def setUp(self):
"""Create test cubes and plugin instance.
The cube coordinates look like this:
Dimension coordinates:
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Scalar coordinates:
time: 2015-11-23 07:00:00
forecast_reference_time: 2015-11-23 07:00:00
forecast_period: 0 seconds
self.cube:
Metadata describes the nowcast lightning fields to be calculated.
forecast_period: 0 seconds (simulates nowcast data)
self.fg_cube:
Has 4 hour forecast period, to test impact at lr2
self.ltng_cube:
forecast_period: 0 seconds (simulates nowcast data)
self.precip_cube:
Has extra coordinate of length(3) "threshold" containing
points [0.5, 7., 35.] mm h-1.
self.vii_cube:
Has extra coordinate of length(3) "threshold" containing
points [0.5, 1., 2.] kg m-2.
"""
(
self.cube,
self.fg_cube,
self.ltng_cube,
self.precip_cube,
self.vii_cube,
) = set_up_lightning_test_cubes()
self.plugin = Plugin()
def test_with_radius(self):
"""
Test that the radius keyword is accepted.
"""
radius = 20000.0
plugin = Plugin(radius=radius)
self.assertEqual(plugin.radius, radius)
def setUp(self):
"""Create a cube like this:
probability_of_lwe_precipitation_rate_above_threshold / (1)
Dimension coordinates:
lwe_precipitation_rate: 1;
projection_y_coordinate: 16;
projection_x_coordinate: 16;
Scalar coordinates:
forecast_period: 14400 seconds
forecast_reference_time: 2017-11-10 00:00:00
time: 2017-11-10 04:00:00
Cell methods:
mean: realization
The lwe_precipitation_rate coordinate will have the attribute:
spp__relative_to_threshold: above.
"""
data = np.ones((1, 16, 16), dtype=np.float32)
thresholds = np.array([0.5], dtype=np.float32)
self.cube = set_up_probability_cube(
data,
thresholds,
variable_name="lwe_precipitation_rate",
threshold_units="mm h-1",
)
self.cube.add_cell_method(CellMethod("mean", coords="realization"))
self.plugin = Plugin()
def test_basic(self):
"""Test that the __repr__ returns the expected string."""
plugin = Plugin()
result = str(plugin)
msg = ("""<NowcastLightning: radius={radius},
lightning mapping (lightning rate in "min^-1"):
upper: lightning rate {lthru} => min lightning prob {lprobu}
lower: lightning rate {lthrl} => min lightning prob {lprobl}
>""".format(radius=10000.,
lthru="<class 'function'>", lthrl=0.,
lprobu=1., lprobl=0.25)
)
self.assertEqual(result, msg)
def test_result_with_vii(self):
"""Test that the method returns the expected data when vii is
present"""
# Set precip_cube forecast period to be zero.
self.precip_cube.coord("forecast_period").points = [0.0]
expected = self.set_up_vii_input_output()
# No halo - we're only testing this method.
# 2000m is the grid-length, so halo includes only one pixel.
plugin = Plugin(2000.0)
result = plugin(
CubeList([self.fg_cube, self.ltng_cube, self.precip_cube, self.vii_cube])
)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected.data)
def setUp(self):
"""Create test cubes and plugin instance.
The cube coordinates look like this:
Dimension coordinates:
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Scalar coordinates:
time: 2015-11-23 07:00:00
forecast_reference_time: 2015-11-23 07:00:00
forecast_period: 0 seconds
self.vii_cube:
Has extra coordinate of length(3) "threshold" containing
points [0.5, 1., 2.] kg m-2.
"""
(_, self.fg_cube, _, _, self.ice_cube) = set_up_lightning_test_cubes(
validity_time=dt(2015, 11, 23, 7), fg_frt=dt(2015, 11, 23, 7))
self.plugin = Plugin()
self.ice_threshold_coord = find_threshold_coordinate(self.ice_cube)
def setUp(self):
"""Create cubes with a single zero prob(precip) point.
