to resolve by using assert_allclose instead... not sure, though, especially given numpy/numpy#23773. Otherwise we could just defer to whatever numpy is doing (of course, that assumes that full_like works properly, which might not be a good idea for a unit test):

@pytest.mark.parametrize(
    "a",
    [
        xr.Variable(["x"], [0, 0]),
        xr.DataArray([0, 0], dims="x"),
        xr.Dataset({"y": ("x", [0, 0])}),
    ],
)
def test_unary(a):
    fill_value = np.cos(0)

    expected = xr.full_like(a, fill_value=fill_value, dtype=fill_value.dtype)
    actual = np.cos(a)

    assert_identical(actual, expected)

Edit: if relying on full_like turns out to be a concern, maybe we could use "copy + assign" instead?

Uh oh!

⚠️ Nightly upstream-dev CI failed ⚠️ #7707

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