def test_process_derived_var_adds_to_dict(self):
# the one that's usually in the parameters file
derived_var_dict = {
'PRECT': {('test'): lambda x: x}
}
# use this instead of the acme.derived_variables one
default_derived_vars = {
'PRECT': {
('pr'): lambda x: x,
('PRECC'): lambda x: 'this is some function'
}
}
# add derived_var_dict to default_derived_vars
precc_file = cdms2.open(get_abs_file_path('precc.nc'))
self.parameter.derived_variables = derived_var_dict
acme.process_derived_var('PRECT', default_derived_vars, precc_file, self.parameter)
precc_file.close()
if 'test' not in default_derived_vars['PRECT']:
self.fail("Failed to insert test derived variable")
# make sure that test is the first one
if 'test' != default_derived_vars['PRECT'].keys()[0]:
self.fail("Failed to insert test derived variable before default derived vars")
def test_process_derived_var_passes(self):
derived_var = {
'PRECT': {
('pr'): lambda x: x,
('PRECC'): lambda x: 'this is some function'
}
}
precc_file = cdms2.open(get_abs_file_path('precc.nc'))
acme.process_derived_var('PRECT', derived_var, precc_file, self.parameter)
precc_file.close()
def test_process_derived_var_with_wrong_dict(self):
# pr, nothing, and nothing2 are not variables in the file we open
wrong_derived_var = {
'PRECT': {
('pr'): lambda x: x,
('nothing1', 'nothing2'): lambda x, y: 'this is some function'
}
}
precc_file = cdms2.open(get_abs_file_path('precc.nc'))
with self.assertRaises(RuntimeError):
acme.process_derived_var('PRECT', wrong_derived_var, precc_file, self.parameter)
precc_file.close()
def test_process_derived_var_works_with_ordereddict(self):
derived_var_dict = {
'PRECT': OrderedDict([
(('something'), lambda x: 'something')
])
}
default_derived_vars = {
'PRECT': OrderedDict([
(('pr'), lambda x: x),
(('PRECC'), lambda x: 'this is some function')
])
}
precc_file = cdms2.open(get_abs_file_path('precc.nc'))
self.parameter.derived_variables = derived_var_dict
acme.process_derived_var('PRECT', default_derived_vars, precc_file, self.parameter)
precc_file.close()
# Check that 'something' was inserted first
self.assertEqual(['something', 'pr', 'PRECC'], default_derived_vars['PRECT'].keys())
def test_process_derived_var_adds_duplicate_to_dict(self):
# the one that's usually in the parameters file
# the function for PRECC below is different than the one in default_derived_vars
derived_var_dict = {
'PRECT': {('PRECC'): lambda x: 'PRECC'}
}
# use this instead of the acme.derived_variables one
default_derived_vars = {
'PRECT': {
('pr'): lambda x: x,
('PRECC'): lambda x: 'this is some function'
}
}
# add derived_var_dict to default_derived_vars
precc_file = cdms2.open(get_abs_file_path('precc.nc'))
self.parameter.derived_variables = derived_var_dict
msg = acme.process_derived_var('PRECT', default_derived_vars, precc_file, self.parameter)
precc_file.close()
if msg != 'PRECC':
self.fail("Failed to insert a duplicate test derived variable")
def run_diag(parameter):
parameter.reference_data_path
parameter.test_data_path
variables = parameter.variables
seasons = parameter.seasons
ref_name = parameter.ref_name
regions = parameter.regions
for season in seasons:
try:
filename1 = utils.get_test_filename(parameter, season)
filename2 = utils.get_ref_filename(parameter, season)
except IOError as e:
print(e)
# the file for the current parameters wasn't found, move to next
# parameters
continue
print('test file: {}'.format(filename1))
print('reference file: {}'.format(filename2))
f_mod = cdms2.open(filename1)
f_obs = cdms2.open(filename2)
if parameter.short_test_name:
parameter.test_name_yrs = parameter.short_test_name
else:
parameter.test_name_yrs = parameter.test_name
try:
yrs_averaged = f_mod.getglobal('yrs_averaged')
parameter.test_name_yrs = parameter.test_name_yrs + ' (' + yrs_averaged + ')'
except:
print('No yrs_averaged exists in global attributes')
parameter.test_name_yrs = parameter.test_name_yrs
# save land/ocean fraction for masking
try:
land_frac = f_mod('LANDFRAC')
ocean_frac = f_mod('OCNFRAC')
except BaseException:
mask_path = os.path.join(acme_diags.INSTALL_PATH,
'acme_ne30_ocean_land_mask.nc')
f0 = cdms2.open(mask_path)
land_frac = f0('LANDFRAC')
ocean_frac = f0('OCNFRAC')
f0.close()
for var in variables:
print('Variable: {}'.format(var))
parameter.var_id = var
mv1 = acme.process_derived_var(var, acme.derived_variables, f_mod,
parameter)
mv2 = acme.process_derived_var(var, acme.derived_variables, f_obs,
parameter)
parameter.viewer_descr[var] = mv1.long_name if hasattr(
mv1, 'long_name') else 'No long_name attr in test data.'
