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_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_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 setUp(self):
"""Set up cube """
data = np.array([0.1, 0.3, 0.4, 0.2, 0.6, 0.7, 0.4, 0.2, 0.1,
0.2, 0.2, 0.5, 0.1, 0.3, 0.9, 0.8, 0.5, 0.3,
0.6, 0.3, 0.5, 0.6, 0.8, 0.2,
0.8, 0.1, 0.2]).reshape(3, 1, 3, 3)
self.cube = set_up_probability_threshold_cube(
data, 'air_temperature', 'K', spp__relative_to_threshold='above')
self.wxcode = np.array(list(WX_DICT.keys()))
self.wxmeaning = " ".join(WX_DICT.values())
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., 8.],
[-8., 8.66666667]],
[[8., -16.],
[8., -16.]]],
[[[12., 12.],
[12., 12.]],
[[10.5, 10.],
[10.5, 10.]]],
[[[31., 31.],
[31., 31.]],
[[11.5, 11.33333333],
[11.5, 12.]]]], 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 set_up_wxcubes():
"""Set up cubes required for Weather Symbols """
data_snow = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0]).reshape(3, 1, 3, 3)
snowfall_rate = (
set_up_probability_threshold_cube(
data_snow,
'lwe_snowfall_rate',
'm s-1',
spp__relative_to_threshold='above',
forecast_thresholds=np.array([8.33333333e-09,
2.77777778e-08,
2.77777778e-07])))
data_rain = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0]).reshape(3, 1, 3, 3)
rainfall_rate = (
set_up_probability_threshold_cube(
data_rain,
'rainfall_rate',
'm s-1',
spp__relative_to_threshold='above',
forecast_thresholds=np.array([8.33333333e-09,
2.77777778e-08,
2.77777778e-07])))
data_snowv = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0]).reshape(3, 1, 3, 3)
snowfall_vicinity = (
set_up_probability_threshold_cube(
data_snowv,
'lwe_snowfall_rate_in_vicinity',
'm s-1',
spp__relative_to_threshold='above',
forecast_thresholds=np.array([8.33333333e-09,
2.77777778e-08,
2.77777778e-07])))
data_rainv = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0]).reshape(3, 1, 3, 3)
rainfall_vicinity = (
set_up_probability_threshold_cube(
data_rainv,
'rainfall_rate_in_vicinity',
'm s-1',
spp__relative_to_threshold='above',
forecast_thresholds=np.array([8.33333333e-09,
2.77777778e-08,
2.77777778e-07])))
data_cloud = np.array([0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 1.0, 1.0,
0.0, 0.0, 1.0]).reshape(2, 1, 3, 3)
cloud = (set_up_probability_threshold_cube(
data_cloud,
'cloud_area_fraction',
'1',
spp__relative_to_threshold='above',
forecast_thresholds=np.array([0.1875, 0.8125])))
data_cld_low = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 1.0,
0.0, 0.0, 0.0]).reshape(1, 1, 3, 3)
cloud_low = (
set_up_probability_threshold_cube(
data_cld_low,
'low_type_cloud_area_fraction',
'1',
spp__relative_to_threshold='above',
forecast_thresholds=np.array([0.85])))
data_vis = np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
0.0, 1.0, 0.0]).reshape(2, 1, 3, 3)
visibility = (
set_up_probability_threshold_cube(
data_vis,
'visibility_in_air',
'm',
spp__relative_to_threshold='below',
forecast_thresholds=np.array([1000.0, 5000.0])))
visibility.attributes['relative_to_threshold'] = 'below'
cubes = iris.cube.CubeList([snowfall_rate, rainfall_rate,
snowfall_vicinity, rainfall_vicinity,
cloud, cloud_low,
visibility])
return cubes
def set_up_wxcubes_global():
"""Set up cubes required for Weather Symbols """
data_snow = np.array([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]).reshape(3, 1, 3, 3)
snowfall_rate = (set_up_probability_threshold_cube(
data_snow,
'lwe_snowfall_rate',
'm s-1',
relative_to_threshold='above',
forecast_thresholds=np.array(
[8.33333333e-09, 2.77777778e-08, 2.77777778e-07])))
data_rain = np.array([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]).reshape(3, 1, 3, 3)
rainfall_rate = (set_up_probability_threshold_cube(
data_rain,
'rainfall_rate',
'm s-1',
relative_to_threshold='above',
forecast_thresholds=np.array(
[8.33333333e-09, 2.77777778e-08, 2.77777778e-07])))
data_cloud = np.array([
0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0,
1.0, 0.0, 0.0, 1.0
]).reshape(2, 1, 3, 3)
cloud = (set_up_probability_threshold_cube(data_cloud,
'cloud_area_fraction',
'1',
relative_to_threshold='above',
forecast_thresholds=np.array(
[0.1875, 0.8125])))
data_cld_1000ft = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0]).reshape(1, 1, 3, 3)
cloud_below_1000ft = (set_up_probability_threshold_cube(
data_cld_1000ft, 'cloud_area_fraction_assuming_only'
'_consider_surface_to_1000_feet_asl',
'1',
relative_to_threshold='above',
forecast_thresholds=np.array([0.85])))
data_vis = np.array([
0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0,
0.0, 0.0, 1.0, 0.0
]).reshape(2, 1, 3, 3)
visibility = (set_up_probability_threshold_cube(
data_vis,
'visibility_in_air',
'm',
relative_to_threshold='below',
forecast_thresholds=np.array([1000.0, 5000.0])))
visibility.attributes['relative_to_threshold'] = 'below'
cubes = iris.cube.CubeList(
[snowfall_rate, rainfall_rate, cloud, cloud_below_1000ft, visibility])
return cubes
