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': min_cdms(ref),
'max': max_cdms(ref),
'mean': mean(ref)
}
metrics_dict['test'] = {
'min': min_cdms(test),
'max': max_cdms(test),
'mean': mean(test)
}
metrics_dict['diff'] = {
'min': min_cdms(diff),
'max': max_cdms(diff),
'mean': mean(diff)
}
metrics_dict['misc'] = {
'rmse': rmse(test_regrid, ref_regrid),
'corr': 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"""
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 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.'
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
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": min_cdms(ref),
"max": max_cdms(ref),
"mean": mean(ref),
}
metrics_dict["test"] = {
"min": min_cdms(test),
"max": max_cdms(test),
"mean": mean(test),
}
metrics_dict["diff"] = {
"min": min_cdms(diff),
"max": max_cdms(diff),
"mean": mean(diff),
}
metrics_dict["misc"] = {
"rmse": rmse(test_regrid, ref_regrid),
"corr": 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': numpy.nan #mean(ref, axis='yz')
'mean': mean(ref, axis='yz')
}
metrics_dict['test'] = {
'min': min_cdms(test),
'max': max_cdms(test),
#'mean': numpy.nan #mean(test, axis='yz')
'mean': mean(test, axis='yz')
}
metrics_dict['diff'] = {
'min': min_cdms(diff),
'max': max_cdms(diff),
#'mean': numpy.nan #mean(diff, axis='yz')
'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_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="xz"),
}
metrics_dict["test"] = {
"min": min_cdms(test),
"max": max_cdms(test),
"mean": mean(test, axis="xz"),
}
metrics_dict["diff"] = {
"min": min_cdms(diff),
"max": max_cdms(diff),
"mean": mean(diff, axis="xz"),
}
metrics_dict["misc"] = {
"rmse": rmse(test_regrid, ref_regrid, axis="xz"),
"corr": corr(test_regrid, ref_regrid, axis="xz"),
}
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_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):
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
