def testMaskingFunctions(self):
xouter = MV2.outerproduct(MV2.arange(5.), [1] * 10)
masked = MV2.masked_greater(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[2:], True))
self.assertTrue(MV2.allequal(masked.mask[:2], False))
masked = MV2.masked_greater_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1:], True))
self.assertTrue(MV2.allequal(masked.mask[:1], False))
masked = MV2.masked_less(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:], False))
masked = MV2.masked_less_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_not_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], False))
self.assertTrue(MV2.allequal(masked.mask[0], True))
self.assertTrue(MV2.allequal(masked.mask[2:], True))
masked = MV2.masked_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], True))
self.assertTrue(MV2.allequal(masked.mask[0], False))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_outside(xouter, 1, 3)
self.assertTrue(MV2.allequal(masked.mask[0:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
masked = MV2.masked_where(
MV2.logical_or(MV2.greater(xouter, 3), MV2.less(xouter, 2)),
xouter)
self.assertTrue(MV2.allequal(masked.mask[0:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
def myMakeMask(array, range):
"""Returns the input array masked where the values are not between range[0] and range[1]"""
m1 = MV2.less(array,
range[0]) # mask where it is less than the 1st value
m2 = MV2.greater(
array, range[1]) # mask where it is more than the 2nd value
return MV2.logical_or(m1, m2)
def loop(potential,potential_reg,c2,w3,region):
nmax = potential.shape[0]
c3 = MV2.not_equal(w3,0.)
c = MV2.logical_and(c2,c3)
thisturn = MV2.ones(c.shape)
for i in range(nmax):
c1 = MV2.logical_or(MV2.equal(potential_reg[i],region),MV2.equal(potential[i],-999))
c2 = MV2.logical_and(c,c1)
c2 = MV2.logical_and(c2,thisturn)
potential_reg[i] = MV2.where(c2,region,potential_reg[i])
thisturn = MV2.where(c2,0,thisturn)
c1 = MV2.logical_and(c2,MV2.equal(potential[i],-999))
c2 = MV2.logical_and(c2,MV2.not_equal(potential[i],-999))
potential[i] = MV2.where(c1,w3,potential[i])
potential[i] = MV2.where(c2,potential[i]+w3,potential[i])
## Ultimate test to see if more would be needed !
if not MV2.allequal(MV2.logical_and(c,thisturn),0):
raise 'OOOPS WE COULD USE MORE REGIONS BUDDY !'
return
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):
variables = parameter.variables
seasons = parameter.seasons
ref_name = getattr(parameter, 'ref_name', '')
regions = parameter.regions
test_data = utils.dataset.Dataset(parameter, test=True)
ref_data = utils.dataset.Dataset(parameter, ref=True)
for season in seasons:
# Get the name of the data, appended with the years averaged.
parameter.test_name_yrs = utils.general.get_name_and_yrs(
parameter, test_data, season)
parameter.ref_name_yrs = utils.general.get_name_and_yrs(
parameter, ref_data, season)
# Get land/ocean fraction for masking.
try:
land_frac = test_data.get_climo_variable('LANDFRAC', season)
ocean_frac = test_data.get_climo_variable('OCNFRAC', season)
except:
mask_path = os.path.join(acme_diags.INSTALL_PATH,
'acme_ne30_ocean_land_mask.nc')
with cdms2.open(mask_path) as f:
land_frac = f('LANDFRAC')
ocean_frac = f('OCNFRAC')
for var in variables:
print('Variable: {}'.format(var))
parameter.var_id = var
mv1 = test_data.get_climo_variable(var, season)
mv2 = ref_data.get_climo_variable(var, season)
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 Denis.
