def test_hybrid_height_cubes(self):
hh1 = stock.simple_4d_with_hybrid_height()
hh1.attributes["cube"] = "hh1"
hh2 = stock.simple_4d_with_hybrid_height()
hh2.attributes["cube"] = "hh2"
sa = hh2.coord("surface_altitude")
sa.points = sa.points * 10
with self.temp_filename(".nc") as fname:
iris.save([hh1, hh2], fname)
cubes = iris.load(fname, "air_temperature")
cubes = sorted(cubes, key=lambda cube: cube.attributes["cube"])
self.assertCML(cubes)
def test_hybrid_height_cubes(self):
hh1 = stock.simple_4d_with_hybrid_height()
hh1.attributes['cube'] = 'hh1'
hh2 = stock.simple_4d_with_hybrid_height()
hh2.attributes['cube'] = 'hh2'
sa = hh2.coord('surface_altitude')
sa.points = sa.points * 10
with self.temp_filename('.nc') as fname:
iris.save([hh1, hh2], fname)
cubes = iris.load(fname)
cubes = sorted(cubes, key=lambda cube: cube.attributes['cube'])
self.assertCML(cubes)
def test_hybrid_height_cubes(self):
hh1 = stock.simple_4d_with_hybrid_height()
hh1.attributes['cube'] = 'hh1'
hh2 = stock.simple_4d_with_hybrid_height()
hh2.attributes['cube'] = 'hh2'
sa = hh2.coord('surface_altitude')
sa.points = sa.points * 10
with self.temp_filename('.nc') as fname:
iris.save([hh1, hh2], fname)
cubes = iris.load(fname)
cubes = sorted(cubes, key=lambda cube: cube.attributes['cube'])
self.assertCML(cubes)
def test_cube__anon_dim(self):
cube = simple_4d_with_hybrid_height()
cube.remove_coord('model_level_number') # Make cube dim 1 anonymous.
waypoints = [{
'grid_latitude': 21,
'grid_longitude': 31
}, {
'grid_latitude': 23,
'grid_longitude': 33
}]
sample_count = 4
new_coord_name = 'index'
trajectory = Trajectory(waypoints, sample_count=sample_count)
result = trajectory.interpolate(cube)
dim_names, named_dims, anon_dims = self._result_cube_metadata(result)
new_coord = result.coord(new_coord_name)
exp_named_dims = [0, 2]
exp_anon_dims = [1]
self.assertEqual(result.ndim, cube.ndim - 1)
self.assertIn(new_coord_name, dim_names)
self.assertEqual(named_dims, exp_named_dims)
self.assertEqual(anon_dims, exp_anon_dims)
self.assertEqual(len(new_coord.points), sample_count)
def test_hybrid_height(self):
cube = istk.simple_4d_with_hybrid_height()
# Put a biggus array on the cube so we can test deferred loading.
cube.lazy_data(biggus.NumpyArrayAdapter(cube.data))
traj = (('grid_latitude', [20.5, 21.5, 22.5, 23.5]),
('grid_longitude', [31, 32, 33, 34]))
xsec = traj_interpolate(cube, traj, method='nearest')
# Check that creating the trajectory hasn't led to the original
# data being loaded.
self.assertTrue(cube.has_lazy_data())
self.assertCML([cube, xsec], ('trajectory', 'hybrid_height.cml'))
def test_hybrid_height(self):
cube = istk.simple_4d_with_hybrid_height()
# Put a lazy array into the cube so we can test deferred loading.
cube.data = as_lazy_data(cube.data)
traj = (
("grid_latitude", [20.5, 21.5, 22.5, 23.5]),
("grid_longitude", [31, 32, 33, 34]),
)
xsec = traj_interpolate(cube, traj, method="nearest")
# Check that creating the trajectory hasn't led to the original
# data being loaded.
self.assertTrue(cube.has_lazy_data())
self.assertCML([cube, xsec], ("trajectory", "hybrid_height.cml"))
def test_hybrid_height(self):
cube = istk.simple_4d_with_hybrid_height()
# Put a lazy array into the cube so we can test deferred loading.
cube.data = as_lazy_data(cube.data)
# Use opionated grid-latitudes to avoid the issue of platform
# specific behaviour within SciPy cKDTree choosing a different
# equi-distant nearest neighbour point when there are multiple
# valid candidates.
traj = (
("grid_latitude", [20.4, 21.6, 22.6, 23.6]),
("grid_longitude", [31, 32, 33, 34]),
)
xsec = traj_interpolate(cube, traj, method="nearest")
# Check that creating the trajectory hasn't led to the original
# data being loaded.
self.assertTrue(cube.has_lazy_data())
self.assertCML([cube, xsec], ("trajectory", "hybrid_height.cml"))
def forecast():
"""Creates a set of files with different lead times
"""
cube = stock.simple_4d_with_hybrid_height()
times = grid.get_datetime(cube)
mapping = {}
for n, time in enumerate(times):
filename = 'forecast_' + str(n) + '.nc'
iris.save(cube[n], filename)
mapping[datetime.datetime(
time.year,
time.month,
time.day,
time.hour,
time.minute,
)] = filename
yield Forecast(sorted(mapping.keys())[0], mapping)
def test_cube__anon_dim(self):
cube = simple_4d_with_hybrid_height()
cube.remove_coord('model_level_number') # Make cube dim 1 anonymous.
waypoints = [{'grid_latitude': 21, 'grid_longitude': 31},
{'grid_latitude': 23, 'grid_longitude': 33}]
sample_count = 4
new_coord_name = 'index'
trajectory = Trajectory(waypoints, sample_count=sample_count)
result = trajectory.interpolate(cube)
dim_names, named_dims, anon_dims = self._result_cube_metadata(result)
new_coord = result.coord(new_coord_name)
exp_named_dims = [0, 2]
exp_anon_dims = [1]
self.assertEqual(result.ndim, cube.ndim - 1)
self.assertIn(new_coord_name, dim_names)
self.assertEqual(named_dims, exp_named_dims)
self.assertEqual(anon_dims, exp_anon_dims)
self.assertEqual(len(new_coord.points), sample_count)
def setUp(self):
self.cube = stock.simple_4d_with_hybrid_height()
mask = np.isnan(self.cube.data)
mask[::3, ::3] = True
self.cube.data = np.ma.masked_array(self.cube.data, mask=mask)
def setUp(self):
self.cube = stock.simple_4d_with_hybrid_height()
mask = np.isnan(self.cube.data)
mask[::3, ::3] = True
self.cube.data = np.ma.masked_array(self.cube.data,
mask=mask)
