Parameter checking & inferring – for developers

There are many parameter-handling functions.

In utils.py:

• get_value_from_parameter: check if parameter is in the configuration dictionary. If not, if inference is turned on (the default), then just use the value of second_choice_parameter. If inference is turned off, raise a ParameterNotProvidedError. Use this function if the backup option is another parameter (rather than a fixed value).

• set_value_of_parameter_if_undefined: check if parameter is in the configuration dictionary. If not, if inference is turned on (the default), then just set the parameter’s value to the backup_option. If inference is turned off, raise a ParameterNotProvidedError.

In e3sm_diags.py:

• check_parameter_defined: check if parameter is in the configuration dictionary, and if not raise a ParameterNotProvidedError.

• check_set_specific_parameter: if any requested e3sm_diags sets require this parameter, make sure it is present. If not, raise a ParameterNotProvidedError.

• check_parameters_for_bash: use check_set_specific_parameter to check the existence of parameters that aren’t used until the bash script.

• check_mvm_only_parameters_for_bash: similar, but these are specifically parameters used for model-vs-model runs. Uses check_parameter_defined in addition to check_set_specific_parameter.

• check_and_define_parameters: make sure all parameters are defined, using utils.py get_value_from_parameter, utils.py set_value_of_parameter_if_undefined, and check_mvm_only_parameters_for_bash.

check_parameters_for_bash can be run immediately for each subtask because it has very few conditions. Other checks are included in check_and_define_parameters later on in the code.