def _remove_zero_weighted_slices(self, cube: Cube,
weights: Cube) -> Tuple[Cube, Cube]:
"""Removes any cube and weights slices where the 1D weighting factor
is zero
Args:
cube:
The data cube to be blended
weights:
1D cube of weights varying along self.blend_coord
Returns:
- Data cube without zero-weighted slices
- Weights without zeroes
"""
slice_out_vals = []
for wslice in weights.slices_over(self.blend_coord):
if np.sum(wslice.data) == 0:
slice_out_vals.append(wslice.coord(self.blend_coord).points[0])
if not slice_out_vals:
return cube, weights
constraint = iris.Constraint(
coord_values={self.blend_coord: lambda x: x not in slice_out_vals})
cube = cube.extract(constraint)
weights = weights.extract(constraint)
return cube, weights
def slice_mask_cube_by_domain(
cube_in: Cube, cube_in_mask: Cube,
output_domain: Tuple[float, float, float, float]) -> Tuple[Cube, Cube]:
"""
Extract cube domain to be consistent as cube_reference's domain.
Args:
cube_in:
Input data cube to be sliced.
cube_in_mask:
Input mask cube to be sliced.
output_domain:
Lat_max, lon_max, lat_min, lon_min.
Returns:
- Data cube after slicing.
- Mask cube after slicing.
"""
lat_max, lon_max, lat_min, lon_min = output_domain
lat_d_1, lon_d_1 = calculate_input_grid_spacing(cube_in)
lat_d_2, lon_d_2 = calculate_input_grid_spacing(cube_in_mask)
lat_d = lat_d_1 if lat_d_1 > lat_d_2 else lat_d_2
lon_d = lon_d_1 if lon_d_1 > lon_d_2 else lon_d_2
domain = iris.Constraint(latitude=lambda val: lat_min - 2.0 * lat_d < val <
lat_max + 2.0 * lat_d) & iris.Constraint(
longitude=lambda val: lon_min - 2.0 * lon_d <
val < lon_max + 2.0 * lon_d)
cube_in = cube_in.extract(domain)
cube_in_mask = cube_in_mask.extract(domain)
return cube_in, cube_in_mask
def apply_extraction(
cube: Cube,
constraint: Constraint,
units: Optional[Dict] = None,
use_original_units: bool = True,
) -> Cube:
"""
Using a set of constraints, extract a subcube from the provided cube if it
is available.
Args:
cube:
The cube from which a subcube is to be extracted.
constraint:
The constraint or ConstraintCombination that will be used to
extract a subcube from the input cube.
units:
A dictionary of units for the constraints. Supplied if any
coordinate constraints are provided in different units from those
of the input cube (eg precip in mm/h for cube threshold in m/s).
use_original_units:
Boolean to state whether the coordinates used in the extraction
should be converted back to their original units. The default is
True, indicating that the units should be converted back to the
original units.
Returns:
A single cube matching the input constraints, or None if no subcube
is found within cube that matches the constraints.
"""
if units is None:
output_cube = cube.extract(constraint)
else:
original_units = {}
for coord in units.keys():
original_units[coord] = cube.coord(coord).units
cube.coord(coord).convert_units(units[coord])
output_cube = cube.extract(constraint)
if use_original_units:
for coord in original_units:
cube.coord(coord).convert_units(original_units[coord])
try:
output_cube.coord(coord).convert_units(
original_units[coord])
except AttributeError:
# an empty output cube (None) is handled by the CLI
pass
return output_cube
def slice_cube_by_domain(
cube_in: Cube, output_domain: Tuple[float, float, float,
float]) -> Cube:
"""
Extract cube domain to be consistent as cube_reference's domain.
Args:
cube_in:
Input data cube to be sliced.
output_domain:
Lat_max, lon_max, lat_min, lon_min.
Returns:
Data cube after slicing.
"""
lat_max, lon_max, lat_min, lon_min = output_domain
lat_d, lon_d = calculate_input_grid_spacing(cube_in)
domain = iris.Constraint(latitude=lambda val: lat_min - 2.0 * lat_d < val <
lat_max + 2.0 * lat_d) & iris.Constraint(
longitude=lambda val: lon_min - 2.0 * lon_d <
val < lon_max + 2.0 * lon_d)
cube_in = cube_in.extract(domain)
return cube_in
def extract_percentile_data(cube: Cube, req_percentile: float,
standard_name: str) -> Tuple[Cube, Coord]:
"""Extract percentile data from cube.
Args:
cube:
Cube contain one or more percentiles.
req_percentile:
Required percentile value
standard_name:
Standard name of the data.
Returns:
- Cube containing the required percentile data
- Percentile coordinate.
"""
if not isinstance(cube, iris.cube.Cube):
msg = ("Expecting {0:s} data to be an instance of "
"iris.cube.Cube but is"
" {1}.".format(standard_name, type(cube)))
raise TypeError(msg)
perc_coord = find_percentile_coordinate(cube)
if cube.standard_name != standard_name:
msg = ("Warning mismatching name for data expecting"
" {0:s} but found {1:s}".format(standard_name,
cube.standard_name))
warnings.warn(msg)
constraint = iris.Constraint(
coord_values={perc_coord.name(): req_percentile})
result = cube.extract(constraint)
if result is None:
msg = "Could not find required percentile " "{0:3.1f} in cube".format(
req_percentile)
raise ValueError(msg)
return result, perc_coord
def _find_central_point(self, cube: Cube) -> Cube:
"""
Find the cube that contains the central point, otherwise, raise
an exception.
Args:
cube:
Cube containing input for blending.
Returns:
Cube containing central point.
Raises:
ValueError: Central point is not available within the input cube.
