def test_unmatched_cube_error(self):
"""Test that an error is raised if the neighbour cube and diagnostic
cube do not have the expected attributes matching."""
plugin = SpotExtraction(grid_metadata_identifier='mosg')
msg = 'Cubes do not share the metadata identified '
with self.assertRaisesRegex(ValueError, msg):
plugin.process(self.neighbour_cube, self.diagnostic_cube_xy)
def test_unmatched_cube_error(self):
"""Test that an error is raised if the neighbour cube and diagnostic
cube do not have matching grids."""
self.neighbour_cube.attributes["model_grid_hash"] = "123"
plugin = SpotExtraction()
msg = ("Cubes do not share or originate from the same grid, so cannot "
"be used together.")
with self.assertRaisesRegex(ValueError, msg):
plugin.process(self.neighbour_cube, self.diagnostic_cube_xy)
def test_cube_with_leading_dimensions(self):
"""Test that a cube with a leading dimension such as realization or
probability results in a spotdata cube with the same leading
dimension."""
realization0 = iris.coords.DimCoord([0],
standard_name="realization",
units=1)
realization1 = iris.coords.DimCoord([1],
standard_name="realization",
units=1)
cube0 = self.diagnostic_cube_xy.copy()
cube1 = self.diagnostic_cube_xy.copy()
cube0.add_aux_coord(realization0)
cube1.add_aux_coord(realization1)
cubes = iris.cube.CubeList([cube0, cube1])
cube = cubes.merge_cube()
plugin = SpotExtraction()
expected = [[0, 0, 12, 12], [0, 0, 12, 12]]
expected_coord = iris.coords.DimCoord([0, 1],
standard_name="realization",
units=1)
result = plugin.process(self.neighbour_cube, cube)
self.assertArrayEqual(result.data, expected)
self.assertEqual(result.name(), cube.name())
self.assertEqual(result.units, cube.units)
self.assertArrayEqual(result.coord("latitude").points, self.latitudes)
self.assertArrayEqual(
result.coord("longitude").points, self.longitudes)
self.assertEqual(result.coord("realization"), expected_coord)
self.assertDictEqual(result.attributes, self.expected_attributes)
def process(self, spot_data_cube, neighbour_cube, gridded_lapse_rate_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 (iris.cube.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 (iris.cube.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 (iris.cube.Cube):
A cube of temperature lapse rates on the same grid as that from
which the spot data temperatures were extracted.
Returns:
iris.cube.Cube:
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.
extraction_plugin = SpotExtraction(
neighbour_selection_method=self.neighbour_selection_method)
spot_lapse_rate = extraction_plugin.process(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_removal_of_internal_metadata(self):
"""Test that internal metadata used to identify the unique id coordinate
is removed in the resulting spot diagnostic cube."""
plugin = SpotExtraction()
result = plugin.process(self.neighbour_cube, self.diagnostic_cube_xy)
self.assertNotIn(
UNIQUE_ID_ATTRIBUTE,
[att for att in result.coord(self.unique_site_id_key).attributes],
)
def test_new_title(self):
"""Test title is updated as expected"""
expected_attributes = self.expected_attributes
expected_attributes["title"] = "IMPROVER Spot Forecast"
plugin = SpotExtraction(neighbour_selection_method='nearest_land')
result = plugin.process(self.neighbour_cube,
self.diagnostic_cube_xy,
new_title="IMPROVER Spot Forecast")
self.assertDictEqual(result.attributes, expected_attributes)
def test_cell_methods(self):
"""Test cell methods from the gridded input cube are retained on the
spotdata cube."""
plugin = SpotExtraction(neighbour_selection_method="nearest_land")
result = plugin.process(
self.neighbour_cube,
self.diagnostic_cube_xy,
new_title="IMPROVER Spot Forecast",
)
self.assertEqual(result.cell_methods, self.cell_methods)
def test_2d_aux_coords(self):
"""Test 2D auxiliary coordinates from the gridded input cube are
retained as 1D coordinates associated with the spot-index on the
spotdata cube."""
plugin = SpotExtraction()
result = plugin.process(
self.neighbour_cube,
self.diagnostic_cube_2d_time,
new_title="IMPROVER Spot Forecast",
)
self.assertEqual(result.coord("time"), self.expected_spot_time_coord)
def test_returned_cube_nearest_land(self):
"""Test that data within the returned cube is as expected for the
nearest land neighbours."""
