def check_strattrop(infile):
""" Check if the model output is from a StratTrop(CheST)
or TropIsop (CheT) run.
Method:- Crude, checks for following species in file:
ClO, N2O --> CheST; NOy (34004) --> CheT
Returns True for StratTrop/ CheST
"""
stash_codes_str = ['m01s34i042', 'm01s34i049'] # ClO, N2O
stc_trop = 'm01s34i004' # NO2/NOy
fieldcons = [] # List of stash constraints
for spc in stash_codes_str:
fieldcons.append(iris.AttributeConstraint(STASH=spc))
stcubes = iris.load_cubes(infile, constraints=fieldcons)
if len(stcubes) == len(stash_codes_str):
stcubes = 'a'
return True
else:
# Assume not StratTrop - confirm if Trop
fieldcons = iris.AttributeConstraint(STASH=stc_trop)
trcube = iris.load_cube(infile, constraints=fieldcons)
if trcube != None:
trcube = 'a'
return False
else: # Something wrong, not all diagnostics in place
print 'CHK_STRATTROP: Diagnostics missing -Not able to determine'
return True # Default --may cause failure in some scripts
def convec_2_precipflux(cube_list):
'''
Function to add together large scale rain and snow to
get the equivalent of 05216 (total precip flux) in a
non convection resolving model. Input:
cube_list = a list of cubes which contains 4203 and 4204 (large
scale rain and snow)
Output:
cube_list = the same cube list but with the new precip field appended
'''
print('Downscaled model is convection permitting . . .')
print('Correcting for stash difference between downscaled and driving model . . . ')
print('large scale rainfall (m01s04i203) + large scale snowfall (m01s04i204) = total precipitation (m01s05i216)')
ls_rain_constraint = iris.AttributeConstraint(STASH="m01s04i203")
ls_rain_cube, = cube_list.extract(ls_rain_constraint)
ls_snow_constraint = iris.AttributeConstraint(STASH="m01s04i204")
ls_snow_cube, = cube_list.extract(ls_snow_constraint)
var_cube = cube_list[0].copy()
var_cube.data = ls_snow_cube.data + ls_rain_cube.data # add snow and rain to get total precip
precip_stash = iris.fileformats.pp.STASH(1, 5, 216) # create instance of stash
var_cube.attributes['STASH'] = precip_stash # set stash that will be used for driving model
var_cube.rename('precipitation_flux')
cube_list.append(var_cube) # add calculated precip flux to cube list
cube_list.remove(ls_rain_cube) # remove large scale rain from cube list
cube_list.remove(ls_snow_cube) # remove large scale snow from cube list
return cube_list
def toa_cloud_radiative_effect(cubelist, kind):
"""
Calculate domain-average TOA cloud radiative effect (CRE).
Parameters
----------
cubelist: iris.cube.CubeList
Input list of cubes
kind: str
Shortwave ('sw'), longwave ('lw'), or 'total' CRE
Returns
-------
iris.cube.Cube
Cube of CRE with reduced dimensions.
"""
name = f"toa_cloud_radiative_effect_{kind}"
if kind == "sw":
all_sky = "m01s01i208"
clr_sky = "m01s01i209"
elif kind == "lw":
all_sky = "m01s02i205"
clr_sky = "m01s02i206"
elif kind == "total":
sw = toa_cloud_radiative_effect(cubelist, "sw")
lw = toa_cloud_radiative_effect(cubelist, "lw")
cre = sw + lw
cre.rename(name)
return cre
cube_clr = spatial(cubelist.extract_strict(iris.AttributeConstraint(STASH=clr_sky)), "mean")
cube_all = spatial(cubelist.extract_strict(iris.AttributeConstraint(STASH=all_sky)), "mean")
cre = cube_clr - cube_all
cre.rename(name)
return cre
def load_ts(basedir,
stashcode,
start_year,
stop_year,
months,
overwrite=False):
constraint = iris.AttributeConstraint(STASH=stashcode)
if stashcode == 'm01s05i216':
time_constraint = iris.AttributeConstraint(lbtim='122')
ts_file = basedir + '/' + stashcode + '_' + str(start_year) + '-' + str(
stop_year) + '_' + months[0] + '-' + months[-1] + '.nc'
if os.path.exists(ts_file) and not overwrite:
ts_cube = iris.load_cube(ts_file, constraint)
#raise Exception("File "+ts_file+" exists and will not overwrite.")