# special case, cdms didn't properly convert mask with fill value
# -999.0, filed issue with denise
if ref_name == 'WARREN':
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 == -0.9, mv2)
# following should move to derived variable
if ref_name == 'AIRS':
# mv2=MV2.masked_where(mv2==mv2.fill_value,mv2)
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 > 1e+20, mv2)
if ref_name == 'WILLMOTT' or ref_name == 'CLOUDSAT':
print(mv2.fill_value)
# mv2=MV2.masked_where(mv2==mv2.fill_value,mv2)
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 == -999., mv2)
print(mv2.fill_value)
# following should move to derived variable
if var == 'PRECT_LAND':
days_season = {
'ANN': 365,
'DJF': 90,
'MAM': 92,
'JJA': 92,
'SON': 91
}
# mv1 = mv1 * days_season[season] * 0.1 #following AMWG
# approximate way to convert to seasonal cumulative
# precipitation, need to have solution in derived variable,
# unit convert from mm/day to cm
mv2 = mv2 / days_season[season] / \
0.1 # convert cm to mm/day instead
mv2.units = 'mm/day'
if mv1.getLevel() and mv2.getLevel(): # for variables with z axis:
plev = parameter.plevs
print('Selected pressure level: {}'.format(plev))
f_ins = [f_mod, f_obs]
for f_ind, mv in enumerate([mv1, mv2]):
mv_plv = mv.getLevel()
# var(time,lev,lon,lat) convert from hybrid level to
# pressure
if mv_plv.long_name.lower().find('hybrid') != -1:
f_in = f_ins[f_ind]
hyam = f_in('hyam')
hybm = f_in('hybm')
ps = f_in('PS') # Pa
mv_p = utils.hybrid_to_plevs(mv, hyam, hybm, ps, plev)
# levels are presure levels
elif mv_plv.long_name.lower().find(
'pressure') != -1 or mv_plv.long_name.lower().find(
'isobaric') != -1:
mv_p = utils.pressure_to_plevs(mv, plev)
else:
raise RuntimeError(
"Vertical level is neither hybrid nor pressure. Abort"
)
if f_ind == 0:
mv1_p = mv_p
if f_ind == 1:
mv2_p = mv_p
# select plev
for ilev in range(len(plev)):
mv1 = mv1_p[ilev, ]
mv2 = mv2_p[ilev, ]
# select region
if len(regions) == 0:
regions = ['global']
for region in regions:
print("Selected region: {}".format(region))
mv1_domain, mv2_domain = utils.select_region(
region, mv1, mv2, land_frac, ocean_frac, parameter)
parameter.output_file = '-'.join([
ref_name, var,
str(int(plev[ilev])), season, region
])
parameter.main_title = str(' '.join(
[var,
str(int(plev[ilev])), 'mb', season, region]))
# Regrid towards lower resolution of two variables for
# calculating difference
mv1_reg, mv2_reg = utils.regrid_to_lower_res(
mv1_domain, mv2_domain, parameter.regrid_tool,
parameter.regrid_method)
# Plotting
diff = mv1_reg - mv2_reg
metrics_dict = create_metrics(mv2_domain, mv1_domain,
mv2_reg, mv1_reg, diff)
parameter.var_region = region
plot(parameter.current_set, mv2_domain, mv1_domain,
diff, metrics_dict, parameter)
utils.save_ncfiles(parameter.current_set, mv1_domain,
mv2_domain, diff, parameter)
f_in.close()