plugin = SpotExtraction(neighbour_selection_method="nearest_land")
expected = [6, 6, 12, 12]
result = plugin.process(self.neighbour_cube, self.diagnostic_cube_xy)
self.assertArrayEqual(result.data, expected)
self.assertEqual(result.name(), self.diagnostic_cube_xy.name())
self.assertEqual(result.units, self.diagnostic_cube_xy.units)
self.assertArrayEqual(result.coord("latitude").points, self.latitudes)
self.assertArrayEqual(result.coord("longitude").points, self.longitudes)
self.assertDictEqual(result.attributes, self.expected_attributes)
def test_returned_cube_nearest(self):
"""Test that data within the returned cube is as expected for the
nearest neigbours."""
plugin = SpotExtraction(grid_metadata_identifier=None)
expected = [0, 0, 12, 12]
result = plugin.process(self.neighbour_cube, self.diagnostic_cube_xy)
self.assertArrayEqual(result.data, expected)
self.assertEqual(result.name(), self.diagnostic_cube_xy.name())
self.assertEqual(result.units, self.diagnostic_cube_xy.units)
self.assertDictEqual(result.attributes,
self.diagnostic_cube_xy.attributes)
self.assertArrayEqual(result.coord('latitude').points, self.latitudes)
self.assertArrayEqual(
result.coord('longitude').points, self.longitudes)
def test_yx_ordered_cube(self):
"""Test extraction of diagnostic data that is natively ordered yx."""
plugin = SpotExtraction()
expected = [0, 0, 12, 12]
result = plugin.process(self.coordinate_cube, self.diagnostic_cube_yx)
self.assertArrayEqual(result.data, expected)
def main(argv=None):
"""Load in arguments and start spotdata extraction process."""
parser = ArgParser(
description="Extract diagnostic data from gridded fields for spot data"
" sites. It is possible to apply a temperature lapse rate adjustment"
" to temperature data that helps to account for differences between"
" the spot sites real altitude and that of the grid point from which"
" the temperature data is extracted.")
# Input and output files required.
parser.add_argument("neighbour_filepath", metavar="NEIGHBOUR_FILEPATH",
help="Path to a NetCDF file of spot-data neighbours. "
"This file also contains the spot site information.")
parser.add_argument("diagnostic_filepath", metavar="DIAGNOSTIC_FILEPATH",
help="Path to a NetCDF file containing the diagnostic "
"data to be extracted.")
parser.add_argument("temperature_lapse_rate_filepath",
metavar="LAPSE_RATE_FILEPATH", nargs='?',
help="(Optional) Filepath to a NetCDF file containing"
" temperature lapse rates. If this cube is provided,"
" and a screen temperature cube is being processed,"
" the lapse rates will be used to adjust the"
" temperatures to better represent each spot's"
" site-altitude.")
parser.add_argument("output_filepath", metavar="OUTPUT_FILEPATH",
help="The output path for the resulting NetCDF")
parser.add_argument(
"--apply_lapse_rate_correction",
default=False, action="store_true",
help="If the option is set and a lapse rate cube has been "
"provided, extracted screen temperatures will be adjusted to "
"better match the altitude of the spot site for which they have "
"been extracted.")
method_group = parser.add_argument_group(
title="Neighbour finding method",
description="If none of these options are set, the nearest grid point "
"to a spot site will be used without any other constraints.")
method_group.add_argument(
"--land_constraint", default=False, action='store_true',
help="If set the neighbour cube will be interrogated for grid point"
" neighbours that were identified using a land constraint. This means"
" that the grid points should be land points except for sites where"
" none were found within the search radius when the neighbour cube was"
" created. May be used with minimum_dz.")
method_group.add_argument(
"--minimum_dz", default=False, action='store_true',
help="If set the neighbour cube will be interrogated for grid point"
" neighbours that were identified using a minimum height difference"
" constraint. These are grid points that were found to be the closest"
" in altitude to the spot site within the search radius defined when"
" the neighbour cube was created. May be used with land_constraint.")
percentile_group = parser.add_argument_group(
title="Extract percentiles",
description="Extract particular percentiles from probabilistic, "
"percentile, or realization inputs. If deterministic input is "
"provided a warning is raised and all leading dimensions are included "
"in the returned spot-data cube.")
