cost_eco_model_linker.sampling

Attributes

THIS_DIR

DEFAULT_PROD_VER

DEFAULT_DEPLOY_VER

Functions

evaluate_spreadsheet(→ tuple[float, float])

Set input cells, trigger recalculation, and read back capex and opex.

calculate_deployment_cost(wb, model_spec, factors)

Calculates set up and operational costs in the deployment cost model (wb), given a set of parameters to sample.

calculate_production_cost(wb, factor_spec, factors)

Calculates set up and operational costs in the production cost model (wb), given a set of parameters to sample.

load_config()

Load configuration file for model sampling

load_internal_config(fp)

Load internal config for model sampling

problem_spec(cost_type)

Create a problem specification for sampling cost models using SALib.

convert_factor_types(factors_df, is_cat)

SALib samples floats, so convert categorical variables to integers by taking the ceiling.

apply_discrete_mapping(factors_df, model_spec)

Map sampled integer indices back to their actual discrete values.

collect_production_costs(wb, cost_factors, factor_spec)

Run the production cost model.

collect_deployment_costs(wb, cost_factors, factor_spec)

Run the deployment cost model.

run_deployment_model(cost_model, N)

Generate Sobol' samples for the deployment model and run

run_production_model(cost_model, N[, nprocs])

Generate Sobol' samples for the production model and run

extract_sa_results(sp[, fig_path])

Module Contents

cost_eco_model_linker.sampling.THIS_DIR
cost_eco_model_linker.sampling.DEFAULT_PROD_VER = '3.9.1'
cost_eco_model_linker.sampling.DEFAULT_DEPLOY_VER = '3.9.0'
cost_eco_model_linker.sampling.evaluate_spreadsheet(wb, model_spec, params) → tuple[float, float]

Set input cells, trigger recalculation, and read back capex and opex.

This is the inner function for both cost models. All model-specific parameter transformations (yield adjustment, reef name resolution, vessel type switching) must be applied to params before calling this function.

Parameters:

  • wb (Workbook) – Open Excel workbook.

  • model_spec (DataFrame) – Factor specification with columns: factor_names, sheet, cell_pos.

  • params (Series) – Fully-resolved parameter values indexed by factor_names.

Returns:

  • capex (float)

  • opex (float)

cost_eco_model_linker.sampling.calculate_deployment_cost(wb, model_spec, factors)

Calculates set up and operational costs in the deployment cost model (wb), given a set of parameters to sample.

Parameters:

  • wb (Workbook) – The cost model as an excel workbook

  • model_spec (DataFrame) – The cost model specification, detailing where cells are in the spreadsheet

  • factors (DataFrameRow) – Factor values to run cost model with

Returns:

  • capex (float) – Setup cost (CAPEX)

  • opex (float) – Operational cost (OPEX)

cost_eco_model_linker.sampling.calculate_production_cost(wb, factor_spec, factors)

Calculates set up and operational costs in the production cost model (wb), given a set of parameters to sample.

Parameters:

  • wb (Workbook) – The cost model as an excel workbook

  • factor_spec (DataFrame) – Factor specification, as loaded from the config.csv

  • factors (DataFrameRow) – Factor values to run model with

Returns:

  • capex (float) – Setup cost (CAPEX)

  • opex (float) – Operational cost (OPEX)

cost_eco_model_linker.sampling.load_config()

Load configuration file for model sampling

Parameters:

config_filepath (str) – String specifying filepath of config file, default is the default package config file

cost_eco_model_linker.sampling.load_internal_config(fp)

Load internal config for model sampling

Parameters:

fp (str) – Filename of config file within the package structure

cost_eco_model_linker.sampling.problem_spec(cost_type)

Create a problem specification for sampling cost models using SALib.

Parameters:

  • cost_type (str) – String specifying cost model type, “production_params” or “deployment_params”

  • config_filepath (str) – String specifying filepath of config file, default is the default package config file

Returns:

  • sp (dict) – ProblemSpec for sampling with SALib

  • model_spec (dataframe) – factor specification, as loaded from the config.csv

cost_eco_model_linker.sampling.convert_factor_types(factors_df, is_cat)

SALib samples floats, so convert categorical variables to integers by taking the ceiling.

Parameters:

  • factors_df (dataframe) – A dataframe of sampled factors

  • is_cat (list{bool}) – Boolian vector specifian whether each factor is categorical

Returns:

factors_df

Return type:

Updated sampled factor dataframe with categorical factors as integers

cost_eco_model_linker.sampling.apply_discrete_mapping(factors_df, model_spec)

Map sampled integer indices back to their actual discrete values. Only applies to factors with a discrete_values entry in model_spec. Factors marked as is_cat without discrete_values are already handled by the flooring trick in convert_factor_types.

Parameters:

  • factors_df (dataframe) – A dataframe of sampled factors

  • model_spec (dataframe) – Factor specification, as loaded from the config CSV

Returns:

factors_df

Return type:

Updated sampled factor dataframe with discrete mappings applied

Raises:

ValueError – If the min/max of discrete_values does not match range_lower/range_upper

cost_eco_model_linker.sampling.collect_production_costs(wb, cost_factors, factor_spec)

Run the production cost model.

Parameters:

  • wb_file_path (Workbook file path) – A cost model as an excel workbook

  • cost_factors (dataframe) – Dataframe of factors to input in the cost model

  • factor_spec (dataframe) – Factor specification, as loaded from the config.csv

Returns:

cost_factors – Updated sampled factor dataframe with costs added

Return type:

dataframe

cost_eco_model_linker.sampling.collect_deployment_costs(wb, cost_factors, factor_spec)

Run the deployment cost model.

Parameters:

  • wb_file_path (str) – Filepath to a cost model as an excel workbook

  • cost_factors (dataframe) – Dataframe of factors to input in the cost model

  • factor_spec (dataframe) – Factor specification, as loaded from the config.csv

Returns:

cost_factors – Updated sampled factor dataframe with costs added

Return type:

dataframe

cost_eco_model_linker.sampling.run_deployment_model(cost_model: str, N: int)

Generate Sobol’ samples for the deployment model and run

Parameters:

  • cost_model (str) – Path to cost (spreadsheet) model

  • N (int) – Number of desired Sobol’ sample points (resolves to N * (2D + 2) samples) where D is the number of model factors

Return type:

SALib ProblemSpec with cost_model_results added as a field.

cost_eco_model_linker.sampling.run_production_model(cost_model: str, N: int, nprocs=1)

Generate Sobol’ samples for the production model and run

Parameters:

  • cost_model (str) – Path to cost (spreadsheet) model, including extension (.xlsx)

  • N (int) – Number of desired Sobol’ sample points (resolves to N * (2D + 2) samples) where D is the number of model factors

Return type:

SALib ProblemSpec with cost_model_results added as a field.

cost_eco_model_linker.sampling.extract_sa_results(sp: SALib.ProblemSpec, fig_path: str = './figs/')