def _plot_model_point(model, psi_cube, ecs_cube, lambda_cube):
"""Plot a single model point for emergent relationship."""
col = _get_model_color(model, lambda_cube)
style = plot.get_dataset_style(model, 'cox18nature')
AXES.plot(psi_cube.extract(iris.Constraint(dataset=model)).data,
ecs_cube.extract(iris.Constraint(dataset=model)).data,
linestyle='none',
marker=style['mark'],
markeredgecolor=col,
markerfacecolor=col,
markersize=style['size'])
def plot_temperature_anomaly(cfg, tas_cubes, lambda_cube, obs_name):
"""Plot temperature anomaly versus time."""
for cube in tas_cubes.values():
cube.data -= np.mean(
cube.extract(
iris.Constraint(year=lambda cell: 1961 <= cell <= 1990)).data)
# Save netcdf file and provenance
filename = 'temperature_anomaly_{}'.format(obs_name)
netcdf_path = get_diagnostic_filename(filename, cfg)
io.save_1d_data(tas_cubes, netcdf_path, 'year', TASA_ATTRS)
project = _get_project(cfg)
provenance_record = get_provenance_record(
"Simulated change in global temperature from {} models (coloured "
"lines), compared to the global temperature anomaly from the {} "
"dataset (black dots). The anomalies are relative to a baseline "
"period of 1961-1990.".format(project, obs_name), ['anomaly'],
['times'], _get_ancestor_files(cfg, obs_name))
# Plot
if cfg['write_plots']:
models = lambda_cube.coord('dataset').points
# Plot lines
for model in models:
cube = tas_cubes[model]
AXES.plot(cube.coord('year').points,
cube.data,
color=_get_model_color(model, lambda_cube))
obs_style = plot.get_dataset_style('OBS', 'cox18nature')
obs_cube = tas_cubes[obs_name]
AXES.plot(obs_cube.coord('year').points,
obs_cube.data,
linestyle='none',
marker='o',
markeredgecolor=obs_style['color'],
markerfacecolor=obs_style['color'])
# Plot appearance
AXES.set_title('Simulation of global warming record')
AXES.set_xlabel('Year')
AXES.set_ylabel('Temperature anomaly / K')
legend = _get_line_plot_legend()
# 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_data(cfg, cube):
"""Create scatterplot for cube."""
if not cfg['write_plots']:
return None
logger.debug("Plotting scatterplot for cube %s",
cube.summary(shorten=True))
(_, axes) = plt.subplots()
project = cube.attributes.get('project')
# Plot
for (idx, dataset_name) in enumerate(cube.coord('dataset').points):
style = plot.get_dataset_style(dataset_name, cfg.get('dataset_style'))
y_data = cube.extract(iris.Constraint(dataset=dataset_name)).data
axes.plot(idx + 1,
y_data,
marker=style['mark'],
linestyle='none',
markeredgecolor=style['color'],
markerfacecolor=style['facecolor'],
label=dataset_name)
# Plot appearance
title = cube.long_name
if project is not None:
title += f' for {project}'
axes.set_title(title)
axes.tick_params(axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
axes.set_ylabel(f"{cube.var_name.upper()} / {cube.units}")
axes.set_ylim(cfg.get('y_range'))
legend = axes.legend(loc='center left',
bbox_to_anchor=[1.05, 0.5],
borderaxespad=0.0,
ncol=2)
# Save plot
plot_path = get_plot_filename(cube.var_name, cfg)
plt.savefig(plot_path,
orientation='landscape',
bbox_inches='tight',
additional_artists=[legend])
logger.info("Wrote %s", plot_path)
plt.close()
return plot_path
def plot_psi(cfg, psi_cubes, lambda_cube, obs_name):
"""Plot temperature variability metric psi versus time."""
filename = 'temperature_variability_metric_{}'.format(obs_name)
netcdf_path = get_diagnostic_filename(filename, cfg)
io.save_1d_data(psi_cubes, netcdf_path, 'year', PSI_ATTRS)
project = _get_project(cfg)
provenance_record = get_provenance_record(
"Psi metric of variability versus time, from the {0} models "
"(coloured lines), and the {1} observational data (black circles). "
"The psi values are calculated for windows of width {2} yr, after "
"linear de-trending in each window. These {2}-yr windows are shown "
"for different end times.".format(project, obs_name,
cfg.get('window_length', 55)),
['corr', 'var'], ['times'], _get_ancestor_files(cfg, obs_name))
# Plot
if cfg['write_plots']:
models = lambda_cube.coord('dataset').points
# Plot lines
for model in models:
cube = psi_cubes[model]
AXES.plot(cube.coord('year').points,
cube.data,
color=_get_model_color(model, lambda_cube))
obs_style = plot.get_dataset_style('OBS', 'cox18nature')
obs_cube = psi_cubes[obs_name]
AXES.plot(obs_cube.coord('year').points,
obs_cube.data,
linestyle='none',
marker='o',
markeredgecolor=obs_style['color'],
markerfacecolor=obs_style['color'])
# Plot appearance
AXES.set_title('Metric of variability versus time')
AXES.set_xlabel('Year')
AXES.set_ylabel(r'$\Psi$ / K')
legend = _get_line_plot_legend()
# 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_line_plot_legend():
"""Add legend for line plots."""