if ref_name == 'WARREN':
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 == -0.9, mv2)
# The following should be moved to a derived variable.
if ref_name == 'AIRS':
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 > 1e+20, mv2)
if ref_name == 'WILLMOTT' or ref_name == 'CLOUDSAT':
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 == -999., mv2)
# The following should be moved to a 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'
# For variables with a z-axis.
if mv1.getLevel() and mv2.getLevel():
plev = parameter.plevs
print('Selected pressure level: {}'.format(plev))
mv1_p = utils.general.convert_to_pressure_levels(
mv1, plev, test_data, var, season)
mv2_p = utils.general.convert_to_pressure_levels(
mv2, plev, ref_data, var, season)
# Select plev.
for ilev in range(len(plev)):
mv1 = mv1_p[ilev, ]
mv2 = mv2_p[ilev, ]
for region in regions:
print("Selected region: {}".format(region))
mv1_domain = utils.general.select_region(
region, mv1, land_frac, ocean_frac, parameter)
mv2_domain = utils.general.select_region(
region, 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 the lower resolution of the two
# variables for calculating the difference.
mv1_reg, mv2_reg = utils.general.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.general.save_ncfiles(parameter.current_set,
mv1_domain, mv2_domain,
diff, parameter)
# For variables without a z-axis.
elif mv1.getLevel() is None and mv2.getLevel() is None:
for region in regions:
print("Selected region: {}".format(region))
mv1_domain = utils.general.select_region(
region, mv1, land_frac, ocean_frac, parameter)
mv2_domain = utils.general.select_region(
region, 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 the lower resolution of the two
# variables for calculating the difference.
mv1_reg, mv2_reg = utils.general.regrid_to_lower_res(
mv1_domain, mv2_domain, parameter.regrid_tool,
parameter.regrid_method)
# Special case.
if var == 'TREFHT_LAND' or var == 'SST':
if ref_name == 'WILLMOTT':
mv2_reg = MV2.masked_where(
mv2_reg == mv2_reg.fill_value, mv2_reg)
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.general.save_ncfiles(parameter.current_set,
mv1_domain, mv2_domain, diff,
parameter)
else:
raise RuntimeError(
"Dimensions of the two variables are different. Aborting.")
return parameter
def myMakeMask(array, range):
"""Returns the input array masked where the values are not between range[0] and range[1]"""
m1=MV.less (array, range[0]) # mask where it is less than the 1st value
m2=MV.greater(array, range[1]) # mask where it is more than the 2nd value
return MV.logical_or(m1,m2)
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):
variables = parameter.variables
seasons = parameter.seasons
ref_name = getattr(parameter, "ref_name", "")
regions = parameter.regions
test_data = utils.dataset.Dataset(parameter, test=True)
ref_data = utils.dataset.Dataset(parameter, ref=True)
for season in seasons:
# Get the name of the data, appended with the years averaged.
parameter.test_name_yrs = utils.general.get_name_and_yrs(
parameter, test_data, season)
parameter.ref_name_yrs = utils.general.get_name_and_yrs(
parameter, ref_data, season)
# Get land/ocean fraction for masking.
try:
land_frac = test_data.get_climo_variable("LANDFRAC", season)
ocean_frac = test_data.get_climo_variable("OCNFRAC", season)
except Exception:
mask_path = os.path.join(acme_diags.INSTALL_PATH,
"acme_ne30_ocean_land_mask.nc")
with cdms2.open(mask_path) as f:
land_frac = f("LANDFRAC")
ocean_frac = f("OCNFRAC")
for var in variables:
print("Variable: {}".format(var))
parameter.var_id = var
mv1 = test_data.get_climo_variable(var, season)
mv2 = ref_data.get_climo_variable(var, season)
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 Denis.
if ref_name == "WARREN":
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 == -0.9, mv2)
# The following should be moved to a derived variable.
if ref_name == "AIRS":
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 > 1e20, mv2)
if ref_name == "WILLMOTT" or ref_name == "CLOUDSAT":
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 == -999.0, mv2)
# The following should be moved to a 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"
# For variables with a z-axis.
if mv1.getLevel() and mv2.getLevel():
plev = parameter.plevs
print("Selected pressure level: {}".format(plev))
mv1_p = utils.general.convert_to_pressure_levels(
mv1, plev, test_data, var, season)
mv2_p = utils.general.convert_to_pressure_levels(
mv2, plev, ref_data, var, season)
# Select plev.
for ilev in range(len(plev)):
mv1 = mv1_p[ilev, ]
mv2 = mv2_p[ilev, ]
for region in regions:
# print("Selected region: {}".format(region))
mv1_domain = utils.general.select_region(
region, mv1, land_frac, ocean_frac, parameter)
mv2_domain = utils.general.select_region(
region, 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 the lower resolution of the two