else:
ts_cubelist = iris.cube.CubeList()
for year in xrange(start_year, stop_year + 1):
for month in months:
if stashcode == 'm01s05i216':
month_cube = iris.load_cube(
basedir + '/*.p?' + str(year) + month + '.nc',
constraint & time_constraint)
else:
month_cube = iris.load_cube(
basedir + '/*.p?' + str(year) + month + '.nc',
constraint)
ts_cubelist.append(month_cube)
print year, month
print ts_cubelist
equalise_attributes(ts_cubelist)
unify_time_units(ts_cubelist)
ts_cube = ts_cubelist.concatenate_cube()
iris.save(ts_cube, ts_file)
return ts_cube
def test_pp_with_stash_constraints(self):
filenames = [tests.get_data_path(("PP", "globClim1", "dec_subset.pp"))]
stcon1 = iris.AttributeConstraint(STASH="m01s00i004")
stcon2 = iris.AttributeConstraint(STASH="m01s00i010")
pp_constraints = pp._convert_constraints([stcon1, stcon2])
pp_loader = iris.fileformats.rules.Loader(pp.load, {}, convert)
cubes = list(load_cubes(filenames, None, pp_loader, pp_constraints))
self.assertEqual(len(cubes), 76)
def loadData(self):
slpConstraint = iris.AttributeConstraint(STASH=self.slpStash)
uConstraint = iris.AttributeConstraint(STASH=self.uStash)
vConstraint = iris.AttributeConstraint(STASH=self.vStash)
hrdtConstraint = iris.Constraint(forecast_period = lambda cell: cell % 3 == 0)
self._getBasinConstraint(self.initLat,self.initLon)
attrConstraint = self.basinConstraint&hrdtConstraint
self.slpCube = loadIrisCube(self.dataloc,
attributeConstraint=slpConstraint,
dataConstraints = attrConstraint)
def test_double_stash(self):
stcube236 = mock.Mock(stash=STASH.from_msi('m01s03i236'))
stcube4 = mock.Mock(stash=STASH.from_msi('m01s00i004'))
stcube7 = mock.Mock(stash=STASH.from_msi('m01s00i007'))
constraints = [iris.AttributeConstraint(STASH='m01s03i236'),
iris.AttributeConstraint(STASH='m01s00i004')]
pp_filter = _convert_constraints(constraints)
self.assertTrue(pp_filter(stcube236))
self.assertTrue(pp_filter(stcube4))
self.assertFalse(pp_filter(stcube7))
def loadPP(args, reqd):
"""Loads a pp file with a callback"""
if args.stash:
constr = [
iris.AttributeConstraint(STASH=args.stash[0]),
iris.AttributeConstraint(STASH=get_pressure_stash(args.pgrid))
]
else:
constr = None
cubes = iris.load(args.files, constraints=constr, callback=reqd.callback)
return cubes
def test_callable_stash(self):
stcube236 = mock.Mock(stash=STASH.from_msi('m01s03i236'))
stcube4 = mock.Mock(stash=STASH.from_msi('m01s00i004'))
stcube7 = mock.Mock(stash=STASH.from_msi('m01s00i007'))
con1 = iris.AttributeConstraint(STASH=lambda s: s.endswith("004"))
con2 = iris.AttributeConstraint(STASH=lambda s: s == "m01s00i007")
constraints = [con1, con2]
pp_filter = _convert_constraints(constraints)
self.assertFalse(pp_filter(stcube236))
self.assertTrue(pp_filter(stcube4))
self.assertTrue(pp_filter(stcube7))
def stash_vars(self):
"""stash_vars
Description
Args:
Returns
"""
stash_df = pd.read_csv(self.stashfile + '.csv')
u_stash = stash_df.u_stash[0]
v_stash = stash_df.v_stash[0]
u_constraint = iris.AttributeConstraint(STASH=u_stash)
v_constraint = iris.AttributeConstraint(STASH=v_stash)
p_constraint = iris.Constraint(pressure=self.plev)
return u_constraint, v_constraint, p_constraint
def _load_cubes(self, filename):
cubes = iris.load(
self.root + filename,
iris.AttributeConstraint(STASH=[p['stash'] for p in self.params]))
hr_dict = {}
lr_dict = {}
for p in self.params:
c_hr = cubes.extract(iris.AttributeConstraint(STASH=p['stash']))[0]