color_obs = plot.get_dataset_style('OBS', 'cox18nature')['color']
handles = [
mlines.Line2D([], [],
color=COLOR_SMALL_LAMBDA,
label=r'$\lambda < 1.0$ Wm$^{-2}$K$^{-1}$'),
mlines.Line2D([], [],
color=COLOR_LARGE_LAMBDA,
label=r'$\lambda > 1.0$ Wm$^{-2}$K$^{-1}$'),
mlines.Line2D([], [],
linestyle='none',
marker='o',
markeredgecolor=color_obs,
markerfacecolor=color_obs,
label='Observations'),
]
return AXES.legend(handles=handles, loc='upper left')
def _get_style(dataset_name, cfg):
"""Get style for individual data points."""
style = plot.get_dataset_style(dataset_name, cfg.get('dataset_style'))
if not cfg.get('marker_file'):
return style
marker_file = os.path.expanduser(cfg['marker_file'])
if not os.path.isabs(marker_file):
marker_file = io.get_ancestor_file(cfg, marker_file)
data_frame = pd.read_csv(marker_file)
marker_column = cfg['marker_column']
for column in ('dataset', marker_column):
if column not in data_frame.columns:
raise ValueError(
f"Marker file '{marker_file}' does not contain necessary "
f"column '{column}'")
marker = data_frame[marker_column][data_frame['dataset'] == dataset_name]
if len(marker) != 1:
raise ValueError(
f"Expected exactly one entry for marker of '{dataset_name}' in "
f"file '{marker_file}', got {len(marker):d}")
style['mark'] = marker.values[0]
return style
def plot_data(cfg, ecs_cube, tcr_cube):
"""Plot data."""
if not cfg['write_plots']:
return None
logger.debug("Plotting Fig. 9.42b of IPCC AR5")
(_, axes) = plt.subplots()
project = ecs_cube.attributes['project']
# Apply logarithms if desired
if cfg.get('log_x'):
ecs_cube.data = np.ma.log(ecs_cube.data)
if cfg.get('log_y'):
tcr_cube.data = np.ma.log(tcr_cube.data)
# Plot scatterplot
for dataset_name in ecs_cube.coord('dataset').points:
style = plot.get_dataset_style(dataset_name, cfg.get('dataset_style'))
ecs = ecs_cube.extract(iris.Constraint(dataset=dataset_name)).data
tcr = tcr_cube.extract(iris.Constraint(dataset=dataset_name)).data
# Plot single point
axes.plot(ecs,
tcr,
marker=style['mark'],
linestyle='none',
markeredgecolor=style['color'],
markerfacecolor=style['facecolor'],
label=dataset_name)
# Plot regression line and MMM
(reg_line, mmm, r_value) = _get_reg_line(ecs_cube, tcr_cube)
axes.plot(reg_line[0], reg_line[1], 'k-')
axes.plot(mmm[0], mmm[1], 'ro', label=f'{project} mean')
# Plot appearance
axes.set_title(f"TCR vs. ECS for {project} models")
if cfg.get('log_x'):
axes.set_xlim([0.8, 2.0])
axes.set_xlabel('log(ECS / K)')
else:
axes.set_xlim([1.5, 6.0])
axes.set_xlabel('ECS / K')
if cfg.get('log_y'):
axes.set_ylim([0.3, 1.2])
axes.set_ylabel('log(TCR / K)')
else:
axes.set_ylim([0.5, 3.5])
axes.set_ylabel('TCR / K')
legend = axes.legend(loc='center left',
bbox_to_anchor=[1.05, 0.5],
borderaxespad=0.0,
ncol=2)
axes.text(0.05, 0.9, f'$R^2$ = {r_value**2:.2f}', transform=axes.transAxes)
# Save plot
plot_path = get_plot_filename('ch09_fig09_42b', cfg)
plt.savefig(plot_path,
dpi=300,
orientation='landscape',
bbox_inches='tight',
additional_artists=[legend])
logger.info("Wrote %s", plot_path)
plt.close()
return plot_path
def plot_deangelis_fig3b4(cfg, data_model, reg_prw_obs):
"""Plot DeAngelis Fig. 3b and prepare 4."""
