def __new__(cls, *args, **kwargs):
# Input array is an already formed ndarray instance
longName = kwargs.pop('longName', None)
units = kwargs.pop('units', None)
missingValue = kwargs.pop('missingValue', -9999.)
obj = MaskedArray.__new__(cls, *args, **kwargs)
# Add the new attributes to the created instance
obj.longName = longName
obj.units = units
obj.missingValue = missingValue
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
np.matrix.__array_finalize__(self, obj)
MaskedArray.__array_finalize__(self, obj)
return
def __new__(cls, data, mask=nomask):
mat = np.matrix(data)
_data = MaskedArray.__new__(cls, data=mat, mask=mask)
return _data
def __new__(cls, data, info={}, mask=nomask):
subarr = SubArray(data, info)
_data = MaskedArray.__new__(cls, data=subarr, mask=mask)
_data.info = subarr.info
return _data
def __array_finalize__(self, obj):
np.matrix.__array_finalize__(self, obj)
MaskedArray.__array_finalize__(self, obj)
return
def __new__(cls, data, mask=nomask):
mat = np.matrix(data)
_data = MaskedArray.__new__(cls, data=mat, mask=mask)
return _data
def __new__(cls, data, info={}, mask=nomask):
subarr = SubArray(data, info)
_data = MaskedArray.__new__(cls, data=subarr, mask=mask)
_data.info = subarr.info
return _data
def _make_mask_cube(
mask_data: MaskedArray,
coords: List,
topographic_bounds: List[float],
topographic_units: str,
sea_points_included: bool = False,
) -> Cube:
"""
Makes cube from numpy masked array generated from orography fields.
Args:
mask_data:
The numpy array to make a cube from.
coords:
Dictionary of coordinate on the model ancillary file.
topographic_bounds:
List containing the lower and upper thresholds defining the mask
topographic_units:
Name of the units of the topographic zone coordinate of the output
cube.
sea_points_included:
Default is False. Value for the output cube attribute
'topographic_zones_include_seapoints', signifying whether sea
points have been included when the ancillary is generated.
Returns:
Cube containing the mask_data array, with appropriate coordinate
and attribute information.
"""
mask_data = mask_data.astype(np.int32)
mask_cube = iris.cube.Cube(mask_data, long_name="topography_mask")
if any(item is None for item in topographic_bounds):
msg = ("The topographic bounds variable should have both an "
"upper and lower limit: "
"Your topographic_bounds are {}")
raise TypeError(msg.format(topographic_bounds))
if len(topographic_bounds) != 2:
msg = ("The topographic bounds variable should have only an "
"upper and lower limit: "
"Your topographic_bounds variable has length {}")
raise TypeError(msg.format(len(topographic_bounds)))
coord_name = "topographic_zone"
central_point = np.mean(topographic_bounds)
threshold_coord = iris.coords.AuxCoord(
central_point,
bounds=topographic_bounds,
long_name=coord_name,
units=Unit(topographic_units),
)
mask_cube.add_aux_coord(threshold_coord)
# We can't save attributes with boolean values so convert to string.
mask_cube.attributes.update(
{"topographic_zones_include_seapoints": str(sea_points_included)})
for coord in coords:
if coord.name() in ["projection_y_coordinate", "latitude"]:
mask_cube.add_dim_coord(coord, 0)
elif coord.name() in ["projection_x_coordinate", "longitude"]:
mask_cube.add_dim_coord(coord, 1)
else:
mask_cube.add_aux_coord(coord)
mask_cube = iris.util.new_axis(mask_cube, scalar_coord=coord_name)
return mask_cube