class Test_gradient_from_diff(IrisTest):
"""Test for correct behaviour when calculating a gradient"""
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3],
[2, 4, 2],
[5, 10, 15]])
x_coord = DimCoord(2.*np.arange(3), "projection_x_coordinate")
y_coord = DimCoord(2.*np.arange(3), "projection_y_coordinate")
self.cube = iris.cube.Cube(data, "wind_speed", units="m s-1",
dim_coords_and_dims=[(y_coord, 0),
(x_coord, 1)])
self.plugin = DifferenceBetweenAdjacentGridSquares(gradient=True)
def test_basic(self):
"""Test contents and metadata"""
expected = np.array([[0.5, 0.5, 0.5],
[2.0, 0.0, -2.0],
[2.5, 2.5, 2.5]])
xdiff = self.plugin.calculate_difference(self.cube, "x")
result = self.plugin.gradient_from_diff(xdiff, self.cube, "x")
self.assertArrayAlmostEqual(result.data, expected)
self.assertEqual(result.name(), "gradient_of_wind_speed")
class Test_process(IrisTest):
"""Test the process method."""
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3], [2, 4, 6], [5, 10, 15]])
self.cube = set_up_cube(data, "wind_speed", "m s-1")
self.plugin = DifferenceBetweenAdjacentGridSquares()
def test_basic(self):
"""Test that differences are calculated along both the x and
y dimensions and returned as separate cubes."""
expected_x = np.array([[1, 1], [2, 2], [5, 5]])
expected_y = np.array([[1, 2, 3], [3, 6, 9]])
result = self.plugin.process(self.cube)
self.assertIsInstance(result[0], Cube)
self.assertArrayAlmostEqual(result[0].data, expected_x)
self.assertIsInstance(result[1], Cube)
self.assertArrayAlmostEqual(result[1].data, expected_y)
def test_3d_cube(self):
"""Test the differences are calculated along both the x and
y dimensions and returned as separate cubes when a 3d cube is input."""
data = np.array(
[[[1, 2, 3], [2, 4, 6], [5, 10, 15]], [[1, 2, 3], [2, 2, 6], [5, 10, 20]]]
)
expected_x = np.array([[[1, 1], [2, 2], [5, 5]], [[1, 1], [0, 4], [5, 10]]])
expected_y = np.array([[[1, 2, 3], [3, 6, 9]], [[1, 0, 3], [3, 8, 14]]])
cube = set_up_cube(data, "wind_speed", "m s-1", realizations=np.array([1, 2]))
result = self.plugin.process(cube)
self.assertIsInstance(result[0], iris.cube.Cube)
self.assertArrayAlmostEqual(result[0].data, expected_x)
self.assertIsInstance(result[1], iris.cube.Cube)
self.assertArrayAlmostEqual(result[1].data, expected_y)
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3],
[2, 4, 6],
[5, 10, 15]])
self.cube = set_up_cube(data, "wind_speed", "m s-1")
self.plugin = DifferenceBetweenAdjacentGridSquares()
class Test_calculate_difference(IrisTest):
"""Test the calculate_difference method."""
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3],
[2, 4, 6],
[5, 10, 15]])
self.cube = set_up_cube(data, "wind_speed", "m s-1")
self.plugin = DifferenceBetweenAdjacentGridSquares()
def test_x_dimension(self):
"""Test differences calculated along the x dimension."""
expected = np.array([[1, 1],
[2, 2],
[5, 5]])
result = self.plugin.calculate_difference(self.cube, "x")
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected)
def test_y_dimension(self):
"""Test differences calculated along the y dimension."""
expected = np.array([[1, 2, 3],
[3, 6, 9]])
result = self.plugin.calculate_difference(self.cube, "y")
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected)
def test_missing_data(self):
"""Test that the result is as expected when data is missing."""
data = np.array([[1, 2, 3],
[np.nan, 4, 6],
[5, 10, 15]])
cube = set_up_cube(data, "wind_speed", "m s-1")
expected = np.array([[np.nan, 2, 3],
[np.nan, 6, 9]])
result = self.plugin.calculate_difference(cube, "y")
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, expected)
def test_masked_data(self):
"""Test that the result is as expected when data is masked."""
data = ma.array([[1, 2, 3],
[2, 4, 6],
[5, 10, 15]],
mask=[[0, 0, 0],
[1, 0, 0],
[0, 0, 0]])
cube = set_up_cube(data, "wind_speed", "m s-1")
expected = ma.array([[1, 2, 3],
[3, 6, 9]],
mask=[[1, 0, 0],
[1, 0, 0]])
result = self.plugin.calculate_difference(cube, "y")
self.assertIsInstance(result, Cube)
self.assertArrayEqual(result.data, expected)
self.assertArrayEqual(result.data.mask, expected.mask)
class Test_create_difference_cube(IrisTest):
"""Test the create_difference_cube method."""