# Fig 4
# model_scheme_name = dict([(1, ['Correlated-k-distribution, N >= 20',
# 'dodgerblue']),
# (2, ['Correlated-k-distribution, 10 < N < 20',
# 'limegreen']),
# (3, ['Pseudo-k-distribution, N >= 20',
# 'gold']),
# (4, ['Pseudo-k-distribution, 10 < N < 20',
# 'darkorange']),
# (5, ['7-band parameterization (N = 7)',
# 'crimson']),
# (6, ['Pade approximants, higher res.',
# 'slategrey']),
# (7, ['Pade approximants, lower res.',
# 'silver']),
# (8, ['unknown',
# 'xkcd:pale purple'])])
# Plot data
fig, axx = plt.subplots(figsize=(8, 8))
mdrsnstdts = np.zeros((len(data_model.keys()), 3))
for iii, modelkey in enumerate(data_model.keys()):
mdrsnstdts[iii, 0] = (list(data_model[modelkey]))[0]
mdrsnstdts[iii, 1] = (list(data_model[modelkey]))[2]
mdrsnstdts[iii, 2] = (list(data_model[modelkey]))[3]
axx.fill([
mdrsnstdts[iii, 1] - 2.0 * mdrsnstdts[iii, 2],
mdrsnstdts[iii, 1] + 2.0 * mdrsnstdts[iii, 2],
mdrsnstdts[iii, 1] + 2.0 * mdrsnstdts[iii, 2],
mdrsnstdts[iii, 1] - 2.0 * mdrsnstdts[iii, 2]
], [
mdrsnstdts[iii, 0] - 0.01, mdrsnstdts[iii, 0] - 0.01,
mdrsnstdts[iii, 0] + 0.01, mdrsnstdts[iii, 0] + 0.01
],
color=(0.8, 0.8, 0.8))
style = (select_metadata(cfg['input_data'].values(),
dataset=modelkey))[0]['project']
style = plot.get_dataset_style(modelkey, style_file=style.lower())
axx.plot(mdrsnstdts[iii, 1],
mdrsnstdts[iii, 0],
marker=style['mark'],
color=style['color'],
markerfacecolor=style['facecolor'],
linestyle='none',
markersize=10,
markeredgewidth=2.0,
label=modelkey)
prw = {}
if reg_prw_obs:
prw["min"] = np.zeros(len(reg_prw_obs.keys()))
prw["max"] = np.zeros(len(reg_prw_obs.keys()))
for iii, modelkey in enumerate(reg_prw_obs.keys()):
(prw["min"])[iii] = reg_prw_obs[modelkey].slope - 2.0 * \
reg_prw_obs[modelkey].stderr
(prw["max"])[iii] = reg_prw_obs[modelkey].slope + 2.0 * \
reg_prw_obs[modelkey].stderr
prw["mean"] = (np.min(prw["min"]) + np.max(prw["max"])) / 2.0
prw["diff"] = np.max(prw["max"]) - prw["mean"]
else:
prw["mean"] = 0.0
prw["diff"] = 0.0
axx = set_axx_deangelis3b(axx, prw["mean"], prw["diff"])
# Regression line
reg = stats.linregress(mdrsnstdts[:, 1], mdrsnstdts[:, 0])
axx.plot(np.linspace(0.0, 0.15, 2),
reg.slope * np.linspace(0.0, 0.15, 2) + reg.intercept,
linestyle='solid',
color='r',
label='Fit (r ={:.2f})'.format(reg.rvalue))
axx.legend(ncol=2, loc=2)
fig.tight_layout()
fig.savefig(get_plot_filename('fig3b', cfg), dpi=300)
plt.close()
caption = 'Scatter plot and regression line the between the ratio ' + \
'of the change of ' + \
'netto short wave radiation (rsnst) and the change of the ' + \
'Water Vapor Path (prw) against the ratio of the change of ' + \
'netto short wave radiation for clear skye (rsnstcs) and the ' + \
'the change of surface temperature (tas).' + \
'The width of horizontal ' + \
'shading for models and the vertical dashed lines for ' + \
'observations (Obs.) represent statistical uncertainties of ' + \
'the ratio, as the 95% confidence interval (CI) of the regression ' + \
'slope to the rsnst versus prw curve. For the observations ' + \
'the minimum of the lower bounds of all CIs to the maximum of ' + \
'the upper bounds of all CIs is shown.'
provenance_record = get_provenance_record(
_get_sel_files_var(cfg, ['prw', 'tas', 'rsnst', 'rsnstcs']), caption,
['other'], ['global'])
diagnostic_file = get_diagnostic_filename('fig3b', cfg)
logger.info("Saving analysis results to %s", diagnostic_file)
iris.save(cube_to_save_matrix(
mdrsnstdts, {
'var_name':
'mdrsnstdts',
'long_name':
'Change of Netto' + 'Short Wave ' + 'Radiation ' + 'with Change ' +
'of the ' + 'Water Vapor Path',
'units':
'% kg-1 m2'
}),
target=diagnostic_file)
logger.info("Recording provenance of %s:\n%s", diagnostic_file,
pformat(provenance_record))
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(diagnostic_file, provenance_record)
# Fig 4
plot_deangelis_fig4(cfg, data_model, mdrsnstdts, prw)