# variables for calculating the difference.
mv1_reg, mv2_reg = utils.general.regrid_to_lower_res(
mv1_domain,
mv2_domain,
parameter.regrid_tool,
parameter.regrid_method,
)
diff = mv1_reg - mv2_reg
metrics_dict = create_metrics(mv2_domain, mv1_domain,
mv2_reg, mv1_reg, diff)
# Saving the metrics as a json.
metrics_dict["unit"] = mv1_reg.units
fnm = os.path.join(
utils.general.get_output_dir(
parameter.current_set, parameter),
parameter.output_file + ".json",
)
with open(fnm, "w") as outfile:
json.dump(metrics_dict, outfile)
# Get the filename that the user has passed in and display that.
# When running in a container, the paths are modified.
fnm = os.path.join(
utils.general.get_output_dir(
parameter.current_set,
parameter,
ignore_container=True,
),
parameter.output_file + ".json",
)
# print('Metrics saved in: ' + fnm)
parameter.var_region = region
plot(
parameter.current_set,
mv2_domain,
mv1_domain,
diff,
metrics_dict,
parameter,
)
utils.general.save_ncfiles(
parameter.current_set,
mv1_domain,
mv2_domain,
diff,
parameter,
)
# For variables without a z-axis.
elif mv1.getLevel() is None and mv2.getLevel() is None:
for region in regions:
# print("Selected region: {}".format(region))
mv1_domain = utils.general.select_region(
region, mv1, land_frac, ocean_frac, parameter)
mv2_domain = utils.general.select_region(
region, 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 the lower resolution of the two
# variables for calculating the difference.
mv1_reg, mv2_reg = utils.general.regrid_to_lower_res(
mv1_domain,
mv2_domain,
parameter.regrid_tool,
parameter.regrid_method,
)
# Special case.
if var == "TREFHT_LAND" or var == "SST":
if ref_name == "WILLMOTT":
mv2_reg = MV2.masked_where(
mv2_reg == mv2_reg.fill_value, mv2_reg)
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)
# Saving the metrics as a json.
metrics_dict["unit"] = mv1_reg.units
fnm = os.path.join(
utils.general.get_output_dir(parameter.current_set,
parameter),
parameter.output_file + ".json",
)
with open(fnm, "w") as outfile:
json.dump(metrics_dict, outfile)
# Get the filename that the user has passed in and display that.
# When running in a container, the paths are modified.
fnm = os.path.join(
utils.general.get_output_dir(
parameter.current_set,
parameter,
ignore_container=True,
),
parameter.output_file + ".json",
)
# print('Metrics saved in: ' + fnm)
parameter.var_region = region
plot(
parameter.current_set,
mv2_domain,
mv1_domain,
diff,
metrics_dict,
parameter,
)
utils.general.save_ncfiles(
parameter.current_set,
mv1_domain,
mv2_domain,
diff,
parameter,
)
else:
raise RuntimeError(
"Dimensions of the two variables are different. Aborting.")
return parameter
def harmonic(data, k=3):
data = data.reorder('t...')
cdutil.setAxisTimeBoundsDaily(data.getTime())
axislist = data.getAxisList()
dataid = data.id
daily = True
monthly = False
timeAxis = axislist[0]
N = 365. #len(timeAxis)
# P = 10. # 10 year, yearly harmonic oscilation
# P = 10*12 # 10 year, monthly harmonic oscilation
# P = 10*365 # 10 year, daily harmonic oscilation
# if P > N:
# raise ValueError("P('%d') value should not exceed N(%d)" % (P,N))
if k > N/2:
raise ValueError("k value should not exceed (%d) i.e. N/2 value" % (N/2))
if len(timeAxis) > 366:
print 'found more than 1 year data.'
