def test_probabilities_not_monotonically_increasing(
self, warning_list=None):
"""
Test that the plugin raises a Warning when the probabilities
of the Cumulative Distribution Function are not monotonically
increasing.
"""
data = np.array([0.05, 0.7, 0.95])
data = data[:, np.newaxis, np.newaxis, np.newaxis]
self.current_temperature_forecast_cube = add_forecast_reference_time_and_forecast_period(
set_up_probability_threshold_cube(
data,
"air_temperature",
"degreesC",
forecast_thresholds=[8, 10, 12],
y_dimension_length=1,
x_dimension_length=1,
spp__relative_to_threshold="above",
))
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
warning_msg = "The probability values used to construct the"
plugin._probabilities_to_percentiles(cube, percentiles, bounds_pairing)
self.assertTrue(any(warning_msg in str(item) for item in warning_list))
def test_transpose_cube_dimensions(self):
"""
Test that the plugin returns an the expected data, when comparing
input cubes which have dimensions in a different order.
"""
# Calculate result for nontransposed cube.
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
nontransposed_result = plugin._probabilities_to_percentiles(
cube, percentiles, bounds_pairing)
# Calculate result for transposed cube.
# Original cube dimensions are [P, T, Y, X].
# Transposed cube dimensions are [X, Y, T, P].
cube.transpose([3, 2, 1, 0])
transposed_result = plugin._probabilities_to_percentiles(
cube, percentiles, bounds_pairing)
# Result cube will be [P, X, Y, T]
# Transpose cube to be [P, T, Y, X]
transposed_result.transpose([0, 3, 2, 1])
self.assertArrayAlmostEqual(nontransposed_result.data,
transposed_result.data)
def test_unknown_thresholding(self):
"""Test that the plugin returns an Iris.cube.Cube."""
cube = self.current_temperature_forecast_cube
cube.coord(var_name="threshold"
).attributes["spp__relative_to_threshold"] = "between"
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
msg = "Probabilities to percentiles only implemented for"
with self.assertRaisesRegex(NotImplementedError, msg):
plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
def test_simple_check_data_below(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles when input probabilities are given
for being below a threshold.
The input cube contains probabilities that values are below a given
threshold.
"""
expected = np.array([8.4, 10.61538462, 11.84615385])
expected = expected[:, np.newaxis, np.newaxis, np.newaxis]
data = np.array([0.95, 0.3, 0.05])[::-1]
data = data[:, np.newaxis, np.newaxis, np.newaxis]
self.current_temperature_forecast_cube = add_forecast_reference_time_and_forecast_period(
set_up_probability_threshold_cube(
data,
"air_temperature",
"degreesC",
forecast_thresholds=[8, 10, 12],
y_dimension_length=1,
x_dimension_length=1,
spp__relative_to_threshold="above",
))
cube = self.current_temperature_forecast_cube
cube.coord(var_name="threshold"
).attributes["spp__relative_to_threshold"] = "below"
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertArrayAlmostEqual(result.data, expected)
def test_lots_of_percentiles(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles, if lots of percentile values are
requested.
"""
data = np.array(
[[[[13.9, -16., 10.2], [-28., -16., -35.2], [-35.2, -37., -37.6]]],
[[[17.7, 8.25, 10.6], [-4., 8.25, -25.6], [-25.6, -31., -32.8]]],
[[[21.5, 8.75, 11.], [8.33333333, 8.75, -16.], [-16., -25., -28.]]
], [[[25.3, 9.25, 11.4], [9., 9.25, -6.4], [-6.4, -19., -23.2]]],
[[[29.1, 9.75, 11.8], [9.66666667, 9.75, 3.2], [3.2, -13., -18.4]]
],
[[[32.9, 10.33333333, 15.8], [10.33333333, 10.2, 8.5],
[8.33333333, -7., -13.6]]],
[[[36.7, 11., 23.4], [11., 10.6, 9.5], [9., -1., -8.8]]],
[[[40.5, 11.66666667, 31.], [11.66666667, 11., 10.5],
[9.66666667, 5., -4.]]],
[[[44.3, 21.5, 38.6], [21.5, 11.4, 11.5], [10.5, 8.5, 0.8]]],
[[[48.1, 40.5, 46.2], [40.5, 11.8, 31.], [11.5, 9.5, 5.6]]]],
dtype=np.float32)
cube = self.current_temperature_forecast_cube
percentiles = np.arange(5, 100, 10)
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertArrayAlmostEqual(result.data, data, decimal=5)
def test_lots_of_probability_thresholds(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles, if there are lots of thresholds.
