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)