c_lr = self._bilinear_downscale(self._upscale(c_hr), target=c_hr)
hr_dict[p['name']] = c_hr
lr_dict[p['name']] = c_lr
self.cubes_hr[filename] = hr_dict
self.cubes_lr[filename] = lr_dict
def test_no_stash(self):
constraints = [
iris.Constraint('air_potential_temperature'),
iris.AttributeConstraint(source='asource')
]
pp_filter = _convert_constraints(constraints)
self.assertIsNone(pp_filter)
def test_multiple_with_stash(self):
constraints = [
iris.Constraint('air_potential_temperature'),
iris.AttributeConstraint(STASH='m01s00i004')
]
pp_filter = _convert_constraints(constraints)
self.assertIsNone(pp_filter)
def _get_slice_at_hour_at_timestep(variable,
validity_time,
methods=None,
model='global'):
"""Get the cube with the data, given that the specified time
matches a data timestep."""
file_name = _get_file_name(variable,
validity_time,
model=model,
methods=methods)
if not os.path.isfile(file_name):
raise Exception(
("Data for %04d/%02d/%02d not available" +
" might need oper.fetch") % validity_time.strftime("%Y-%m-%d"))
if variable == 'prate_a':
tp = _get_fcst_period(variable,
validity_time,
model=model,
methods=methods)
tp[0] = tp[0].days * 24 + tp[0].seconds // 3600
tp[1] = tp[1].days * 24 + tp[1].seconds // 3600
ftco = iris.Constraint(forecast_period=lambda t: t.bound is not None
and t.bound[0] == tp[0] and t.bound[1] == tp[1])
else:
ftco = iris.Constraint(forecast_period=_get_fcst_period(
variable, validity_time, model=model, methods=methods).seconds //
3600)
ftfr = iris.Constraint(forecast_reference_time=_get_fcst_reference_time(
variable, validity_time, model=model, methods=methods))
stco = iris.AttributeConstraint(
STASH=_stash_from_variable_names(variable, model=model))
hslice = iris.load_cube(file_name, stco & ftco & ftfr)
return hslice
def _find_required_cubes_using_metadata(cubes, input_dict):
"""
Extract the cube that matches the information within the input
dictionary.
Args:
cubes (iris.cube.CubeList):
The cubes that will be checked for matches against the
metadata specified in the input_dict.
input_dict (dict):
A dictionary containing the metadata that will be used to
identify the desired cubes.
Returns:
iris.cube.CubeList:
CubeList containing cubes that match the metadata supplied
within the input dictionary.
Raises:
ValueError:
The metadata supplied resulted in no matching cubes.
"""
constr = iris.AttributeConstraint(**input_dict["attributes"])
cubelist = cubes.extract(constr)
if not cubelist:
msg = ("The metadata to identify the desired historic forecast or "
"truth has found nothing matching the metadata information "
"supplied: {}".format(input_dict))
raise ValueError(msg)
return cubelist
def load_stash_for_time_and_region(file, stash, region):
valid_time_in_hours = file_valid_time(file).timestamp() / (60 * 60)
return iris.load(file,
iris.AttributeConstraint(STASH=stash) &
region &
iris.Constraint(time=lambda t: valid_time_in_hours - 0.1 < t < valid_time_in_hours + 0.1 ))
def test_pp_with_stash_constraint(self):
filenames = [tests.get_data_path(('PP', 'globClim1', 'dec_subset.pp'))]
stcon = iris.AttributeConstraint(STASH='m01s00i004')
pp_constraints = pp._convert_constraints(stcon)
pp_loader = iris.fileformats.rules.Loader(pp.load, {}, convert)
cubes = list(load_cubes(filenames, None, pp_loader, pp_constraints))
self.assertEqual(len(cubes), 38)
def pp_to_cube(suite_name, stream, suite_rootdir, stash_code, path_style,
years):
"""Find and convert many pp files to a single netCDF (no CMORization!)"""
