def plot_data(*, cfg: dict, datasets: dict, xaxis: str, yaxis: str,
xlabel: str, ylabel: str, caption: str, name: str,
ancestors: list):
"""Plot data."""
figure, _ = plt.subplots(dpi=300)
for label in datasets.dataset:
label = str(label.data)
dataset = datasets.sel(dataset=label)
if 'time' in dataset:
dataset = dataset.dropna(dim='time') # remove nan
figure.autofmt_xdate() # rotate date labels
plt.plot(dataset[xaxis], dataset[yaxis], label=label)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(caption)
plt.legend()
plt.show()
filename_plot = get_plot_filename(name + '_plot', cfg)
figure.savefig(filename_plot, dpi=300, bbox_inches='tight')
plt.close(figure)
# Store provenance
log_provenance(caption, filename_plot, cfg, ancestors)
def visualize_and_save_independence(independence: 'xr.DataArray', cfg: dict,
ancestors: list):
"""Visualize independence."""
variable = independence.variable_group
labels = list(independence.model_ensemble.values)
figure, axes = plt.subplots(figsize=(15, 15),
subplot_kw={'aspect': 'equal'})
chart = sns.heatmap(
independence,
linewidths=1,
cmap="YlGn",
xticklabels=labels,
yticklabels=labels,
cbar_kws={'label': f'Euclidean distance ({independence.units})'},
ax=axes,
)
chart.set_title(f'Distance matrix for {variable}')
filename_plot = get_plot_filename(f'independence_{variable}', cfg)
figure.savefig(filename_plot, dpi=300, bbox_inches='tight')
plt.close(figure)
filename_data = get_diagnostic_filename(f'independence_{variable}',
cfg,
extension='nc')
independence.to_netcdf(filename_data)
caption = f'Euclidean distance matrix for variable {variable}'
log_provenance(caption, filename_plot, cfg, ancestors)
log_provenance(caption, filename_data, cfg, ancestors)
def _plot_boxplot(cfg, data_frame, plot_name):
"""Plot boxplot."""
boxplot_kwargs = {
'color': 'b',
'data': data_frame,
'showfliers': False,
'showmeans': True,
'meanprops': {
'marker': 'x',
'markeredgecolor': 'k',
'markerfacecolor': 'k',
'markersize': 8,
},
'whis': [0, 100],
}
boxplot_kwargs.update(mlr_plot.get_plot_kwargs(cfg, plot_name))
sns.boxplot(**boxplot_kwargs)
sns.swarmplot(data=data_frame, color='k', alpha=0.6)
# Plot appearance
plt.ylim(0.0, plt.ylim()[1])
mlr_plot.process_pyplot_kwargs(cfg, plot_name)
# Save plot
plot_path = get_plot_filename(plot_name, cfg)
plt.savefig(plot_path, **mlr_plot.get_savefig_kwargs(cfg))
logger.info("Wrote %s", plot_path)
plt.close()
return plot_path
def _plot_cube(cube, cfg):
"""Plot the transition cube."""
# Also plot the transition_cube
if not cube.ndim == 2:
raise ValueError("Cube should be two-dimensional")
plt.clf()
cow = plt.axes(projection=crs.PlateCarree())
cow.add_feature(cfeature.LAND)
iris.quickplot.pcolormesh(cube, vmin=-.24, vmax=.24, cmap='bwr')
# Set title/suptitle for plot
if 'plottitle' in cube.attributes:
plt.title(cube.attributes['plottitle'])
if 'suptitle' in cube.attributes:
plt.suptitle(cube.attributes['plotsuptitle'])
# Draw coast lines
plt.gca().coastlines()
# Get right path for saving plots from the cfg dictionary.
if 'parent_mip_era' in cube.attributes:
model_attr_name = 'source_id' if\
cube.attributes['parent_mip_era'] == 'CMIP6'\
else 'model_id'
else: # In this case it must be OBS, and we set it to model_id explicitly
model_attr_name = 'model_id'
basename = cube.attributes[model_attr_name] + '_'\
+ cube.name().replace(' ', '_')
savename_fig = get_plot_filename(basename, cfg)
logger.info("Saving figure as: %s", savename_fig)
plt.savefig(savename_fig)
def plot_bar_deangelis(cfg, data_var_sum, available_exp, available_vars):
"""Plot linear regression used to calculate ECS."""