"""
input_probs_1d = np.linspace(1, 0, 30)
input_probs = np.tile(input_probs_1d, (3, 3, 1, 1)).T
data = np.array(
[[[[2.9, 2.9, 2.9], [2.9, 2.9, 2.9], [2.9, 2.9, 2.9]]],
[[[14.5, 14.5, 14.5], [14.5, 14.5, 14.5], [14.5, 14.5, 14.5]]],
[[[26.099998, 26.099998, 26.099998],
[26.099998, 26.099998, 26.099998],
[26.099998, 26.099998, 26.099998]]]],
dtype=np.float32)
temperature_values = np.arange(0, 30)
cube = (add_forecast_reference_time_and_forecast_period(
set_up_probability_threshold_cube(
input_probs,
"air_temperature",
"degreesC",
forecast_thresholds=temperature_values,
spp__relative_to_threshold='above')))
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertArrayAlmostEqual(result.data, data)
def test_check_single_threshold(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles, if a single threshold is used for
constructing the percentiles.
"""
data = np.array(
[[[[12.2, 8., 12.2], [-16., 8., -30.4], [-30.4, -34., -35.2]]],
[[[29., 26.66666667, 29.], [23.75, 26.66666667, 8.],
[8., -10., -16.]]],
[[[45.8, 45.33333333, 45.8], [44.75, 45.33333333, 41.6],
[41.6, 29., 3.2]]]],
dtype=np.float32)
threshold_coord = find_threshold_coordinate(
self.current_temperature_forecast_cube)
for acube in self.current_temperature_forecast_cube.slices_over(
threshold_coord):
cube = acube
break
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertArrayAlmostEqual(result.data, data, decimal=5)
def test_basic(self):
"""Test that the plugin returns an Iris.cube.Cube."""
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertIsInstance(result, Cube)
def test_return_name(self):
"""Test that the plugin returns an Iris.cube.Cube with an appropriate
name.
"""
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertEqual(result.name(), "air_temperature")
def test_result_cube_has_no_air_temperature_threshold_coordinate(self):
"""
Test that the plugin returns a cube with coordinates that
do not include the air_temperature_threshold coordinate.
"""
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
for coord in result.coords():
self.assertNotEqual(coord.name(), "threshold")
def test_return_coord_units(self):
"""Test that the plugin returns an Iris.cube.Cube with an appropriate
percentile coordinate with suitable units.
"""
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertIsInstance(result.coord("percentile"), DimCoord)
self.assertArrayEqual(result.coord("percentile").points, percentiles)
self.assertEqual(result.coord("percentile").units, unit.Unit("%"))
def test_check_data_multiple_timesteps(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles.
"""
expected = np.array(
[
[[[8.0, 8.0], [-8.0, 8.66666667]], [[8.0, -16.0], [8.0, -16.0]]
],
[[[12.0, 12.0], [12.0, 12.0]], [[10.5, 10.0], [10.5, 10.0]]],
[[[31.0, 31.0], [31.0, 31.0]],
[[11.5, 11.33333333], [11.5, 12.0]]],
],
dtype=np.float32,
)
data = np.array(
[
[[[0.8, 0.8], [0.7, 0.9]], [[0.8, 0.6], [0.8, 0.6]]],
[[[0.6, 0.6], [0.6, 0.6]], [[0.5, 0.4], [0.5, 0.4]]],
[[[0.4, 0.4], [0.4, 0.4]], [[0.1, 0.1], [0.1, 0.2]]],
],
dtype=np.float32,
)
cube = set_up_probability_threshold_cube(
data,
"air_temperature",
"degreesC",
timesteps=2,
x_dimension_length=2,
y_dimension_length=2,
spp__relative_to_threshold="above",
)
self.probability_cube = add_forecast_reference_time_and_forecast_period(
cube,
time_point=np.array([402295.0, 402296.0]),
fp_point=[2.0, 3.0])
cube = self.probability_cube
percentiles = [20, 60, 80]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertArrayAlmostEqual(result.data, expected, decimal=5)
def test_check_data_spot_forecasts(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles for spot forecasts.
"""
data = np.array(
[[[15.8, 8., 10.4, -16., 8., -30.4, -30.4, -34., -35.2]],
[[31., 10., 12., 10., 10., 8., 8., -10., -16.]],
[[46.2, 31., 42.4, 31., 11.6, 12., 11., 9., 3.2]]],
dtype=np.float32)
cube = self.current_temperature_spot_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
self.assertArrayAlmostEqual(result.data, data, decimal=5)