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3],
[2, 4, 6],
[5, 10, 15]])
self.cube = set_up_cube(data, "wind_speed", "m s-1")
self.plugin = DifferenceBetweenAdjacentGridSquares()
def test_y_dimension(self):
"""Test differences calculated along the y dimension."""
diff_array = np.array([[1, 2, 3],
[3, 6, 9]])
result = self.plugin.create_difference_cube(
self.cube, "projection_y_coordinate", diff_array)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, diff_array)
def test_x_dimension(self):
"""Test differences calculated along the x dimension."""
diff_array = np.array([[1, 1],
[2, 2],
[5, 5]])
result = self.plugin.create_difference_cube(
self.cube, "projection_x_coordinate", diff_array)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, diff_array)
def test_metadata(self):
"""Test that the result has the expected metadata."""
diff_array = np.array([[1, 2, 3],
[3, 6, 9]])
cell_method = CellMethod(
"difference", coords=["projection_y_coordinate"],
intervals='1 grid length')
result = self.plugin.create_difference_cube(
self.cube, "projection_y_coordinate", diff_array)
self.assertEqual(
result.cell_methods[0], cell_method)
self.assertEqual(
result.attributes["form_of_difference"],
"forward_difference")
self.assertEqual(result.name(), 'difference_of_wind_speed')
def test_othercoords(self):
"""Test that other coords are transferred properly"""
diff_array = np.array([[1, 2, 3],
[3, 6, 9]])
time_coord = self.cube.coord('time')
proj_x_coord = self.cube.coord(axis='x')
result = self.plugin.create_difference_cube(
self.cube, "projection_y_coordinate", diff_array)
self.assertEqual(result.coord(axis='x'), proj_x_coord)
self.assertEqual(result.coord('time'), time_coord)
def test_gradient(self):
"""Test that the correct metadata is set if the desired output is a
gradient not a difference (except name)"""
plugin = DifferenceBetweenAdjacentGridSquares(gradient=True)
diff_array = np.array([[1, 2, 3],
[3, 6, 9]])
result = plugin.create_difference_cube(
self.cube, "projection_y_coordinate", diff_array)
self.assertNotIn("form_of_difference", result.attributes)
self.assertFalse(result.cell_methods)
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3], [2, 4, 6], [5, 10, 15]])
self.diff_in_y_array = np.array([[1, 2, 3], [3, 6, 9]])
self.cube = set_up_variable_cube(
data,
"wind_speed",
"m s-1",
"equalarea",
)
self.plugin = DifferenceBetweenAdjacentGridSquares()
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3],
[2, 4, 2],
[5, 10, 15]])
x_coord = DimCoord(2.*np.arange(3), "projection_x_coordinate")
y_coord = DimCoord(2.*np.arange(3), "projection_y_coordinate")
self.cube = iris.cube.Cube(data, "wind_speed", units="m s-1",
dim_coords_and_dims=[(y_coord, 0),
(x_coord, 1)])
self.plugin = DifferenceBetweenAdjacentGridSquares(gradient=True)
def absolute_differences_between_adjacent_grid_squares(cube, threshold):
"""
Compute the absolute differences between grid squares and put the
resulting cubes into a cubelist.
Args:
cube : Iris.cube.Cube
The cube from which adjacent grid square differences will be
calculated.
threshold : float
The threshold that will be applied.
Returns:
cubelist : Iris.cube.CubeList
Cubelist containing cubes with the absolute difference
between adjacent grid squares along x and y, respectively.
"""
diff_along_x_cube, diff_along_y_cube = (
DifferenceBetweenAdjacentGridSquares().process(cube))
# Compute the absolute values of the differences to ensure that
# negative differences are included.
diff_along_x_cube.data = np.absolute(diff_along_x_cube.data)
diff_along_y_cube.data = np.absolute(diff_along_y_cube.data)
cubelist = iris.cube.CubeList([diff_along_x_cube, diff_along_y_cube])
return cubelist
def setUp(self):
"""Set up cube & plugin"""
self.plugin = OrographicAlphas(min_alpha=0.3, max_alpha=0.5)
self.cube = set_up_cube()
self.gradient_x, self.gradient_y = \
DifferenceBetweenAdjacentGridSquares(gradient=True).process(
self.cube)
def setUp(self):
"""Set up cube & plugin"""
self.plugin = OrographicSmoothingCoefficients(
min_smoothing_coefficient=0.5, max_smoothing_coefficient=0.3)
self.cube = set_up_cube()
self.gradient_x, self.gradient_y = DifferenceBetweenAdjacentGridSquares(
gradient=True).process(self.cube)
class Test_create_difference_cube(IrisTest):
"""Test the create_difference_cube method."""
