def test_FAIL_phenomena_nostash(self):
# If we remove the 'STASH' attributes, certain phenomena can still be
# successfully encoded+decoded by standard load using LBFC values.
# Structured loading gets this wrong, because it does not use LBFC in
# characterising phenomena.
flds = self.fields(c_t="1122", phn="0101")
for fld in flds:
del fld.attributes["STASH"]
file = self.save_fieldcubes(flds)
results = iris.load(file)
if not self.do_fast_loads:
# This is what we'd LIKE to get (what iris.load gives).
expected = CubeList(flds).merge()
else:
# At present, we get a cube incorrectly combined together over all
# 4 timepoints, with the same phenomenon for all (!wrong!).
# It's a bit tricky to arrange the existing data like that.
# Do it by hacking the time values to allow merge, and then fixing
# up the time
old_t1, old_t2 = (
fld.coord("time").points[0] for fld in (flds[0], flds[2])
)
for i_fld, fld in enumerate(flds):
# Hack the phenomena to all look like the first one.
fld.rename("air_temperature")
fld.units = "K"
# Hack the time points so the 4 cube can merge into one.
fld.coord("time").points = [old_t1 + i_fld]
one_cube = CubeList(flds).merge_cube()
# Replace time dim with an anonymous dim.
co_t_fake = one_cube.coord("time")
one_cube.remove_coord(co_t_fake)
# Reconstruct + add back the expected auxiliary time coord.
co_t_new = AuxCoord(
[old_t1, old_t1, old_t2, old_t2],
standard_name="time",
units=co_t_fake.units,
)
one_cube.add_aux_coord(co_t_new, 0)
expected = [one_cube]
self.assertEqual(results, expected)
def test_FAIL_phenomena_nostash(self):
# If we remove the 'STASH' attributes, certain phenomena can still be
# successfully encoded+decoded by standard load using LBFC values.
# Structured loading gets this wrong, because it does not use LBFC in
# characterising phenomena.
flds = self.fields(c_t='1122', phn='0101')
for fld in flds:
del fld.attributes['STASH']
file = self.save_fieldcubes(flds)
results = iris.load(file)
if not self.do_fast_loads:
# This is what we'd LIKE to get (what iris.load gives).
expected = CubeList(flds).merge()
else:
# At present, we get a cube incorrectly combined together over all
# 4 timepoints, with the same phenomenon for all (!wrong!).
# It's a bit tricky to arrange the existing data like that.
# Do it by hacking the time values to allow merge, and then fixing
# up the time
old_t1, old_t2 = (fld.coord('time').points[0]
for fld in (flds[0], flds[2]))
for i_fld, fld in enumerate(flds):
# Hack the phenomena to all look like the first one.
fld.rename('air_temperature')
fld.units = 'K'
# Hack the time points so the 4 cube can merge into one.
fld.coord('time').points = [old_t1 + i_fld]
one_cube = CubeList(flds).merge_cube()
# Replace time dim with an anonymous dim.
co_t_fake = one_cube.coord('time')
one_cube.remove_coord(co_t_fake)
# Reconstruct + add back the expected auxiliary time coord.
co_t_new = AuxCoord([old_t1, old_t1, old_t2, old_t2],
standard_name='time', units=co_t_fake.units)
one_cube.add_aux_coord(co_t_new, 0)
expected = [one_cube]
self.assertEqual(results, expected)
class Test__create_template_slice(IrisTest):
"""Test create_template_slice method"""
def setUp(self):
"""
Set up a basic input cube. Input cube has 2 thresholds on and 3
forecast_reference_times
"""
thresholds = [10, 20]
data = np.ones((2, 2, 3), dtype=np.float32)
cycle1 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 0, 0),
)
cycle2 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 1, 0),
)
cycle3 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 2, 0),
)
self.cube_to_collapse = CubeList([cycle1, cycle2, cycle3]).merge_cube()
self.cube_to_collapse = squeeze(self.cube_to_collapse)
self.cube_to_collapse.rename("weights")
# This input array has 3 forecast reference times and 2 thresholds.
# The two thresholds have the same weights.
self.cube_to_collapse.data = np.array(
[
[[[1, 0, 1], [1, 1, 1]], [[1, 0, 1], [1, 1, 1]]],
[[[0, 0, 1], [0, 1, 1]], [[0, 0, 1], [0, 1, 1]]],
[[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]],
],
dtype=np.float32,
)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 0)
self.plugin = SpatiallyVaryingWeightsFromMask(
"forecast_reference_time")
def test_multi_dim_blend_coord_fail(self):
"""Test error is raised when we have a multi-dimensional blend_coord"""
# Add a surface altitude coordinate which covers x and y dimensions.
altitudes = np.array([[10, 20, 30], [20, 30, 10]])
altitudes_coord = AuxCoord(altitudes,
standard_name="surface_altitude",
units="m")
self.cube_to_collapse.add_aux_coord(altitudes_coord, data_dims=(2, 3))
message = "Blend coordinate must only be across one dimension."
plugin = SpatiallyVaryingWeightsFromMask("surface_altitude")
with self.assertRaisesRegex(ValueError, message):
plugin._create_template_slice(self.cube_to_collapse)
def test_varying_mask_fail(self):
"""Test error is raised when mask varies along collapsing dim"""
# Check fails when blending along threshold coordinate, as mask
# varies along this coordinate.
threshold_coord = find_threshold_coordinate(self.cube_to_collapse)
message = "The mask on the input cube can only vary along the blend_coord"
plugin = SpatiallyVaryingWeightsFromMask(threshold_coord.name())
with self.assertRaisesRegex(ValueError, message):
plugin._create_template_slice(self.cube_to_collapse)
def test_scalar_blend_coord_fail(self):
"""Test error is raised when blend_coord is scalar"""
message = "Blend coordinate must only be across one dimension."
with self.assertRaisesRegex(ValueError, message):
self.plugin._create_template_slice(self.cube_to_collapse[0])
def test_basic(self):
"""Test a correct template slice is returned for simple case"""
expected = self.cube_to_collapse.copy()[:, 0, :, :]
result = self.plugin._create_template_slice(self.cube_to_collapse)
self.assertEqual(expected.metadata, result.metadata)
self.assertArrayAlmostEqual(expected.data, result.data)
def test_basic_no_change(self):
"""Test a correct template slice is returned for a case where
no slicing is needed"""
input_cube = self.cube_to_collapse.copy()[:, 0, :, :]
expected = input_cube.copy()
result = self.plugin._create_template_slice(input_cube)
self.assertEqual(expected.metadata, result.metadata)
self.assertArrayAlmostEqual(expected.data, result.data)
def test_aux_blending_coord(self):
"""Test a correct template slice is returned when blending_coord is
an AuxCoord"""
expected = self.cube_to_collapse.copy()[:, 0, :, :]
plugin = SpatiallyVaryingWeightsFromMask("forecast_period")
result = self.plugin._create_template_slice(self.cube_to_collapse)
self.assertEqual(expected.metadata, result.metadata)
self.assertArrayAlmostEqual(expected.data, result.data)
