def test_error(self):
"""Test correct exception raised when the distance is larger than the
corner-to-corner distance of the domain."""
distance = 550000.0
msg = "Distance of 550000.0m exceeds max domain distance of "
with self.assertRaisesRegex(ValueError, msg):
check_radius_against_distance(self.cube, distance)
def run(self, cube, radius, mask_cube=None):
"""
Call the methods required to apply a square neighbourhood
method to a cube.
The steps undertaken are:
1. Set up cubes by determining, if the arrays are masked.
2. Pad the input array with a halo and then calculate the neighbourhood
of the haloed array.
3. Remove the halo from the neighbourhooded array and deal with a mask,
if required.
Args:
cube (iris.cube.Cube):
Cube containing the array to which the square neighbourhood
will be applied.
radius (float):
Radius in metres for use in specifying the number of
grid cells used to create a square neighbourhood.
mask_cube (iris.cube.Cube):
Cube containing the array to be used as a mask.
Returns:
iris.cube.Cube:
Cube containing the smoothed field after the square
neighbourhood method has been applied.
"""
# If the data is masked, the mask will be processed as well as the
# original_data * mask array.
check_radius_against_distance(cube, radius)
original_attributes = cube.attributes
original_methods = cube.cell_methods
grid_cells = distance_to_number_of_grid_cells(cube, radius)
nb_size = 2 * grid_cells + 1
try:
mask_cube_data = mask_cube.data
except AttributeError:
mask_cube_data = None
result_slices = iris.cube.CubeList()
for cube_slice in cube.slices([cube.coord(axis="y"), cube.coord(axis="x")]):
cube_slice.data = self._calculate_neighbourhood(
cube_slice.data,
mask_cube_data,
nb_size,
self.sum_or_fraction == "sum",
self.re_mask,
)
result_slices.append(cube_slice)
neighbourhood_averaged_cube = result_slices.merge_cube()
neighbourhood_averaged_cube.cell_methods = original_methods
neighbourhood_averaged_cube.attributes = original_attributes
neighbourhood_averaged_cube = check_cube_coordinates(
cube, neighbourhood_averaged_cube
)
return neighbourhood_averaged_cube
def run(self, cube, radius, mask_cube=None):
"""
Call the methods required to apply a square neighbourhood
method to a cube.
The steps undertaken are:
1. Set up cubes by determining, if the arrays are masked.
2. Pad the input array with a halo and then calculate the neighbourhood
of the haloed array.
3. Remove the halo from the neighbourhooded array and deal with a mask,
if required.
Args:
cube (iris.cube.Cube):
Cube containing the array to which the square neighbourhood
will be applied.
radius (float):
Radius in metres for use in specifying the number of
grid cells used to create a square neighbourhood.
mask_cube (iris.cube.Cube):
Cube containing the array to be used as a mask.
Returns:
iris.cube.Cube:
Cube containing the smoothed field after the square
neighbourhood method has been applied.
"""
# If the data is masked, the mask will be processed as well as the
# original_data * mask array.
check_radius_against_distance(cube, radius)
original_attributes = cube.attributes
original_methods = cube.cell_methods
grid_cells = distance_to_number_of_grid_cells(cube, radius)
result_slices = iris.cube.CubeList()
for cube_slice in cube.slices(
[cube.coord(axis='y'), cube.coord(axis='x')]):
(cube_slice, mask,
nan_array) = (self.set_up_cubes_to_be_neighbourhooded(
cube_slice, mask_cube))
neighbourhood_averaged_cube = (
self._pad_and_calculate_neighbourhood(cube_slice, mask,
grid_cells))
neighbourhood_averaged_cube = (self._remove_padding_and_mask(
neighbourhood_averaged_cube, cube_slice, mask, grid_cells))
neighbourhood_averaged_cube.data[nan_array.astype(bool)] = np.nan
result_slices.append(neighbourhood_averaged_cube)
neighbourhood_averaged_cube = result_slices.merge_cube()
neighbourhood_averaged_cube.cell_methods = original_methods
neighbourhood_averaged_cube.attributes = original_attributes
neighbourhood_averaged_cube = check_cube_coordinates(
cube, neighbourhood_averaged_cube)
return neighbourhood_averaged_cube
def test_passes(self):
"""Test no exception raised when the distance is smaller than the
corner-to-corner distance of the domain."""
distance = 6100
check_radius_against_distance(self.cube, distance)