"""
# Convert central point into the units of the cube, so that a
# central point can be extracted.
central_point = Unit(self.parameter_units).convert(
self.central_point,
cube.coord(self.coord).units)
constr = iris.Constraint(
coord_values={
self.coord: lambda cell: cell.point == central_point
})
central_point_cube = cube.extract(constr)
if central_point_cube is None:
msg = ("The central point {} in units of {} not available "
"within input cube coordinate points: {}.".format(
self.central_point,
self.parameter_units,
cube.coord(self.coord).points,
))
raise ValueError(msg)
return central_point_cube
def apply_ice(self, prob_lightning_cube: Cube, ice_cube: Cube) -> Cube:
"""
Modify Nowcast of lightning probability with ice data from a radar
composite (VII; Vertically Integrated Ice)
Args:
prob_lightning_cube:
First-guess lightning probability.
The forecast_period coord is modified in-place to "minutes".
ice_cube:
Analysis of vertically integrated ice (VII) from radar
thresholded at self.ice_thresholds.
Units of threshold coord modified in-place to kg m^-2
Returns:
Output cube containing updated nowcast lightning probability.
This cube will have the same dimensions and meta-data as
prob_lightning_cube.
The influence of the data in ice_cube reduces linearly to zero
as forecast_period increases to 2H30M.
Raises:
iris.exceptions.ConstraintMismatchError:
If ice_cube does not contain the expected thresholds.
"""
prob_lightning_cube.coord("forecast_period").convert_units("minutes")
# check prob-ice threshold units are as expected
ice_threshold_coord = find_threshold_coordinate(ice_cube)
ice_threshold_coord.convert_units("kg m^-2")
new_cube_list = iris.cube.CubeList([])
err_string = "No matching prob(Ice) cube for threshold {}"
for cube_slice in prob_lightning_cube.slices_over("time"):
fcmins = cube_slice.coord("forecast_period").points[0]
for threshold, prob_max in zip(self.ice_thresholds,
self.ice_scaling):
ice_slice = ice_cube.extract(
iris.Constraint(coord_values={
ice_threshold_coord:
lambda t: isclose(t.point, threshold)
}))
if not isinstance(ice_slice, iris.cube.Cube):
raise ConstraintMismatchError(err_string.format(threshold))
# Linearly reduce impact of ice as fcmins increases to 2H30M.
ice_scaling = [0.0, (prob_max * (1.0 - (fcmins / 150.0)))]
if ice_scaling[1] > 0:
cube_slice.data = np.maximum(
rescale(
ice_slice.data,
data_range=(0.0, 1.0),
scale_range=ice_scaling,
clip=True,
),
cube_slice.data,
)
new_cube_list.append(cube_slice)
new_cube = new_cube_list.merge_cube()
new_cube = check_cube_coordinates(prob_lightning_cube, new_cube)
return new_cube
def wind_dir_decider(self, where_low_r: ndarray, wdir_cube: Cube) -> None:
"""If the wind direction is so widely scattered that the r value
is nearly zero then this indicates that the average wind direction
is essentially meaningless.
We therefore substitute this meaningless average wind
direction value for the wind direction calculated from a larger
sample by smoothing across a neighbourhood of points before
rerunning the main technique.
This is invoked rarely (1 : 100 000)
Args:
where_low_r:
Array of boolean values. True where original wind direction
estimate has low confidence. These points are replaced
according to self.backup_method
wdir_cube:
Contains array of wind direction data (realization, y, x)
Uses:
self.wdir_slice_mean:
Containing average wind direction angle (in degrees).
self.wdir_complex:
3D array - wind direction angles from ensembles (in complex).
self.r_vals_slice.data:
2D array - Radius taken from average complex wind direction
angle.
self.r_thresh:
Any r value below threshold is regarded as meaningless.
self.realization_axis:
Axis to collapse over.
self.n_realizations:
Number of realizations available in the plugin. Used to set the
neighbourhood radius as this is used to adjust the radius again
in the neighbourhooding plugin.
Defines:
self.wdir_slice_mean.data:
2D array - Wind direction degrees where ambigious values have
been replaced with data from first ensemble realization.
"""
if self.backup_method == "neighbourhood":
# Performs smoothing over a 6km square neighbourhood.
# Then calculates the mean wind direction.
child_class = WindDirection(backup_method="first_realization")
child_class.wdir_complex = self.nbhood(
wdir_cube.copy(data=self.wdir_complex)).data
child_class.realization_axis = self.realization_axis
child_class.wdir_slice_mean = self.wdir_slice_mean.copy()
child_class.calc_wind_dir_mean()
improved_values = child_class.wdir_slice_mean.data
else:
# Takes realization zero (control member).
improved_values = wdir_cube.extract(
iris.Constraint(realization=0)).data
# If the r-value is low - substitute average wind direction value for
# the wind direction taken from the first ensemble realization.
self.wdir_slice_mean.data = np.where(where_low_r, improved_values,
self.wdir_slice_mean.data)
def test_scalar_cube_coord_nomatch(self):
# Ensure that extract is not extracting a scalar cube with scalar
# coordinate that does not match the constraint.
constraint = iris.Constraint(scalar_coord=1)
cube = Cube(1, long_name='a1')
coord = iris.coords.AuxCoord(0, long_name='scalar_coord')
cube.add_aux_coord(coord, None)
res = cube.extract(constraint)
self.assertIs(res, None)
def process(self, spot_data_cube: Cube, neighbour_cube: Cube,
gridded_lapse_rate_cube: Cube) -> Cube:
"""
Extract lapse rates from the appropriate grid points and apply them to
the spot extracted temperatures.