if not cfg[n.WRITE_PLOTS]:
return
# Plot data
fig, axx = plt.subplots()
set_colors = [
'cornflowerblue', 'orange', 'silver', 'limegreen', 'rosybrown',
'orchid'
]
bar_width = 1.0 / float(len(available_vars))
for iii, iexp in enumerate(available_exp):
axx.bar(np.arange(len(available_vars)) + bar_width * float(iii),
data_var_sum[iexp],
bar_width,
color=set_colors[iii],
label=iexp)
axx.set_xlabel(' ')
axx.set_ylabel(r'Model mean (W m$^{-2}$)')
axx.set_title(' ')
axx.set_xticks(np.arange(len(available_vars)) + bar_width)
axx.set_xticklabels(available_vars)
axx.legend(loc=1)
fig.tight_layout()
fig.savefig(get_plot_filename('bar_all', cfg), dpi=300)
plt.close()
caption = 'Global average multi-model mean comparing different ' + \
'model experiments and flux variables.'
provenance_record = get_provenance_record(
_get_sel_files_var(cfg, available_vars), caption, ['mean'], ['global'])
diagnostic_file = get_diagnostic_filename('bar_all', cfg)
logger.info("Saving analysis results to %s", diagnostic_file)
list_dict = {}
list_dict["data"] = []
list_dict["name"] = []
for iexp in available_exp:
list_dict["data"].append(data_var_sum[iexp])
list_dict["name"].append({
'var_name': iexp + '_all',
'long_name': 'Fluxes for ' + iexp + ' experiment',
'units': 'W m-2'
})
iris.save(cube_to_save_vars(list_dict), 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)
def _create_feedback_plot(tas_cube, cube, dataset_name, cfg, description=None):
"""Plot feedback parameter vs. remaining dimensions."""
var = cube.var_name
logger.debug("Plotting '%s' vs. %s for '%s'", SHORTER_NAMES.get(var, var),
COORDS['rad'], dataset_name)
x_data = _get_data_time_last(tas_cube)
y_data = _get_data_time_last(cube)
coords = [(coord, idx - 1)
for (idx, coord) in enumerate(cube.coords(dim_coords=True))
if coord.name() != 'time']
feedback_cube = iris.cube.Cube(_get_slope(x_data, y_data),
var_name=var,
dim_coords_and_dims=coords,
units='W m-2 K-1')
# Plot
if feedback_cube.ndim == 1:
iplt.plot(feedback_cube)
plt.xlabel(f"{COORDS['rad'][0]} / "
f"{cube.coord(COORDS['rad'][0]).units.origin}")
plt.ylabel(f"{NICE_SYMBOLS.get(var, var)} / "
f"{NICE_UNITS.get(feedback_cube.units.origin, 'unknown')}")
colorbar = None
elif feedback_cube.ndim == 2:
iplt.contourf(feedback_cube, cmap='bwr', levels=_get_levels())
colorbar = plt.colorbar(orientation='horizontal')
colorbar.set_label(
f"{NICE_SYMBOLS.get(var, var)} / "
f"{NICE_UNITS.get(feedback_cube.units.origin, 'unknown')}")
ticks = [-8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0]
colorbar.set_ticks(ticks)
colorbar.set_ticklabels([str(tick) for tick in ticks])
if COORDS['rad'] == ['latitude', 'longitude']:
plt.gca().coastlines()
else:
plt.xlabel(f"{COORDS['rad'][0]} / "
f"{cube.coord(COORDS['rad'][0]).units.origin}")
plt.ylabel(f"{COORDS['rad'][1]} / "
f"{cube.coord(COORDS['rad'][1]).units.origin}")
else:
raise ValueError(f"Cube dimension {feedback_cube.ndim} not supported")
# Appearance
title = f'{SHORTER_NAMES.get(var, var)} for {dataset_name}'
filename = ('{}_vs_{}_{}'.format(VAR_NAMES.get(var, var),
'-'.join(COORDS['rad']), dataset_name))
if description is not None:
title += f' ({description})'
filename += f"_{description.replace(' ', '_')}"
plt.title(title)
plot_path = get_plot_filename(filename, cfg)
plt.savefig(plot_path,
bbox_inches='tight',
orientation='landscape',
additional_artists=[colorbar])
logger.info("Wrote %s", plot_path)
plt.close()
return (plot_path, feedback_cube)
def su(grouped_data, cfg):
"""Su et al. (2014) constraint."""
