def model_run_from_dict(config_dict, scenario=None, override_dict=None):
"""
Generate processed ModelRun configuration from a
model configuration dictionary.
Parameters
----------
config_dict : dict or AttrDict
scenario : str, optional
Name of scenario to apply. Can either be a named scenario, or a
comman-separated list of individual overrides to be combined
ad-hoc, e.g. 'my_scenario_name' or 'override1,override2'.
override_dict : dict or AttrDict, optional
"""
if not isinstance(config_dict, AttrDict):
config = AttrDict(config_dict)
else:
config = config_dict
config.config_path = None
config_with_overrides, debug_comments, overrides, scenario = apply_overrides(
config, scenario=scenario, override_dict=override_dict
)
return generate_model_run(
config_with_overrides, debug_comments, overrides, scenario)
def model_run_from_dict(config_dict, scenario=None, override_dict=None):
"""
Generate processed ModelRun configuration from a
model configuration dictionary.
Parameters
----------
config_dict : dict or AttrDict
scenario : str, optional
Name of scenario to apply. Can either be a named scenario, or a
comman-separated list of individual overrides to be combined
ad-hoc, e.g. 'my_scenario_name' or 'override1,override2'.
override_dict : dict or AttrDict, optional
"""
if not isinstance(config_dict, AttrDict):
config = AttrDict(config_dict)
else:
config = config_dict
config.config_path = None
config_with_overrides, debug_comments, overrides, scenario = apply_overrides(
config, scenario=scenario, override_dict=override_dict
)
return generate_model_run(
config_with_overrides, debug_comments, overrides, scenario)
def model_run_from_dict(config_dict,
timeseries_dataframes=None,
scenario=None,
override_dict=None):
"""
Generate processed ModelRun configuration from a
model configuration dictionary.
Parameters
----------
config_dict : dict or AttrDict
timeseries_dataframes : dict, optional
Dictionary of timeseries dataframes. The keys are strings
corresponding to the dataframe names given in the yaml files and
the values are dataframes with time series data.
scenario : str, optional
Name of scenario to apply. Can either be a named scenario, or a
comma-separated list of individual overrides to be combined
ad-hoc, e.g. 'my_scenario_name' or 'override1,override2'.
override_dict : dict or AttrDict, optional
"""
if not isinstance(config_dict, AttrDict):
config = AttrDict(config_dict)
else:
config = config_dict
config.config_path = None
config_with_overrides, debug_comments, overrides, scenario = apply_overrides(
config, scenario=scenario, override_dict=override_dict)
subsets = AttrDict.from_yaml(
os.path.join(os.path.dirname(calliope.__file__), "config",
"subsets.yaml"))
return generate_model_run(
config_with_overrides,
timeseries_dataframes,
debug_comments,
overrides,
scenario,
subsets,
)
def model_run_from_dict(config_dict, override_dict=None):
"""
Generate processed ModelRun configuration from a
model configuration dictionary.
Parameters
----------
config_dict : dict or AttrDict
override_dict : dict or AttrDict, optional
"""
if not isinstance(config_dict, AttrDict):
config = AttrDict(config_dict)
else:
config = config_dict
config.config_path = None
config_with_overrides, debug_comments = apply_overrides(
config, override_dict=override_dict)
return generate_model_run(config_with_overrides, debug_comments)
