cost_eco_model_linker.runner

Attributes

SEMVER_RE

Functions

evaluate(→ list[str])

Evaluate costs of intervention scenarios.

parallel_evaluate(rme_files_path, nsims, ncores, ...)

evaluate_production_cost(→ tuple[float, float])

Wrapper that converts factor kwargs into the format expected by

evaluate_deployment_cost(→ tuple[float, float])

Wrapper that converts factor kwargs into the format expected by

run_cost_model(→ pandas.DataFrame)

Evaluate a cost model over a set of parameter combinations.

run_parameter_sweep(prod_model, deploy_model, ...[, ...])

Run a parameter sweep over a range of values for any single parameter.

Module Contents

cost_eco_model_linker.runner.SEMVER_RE
cost_eco_model_linker.runner.evaluate(rme_files_path: str, nsims: int, deploy_model_fn: str, prod_model_fn: str, results_dir: str, metrics: list = None, uncertainty_dict: dict = None) → list[str]

Evaluate costs of intervention scenarios.

Parameters:

  • rme_files_path (str) – Path to ReefMod Engine results.

  • nsims (int) – Number of simulations to evaluate.

  • deploy_model_fn (str) – Path to deployment spreadsheet model, including filename but excluding file extension.

  • prod_model_fn (str) – Path to production spreadsheet model, including filename but excluding file extension.

  • results_dir (str) – Path to directory for storing results.

  • metrics (list, optional) – List of metrics to calculate. Default is None.

  • uncertainty_dict (dict, optional) – Dictionary specifying uncertainty parameters. Default is None.

Returns:

Paths to result files.

Return type:

list[str]

cost_eco_model_linker.runner.parallel_evaluate(rme_files_path: str, nsims: int, ncores: int, deploy_model_fn: str, prod_model_fn: str, results_dir: str, metrics: list = None, uncertainty_dict: dict = None)
cost_eco_model_linker.runner.evaluate_production_cost(workbook_path: str, **factors) → tuple[float, float]

Wrapper that converts factor kwargs into the format expected by calculate_production_cost and returns operational and setup costs. Only the factors to be overridden need to be provided; all others default to their current values in the workbook.

Parameters:

  • workbook_path (str) – Absolute path to the Excel workbook including extension (.xlsx).

  • **factors – Factor name-value pairs keyed by factor_name from the config CSV. Only factors to be overridden need to be supplied.

Returns:

  • op_cost (float) – Operational cost.

  • setup_cost (float) – Setup cost.

cost_eco_model_linker.runner.evaluate_deployment_cost(workbook_path: str, **factors) → tuple[float, float]

Wrapper that converts factor kwargs into the format expected by calculate_deployment_cost and returns operational and setup costs. Only the factors to be overridden need to be provided; all others default to their current values in the workbook.

Parameters:

  • workbook_path (str) – Absolute path to the Excel workbook.

  • **factors – Factor name-value pairs keyed by factor_name from the config CSV. Only factors to be overridden need to be supplied. due to the leading digit making it an invalid Python identifier.

Returns:

  • op_cost (float) – Operational cost.

  • setup_cost (float) – Setup cost.

cost_eco_model_linker.runner.run_cost_model(workbook_path: str, params_df: pandas.DataFrame, *, model_type: str | None = None, nprocs: int | None = None) → pandas.DataFrame

Evaluate a cost model over a set of parameter combinations.

The model type (production or deployment) and config version are inferred from the workbook filename — e.g. '3.9.1 CA Production Model.xlsx'. Pass model_type explicitly only when the filename does not follow the standard naming convention.

When nprocs is greater than 1, params_df is split into chunks and evaluated in parallel — each worker opens its own temporary workbook copy. Otherwise the whole DataFrame is evaluated serially in a single workbook session.

Columns not present in params_df default to the values currently in the workbook.

Parameters:

  • workbook_path (str) – Absolute path to the Excel workbook including extension (.xlsx).

  • params_df (pd.DataFrame) – Each row is one model evaluation. Column names must be factor names from the relevant config CSV. Only factors to be overridden from workbook defaults need to be included.

  • model_type (str, optional) – "production" or "deployment". Inferred from the filename if not provided.

  • nprocs (int, optional) – Number of parallel worker processes. Values <= 1 run serially. Defaults to serial (None).

Returns:

Input DataFrame with three appended columns: capex, opex, and total_cost.

Return type:

pd.DataFrame

cost_eco_model_linker.runner.run_parameter_sweep(prod_model, deploy_model, sweep_param, search_range, prod_params=None, dep_params=None)

Run a parameter sweep over a range of values for any single parameter.

Parameters:

  • prod_model (model) – Production cost model

  • deploy_model (model) – Deployment cost model

  • sweep_param (str) – Name of the parameter to sweep over

  • search_range (iterable) – Range of values to sweep over for sweep_param

  • prod_params (dict, optional) – Fixed keyword arguments passed to evaluate_production_cost

  • dep_params (dict, optional) – Fixed keyword arguments passed to evaluate_deployment_cost

Returns:

  • search_range (iterable) – The input search range

  • prod_capex (np.ndarray)

  • prod_opex (np.ndarray)

  • dep_capex (np.ndarray)

  • dep_opex (np.ndarray)

  • totals (np.ndarray)