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3], [2, 4, 6], [5, 10, 15]])
self.diff_in_y_array = np.array([[1, 2, 3], [3, 6, 9]])
self.cube = set_up_variable_cube(
data,
"wind_speed",
"m s-1",
"equalarea",
)
self.plugin = DifferenceBetweenAdjacentGridSquares()
def test_y_dimension(self):
"""Test differences calculated along the y dimension."""
points = self.cube.coord(axis="y").points
expected_y = (points[1:] + points[:-1]) / 2
result = self.plugin.create_difference_cube(self.cube,
"projection_y_coordinate",
self.diff_in_y_array)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.coord(axis="y").points, expected_y)
self.assertArrayEqual(result.data, self.diff_in_y_array)
def test_x_dimension(self):
"""Test differences calculated along the x dimension."""
diff_array = np.array([[1, 1], [2, 2], [5, 5]])
points = self.cube.coord(axis="x").points
expected_x = (points[1:] + points[:-1]) / 2
result = self.plugin.create_difference_cube(self.cube,
"projection_x_coordinate",
diff_array)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.coord(axis="x").points, expected_x)
self.assertArrayEqual(result.data, diff_array)
def test_othercoords(self):
"""Test that other coords are transferred properly"""
time_coord = self.cube.coord("time")
proj_x_coord = self.cube.coord(axis="x")
result = self.plugin.create_difference_cube(self.cube,
"projection_y_coordinate",
self.diff_in_y_array)
self.assertEqual(result.coord(axis="x"), proj_x_coord)
self.assertEqual(result.coord("time"), time_coord)
def test_basic(self):
"""Test data are as expected"""
expected = np.array([[1.53125, 2.53125, 3.78125], [0., 0.5, 2.],
[1.53125, 0.03125, 0.78125]])
gradient_x, _ = \
DifferenceBetweenAdjacentGridSquares(gradient=True).process(
self.cube)
alpha_x = self.plugin.unnormalised_alphas(gradient_x)
self.assertArrayAlmostEqual(alpha_x.data, expected)
def _orography_gradients(self):
"""
Calculates the dimensionless gradient of self.topography along both
spatial axes, smoothed along the perpendicular axis. If spatial
coordinates are not in the same units as topography height (m),
converts coordinate units in place.
Returns:
(tuple): tuple containing:
**gradx** (iris.cube.Cube):
2D cube of dimensionless topography gradients in the
positive x direction
**grady** (iris.cube.Cube):
2D cube of dimensionless topography gradients in the
positive y direction
"""
self.topography.coord(axis='x').convert_units(self.topography.units)
xdim = self.topography.coord_dims(self.topography.coord(axis='x'))[0]
self.topography.coord(axis='y').convert_units(self.topography.units)
ydim = self.topography.coord_dims(self.topography.coord(axis='y'))[0]
# smooth topography by +/- one grid cell along the perpendicular axis
# before calculating each gradient (as done in STEPS)
topo_smx = uniform_filter1d(self.topography.data, 3, axis=ydim)
topo_smx_cube = self.topography.copy(data=topo_smx)
gradx, _ = DifferenceBetweenAdjacentGridSquares(
gradient=True).process(topo_smx_cube)
gradx.units = '1'
topo_smy = uniform_filter1d(self.topography.data, 3, axis=xdim)
topo_smy_cube = self.topography.copy(data=topo_smy)
_, grady = DifferenceBetweenAdjacentGridSquares(
gradient=True).process(topo_smy_cube)
grady.units = '1'
return gradx, grady
def process(self, cube):
"""
This creates the smoothing_coefficient cubes. It returns one for the x
direction and one for the y direction. It uses the
DifferenceBetweenAdjacentGridSquares plugin to calculate an average
gradient across each grid square. These gradients are then used to
calculate "smoothing_coefficient" arrays that are normalised between a
user-specified max and min.
Args:
cube (iris.cube.Cube):
A 2D field of orography on the grid for which
smoothing_coefficients are to be generated.
Returns:
(iris.cube.CubeList): containing:
**smoothing_coefficient_x** (iris.cube.Cube): A cube of
orography-dependent smoothing_coefficients calculated in
the x direction.
