def test_fewer_inputs(self):
"""Test routine can produce output from a shorter list of inputs"""
result = generate_optical_flow_components(
[self.second_cube, self.third_cube], self.ofc_box_size, self.iterations
)
for cube in result:
self.assertAlmostEqual(cube.coord("time").points[0], self.expected_time)
def process(original_cube_list,
orographic_enhancement_cube=None,
attributes_dict=None,
ofc_box_size=30,
smart_smoothing_iterations=100):
"""Calculate optical flow components from input fields.
Args:
original_cube_list (iris.cube.CubeList):
Cubelist from which to calculate optical flow velocities.
The cubes require a 'time' coordinate on which they are sorted,
so the order of cubes does not matter.
orographic_enhancement_cube (iris.cube.Cube):
Cube containing the orographic enhancement fields.
Default is None.
attributes_dict (dict):
Dictionary containing required changes to the attributes.
Every output file will have the attributes_dict applied.
Default is None.
ofc_box_size (int):
Size of square 'box' (in grid spaces) within which to solve
the optical flow equations.
Default is 30.
smart_smoothing_iterations (int):
Number of iterations to perform in enforcing smoothness constraint
for optical flow velocities.
Default is 100.
Returns:
iris.cube.CubeList:
List of the umean and vmean cubes.
Raises:
ValueError:
If there is no oe_cube but a cube is called 'precipitation_rate'.
"""
# order input files by validity time
original_cube_list.sort(key=lambda x: x.coord("time").points[0])
# subtract orographic enhancement
if orographic_enhancement_cube:
cube_list = ApplyOrographicEnhancement("subtract").process(
original_cube_list, orographic_enhancement_cube)
else:
cube_list = original_cube_list
if any("precipitation_rate" in cube.name() for cube in cube_list):
cube_names = [cube.name() for cube in cube_list]
msg = ("For precipitation fields, orographic enhancement "
"filepaths must be supplied. The names of the cubes "
"supplied were: {}".format(cube_names))
raise ValueError(msg)
# calculate optical flow velocities from T-1 to T and T-2 to T-1, and
# average to produce the velocities for use in advection
u_mean, v_mean = generate_optical_flow_components(
cube_list, ofc_box_size, smart_smoothing_iterations, attributes_dict)
return CubeList([u_mean, v_mean])
def test_time_ordering(self):
"""Test output timestamps are insensitive to input cube order"""
cubelist = [self.second_cube, self.third_cube, self.first_cube]
result = generate_optical_flow_components(cubelist, self.ofc_box_size,
self.iterations)
for cube in result:
self.assertAlmostEqual(
cube.coord("time").points[0], self.expected_time)
def test_basic(self):
"""Test output is a tuple of cubes"""
cubelist = [self.first_cube, self.second_cube, self.third_cube]
result = generate_optical_flow_components(cubelist, self.ofc_box_size,
self.iterations)
for cube in result:
self.assertIsInstance(cube, iris.cube.Cube)
self.assertAlmostEqual(
cube.coord("time").points[0], self.expected_time)
def process(orographic_enhancement: cli.inputcube,
*cubes: cli.inputcube,
attributes_config: cli.inputjson = None,
ofc_box_size: int = 30,
smart_smoothing_iterations: int = 100):
"""Calculate optical flow components from input fields.
Args:
orographic_enhancement (iris.cube.Cube):
Cube containing the orographic enhancement fields.
cubes (iris.cube.CubeList):
Cubes from which to calculate optical flow velocities.
These three cubes will be sorted by their time coords.
attributes_config (dict):
Dictionary containing required changes to the attributes.
Every output file will have the attributes_config applied.
ofc_box_size (int):
Size of square 'box' (in grid spaces) within which to solve
the optical flow equations.
smart_smoothing_iterations (int):
Number of iterations to perform in enforcing smoothness constraint
for optical flow velocities.
Returns:
iris.cube.CubeList:
List of the umean and vmean cubes.
"""
from iris.cube import CubeList
from improver.nowcasting.optical_flow import \
generate_optical_flow_components
from improver.nowcasting.utilities import ApplyOrographicEnhancement
original_cube_list = CubeList(cubes)
# order input files by validity time
original_cube_list.sort(key=lambda x: x.coord("time").points[0])
# subtract orographic enhancement
cube_list = ApplyOrographicEnhancement("subtract").process(
original_cube_list, orographic_enhancement)
# calculate optical flow velocities from T-1 to T and T-2 to T-1, and
# average to produce the velocities for use in advection
u_mean, v_mean = generate_optical_flow_components(
cube_list, ofc_box_size, smart_smoothing_iterations, attributes_config)
return CubeList([u_mean, v_mean])
def process(
orographic_enhancement: cli.inputcube, *cubes: cli.inputcube,
):
"""Calculate optical flow components from input fields.
Args:
orographic_enhancement (iris.cube.Cube):
Cube containing the orographic enhancement fields.
cubes (iris.cube.CubeList):
Cubes from which to calculate optical flow velocities.
These three cubes will be sorted by their time coords.
Returns:
iris.cube.CubeList:
List of the umean and vmean cubes.
"""
from iris.cube import CubeList
from improver.nowcasting.optical_flow import generate_optical_flow_components
from improver.nowcasting.utilities import ApplyOrographicEnhancement
original_cube_list = CubeList(cubes)
# order input files by validity time
original_cube_list.sort(key=lambda x: x.coord("time").points[0])
# subtract orographic enhancement
cube_list = ApplyOrographicEnhancement("subtract")(
original_cube_list, orographic_enhancement
)
# calculate optical flow velocities from T-1 to T and T-2 to T-1, and
# average to produce the velocities for use in advection
u_mean, v_mean = generate_optical_flow_components(
cube_list, ofc_box_size=30, smart_smoothing_iterations=100
)
return CubeList([u_mean, v_mean])
