def check_input_data(cfg):
"""Check input data."""
if not variables_available(cfg, ['tas']):
raise ValueError(
"This diagnostic needs variable 'tas' if 'read_external_file' is "
"not given")
if not (variables_available(cfg, ['rtnt'])
or variables_available(cfg, ['rtmt'])):
raise ValueError(
"This diagnostic needs the variable 'rtnt' or 'rtmt' if "
"'read_external_file' is not given")
input_data = cfg['input_data'].values()
project_group = group_metadata(input_data, 'project')
projects = list(project_group.keys())
if len(projects) > 1:
raise ValueError(
f"This diagnostic supports only unique 'project' attributes, got "
f"{projects}")
project = projects[0]
if project not in EXP_4XCO2:
raise ValueError(f"Project '{project}' not supported yet")
exp_group = group_metadata(input_data, 'exp')
exps = set(exp_group.keys())
if exps != {'piControl', EXP_4XCO2[project]}:
raise ValueError(
f"This diagnostic needs 'piControl' and '{EXP_4XCO2[project]}' "
f"experiments, got {exps}")
def test_variables_available():
cfg = {
'input_data': {
'file1.nc': {
'short_name': 'ta'
},
}
}
assert shared.variables_available(cfg, ['ta']) is True
assert shared.variables_available(cfg, ['pr']) is False
def main(cfg):
"""Run the diagnostic.
Parameters :
----------
cfg : dict
Configuration dictionary of the recipe.
"""
###########################################################################
# Read recipe data
###########################################################################
# Get Input data
input_data = (cfg['input_data'].values())
required_vars = ('tas', 'rsnstcs', 'rsnstcsnorm', 'prw')
if not variables_available(cfg, required_vars):
raise ValueError("This diagnostic needs {required_vars} variables")
available_exp = list(group_metadata(input_data, 'exp'))
if 'abrupt-4xCO2' not in available_exp:
if 'abrupt4xCO2' not in available_exp:
raise ValueError("The diagnostic needs an experiment with " +
"4 times CO2.")
if 'piControl' not in available_exp:
raise ValueError("The diagnostic needs a pre industrial control " +
"experiment.")
###########################################################################
# Read data
###########################################################################
# Create iris cube for each dataset and save annual means
cubes = reform_data_iris_deangelis3b4(input_data)
meas_tub_rsnstcsnorm = []
meas_tub_prw = []
for ctub in cubes:
if ctub[2] == 'nomodel':
if ctub[1] == 'prw':
meas_tub_prw.append(ctub)
if ctub[1] == 'rsnstcsnorm':
meas_tub_rsnstcsnorm.append(ctub)
if len(meas_tub_rsnstcsnorm) > 1:
raise ValueError("This diagnostic expects one (or no) observational "
"dataset for rsnstcsnorm")
###########################################################################
# Process data
###########################################################################
[grid_pw, reg_prw_obs] = set_grid_pw_reg_obs(cubes, meas_tub_rsnstcsnorm,
meas_tub_prw)
data_model = substract_and_reg_deangelis(cfg, cubes, grid_pw, reg_prw_obs)
plot_deangelis_fig3b4(cfg, data_model, reg_prw_obs)
def main(cfg):
"""Run the diagnostic."""