percentile_group.add_argument(
"--extract_percentiles", default=None, nargs='+', type=int,
help="If set to a percentile value or a list of percentile values, "
"data corresponding to those percentiles will be returned. For "
"example setting '--extract_percentiles 25 50 75' will result in the "
"25th, 50th, and 75th percentiles being returned from a cube of "
"probabilities, percentiles, or realizations. Note that for "
"percentile inputs, the desired percentile(s) must exist in the input "
"cube.")
parser.add_argument(
"--ecc_bounds_warning", default=False, action="store_true",
help="If True, where calculated percentiles are outside the ECC "
"bounds range, raise a warning rather than an exception.")
meta_group = parser.add_argument_group("Metadata")
meta_group.add_argument(
"--metadata_json", metavar="METADATA_JSON", default=None,
help="If provided, this JSON file can be used to modify the metadata "
"of the returned netCDF file. Defaults to None.")
output_group = parser.add_argument_group("Suppress Verbose output")
# This CLI may be used to prepare data for verification without knowing the
# form of the input, be it deterministic, realizations or probabilistic.
# A warning is normally raised when attempting to extract a percentile from
# deterministic data as this is not possible; the spot-extraction of the
# entire cube is returned. When preparing data for verification we know
# that we will produce a large number of these warnings when passing in
# deterministic data. This option to suppress warnings is provided to
# reduce the amount of unneeded logging information that is written out.
output_group.add_argument(
"--suppress_warnings", default=False, action="store_true",
help="Suppress warning output. This option should only be used if "
"it is known that warnings will be generated but they are not "
"required.")
args = parser.parse_args(args=argv)
neighbour_cube = load_cube(args.neighbour_filepath)
diagnostic_cube = load_cube(args.diagnostic_filepath)
neighbour_selection_method = NeighbourSelection(
land_constraint=args.land_constraint,
minimum_dz=args.minimum_dz).neighbour_finding_method_name()
plugin = SpotExtraction(
neighbour_selection_method=neighbour_selection_method)
result = plugin.process(neighbour_cube, diagnostic_cube)
# If a probability or percentile diagnostic cube is provided, extract
# the given percentile if available. This is done after the spot-extraction
# to minimise processing time; usually there are far fewer spot sites than
# grid points.
if args.extract_percentiles:
try:
perc_coordinate = find_percentile_coordinate(result)
except CoordinateNotFoundError:
if 'probability_of_' in result.name():
result = GeneratePercentilesFromProbabilities(
ecc_bounds_warning=args.ecc_bounds_warning).process(
result, percentiles=args.extract_percentiles)
result = iris.util.squeeze(result)
elif result.coords('realization', dim_coords=True):
fast_percentile_method = (
False if np.ma.isMaskedArray(result.data) else True)
result = PercentileConverter(
'realization', percentiles=args.extract_percentiles,
fast_percentile_method=fast_percentile_method).process(
result)
else:
msg = ('Diagnostic cube is not a known probabilistic type. '
'The {} percentile could not be extracted. Extracting '
'data from the cube including any leading '
'dimensions.'.format(
args.extract_percentiles))
if not args.suppress_warnings:
warnings.warn(msg)
else:
constraint = ['{}={}'.format(perc_coordinate.name(),
args.extract_percentiles)]
perc_result = extract_subcube(result, constraint)
if perc_result is not None:
result = perc_result
else:
msg = ('The percentile diagnostic cube does not contain the '
'requested percentile value. Requested {}, available '
'{}'.format(args.extract_percentiles,
perc_coordinate.points))
raise ValueError(msg)
# Check whether a lapse rate cube has been provided and we are dealing with
# temperature data and the lapse-rate option is enabled.
if (args.temperature_lapse_rate_filepath and
args.apply_lapse_rate_correction):
if not result.name() == "air_temperature":
msg = ("A lapse rate cube was provided, but the diagnostic being "
"processed is not air temperature and cannot be adjusted.")
raise ValueError(msg)
lapse_rate_cube = load_cube(args.temperature_lapse_rate_filepath)
if not 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(lapse_rate_cube.name()))
raise ValueError(msg)
try:
lapse_rate_height_coord = lapse_rate_cube.coord("height")
except (ValueError, 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 ValueError(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 diagnostic_cube.coord("height") == lapse_rate_height_coord:
plugin = SpotLapseRateAdjust(
neighbour_selection_method=neighbour_selection_method)
result = plugin.process(result, neighbour_cube, lapse_rate_cube)
else:
msg = ("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.")
