def test_interpolation(self):
"""Test that interpolation is working as expected in _find_radii."""
fp_points = np.array([2, 3, 4])
radii = [10000, 30000]
lead_times = [2, 4]
plugin = NBHood(radii=radii, lead_times=lead_times)
result = plugin._find_radii(cube_lead_times=fp_points)
expected_result = np.array([10000.0, 20000.0, 30000.0])
self.assertArrayAlmostEqual(result, expected_result)
def test_basic_float_cube_lead_times_is_none(self):
"""Test _find_radii returns an unaltered radius if
the lead times are none, and this radius is a float."""
neighbourhood_method = CircularNeighbourhood()
radius = 6300
plugin = NBHood(neighbourhood_method, radius)
result = plugin._find_radii(cube_lead_times=None)
expected_result = 6300.0
self.assertIsInstance(result, float)
self.assertAlmostEqual(result, expected_result)
def test_basic_array_cube_lead_times_an_array(self):
"""Test _find_radii returns an array with the correct values."""
fp_points = np.array([2, 3, 4])
radii = [10000, 20000, 30000]
lead_times = [1, 3, 5]
plugin = NBHood(radii, lead_times=lead_times)
result = plugin._find_radii(cube_lead_times=fp_points)
expected_result = np.array([15000.0, 20000.0, 25000.0])
self.assertIsInstance(result, np.ndarray)
self.assertArrayAlmostEqual(result, expected_result)
def test_basic_float_cube_lead_times_is_none(self):
"""Test _find_radii returns a float with the correct value."""
neighbourhood_method = CircularNeighbourhood()
ens_factor = 0.8
num_ens = 2.0
radius = 6300
plugin = NBHood(neighbourhood_method, radius, ens_factor=ens_factor)
result = plugin._find_radii(num_ens)
expected_result = 3563.8181771801998
self.assertIsInstance(result, float)
self.assertAlmostEquals(result, expected_result)
def test_interpolation(self):
"""Test that interpolation is working as expected in _find_radii."""
neighbourhood_method = CircularNeighbourhood
fp_points = np.array([2, 3, 4])
radii = [10000, 30000]
lead_times = [2, 4]
plugin = NBHood(neighbourhood_method(),
radii=radii,
lead_times=lead_times)
result = plugin._find_radii(cube_lead_times=fp_points)
expected_result = np.array([10000., 20000., 30000.])
self.assertArrayAlmostEqual(result, expected_result)
def test_source_realizations(self):
"""Test when the array has source_realization attribute."""
realization_list = [0, 1, 2, 3]
cube = (set_up_cube_with_no_realizations(
source_realizations=realization_list))
radii = 5600
neighbourhood_method = CircularNeighbourhood()
plugin = NBHood(neighbourhood_method, radii)
result = plugin.process(cube)
self.assertIsInstance(result, Cube)
expected = np.ones([1, 16, 16])
expected[0, 6:9, 6:9] = ([0.91666667, 0.875,
0.91666667], [0.875, 0.83333333, 0.875],
[0.91666667, 0.875, 0.91666667])
self.assertArrayAlmostEqual(result.data, expected)
def test_radii_varying_with_lead_time(self):
"""
Test that a cube is returned when the radius varies with lead time.
"""
cube = set_up_cube(num_time_points=3)
iris.util.promote_aux_coord_to_dim_coord(cube, "time")
time_points = cube.coord("time").points
fp_points = [2, 3, 4]
cube = add_forecast_reference_time_and_forecast_period(
cube, time_point=time_points, fp_point=fp_points)
radii = [10000, 20000, 30000]
lead_times = [2, 3, 4]
neighbourhood_method = CircularNeighbourhood()
plugin = NBHood(neighbourhood_method, radii, lead_times)
result = plugin.process(cube)
self.assertIsInstance(result, Cube)
def test_interpolation(self):
"""Test that interpolation is working as expected in _find_radii."""
fp_points = np.array([2, 3, 4])
neighbourhood_method = CircularNeighbourhood()
ens_factor = 0.8
num_ens = 4.0
fp_points = np.array([2, 3, 4])
radii = [10000, 30000]
lead_times = [2, 4]
plugin = NBHood(neighbourhood_method,
radii,
lead_times=lead_times,
ens_factor=ens_factor)
result = plugin._find_radii(num_ens, cube_lead_times=fp_points)
expected_result = np.array([4000., 8000., 12000.])
self.assertArrayAlmostEqual(result, expected_result)
def test_basic_array_cube_lead_times_an_array(self):
"""Test _find_radii returns an array with the correct values."""
neighbourhood_method = CircularNeighbourhood
ens_factor = 0.9
num_ens = 2.0
fp_points = np.array([2, 3, 4])
radii = [10000, 20000, 30000]
lead_times = [2, 3, 4]
plugin = NBHood(neighbourhood_method(),
radii,
lead_times=lead_times,
ens_factor=ens_factor)
result = plugin._find_radii(num_ens, cube_lead_times=fp_points)
expected_result = np.array([6363.961031, 12727.922061, 19091.883092])
self.assertIsInstance(result, np.ndarray)
self.assertArrayAlmostEqual(result, expected_result)
def test_radii_varying_with_lead_time_with_interpolation(self):
"""Test that a cube is returned for the following conditions:
1. The radius varies with lead time.
2. Linear interpolation is required to create values for the radii
which are required but were not specified within the 'radii'
argument."""
cube = set_up_cube(num_time_points=3)
iris.util.promote_aux_coord_to_dim_coord(cube, "time")
time_points = cube.coord("time").points
fp_points = [2, 3, 4]
cube = add_forecast_reference_time_and_forecast_period(
cube, time_point=time_points, fp_point=fp_points)
radii = [10000, 30000]
lead_times = [2, 4]
neighbourhood_method = CircularNeighbourhood()
plugin = NBHood(neighbourhood_method, radii, lead_times)
result = plugin.process(cube)
self.assertIsInstance(result, Cube)
def test_radii_varying_with_lead_time_fp_seconds(self):
"""
Test that a cube fp coord is unchanged by the lead time calculation.