The calculation is::
lapse_rate_adjusted_temperatures = temperatures + lapse_rate *
vertical_displacement
Args:
spot_data_cube:
A spot data cube of temperatures for the spot data sites,
extracted from the gridded temperature field. These
temperatures will have been extracted using the same
neighbour_cube and neighbour_selection_method that are being
used here.
neighbour_cube:
The neighbour_cube that contains the grid coordinates at which
lapse rates should be extracted and the vertical displacement
between those grid points on the model orography and the spot
data sites actual altitudes. This cube is only updated when
a new site is added.
gridded_lapse_rate_cube:
A cube of temperature lapse rates on the same grid as that from
which the spot data temperatures were extracted.
Returns:
A copy of the input spot_data_cube with the data modified by
the lapse rates to give a better representation of the site's
temperatures.
"""
# Check the cubes are compatible.
check_grid_match(
[neighbour_cube, spot_data_cube, gridded_lapse_rate_cube])
# Extract the lapse rates that correspond to the spot sites.
spot_lapse_rate = SpotExtraction(
neighbour_selection_method=self.neighbour_selection_method)(
neighbour_cube, gridded_lapse_rate_cube)
# Extract vertical displacements between the model orography and sites.
method_constraint = iris.Constraint(
neighbour_selection_method_name=self.neighbour_selection_method)
data_constraint = iris.Constraint(
grid_attributes_key="vertical_displacement")
vertical_displacement = neighbour_cube.extract(method_constraint
& data_constraint)
# Apply lapse rate adjustment to the temperature at each site.
new_temperatures = (
spot_data_cube.data +
(spot_lapse_rate.data * vertical_displacement.data)).astype(
np.float32)
new_spot_cube = spot_data_cube.copy(data=new_temperatures)
return new_spot_cube
def test_scalar_cube_coord_match(self):
# Ensure that extract is able to extract a scalar cube according to
# constrained scalar coordinate.
constraint = iris.Constraint(scalar_coord=0)
cube = Cube(1, long_name='a1')
coord = iris.coords.AuxCoord(0, long_name='scalar_coord')
cube.add_aux_coord(coord, None)
res = cube.extract(constraint)
self.assertIs(res, cube)
def filter_non_matching_cubes(historic_forecast: Cube,
truth: Cube) -> Tuple[Cube, Cube]:
"""
Provide filtering for the historic forecast and truth to make sure
that these contain matching validity times. This ensures that any
mismatch between the historic forecasts and truth is dealt with.
Args:
historic_forecast:
Cube of historic forecasts that potentially contains
a mismatch compared to the truth.
truth:
Cube of truth that potentially contains a mismatch
compared to the historic forecasts.
Returns:
- Cube of historic forecasts where any mismatches with
the truth cube have been removed.
- Cube of truths where any mismatches with
the historic_forecasts cube have been removed.
Raises:
ValueError: The filtering has found no matches in validity time
between the historic forecasts and the truths.
"""
matching_historic_forecasts = iris.cube.CubeList([])
matching_truths = iris.cube.CubeList([])
for hf_slice in historic_forecast.slices_over("time"):
if hf_slice.coord("time").has_bounds():
point = iris_time_to_datetime(hf_slice.coord("time"),
point_or_bound="point")
(bounds, ) = iris_time_to_datetime(hf_slice.coord("time"),
point_or_bound="bound")
coord_values = {
"time":
lambda cell: point[0] == cell.point and bounds[0] == cell.
bound[0] and bounds[1] == cell.bound[1]
}
else:
coord_values = {
"time":
iris_time_to_datetime(hf_slice.coord("time"),
point_or_bound="point")
}
constr = iris.Constraint(coord_values=coord_values)
truth_slice = truth.extract(constr)
if truth_slice:
matching_historic_forecasts.append(hf_slice)
matching_truths.append(truth_slice)
if not matching_historic_forecasts and not matching_truths:
msg = ("The filtering has found no matches in validity time "
"between the historic forecasts and the truths.")
raise ValueError(msg)
return (matching_historic_forecasts.merge_cube(),
matching_truths.merge_cube())
def extract_nearest_time_point(
cube: Cube, dt: datetime, time_name: str = "time", allowed_dt_difference: int = 0
) -> Cube:
"""Find the nearest time point to the time point provided.
Args:
cube:
Cube or CubeList that will be extracted from using the supplied
time_point
dt:
Datetime representation of a time that will be used within the
extraction from the cube supplied.
time_name:
Name of the "time" coordinate that will be extracted. This must be
"time" or "forecast_reference_time".
allowed_dt_difference:
An int in seconds to define a limit to the maximum difference
between the datetime provided and the time points available within
the cube. If this limit is exceeded, then an error is raised.
This must be defined in seconds.
Default is 0.
Returns:
Cube following extraction to return the cube that is nearest
to the time point supplied.
Raises:
ValueError: The requested datetime is not available within the
allowed difference.
"""
if time_name not in ["time", "forecast_reference_time"]:
msg = (
"{} is not a valid time_name. "
"The time_name must be either "
"'time' or 'forecast_reference_time'."
)
raise ValueError(msg)
time_point = datetime_to_iris_time(dt)
time_point_index = cube.coord(time_name).nearest_neighbour_index(time_point)
(nearest_dt,) = iris_time_to_datetime(
cube.coord(time_name).copy()[time_point_index]
)
if abs((dt - nearest_dt).total_seconds()) > allowed_dt_difference:
msg = (
"The datetime {} is not available within the input "
"cube within the allowed difference {} seconds. "
"The nearest datetime available was {}".format(
dt, allowed_dt_difference, nearest_dt
)
)
raise ValueError(msg)
constr = iris.Constraint(coord_values={time_name: nearest_dt})
cube = cube.extract(constr)
return cube
def different_projection(self, method, ancillary_data, additional_data,
expected, **kwargs):
"""Test that the plugin copes with non-lat/lon grids."""