The cubes look like this:
precipitation_amount / (kg m^-2)
Dimension coordinates:
time: 1;
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Auxiliary coordinates:
forecast_period (on time coord): 4.0 hours (simulates UM data)
Scalar coordinates:
forecast_reference_time: 2015-11-23 03:00:00
Data:
self.fg_cube:
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
All points contain float(1.)
Cube name is "probability_of_lightning".
self.precip_cube:
With extra coordinate of length(3) "threshold" containing
points [0.5, 7., 35.] mm hr-1.
All points contain float(1.) except the
zero point [0, 0, 1, 1] which is float(0.)
and [1:, 0, ...] which are float(0.)
Cube name is "probability_of_precipitation".
Cube has added attribute {'relative_to_threshold': 'above'}
"""
self.fg_cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=[],
num_grid_points=3),
fp_point=0.0)
self.fg_cube.rename("probability_of_lightning")
self.precip_cube = (add_forecast_reference_time_and_forecast_period(
set_up_cube(num_realization_points=3,
zero_point_indices=((0, 1, 1), ),
num_grid_points=3)))
threshold_coord = self.precip_cube.coord('realization')
threshold_coord.points = [0.5, 7.0, 35.0]
threshold_coord.rename('threshold')
threshold_coord.units = cf_units.Unit('mm hr-1')
self.precip_cube.rename("probability_of_precipitation")
self.precip_cube.attributes.update({'relative_to_threshold': 'above'})
self.precip_cube.data[1:, 0, ...] = 0.
self.plugin = Plugin()
def setUp(self):
"""Create test cubes and plugin instance.
The cube coordinates look like this:
Dimension coordinates:
projection_y_coordinate: 16;
projection_x_coordinate: 16;
Scalar coordinates:
time: 2015-11-23 07:00:00
forecast_reference_time: 2015-11-23 07:00:00
forecast_period: 0 seconds
self.fg_cube:
Has 4 hour forecast period, to test impact on "lightning risk
2 level" output (see improver.nowcasting.lightning for details)
self.ltng_cube:
forecast_period: 0 seconds (simulates nowcast data)
self.precip_cube:
Has extra coordinate of length(3) "threshold" containing
points [0.5, 7., 35.] mm h-1. Has a 4 hour forecast period.
self.vii_cube:
Has extra coordinate of length(3) "threshold" containing
points [0.5, 1., 2.] kg m-2.
"""
(
_,
self.fg_cube,
self.ltng_cube,
self.precip_cube,
self.vii_cube,
) = set_up_lightning_test_cubes(grid_points=16)
# reset some data and give precip cube a 4 hour forecast period
self.precip_cube.data[0, ...] = 1.0
self.precip_cube.coord("forecast_period").points = [4 * 3600.0]
# sort out spatial coordinates - need smaller grid length (set to 2 km)
for cube in [
self.fg_cube, self.ltng_cube, self.precip_cube, self.vii_cube
]:
points_array = 2000.0 * np.arange(16).astype(np.float32)
cube.coord(axis="x").points = points_array
cube.coord(axis="y").points = points_array
self.plugin = Plugin()
def setUp(self):
"""Create test cubes and plugin instance.
The cube coordinates look like this:
Dimension coordinates:
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Scalar coordinates:
time: 2015-11-23 07:00:00
forecast_reference_time: 2015-11-23 07:00:00
forecast_period: 0 seconds
self.fg_cube:
Has 4 hour forecast period
self.precip_cube:
Has extra coordinate of length(3) "threshold" containing
points [0.5, 7., 35.] mm h-1.
"""
(_, self.fg_cube, _, self.precip_cube, _) = set_up_lightning_test_cubes()
self.plugin = Plugin()
self.precip_threshold_coord = find_threshold_coordinate(self.precip_cube)
def setUp(self):
"""Create cubes with a single zero prob(precip) point.
The cubes look like this:
precipitation_amount / (kg m^-2)
Dimension coordinates:
time: 1;
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Auxiliary coordinates:
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
Scalar coordinates:
forecast_reference_time: 2015-11-23 03:00:00
Data:
self.fg_cube:
All points contain float(1.)