# y_t = dailyClimatology(data, action='sum')
else:
y_t = data
# end of if len(timeAxis) > 366:
Y_0 = cdutil.averager(data, axis='t', action='average', weights='equal')
# make memory free
del data
t = numpy.arange(1, N+1, dtype='float')
otheraxis = list(Y_0.shape)
ax_product = 1
for ax in otheraxis:
ax_product *= ax
otheraxis.insert(0,N)
t = t.repeat(ax_product).reshape(otheraxis)
angle = 2 * math.pi * t/N
Y_k = 0.
for i in range(1,k+1):
kangle = angle*i
A_k = (2./N) * cdutil.averager(y_t * numpy.cos(kangle), axis='t', action='sum')
B_k = (2./N) * cdutil.averager(y_t * numpy.sin(kangle), axis='t', action='sum')
C_k = MV2.sqrt((A_k*A_k) + (B_k*B_k))
# if A_k is positiv, then retain this phase_angle as it is.
# phase_angle should be in degrees
phase_angle = phase_arc_angle = MV2.arctan(B_k/A_k)
# if A_k is zero, then replace phase_angle with pi/2 else retain same
phase_angle = MV2.where(MV2.equal(A_k, 0.), math.pi/2.0, phase_arc_angle)
# if A_k is negative, then add pi with phase_angle (if it is <= pi )
condition1 = MV2.logical_and(MV2.less(A_k, 0.), MV2.less_equal(phase_arc_angle, math.pi))
phase_angle = MV2.where(condition1, phase_arc_angle+math.pi, phase_arc_angle)
# if A_k is negative, then subtract pi from phase_angle (if it is > pi )
condition2 = MV2.logical_and(MV2.less(A_k, 0.), MV2.greater(phase_arc_angle, math.pi))
condition3 = MV2.logical_or(condition1, condition2)
phase_angle = MV2.where(condition3, phase_arc_angle-math.pi, phase_arc_angle)
# make memory free
del phase_arc_angle
if daily and not monthly:
# subtract 15 days lag to adjust phase_angle w.r.t daily
print "Daily Subtraction"
phase_angle -= (15.*2*math.pi)/N
# end of if daily and not monthly:
phase_angle = numpy.array(phase_angle)
# phase_angle = numpy.tile(phase_angle, N).reshape(kangle.shape)
kangle = numpy.array(kangle)
Y_k += C_k * MV2.cos(kangle - phase_angle)
# end of for i in range(1,k+1):
# add mean to the sum of first k-th harmonic of data
Y_k += Y_0
# make memory free
del y_t, Y_0
sumOfMean_and_first_k_harmonic = cdms2.createVariable(Y_k, id=dataid)
sumOfMean_and_first_k_harmonic.setAxisList(axislist)
sumOfMean_and_first_k_harmonic.comments = 'sumOfMean_and_first_%d_harmonic' % k
# make memory free
del Y_k
# return result
return sumOfMean_and_first_k_harmonic
def run_diag(parameter):
variables = parameter.variables
seasons = parameter.seasons
ref_name = getattr(parameter, "ref_name", "")
regions = parameter.regions
test_data = utils.dataset.Dataset(parameter, test=True)
ref_data = utils.dataset.Dataset(parameter, ref=True)
for season in seasons:
# Get the name of the data, appended with the years averaged.
parameter.test_name_yrs = utils.general.get_name_and_yrs(
parameter, test_data, season)
parameter.ref_name_yrs = utils.general.get_name_and_yrs(
parameter, ref_data, season)
# Get land/ocean fraction for masking.
try:
land_frac = test_data.get_climo_variable("LANDFRAC", season)
ocean_frac = test_data.get_climo_variable("OCNFRAC", season)
except Exception:
mask_path = os.path.join(acme_diags.INSTALL_PATH,
"acme_ne30_ocean_land_mask.nc")
with cdms2.open(mask_path) as f:
land_frac = f("LANDFRAC")
ocean_frac = f("OCNFRAC")
for var in variables:
print("Variable: {}".format(var))
parameter.var_id = var
mv1 = test_data.get_climo_variable(var, season)
mv2 = ref_data.get_climo_variable(var, season)
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 Denis.