trg_crs = None
src_crs = ccrs.PlateCarree()
trg_crs = ccrs.LambertConformal(central_longitude=50,
central_latitude=10)
trg_crs_iris = coord_systems.LambertConformal(central_lon=50,
central_lat=10)
lons = self.cube.coord('longitude').points
lats = self.cube.coord('latitude').points
x, y = [], []
for lon, lat in zip(lons, lats):
x_trg, y_trg = trg_crs.transform_point(lon, lat, src_crs)
x.append(x_trg)
y.append(y_trg)
new_x = AuxCoord(x,
standard_name='projection_x_coordinate',
units='m',
coord_system=trg_crs_iris)
new_y = AuxCoord(y,
standard_name='projection_y_coordinate',
units='m',
coord_system=trg_crs_iris)
cube = Cube(self.cube.data,
long_name="air_temperature",
dim_coords_and_dims=[(self.cube.coord('time'), 0)],
aux_coords_and_dims=[(new_y, 1), (new_x, 2)],
units="K")
plugin = Plugin(method)
with iris.FUTURE.context(cell_datetime_objects=True):
cube = cube.extract(self.time_extract)
result = plugin.process(cube, self.sites, self.neighbour_list,
ancillary_data, additional_data, **kwargs)
self.assertEqual(cube.coord_system(), trg_crs_iris)
self.assertAlmostEqual(result.data, expected)
self.assertEqual(result.coord(axis='y').name(), 'latitude')
self.assertEqual(result.coord(axis='x').name(), 'longitude')
self.assertAlmostEqual(result.coord(axis='y').points, 4.74)
self.assertAlmostEqual(result.coord(axis='x').points, 9.47)
def extract_coordinates(self, neighbour_cube: Cube) -> Cube:
"""
Extract the desired set of grid coordinates that correspond to spot
sites from the neighbour cube.
Args:
neighbour_cube:
A cube containing information about the spot data sites and
their grid point neighbours.
Returns:
A cube containing only the x and y grid coordinates for the
grid point neighbours given the chosen neighbour selection
method. The neighbour cube contains the indices stored as
floating point values, so they are converted to integers
in this cube.
Raises:
ValueError if the neighbour_selection_method expected is not found
in the neighbour cube.
"""
method = iris.Constraint(
neighbour_selection_method_name=self.neighbour_selection_method
)
index_constraint = iris.Constraint(grid_attributes_key=["x_index", "y_index"])
coordinate_cube = neighbour_cube.extract(method & index_constraint)
if coordinate_cube:
coordinate_cube.data = np.rint(coordinate_cube.data).astype(int)
return coordinate_cube
available_methods = neighbour_cube.coord(
"neighbour_selection_method_name"
).points
raise ValueError(
'The requested neighbour_selection_method "{}" is not available in'
" this neighbour_cube. Available methods are: {}.".format(
self.neighbour_selection_method, available_methods
)
)
def process(self, spot_data_cube: Cube, neighbour_cube: Cube,
gridded_lapse_rate_cube: Cube) -> Cube:
"""
Extract lapse rates from the appropriate grid points and apply them to
the spot extracted temperatures.
The calculation is::
lapse_rate_adjusted_temperatures = temperatures + lapse_rate *
vertical_displacement
Args:
spot_data_cube:
A spot data cube of temperatures for the spot data sites,
extracted from the gridded temperature field. These
temperatures will have been extracted using the same
neighbour_cube and neighbour_selection_method that are being
used here.
neighbour_cube:
The neighbour_cube that contains the grid coordinates at which
lapse rates should be extracted and the vertical displacement
between those grid points on the model orography and the spot
data sites actual altitudes. This cube is only updated when
a new site is added.
gridded_lapse_rate_cube:
A cube of temperature lapse rates on the same grid as that from
which the spot data temperatures were extracted.
Returns:
A copy of the input spot_data_cube with the data modified by
the lapse rates to give a better representation of the site's
temperatures.
Raises:
ValueError:
If the lapse rate cube was provided but the diagnostic being
processed is not air temperature.
ValueError:
If the lapse rate cube provided does not have the name
"air_temperature_lapse_rate"
ValueError:
If the lapse rate cube does not contain a single valued height
coordinate.
Warns:
warning:
If a lapse rate cube was provided, but the height of the
temperature does not match that of the data used.
"""
if is_probability(spot_data_cube):
msg = (
"Input cube has a probability coordinate which cannot be lapse "
"rate adjusted. Input data should be in percentile or "
"deterministic space only.")
raise ValueError(msg)
# Check that we are dealing with temperature data.
if spot_data_cube.name() not in [
"air_temperature", "feels_like_temperature"
]:
msg = (
"The diagnostic being processed is not air temperature "
"or feels like temperature and therefore cannot be adjusted.")
raise ValueError(msg)
if not gridded_lapse_rate_cube.name() == "air_temperature_lapse_rate":
msg = ("A cube has been provided as a lapse rate cube but does "
"not have the expected name air_temperature_lapse_rate: "
"{}".format(gridded_lapse_rate_cube.name()))
raise ValueError(msg)
try:
lapse_rate_height_coord = gridded_lapse_rate_cube.coord("height")
except (CoordinateNotFoundError):
msg = ("Lapse rate cube does not contain a single valued height "
"coordinate. This is required to ensure it is applied to "
"equivalent temperature data.")
raise CoordinateNotFoundError(msg)
# Check the height of the temperature data matches that used to
# calculate the lapse rates. If so, adjust temperatures using the lapse
# rate values.
if not spot_data_cube.coord("height") == lapse_rate_height_coord:
raise ValueError(
"A lapse rate cube was provided, but the height of the "
"temperature data does not match that of the data used "
"to calculate the lapse rates. As such the temperatures "
"were not adjusted with the lapse rates.")
