=================================== FAILURES =================================== ___________________ test_cf_timedelta[timedeltas7-days-nan] ____________________ timedeltas = numpy.datetime64('NaT'), units = 'days', numbers = array(nan) @pytest.mark.parametrize( ['timedeltas', 'units', 'numbers'], [('1D', 'days', np.int64(1)), (['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')), ('1h', 'hours', np.int64(1)), ('1ms', 'milliseconds', np.int64(1)), ('1us', 'microseconds', np.int64(1)), (['NaT', '0s', '1s'], None, [np.nan, 0, 1]), (['30m', '60m'], 'hours', [0.5, 1.0]), (np.timedelta64('NaT', 'ns'), 'days', np.nan), (['NaT', 'NaT'], 'days', [np.nan, np.nan])]) def test_cf_timedelta(timedeltas, units, numbers): timedeltas = pd.to_timedelta(timedeltas, box=False) numbers = np.array(numbers) expected = numbers > actual, _ = coding.times.encode_cf_timedelta(timedeltas, units) xarray/tests/test_coding_times.py:550: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ timedeltas = numpy.datetime64('NaT'), units = 'days' def encode_cf_timedelta(timedeltas, units=None): if units is None: units = infer_timedelta_units(timedeltas) np_unit = _netcdf_to_numpy_timeunit(units) > num = 1.0 * timedeltas / np.timedelta64(1, np_unit) E TypeError: ufunc multiply cannot use operands with types dtype('float64') and dtype('<M8[ns]') xarray/coding/times.py:379: TypeError _____________________ TestDataArray.test_struct_array_dims _____________________ self = <xarray.tests.test_dataarray.TestDataArray object at 0x7fb508944a90> def test_struct_array_dims(self): """ This test checks subraction of two DataArrays for the case when dimension is a structured array. """ # GH837, GH861 # checking array subraction when dims are the same p_data = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) p_data_1 = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) p_data_2 = np.array([('John', 180), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) weights_0 = DataArray([80, 56, 120], dims=['participant'], coords={'participant': p_data}) weights_1 = DataArray([81, 52, 115], dims=['participant'], coords={'participant': p_data_1}) actual = weights_1 - weights_0 expected = DataArray([1, -4, -5], dims=['participant'], coords={'participant': p_data}) assert_identical(actual, expected) # checking array subraction when dims are not the same p_data_1 = np.array([('John', 180), ('Stacy', 151), ('Dick', 200)], dtype=[('name', '|S256'), ('height', object)]) weights_1 = DataArray([81, 52, 115], dims=['participant'], coords={'participant': p_data_1}) actual = weights_1 - weights_0 expected = DataArray([1, -5], dims=['participant'], coords={'participant': p_data_2}) > assert_identical(actual, expected) E AssertionError: Left and right DataArray objects are not identical E E Differing values: E L E array([-5, 1]) E R E array([ 1, -5]) E Differing coordinates: E L * participant (participant) object (b'Dick', 200) (b'John', 180) E R * participant (participant) [('name', 'S256'), ('height', 'O')] (b'John', 180) (b'Dick', 200)