if not args.suppress_warnings:
warnings.warn(msg)
elif (args.apply_lapse_rate_correction and
not args.temperature_lapse_rate_filepath):
msg = ("A lapse rate cube was not provided, but the option to "
"apply the lapse rate correction was enabled. No lapse rate "
"correction could be applied.")
if not args.suppress_warnings:
warnings.warn(msg)
# Modify final metadata as described by provided JSON file.
if args.metadata_json:
with open(args.metadata_json, 'r') as input_file:
metadata_dict = json.load(input_file)
result = amend_metadata(result, **metadata_dict)
# Remove the internal model_grid_hash attribute if present.
result.attributes.pop('model_grid_hash', None)
# Save the spot data cube.
save_netcdf(result, args.output_filepath)
def process(neighbour_cube,
diagnostic_cube,
lapse_rate_cube=None,
apply_lapse_rate_correction=False,
land_constraint=False,
minimum_dz=False,
extract_percentiles=None,
ecc_bounds_warning=False,
metadata_dict=None,
suppress_warnings=False):
"""Module to run spot data extraction.
Extract diagnostic data from gridded fields for spot data sites. It is
possible to apply a temperature lapse rate adjustment to temperature data
that helps to account for differences between the spot site's real altitude
and that of the grid point from which the temperature data is extracted.
Args:
neighbour_cube (iris.cube.Cube):
Cube of spot-data neighbours and the spot site information.
diagnostic_cube (iris.cube.Cube):
Cube containing the diagnostic data to be extracted.
lapse_rate_cube (iris.cube.Cube):
Cube containing temperature lapse rates. If this cube is provided
and a screen temperature cube is being processed, the lapse rates
will be used to adjust the temperature to better represent each
spot's site-altitude.
apply_lapse_rate_correction (bool):
If True, and a lapse rate cube has been provided, extracted
screen temperature will be adjusted to better match the altitude
of the spot site for which they have been extracted.
Default is False.
land_constraint (bool):
If True, the neighbour cube will be interrogated for grid point
neighbours that were identified using a land constraint. This means
that the grid points should be land points except for sites where
none were found within the search radius when the neighbour cube
was created. May be used with minimum_dz.
Default is False.
minimum_dz (bool):
If True, the neighbour cube will be interrogated for grid point
neighbours that were identified using the minimum height
difference constraint. These are grid points that were found to be
the closest in altitude to the spot site within the search radius
defined when the neighbour cube was created. May be used with
land_constraint.
Default is False.
extract_percentiles (list or int):
If set to a percentile value or a list of percentile values,
data corresponding to those percentiles will be returned. For
example [25, 50, 75] will result in the 25th, 50th and 75th
percentiles being returned from a cube of probabilities,
percentiles or realizations.
Note that for percentiles inputs, the desired percentile(s) must
exist in the input cube.
Default is None.
ecc_bounds_warning (bool):
If True, where calculated percentiles are outside the ECC bounds
range, raises a warning rather than an exception.
Default is False.
metadata_dict (dict):
If provided, this dictionary can be used to modify the metadata
of the returned cube.
Default is None.
suppress_warnings (bool):
Suppress warning output. This option should only be used if it
is known that warnings will be generated but they are not required.
Default is None.
Returns:
result (iris.cube.Cube):
The processed cube.
Raises:
ValueError:
If the percentile diagnostic cube does not contain the requested
percentile value.
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 diagnostic cube is not a known probabilistic type.
warning:
If a lapse rate cube was provided, but the height of the
temperature does not match that of the data used.
warning:
If a lapse rate cube was not provided, but the option to apply
the lapse rate correction was enabled.
"""
neighbour_selection_method = NeighbourSelection(
land_constraint=land_constraint,
minimum_dz=minimum_dz).neighbour_finding_method_name()
plugin = SpotExtraction(
neighbour_selection_method=neighbour_selection_method)
result = plugin.process(neighbour_cube, diagnostic_cube)