# Check the cubes are compatible.
check_grid_match(
[neighbour_cube, spot_data_cube, gridded_lapse_rate_cube])
# Extract the lapse rates that correspond to the spot sites.
spot_lapse_rate = SpotExtraction(
neighbour_selection_method=self.neighbour_selection_method)(
neighbour_cube, gridded_lapse_rate_cube)
# Extract vertical displacements between the model orography and sites.
method_constraint = iris.Constraint(
neighbour_selection_method_name=self.neighbour_selection_method)
data_constraint = iris.Constraint(
grid_attributes_key="vertical_displacement")
vertical_displacement = neighbour_cube.extract(method_constraint
& data_constraint)
# Apply lapse rate adjustment to the temperature at each site.
new_spot_lapse_rate = iris.util.broadcast_to_shape(
spot_lapse_rate.data, spot_data_cube.shape, [-1])
new_temperatures = (
spot_data_cube.data +
(new_spot_lapse_rate * vertical_displacement.data)).astype(
np.float32)
new_spot_cube = spot_data_cube.copy(data=new_temperatures)
return new_spot_cube
def _create_output_cube(
self,
template: Cube,
data: Union[List[float], ndarray],
points: Union[List[float], ndarray],
bounds: Union[List[float], ndarray],
) -> Cube:
"""
Populates a template cube with data from the integration
Args:
template:
Copy of upper or lower bounds cube, based on direction of
integration
data:
Integrated data
points:
Points values for the integrated coordinate. These will not
match the template cube if any slices were skipped in the
integration, and therefore are used to slice the template cube
to match the data array.
bounds:
Bounds values for the integrated coordinate
Returns:
Cube with data from integration
"""
# extract required slices from template cube
template = template.extract(
iris.Constraint(coord_values={
self.coord_name_to_integrate: lambda x: x in points
}))
# re-promote integrated coord to dimension coord if need be
aux_coord_names = [coord.name() for coord in template.aux_coords]
if self.coord_name_to_integrate in aux_coord_names:
template = iris.util.new_axis(template,
self.coord_name_to_integrate)
# order dimensions on the template cube so that the integrated
# coordinate is first (as this is the leading dimension on the
# data array)
enforce_coordinate_ordering(template, self.coord_name_to_integrate)
# generate appropriate metadata for new cube
attributes = generate_mandatory_attributes([template])
coord_dtype = template.coord(self.coord_name_to_integrate).dtype
name, units = self._generate_output_name_and_units()
# create new cube from template
integrated_cube = create_new_diagnostic_cube(name,
units,
template,
attributes,
data=np.array(data))
integrated_cube.coord(self.coord_name_to_integrate).bounds = np.array(
bounds).astype(coord_dtype)
# re-order cube to match dimensions of input cube
ordered_dimensions = get_dim_coord_names(self.input_cube)
enforce_coordinate_ordering(integrated_cube, ordered_dimensions)
return integrated_cube
def _modify_first_guess(
self,
cube: Cube,
first_guess_lightning_cube: Cube,
lightning_rate_cube: Cube,
prob_precip_cube: Cube,
prob_vii_cube: Optional[Cube] = None,
) -> Cube:
"""
Modify first-guess lightning probability with nowcast data.
Args:
cube:
Provides the meta-data for the Nowcast lightning probability
output cube.
first_guess_lightning_cube:
First-guess lightning probability.
Must have same x & y dimensions as cube.
Time dimension should overlap that of cube (closest slice in
time is used with a maximum time mismatch of 2 hours).
This is included to allow this cube to come from a different
modelling system, such as the UM.
lightning_rate_cube:
Nowcast lightning rate.
Must have same dimensions as cube.
prob_precip_cube:
Nowcast precipitation probability (threshold > 0.5, 7, 35).
Must have same other dimensions as cube.
prob_vii_cube:
Radar-derived vertically integrated ice content (VII).
Must have same x and y dimensions as cube.
Time should be a scalar coordinate.
Must have a threshold coordinate with points matching.
self.vii_thresholds.
Can be <No cube> or None or anything that evaluates to False.
Returns:
Output cube containing Nowcast lightning probability.
Raises:
iris.exceptions.ConstraintMismatchError:
If lightning_rate_cube or first_guess_lightning_cube do not
contain the expected times.
"""
new_cube_list = iris.cube.CubeList([])
# Loop over required forecast validity times
for cube_slice in cube.slices_over("time"):
this_time = iris_time_to_datetime(
cube_slice.coord("time").copy())[0]
lightning_rate_slice = lightning_rate_cube.extract(
iris.Constraint(time=this_time))
err_string = "No matching {} cube for {}"
if not isinstance(lightning_rate_slice, iris.cube.Cube):
raise ConstraintMismatchError(
err_string.format("lightning", this_time))
first_guess_slice = extract_nearest_time_point(
first_guess_lightning_cube,
this_time,
allowed_dt_difference=7201)
first_guess_slice = cube_slice.copy(data=first_guess_slice.data)
first_guess_slice.coord("forecast_period").convert_units("minutes")
fcmins = first_guess_slice.coord("forecast_period").points[0]
# Increase prob(lightning) to Risk 2 (pl_dict[2]) when
# lightning nearby (lrt_lev2)
# (and leave unchanged when condition is not met):
first_guess_slice.data = np.where(
(lightning_rate_slice.data >= self.lrt_lev2)
& (first_guess_slice.data < self.pl_dict[2]),
self.pl_dict[2],
first_guess_slice.data,
)
# Increase prob(lightning) to Risk 1 (pl_dict[1]) when within
# lightning storm (lrt_lev1):
# (and leave unchanged when condition is not met):
lratethresh = self.lrt_lev1(fcmins)
first_guess_slice.data = np.where(
(lightning_rate_slice.data >= lratethresh)
& (first_guess_slice.data < self.pl_dict[1]),
self.pl_dict[1],
first_guess_slice.data,
)
new_cube_list.append(first_guess_slice)
new_prob_lightning_cube = new_cube_list.merge_cube()
new_prob_lightning_cube = check_cube_coordinates(
cube, new_prob_lightning_cube)