# set the file pattern
stream_part_2 = stream[1:]
cube_list = []
for yr in years:
for mo in nr_months.keys():
fileDescriptor = suite_name + '.' + stream_part_2 \
+ yr + nr_months[mo] + '.pp'
if path_style == 'jasmin':
basedir = os.path.join(suite_rootdir, yr + mo + tz_root)
elif path_style == 'moose':
basedir = os.path.join(suite_rootdir.strip(suite_rootdir[-1]),
stream + '.pp')
elif path_style == 'mangled':
basedir = suite_rootdir
result = []
for path, _, files in os.walk(basedir, followlinks=True):
files = fnmatch.filter(files, fileDescriptor)
if files:
result.extend(os.path.join(path, f) for f in files)
if os.path.isfile(result[0]):
stash_cons = iris.AttributeConstraint(STASH=stash_code)
cb = iris.load(result[0], stash_cons)[0]
cbr = recube(cb)
cube_list.append(cbr)
try:
cube = iris.cube.CubeList(cube_list).concatenate_cube()
return cube
except iris.exceptions.ConcatenateError as ex:
print('Can not concatenate cubes: ', ex)
print('Differences: %s', ex.differences)
raise ex
def PlumeandMet(workdir, metDir):
times = ['201303060600']
for time in times:
filenames = glob.glob(workdir + '*' + time + '.txt')
filename = filenames[0]
attConstraint = iris.AttributeConstraint(Name='TotalAC')
cube = iris.load_cube(filename, attConstraint)
conc = cube
filename = metDir + '*' + time + '.txt'
precip = iris.load_cube(filename)
colorscale = ('#ffffff', '#b4dcff', '#04fdff', '#00ff00', '#fdff00',
'#ffbd02', '#ff6a00', '#fe0000', '#0000FF', '#800080',
'#008000')
# Set up axes
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-23, -13, 63, 67])
# Set up country outlines
countries = cfeature.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='10m',
facecolor='none')
ax.add_feature(countries, edgecolor='black', zorder=2)
# Set-up the gridlines
gl = ax.gridlines(draw_labels=True, linewidth=0.8, alpha=0.9)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlocator = mticker.FixedLocator(
[-23, -22, -21, -20, -19, -18, -17, -16, -15, -14])
gl.ylocator = mticker.FixedLocator([63, 64, 65, 66, 67])
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Plot
cf1 = iplt.contourf(precip,
levels=[0.0, 0.01, 0.1, 1.0, 10],
colors=colorscale)
cf = iplt.contour(conc,
levels=[
1e-9, 3.16e-8, 1e-8, 3.16e-7, 1e-7, 3.16e-6,
1e-6, 3.16e-5, 1e-5
])
cb = plt.colorbar(cf1, orientation='horizontal', shrink=0.9)
cb.set_label(str(precip.units))
plt.title(
'Precipitation (coloured) and Air Concentration (contoured) \n' +
time,
fontsize=12)
plt.show()
def stash_vars(self):
"""stash_vars
Description:
Uses Stash file to create iris cube contraints (variables)
Args: none
Returns:
u_constraint (iris cube constraint): Uvel constraint
v_constraint (iris cube constraint): Vvel constraint
p_constraint (iris cube constraint): Pressure constraint
"""
stash_df = pd.read_csv(self.stashfile + '.csv')
u_stash = stash_df.u_stash[0]
v_stash = stash_df.v_stash[0]
u_constraint = iris.AttributeConstraint(STASH=u_stash)
v_constraint = iris.AttributeConstraint(STASH=v_stash)
p_constraint = iris.Constraint(pressure=self.plev)
return u_constraint, v_constraint, p_constraint
def load_cube( self , job ):
variables = [] ; cubes_to_sum = []
for stash in range( 0 , len( self.stash ) ):
variables.append( iris.AttributeConstraint( STASH = self.stash[ stash ] ) )
cubes_to_sum.append( iris.load_cube( self.job_files_dict[job] , variables[ stash ] ) )
cube = sum( cubes_to_sum )
return cube
def load_cube( self , job ):
variable = iris.AttributeConstraint( STASH = self.stash )
cube = iris.load_cube( self.job_files_dict[job] , variable )
cubes_to_sum = []
for layer in range( 0 , len( self.cell_number ) ):
cubes_to_sum.append( cube.extract( iris.Constraint( soil_model_level_number = lambda cell: cell == self.cell_number[ layer ] ) ) )
cube = sum( cubes_to_sum )
return cube
def loadPP_cella(file=file, stash=None):
"""Loads a pp file with a callback"""
if stash is not None:
constr = [iris.AttributeConstraint(STASH=stash)]
else:
constr = None
#print file
#print constr
cube = iris.load_cube(file, constr)
return cube
def test_stash_constraint(self):
