def test_multiply_preserves_bounds(self):
"""Test specific case for precipitation type, where multiplying a
precipitation accumulation by a point-time probability of snow retains
the bounds on the original accumulation."""
validity_time = datetime(2015, 11, 19, 0)
time_bounds = [datetime(2015, 11, 18, 23), datetime(2015, 11, 19, 0)]
forecast_reference_time = datetime(2015, 11, 18, 22)
precip_accum = set_up_variable_cube(
np.full((2, 3, 3), 1.5, dtype=np.float32),
name="lwe_thickness_of_precipitation_amount",
units="mm",
time=validity_time,
time_bounds=time_bounds,
frt=forecast_reference_time,
)
snow_prob = set_up_variable_cube(
np.full(precip_accum.shape, 0.2, dtype=np.float32),
name="probability_of_snow",
units="1",
time=validity_time,
frt=forecast_reference_time,
)
result = CubeMultiplier()(
[precip_accum, snow_prob],
"lwe_thickness_of_snowfall_amount",
)
self.assertArrayAlmostEqual(result.data, np.full((2, 3, 3), 0.3))
self.assertArrayEqual(result.coord("time"), precip_accum.coord("time"))
def test_vicinity_names(self):
"""Test plugin names the cube and threshold coordinate correctly for a
vicinity diagnostic"""
input = "lwe_thickness_of_precipitation_amount_in_vicinity"
output = "thickness_of_rainfall_amount_in_vicinity"
self.cube.rename(f"probability_of_{input}_above_threshold")
cubelist = iris.cube.CubeList([self.cube.copy(), self.multiplier])
result = CubeMultiplier(broadcast_to_threshold=True)(cubelist, output)
self.assertEqual(result.name(),
f"probability_of_{output}_above_threshold")
self.assertEqual(
result.coord(var_name="threshold").name(),
"thickness_of_rainfall_amount")
def test_vicinity_names(self):
"""Test plugin names the cube and threshold coordinate correctly for a
vicinity diagnostic"""
input = "lwe_thickness_of_precipitation_amount_in_vicinity"
output = "thickness_of_rainfall_amount_in_vicinity"
self.cube4.rename(f"probability_of_{input}_above_threshold")
cube = self.cube4[:, 0, ...].copy()
cube.data = np.ones_like(cube.data)
cube.remove_coord("lwe_thickness_of_precipitation_amount")
cubelist = iris.cube.CubeList([self.cube4.copy(), cube])
input_copy = deepcopy(cubelist)
result = CubeMultiplier()(cubelist, output, broadcast_to_threshold=True)
self.assertEqual(result.name(), f"probability_of_{output}_above_threshold")
self.assertEqual(
result.coord(var_name="threshold").name(), "thickness_of_rainfall_amount"
)
def test_broadcast_coord(self):
"""Test that plugin broadcasts to threshold coord without changing inputs.
Using the broadcast_to_coords argument including a value of "threshold"
will result in the returned cube maintaining the probabilistic elements
of the name of the first input cube."""
cubelist = iris.cube.CubeList([self.cube.copy(), self.multiplier])
input_copy = deepcopy(cubelist)
result = CubeMultiplier(broadcast_to_threshold=True)(cubelist,
"new_cube_name")
self.assertIsInstance(result, Cube)
self.assertEqual(result.name(),
"probability_of_new_cube_name_above_threshold")
self.assertEqual(
result.coord(var_name="threshold").name(), "new_cube_name")
self.assertArrayAlmostEqual(result.data, self.cube.data)
self.assertCubeListEqual(input_copy, cubelist)
def test_broadcast_coord(self):
"""Test that plugin broadcasts to threshold coord without changing inputs.
Using the broadcast_to_coords argument including a value of "threshold"
will result in the returned cube maintaining the probabilistic elements
of the name of the first input cube."""
cube = self.cube4[:, 0, ...].copy()
cube.data = np.ones_like(cube.data)
cube.remove_coord("lwe_thickness_of_precipitation_amount")
cubelist = iris.cube.CubeList([self.cube4.copy(), cube])
input_copy = deepcopy(cubelist)
result = CubeMultiplier()(
cubelist, "new_cube_name", broadcast_to_threshold=True
)
self.assertIsInstance(result, Cube)
self.assertEqual(result.name(), "probability_of_new_cube_name_above_threshold")
self.assertEqual(result.coord(var_name="threshold").name(), "new_cube_name")
self.assertArrayAlmostEqual(result.data, self.cube4.data)
self.assertCubeListEqual(input_copy, cubelist)