# Apply precipitation adjustments.
new_prob_lightning_cube = self.apply_precip(new_prob_lightning_cube,
prob_precip_cube)
# If we have VII data, increase prob(lightning) accordingly.
if prob_vii_cube:
new_prob_lightning_cube = self.apply_ice(new_prob_lightning_cube,
prob_vii_cube)
return new_prob_lightning_cube
def test_scalar_cube_exists(self):
# Ensure that extract is able to extract a scalar cube.
constraint = iris.Constraint(name='a1')
cube = Cube(1, long_name='a1')
res = cube.extract(constraint)
self.assertIs(res, cube)
def apply_precip(self, prob_lightning_cube: Cube,
prob_precip_cube: Cube) -> Cube:
"""
Modify Nowcast of lightning probability with precipitation rate
probabilities at thresholds of 0.5, 7 and 35 mm/h.
Args:
prob_lightning_cube:
First-guess lightning probability.
prob_precip_cube:
Nowcast precipitation probability
(threshold > 0.5, 7., 35. mm hr-1)
Units of threshold coord modified in-place to mm hr-1
Returns:
Output cube containing updated nowcast lightning probability.
This cube will have the same dimensions and meta-data as
prob_lightning_cube.
Raises:
iris.exceptions.ConstraintMismatchError:
If prob_precip_cube does not contain the expected thresholds.
"""
new_cube_list = iris.cube.CubeList([])
# check prob-precip threshold units are as expected
precip_threshold_coord = find_threshold_coordinate(prob_precip_cube)
precip_threshold_coord.convert_units("mm hr-1")
# extract precipitation probabilities at required thresholds
for cube_slice in prob_lightning_cube.slices_over("time"):
this_time = iris_time_to_datetime(
cube_slice.coord("time").copy())[0]
this_precip = prob_precip_cube.extract(
iris.Constraint(time=this_time)
& iris.Constraint(coord_values={
precip_threshold_coord:
lambda t: isclose(t.point, 0.5)
}))
high_precip = prob_precip_cube.extract(
iris.Constraint(time=this_time)
& iris.Constraint(coord_values={
precip_threshold_coord:
lambda t: isclose(t.point, 7.0)
}))
torr_precip = prob_precip_cube.extract(
iris.Constraint(time=this_time)
& iris.Constraint(coord_values={
precip_threshold_coord:
lambda t: isclose(t.point, 35.0)
}))
err_string = "No matching {} cube for {}"
if not isinstance(this_precip, iris.cube.Cube):
raise ConstraintMismatchError(
err_string.format("any precip", this_time))
if not isinstance(high_precip, iris.cube.Cube):
raise ConstraintMismatchError(
err_string.format("high precip", this_time))
if not isinstance(torr_precip, iris.cube.Cube):
raise ConstraintMismatchError(
err_string.format("intense precip", this_time))
# Increase prob(lightning) to Risk 2 (pl_dict[2]) when
# prob(precip > 7mm/hr) > phighthresh
cube_slice.data = np.where(
(high_precip.data >= self.phighthresh)
& (cube_slice.data < self.pl_dict[2]),
self.pl_dict[2],
cube_slice.data,
)
# Increase prob(lightning) to Risk 1 (pl_dict[1]) when
# prob(precip > 35mm/hr) > ptorrthresh
cube_slice.data = np.where(
(torr_precip.data >= self.ptorrthresh)
& (cube_slice.data < self.pl_dict[1]),
self.pl_dict[1],
cube_slice.data,
)
# Decrease prob(lightning) where prob(precip > 0.5 mm hr-1) is low.
cube_slice.data = apply_double_scaling(this_precip, cube_slice,
self.precipthr,
self.ltngthr)
new_cube_list.append(cube_slice)
new_cube = new_cube_list.merge_cube()
new_cube = check_cube_coordinates(prob_lightning_cube, new_cube)
return new_cube
def test_1d_cube_exists(self):
# Ensure that extract is able to extract from a 1d cube.
constraint = iris.Constraint(name='a1')
cube = Cube([1], long_name='a1')
res = cube.extract(constraint)
self.assertIs(res, cube)
def process(self, input_cube: Cube) -> Cube:
"""
Calculates a field of texture to use in differentiating solid and
more scattered features.
Args:
input_cube:
Input data in cube format containing the field for which the
texture is to be assessed.
Returns:
A cube containing either the mean across realization of the
thresholded ratios to give the field texture, if a realization
coordinate is present, or the thresholded ratios directly, if
no realization coordinate is present.
"""
values = np.unique(input_cube.data)
non_binary = np.where((values != 0) & (values != 1), True, False)
if non_binary.any():
raise ValueError("Incorrect input. Cube should hold binary data only")
# Create new cube name for _calculate_ratio method.
cube_name = find_threshold_coordinate(input_cube).name()
# Extract threshold from input data to work with, taking into account floating
# point comparisons.
cube = input_cube.extract(
iris.Constraint(
coord_values={
cube_name: lambda cell: np.isclose(
cell.point, self.diagnostic_threshold
)
}
)
)
try:
cube.remove_coord(cube_name)
except AttributeError:
msg = "Threshold {} is not present on coordinate with values {} {}"
raise ValueError(
msg.format(
self.diagnostic_threshold,
input_cube.coord(cube_name).points,
input_cube.coord(cube_name).units,
)
)
ratios = iris.cube.CubeList()
try:
cslices = cube.slices_over("realization")
except CoordinateNotFoundError:
cslices = [cube]
for cslice in cslices:
ratios.append(self._calculate_ratio(cslice, cube_name, self.nbhood_radius))
ratios = ratios.merge_cube()
thresholded = BasicThreshold(self.textural_threshold).process(ratios)
# Squeeze scalar threshold coordinate.
try:
field_texture = iris.util.squeeze(collapse_realizations(thresholded))
except CoordinateNotFoundError:
field_texture = iris.util.squeeze(thresholded)
return field_texture
def test_scalar_cube_noexists(self):
# Ensure that extract does not return a non-matching scalar cube.
constraint = iris.Constraint(name='a2')
cube = Cube(1, long_name='a1')
res = cube.extract(constraint)
self.assertIs(res, None)
def setUp(self):
"""Create a cube containing a regular lat-lon grid.
