cost_eco_model_linker.sampling
==============================

.. py:module:: cost_eco_model_linker.sampling


Attributes
----------

.. autoapisummary::

   cost_eco_model_linker.sampling.THIS_DIR
   cost_eco_model_linker.sampling.DEFAULT_VER


Functions
---------

.. autoapisummary::

   cost_eco_model_linker.sampling.calculate_deployment_cost
   cost_eco_model_linker.sampling.calculate_production_cost
   cost_eco_model_linker.sampling.load_config
   cost_eco_model_linker.sampling.load_internal_config
   cost_eco_model_linker.sampling.problem_spec
   cost_eco_model_linker.sampling.convert_factor_types
   cost_eco_model_linker.sampling.collect_production_costs
   cost_eco_model_linker.sampling.collect_deployment_costs
   cost_eco_model_linker.sampling.run_deployment_model
   cost_eco_model_linker.sampling.run_production_model
   cost_eco_model_linker.sampling.extract_sa_results


Module Contents
---------------

.. py:data:: THIS_DIR

.. py:data:: DEFAULT_VER
   :value: '3.8.0'


.. py:function:: 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.

   :param wb: The cost model as an excel workbook
   :type wb: Workbook
   :param model_spec: The cost model specification, detailing where cells are in the spreadsheet
   :type model_spec: DataFrame
   :param factors: Factor values to run cost model with
   :type factors: DataFrameRow

   :returns: * **op_cost** (*float*) -- Operational cost
             * **setup_cost** (*float*) -- Setup cost


.. py:function:: 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.

   :param wb: The cost model as an excel workbook
   :type wb: Workbook
   :param factor_spec: factor specification, as loaded from the config.csv
   :type factor_spec: dataframe
   :param factors: Row of a pandas dataframe with factors to sample
   :type factors: dataframerow

   :returns: * **op_cost** (*float*) -- Operational cost
             * **setup_cost** (*float*) -- Setup cost


.. py:function:: load_config()

   Load configuration file for model sampling

   :param config_filepath: String specifying filepath of config file, default is the default package config file
   :type config_filepath: str


.. py:function:: load_internal_config(fp)

   Load internal config for model sampling

   :param fp: Filename of config file within the package structure
   :type fp: str


.. py:function:: problem_spec(cost_type)

   Create a problem specification for sampling cost models using SALib.

   :param cost_type: String specifying cost model type, "production_params" or "deployment_params"
   :type cost_type: str
   :param config_filepath: String specifying filepath of config file, default is the default package config file
   :type config_filepath: str

   :returns: * **sp** (*dict*) -- ProblemSpec for sampling with SALib
             * **model_spec** (*dataframe*) -- factor specification, as loaded from the config.csv


.. py:function:: convert_factor_types(factors_df, is_cat)

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

   :param factors_df: A dataframe of sampled factors
   :type factors_df: dataframe
   :param is_cat: Boolian vector specifian whether each factor is categorical
   :type is_cat: list{bool}

   :returns: **factors_df**
   :rtype: Updated sampled factor dataframe with categorical factors as integers


.. py:function:: collect_production_costs(wb, cost_factors, factor_spec)

   Run the production cost model.

   :param wb_file_path: A cost model as an excel workbook
   :type wb_file_path: Workbook file path
   :param cost_factors: Dataframe of factors to input in the cost model
   :type cost_factors: dataframe
   :param factor_spec: Factor specification, as loaded from the config.csv
   :type factor_spec: dataframe

   :returns: **cost_factors** -- Updated sampled factor dataframe with costs added
   :rtype: dataframe


.. py:function:: collect_deployment_costs(wb, cost_factors, factor_spec)

   Run the deployment cost model.

   :param wb_file_path: Filepath to a cost model as an excel workbook
   :type wb_file_path: str
   :param cost_factors: Dataframe of factors to input in the cost model
   :type cost_factors: dataframe
   :param factor_spec: Factor specification, as loaded from the config.csv
   :type factor_spec: dataframe

   :returns: **cost_factors** -- Updated sampled factor dataframe with costs added
   :rtype: dataframe


.. py:function:: run_deployment_model(cost_model: str, N: int)

   Generate Sobol' samples for the deployment model and run

   :param cost_model: Path to cost (spreadsheet) model
   :type cost_model: str
   :param N: Number of desired Sobol' sample points (resolves to `N * (2D + 2)` samples)
             where `D` is the number of model factors
   :type N: int

   :rtype: SALib ProblemSpec with `cost_model_results` added as a field.


.. py:function:: run_production_model(cost_model: str, N: int)

   Generate Sobol' samples for the production model and run

   :param cost_model: Path to cost (spreadsheet) model
   :type cost_model: str
   :param N: Number of desired Sobol' sample points (resolves to `N * (2D + 2)` samples)
             where `D` is the number of model factors
   :type N: int

   :rtype: SALib ProblemSpec with `cost_model_results` added as a field.


.. py:function:: extract_sa_results(sp: SALib.ProblemSpec, fig_path: str = './figs/')