# If a probability or percentile diagnostic cube is provided, extract
# the given percentile if available. This is done after the spot-extraction
# to minimise processing time; usually there are far fewer spot sites than
# grid points.
if extract_percentiles is not None:
try:
perc_coordinate = find_percentile_coordinate(result)
except CoordinateNotFoundError:
if 'probability_of_' in result.name():
result = GeneratePercentilesFromProbabilities(
ecc_bounds_warning=ecc_bounds_warning).process(
result, percentiles=extract_percentiles)
result = iris.util.squeeze(result)
elif result.coords('realization', dim_coords=True):
fast_percentile_method = (False if np.ma.isMaskedArray(
result.data) else True)
result = PercentileConverter(
'realization',
percentiles=extract_percentiles,
fast_percentile_method=fast_percentile_method).process(
result)
else:
msg = ('Diagnostic cube is not a known probabilistic type. '
'The {} percentile could not be extracted. Extracting '
'data from the cube including any leading '
'dimensions.'.format(extract_percentiles))
if not suppress_warnings:
warnings.warn(msg)
else:
constraint = [
'{}={}'.format(perc_coordinate.name(), extract_percentiles)
]
perc_result = extract_subcube(result, constraint)
if perc_result is not None:
result = perc_result
else:
msg = ('The percentile diagnostic cube does not contain the '
'requested percentile value. Requested {}, available '
'{}'.format(extract_percentiles,
perc_coordinate.points))
raise ValueError(msg)
# Check whether a lapse rate cube has been provided and we are dealing with
# temperature data and the lapse-rate option is enabled.
if apply_lapse_rate_correction and lapse_rate_cube:
if not result.name() == "air_temperature":
msg = ("A lapse rate cube was provided, but the diagnostic being "
"processed is not air temperature and cannot be adjusted.")
raise ValueError(msg)
if not 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(lapse_rate_cube.name()))
raise ValueError(msg)
try:
lapse_rate_height_coord = lapse_rate_cube.coord("height")
except (ValueError, 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 ValueError(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 diagnostic_cube.coord("height") == lapse_rate_height_coord:
plugin = SpotLapseRateAdjust(
neighbour_selection_method=neighbour_selection_method)
result = plugin.process(result, neighbour_cube, lapse_rate_cube)
elif not suppress_warnings:
warnings.warn(
"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.")
elif apply_lapse_rate_correction and not lapse_rate_cube:
if not suppress_warnings:
warnings.warn(
"A lapse rate cube was not provided, but the option to "
"apply the lapse rate correction was enabled. No lapse rate "
"correction could be applied.")
# Modify final metadata as described by provided JSON file.
if metadata_dict:
result = amend_metadata(result, **metadata_dict)
# Remove the internal model_grid_hash attribute if present.
result.attributes.pop('model_grid_hash', None)
return result
def process(neighbour_cube: cli.inputcube,
cube: cli.inputcube,
lapse_rate: cli.inputcube = None,
*,
apply_lapse_rate_correction=False,
land_constraint=False,
similar_altitude=False,
extract_percentiles: cli.comma_separated_list = None,
ignore_ecc_bounds=False,
new_title: str = None,
suppress_warnings=False):
"""Module to run spot data extraction.
Extract diagnostic data from gridded fields for spot data sites. It is
possible to apply a temperature lapse rate adjustment to temperature data
that helps to account for differences between the spot site's real altitude
and that of the grid point from which the temperature data is extracted.
Args:
neighbour_cube (iris.cube.Cube):
Cube of spot-data neighbours and the spot site information.
cube (iris.cube.Cube):
Cube containing the diagnostic data to be extracted.
lapse_rate (iris.cube.Cube):
Optional cube containing temperature lapse rates. If this cube is
provided and a screen temperature cube is being processed, the
lapse rates will be used to adjust the temperature to better
represent each spot's site-altitude.
apply_lapse_rate_correction (bool):
Use to apply a lapse-rate correction to screen temperature data so
that the data are a better match the altitude of the spot site for
which they have been extracted.
land_constraint (bool):
Use to select the nearest-with-land-constraint neighbour-selection
method from the neighbour_cube. This means that the grid points
should be land points except for sites where none were found within
the search radius when the neighbour cube was created. May be used
with similar_altitude.
similar_altitude (bool):
Use to select the nearest-with-height-constraint
neighbour-selection method from the neighbour_cube. These are grid
points that were found to be the closest in altitude to the spot
site within the search radius defined when the neighbour cube was
created. May be used with land_constraint.
extract_percentiles (list or int):
If set to a percentile value or a list of percentile values,
data corresponding to those percentiles will be returned. For
example "25, 50, 75" will result in the 25th, 50th and 75th
percentiles being returned from a cube of probabilities,
percentiles or realizations. Deterministic input data will raise
a warning message.
