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 get_ecs_range(cfg, ecs_lin, ecs_pdf):
"""Get constrained ecs range."""
confidence_level = cfg.get('confidence_level', 0.66)
conf_low = (1.0 - confidence_level) / 2.0
conf_high = (1.0 + confidence_level) / 2.0
# Calculate CDF
ecs_cdf = ec.cdf(ecs_lin, ecs_pdf)
# Calculate constrained ECS range
ecs_mean = ecs_lin[np.argmax(ecs_pdf)]
ecs_index_range = np.where((ecs_cdf >= conf_low)
& (ecs_cdf <= conf_high))[0]
ecs_range = ecs_lin[ecs_index_range]
ecs_low = min(ecs_range)
ecs_high = max(ecs_range)
return (ecs_mean, ecs_low, ecs_high)
def get_ecs_range(cfg, psi_cube, ecs_cube, obs_cube):
"""Get constrained ecs range."""
confidence_level = cfg.get('confidence_level', 0.66)
conf_low = (1.0 - confidence_level) / 2.0
conf_high = (1.0 + confidence_level) / 2.0
# Calculate PDF and CDF
(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)
# Calculate constrained ECS range
ecs_mean = ecs_lin[np.argmax(ecs_pdf)]
ecs_index_range = np.where((ecs_cdf >= conf_low)
& (ecs_cdf <= conf_high))[0]
ecs_range = ecs_lin[ecs_index_range]
ecs_low = min(ecs_range)
ecs_high = max(ecs_range)
return (ecs_mean, ecs_low, ecs_high)