def _lbproc_rules(cube, pp):
"""
Rules for setting the horizontal grid and pole location of the PP field.
Note: `pp.lbproc` must be set to 0 before these rules are run.
Args:
cube: the cube being saved as a series of PP fields.
pp: the current PP field having save rules applied.
Returns:
The PP field with updated metadata.
"""
# Basic setting (this may be overridden by subsequent rules).
pp.lbproc = 0
if cube.attributes.get("ukmo__process_flags", None):
pp.lbproc += sum(
[
LBPROC_MAP[name]
for name in cube.attributes["ukmo__process_flags"]
]
)
# Zonal-mean: look for a CellMethod which is a "mean" over "longitude" or
# "grid_longitude".
if (
scalar_cell_method(cube, "mean", "longitude") is not None
or scalar_cell_method(cube, "mean", "grid_longitude") is not None
):
pp.lbproc += 64
# Time-mean: look for a CellMethod which is a "mean" over "time".
if scalar_cell_method(cube, "mean", "time") is not None:
pp.lbproc += 128
# Time-minimum: look for a CellMethod which is a "minimum" over "time".
if scalar_cell_method(cube, "minimum", "time") is not None:
pp.lbproc += 4096
# Time-maximum: look for a CellMethod which is a "maximum" over "time".
if scalar_cell_method(cube, "maximum", "time") is not None:
pp.lbproc += 8192
return pp
def _lbproc_rules(cube, pp):
"""
Rules for setting the horizontal grid and pole location of the PP field.
Note: `pp.lbproc` must be set to 0 before these rules are run.
Args:
cube: the cube being saved as a series of PP fields.
pp: the current PP field having save rules applied.
Returns:
The PP field with updated metadata.
"""
# Basic setting (this may be overridden by subsequent rules).
pp.lbproc = 0
if cube.attributes.get("ukmo__process_flags", None):
pp.lbproc += sum([LBPROC_MAP[name]
for name in cube.attributes["ukmo__process_flags"]])
# Zonal-mean: look for a CellMethod which is a "mean" over "longitude" or
# "grid_longitude".
if (scalar_cell_method(cube, 'mean', 'longitude') is not None or
scalar_cell_method(cube, 'mean', 'grid_longitude') is not None):
pp.lbproc += 64
# Time-mean: look for a CellMethod which is a "mean" over "time".
if scalar_cell_method(cube, 'mean', 'time') is not None:
pp.lbproc += 128
# Time-minimum: look for a CellMethod which is a "minimum" over "time".
if scalar_cell_method(cube, 'minimum', 'time') is not None:
pp.lbproc += 4096
# Time-maximum: look for a CellMethod which is a "maximum" over "time".
if scalar_cell_method(cube, 'maximum', 'time') is not None:
pp.lbproc += 8192
return pp
def test_double_coord_fails(self):
self.cube.cell_methods = (CellMethod("mean", ("foo", "bar"),
("1 hour", "1 hour")), )
actual = scalar_cell_method(self.cube, "mean", "foo")
self.assertIsNone(actual)
def test_coord_name_different(self):
actual = scalar_cell_method(self.cube, "average", "bar")
self.assertIsNone(actual)
def test_method_different(self):
actual = scalar_cell_method(self.cube, "average", "foo")
self.assertIsNone(actual)
def test_cell_method_found(self):
actual = scalar_cell_method(self.cube, "mean", "foo")
self.assertEqual(actual, self.cm)
def _general_time_rules(cube, pp):
"""
Rules for setting time metadata of the PP field.
Args:
cube: the cube being saved as a series of PP fields.
pp: the current PP field having save rules applied.
Returns:
The PP field with updated metadata.
"""
time_coord = scalar_coord(cube, 'time')
fp_coord = scalar_coord(cube, 'forecast_period')
frt_coord = scalar_coord(cube, 'forecast_reference_time')
clim_season_coord = scalar_coord(cube, 'clim_season')
cm_time_mean = scalar_cell_method(cube, 'mean', 'time')
cm_time_min = scalar_cell_method(cube, 'minimum', 'time')
cm_time_max = scalar_cell_method(cube, 'maximum', 'time')
# No forecast.
if time_coord is not None and fp_coord is None and frt_coord is None:
pp.lbtim.ia = 0
pp.lbtim.ib = 0
pp.t1 = time_coord.units.num2date(time_coord.points[0])
pp.t2 = netcdftime.datetime(0, 0, 0)
# Forecast.
if (time_coord is not None and not time_coord.has_bounds()
and fp_coord is not None):
pp.lbtim.ia = 0
pp.lbtim.ib = 1
pp.t1 = time_coord.units.num2date(time_coord.points[0])
pp.t2 = time_coord.units.num2date(time_coord.points[0] -
fp_coord.points[0])
pp.lbft = fp_coord.points[0]