# for variables without z axis:
elif mv1.getLevel() is None and mv2.getLevel() is None:
# select region
if len(regions) == 0:
regions = ['global']
for region in regions:
print("Selected region: {}".format(region))
mv1_domain, mv2_domain = utils.select_region(
region, mv1, mv2, land_frac, ocean_frac, parameter)
parameter.output_file = '-'.join(
[ref_name, var, season, region])
parameter.main_title = str(' '.join([var, season, region]))
# regrid towards lower resolution of two variables for
# calculating difference
mv1_reg, mv2_reg = utils.regrid_to_lower_res(
mv1_domain, mv2_domain, parameter.regrid_tool,
parameter.regrid_method)
# if var is 'SST' or var is 'TREFHT_LAND': #special case
if var == 'TREFHT_LAND' or var == 'SST': # use "==" instead of "is"
if ref_name == 'WILLMOTT':
mv2_reg = MV2.masked_where(
mv2_reg == mv2_reg.fill_value, mv2_reg)
print(ref_name)
# if mv.mask is False:
# mv = MV2.masked_less_equal(mv, mv._FillValue)
# print("*************",mv.count())
land_mask = MV2.logical_or(mv1_reg.mask, mv2_reg.mask)
mv1_reg = MV2.masked_where(land_mask, mv1_reg)
mv2_reg = MV2.masked_where(land_mask, mv2_reg)
diff = mv1_reg - mv2_reg
metrics_dict = create_metrics(mv2_domain, mv1_domain,
mv2_reg, mv1_reg, diff)
parameter.var_region = region
plot(parameter.current_set, mv2_domain, mv1_domain, diff,
metrics_dict, parameter)
utils.save_ncfiles(parameter.current_set, mv1_domain,
mv2_domain, diff, parameter)
else:
raise RuntimeError(
"Dimensions of two variables are difference. Abort")
f_obs.close()
f_mod.close()
return parameter
def run_diag(parameter):
reference_data_path = parameter.reference_data_path
test_data_path = parameter.test_data_path
variables = parameter.variables
seasons = parameter.seasons
ref_name = parameter.ref_name
regions = parameter.regions
for season in seasons:
try:
filename1 = utils.get_test_filename(parameter, season)
filename2 = utils.get_ref_filename(parameter, season)
except IOError as e:
print(e)
# the file for the current parameters wasn't found, move to next parameters
continue
print('test file: {}'.format(filename1))
print('reference file: {}'.format(filename2))
f_mod = cdms2.open(filename1)
f_obs = cdms2.open(filename2)
#save land/ocean fraction for masking
try:
land_frac = f_mod('LANDFRAC')
ocean_frac = f_mod('OCNFRAC')
except:
mask_path = os.path.join(sys.prefix, 'share', 'acme_diags',
'acme_ne30_ocean_land_mask.nc')
f0 = cdms2.open(mask_path)
land_frac = f0('LANDFRAC')
ocean_frac = f0('OCNFRAC')
f0.close()
for var in variables:
print('Variable: {}'.format(var))
parameter.var_id = var
mv1 = acme.process_derived_var(var, acme.derived_variables, f_mod,
parameter)
mv2 = acme.process_derived_var(var, acme.derived_variables, f_obs,
parameter)
parameter.viewer_descr[var] = mv1.long_name if hasattr(
mv1, 'long_name') else 'No long_name attr in test data.'
