def test_zonal_mean_2d(self):
# Running zonal_mean_2d with the core param only.
self.runner.sets_to_run = ['zonal_mean_2d']
core_only_results = self.runner.get_final_parameters([self.core_param])
# Running zonal_mean_2d with a set-specific param.
# We pass in both the core and this parameter.
zonal_mean_2d_param = ZonalMean2dParameter()
zonal_mean_2d_param.plevs = [10.0, 20.0, 30.0]
both_results = self.runner.get_final_parameters(
[self.core_param, zonal_mean_2d_param])
# Check that the plevs value is actually changed.
for param in both_results:
if param.plevs != zonal_mean_2d_param.plevs:
msg = 'plevs are {} when they should be {}.'
self.fail(msg.format(param.plevs, zonal_mean_2d_param.plevs))
# Running zonal_mean_2d without a CoreParameter.
# When doing so, we still must have valid values for
# certain parameters.
another_zonal_mean_2d_param = ZonalMean2dParameter()
another_zonal_mean_2d_param.reference_data_path = '/something'
another_zonal_mean_2d_param.test_data_path = '/something/else'
another_zonal_mean_2d_param.results_dir = '/something/else/too'
zm_2d_only_results = self.runner.get_final_parameters(
[another_zonal_mean_2d_param])
# Now checking that all three configs have the same output.
len1, len2, len3 = len(core_only_results), len(both_results), len(
zm_2d_only_results)
if len1 == 0 or not (len1 == len2 == len3):
msg = 'The lengths are either 0 or not equal: {} {} {}'
self.fail(msg.format(len1, len2, len3))
# Running the software with the API:
# python all_sets.py -d all_sets.py
import numpy
from acme_diags.parameter.area_mean_time_series_parameter import AreaMeanTimeSeriesParameter
from acme_diags.parameter.core_parameter import CoreParameter
from acme_diags.parameter.enso_diags_parameter import EnsoDiagsParameter
from acme_diags.parameter.meridional_mean_2d_parameter import MeridionalMean2dParameter
from acme_diags.parameter.zonal_mean_2d_parameter import ZonalMean2dParameter
from acme_diags.run import Run
run_object = Run()
param = CoreParameter()
ts_param = AreaMeanTimeSeriesParameter()
m2d_param = MeridionalMean2dParameter()
m2d_param.plevs = [
200.,
500.,
]
z2d_param = ZonalMean2dParameter()
z2d_param.plevs = [
200.,
300.,
]
enso_param = EnsoDiagsParameter()
enso_param.test_name = 'e3sm_v1'
run_object.run_diags([param, ts_param, m2d_param, z2d_param, enso_param])
from acme_diags.parameter.core_parameter import CoreParameter
from acme_diags.parameter.zonal_mean_2d_parameter import ZonalMean2dParameter
from acme_diags.run import runner
param = CoreParameter()
param.reference_data_path = (
"/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/")
param.test_data_path = (
"/global/cfs/cdirs/e3sm/acme_diags/test_model_data_for_acme_diags/climatology/"
)
param.test_name = "20161118.beta0.FC5COSP.ne30_ne30.edison"
param.seasons = ["ANN"]
# Name of the folder where the results are stored.
# Change `prefix` to use your directory.
prefix = "/global/cfs/cdirs/e3sm/www/<your directory>/examples"
param.results_dir = os.path.join(prefix, "ex6_zonal_mean_2d_and_lat_lon_demo")
# Uncomment the two lines below to just
# run the diags with T and PRECT.
# param.selectors += ['variables']
# param.variables = ['T', 'PRECT']
# The new changes are below.
zonal_mean_2d_param = ZonalMean2dParameter()
zonal_mean_2d_param.plevs = [10.0, 20.0, 30.0]
runner.sets_to_run = ["zonal_mean_2d", "lat_lon"]
runner.run_diags([param, zonal_mean_2d_param])
def run_diag(parameter):
variables = parameter.variables
seasons = parameter.seasons
ref_name = getattr(parameter, "ref_name", "")
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)
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.")
# For variables with a z-axis.
if mv1.getLevel() and mv2.getLevel():
# Since the default is now stored in MeridionalMean2dParameter,
# 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="y")
mv2_p = cdutil.averager(mv2_p, axis="y")
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.getLongitude()) < len(mv2_p.getLongitude()):
mv1_reg = mv1_p
lev_out = mv1_p.getLevel()
lon_out = mv1_p.getLongitude()
# in order to use regrid tool we need to have at least two latitude bands, so generate new grid first
lat = cdms2.createAxis([0])
lat.setBounds(numpy.array([-1, 1]))
lat.designateLatitude()
grid = cdms2.createRectGrid(lat, lon_out)
data_shape = list(mv2_p.shape)
data_shape.append(1)
mv2_reg = MV2.resize(mv2_p, data_shape)
mv2_reg.setAxis(-1, lat)
for i, ax in enumerate(mv2_p.getAxisList()):
mv2_reg.setAxis(i, ax)
mv2_reg = mv2_reg.regrid(grid, regridTool="regrid2")[...,
0]
# 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.getLongitude()) > len(mv2_p.getLongitude()):
mv2_reg = mv2_p
lev_out = mv2_p.getLevel()
lon_out = mv2_p.getLongitude()
mv1_reg = mv1_p.crossSectionRegrid(lev_out, lon_out)
# In order to use regrid tool we need to have at least two
# latitude bands, so generate new grid first.
lat = cdms2.createAxis([0])
lat.setBounds(numpy.array([-1, 1]))
lat.designateLatitude()
grid = cdms2.createRectGrid(lat, lon_out)
data_shape = list(mv1_p.shape)
data_shape.append(1)
mv1_reg = MV2.resize(mv1_p, data_shape)
mv1_reg.setAxis(-1, lat)
for i, ax in enumerate(mv1_p.getAxisList()):
mv1_reg.setAxis(i, ax)
mv1_reg = mv1_reg.regrid(grid, regridTool="regrid2")[...,
0]
# 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:
raise RuntimeError(
"One of or both data doesn't have z dimention. Aborting.")
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():
# 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