# Check that an attribute constraint functions correctly.
# Note: this is a special case in "fileformats.pp".
flds = self.fields(c_t='1122', phn='0101')
file = self.save_fieldcubes(flds)
airtemp_flds = [fld for fld in flds if fld.name() == 'air_temperature']
stash_attribute = airtemp_flds[0].attributes['STASH']
results = iris.load(file,
iris.AttributeConstraint(STASH=stash_attribute))
expected = CubeList(airtemp_flds).merge()
self.assertEqual(results, expected)
def __init__(self, cubelist):
"""Initialise CloudPlotter from `iris.cube.CubeList` containing cloud fractions."""
self.factor = 10.0 # scaling factor
self.cubes = {}
for key, stash in self._stash_items.items():
try:
self.cubes[key] = cubelist.extract_strict(
iris.AttributeConstraint(STASH=stash))
except iris.exceptions.ConstraintMismatchError:
warnings.warn(
f"Warning!\n{key} ({stash}) is not found in\n\n{cubelist}")
def create_new_cubes(self, cubes):
stashcube = cubes.extract(
iris.AttributeConstraint(STASH=self.STASH))[0]
# what are the dimensions of this cube
if stashcube.ndim == 3:
self.is3D = False
elif stashcube.ndim == 4:
self.is3D = True
newcube = self.convert_pressure(stashcube, cubes)
return self.add_metadata(newcube, stashcube)
def load_stash(files, code, name, units=None):
print name
print code
stash_constraint = iris.AttributeConstraint(STASH=code)
cube = iris.load_cube(files, stash_constraint)
cube.var_name = name
cube.standard_name = None
if (units is not None): cube.units = units
return cube
def cmorization(in_dir, out_dir, cfg, cfg_user):
"""Cmorization func call."""
