def process(self, cube):
"""
Identify the probability of having a phenomenon occur within a
vicinity.
The steps for this are as follows:
1. Calculate the occurrence of a phenomenon within
a defined vicinity.
2. If the cube contains a realization dimension coordinate,
find the mean.
3. Compute neighbourhood processing.
Args:
cube (iris.cube.Cube):
A cube that has been thresholded.
Returns:
cube (iris.cube.Cube):
A cube containing neighbourhood probabilities to represent the
probability of an occurrence within the vicinity given a
pre-defined spatial uncertainty.
"""
cube = OccurrenceWithinVicinity(self.distance).process(cube)
try:
if cube.coord_dims('realization'):
ens_members = cube.coord('realization').points
# BUG in iris: collapsed returns a masked cube regardless of
# input status. If input is not masked, output mask does not
# match data. Fix is to re-cast output to an unmasked array.
cube_is_masked = isinstance(cube.data, np.ma.MaskedArray)
cube = cube.collapsed('realization', iris.analysis.MEAN)
if not cube_is_masked:
cube.data = np.array(cube.data)
cube.remove_coord('realization')
cube.attributes['source_realizations'] = ens_members
except iris.exceptions.CoordinateNotFoundError:
pass
cube = NeighbourhoodProcessing(
self.neighbourhood_method,
self.radii,
lead_times=self.lead_times,
weighted_mode=self.weighted_mode,
ens_factor=self.ens_factor).process(cube)
cube.rename(cube.name() + '_in_vicinity')
return cube
def process(self, cube):
"""
Identify the probability of having a phenomenon occur within a
vicinity.
The steps for this are as follows:
1. Calculate the occurrence of a phenomenon within
a defined vicinity.
2. If the cube contains a realization dimension coordinate,
find the mean.
3. Compute neighbourhood processing.
Args:
cube (iris.cube.Cube):
A cube that has been thresholded.
Returns:
cube (iris.cube.Cube):
A cube containing neighbourhood probabilities to represent the
probability of an occurrence within the vicinity given a
pre-defined spatial uncertainty.
"""
cube = OccurrenceWithinVicinity(self.distance).process(cube)
try:
if cube.coord_dims('realization'):
ens_members = cube.coord('realization').points
cube = cube.collapsed('realization', iris.analysis.MEAN)
cube.remove_coord('realization')
cube.attributes['source_realizations'] = ens_members
except iris.exceptions.CoordinateNotFoundError:
pass
cube = NeighbourhoodProcessing(
self.neighbourhood_method, self.radii,
lead_times=self.lead_times,
weighted_mode=self.weighted_mode,
ens_factor=self.ens_factor).process(cube)
cube.rename(cube.name() + '_in_vicinity')
return cube
