issue_comments: 1555902158

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): ```python @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 onfull_like` turns out to be a concern, maybe we could use "copy + assign" instead?