def test_convert_to_iris(dict_in, dict_out):
"""Test converting metadata dictionary checking of coordinates."""
if 'short_name' in dict_in and 'var_name' in dict_in:
with pytest.raises(KeyError):
ih.convert_to_iris(dict_in)
return
new_dict = ih.convert_to_iris(dict_in)
assert new_dict == dict_out
assert new_dict is not dict_in
def plot_cdf(cfg, psi_cube, ecs_cube, obs_cube):
"""Plot cumulative distribution function of ECS."""
confidence_level = cfg.get('confidence_level', 0.66)
(ecs_lin, ecs_pdf) = ec.gaussian_pdf(psi_cube.data, ecs_cube.data,
np.mean(obs_cube.data),
np.std(obs_cube.data))
ecs_cdf = ec.cdf(ecs_lin, ecs_pdf)
# Provenance
filename = 'cdf_{}'.format(obs_cube.attributes['dataset'])
netcdf_path = get_diagnostic_filename(filename, cfg)
cube = iris.cube.Cube(ecs_cdf,
var_name='cdf',
long_name='Cumulative distribution function',
units='1')
cube.add_aux_coord(
iris.coords.AuxCoord(ecs_lin, **ih.convert_to_iris(ECS_ATTRS)), 0)
io.iris_save(cube, netcdf_path)
project = _get_project(cfg)
provenance_record = get_provenance_record(
"The CDF for ECS. The horizontal dot-dashed lines show the {}% "
"confidence limits. The orange histograms show the prior "
"distributions that arise from equal weighting of the {} models in "
"0.5 K bins.".format(int(confidence_level * 100), project), ['mean'],
['other'], _get_ancestor_files(cfg, obs_cube.attributes['dataset']))
# Plot
if cfg['write_plots']:
AXES.plot(ecs_lin,
ecs_cdf,
color='black',
linewidth=2.0,
label='Emergent constraint')
AXES.hist(ecs_cube.data,
bins=6,
range=(2.0, 5.0),
cumulative=True,
density=True,
color='orange',
label='{} models'.format(project))
AXES.axhline((1.0 - confidence_level) / 2.0,
color='black',
linestyle='dashdot')
AXES.axhline((1.0 + confidence_level) / 2.0,
color='black',
linestyle='dashdot')
# Plot appearance
AXES.set_title('CDF of emergent constraint')
AXES.set_xlabel('ECS / K')
AXES.set_ylabel('CDF')
legend = AXES.legend(loc='upper left')
# Save plot
provenance_record['plot_file'] = _save_fig(cfg, filename, legend)
# Write provenance
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(netcdf_path, provenance_record)
def test_convert_to_iris(mock_logger, dict_in, dict_out):
"""Test converting metadata dictionary checking of coordinates."""
new_dict = ih.convert_to_iris(dict_in)
assert new_dict == dict_out
assert new_dict is not dict_in
if 'short_name' in dict_in and 'var_name' in dict_in:
mock_logger.warning.assert_called()
else:
mock_logger.warning.assert_not_called()
def plot_pdf(cfg, psi_cube, ecs_cube, obs_cube):
"""Plot probability density function of ECS."""
obs_mean = np.mean(obs_cube.data)
obs_std = np.std(obs_cube.data)
(ecs_lin, ecs_pdf) = ec.gaussian_pdf(psi_cube.data, ecs_cube.data,
obs_mean, obs_std)
# Provenance
filename = 'pdf_{}'.format(obs_cube.attributes['dataset'])
netcdf_path = get_diagnostic_filename(filename, cfg)
cube = iris.cube.Cube(ecs_pdf,
var_name='pdf',
long_name='Probability density function',
units='K-1')
cube.add_aux_coord(
iris.coords.AuxCoord(ecs_lin, **ih.convert_to_iris(ECS_ATTRS)), 0)
io.iris_save(cube, netcdf_path)
project = _get_project(cfg)
provenance_record = get_provenance_record(
"The PDF for ECS. The orange histograms show the prior distributions "
"that arise from equal weighting of the {} models in 0.5 K bins.".
format(project), ['mean'], ['other'],
_get_ancestor_files(cfg, obs_cube.attributes['dataset']))
# Plot
if cfg['write_plots']:
AXES.plot(ecs_lin,
ecs_pdf,
color='black',
linewidth=2.0,
label='Emergent constraint')
AXES.hist(ecs_cube.data,
bins=6,
range=(2.0, 5.0),
density=True,
color='orange',
label='{} models'.format(project))
# Plot appearance
AXES.set_title('PDF of emergent constraint')
AXES.set_xlabel('ECS / K')
AXES.set_ylabel('Probability density')
legend = AXES.legend(loc='upper left')
# Save plot
provenance_record['plot_file'] = _save_fig(cfg, filename, legend)
# Write provenance
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(netcdf_path, provenance_record)
def plot_emergent_relationship(cfg, psi_cube, ecs_cube, lambda_cube, obs_cube):
"""Plot emergent relationship."""
filename = 'emergent_relationship_{}'.format(
obs_cube.attributes['dataset'])
cube = ecs_cube.copy()
cube.add_aux_coord(
iris.coords.AuxCoord(psi_cube.data, **ih.convert_to_iris(PSI_ATTRS)),
0)
netcdf_path = get_diagnostic_filename(filename, cfg)
io.iris_save(cube, netcdf_path)
provenance_record = get_provenance_record(
"Emergent relationship between ECS and the psi metric. The black dot-"
"dashed line shows the best-fit linear regression across the model "
"ensemble, with the prediction error for the fit given by the black "
"dashed lines. The vertical blue lines show the observational "
"constraint from the {} observations: the mean (dot-dashed line) and "
"the mean plus and minus one standard deviation (dashed lines).".
format(obs_cube.attributes['dataset']), ['mean', 'corr', 'var'],
['scatter'], _get_ancestor_files(cfg, obs_cube.attributes['dataset']))
# Plot
if cfg['write_plots']:
obs_mean = np.mean(obs_cube.data)
obs_std = np.std(obs_cube.data)
# Calculate regression line
lines = ec.regression_surface(psi_cube.data, ecs_cube.data,
n_points=1000)
logger.info("Found emergent relationship with slope %.2f (R2 = %.2f)",
lines['coef'], lines['R2'])
# Plot points
for model in psi_cube.coord('dataset').points:
_plot_model_point(model, psi_cube, ecs_cube, lambda_cube)
# Plot lines
AXES.set_xlim(auto=False)
AXES.set_ylim(auto=False)
AXES.plot(lines['x'],
lines['y'],
color='black',
linestyle='dashdot',
label='Linear regression')
AXES.plot(lines['x'],
lines['y_minus_err'],
color='black',
linestyle='dashed')
AXES.plot(lines['x'],
lines['y_plus_err'],
color='black',
linestyle='dashed')
AXES.axvline(obs_mean,
color='blue',
linestyle='dashdot',
label='Observational constraint')
AXES.axvline(obs_mean - obs_std, color='blue', linestyle='dashed')
AXES.axvline(obs_mean + obs_std, color='blue', linestyle='dashed')
# Plot appearance
AXES.set_title('Emergent relationship fit')
AXES.set_xlabel(r'$\Psi$ / K')
AXES.set_ylabel('ECS / K')
legend = AXES.legend(loc='upper left')
# Save plot
provenance_record['plot_file'] = _save_fig(cfg, filename, legend)
# Write provenance
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(netcdf_path, provenance_record)