metric = cfg['metric']
logger.info("Found metric '%s' for Su et al. (2014) constraint", metric)
# Extract cubes
(var_name, reference_datasets) = _get_su_variable(grouped_data)
cube_dict = _get_su_cube_dict(grouped_data, var_name, reference_datasets)
diag_data = {}
ref_cube = cube_dict[reference_datasets]
# Variable attributes
var_attrs = {
'short_name': 'alpha' if metric == 'regression_slope' else 'rho',
'long_name': f"Error in vertically-resolved tropospheric "
f"zonal-average {ref_cube.long_name} between 40°N and "
f"45°S expressed as {metric.replace('_', ' ')} between "
f"model data and observations",
'units': '1',
}
attrs = {
'plot_xlabel': f'Model performance in {ref_cube.long_name} [1]',
'plot_title': 'Su et al. (2014) constraint',
'provenance_authors': ['schlund_manuel'],
'provenance_domains': ['trop', 'midlat'],
'provenance_realms': ['atmos'],
'provenance_references': ['su14jgr'],
'provenance_statistics': ['corr'],
'provenance_themes': ['EC'],
}
# Calculate constraint
for (dataset_name, cube) in cube_dict.items():
logger.info("Processing dataset '%s'", dataset_name)
# Plot cube
if cube.ndim == 2:
iris.quickplot.contourf(cube)
filename = f"su_{dataset_name.replace('|', '_')}"
plot_path = get_plot_filename(filename, cfg)
plt.savefig(plot_path, **cfg['savefig_kwargs'])
logger.info("Wrote %s", plot_path)
plt.close()
# Provenance
netcdf_path = get_diagnostic_filename(filename, cfg)
io.iris_save(cube, netcdf_path)
ancestors = cube.attributes['ancestors'].split('|')
provenance_record = ec.get_provenance_record(
{'su': attrs}, ['su'],
caption=f'{cube.long_name} for {dataset_name}.',
plot_type='zonal', plot_file=plot_path, ancestors=ancestors)
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(netcdf_path, provenance_record)
# Similarity metric
diag_data[dataset_name] = _similarity_metric(cube, ref_cube, metric)
return (diag_data, var_attrs, attrs)
def test_get_plot_filename():
cfg = {
'plot_dir': '/some/path',
'output_file_type': 'png',
}
filename = shared.get_plot_filename('test', cfg)
assert filename == '/some/path/test.png'
def mapplot(dataarray, cfg, title_pattern, filename_part, ancestors,
**colormesh_args):
"""Visualize weighted temperature."""
period = '{start_year}-{end_year}'.format(**read_metadata(cfg)['tas'][0])
if 'tas_reference' in read_metadata(cfg).keys():
meta = read_metadata(cfg)['tas_reference']
period = 'change: {} minus {start_year}-{end_year}'.format(
period, **meta[0])
metric = cfg['model_aggregation']
if isinstance(metric, int):
metric = f'{metric}perc'
proj = ccrs.PlateCarree(central_longitude=0)
figure, axes = plt.subplots(subplot_kw={'projection': proj})
dataarray = set_antimeridian(dataarray, cfg.get('antimeridian', 'pacific'))
dataarray = dataarray.dropna('lon', how='all').dropna('lat', how='all')
dataarray.plot.pcolormesh(
ax=axes,
transform=ccrs.PlateCarree(),
levels=9,
robust=True,
extend='both',
**colormesh_args
# colorbar size often does not fit nicely
# https://stackoverflow.com/questions/18195758/set-matplotlib-colorbar-size-to-match-graph
# cbar_kwargs={'fraction': .021}
)
lons = dataarray.lon.values
lats = dataarray.lat.values
longitude_formatter = LongitudeFormatter()
latitude_formatter = LatitudeFormatter()
default_xticks = np.arange(np.floor(lons.min()), np.ceil(lons.max()), 10)
default_yticks = np.arange(np.floor(lats.min()), np.ceil(lats.max()), 10)
axes.coastlines()
axes.set_xticks(cfg.get('xticks', default_xticks), crs=proj)
axes.set_yticks(cfg.get('yticks', default_yticks), crs=proj)
axes.xaxis.set_ticks_position('both')
axes.yaxis.set_ticks_position('both')
axes.xaxis.set_major_formatter(longitude_formatter)
axes.yaxis.set_major_formatter(latitude_formatter)
axes.set_xlabel('')
axes.set_ylabel('')
title = title_pattern.format(metric=metric, period=period)
axes.set_title(title)
filename_plot = get_plot_filename(filename_part, cfg)
figure.savefig(filename_plot, dpi=300, bbox_inches='tight')
plt.close(figure)
filename_data = get_diagnostic_filename(filename_part, cfg, extension='nc')
dataarray.to_netcdf(filename_data)
log_provenance(title, filename_plot, cfg, ancestors)
log_provenance(title, filename_data, cfg, ancestors)
def make_plot(metadata, scenarios, cfg, provenance):
"""Make figure 3, left graph.
Multimodel values as line, reference value in black square,
steering variables in dark dots.