# special case, cdms didn't properly convert mask with fill value
# -999.0, filed issue with denise
if ref_name == 'WARREN':
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 == -0.9, mv2)
# following should move to derived variable
if ref_name == 'AIRS':
# mv2=MV2.masked_where(mv2==mv2.fill_value,mv2)
print(mv2.fill_value)
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 > 1e+20, mv2)
if ref_name == 'WILLMOTT' or ref_name == 'CLOUDSAT':
print(mv2.fill_value)
# mv2=MV2.masked_where(mv2==mv2.fill_value,mv2)
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 == -999., mv2)
print(mv2.fill_value)
# following should move to derived variable
if var == 'PRECT_LAND':
days_season = {
'ANN': 365,
'DJF': 90,
'MAM': 92,
'JJA': 92,
'SON': 91
}
# mv1 = mv1 * days_season[season] * 0.1 #following AMWG
# approximate way to convert to seasonal cumulative
# precipitation, need to have solution in derived variable,
# unit convert from mm/day to cm
mv2 = mv2 / days_season[season] / \
0.1 # convert cm to mm/day instead
mv2.units = 'mm/day'
if mv1.getLevel() and mv2.getLevel(): # for variables with z axis:
#plev = parameter.plevs
plev = numpy.logspace(2.0, 3.0, num=17)
#plev = [30.,50.,70.,100.,150.,200.,250.,300.,400.,500.,600.,700.,775.,850.,925.,1000.]
print('Selected pressure level: {}'.format(plev))
f_ins = [f_mod, f_obs]
for f_ind, mv in enumerate([mv1, mv2]):
mv_plv = mv.getLevel()
# var(time,lev,lon,lat) convert from hybrid level to pressure
if mv_plv.long_name.lower().find('hybrid') != -1:
f_in = f_ins[f_ind]
hyam = f_in('hyam')
hybm = f_in('hybm')
ps = f_in('PS') #Pa
mv_p = utils.hybrid_to_plevs(mv, hyam, hybm, ps, plev)
elif mv_plv.long_name.lower().find(
'pressure') != -1 or mv_plv.long_name.lower().find(
'isobaric') != -1: # levels are presure levels
mv_p = utils.pressure_to_plevs(mv, plev)
else:
raise RuntimeError(
"Vertical level is neither hybrid nor pressure. Abort"
)
#calculate zonal mean
mv_p = cdutil.averager(mv_p, axis='x')
if f_ind == 0:
mv1_p = mv_p
elif f_ind == 1:
mv2_p = mv_p
parameter.output_file = '-'.join(
[ref_name, var, season, parameter.regions[0]])
parameter.main_title = str(' '.join([var, season]))
# Regrid towards lower resolution of two variables for
# calculating difference
if len(mv1_p.getLatitude()) <= len(mv2_p.getLatitude()):
mv1_reg = mv1_p
lev_out = mv1_p.getLevel()
lat_out = mv1_p.getLatitude()
mv2_reg = mv2_p.crossSectionRegrid(lev_out, lat_out)
#apply mask back, since crossSectionRegrid doesn't preserve mask
mv2_reg = MV2.masked_where(mv2_reg == mv2_reg.fill_value,
mv2_reg)
print(mv2_reg.fill_value)
else:
mv2_reg = mv2_p
lev_out = mv2_p.getLevel()
lat_out = mv2_p.getLatitude()
mv1_reg = mv1_p.crossSectionRegrid(lev_out, lat_out)
#apply mask back, since crossSectionRegrid doesn't preserve mask
mv1_reg = MV2.masked_where(mv1_reg == mv1_reg.fill_value,
mv1_reg)
#print(mv1_p.shape, mv2_p.shape)
#mv1_reg, mv2_reg = utils.sregrid_to_lower_res(
# mv1_p, mv2_p, parameter.regrid_tool, parameter.regrid_method)
#reg_mask = MV2.logical_and(mv1_reg.mask, mv2_reg.mask)
#print('reg_mask', reg_mask[:,-1])
#mv1_reg = MV2.masked_where(reg_mask,mv1_reg)
#mv2_reg = MV2.masked_where(reg_mask,mv2_reg)
#print(mv1_reg.shape)
#print(mv2_reg.shape)