if ref_name == "WARREN":
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 == -0.9, mv2)
# The following should be moved to a derived variable.
if ref_name == "AIRS":
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 > 1e20, mv2)
if ref_name == "WILLMOTT" or ref_name == "CLOUDSAT":
# This is cdms2 fix for bad mask, Denis' fix should fix this.
mv2 = MV2.masked_where(mv2 == -999.0, mv2)
# The following should be moved to a 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"
# For variables with a z-axis.
if mv1.getLevel() and mv2.getLevel():
# Since the default is now stored in ZonalMean2dParameter,
# we must get it from there if the plevs param is blank.
plevs = parameter.plevs
if (isinstance(plevs, numpy.ndarray) and not plevs.all()) or (
not isinstance(plevs, numpy.ndarray) and not plevs):
plevs = ZonalMean2dParameter().plevs
# print('Selected pressure level: {}'.format(plevs))
mv1_p = utils.general.convert_to_pressure_levels(
mv1, plevs, test_data, var, season)
mv2_p = utils.general.convert_to_pressure_levels(
mv2, plevs, ref_data, var, season)
mv1_p = cdutil.averager(mv1_p, axis="x")
mv2_p = cdutil.averager(mv2_p, axis="x")
parameter.output_file = "-".join(
[ref_name, var, season, parameter.regions[0]])
parameter.main_title = str(" ".join([var, season]))
# Regrid towards the lower resolution of the two
# variables for calculating the 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 the mask back, since crossSectionRegrid
# doesn't preserve the mask.
mv2_reg = MV2.masked_where(mv2_reg == mv2_reg.fill_value,
mv2_reg)
elif len(mv1_p.getLatitude()) > len(mv2_p.getLatitude()):
mv2_reg = mv2_p
lev_out = mv2_p.getLevel()
lat_out = mv2_p.getLatitude()
mv1_reg = mv1_p.crossSectionRegrid(lev_out, lat_out)
# Apply the mask back, since crossSectionRegrid
# doesn't preserve the mask.
mv1_reg = MV2.masked_where(mv1_reg == mv1_reg.fill_value,
mv1_reg)
else:
mv1_reg = mv1_p
mv2_reg = mv2_p
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.general.save_ncfiles(parameter.current_set, mv1_p, mv2_p,
diff, parameter)
# For variables without a z-axis.
elif mv1.getLevel() is None and mv2.getLevel() is None:
for region in regions:
# print("Selected region: {}".format(region))
mv1_domain = utils.general.select_region(
region, mv1, land_frac, ocean_frac, parameter)
mv2_domain = utils.general.select_region(
region, 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 the lower resolution of the two
# variables for calculating the difference.
mv1_reg, mv2_reg = utils.general.regrid_to_lower_res(
mv1_domain,
mv2_domain,
parameter.regrid_tool,
parameter.regrid_method,
)
# Special case.
if var == "TREFHT_LAND" or var == "SST":
if ref_name == "WILLMOTT":
mv2_reg = MV2.masked_where(
mv2_reg == mv2_reg.fill_value, mv2_reg)
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.general.save_ncfiles(
parameter.current_set,
mv1_domain,
mv2_domain,
diff,
parameter,
)
else:
raise RuntimeError(
"Dimensions of the two variables are different. Aborting.")
return parameter
def harmonic(data, k=3, time_type='daily', phase_shift=15):
"""
Inputs :
data : climatology data
k : Integer no to compute K th harmonic. By default it takes 3.
time_type : daily | monthly | full (time type of input climatology)
'daily' -> it returns 365 days harmonic,
'monthly' -> it returns 12 month harmonic,
'full' -> it retuns harmonic for full length of
input data.
phase_shift : Used to subtract 'phase_shift' days lag to adjust
phase_angle w.r.t daily or monthly. By default it takes
15 days lag to adjust phase_angle w.r.t daily data.
User can pass None disable this option.
Returns :
Returns "sum mean of mean and first K th harmonic" of input
climatology data.
Concept :
Earth science data consists of a strong seasonality component as
indicated by the cycles of repeated patterns in climate variables such
as air pressure, temperature and precipitation. The seasonality forms
the strongest signals in this data and in order to find other patterns,
the seasonality is removed by subtracting the monthly mean values of the
raw data for each month. However since the raw data like air temperature,
pressure, etc. are constantly being generated with the help of satellite
observations, the climate scientists usually use a moving reference base
interval of some years of raw data to calculate the mean in order to
generate the anomaly time series and study the changes with respect to
that.