"""
fig, axes = plt.subplots()
for member in select_metadata(metadata, variable_group='tas_cmip'):
filename = member['filename']
dataset = xr.open_dataset(filename)
if 'MultiModel' not in filename:
axes.plot(dataset.time.dt.year,
dataset.tas.values,
c='grey',
alpha=0.3,
lw=.5,
label='CMIP members')
else:
# Only display stats for the future period:
dataset = dataset.sel(time=slice('2010', None, None))
axes.plot(dataset.time.dt.year,
dataset.tas.values,
color='k',
linewidth=2,
label='CMIP ' + Path(filename).stem.split('_')[0][10:])
for member in select_metadata(metadata, variable_group='tas_target'):
filename = member['filename']
dataset = xr.open_dataset(filename)
if 'MultiModel' not in filename:
axes.plot(dataset.time.dt.year,
dataset.tas.values,
color='blue',
linewidth=1,
label=member['dataset'])
# Add the scenario's with dots at the cmip dt and bars for the periods
for i, scenario in enumerate(scenarios):
axes.scatter(scenario['year'],
scenario['cmip_dt'],
s=50,
zorder=10,
color='r',
label=r"Scenarios' steering $\Delta T_{CMIP}$")
_timeline(axes, i, scenario['period_bounds'])
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles)) # dict removes dupes
axes.legend(by_label.values(), by_label.keys())
axes.set_xlabel('Year')
axes.set_ylabel(r'Global mean $\Delta T$ (K) w.r.t. reference period')
# Save figure
filename = get_plot_filename('global_matching', cfg)
fig.savefig(filename, bbox_inches='tight', dpi=300)
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(filename, provenance)
def plot_time_series_spei(cfg, cube, filename, add_to_filename=''):
"""Plot time series."""
# SPEI vector to plot
spei = cube.data
# Get time from cube
print("cube.coord(time)")
print(cube.coord('time'))
time = cube.coord('time').points
# Adjust (ncdf) time to the format matplotlib expects
add_m_delta = mda.datestr2num('1850-01-01 00:00:00')
time = time + add_m_delta
# Get data set name from cube
try:
dataset_name = cube.metadata.attributes['model_id']
except KeyError:
try:
dataset_name = cube.metadata.attributes['source_id']
except KeyError:
dataset_name = 'Observations'
data_dict = {'data': spei,
'time': time,
'var': cfg['indexname'],
'dataset_name': dataset_name,
'unit': '1',
'filename': filename,
'area': add_to_filename}
fig, axx = plt.subplots(figsize=(16, 4))
axx.plot_date(time, spei, '-', tz=None, xdate=True, ydate=False,
color='r', linewidth=4., linestyle='-', alpha=1.,
marker='x')
axx.axhline(y=-2, color='k')
# Plot labels and title
axx.set_xlabel('Time')
axx.set_ylabel(cfg['indexname'])
axx.set_title('Mean ' + cfg['indexname'] + ' ' +
data_dict['dataset_name'] + ' '
+ data_dict['area'])
# Set limits for y-axis
axx.set_ylim(-4.0, 4.0)
# Often improves the layout
fig.tight_layout()
# Save plot to file
fig.savefig(get_plot_filename(cfg['indexname'] +
'_time_series_' +
data_dict['area'] +
'_' +
data_dict['dataset_name'], cfg), dpi=300)
plt.close()
_provenance_time_series_spei(cfg, data_dict)
def _get_plot_filename(var_meta, cfg):
"""Return an output filename for RWR map plots."""
basename = "_".join([var_meta["project"],
var_meta["dataset"],
var_meta["exp"],
var_meta["ensemble"],
"rwr"])
filename = get_plot_filename(basename, cfg)
return filename
def plot_table(dataframe, ancestors, cfg):
"""Render pandas table as a matplotlib figure."""
fig, axes = plt.subplots()
pd.plotting.table(axes, dataframe.reset_index().round(2))
axes.set_axis_off()
filename = get_plot_filename('table', cfg)
fig.savefig(filename, bbox_inches='tight')
caption = "Bias and change for each variable"
log_provenance(filename, ancestors, caption, cfg)
def main(diag_config):
"""
Diagnostic function to compare the zonal turnover time.