#print(mv1_p[:,-1].mask)
#print(mv2_p[:,-1].mask)
#print(mv1_reg[:,-1].mask)
#print(mv2_reg[:,-1].mask)
# Plotting
diff = mv1_reg - mv2_reg
metrics_dict = create_metrics(mv2_p, mv1_p, mv2_reg, mv1_reg,
diff)
parameter.var_region = 'global'
plot(parameter.current_set, mv2_p, mv1_p, diff, metrics_dict,
parameter)
utils.save_ncfiles(parameter.current_set, mv1_p, mv2_p, diff,
parameter)
# for variables without z axis:
elif mv1.getLevel() == None and mv2.getLevel() == None:
#select region
if len(regions) == 0:
regions = ['global']
for region in regions:
print("Selected region: {}".format(region))
mv1_domain, mv2_domain = utils.select_region(
region, mv1, mv2, land_frac, ocean_frac, parameter)
parameter.output_file = '-'.join(
[ref_name, var, season, region])
parameter.main_title = str(' '.join([var, season, region]))
# regrid towards lower resolution of two variables for
# calculating difference
mv1_reg, mv2_reg = utils.regrid_to_lower_res(
mv1_domain, mv2_domain, parameter.regrid_tool,
parameter.regrid_method)
# if var is 'SST' or var is 'TREFHT_LAND': #special case
if var == 'TREFHT_LAND' or var == 'SST': # use "==" instead of "is"
if ref_name == 'WILLMOTT':
mv2_reg = MV2.masked_where(
mv2_reg == mv2_reg.fill_value, mv2_reg)
print(ref_name)
# if mv.mask is False:
# mv = MV2.masked_less_equal(mv, mv._FillValue)
# print("*************",mv.count())
land_mask = MV2.logical_or(mv1_reg.mask, mv2_reg.mask)
mv1_reg = MV2.masked_where(land_mask, mv1_reg)
mv2_reg = MV2.masked_where(land_mask, mv2_reg)
diff = mv1_reg - mv2_reg
metrics_dict = create_metrics(mv2_domain, mv1_domain,
mv2_reg, mv1_reg, diff)
parameter.var_region = region
plot(parameter.current_set, mv2_domain, mv1_domain, diff,
metrics_dict, parameter)
utils.save_ncfiles(parameter.current_set, mv1_domain,
mv2_domain, diff, parameter)
else:
raise RuntimeError(
"Dimensions of two variables are difference. Abort")
f_obs.close()
f_mod.close()
return parameter
def run_diag(parameter):
reference_data_path = parameter.reference_data_path
test_data_path = parameter.test_data_path
variables = parameter.variables
seasons = parameter.seasons
ref_name = parameter.ref_name
regions = parameter.regions
for season in seasons:
try:
filename1 = utils.get_test_filename(parameter, season)
filename2 = utils.get_ref_filename(parameter, season)
except IOError as e:
print(e)
# the file for the current parameters wasn't found, move to next parameters
continue
print('test file: {}'.format(filename1))
print('reference file: {}'.format(filename2))
f_mod = cdms2.open(filename1)
f_obs = cdms2.open(filename2)
#save land/ocean fraction for masking
try:
land_frac = f_mod('LANDFRAC')
ocean_frac = f_mod('OCNFRAC')
except:
mask_path = os.path.join(sys.prefix, 'share', 'acme_diags',
'acme_ne30_ocean_land_mask.nc')
f0 = cdms2.open(mask_path)
land_frac = f0('LANDFRAC')
ocean_frac = f0('OCNFRAC')
f0.close()
for var in variables:
print('Variable: {}'.format(var))
parameter.var_id = var
mv1 = acme.process_derived_var(var, acme.derived_variables, f_mod,
parameter)
mv2 = acme.process_derived_var(var, acme.derived_variables, f_obs,
parameter)
parameter.viewer_descr[var] = mv1.long_name if hasattr(
mv1, 'long_name') else 'No long_name attr in test data.'