**smoothing_coefficient_y** (iris.cube.Cube): A cube of
orography-dependent smoothing_coefficients calculated in
the y direction.
"""
if not isinstance(cube, iris.cube.Cube):
raise ValueError("OrographicSmoothingCoefficients() expects cube "
"input, got {}".format(type(cube)))
if len(cube.data.shape) != 2:
raise ValueError(
"Expected orography on 2D grid, got {} dims".format(
len(cube.data.shape)))
gradient_x, gradient_y = DifferenceBetweenAdjacentGridSquares(
gradient=True)(cube)
(
smoothing_coefficient_x,
smoothing_coefficient_y,
) = self.gradient_to_smoothing_coefficient(gradient_x, gradient_y)
return iris.cube.CubeList(
[smoothing_coefficient_x, smoothing_coefficient_y])
def process(self, cube):
"""
This creates the alpha cubes. It returns one for the x direction and
one for the y direction. It uses the
DifferenceBetweenAdjacentGridSquares plugin to calculate an average
gradient across each grid square. These gradients are then used to
calculate "alpha" smoothing arrays that are normalised between a
user-specified max and min.
Args:
cube (iris.cube.Cube):
A 2D cube of the orography for the grid we want to get alphas
for.
Returns:
(tuple): tuple containing
**alpha_x** (iris.cube.Cube) - A cube of orography-dependent
alphas calculated in the x direction.
**alpha_y** (iris.cube.Cube) - A cube of orography-dependent
alphas calculated in the y direction.
"""
if not isinstance(cube, iris.cube.Cube):
raise ValueError(
'OrographicAlphas() expects cube input, got {}'.format(
type(cube)))
if len(cube.data.shape) != 2:
raise ValueError(
'Expected orography on 2D grid, got {} dims'.format(
len(cube.data.shape)))
gradient_x, gradient_y = \
DifferenceBetweenAdjacentGridSquares(gradient=True).process(cube)
alpha_x, alpha_y = self.gradient_to_alpha(gradient_x, gradient_y)
return alpha_x, alpha_y
class Test_process(IrisTest):
"""Test the process method."""
def setUp(self):
"""Set up cube."""
data = np.array([[1, 2, 3], [2, 4, 6], [5, 10, 15]])
self.cube = set_up_variable_cube(
data,
"wind_speed",
"m s-1",
"equalarea",
realizations=np.array([1, 2]),
)
self.plugin = DifferenceBetweenAdjacentGridSquares()
def test_basic(self):
"""Test that differences are calculated along both the x and
y dimensions and returned as separate cubes."""
expected_x = np.array([[1, 1], [2, 2], [5, 5]])
expected_y = np.array([[1, 2, 3], [3, 6, 9]])
result = self.plugin.process(self.cube)
self.assertIsInstance(result[0], Cube)
self.assertArrayEqual(result[0].data, expected_x)
self.assertIsInstance(result[1], Cube)
self.assertArrayEqual(result[1].data, expected_y)
def test_metadata(self):
"""Test the resulting metadata is correct."""
cell_method_x = CellMethod("difference",
coords=["projection_x_coordinate"],
intervals="1 grid length")
cell_method_y = CellMethod("difference",
coords=["projection_y_coordinate"],
intervals="1 grid length")
result = self.plugin.process(self.cube)
for cube, cm in zip(result, [cell_method_x, cell_method_y]):
self.assertEqual(cube.cell_methods[0], cm)
self.assertEqual(cube.attributes["form_of_difference"],
"forward_difference")
self.assertEqual(cube.name(), "difference_of_wind_speed")
def test_3d_cube(self):
"""Test the differences are calculated along both the x and
y dimensions and returned as separate cubes when a 3d cube is input."""
data = np.array([[[1, 2, 3], [2, 4, 6], [5, 10, 15]],
[[1, 2, 3], [2, 2, 6], [5, 10, 20]]])
expected_x = np.array([[[1, 1], [2, 2], [5, 5]],
[[1, 1], [0, 4], [5, 10]]])
expected_y = np.array([[[1, 2, 3], [3, 6, 9]], [[1, 0, 3], [3, 8,
14]]])
cube = set_up_variable_cube(
data,
"wind_speed",
"m s-1",
"equalarea",
realizations=np.array([1, 2]),
)
result = self.plugin.process(cube)
self.assertIsInstance(result[0], iris.cube.Cube)
self.assertArrayEqual(result[0].data, expected_x)
self.assertIsInstance(result[1], iris.cube.Cube)
self.assertArrayEqual(result[1].data, expected_y)