# run the cmorization
# Pass on the workdir to the cfg dictionary
cfg['work_dir'] = cfg_user['work_dir']
# If it doesn't exist, create it
if not os.path.isdir(cfg['work_dir']):
logger.info("Creating working directory for regridding: %s",
cfg['work_dir'])
os.mkdir(cfg['work_dir'])
for short_name, var in cfg['variables'].items():
var['short_name'] = short_name
logger.info("Processing var %s", short_name)
# Regridding
logger.info("Start regridding to: %s",
cfg['custom']['regrid_resolution'])
_regrid_dataset(in_dir, var, cfg)
logger.info("Finished regridding")
# File concatenation
logger.info("Start setting time_bnds")
cubelist = _set_time_bnds(cfg['work_dir'], var)
attrs_to_remove = ['identifier', 'date_created']
for cube in cubelist:
for attr in attrs_to_remove:
cube.attributes.pop(attr)
# Loop over two different platform names
for platformname in ['SPOT-4', 'SPOT-5']:
# Now split the cubelist on the different platform
logger.info("Start processing part of dataset: %s", platformname)
cubelist_platform = cubelist.extract(
iris.AttributeConstraint(platform=platformname))
if cubelist_platform:
assert _attrs_are_the_same(cubelist_platform)
cube = cubelist_platform.concatenate_cube()
else:
logger.warning(
"No files found for platform %s \
(check input data)", platformname)
continue
savename = os.path.join(cfg['work_dir'],
var['short_name'] + platformname + '.nc')
logger.info("Saving as: %s", savename)
iris.save(cube, savename)
logger.info("Finished file concatenation over time")
in_file = savename
logger.info("Start CMORization of file %s", in_file)
_cmorize_dataset(in_file, var, cfg, out_dir)
logger.info("Finished regridding and CMORizing %s", in_file)
def test_attribute_constraint(self):
# there is no my_attribute attribute on the cube, so ensure it returns None
cube = self.cube.extract(
iris.AttributeConstraint(my_attribute='foobar'))
self.assertIsNone(cube)
orig_cube = self.cube
# add an attribute to the cubes
orig_cube.attributes['my_attribute'] = 'foobar'
cube = orig_cube.extract(
iris.AttributeConstraint(my_attribute='foobar'))
self.assertCML(cube, ('constrained_load', 'attribute_constraint.cml'))
cube = orig_cube.extract(
iris.AttributeConstraint(my_attribute='not me'))
self.assertIsNone(cube)
cube = orig_cube.extract(
iris.AttributeConstraint(
my_attribute=lambda val: val.startswith('foo')))
self.assertCML(cube, ('constrained_load', 'attribute_constraint.cml'))
cube = orig_cube.extract(
iris.AttributeConstraint(
my_attribute=lambda val: not val.startswith('foo')))
self.assertIsNone(cube)
cube = orig_cube.extract(
iris.AttributeConstraint(my_non_existant_attribute='hello world'))
self.assertIsNone(cube)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--start-date', help='ISO-formatted start date')
parser.add_argument('--end-date', help='ISO-formatted end date')
parser.add_argument('--output', '-o', help='Output file name', required=True)
parser.add_argument('--target-mask', help='Target UM land mask', required=True)
parser.add_argument('--frequency', choices=[6, 12, 24],
type=int, help='Update frequency (hours)', default=24)
args = parser.parse_args()
# Read in the source mask
tos = xarray.open_mfdataset('/g/data1a/ub4/erai/netcdf/6hr/ocean/'
'oper_an_sfc/v01/tos/'
'tos_6hrs_ERAI_historical_an-sfc_2001*.nc',
coords='all')
src_mask = tos.tos.isel(time=0)
# Read in the target mask
mask_iris = iris.load_cube(args.target_mask, iris.AttributeConstraint(STASH='m01s00i030'))
mask_iris.coord('latitude').var_name = 'lat'
mask_iris.coord('longitude').var_name = 'lon'
tgt_mask = xarray.DataArray.from_iris(mask_iris).load()
tgt_mask = tgt_mask.where(tgt_mask == 0)
tgt_mask.lon.attrs['standard_name'] = 'longitude'
tgt_mask.lat.attrs['standard_name'] = 'latitude'
tgt_mask.lon.attrs['units'] = 'degrees_east'
tgt_mask.lat.attrs['units'] = 'degrees_north'
print(tgt_mask)
weights = esmf_generate_weights(src_mask, tgt_mask, method='patch')
with ProgressBar():
# Read and slice the source data
tos = xarray.open_mfdataset('/g/data1a/ub4/erai/netcdf/6hr/ocean/'
'oper_an_sfc/v01/tos/'
'tos_6hrs_ERAI_historical_an-sfc_2001*.nc',
coords='all')
sic = xarray.open_mfdataset('/g/data1a/ub4/erai/netcdf/6hr/seaIce/'
'oper_an_sfc/v01/sic/'
'sic_6hrs_ERAI_historical_an-sfc_2001*.nc',
coords='all')
ds = xarray.Dataset({'tos': tos.tos, 'sic': sic.sic})
ds = ds.sel(time=slice(args.start_date, args.end_date))
print(ds)
newds = regrid(ds, weights=weights)
newds['time'] = newds['time'].astype('i4')
newds.to_netcdf(args.output)