Note that for percentiles inputs, the desired percentile(s) must
exist in the input cube.
ignore_ecc_bounds (bool):
Demotes exceptions where calculated percentiles are outside the ECC
bounds range to warnings.
new_title (str):
New title for the spot-extracted data. If None, this attribute is
removed from the output cube since it has no prescribed standard
and may therefore contain grid information that is no longer
correct after spot-extraction.
suppress_warnings (bool):
Suppress warning output. This option should only be used if it
is known that warnings will be generated but they are not required.
Returns:
iris.cube.Cube:
Cube of spot data.
Raises:
ValueError:
If the percentile diagnostic cube does not contain the requested
percentile value.
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 diagnostic cube is not a known probabilistic type.
warning:
If a lapse rate cube was provided, but the height of the
temperature does not match that of the data used.
warning:
If a lapse rate cube was not provided, but the option to apply
the lapse rate correction was enabled.
"""
import warnings
import iris
import numpy as np
from iris.exceptions import CoordinateNotFoundError
from improver.ensemble_copula_coupling.ensemble_copula_coupling import \
GeneratePercentilesFromProbabilities
from improver.metadata.probabilistic import find_percentile_coordinate
from improver.percentile import PercentileConverter
from improver.spotdata.apply_lapse_rate import SpotLapseRateAdjust
from improver.spotdata.neighbour_finding import NeighbourSelection
from improver.spotdata.spot_extraction import SpotExtraction
from improver.utilities.cube_extraction import extract_subcube
neighbour_selection_method = NeighbourSelection(
land_constraint=land_constraint,
minimum_dz=similar_altitude).neighbour_finding_method_name()
plugin = SpotExtraction(
neighbour_selection_method=neighbour_selection_method)
result = plugin.process(neighbour_cube, cube, new_title=new_title)
# If a probability or percentile diagnostic cube is provided, extract
# the given percentile if available. This is done after the spot-extraction
# to minimise processing time; usually there are far fewer spot sites than
# grid points.
if extract_percentiles:
extract_percentiles = [np.float32(x) for x in extract_percentiles]
try:
perc_coordinate = find_percentile_coordinate(result)
except CoordinateNotFoundError:
if 'probability_of_' in result.name():
result = GeneratePercentilesFromProbabilities(
ecc_bounds_warning=ignore_ecc_bounds).process(
result, percentiles=extract_percentiles)
result = iris.util.squeeze(result)
elif result.coords('realization', dim_coords=True):
fast_percentile_method = (False if np.ma.isMaskedArray(
result.data) else True)
result = PercentileConverter(
'realization',
percentiles=extract_percentiles,
fast_percentile_method=fast_percentile_method).process(
result)
else:
msg = ('Diagnostic cube is not a known probabilistic type. '
'The {} percentile could not be extracted. Extracting '
'data from the cube including any leading '
'dimensions.'.format(extract_percentiles))
if not suppress_warnings:
warnings.warn(msg)
else:
constraint = [
'{}={}'.format(perc_coordinate.name(), extract_percentiles)
]
perc_result = extract_subcube(result, constraint)
if perc_result is not None:
result = perc_result
else:
msg = ('The percentile diagnostic cube does not contain the '
'requested percentile value. Requested {}, available '
'{}'.format(extract_percentiles,
perc_coordinate.points))
raise ValueError(msg)
# Check whether a lapse rate cube has been provided and we are dealing with
# temperature data and the lapse-rate option is enabled.
if apply_lapse_rate_correction and lapse_rate:
if not result.name() == "air_temperature":
msg = ("A lapse rate cube was provided, but the diagnostic being "
"processed is not air temperature and cannot be adjusted.")
raise ValueError(msg)
if not lapse_rate.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(lapse_rate.name()))
raise ValueError(msg)
try:
lapse_rate_height_coord = lapse_rate.coord("height")
except (ValueError, 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 ValueError(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 cube.coord("height") == lapse_rate_height_coord:
plugin = SpotLapseRateAdjust(
neighbour_selection_method=neighbour_selection_method)
result = plugin.process(result, neighbour_cube, lapse_rate)
elif not suppress_warnings:
warnings.warn(
"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.")
elif apply_lapse_rate_correction and not lapse_rate:
if not suppress_warnings:
warnings.warn(
"A lapse rate cube was not provided, but the option to "
"apply the lapse rate correction was enabled. No lapse rate "
"correction could be applied.")
# Remove the internal model_grid_hash attribute if present.
result.attributes.pop('model_grid_hash', None)
return result
