def test_exception(self):
"""Test the expected exception is raised."""
distance = 2000
radius = 2000
msg = "Only a square neighbourhood is accepted"
with self.assertRaisesRegexp(ValueError, msg):
ProbabilityOfOccurrence(distance, "circular", radius)
def test_basic(self):
"""Test that the __repr__ returns the expected string."""
result = str(ProbabilityOfOccurrence(2000, "square", 2000))
msg = ('<ProbabilityOfOccurrence: distance: 2000; '
'neighbourhood_method: square; radii: 2000; '
'lead_times: None; weighted_mode: True; '
'ens_factor: 1.0>')
self.assertEqual(result, msg)
def test_with_realization(self):
"""Test when a realization coordinate is present."""
expected = np.array(
[[[1, 1., 0.77777778, 0.55555556, 0.33333333],
[0.66666667, 0.77777778, 0.77777778, 0.77777778, 0.66666667],
[0.33333333, 0.55555556, 0.77777778, 1., 1.],
[0., 0.22222222, 0.44444444, 0.66666667, 0.66666667],
[0., 0.11111111, 0.22222222, 0.33333333, 0.33333333]]])
distance = 2000
neighbourhood_method = "square"
radii = 2000
result = (ProbabilityOfOccurrence(distance, neighbourhood_method,
radii).process(self.cube))
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected)
def test_additional_arguments(self):
"""Test when all keyword arguments are passed in."""
expected = np.array(
[[[1., 1., 0.8333333, 0.666667, 0.5],
[0.66666667, 0.77777778, 0.77777778, 0.77777778, 0.66666667],
[0.33333333, 0.55555556, 0.77777778, 1., 1.],
[0., 0.22222222, 0.44444444, 0.66666667, 0.66666667],
[0., 0.16666667, 0.33333333, 0.5, 0.5]],
[[0.77777778, 0.75, 0.73333333, 0.75, 0.77777778],
[0.66666667, 0.6875, 0.7, 0.75, 0.8333333],
[0.53333333, 0.55, 0.56, 0.6, 0.66666667],
[0.41666667, 0.5, 0.55, 0.625, 0.75],
[0.22222222, 0.33333333, 0.4, 0.5, 0.66666667]]])
data = np.zeros((1, 2, 5, 5))
data[0, :, 0, 1] = 1.0
data[0, :, 2, 3] = 1.0
y_dimension_values = np.arange(0.0, 10000.0, 2000.0)
cube = set_up_cube(data,
"lwe_precipitation_rate",
"m s-1",
y_dimension_values=y_dimension_values,
x_dimension_values=y_dimension_values,
timesteps=np.array([402192.5, 402195.5]))
distance = 2000
neighbourhood_method = "square"
radii = [2000, 4000]
lead_times = [3, 6]
cube.add_aux_coord(
AuxCoord(lead_times, "forecast_period", units="hours"), 1)
weighted_mode = False
ens_factor = 0.9
expected_cube_for_shape = cube.copy()
expected_cube_for_shape = expected_cube_for_shape.collapsed(
"realization", MEAN)
orig_shape = expected_cube_for_shape.data.copy().shape
result = (ProbabilityOfOccurrence(distance,
neighbourhood_method,
radii,
lead_times=lead_times,
weighted_mode=weighted_mode,
ens_factor=ens_factor).process(cube))
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected)
self.assertEqual(result.data.shape, orig_shape)
self.assertEqual(result.coord("forecast_period").units, "hours")
def test_without_realization(self):
"""Test when a realization coordinate is not present."""
expected = np.array(
[[[1., 1., 0.8333333, 0.666667, 0.5],
[0.66666667, 0.77777778, 0.77777778, 0.77777778, 0.66666667],
[0.33333333, 0.55555556, 0.77777778, 1., 1.],
[0., 0.22222222, 0.44444444, 0.66666667, 0.66666667],
[0., 0.16666667, 0.33333333, 0.5, 0.5]]])
cube = self.cube[0, :, :, :]
cube.remove_coord("realization")
distance = 2000
neighbourhood_method = "square"
radii = 2000
orig_shape = cube.data.copy().shape
result = (ProbabilityOfOccurrence(distance, neighbourhood_method,
radii).process(self.cube))
self.assertIsInstance(result, Cube)
self.assertEqual(result.data.shape, orig_shape)
self.assertArrayAlmostEqual(result.data, expected)
def test_additional_arguments(self):
"""Test when all keyword arguments are passed in."""
expected = np.array(
[[[[1., 1., 0.77777778, 0.55555556, 0.333333],
[0.66666667, 0.77777778, 0.77777778, 0.77777778, 0.66666667],
[0.33333333, 0.55555556, 0.77777778, 1., 1.],
[0., 0.22222222, 0.44444444, 0.66666667, 0.66666667],
[0., 0.11111111, 0.22222222, 0.33333333, 0.33333333]],
[[0.84, 0.8, 0.76, 0.72, 0.68], [0.68, 0.7, 0.72, 0.74, 0.76],
[0.48, 0.52, 0.56, 0.6, 0.64], [0.3, 0.4, 0.5, 0.6, 0.7],
[0.12, 0.24, 0.36, 0.48, 0.6]]]])
data = np.zeros((1, 2, 5, 5))
data[0, :, 0, 1] = 1.0
data[0, :, 2, 3] = 1.0
y_dimension_values = np.arange(0.0, 10000.0, 2000.0)
cube = set_up_cube(data,
"lwe_precipitation_rate",
"m s-1",
y_dimension_values=y_dimension_values,
x_dimension_values=y_dimension_values,
timesteps=np.array([402192.5, 402195.5]))
distance = 2000
neighbourhood_method = "square"
radii = [2000, 4000]
lead_times = [3, 6]
cube.add_aux_coord(
AuxCoord(lead_times, "forecast_period", units="hours"), 1)
unweighted_mode = True
ens_factor = 0.9
result = (ProbabilityOfOccurrence(distance,
neighbourhood_method,
radii,
lead_times=lead_times,
unweighted_mode=unweighted_mode,
ens_factor=ens_factor).process(cube))
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected)