#select region
if len(regions) == 0:
regions = ['global']
for region in regions:
print("Selected region: {}".format(region))
mv1_domain, mv2_domain = utils.select_region(
region, mv1, mv2, land_frac, ocean_frac, parameter)
parameter.output_file = '-'.join(
[ref_name, var, season, region])
parameter.main_title = str(' '.join([var, season, region]))
mv1_domain_mean = mean(mv1_domain)
mv2_domain_mean = mean(mv2_domain)
diff = mv1_domain_mean - mv2_domain_mean
mv1_domain_mean.id = var
mv2_domain_mean.id = var
diff.id = var
parameter.backend = 'cartopy'
plot(parameter.current_set, mv2_domain_mean, mv1_domain_mean,
diff, {}, parameter)
# plot(parameter.current_set, mv2_domain, mv1_domain, diff, metrics_dict, parameter)
# utils.save_ncfiles(parameter.current_set, mv1_domain, mv2_domain, diff, parameter)
f_obs.close()
f_mod.close()
return parameter
def run_diag(parameter):
reference_data_path = parameter.reference_data_path
test_data_path = parameter.test_data_path
variables = parameter.variables
seasons = parameter.seasons
ref_name = parameter.ref_name
regions = parameter.regions
for season in seasons:
try:
filename1 = utils.get_test_filename(parameter, season)
filename2 = utils.get_ref_filename(parameter, season)
except IOError as e:
print e
# the file for the current parameters wasn't found, move to next parameters
continue
print('test file: {}'.format(filename1))
print('reference file: {}'.format(filename2))
f_mod = cdms2.open(filename1)
f_obs = cdms2.open(filename2)
# save land/ocean fraction for masking
land_frac = f_mod('LANDFRAC')
ocean_frac = f_mod('OCNFRAC')
for var in variables:
print '***********', variables
print var
parameter.var_id = var
mv1 = acme.process_derived_var(var, acme.derived_variables, f_mod,
parameter)
mv2 = acme.process_derived_var(var, acme.derived_variables, f_obs,
parameter)
# special case, cdms didn't properly convert mask with fill value
# -999.0, filed issue with denise
if ref_name == 'WARREN':
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 == -0.9, mv2)
# following should move to derived variable
if ref_name == 'AIRS':
# mv2=MV2.masked_where(mv2==mv2.fill_value,mv2)
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 > 1e+20, mv2)
if ref_name == 'WILLMOTT' or ref_name == 'CLOUDSAT':
print mv2.fill_value
# mv2=MV2.masked_where(mv2==mv2.fill_value,mv2)
# this is cdms2 for bad mask, denise's fix should fix
mv2 = MV2.masked_where(mv2 == -999., mv2)
print mv2.fill_value
# following should move to derived variable
if var == 'PRECT_LAND':
days_season = {
'ANN': 365,
'DJF': 90,
'MAM': 92,
'JJA': 92,
'SON': 91
}
# mv1 = mv1 * days_season[season] * 0.1 #following AMWG
# approximate way to convert to seasonal cumulative
# precipitation, need to have solution in derived variable,
# unit convert from mm/day to cm
mv2 = mv2 / days_season[season] / \
0.1 # convert cm to mm/day instead
mv2.units = 'mm/day'
if mv1.getLevel() and mv2.getLevel(): # for variables with z axis:
plev = parameter.plevs
print 'selected pressure level', plev
f_ins = [f_mod, f_obs]
for f_ind, mv in enumerate([mv1, mv2]):
mv_plv = mv.getLevel()
# var(time,lev,lon,lat) convert from hybrid level to pressure
if mv_plv.long_name.lower().find('hybrid') != -1:
f_in = f_ins[f_ind]
hyam = f_in('hyam')
hybm = f_in('hybm')
ps = f_in('PS') #Pa
mv_p = utils.hybrid_to_plevs(mv, hyam, hybm, ps, plev)
elif mv_plv.long_name.lower().find(
'pressure') != -1 or mv_plv.long_name.lower().find(
'isobaric') != -1: # levels are presure levels
mv_p = utils.pressure_to_plevs(mv, plev)
else:
raise RuntimeError(
"Vertical level is neither hybrid nor pressure. Abort"
)
if f_ind == 0:
mv1_p = mv_p
if f_ind == 1:
