def _prepare_features_dataframe(self,
feature_cubes: CubeList) -> DataFrame:
"""Convert gridded feature cubes into a dataframe, with feature variables
sorted alphabetically.
Note: It is expected that feature_cubes has been aligned using
_align_feature_variables prior to calling this function.
Args:
feature_cubes:
Cubelist containing the independent feature variables for prediction.
Returns:
Dataframe containing flattened feature variables.
Raises:
ValueError:
If flattened cubes have differing length.
"""
# Get the names of features and sort alphabetically
feature_variables = [cube.name() for cube in feature_cubes]
feature_variables.sort()
# Unpack the cube-data into dataframe to feed into the tree-models.
features_df = pd.DataFrame()
for feature in feature_variables:
cube = feature_cubes.extract_cube(feature)
features_df[feature] = cube.data.ravel()
return features_df
def process(self, cubelist: CubeList) -> Cube:
"""
Produce Nowcast of lightning probability.
Args:
cubelist:
Where thresholds are listed, only these threshold values will
be used.
Contains cubes of
* First-guess lightning probability
* Nowcast precipitation probability
(required thresholds: > 0.5, 7., 35. mm hr-1)
* Nowcast lightning rate
* (optional) Analysis of vertically integrated ice (VII)
from radar thresholded into probability slices
at self.ice_thresholds.
Returns:
Output cube containing Nowcast lightning probability.
This cube will have the same dimensions as the input
Nowcast precipitation probability after the threshold coord
has been removed.
Raises:
iris.exceptions.ConstraintMismatchError:
If cubelist does not contain the expected cubes.
"""
first_guess_lightning_cube = cubelist.extract_cube(
"probability_of_rate_of_lightning_above_threshold")
lightning_rate_cube = cubelist.extract_cube("rate_of_lightning")
lightning_rate_cube.convert_units("min^-1") # Ensure units are correct
prob_precip_cube = cubelist.extract_cube(
"probability_of_lwe_precipitation_rate_above_threshold")
# Now find prob_vii_cube. Can't use strict=True here as cube may not be
# present, so will use a normal extract and then merge_cube if needed.
prob_vii_cube = cubelist.extract(
"probability_of_vertical_integral_of_ice_above_threshold")
if prob_vii_cube:
prob_vii_cube = prob_vii_cube.merge_cube()
precip_threshold_coord = find_threshold_coordinate(prob_precip_cube)
precip_threshold_coord.convert_units("mm hr-1")
precip_slice = prob_precip_cube.extract(
iris.Constraint(
coord_values={
precip_threshold_coord: lambda t: isclose(t.point, 0.5)
}))
if not isinstance(precip_slice, iris.cube.Cube):
raise ConstraintMismatchError(
"Cannot find prob(precip > 0.5 mm hr-1) cube in cubelist.")
template_cube = self._update_metadata(precip_slice)
new_cube = self._modify_first_guess(
template_cube,
first_guess_lightning_cube,
lightning_rate_cube,
prob_precip_cube,
prob_vii_cube,
)
# Adjust data so that lightning probability does not decrease too
# rapidly with distance.
self.neighbourhood(new_cube)
return new_cube