def test_endpoints_of_distribution_exceeded(self):
"""
Test that the plugin raises a ValueError when the constant
end points of the distribution are exceeded by a threshold value
used in the forecast.
"""
probabilities_for_cdf = np.array([[0.05, 0.7, 0.95]])
threshold_points = np.array([8, 10, 60])
bounds_pairing = (-40, 50)
plugin = Plugin()
msg = "The calculated threshold values"
with self.assertRaisesRegex(ValueError, msg):
plugin._add_bounds_to_thresholds_and_probabilities(
threshold_points, probabilities_for_cdf, bounds_pairing)
def test_endpoints_of_distribution_exceeded_warning(
self, warning_list=None):
"""
Test that the plugin raises a warning message when the constant
end points of the distribution are exceeded by a threshold value
used in the forecast and the ecc_bounds_warning keyword argument
has been specified.
"""
probabilities_for_cdf = np.array([[0.05, 0.7, 0.95]])
threshold_points = np.array([8, 10, 60])
bounds_pairing = (-40, 50)
plugin = Plugin(ecc_bounds_warning=True)
warning_msg = "The calculated threshold values"
plugin._add_bounds_to_thresholds_and_probabilities(
threshold_points, probabilities_for_cdf, bounds_pairing)
self.assertTrue(any(warning_msg in str(item) for item in warning_list))
def test_basic(self):
"""Test that the plugin returns two numpy arrays."""
cube = self.current_temperature_forecast_cube
probabilities_for_cdf = cube.data.reshape(3, 9)
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._add_bounds_to_thresholds_and_probabilities(
self.threshold_points, probabilities_for_cdf, bounds_pairing)
self.assertIsInstance(result[0], np.ndarray)
self.assertIsInstance(result[1], np.ndarray)
def test_new_endpoints_generation(self):
"""Test that the plugin re-applies the threshold bounds using the
maximum and minimum threshold points values when the original bounds
have been exceeded and ecc_bounds_warning has been set."""
probabilities_for_cdf = np.array([[0.05, 0.7, 0.95]])
threshold_points = np.array([-50, 10, 60])
bounds_pairing = (-40, 50)
plugin = Plugin(ecc_bounds_warning=True)
result = plugin._add_bounds_to_thresholds_and_probabilities(
threshold_points, probabilities_for_cdf, bounds_pairing)
self.assertEqual(max(result[0]), max(threshold_points))
self.assertEqual(min(result[0]), min(threshold_points))
def test_bounds_of_threshold_points(self):
"""
Test that the plugin returns the expected results for the
threshold_points, where they've been padded with the values from
the bounds_pairing.
"""
cube = self.current_temperature_forecast_cube
probabilities_for_cdf = cube.data.reshape(3, 9)
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._add_bounds_to_thresholds_and_probabilities(
self.threshold_points, probabilities_for_cdf, bounds_pairing)
self.assertArrayAlmostEqual(result[0][0], bounds_pairing[0])
self.assertArrayAlmostEqual(result[0][-1], bounds_pairing[1])
def test_probability_data(self):
"""
Test that the plugin returns the expected results for the
probabilities, where they've been padded with zeros and ones to
represent the extreme ends of the Cumulative Distribution Function.
"""
cube = self.current_temperature_forecast_cube
probabilities_for_cdf = cube.data.reshape(3, 9)
zero_array = np.zeros(probabilities_for_cdf[:, 0].shape)
one_array = np.ones(probabilities_for_cdf[:, 0].shape)
bounds_pairing = (-40, 50)
plugin = Plugin()
result = plugin._add_bounds_to_thresholds_and_probabilities(
self.threshold_points, probabilities_for_cdf, bounds_pairing)
self.assertArrayAlmostEqual(result[1][:, 0], zero_array)
self.assertArrayAlmostEqual(result[1][:, -1], one_array)
