def test_mean(self):
"""Test that the function adds the cubes correctly for mean."""
operation = 'mean'
plugin = CubeCombiner(operation)
result = plugin.combine(self.cube1, self.cube2)
expected_data = np.full((1, 2, 2), 1.1, dtype=np.float32)
self.assertArrayAlmostEqual(result.data, expected_data)
def test_min(self):
"""Test combine finds the min of the cubes correctly."""
operation = 'min'
plugin = CubeCombiner(operation)
result = plugin.combine(self.cube1, self.cube2)
expected_data = np.full((1, 2, 2), 0.5, dtype=np.float32)
self.assertArrayAlmostEqual(result.data, expected_data)
def test_basic(self):
"""Test that the function returns a Cube. """
operation = '*'
plugin = CubeCombiner(operation)
result = plugin.combine(self.cube1, self.cube2)
self.assertIsInstance(result, Cube)
expected_data = np.full((1, 2, 2), 0.3, dtype=np.float32)
self.assertArrayAlmostEqual(result.data, expected_data)
def test_mean(self):
"""Test that the function adds the cubes correctly for mean."""
operation = '+'
plugin = CubeCombiner(operation)
result = plugin.combine(self.cube1, self.cube3, operation)
expected_data = np.zeros((1, 2, 2, 2))
expected_data[0, 0, :, :] = 0.6
expected_data[0, 1, :, :] = 1.4
self.assertArrayAlmostEqual(result.data, expected_data)
def test_min(self):
"""Test combine finds the min of the cubes correctly."""
operation = 'min'
plugin = CubeCombiner(operation)
result = plugin.combine(self.cube1, self.cube3, operation)
expected_data = np.zeros((1, 2, 2, 2))
expected_data[0, 0, :, :] = 0.1
expected_data[0, 1, :, :] = 0.6
self.assertArrayAlmostEqual(result.data, expected_data)
def test_minus(self):
"""Test combine minus the cubes correctly. """
operation = '-'
plugin = CubeCombiner(operation)
result = plugin.combine(self.cube1, self.cube2, operation)
expected_data = np.zeros((1, 2, 2, 2))
expected_data[0, 0, :, :] = 0.4
expected_data[0, 1, :, :] = 0.2
self.assertArrayAlmostEqual(result.data, expected_data)
def test_basic(self):
"""Test that the function returns a Cube. """
operation = '*'
plugin = CubeCombiner(operation)
cube1 = self.cube1
cube2 = cube1.copy()
result = plugin.combine(cube1, cube2, operation)
self.assertIsInstance(result, Cube)
expected_data = np.zeros((1, 2, 2, 2))
expected_data[0, 0, :, :] = 0.25
expected_data[0, 1, :, :] = 0.36
self.assertArrayAlmostEqual(result.data, expected_data)
def process(self, cube_gust, cube_ws):
"""
Create a cube containing the wind_gust diagnostic.
Args:
cube_gust (iris.cube.Cube):
Cube contain one or more percentiles of wind_gust data.
cube_ws (iris.cube.Cube):
Cube contain one or more percentiles of wind_speed data.
Returns:
result (iris.cube.Cube):
Cube containing the wind-gust diagnostic data.
"""
# Extract wind-gust data
(req_cube_gust, perc_coord_gust) = self.extract_percentile_data(
cube_gust, self.percentile_gust, "wind_speed_of_gust")
# Extract wind-speed data
(req_cube_ws, perc_coord_ws) = (self.extract_percentile_data(
cube_ws, self.percentile_windspeed, "wind_speed"))
if perc_coord_gust.name() != perc_coord_ws.name():
msg = ('Percentile coord of wind-gust data'
'does not match coord of wind-speed data'
' {0:s} {1:s}.'.format(perc_coord_gust.name(),
perc_coord_ws.name()))
raise ValueError(msg)
# Check times are compatible.
msg = ('Could not match time coordinate')
wg_time = req_cube_gust.coords('time')
ws_time = req_cube_ws.coords('time')
if len(wg_time) == 0 or len(ws_time) == 0:
raise ValueError(msg)
wg_time = wg_time[0]
ws_time = ws_time[0]
for i, point in enumerate(wg_time.points):
if point != ws_time.points[i]:
if wg_time.bounds is None:
raise ValueError(msg)
if len(wg_time.bounds) >= i:
if len(wg_time.bounds[i]) == 2:
if ws_time.points[i] < wg_time.bounds[i][0]:
raise ValueError(msg)
elif ws_time.points[i] > wg_time.bounds[i][1]:
raise ValueError(msg)
else:
raise ValueError(msg)
else:
raise ValueError(msg)
# Add metadata to gust cube
req_cube_gust = self.add_metadata(req_cube_gust)
# Calculate wind-gust diagnostic using CubeCombiner
plugin = CubeCombiner('max')
result = plugin.combine(req_cube_gust, req_cube_ws)
# Update metadata
result = self.update_metadata_after_max(result, perc_coord_gust.name())
return result