Argument:
--------
diag_config - nested dictionary of metadata
"""
model_data_dict = group_metadata(diag_config['input_data'].values(),
'dataset')
fig_config = _get_fig_config(diag_config)
zonal_tau_mod = {}
for model_name, model_dataset in model_data_dict.items():
zonal_tau_mod[model_name] = {}
ctotal = _load_variable(model_dataset, 'ctotal')
gpp = _load_variable(model_dataset, 'gpp')
zonal_tau_mod[model_name] = _calc_zonal_tau(gpp, ctotal, fig_config)
zonal_tau_obs = _get_obs_data_zonal(diag_config)
obs_var = diag_config.get('obs_variable')[0]
tau_obs = zonal_tau_obs[obs_var]
base_name = '{title}_{source_label}_{grid_label}z'.format(
title=tau_obs.long_name,
source_label=diag_config['obs_info']['source_label'],
grid_label=diag_config['obs_info']['grid_label'])
provenance_record = _get_provenance_record(
"Comparison of latitudinal (zonal) variations of observation-based and"
" modelled ecosystem carbon turnover time. The zonal turnover time is"
" calculated as the ratio of zonal `ctotal` and `gpp`. Reproduces "
" figure 2a and 2b in Carvalhais et al. (2014).", ['mean', 'perc'],
['zonal'], _get_ancestor_files(diag_config, obs_var))
if diag_config['write_netcdf']:
model_cubes = [
c for c in zonal_tau_mod.values() if isinstance(c, iris.cube.Cube)
]
obs_cubes = [
c for c in zonal_tau_obs.values() if isinstance(c, iris.cube.Cube)
]
netcdf_path = get_diagnostic_filename(base_name, diag_config)
save_cubes = iris.cube.CubeList(model_cubes + obs_cubes)
iris.save(save_cubes, netcdf_path)
with ProvenanceLogger(diag_config) as provenance_logger:
provenance_logger.log(netcdf_path, provenance_record)
if diag_config['write_plots']:
plot_path = get_plot_filename(base_name, diag_config)
_plot_zonal_tau(plot_path, zonal_tau_mod, zonal_tau_obs, diag_config)
with ProvenanceLogger(diag_config) as provenance_logger:
provenance_logger.log(plot_path, provenance_record)
def _plot(cfg, cube, dataset_name, tcr):
"""Create scatterplot of temperature anomaly vs. time."""
if not cfg.get('plot', True):
return (None, None)
logger.debug("Plotting temperature anomaly vs. time for '%s'",
dataset_name)
(_, axes) = plt.subplots()
# Plot data
x_data = np.arange(cube.shape[0])
y_data = cube.data
axes.scatter(x_data, y_data, color='b', marker='o')
# Plot lines
line_kwargs = {'color': 'k', 'linewidth': 1.0, 'linestyle': '--'}
axes.axhline(tcr, **line_kwargs)
axes.axvline(START_YEAR_IDX, **line_kwargs)
axes.axvline(END_YEAR_IDX, **line_kwargs)
# Appearance
units_str = (cube.units.symbol
if cube.units.origin is None else cube.units.origin)
axes.set_title(dataset_name)
axes.set_xlabel('Years after experiment start')
axes.set_ylabel(f'Temperature anomaly / {units_str}')
axes.set_ylim([x_data[0] - 1, x_data[-1] + 1])
axes.set_ylim([-1.0, 7.0])
axes.text(0.0, tcr + 0.1, 'TCR = {:.1f} {}'.format(tcr, units_str))
# Save cube
netcdf_path = get_diagnostic_filename(dataset_name, cfg)
io.iris_save(cube, netcdf_path)
# Save plot
plot_path = get_plot_filename(dataset_name, cfg)
plt.savefig(plot_path, **cfg['savefig_kwargs'])
logger.info("Wrote %s", plot_path)
plt.close()
# Provenance
provenance_record = get_provenance_record(
f"Time series of the global mean surface air temperature anomaly "
f"(relative to the linear fit of the pre-industrial control run) of "
f"{dataset_name} for the 1% CO2 increase per year experiment. The "
f"horizontal dashed line indicates the transient climate response "
f"(TCR) defined as the 20 year average temperature anomaly centered "
f"at the time of CO2 doubling (vertical dashed lines).")
provenance_record.update({
'plot_file': plot_path,
'plot_types': ['times'],
})
return (netcdf_path, provenance_record)
def plot_map_rel_biases(cfg, cube_dict):
"""Plot global maps showing relative biases of datasets."""
logger.info("Creating relative bias map plots")
for (key_1, key_2) in itertools.permutations(cube_dict, 2):
logger.debug("Plotting relative bias ('%s' - '%s') / '%s'", key_1,
key_2, key_2)
cube_1 = cube_dict[key_1]
cube_2 = cube_dict[key_2]
attrs_1 = cube_1.attributes
attrs_2 = cube_2.attributes
alias_1 = _get_alias(cfg, key_1)
alias_2 = _get_alias(cfg, key_2)
# Mask cube to avoid division by zero
cube_2 = _mask_cube(cube_2)
# Plot
bias_cube = cube_1.copy()
bias_cube.data = (cube_1.data - cube_2.data) / cube_2.data
plot_kwargs = {
