def create_metrics(ref, test, ref_regrid, test_regrid, diff):
"""Creates the mean, max, min, rmse, corr in a dictionary"""
metrics_dict = {}
metrics_dict["ref"] = {
"min": float(min_cdms(ref)),
"max": float(max_cdms(ref)),
"mean": float(mean(ref)),
}
metrics_dict["ref_regrid"] = {
"min": float(min_cdms(ref_regrid)),
"max": float(max_cdms(ref_regrid)),
"mean": float(mean(ref_regrid)),
"std": float(std(ref_regrid)),
}
metrics_dict["test"] = {
"min": float(min_cdms(test)),
"max": float(max_cdms(test)),
"mean": float(mean(test)),
}
metrics_dict["test_regrid"] = {
"min": float(min_cdms(test_regrid)),
"max": float(max_cdms(test_regrid)),
"mean": float(mean(test_regrid)),
"std": float(std(test_regrid)),
}
metrics_dict["diff"] = {
"min": float(min_cdms(diff)),
"max": float(max_cdms(diff)),
"mean": float(mean(diff)),
}
metrics_dict["misc"] = {
"rmse": float(rmse(test_regrid, ref_regrid)),
"corr": float(corr(test_regrid, ref_regrid)),
}
return metrics_dict
def create_metrics(ref, test, ref_regrid, test_regrid, diff):
"""Creates the mean, max, min, rmse, corr in a dictionary"""
orig_bounds = cdms2.getAutoBounds()
cdms2.setAutoBounds(1)
lev = ref.getLevel()
if lev is not None:
lev.setBounds(None)
lev = test.getLevel()
if lev is not None:
lev.setBounds(None)
lev = test_regrid.getLevel()
if lev is not None:
lev.setBounds(None)
lev = ref_regrid.getLevel()
if lev is not None:
lev.setBounds(None)
lev = diff.getLevel()
if lev is not None:
lev.setBounds(None)
cdms2.setAutoBounds(orig_bounds)
metrics_dict = {}
metrics_dict["ref"] = {
"min": min_cdms(ref),
"max": max_cdms(ref),
"mean": mean(ref, axis="yz"),
}
metrics_dict["test"] = {
"min": min_cdms(test),
"max": max_cdms(test),
"mean": mean(test, axis="yz"),
}
metrics_dict["diff"] = {
"min": min_cdms(diff),
"max": max_cdms(diff),
"mean": mean(diff, axis="yz"),
}
metrics_dict["misc"] = {
"rmse": rmse(test_regrid, ref_regrid, axis="yz"),
"corr": corr(test_regrid, ref_regrid, axis="yz"),
}
return metrics_dict
def create_single_metrics_dict(values):
d = {
"min": float(min_cdms(values)),
"max": float(max_cdms(values)),
"mean": float(mean(values)),
"std": float(std(values)),
}
return d
def create_metrics(ref_domain):
"""
For this plotset, calculate the mean of the
reference data and return a dict of that.
"""
return {"mean": mean(ref_domain)}
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(e3sm_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.")
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]))
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 = (
"mpl" # For now, there's no vcs support for this set.
)
plot(
parameter.current_set,
mv2_domain_mean,
mv1_domain_mean,
diff,
{},
parameter,
)
utils.general.save_ncfiles(
parameter.current_set,
mv1_domain,
mv2_domain,
diff,
parameter,
)
return parameter