Cube name is "probability_of_lightning".
self.ice_cube:
With extra coordinate of length(3) "threshold" containing
points [0.5, 1., 2.] kg m^-2.
Time and forecast_period dimensions "sqeezed" to be Scalar coords.
All points contain float(0.)
Cube name is "probability_of_vertical_integral_of_ice".
"""
self.fg_cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=[],
num_grid_points=3),
fp_point=0.0)
self.fg_cube.rename("probability_of_lightning")
self.ice_cube = squeeze(
add_forecast_reference_time_and_forecast_period(set_up_cube(
num_realization_points=3,
zero_point_indices=[],
num_grid_points=3),
fp_point=0.0))
threshold_coord = self.ice_cube.coord('realization')
threshold_coord.points = [0.5, 1.0, 2.0]
threshold_coord.rename('threshold')
threshold_coord.units = cf_units.Unit('kg m^-2')
self.ice_cube.data = np.zeros_like(self.ice_cube.data)
self.ice_cube.rename("probability_of_vertical_integral_of_ice")
self.plugin = Plugin()
def test_result_with_vii_longfc(self):
"""Test that the method returns the expected data when vii is
present and forecast time is 4 hours"""
expected = self.set_up_vii_input_output()
# test_vii_null with no precip will now return 0.0067
expected.data[5, 5] = 0.0067
# test_vii_small with no and light precip will now return zero
expected.data[7, 5:7] = 0.0
# test_vii_large with no and light precip now return zero
# and 0.25 for heavy precip
expected.data[8, 5:8] = [0.0, 0.0, 0.25]
# No halo - we're only testing this method.
# 2000m is the grid-length, so halo includes only one pixel.
plugin = Plugin(2000.0)
result = plugin(
CubeList([self.fg_cube, self.ltng_cube, self.precip_cube, self.vii_cube])
)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected.data)
def setUp(self):
"""Create a cube with a single non-zero point like this:
precipitation_amount / (kg m^-2)
Dimension coordinates:
realization: 1;
time: 1;
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Auxiliary coordinates:
forecast_period (on time coord): 4.0 hours
Scalar coordinates:
forecast_reference_time: 2015-11-23 03:00:00
threshold: 0.5 mm hr-1
Data:
All points contain float(1.) except the
zero point [0, 0, 1, 1] which is float(0.)
"""
self.cube = add_forecast_reference_time_and_forecast_period(
set_up_cube())
coord = DimCoord(0.5, long_name="threshold", units='mm hr^-1')
self.cube.add_aux_coord(coord)
self.cube.add_cell_method(CellMethod('mean', coords='realization'))
self.plugin = Plugin()
def setUp(self):
"""Create cubes with a single zero prob(precip) point.
The cubes look like this:
precipitation_amount / (kg m^-2)
Dimension coordinates:
time: 1;
projection_y_coordinate: 16;
projection_x_coordinate: 16;
Auxiliary coordinates:
forecast_period (on time coord): 4.0 hours (simulates UM data)
Scalar coordinates:
forecast_reference_time: 2015-11-23 03:00:00
Data:
self.fg_cube:
All points contain float(1.)
Cube name is "probability_of_lightning".
self.ltng_cube:
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
All points contain float(1.)
Cube name is "rate_of_lightning".
Cube units are "min^-1".
self.precip_cube:
With extra coordinate of length(3) "threshold" containing
points [0.5, 7., 35.] mm hr-1.
All points contain float(1.) except the
zero point [0, 0, 7, 7] which is float(0.)
and [1:, 0, ...] which are float(0.)
Cube name is "probability_of_precipitation".
Cube has added attribute {'relative_to_threshold': 'above'}
self.vii_cube:
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
With extra coordinate of length(3) "threshold" containing
points [0.5, 1., 2.] kg m^-2.
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
Time and forecast_period dimensions "sqeezed" to be Scalar coords.
All points contain float(0.)
Cube name is "probability_of_vertical_integral_of_ice".