"""
cube = set_up_cube(num_time_points=3)
iris.util.promote_aux_coord_to_dim_coord(cube, "time")
time_points = cube.coord("time").points
fp_points = [2, 3, 4]
cube = add_forecast_reference_time_and_forecast_period(
cube, time_point=time_points, fp_point=fp_points)
cube.coord("forecast_period").convert_units("seconds")
radii = [10000, 20000, 30000]
lead_times = [2, 3, 4]
neighbourhood_method = CircularNeighbourhood()
plugin = NBHood(neighbourhood_method, radii, lead_times)
result = plugin.process(cube)
self.assertIsInstance(result, Cube)
self.assertEqual(cube.coord("forecast_period").units, "seconds")
def test_radii_varying_with_lead_time_with_interpolation_check_data(self):
"""Test that a cube with the correct data is returned for the
following conditions:
1. The radius varies with lead time.
2. Linear interpolation is required to create values for the radii
which are required but were not specified within the 'radii'
argument."""
cube = set_up_cube(zero_point_indices=((0, 0, 7, 7), (
0,
1,
7,
7,
), (0, 2, 7, 7)),
num_time_points=3)
expected = np.ones_like(cube.data)
expected[0, 0, 6:9, 6:9] = ([0.91666667, 0.875,
0.91666667], [0.875, 0.83333333, 0.875],
[0.91666667, 0.875, 0.91666667])
expected[0, 1, 5:10, 5:10] = SINGLE_POINT_RANGE_3_CENTROID
expected[0, 2, 4:11, 4:11] = ([
1, 0.9925, 0.985, 0.9825, 0.985, 0.9925, 1
], [0.9925, 0.98, 0.9725, 0.97, 0.9725, 0.98,
0.9925], [0.985, 0.9725, 0.965, 0.9625, 0.965, 0.9725, 0.985], [
0.9825, 0.97, 0.9625, 0.96, 0.9625, 0.97, 0.9825
], [0.985, 0.9725, 0.965, 0.9625, 0.965, 0.9725,
0.985], [0.9925, 0.98, 0.9725, 0.97, 0.9725, 0.98,
0.9925], [1, 0.9925, 0.985, 0.9825, 0.985, 0.9925, 1])
iris.util.promote_aux_coord_to_dim_coord(cube, "time")
time_points = cube.coord("time").points
fp_points = [2, 3, 4]
cube = add_forecast_reference_time_and_forecast_period(
cube, time_point=time_points, fp_point=fp_points)
radii = [5600, 9500]
lead_times = [2, 4]
neighbourhood_method = CircularNeighbourhood()
plugin = NBHood(neighbourhood_method, radii, lead_times)
result = plugin.process(cube)
self.assertArrayAlmostEqual(result.data, expected)
def test_radii_varying_with_lead_time_check_data(self):
"""
Test that the expected data is produced when the radius
varies with lead time.
"""
cube = set_up_cube(zero_point_indices=((0, 0, 7, 7), (
0,
1,
7,
7,
), (0, 2, 7, 7)),
num_time_points=3)
expected = np.ones_like(cube.data)
expected[0, 0, 6:9, 6:9] = ([0.91666667, 0.875,
0.91666667], [0.875, 0.83333333, 0.875],
[0.91666667, 0.875, 0.91666667])
expected[0, 1, 5:10, 5:10] = SINGLE_POINT_RANGE_3_CENTROID
expected[0, 2, 4:11, 4:11] = ([
1, 0.9925, 0.985, 0.9825, 0.985, 0.9925, 1
], [0.9925, 0.98, 0.9725, 0.97, 0.9725, 0.98,
0.9925], [0.985, 0.9725, 0.965, 0.9625, 0.965, 0.9725, 0.985], [
0.9825, 0.97, 0.9625, 0.96, 0.9625, 0.97, 0.9825
], [0.985, 0.9725, 0.965, 0.9625, 0.965, 0.9725,
0.985], [0.9925, 0.98, 0.9725, 0.97, 0.9725, 0.98,
0.9925], [1, 0.9925, 0.985, 0.9825, 0.985, 0.9925, 1])
iris.util.promote_aux_coord_to_dim_coord(cube, "time")
time_points = cube.coord("time").points
fp_points = [2, 3, 4]
cube = add_forecast_reference_time_and_forecast_period(
cube, time_point=time_points, fp_point=fp_points)
radii = [5600, 7600, 9500]
lead_times = [2, 3, 4]
neighbourhood_method = CircularNeighbourhood()
plugin = NBHood(neighbourhood_method, radii, lead_times)
result = plugin.process(cube)
self.assertArrayAlmostEqual(result.data, expected)