Fourier series analysis decomposes a signal into an infinite series of
harmonic components. Each of these components is comprised initially of
a sine wave and a cosine wave of equal integer frequency. These two waves
are then combined into a single cosine wave, which has characteristic
amplitude (size of the wave) and phase angle (offset of the wave).
Convergence has been established for bounded piecewise continuous
functions on a closed interval, with special conditions at points of
discontinuity. Its convergence has been established for other conditions
as well, but these are not relevant to the analysis at hand.
Reference: Daniel S Wilks, 'Statistical Methods in the Atmospheric
Sciences' second Edition, page no(372-378).
Written By : Arulalan.T
Date : 16.05.2014
"""
data = data.reorder('t...')
cdutil.setAxisTimeBoundsDaily(data.getTime())
axislist = data.getAxisList()
timeAxis = axislist[0]
dataid = data.id
if time_type in ['daily']:
N = 365.0 # must be float
elif time_type[:3] in ['mon']:
N = 12.0 # must be float
elif time_type in ['full']:
N = float(len(timeAxis))
if k > N/2:
raise ValueError("k value should not exceed (%d) i.e. N/2 value" % (N/2))
if len(timeAxis) > 366:
print 'found more than 1 year data.'
raise ValueError("Kindly pass only climatology data")
else:
y_t = data
# end of if len(timeAxis) > 366:
Y_0 = cdutil.averager(data, axis='t', action='average', weights='equal')
# make memory free
del data
t = numpy.arange(1, N+1, dtype='float')
otheraxis = list(Y_0.shape)
ax_product = 1
for ax in otheraxis:
ax_product *= ax
otheraxis.insert(0,N)
t = t.repeat(ax_product).reshape(otheraxis)
angle = 2 * math.pi * t/N
Y_k = 0.
for i in range(1,k+1):
kangle = angle*i
A_k = (2./N) * cdutil.averager(y_t * numpy.cos(kangle), axis='t', action='sum')
B_k = (2./N) * cdutil.averager(y_t * numpy.sin(kangle), axis='t', action='sum')
C_k = MV2.sqrt((A_k*A_k) + (B_k*B_k))
# if A_k is positiv, then retain this phase_angle as it is.
# phase_angle should be in degrees
phase_angle = phase_arc_angle = MV2.arctan(B_k/A_k)
# if A_k is zero, then replace phase_angle with pi/2 else retain same
phase_angle = MV2.where(MV2.equal(A_k, 0.), math.pi/2.0, phase_arc_angle)
# if A_k is negative, then add pi with phase_angle (if it is <= pi )
condition1 = MV2.logical_and(MV2.less(A_k, 0.), MV2.less_equal(phase_arc_angle, math.pi))
phase_angle = MV2.where(condition1, phase_arc_angle+math.pi, phase_arc_angle)
# if A_k is negative, then subtract pi from phase_angle (if it is > pi )
condition2 = MV2.logical_and(MV2.less(A_k, 0.), MV2.greater(phase_arc_angle, math.pi))
condition3 = MV2.logical_or(condition1, condition2)
phase_angle = MV2.where(condition3, phase_arc_angle-math.pi, phase_arc_angle)
# make memory free
del phase_arc_angle
if phase_shift:
# subtract 15 days lag to adjust phase_angle w.r.t daily
phase_angle -= (phase_shift *2 * math.pi) / N
# end of if daily and not monthly:
phase_angle = numpy.array(phase_angle)
kangle = numpy.array(kangle)
Y_k += C_k * MV2.cos(kangle - phase_angle)
# end of for i in range(1,k+1):
# add mean to the sum of first k-th harmonic of data
Y_k += Y_0
# make memory free
del y_t, Y_0
sumOfMean_and_first_k_harmonic = cdms2.createVariable(Y_k, id=dataid)
sumOfMean_and_first_k_harmonic.setAxisList(axislist)
sumOfMean_and_first_k_harmonic.comments = 'sumOfMean_and_first_%d_harmonic' % k
# make memory free
del Y_k
# return result
return sumOfMean_and_first_k_harmonic