'cbar_label': f"relative change in {attrs_1['tag']} / 1",
'cmap': 'bwr',
}
plot_kwargs.update(get_plot_kwargs(cfg, 'plot_map_rel_biases'))
_get_map_plot_func(cfg)(bias_cube, **plot_kwargs)
# Plot appearance
title = _get_title(cfg,
alias_1,
attrs_1,
alias_2,
attrs_2,
op_type='rel_bias')
plt.title(title)
process_pyplot_kwargs(cfg, 'plot_map_rel_biases')
# Write minimum and maximum
logger.debug("Minimum of '%s': %.2f", title, bias_cube.data.min())
logger.debug("Maximum of '%s': %.2f", title, bias_cube.data.max())
# Save plot
plot_path = get_plot_filename(f'map_rel_bias_{key_1}-{key_2}', cfg)
plt.savefig(plot_path, **get_savefig_kwargs(cfg))
logger.info("Wrote %s", plot_path)
plt.close()
# Provenance
_write_map_provenance(cfg, bias_cube, plot_path, title, attrs_1,
attrs_2)
# Add to global DataFrame
_add_correlation_information(cfg, title, bias_cube)
def main(cfg):
# just load the pre-processed anomlies, and plot them
# first read them in, pop into dictionaries keyed by model name
# group by project first (CMIP5, CMIP6, UKCP)
projects = group_metadata(cfg["input_data"].values(), "project")
results = {}
for p in projects:
results[p] = {}
if p == "UKCP18":
# loop over ensembles
models = group_metadata(projects[p], "ensemble")
else:
# loop over datasets
models = group_metadata(projects[p], "dataset")
for m in models:
if len(models[m]) > 1:
raise ValueError("Too many bits of data")
fname = models[m][0]["filename"]
data = iris.load_cube(fname)
results[p][m] = data.data.item()
# plot and save the results
for p in projects:
# use pandas to create data for a csv file
results_df = pd.DataFrame.from_dict(results[p], orient='index')
# save data as csv
results_df.to_csv(get_diagnostic_filename(f"{p}_global_tas_anom", cfg,
"csv"),
header=False)
# get list of models
models = results[p].keys()
# and corresponding values
vals = [results[p][m] for m in models]
fig, ax = plt.subplots(figsize=(12.8, 9.6))
# plot bar chart
y_pos = np.arange(len(models))
colors = np.empty(len(models, ), dtype=str)
colors[::2] = 'r'
colors[1::2] = 'b'
ax.barh(y_pos, vals, color=colors)
ax.set_yticks(y_pos, labels=models)
plot_fname = get_plot_filename(f'{p}_global_anomaly', cfg)
fig.savefig(plot_fname)
plt.tight_layout()
plt.close(fig)
def plot_data(cfg, all_data, metadata):
"""Create barplot."""
if not cfg['write_plots']:
return None
logger.debug("Plotting barplot")
(_, axes) = plt.subplots(figsize=(8, 4))
# Plot
all_pos = []
x_labels = []
offset = 0.0
for (label, xy_data) in all_data.items():
if cfg.get('sort_ascending'):
sort_idx = np.argsort(xy_data[1])
elif cfg.get('sort_descending'):
sort_idx = np.argsort(xy_data[1])[::-1]
else:
sort_idx = np.arange(len(xy_data[1]))
xy_data = (xy_data[0][sort_idx], xy_data[1][sort_idx])
pos = np.arange(len(xy_data[0])) + offset + 0.5
axes.bar(pos, xy_data[1], align='center', label=label)
all_pos.extend(pos)
x_labels.extend(xy_data[0])
offset += len(pos) + 1.0
# Plot appearance
axes.set_title(metadata['long_name'])
axes.set_xticks(all_pos)
axes.set_xticklabels(x_labels, rotation=45.0, ha='right', size=7.0)
axes.set_ylabel(f"{metadata['var_name']} / {metadata['units']}")
axes.set_ylim(cfg.get('y_range'))
if 'label_attribute' in cfg:
axes.legend(loc='upper right')
if cfg.get('value_labels'):
for rect in axes.patches:
axes.text(rect.get_x() + rect.get_width() / 2.0,
rect.get_height(),
"{:.1f}".format(rect.get_height()),
ha='center',
va='bottom',
size=5.0)
# Save plot
plot_path = get_plot_filename(metadata['var_name'], cfg)
plt.savefig(plot_path,
orientation='landscape',
bbox_inches='tight',
dpi=300)
logger.info("Wrote %s", plot_path)
plt.close()
return plot_path
def _save_fig(cfg, basename, legend=None):
"""Save matplotlib figure."""
path = get_plot_filename(basename, cfg)
if legend is None:
legend = []
else:
legend = [legend]
FIG.savefig(path,
additional_artists=legend,
bbox_inches='tight',
orientation='landscape')
logger.info("Wrote %s", path)
AXES.cla()
return path
def _get_plot_filename(var_meta, cfg, label):
"""Return an output filename for plots."""
basename = "_".join([
var_meta["project"],
var_meta["dataset"],
var_meta["exp"],
var_meta["ensemble"],
label,
str(var_meta["start_year"]),
str(var_meta["end_year"]),
])
filename = get_plot_filename(basename, cfg)
return filename
def plot_data(cfg, hist_cubes, pi_cubes, ecs_cube):
"""Plot data."""