"""
self.fg_cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=[]))
self.fg_cube.rename("probability_of_lightning")
self.ltng_cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=[]),
fp_point=0.0)
self.ltng_cube.rename("rate_of_lightning")
self.ltng_cube.units = cf_units.Unit("min^-1")
self.precip_cube = (add_forecast_reference_time_and_forecast_period(
set_up_cube(num_realization_points=3)))
threshold_coord = self.precip_cube.coord('realization')
threshold_coord.points = [0.5, 7.0, 35.0]
threshold_coord.rename('threshold')
threshold_coord.units = cf_units.Unit('mm hr-1')
self.precip_cube.rename("probability_of_precipitation")
self.precip_cube.attributes.update({'relative_to_threshold': 'above'})
self.precip_cube.data[1:, 0, ...] = 0.
self.vii_cube = squeeze(
add_forecast_reference_time_and_forecast_period(set_up_cube(
num_realization_points=3, zero_point_indices=[]),
fp_point=0.0))
threshold_coord = self.vii_cube.coord('realization')
threshold_coord.points = [0.5, 1.0, 2.0]
threshold_coord.rename('threshold')
threshold_coord.units = cf_units.Unit('kg m^-2')
self.vii_cube.data = np.zeros_like(self.vii_cube.data)
self.vii_cube.rename("probability_of_vertical_integral_of_ice")
self.plugin = Plugin()
def setUp(self):
"""Create cubes with a single zero prob(precip) point.
The cubes look like this:
precipitation_amount / (kg m^-2)
Dimension coordinates:
time: 1;
projection_y_coordinate: 3;
projection_x_coordinate: 3;
Auxiliary coordinates:
forecast_period (on time coord): 4.0 hours (simulates UM data)
Scalar coordinates:
forecast_reference_time: 2015-11-23 03:00:00
Data:
self.cube:
Describes the nowcast fields to be calculated.
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
All points contain float(1.) except the
zero point [0, 1, 1] which is float(0.)
self.fg_cube:
All points contain float(1.)
self.ltng_cube:
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
All points contain float(1.)
self.precip_cube:
With extra coordinate of length(3) "threshold" containing
points [0.5, 7., 35.] mm hr-1.
All points contain float(1.) except the
zero point [0, 0, 1, 1] which is float(0.)
and [1:, 0, ...] which are float(0.)
self.vii_cube:
With extra coordinate of length(3) "threshold" containing
points [0.5, 1., 2.] kg m^-2.
forecast_period (on time coord): 0.0 hours (simulates nowcast data)
Time and forecast_period dimensions "sqeezed" to be Scalar coords.
All points contain float(0.)
"""
self.cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=((0, 1, 1), ),
num_grid_points=3),
fp_point=0.0)
self.fg_cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=[],
num_grid_points=3))
self.ltng_cube = add_forecast_reference_time_and_forecast_period(
set_up_cube_with_no_realizations(zero_point_indices=[],
num_grid_points=3),
fp_point=0.0)
self.precip_cube = (add_forecast_reference_time_and_forecast_period(
set_up_cube(num_realization_points=3,
zero_point_indices=((0, 1, 1), ),
num_grid_points=3),
fp_point=0.0))
threshold_coord = self.precip_cube.coord('realization')
threshold_coord.points = [0.5, 7.0, 35.0]
threshold_coord.rename('threshold')
threshold_coord.units = cf_units.Unit('mm hr-1')
self.precip_cube.data[1:, 0, ...] = 0.
# iris.util.queeze is applied here to demote the singular coord "time"
# to a scalar coord.
self.vii_cube = squeeze(
add_forecast_reference_time_and_forecast_period(set_up_cube(
num_realization_points=3,
zero_point_indices=[],
num_grid_points=3),
fp_point=0.0))
threshold_coord = self.vii_cube.coord('realization')
threshold_coord.points = [0.5, 1.0, 2.0]
threshold_coord.rename('threshold')
threshold_coord.units = cf_units.Unit('kg m^-2')
self.vii_cube.data = np.zeros_like(self.vii_cube.data)
self.plugin = Plugin()