mv2_p = mv_p
#select plev
for ilev in range(len(plev)):
mv1 = mv1_p[ilev, ]
mv2 = mv2_p[ilev, ]
#select region
if len(regions) == 0:
regions = ['global']
for region in regions:
print "selected region", region
mv1_zonal = cdutil.averager(mv1, axis='x')
mv2_zonal = cdutil.averager(mv2, axis='x')
# Regrid towards lower resolution of two variables for
# calculating difference
print mv1_zonal.shape, mv2_zonal.shape
mv1_reg, mv2_reg = regrid_to_lower_res_1d(
mv1_zonal, mv2_zonal)
diff = mv1_reg - mv2_reg
parameter.output_file = '-'.join([
ref_name, var,
str(int(plev[ilev])), season, region
])
parameter.main_title = str(' '.join(
[var,
str(int(plev[ilev])), 'mb', season, region]))
parameter.var_region = region
plot('3', mv2_zonal, mv1_zonal, diff, {}, parameter)
utils.save_ncfiles('3', mv1_zonal, mv2_zonal, diff,
parameter)
# mv1_domain, mv2_domain = utils.select_region(region, mv1, mv2, land_frac,ocean_frac,parameter)
#
# parameter.output_file = '-'.join(
# [ref_name, var, str(int(plev[ilev])), season, region])
# parameter.main_title = str(
# ' '.join([var, str(int(plev[ilev])), 'mb', season, region]))
#
# # Regrid towards lower resolution of two variables for
# # calculating difference
# mv1_reg, mv2_reg = utils.regrid_to_lower_res(
# mv1_domain, mv2_domain, parameter.regrid_tool, parameter.regrid_method)
#
# # Plotting
# diff = mv1_reg - mv2_reg
# metrics_dict = create_metrics(
# mv2_domain, mv1_domain, mv2_reg, mv1_reg, diff)
#
# parameter.var_region = region
# plot('3', mv2_zonal, mv1_zonal, diff parameter)
# utils.save_ncfiles('5', mv1_domain, mv2_domain, diff, parameter)
# for variables without z axis:
elif mv1.getLevel() == None and mv2.getLevel() == None:
#select region
if len(regions) == 0:
regions = ['global']
for region in regions:
print "selected region", region
mv1_zonal = cdutil.averager(mv1, axis='x')
mv2_zonal = cdutil.averager(mv2, axis='x')
print mv1_zonal.shape, mv2_zonal.shape
mv1_reg, mv2_reg = regrid_to_lower_res_1d(
mv1_zonal, mv2_zonal)
diff = mv1_reg - mv2_reg
parameter.output_file = '-'.join(
[ref_name, var, season, region])
parameter.main_title = str(' '.join([var, season, region]))
parameter.var_region = region
plot('3', mv2_zonal, mv1_zonal, diff, {}, parameter)
utils.save_ncfiles('3', mv1_zonal, mv2_zonal, diff,
parameter)
# mv1_domain, mv2_domain = utils.select_region(region, mv1, mv2, land_frac,ocean_frac,parameter)
#
# parameter.output_file = '-'.join(
# [ref_name, var, season, region])
# parameter.main_title = str(' '.join([var, season, region]))
#
# # regrid towards lower resolution of two variables for
# # calculating difference
# mv1_reg, mv2_reg = utils.regrid_to_lower_res(
# mv1_domain, mv2_domain, parameter.regrid_tool, parameter.regrid_method)
#
# # if var is 'SST' or var is 'TREFHT_LAND': #special case
#
# if var == 'TREFHT_LAND'or var == 'SST': # use "==" instead of "is"
# if ref_name == 'WILLMOTT':
# mv2_reg = MV2.masked_where(
# mv2_reg == mv2_reg.fill_value, mv2_reg)
# print ref_name
#
# # if mv.mask is False:
# # mv = MV2.masked_less_equal(mv, mv._FillValue)
# # print "*************",mv.count()
# land_mask = MV2.logical_or(mv1_reg.mask, mv2_reg.mask)
# mv1_reg = MV2.masked_where(land_mask, mv1_reg)
# mv2_reg = MV2.masked_where(land_mask, mv2_reg)
#
# diff = mv1_reg - mv2_reg
# metrics_dict = create_metrics(
# mv2_domain, mv1_domain, mv2_reg, mv1_reg, diff)
# parameter.var_region = region
# plot('5', mv2_domain, mv1_domain, diff, metrics_dict, parameter)
# utils.save_ncfiles('5', mv1_domain, mv2_domain, diff, parameter)
else:
raise RuntimeError(
"Dimensions of two variables are difference. Abort")
f_obs.close()
f_mod.close()