if not cfg['write_plots']:
return None
x_data = []
y_data = []
dataset_names = []
plot_kwargs = []
# Collect data
for dataset in hist_cubes:
ecs = ecs_cube.extract(iris.Constraint(dataset=dataset))
if ecs is None:
raise ValueError(f"No ECS data for '{dataset}' available")
# Historical data
x_data.append(ecs.data)
y_data.append(hist_cubes[dataset].data)
dataset_names.append(dataset)
plot_kwargs.append({
'label': dataset,
'linestyle': 'none',
'markersize': 10,
})
# PiControl data
x_data.append(ecs.data)
y_data.append(pi_cubes[dataset].data)
dataset_names.append(dataset)
plot_kwargs.append({
'label': '_' + dataset,
'linestyle': 'none',
'markersize': 6,
})
# Plot data
path = get_plot_filename('ch09_fig09_42a', cfg)
plot.multi_dataset_scatterplot(
x_data,
y_data,
dataset_names,
path,
plot_kwargs=plot_kwargs,
save_kwargs=cfg.get('save', {}),
axes_functions=cfg.get('axes_functions', {}),
dataset_style_file=cfg.get('dataset_style'),
mpl_style_file=cfg.get('matplotlib_style'),
)
return path
def visualize_and_save_weights(weights: 'xr.DataArray', cfg: dict,
ancestors: list):
"""Visualize weights."""
label = 'Weights'
filename_plot = get_plot_filename('weights', cfg)
barplot(weights, label, filename_plot)
filename_data = get_diagnostic_filename('weights', cfg, extension='nc')
weights.to_netcdf(filename_data)
caption = 'Weights'
log_provenance(caption, filename_plot, cfg, ancestors)
log_provenance(caption, filename_data, cfg, ancestors)
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_data(cfg, all_data, metadata):
"""Create barplot."""
logger.debug("Plotting barplot")
(_, axes) = plt.subplots(**cfg.get('subplots_kwargs', {}))
# Plot
all_pos = []
x_labels = []
offset = 0.0
all_data = _get_ordered_dict(cfg, all_data)
for (label, xy_data) in all_data.items():
xy_data = (xy_data[0], xy_data[1])
pos = np.arange(len(xy_data[0])) + offset + 0.5
bars = axes.bar(pos, xy_data[1], align='center', label=label)
all_pos.extend(pos)
x_labels.extend(xy_data[0])
offset += len(pos) + 1.0
if 'Mean' in xy_data[0]:
mean_idx = np.nonzero(xy_data[0] == 'Mean')[0][0]
bars[mean_idx].set_facecolor(
_adjust_lightness(bars[mean_idx].get_facecolor()[:3]))
# Plot appearance
axes.set_title(metadata['long_name'])
axes.set_xticks(all_pos)
axes.set_xticklabels(x_labels, rotation=45.0, ha='right', size=4.0)
axes.tick_params(axis='x', which='major', pad=-5.0)
axes.set_ylabel(f"{metadata['var_name']} / {metadata['units']}")
axes.set_ylim(cfg.get('y_range'))
if 'label_attribute' in cfg:
axes.legend(loc='upper right')
if cfg.get('value_labels'):
for rect in axes.patches:
axes.text(rect.get_x() + rect.get_width() / 2.0,
rect.get_height() + 0.05,
"{:.2f}".format(rect.get_height()),
rotation=90.0,
ha='center',
va='bottom',
size=5.0)
# Save plot
plot_path = get_plot_filename(metadata['var_name'], cfg)
plt.savefig(plot_path, **cfg['savefig_kwargs'])
logger.info("Wrote %s", plot_path)
plt.close()
return plot_path
def plot_scatter(tidy_df, ancestors, cfg):
"""Plot bias on one axis and change on the other."""
grid = sns.relplot(
data=tidy_df,
x="Bias (RMSD of all gridpoints)",
y="Mean change (Future - Reference)",
hue="dataset",
col="variable",
facet_kws=dict(sharex=False, sharey=False),
kind='scatter',
)
filename = get_plot_filename('bias_vs_change', cfg)
grid.fig.savefig(filename, bbox_inches='tight')
caption = "Bias and change for each variable"
log_provenance(filename, ancestors, caption, cfg)
def make_plots(cfg, scenario_tables):
"""Reproduce figure 5 from the paper."""