# Time mean (non-climatological).
# XXX This only works when we have a single timestep.
if (time_coord is not None and time_coord.has_bounds()
and clim_season_coord is None and fp_coord is not None
and fp_coord.has_bounds()):
# XXX How do we know *which* time to use if there are more than
# one? *Can* there be more than one?
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
if (time_coord is not None and time_coord.has_bounds()
and clim_season_coord is None and fp_coord is None
and frt_coord is not None):
# Handle missing forecast period using time and forecast ref time.
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
stop = time_coord.units.convert(time_coord.bounds[0, 1],
'hours since epoch')
start = frt_coord.units.convert(frt_coord.points[0],
'hours since epoch')
pp.lbft = stop - start
if (time_coord is not None and time_coord.has_bounds()
and clim_season_coord is None
and (fp_coord is not None or frt_coord is not None)
and cm_time_mean is not None and cm_time_mean.intervals != ()
and cm_time_mean.intervals[0].endswith('hour')):
pp.lbtim.ia = int(cm_time_mean.intervals[0][:-5])
if (time_coord is not None and time_coord.has_bounds()
and clim_season_coord is None
and (fp_coord is not None or frt_coord is not None)
and (cm_time_mean is None or cm_time_mean.intervals == ()
or not cm_time_mean.intervals[0].endswith('hour'))):
pp.lbtim.ia = 0
# If the cell methods contain a minimum then overwrite lbtim.ia with this
# interval.
if (time_coord is not None and time_coord.has_bounds()
and clim_season_coord is None
and (fp_coord is not None or frt_coord is not None)
and cm_time_min is not None and cm_time_min.intervals != ()
and cm_time_min.intervals[0].endswith('hour')):
# Set lbtim.ia with the integer part of the cell method's interval
# e.g. if interval is '24 hour' then lbtim.ia becomes 24.
pp.lbtim.ia = int(cm_time_min.intervals[0][:-5])
# If the cell methods contain a maximum then overwrite lbtim.ia with this
# interval.
if (time_coord is not None and time_coord.has_bounds()
and clim_season_coord is None
and (fp_coord is not None or frt_coord is not None)
and cm_time_max is not None and cm_time_max.intervals != ()
and cm_time_max.intervals[0].endswith('hour')):
# Set lbtim.ia with the integer part of the cell method's interval
# e.g. if interval is '1 hour' then lbtim.ia becomes 1.
pp.lbtim.ia = int(cm_time_max.intervals[0][:-5])
if time_coord is not None and time_coord.has_bounds():
lower_bound_yr =\
time_coord.units.num2date(time_coord.bounds[0, 0]).year
upper_bound_yr =\
time_coord.units.num2date(time_coord.bounds[0, 1]).year
else:
lower_bound_yr = None
upper_bound_yr = None
# Climatological time means.
if (time_coord is not None and time_coord.has_bounds()
and lower_bound_yr == upper_bound_yr and fp_coord is not None
and fp_coord.has_bounds() and clim_season_coord is not None
and 'clim_season' in cube.cell_methods[-1].coord_names):
# Climatological time mean - single year.
pp.lbtim.ia = 0
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and time_coord.has_bounds()
and lower_bound_yr != upper_bound_yr and fp_coord is not None
and fp_coord.has_bounds() and clim_season_coord is not None
and 'clim_season' in cube.cell_methods[-1].coord_names
and clim_season_coord.points[0] == 'djf'):
# Climatological time mean - spanning years - djf.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
if pp.t1.month == 12:
pp.t1 = netcdftime.datetime(pp.t1.year)
else:
pp.t1 = netcdftime.datetime(pp.t1.year - 1, 12, 1, 0, 0, 0)
pp.t2 = netcdftime.datetime(pp.t2.year, 3, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and time_coord.has_bounds()
and lower_bound_yr != upper_bound_yr and fp_coord is not None
and fp_coord.has_bounds() and clim_season_coord is not None
and 'clim_season' in cube.cell_methods[-1].coord_names
and clim_season_coord.points[0] == 'mam'):