Data is formatted to increase linearly in x/y dimensions,
e.g.
0 1 2 3
1 2 3 4
2 3 4 5
3 4 5 6
"""
data = np.arange(0, 20, 1)
for i in range(1, 20):
data = np.append(data, np.arange(i, 20 + i))
data.resize(1, 20, 20)
latitudes = np.linspace(-90, 90, 20)
longitudes = np.linspace(-180, 180, 20)
latitude = DimCoord(latitudes,
standard_name='latitude',
units='degrees',
coord_system=GeogCS(6371229.0))
longitude = DimCoord(longitudes,
standard_name='longitude',
units='degrees',
coord_system=GeogCS(6371229.0))
# Use time of 2017-02-17 06:00:00
time = DimCoord([1487311200],
standard_name='time',
units=cf_units.Unit(
'seconds since 1970-01-01 00:00:00',
calendar='gregorian'))
time_dt = dt(2017, 2, 17, 6, 0)
time_extract = Constraint(time=PartialDateTime(
time_dt.year, time_dt.month, time_dt.day, time_dt.hour))
forecast_ref_time = time[0].copy()
forecast_ref_time.rename('forecast_reference_time')
height = AuxCoord([1.5], standard_name='height', units='m')
cube = Cube(data,
standard_name="air_temperature",
dim_coords_and_dims=[(time, 0), (latitude, 1),
(longitude, 2)],
units="K")
cube.add_aux_coord(forecast_ref_time)
cube.add_aux_coord(height)
cube.attributes['institution'] = 'Met Office'
orography = Cube(np.ones((20, 20)),
long_name="surface_altitude",
dim_coords_and_dims=[(latitude, 0), (longitude, 1)],
units="m")
# Western half of grid at altitude 0, eastern half at 10.
# Note that the pressure_on_height_levels data is left unchanged,
# so it is as if there is a sharp front running up the grid with
# differing pressures on either side at equivalent heights above
# the surface (e.g. east 1000hPa at 0m AMSL, west 1000hPa at 10m AMSL).
# So there is higher pressure in the west.
orography.data[0:10] = 0
orography.data[10:] = 10
ancillary_data = {}
ancillary_data.update({'orography': orography})
# Create additional vertical data used to calculate temperature lapse
# rates from model levels.
t_level0 = np.ones((1, 20, 20)) * 20.
t_level1 = np.ones((1, 20, 20)) * 10.
t_level2 = np.ones((1, 20, 20)) * 0.
t_data = np.vstack((t_level0, t_level1, t_level2))
t_data.resize((1, 3, 20, 20))
p_level0 = np.ones((1, 20, 20)) * 1000.
p_level1 = np.ones((1, 20, 20)) * 900.
p_level2 = np.ones((1, 20, 20)) * 800.
p_data = np.vstack((p_level0, p_level1, p_level2))
p_data.resize((1, 3, 20, 20))
height = DimCoord([0., 50., 100.], standard_name='height', units='m')
temperature_on_height_levels = Cube(
t_data,
long_name="temperature_on_height_levels",
dim_coords_and_dims=[(time, 0), (height, 1), (latitude, 2),
(longitude, 3)],
units="degree_Celsius")
pressure_on_height_levels = Cube(p_data,
long_name="pressure_on_height_levels",
dim_coords_and_dims=[(time, 0),
(height, 1),
(latitude, 2),
(longitude, 3)],
units="hPa")
surface_pressure = Cube(p_data[0, 0].reshape(1, 20, 20),
long_name="surface_pressure",
dim_coords_and_dims=[(time, 0), (latitude, 1),
(longitude, 2)],
units="hPa")
with iris.FUTURE.context(cell_datetime_objects=True):
ad = {
'temperature_on_height_levels':
temperature_on_height_levels.extract(time_extract),
'pressure_on_height_levels':
pressure_on_height_levels.extract(time_extract),
'surface_pressure':
surface_pressure.extract(time_extract)
}
sites = OrderedDict()
sites.update({
'100': {
'latitude': 4.74,
'longitude': 9.47,
'altitude': 10,
'utc_offset': 0,
'wmo_site': 0
}
})
neighbour_list = np.empty(1,
dtype=[('i', 'i8'), ('j', 'i8'),
('dz', 'f8'), ('edgepoint', 'bool_')])
neighbour_list[0] = 10, 10, 0, False
self.kwargs = {
'upper_level': 2,
'lower_level': 1,
'dz_tolerance': 2.,
'dthetadz_threshold': 0.02,
'dz_max_adjustment': 70.
}
self.cube = cube
self.ancillary_data = ancillary_data
self.ad = ad
self.sites = sites
self.time_extract = time_extract
self.neighbour_list = neighbour_list
self.latitudes = latitudes
self.latitude = latitude
self.forecast_ref_time = forecast_ref_time
def subset_data(
cube: Cube,
grid_spec: Optional[Dict[str, Dict[str, int]]] = None,
site_list: Optional[List] = None,
) -> Cube:
"""Extract a spatial cutout or subset of sites from data
to generate suite reference outputs.