# Note that quantile is applied twice! Once to get the pdf's of seasonal
# tas/pr and once to get the multimodel pdf of the quantile changes
metadata = cfg['input_data'].values()
climates = {}
for name, info in cfg['scenarios'].items():
climatology = _get_climatology(cfg, name, table=scenario_tables[name])
climates[name] = climatology
for year in [2050, 2085]:
fig, subplots = plt.subplots(2, 2, figsize=(12, 8))
for row, variable in zip(subplots, ['pr', 'tas']):
cmip, prov = _cmip_envelope(metadata, variable, year)
for axes, season in zip(row, ['DJF', 'JJA']):
percentiles = cmip.percentile.values
xlocs = np.arange(len(percentiles))
# Plot the cmip envelope
seasondata = cmip.sel(season=season)
for high, low in [[0.9, 0.1], [0.75, 0.25]]:
upper = seasondata.quantile(high, dim='multimodel')
lower = seasondata.quantile(low, dim='multimodel')
axes.fill_between(xlocs, upper, lower, color='k', alpha=.3)
axes.set_title(f'{variable} / {season}')
# Plot the recombined scenarios
for name, info in cfg['scenarios'].items():
if year == info['scenario_year']:
climate = climates[name].sel(season=season)[variable]
axes.plot(xlocs, climate, lw=3, label=name)
axes.set_xticks(xlocs)
axes.set_xticklabels([f'P{100*x:02.0f}' for x in percentiles])
subplots[0, 0].set_ylabel('change (%)')
subplots[1, 0].set_ylabel('change (K)')
subplots[1, 1].legend()
filename = get_plot_filename(f'local_validation_{year}', cfg)
fig.suptitle(f'Year: {year}')
fig.savefig(filename, bbox_inches='tight', dpi=300)
LOGGER.info("Envelope figure stored as %s", filename)
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(filename, prov)
def visualize_and_save_performance(performance: 'xr.DataArray', cfg: dict,
ancestors: list):
"""Visualize performance."""
label = 'RMS error'
variable_group = performance.variable_group
filename_plot = get_plot_filename(f'performance_{variable_group}', cfg)
barplot(performance, label, filename_plot)
filename_data = get_diagnostic_filename(f'performance_{variable_group}',
cfg,
extension='nc')
performance.to_netcdf(filename_data)
caption = f'Performance metric {label} for variable group {variable_group}'
log_provenance(caption, filename_plot, cfg, ancestors)
log_provenance(caption, filename_data, cfg, ancestors)
def produce_plots(config, data):
"""Produce all elements of the full plot."""
ref_line_style = {'linestyle': '-', 'linewidth': 2.}
fig, axes = setup_figure()
lines, labels = plot_zonal_mean_errors_ensemble(axes[0, 0],
data['zonal_mean_errors'],
ref_line_style)
plot_equatorial_errors(axes[0, 1], data['equatorial_errors'],
ref_line_style)
plot_zonal_mean_errors_project(axes[1, 0], data['zonal_mean_errors'],
ref_line_style)
ref_ls, ref_labels = plot_equatorials(axes[1, 1], data['equatorial_ref'],
data['equatorials'], ref_line_style)
all_lines = ref_ls + lines
all_labels = ref_labels + labels
legend = draw_legend(fig, all_lines, all_labels)
path = get_plot_filename('fig-9-14', config)
fig.savefig(path, additional_artists=[legend], tight_layout=True)
return path
def save_results(cfg, cube, basename, ancestor_files):
"""Create a provenance record describing the diagnostic data and plot."""
basename = basename + '_' + cube.var_name
provenance = {
'caption': cube.long_name.replace('\n', ' '),
'statistics': ['other'],
'domains': ['global'],
'authors': ['berg_peter'],
'references': ['acknow_project'],
'ancestors': ancestor_files,
}
if cfg['write_plots'] and cfg.get('quickplot'):
plot_file = get_plot_filename(basename, cfg)
quickplot(cube, plot_file, **cfg['quickplot'])
provenance['plot_file'] = plot_file
if cfg['write_netcdf']:
netcdf_file = get_diagnostic_filename(basename, cfg)
iris.save(cube, target=netcdf_file)
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(netcdf_file, provenance)
def plot_map(cfg, cube_dict):
"""Plot global maps showing datasets."""
logger.info("Creating map plots")
for (key, cube) in cube_dict.items():
logger.debug("Plotting '%s'", key)
attrs = cube.attributes
# Plot
plot_kwargs = {
'cbar_label': f"{attrs['tag']} / {cube.units}",
'cmap': 'YlGn',
}
plot_kwargs.update(get_plot_kwargs(cfg, 'plot_map', key=key))
_get_map_plot_func(cfg)(cube, **plot_kwargs)
# Plot appearance
alias = _get_alias(cfg, key)
title = _get_title(cfg, alias, attrs)
plt.title(title)
process_pyplot_kwargs(cfg, 'plot_map')
# Write minimum and maximum
logger.debug("Minimum of '%s': %.2f", title, cube.data.min())
logger.debug("Maximum of '%s': %.2f", title, cube.data.max())
# Save plot
plot_path = get_plot_filename(f'map_{key}', cfg)
plt.savefig(plot_path, **get_savefig_kwargs(cfg))
logger.info("Wrote %s", plot_path)
plt.close()
# Provenance
_write_map_provenance(cfg, cube, plot_path, title, attrs)
# Add to global DataFrame
_add_correlation_information(cfg, title, cube)