def create_new_diagnostic_cube(
name: str,
units: Union[Unit, str],
template_cube: Cube,
mandatory_attributes: Union[Dict[str, str], LimitedAttributeDict],
optional_attributes: Optional[Union[Dict[str, str],
LimitedAttributeDict]] = None,
data: Optional[Union[MaskedArray, ndarray]] = None,
dtype: Type = np.float32,
) -> Cube:
"""
Creates a new diagnostic cube with suitable metadata.
Args:
name:
Standard or long name for output cube
units:
Units for output cube
template_cube:
Cube from which to copy dimensional and auxiliary coordinates
mandatory_attributes:
Dictionary containing values for the mandatory attributes
"title", "source" and "institution". These are overridden by
values in the optional_attributes dictionary, if specified.
optional_attributes:
Dictionary of optional attribute names and values. If values for
mandatory attributes are included in this dictionary they override
the values of mandatory_attributes.
data:
Data array. If not set, cube is filled with zeros using a lazy
data object, as this will be overwritten later by the caller
routine.
dtype:
Datatype for dummy cube data if "data" argument is None.
Returns:
Cube with correct metadata to accommodate new diagnostic field
"""
attributes = mandatory_attributes
if optional_attributes is not None:
attributes.update(optional_attributes)
error_msg = ""
for attr in MANDATORY_ATTRIBUTES:
if attr not in attributes:
error_msg += "{} attribute is required\n".format(attr)
if error_msg:
raise ValueError(error_msg)
if data is None:
data = da.zeros_like(template_cube.core_data(), dtype=dtype)
aux_coords_and_dims, dim_coords_and_dims = [[
(coord.copy(), template_cube.coord_dims(coord))
for coord in getattr(template_cube, coord_type)
] for coord_type in ("aux_coords", "dim_coords")]
cube = iris.cube.Cube(
data,
units=units,
attributes=attributes,
dim_coords_and_dims=dim_coords_and_dims,
aux_coords_and_dims=aux_coords_and_dims,
)
cube.rename(name)
return cube
def test_basic(self):
real_data = np.arange(3.0)
cube = Cube(as_lazy_data(real_data))
co_realise_cubes(cube)
self.assertFalse(cube.has_lazy_data())
self.assertArrayAllClose(cube.core_data(), real_data)
def test_basic(self):
real_data = np.arange(3.)
cube = Cube(as_lazy_data(real_data))
co_realise_cubes(cube)
self.assertFalse(cube.has_lazy_data())
self.assertArrayAllClose(cube.core_data(), real_data)
