def test_mismatching_coords_wrong_shape(self):
"""Test raises an error if shape do not match. """
cube1 = create_cube_with_threshold()
cube2 = create_cube_with_threshold(threshold_values=[1.0, 2.0])
msg = "Can not combine cubes, mismatching shapes"
with self.assertRaisesRegex(ValueError, msg):
resolve_metadata_diff(cube1, cube2)
def test_basic(self):
"""Test that the function returns a tuple of Cubes. """
cube2 = self.cube1.copy()
result = resolve_metadata_diff(self.cube1, cube2)
self.assertIsInstance(result, tuple)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], Cube)
self.assertIsInstance(result[1], Cube)
def test_mismatching_coords_1d_coord_pos0_on_cube2(self):
"""Test missing coord on cube1. Coord is leading coord in cube2."""
cube2 = self.cube1.copy()
self.cube1.remove_coord(self.coord_name)
self.cube1 = iris.util.squeeze(self.cube1)
result = resolve_metadata_diff(self.cube1, cube2)
self.assertIsInstance(result, tuple)
self.assertArrayEqual(result[0].shape, np.array([2, 2, 2]))
self.assertArrayEqual(result[1].shape, np.array([2, 2, 2]))
def test_mismatching_coords_same_shape(self):
"""Test works with mismatching coords but coords same shape."""
cube1 = create_cube_with_threshold()
cube2 = create_cube_with_threshold(threshold_values=[2.0])
result = resolve_metadata_diff(cube1, cube2)
self.assertIsInstance(result, tuple)
self.assertArrayEqual(result[0].coord('threshold').points,
np.array([1.0]))
self.assertArrayEqual(result[1].coord('threshold').points,
np.array([2.0]))
def test_mismatching_coords_1d_coord_pos0_on_cube1(self):
"""Test missing coord on cube2. Coord is leading coord in cube1."""
cube1 = create_cube_with_threshold()
cube2 = cube1.copy()
cube2.remove_coord('threshold')
cube2 = iris.util.squeeze(cube2)
result = resolve_metadata_diff(cube1, cube2)
self.assertIsInstance(result, tuple)
self.assertArrayEqual(result[0].shape, np.array([1, 2, 2, 2]))
self.assertArrayEqual(result[1].shape, np.array([1, 2, 2, 2]))
def test_mismatching_coords_1d_coord_pos1_cube1(self):
"""Test missing 1d coord on cube2.
Coord is not leading coord in cube1."""
cube2 = self.cube2.copy()
cube2.remove_coord('threshold')
cube2 = iris.util.squeeze(cube2)
result = resolve_metadata_diff(self.cube2, cube2)
self.assertIsInstance(result, tuple)
self.assertArrayEqual(result[0].shape, np.array([2, 1, 2, 2, 2]))
self.assertArrayEqual(result[1].shape, np.array([2, 1, 2, 2, 2]))
def process(self,
cube_list,
new_diagnostic_name,
revised_coords=None,
revised_attributes=None):
"""
Create a combined cube.
Args:
cube_list (iris.cube.CubeList):
Cube List contain the cubes to combine.
new_diagnostic_name (str):
New name for the combined diagnostic.
Keyword Args:
revised_coords (dict or None):
Revised coordinates for combined cube.
revised_attributes (dict or None):
Revised attributes for combined cube.
Returns:
result (iris.cube.Cube):
Cube containing the combined data.
"""
if not isinstance(cube_list, iris.cube.CubeList):
msg = ('Expecting data to be an instance of '
'iris.cube.CubeList but is'
' {0:s}.'.format(type(cube_list)))
raise TypeError(msg)
if len(cube_list) < 2:
msg = 'Expecting 2 or more cubes in cube_list'
raise ValueError(msg)
# resulting cube will be based on the first cube.
data_type = cube_list[0].dtype
result = cube_list[0].copy()
for ind in range(1, len(cube_list)):
cube1, cube2 = (resolve_metadata_diff(
result.copy(),
cube_list[ind].copy(),
warnings_on=self.warnings_on))
result = self.combine(cube1, cube2, self.operation)
if self.operation == 'mean':
result = result / len(cube_list)
result = amend_metadata(result,
new_diagnostic_name,
data_type,
revised_coords,
revised_attributes,
warnings_on=self.warnings_on)
return result
def test_warnings_on_work(self, warning_list=None):
"""Test warning messages are given if warnings_on is set."""
cube2 = self.cube1.copy()
cube2.remove_coord(self.coord_name)
cube2 = iris.util.squeeze(cube2)
warning_msg = 'Adding new coordinate'
result = resolve_metadata_diff(self.cube1, cube2, warnings_on=True)
self.assertTrue(
any(item.category == UserWarning for item in warning_list))
self.assertTrue(any(warning_msg in str(item) for item in warning_list))
self.assertIsInstance(result, tuple)
self.assertArrayEqual(result[0].shape, np.array([1, 2, 2, 2]))
self.assertArrayEqual(result[1].shape, np.array([1, 2, 2, 2]))
def test_warnings_on_work(self):
"""Test warning messages are given if warnings_on is set."""
cube1 = create_cube_with_threshold()
cube2 = cube1.copy()
cube2.remove_coord('threshold')
cube2 = iris.util.squeeze(cube2)
warning_msg = 'Adding new coordinate'
with warnings.catch_warnings(record=True) as warning_list:
warnings.simplefilter("always")
result = resolve_metadata_diff(cube1, cube2, warnings_on=True)
self.assertTrue(
any(item.category == UserWarning for item in warning_list))
self.assertTrue(
any(warning_msg in str(item) for item in warning_list))
self.assertIsInstance(result, tuple)
self.assertArrayEqual(result[0].shape, np.array([1, 2, 2, 2]))
self.assertArrayEqual(result[1].shape, np.array([1, 2, 2, 2]))
def process(self,
cube_list,
new_diagnostic_name,
revised_coords=None,
revised_attributes=None,
expanded_coord=None):
"""
Create a combined cube.
Args:
cube_list (iris.cube.CubeList):
Cube List contain the cubes to combine.
new_diagnostic_name (str):
New name for the combined diagnostic.
Keyword Args:
revised_coords (dict or None):
Revised coordinates for combined cube.
revised_attributes (dict or None):
Revised attributes for combined cube.
expanded_coord (dict or None):
Coordinates to be expanded as a key, with the value
indicating whether the upper or mid point of the coordinate
should be used as the point value, e.g.
{'time': 'upper'}.
Returns:
result (iris.cube.Cube):
Cube containing the combined data.
Raises:
TypeError: If cube_list is not an iris.cube.CubeList.
ValueError: If the cubelist contains only one cube.
"""
if not isinstance(cube_list, iris.cube.CubeList):
msg = ('Expecting data to be an instance of iris.cube.CubeList '
'but is {}.'.format(type(cube_list)))
raise TypeError(msg)
if len(cube_list) < 2:
msg = 'Expecting 2 or more cubes in cube_list'
raise ValueError(msg)
# resulting cube will be based on the first cube.
data_type = cube_list[0].dtype
result = cube_list[0].copy()
for ind in range(1, len(cube_list)):
cube1, cube2 = (resolve_metadata_diff(
result.copy(),
cube_list[ind].copy(),
warnings_on=self.warnings_on))
result = self.combine(cube1, cube2)
if self.operation == 'mean':
result.data = result.data / len(cube_list)
# If cube has coord bounds that we want to expand
if expanded_coord:
result = expand_bounds(result, cube_list, expanded_coord)
result = amend_metadata(result,
new_diagnostic_name,
data_type,
revised_coords,
revised_attributes,
warnings_on=self.warnings_on)
return result