def test_bad_name(cube_name):
"""Test we get a useful error if one of the input cubes is incorrectly named."""
grid = input_cubes()
(cube,) = grid.extract(cube_name)
cube.rename("kittens")
with assert_raises_regex(ValueError, f"Cannot find a cube named '{cube_name}' in "):
ConvectionRatioFromComponents()(grid)
def process(*cubes: cli.inputcube, model_id_attr: str = None):
""" Calculate the convection ratio from convective and dynamic (stratiform)
precipitation rate components.
Calculates the convective ratio as:
ratio = convective_rate / (convective_rate + dynamic_rate)
Args:
cubes (iris.cube.CubeList):
Cubes of "lwe_convective_precipitation_rate" and "lwe_stratiform_precipitation_rate"
in units that can be converted to "m s-1"
model_id_attr (str):
Name of the attribute used to identify the source model for
blending.
Returns:
iris.cube.Cube:
A cube of convection_ratio of the same dimensions as the input cubes.
"""
from improver.precipitation_type.convection import ConvectionRatioFromComponents
if len(cubes) != 2:
raise IOError(f"Expected 2 input cubes, received {len(cubes)}")
return ConvectionRatioFromComponents()(cubes, model_id_attr=model_id_attr)
def test_bad_units():
"""Test we get a useful error if the input cubes have units that are not rates."""
grid = input_cubes()
for cube in grid:
cube.units = "m"
with assert_raises_regex(
ValueError,
"Input lwe_convective_precipitation_rate cube cannot be converted to 'm s-1' from ",
):
ConvectionRatioFromComponents()(grid)
def test_basic():
"""Ensure Plugin returns object of correct type and meta-data and that
no precip => masked array"""
grid = input_cubes()
expected_array = np.ma.masked_all_like(grid[0].data)
result = ConvectionRatioFromComponents()(grid.copy())
assert isinstance(result, iris.cube.Cube)
assert_allclose(result.data, expected_array)
assert result.attributes == grid[0].attributes
assert result.long_name == "convective_ratio"
assert result.units == "1"
def test_data(data_con_dyn_out, units):
"""Test that the data are calculated as expected for a selection of values on both
grids with SI and non-SI units including either side of the minimum precipitation
rate tolerance 1e-9 m s-1. For each parametrized test, ONE grid point is modified
(point: [0, 0]). Other points remain as zero inputs which gives a masked output.
The special expected value of np.inf is used to indicate that the output is expected
to be a masked value."""
grid = input_cubes()
for i in range(2):
grid[i].data[0, 0] = data_con_dyn_out[i]
for cube in grid:
cube.convert_units(units)
expected_array = np.ma.masked_all_like(grid[0].data)
if np.isfinite(data_con_dyn_out[2]):
expected_array[0, 0] = data_con_dyn_out[2]
result = ConvectionRatioFromComponents()(grid)
assert_allclose(result.data, expected_array)
# assert_allclose doesn't check masks appropriately, so check separately
assert_equal(result.data.mask, expected_array.mask)