Args:
cube:
Input dataset
grid_spec:
Dictionary containing bounding grid points and an integer "thinning
factor" for each of UK and global grid, to create cutouts. Eg a
"thinning factor" of 10 would mean every 10th point being taken for
the cutout. The expected dictionary has keys that are spatial coordinate
names, with values that are dictionaries with "min", "max" and "thin" keys.
site_list:
List of WMO site IDs to extract. These IDs must match the type and format
of the "wmo_id" coordinate on the input spot cube.
Returns:
Subset of input cube as specified by input constraints
Raises:
ValueError:
If site_list is not provided for a spot data cube
ValueError:
If the spot data cube does not contain any of the required sites
ValueError:
If grid_spec is not provided for a gridded cube
ValueError:
If grid_spec does not contain entries for the spatial coordinates on
the input gridded data
ValueError:
If the grid_spec provided does not overlap with the cube domain
"""
if cube.coords("spot_index"):
if site_list is None:
raise ValueError("site_list required to extract from spot data")
constraint = Constraint(
coord_values={"wmo_id": lambda x: x in site_list})
result = cube.extract(constraint)
if result is None:
raise ValueError(
f"Cube does not contain any of the required sites: {site_list}"
)
else:
if grid_spec is None:
raise ValueError("grid_spec required to extract from gridded data")
x_coord = cube.coord(axis="x").name()
y_coord = cube.coord(axis="y").name()
for coord in [y_coord, x_coord]:
if coord not in grid_spec:
raise ValueError(
f"Cube coordinates {y_coord}, {x_coord} are not present within "
f"{grid_spec.keys()}")
def _create_cutout(cube, grid_spec):
"""Given a gridded data cube and boundary limits for cutout dimensions,
create cutout. Expects cube on either lat-lon or equal area grid.
"""
x_coord = cube.coord(axis="x").name()
y_coord = cube.coord(axis="y").name()
xmin = grid_spec[x_coord]["min"]
xmax = grid_spec[x_coord]["max"]
ymin = grid_spec[y_coord]["min"]
ymax = grid_spec[y_coord]["max"]
# need to use cube intersection for circular coordinates (longitude)
if x_coord == "longitude":
lat_constraint = Constraint(
latitude=lambda y: ymin <= y.point <= ymax)
cutout = cube.extract(lat_constraint)
if cutout is None:
return cutout
cutout = cutout.intersection(longitude=(xmin, xmax),
ignore_bounds=True)
# intersection creates a new coordinate with default datatype - we
# therefore need to re-cast to meet the IMPROVER standard
cutout.coord("longitude").points = cutout.coord(
"longitude").points.astype(FLOAT_DTYPE)
if cutout.coord("longitude").bounds is not None:
cutout.coord("longitude").bounds = cutout.coord(
"longitude").bounds.astype(FLOAT_DTYPE)
else:
x_constraint = Constraint(
projection_x_coordinate=lambda x: xmin <= x.point <= xmax)
y_constraint = Constraint(
projection_y_coordinate=lambda y: ymin <= y.point <= ymax)
cutout = cube.extract(x_constraint & y_constraint)
return cutout
cutout = _create_cutout(cube, grid_spec)
if cutout is None:
raise ValueError(
"Cube domain does not overlap with cutout specified:\n"
f"{x_coord}: {grid_spec[x_coord]}, {y_coord}: {grid_spec[y_coord]}"
)
original_coords = get_dim_coord_names(cutout)
thin_x = grid_spec[x_coord]["thin"]
thin_y = grid_spec[y_coord]["thin"]
result_list = CubeList()
try:
for subcube in cutout.slices([y_coord, x_coord]):
result_list.append(subcube[::thin_y, ::thin_x])
except ValueError as cause:
# error is raised if X or Y coordinate are single-valued (non-dimensional)
if "iterator" in str(cause) and "dimension" in str(cause):
raise ValueError(
"Function does not support single point extraction")
else:
raise
result = result_list.merge_cube()
enforce_coordinate_ordering(result, original_coords)
return result
def apply_extraction(
cube: Cube,
constraint: Constraint,
units: Optional[Dict] = None,
use_original_units: bool = True,
longitude_constraint: Optional[List] = None,
) -> Cube:
"""
Using a set of constraints, extract a subcube from the provided cube if it
is available.
Args:
cube:
The cube from which a subcube is to be extracted.
constraint:
The constraint or ConstraintCombination that will be used to
extract a subcube from the input cube.
units:
A dictionary of units for the constraints. Supplied if any
coordinate constraints are provided in different units from those
of the input cube (eg precip in mm/h for cube threshold in m/s).
use_original_units:
Boolean to state whether the coordinates used in the extraction
should be converted back to their original units. The default is
True, indicating that the units should be converted back to the
original units.
longitude_constraint:
List containing the min and max values for the longitude.
This has to be treated separately to the normal constraints due
to the circular nature of longitude.
Returns:
A single cube matching the input constraints, or None if no subcube
is found within cube that matches the constraints.
"""
if units is None:
output_cube = cube.extract(constraint)
else:
original_units = {}
for coord in units.keys():
original_units[coord] = cube.coord(coord).units
cube.coord(coord).convert_units(units[coord])
output_cube = cube.extract(constraint)
if use_original_units:
for coord in original_units:
cube.coord(coord).convert_units(original_units[coord])
try:
output_cube.coord(coord).convert_units(original_units[coord])
except AttributeError:
# an empty output cube (None) is handled by the CLI
pass
if longitude_constraint:
output_cube = output_cube.intersection(
longitude=longitude_constraint, ignore_bounds=True
)
# TODO: Below can be removed when https://github.com/SciTools/iris/issues/4119
# is fixed
output_cube.coord("longitude").points = output_cube.coord(
"longitude"
).points.astype(FLOAT_DTYPE)
if output_cube.coord("longitude").bounds is not None:
output_cube.coord("longitude").bounds = output_cube.coord(
"longitude"
).bounds.astype(FLOAT_DTYPE)
return output_cube