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_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 _write_scalar_data(data, ancestor_files, cfg, description=None):
"""Write scalar data for multiple datasets."""
var_attrs = [
{
'short_name': 'ecs',
'long_name': 'Equilibrium Climate Sensitivity (Gregory method)',
'units': cf_units.Unit('K'),
},
{
'short_name': 'lambda',
'long_name': 'Climate Feedback Parameter',
'units': cf_units.Unit('W m-2 K-1'),
},
]
global_attrs = {'project': list(cfg['input_data'].values())[0]['project']}
if RTMT_DATASETS:
global_attrs['net_toa_radiation'] = RTMT_TEXT.format(RTMT_DATASETS)
for (idx, var_attr) in enumerate(var_attrs):
caption = '{long_name} for multiple climate models'.format(**var_attr)
if description is not None:
filename = '{}_{}'.format(var_attr['short_name'],
description.replace(' ', '_'))
attributes = {'Description': description}
caption += f' for {description}.'
else:
filename = var_attr['short_name']
attributes = {}
caption += '.'
attributes.update(global_attrs)
path = get_diagnostic_filename(filename, cfg)
if not data[idx]:
raise ValueError(f"Cannot write file {path}, no data for variable "
f"'{var_attr['short_name']}' given")
# Scalar data
if NDIMS['rad'] == 1:
io.save_scalar_data({d: data[idx][d].data
for d in data[idx]},
path,
var_attr,
attributes=attributes)
# 1D data
elif NDIMS['rad'] == 2:
io.save_1d_data(data[idx],
path,
COORDS['rad'][0],
var_attr,
attributes=attributes)
# Higher dimensions
else:
logger.info(
"Writing netcdf summary file including ECS and feedback "
"parameters for all datasets is not supported for %iD data "
"yet", NDIMS['rad'])
return
# Provenance
provenance_record = _get_provenance_record(caption)
provenance_record['ancestors'] = ancestor_files
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(path, provenance_record)
def test_save_1d_data(mock_logger, mock_save, var_attrs, attrs):
"""Test saving of 1 dimensional data."""
coord_name = 'inclination'
data = [
np.ma.masked_invalid([1.0, np.nan, -1.0]),
np.arange(2.0) + 100.0,
np.ma.masked_invalid([33.0, 22.0, np.nan, np.nan, -77.0]),
]
coords = [
iris.coords.DimCoord(np.arange(3.0) - 3.0, long_name=coord_name),
iris.coords.DimCoord(np.arange(2.0) + 2.0, long_name=coord_name),
iris.coords.DimCoord(np.array([-7.0, -3.0, -2.71, 3.0, 314.15]),
long_name=coord_name),
]
cubes = OrderedDict([
('model1',
iris.cube.Cube(data[0],
var_name='xy',
units='kg',
attributes={'hi': '!'},
dim_coords_and_dims=[(coords[0], 0)])),
('model2',
iris.cube.Cube(data[1],
var_name='zr',
units='1',
attributes={},
dim_coords_and_dims=[(coords[1], 0)])),
('model3',
iris.cube.Cube(data[2],
var_name='wa',
units='unknown',
attributes={'very': 'long cube'},
dim_coords_and_dims=[(coords[2], 0)])),
])
dataset_dim = iris.coords.AuxCoord(list(cubes.keys()), long_name='dataset')
dim_1 = coords[0].copy([-7.0, -3.0, -2.71, -2.0, -1.0, 2.0, 3.0, 314.15])
output_data = np.ma.masked_invalid(
[[np.nan, 1.0, np.nan, np.nan, -1.0, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, 100.0, 101.0, np.nan],
[33.0, 22.0, np.nan, np.nan, np.nan, np.nan, np.nan, -77.0]])
output_dims = [(dataset_dim, 0), (dim_1, 1)]
# Without cubes
with pytest.raises(ValueError):
io.save_1d_data({}, PATH, coord_name, var_attrs, attrs)
mock_logger.error.assert_not_called()
assert not mock_save.called
mock_logger.reset_mock()
mock_save.reset_mock()
# With cubes
if 'units' not in var_attrs:
with pytest.raises(ValueError):
io.save_1d_data(cubes, PATH, coord_name, var_attrs, attrs)
mock_logger.error.assert_called_once()
assert not mock_save.called
return
io.save_1d_data(cubes, PATH, coord_name, var_attrs, attrs)
iris_var_attrs = deepcopy(var_attrs)
iris_var_attrs['var_name'] = iris_var_attrs.pop('short_name')
new_cube = iris.cube.Cube(output_data,
aux_coords_and_dims=output_dims,
attributes=attrs,
**iris_var_attrs)
mock_logger.error.assert_not_called()
assert mock_save.call_args_list == [mock.call(new_cube, PATH)]
