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_above_threshold_cube(
data_snow,
'lwe_snowfall_rate',
'm s-1',
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_above_threshold_cube(
data_rain,
'rainfall_rate',
'm s-1',
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_above_threshold_cube(
data_cloud,
'cloud_area_fraction',
'1',
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_above_threshold_cube(
data_cld_1000ft, 'cloud_area_fraction_assuming_only'
'_consider_surface_to_1000_feet_asl',
'1',
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_above_threshold_cube(
data_vis,
'visibility_in_air',
'm',
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
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]]],
[[[26.1, 26.1, 26.1], [26.1, 26.1, 26.1],
[26.1, 26.1, 26.1]]]])
temperature_values = np.arange(0, 30)
cube = (add_forecast_reference_time_and_forecast_period(
set_up_probability_above_threshold_cube(
input_probs,
"air_temperature",
"1",
forecast_thresholds=temperature_values)))
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_probabilities_not_monotonically_increasing(self):
"""
Test that the plugin raises a ValueError 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_above_threshold_cube(
data,
"air_temperature",
"1",
forecast_thresholds=[8, 10, 12],
y_dimension_length=1,
x_dimension_length=1)))
cube = self.current_temperature_forecast_cube
percentiles = [10, 50, 90]
bounds_pairing = (-40, 50)
plugin = Plugin()
msg = "The probability values used to construct the"
with self.assertRaisesRegexp(ValueError, msg):
plugin._probabilities_to_percentiles(cube, percentiles,
bounds_pairing)
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.]]]])
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]]]])
cube = set_up_probability_above_threshold_cube(data,
"air_temperature",
"degreesC",
timesteps=2,
x_dimension_length=2,
y_dimension_length=2)
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)
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_above_threshold_cube(
data,
"air_temperature",
"1",
forecast_thresholds=[8, 10, 12],
y_dimension_length=1,
x_dimension_length=1)))
cube = self.current_temperature_forecast_cube
cube.attributes["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_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_above_threshold_cube(
data,
"air_temperature",
"degreesC",
forecast_thresholds=[8, 10, 12],
y_dimension_length=1,
x_dimension_length=1)))
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_simple_check_data(self):
"""
Test that the plugin returns an Iris.cube.Cube with the expected
data values for the percentiles.
The input cube contains probabilities greater than a given threshold.
"""
expected = np.array([8.15384615, 9.38461538, 11.6])
expected = expected[:, np.newaxis, np.newaxis, np.newaxis]
data = np.array([0.95, 0.3, 0.05])
data = data[:, np.newaxis, np.newaxis, np.newaxis]
self.current_temperature_forecast_cube = (
add_forecast_reference_time_and_forecast_period(
set_up_probability_above_threshold_cube(
data,
"air_temperature",
"1",
forecast_thresholds=[8, 10, 12],
y_dimension_length=1,
x_dimension_length=1)))
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.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_above_threshold_cube(
data, 'air_temperature', 'K')
self.wxcode = np.array(WX_DICT.keys())
self.wxmeaning = " ".join(WX_DICT.values())