sns.set(**cfg.get('seaborn_settings', {}))
input_data = cfg['input_data'].values()
project = list(group_metadata(input_data, 'project').keys())
project = [p for p in project if 'obs' not in p.lower()]
if len(project) == 1:
project = project[0]
# Check if tas is available
if not variables_available(cfg, ['tas']):
raise ValueError("This diagnostic needs 'tas' variable")
# Get ECS data
ecs_filepath = io.get_ancestor_file(cfg, 'ecs.nc')
ecs_cube = iris.load_cube(ecs_filepath)
# Create iris cubes for each dataset
hist_cubes = {}
pi_cubes = {}
for data in input_data:
name = data['dataset']
logger.info("Processing %s", name)
cube = iris.load_cube(data['filename'])
# Preprocess cubes
cube.convert_units(cfg.get('tas_units', 'celsius'))
cube = cube.collapsed(['time'], iris.analysis.MEAN)
# Save cubes
if data.get('exp') == 'historical':
hist_cubes[name] = cube
elif data.get('exp') == 'piControl':
pi_cubes[name] = cube
else:
pass
# Plot data
plot_path = plot_data(cfg, hist_cubes, pi_cubes, ecs_cube)
# Write netcdf file
netcdf_path = write_data(cfg, hist_cubes, pi_cubes, ecs_cube)
# Provenance
ancestor_files = [d['filename'] for d in input_data]
ancestor_files.append(ecs_filepath)
provenance_record = get_provenance_record(project, ancestor_files)
if plot_path is not None:
provenance_record.update({
'plot_file': plot_path,
'plot_types': ['scatter'],
})
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(netcdf_path, provenance_record)
def check_input_data(cfg):
"""Check input data."""
if not variables_available(cfg, ['tas']):
raise ValueError(
"This diagnostic needs variable 'tas' if 'read_external_file' is "
"not given")
input_data = cfg['input_data'].values()
project_group = group_metadata(input_data, 'project')
projects = list(project_group.keys())
if len(projects) > 1:
raise ValueError(
f"This diagnostic supports only unique 'project' attributes, got "
f"{projects}")
exp_group = group_metadata(input_data, 'exp')
exps = set(exp_group.keys())
if exps != {'piControl', '1pctCO2'}:
raise ValueError(
f"This diagnostic needs '1pctCO2' and 'piControl' experiment, got "
f"{exps}")
def main(cfg):
"""Run the diagnostic.
Parameters :
----------
cfg : dict
Configuration dictionary of the recipe.
"""
###########################################################################
# Read recipe data
###########################################################################
# Dataset data containers
data = e.Datasets(cfg)
logging.debug("Found datasets in recipe:\n%s", data)
# Variables
var = e.Variables(cfg)
# logging.debug("Found variables in recipe:\n%s", var)
available_vars = list(
group_metadata(cfg['input_data'].values(), 'short_name'))
logging.debug("Found variables in recipe:\n%s", available_vars)
available_exp = list(group_metadata(cfg['input_data'].values(), 'exp'))
# Check for available variables
required_vars = ('tas', 'lvp', 'rlnst', 'rsnst', 'rlnstcs', 'rsnstcs',
'hfss')
if not e.variables_available(cfg, required_vars):
raise ValueError("This diagnostic needs {required_vars} variables")
# Check for experiments
if 'abrupt-4xCO2' not in available_exp:
if 'abrupt4xCO2' not in available_exp:
raise ValueError("The diagnostic needs an experiment with " +
"4 times CO2.")
if 'piControl' not in available_exp:
raise ValueError("The diagnostic needs a pre industrial control " +
"experiment.")
###########################################################################
# Read data
###########################################################################
# Create iris cube for each dataset and save annual means
for dataset_path in data:
cube = iris.load(dataset_path)[0]
cat.add_year(cube, 'time', name='year')
cube = cube.aggregated_by('year', iris.analysis.MEAN)
experiment = data.get_info(n.EXP, dataset_path)
if experiment == PICONTROL:
# DeAngelis use a 21 year running mean on piControl but the
# full extend of 150 years abrupt4xCO2. I could not find out,
# how they tread the edges, currently I just skip the mean for
# the edges. This is not exacly the same as done in the paper,
# small differences remain in extended data Fig 1,
# but closer than other methods I
# tried, e.g. skipping the edges.
# For most data sets it would be also possible to
# extend the piControl for 20 years, but then it would
# not be centered means of piControl for each year of
# abrupt4xCO2 any more.
cube_new = cube.rolling_window('time', iris.analysis.MEAN, 21)
endm10 = len(cube.coord('time').points) - 10
cube.data[10:endm10] = cube_new.data
data.set_data(cube.data, dataset_path)
###########################################################################
# Process data
###########################################################################
data_dict = substract_and_reg_deangelis2(cfg, data, var)
plot_slope_regression(cfg, data_dict)
plot_slope_regression_all(cfg, data_dict, available_vars)