# Climatological time mean - spanning years - mam.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
# TODO: wut?
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.t1 = netcdftime.datetime(pp.t1.year, 3, 1, 0, 0, 0)
pp.t2 = netcdftime.datetime(pp.t2.year, 6, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and time_coord.has_bounds()
and lower_bound_yr != upper_bound_yr and fp_coord is not None
and fp_coord.has_bounds() and clim_season_coord is not None
and 'clim_season' in cube.cell_methods[-1].coord_names
and clim_season_coord.points[0] == 'jja'):
# Climatological time mean - spanning years - jja.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
# TODO: wut?
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.t1 = netcdftime.datetime(pp.t1.year, 6, 1, 0, 0, 0)
pp.t2 = netcdftime.datetime(pp.t2.year, 9, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and time_coord.has_bounds()
and lower_bound_yr != upper_bound_yr and fp_coord is not None
and fp_coord.has_bounds() and clim_season_coord is not None
and 'clim_season' in cube.cell_methods[-1].coord_names
and clim_season_coord.points[0] == 'son'):
# Climatological time mean - spanning years - son.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
# TODO: wut?
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.t1 = netcdftime.datetime(pp.t1.year, 9, 1, 0, 0, 0)
pp.t2 = netcdftime.datetime(pp.t2.year, 12, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
return pp
def _general_time_rules(cube, pp):
"""
Rules for setting time metadata of the PP field.
Args:
cube: the cube being saved as a series of PP fields.
pp: the current PP field having save rules applied.
Returns:
The PP field with updated metadata.
"""
time_coord = scalar_coord(cube, 'time')
fp_coord = scalar_coord(cube, 'forecast_period')
frt_coord = scalar_coord(cube, 'forecast_reference_time')
clim_season_coord = scalar_coord(cube, 'clim_season')
cm_time_mean = scalar_cell_method(cube, 'mean', 'time')
cm_time_min = scalar_cell_method(cube, 'minimum', 'time')
cm_time_max = scalar_cell_method(cube, 'maximum', 'time')
# No forecast.
if time_coord is not None and fp_coord is None and frt_coord is None:
pp.lbtim.ia = 0
pp.lbtim.ib = 0
pp.t1 = time_coord.units.num2date(time_coord.points[0])
pp.t2 = cftime.datetime(0, 0, 0)
# Forecast.
if (time_coord is not None and
not time_coord.has_bounds() and
fp_coord is not None):
pp.lbtim.ia = 0
pp.lbtim.ib = 1
pp.t1 = time_coord.units.num2date(time_coord.points[0])
pp.t2 = time_coord.units.num2date(time_coord.points[0] -
fp_coord.points[0])
pp.lbft = fp_coord.points[0]
# Time mean (non-climatological).
# XXX This only works when we have a single timestep.
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
fp_coord is not None and
fp_coord.has_bounds()):
# XXX How do we know *which* time to use if there are more than
# one? *Can* there be more than one?
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
fp_coord is None and
frt_coord is not None):
# Handle missing forecast period using time and forecast ref time.
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
stop = time_coord.units.convert(time_coord.bounds[0, 1],
'hours since epoch')
start = frt_coord.units.convert(frt_coord.points[0],
'hours since epoch')
pp.lbft = stop - start
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
cm_time_mean is not None):
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
cm_time_mean is not None and
cm_time_mean.intervals != () and
cm_time_mean.intervals[0].endswith('hour')):
pp.lbtim.ia = int(cm_time_mean.intervals[0][:-5])
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
(fp_coord is not None or frt_coord is not None) and
(cm_time_mean is None or cm_time_mean.intervals == () or
not cm_time_mean.intervals[0].endswith('hour'))):
pp.lbtim.ia = 0
# If the cell methods contain a minimum then overwrite lbtim.ia with this
# interval.
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
(fp_coord is not None or frt_coord is not None) and
cm_time_min is not None and
cm_time_min.intervals != () and
cm_time_min.intervals[0].endswith('hour')):
# Set lbtim.ia with the integer part of the cell method's interval
# e.g. if interval is '24 hour' then lbtim.ia becomes 24.
pp.lbtim.ia = int(cm_time_min.intervals[0][:-5])
# If the cell methods contain a maximum then overwrite lbtim.ia with this
# interval.
if (time_coord is not None and
time_coord.has_bounds() and
clim_season_coord is None and
(fp_coord is not None or frt_coord is not None) and
cm_time_max is not None and
cm_time_max.intervals != () and
cm_time_max.intervals[0].endswith('hour')):
# Set lbtim.ia with the integer part of the cell method's interval
# e.g. if interval is '1 hour' then lbtim.ia becomes 1.
pp.lbtim.ia = int(cm_time_max.intervals[0][:-5])
if time_coord is not None and time_coord.has_bounds():
lower_bound_yr =\
time_coord.units.num2date(time_coord.bounds[0, 0]).year
upper_bound_yr =\
time_coord.units.num2date(time_coord.bounds[0, 1]).year
else:
lower_bound_yr = None
upper_bound_yr = None
# Climatological time means.
if (time_coord is not None and
time_coord.has_bounds() and
lower_bound_yr == upper_bound_yr and
fp_coord is not None and
fp_coord.has_bounds() and
clim_season_coord is not None and
'clim_season' in cube.cell_methods[-1].coord_names):
# Climatological time mean - single year.
pp.lbtim.ia = 0
pp.lbtim.ib = 2
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and
time_coord.has_bounds() and
lower_bound_yr != upper_bound_yr and
fp_coord is not None and
fp_coord.has_bounds() and
clim_season_coord is not None and
'clim_season' in cube.cell_methods[-1].coord_names and
clim_season_coord.points[0] == 'djf'):
# Climatological time mean - spanning years - djf.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
if pp.t1.month == 12:
pp.t1 = cftime.datetime(pp.t1.year)
else:
pp.t1 = cftime.datetime(pp.t1.year-1, 12, 1, 0, 0, 0)
pp.t2 = cftime.datetime(pp.t2.year, 3, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and
time_coord.has_bounds() and
lower_bound_yr != upper_bound_yr and
fp_coord is not None and
fp_coord.has_bounds() and
clim_season_coord is not None and
'clim_season' in cube.cell_methods[-1].coord_names and
clim_season_coord.points[0] == 'mam'):
# Climatological time mean - spanning years - mam.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
# TODO: wut?
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.t1 = cftime.datetime(pp.t1.year, 3, 1, 0, 0, 0)
pp.t2 = cftime.datetime(pp.t2.year, 6, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and
time_coord.has_bounds() and
lower_bound_yr != upper_bound_yr and
fp_coord is not None and
fp_coord.has_bounds() and
clim_season_coord is not None and
'clim_season' in cube.cell_methods[-1].coord_names and
clim_season_coord.points[0] == 'jja'):
# Climatological time mean - spanning years - jja.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
# TODO: wut?
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.t1 = cftime.datetime(pp.t1.year, 6, 1, 0, 0, 0)
pp.t2 = cftime.datetime(pp.t2.year, 9, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
elif (time_coord is not None and
time_coord.has_bounds() and
lower_bound_yr != upper_bound_yr and
fp_coord is not None and
fp_coord.has_bounds() and
clim_season_coord is not None and
'clim_season' in cube.cell_methods[-1].coord_names and
clim_season_coord.points[0] == 'son'):
# Climatological time mean - spanning years - son.
pp.lbtim.ia = 0
pp.lbtim.ib = 3
# TODO: wut?
pp.t1 = time_coord.units.num2date(time_coord.bounds[0, 0])
pp.t2 = time_coord.units.num2date(time_coord.bounds[0, 1])
pp.t1 = cftime.datetime(pp.t1.year, 9, 1, 0, 0, 0)
pp.t2 = cftime.datetime(pp.t2.year, 12, 1, 0, 0, 0)
_conditional_warning(
time_coord.bounds[0, 0] != time_coord.units.date2num(pp.t1),
"modified t1 for climatological seasonal mean")
_conditional_warning(
time_coord.bounds[0, 1] != time_coord.units.date2num(pp.t2),
"modified t2 for climatological seasonal mean")
pp.lbft = fp_coord.units.convert(fp_coord.bounds[0, 1], 'hours')
return pp
def test_double_coord_fails(self):
self.cube.cell_methods = (CellMethod('mean', ('foo', 'bar'),
('1 hour', '1 hour')), )
actual = scalar_cell_method(self.cube, 'mean', 'foo')
self.assertIsNone(actual)
def test_method_different(self):
actual = scalar_cell_method(self.cube, 'average', 'foo')
self.assertIsNone(actual)
def test_cell_method_found(self):
actual = scalar_cell_method(self.cube, 'mean', 'foo')
self.assertEqual(actual, self.cm)
def test_double_coord_fails(self):
self.cube.cell_methods = (CellMethod('mean', ('foo', 'bar'),
('1 hour', '1 hour')), )
actual = scalar_cell_method(self.cube, 'mean', 'foo')
self.assertIsNone(actual)
def test_coord_name_different(self):
actual = scalar_cell_method(self.cube, 'average', 'bar')
self.assertIsNone(actual)
def test_method_different(self):
actual = scalar_cell_method(self.cube, 'average', 'foo')
self.assertIsNone(actual)
def test_cell_method_found(self):
actual = scalar_cell_method(self.cube, 'mean', 'foo')
self.assertEqual(actual, self.cm)
