optimization_history module

Provide functions to study optimization history.

load(folder, flag_constraints=False)

Load the threee optimization history files in folder.

Parameters:

• folder (Path)

• flag_constraints (bool, default: False)

Return type:

tuple[DataFrame, DataFrame, DataFrame]

get_optimization_objective_names(objectives)

Get the columns corresponding to optimization objectives.

Also return columns taken from simulation outputs.

Parameters:

objectives (DataFrame)

Return type:

tuple[list[str], list[str]]

plot_optimization_objectives(objectives, opti_cols, subplots=False, logy=None, **kwargs)

Plot evolution of optimization objectives.

Parameters:

• objectives (DataFrame)

• opti_cols (list[str])

• subplots (bool, default: False)

• logy (bool | Literal['sym'] | None, default: None)

Return type:

Axis | ndarray

_qty_sim_output(column_name)

Get the quantity that is stored in the column column_name.

It is expected that the header of the column is qty @ position; it only works for the quantites taken from SimulationOutput and written in the objectives.csv.

Parameters:

column_name (str)

Return type:

str

_post_treat(df, post_treat=None, make_absolute=False)

Post-treat the SimulationOutput data.

Parameters:

• df (DataFrame)

• post_treat (Literal['relative difference', 'difference'] | None, default: None)

• make_absolute (bool, default: False)

Return type:

tuple[DataFrame, str]

plot_additional_objectives(objectives, simulation_output_cols, subplots=False, logy=None, post_treat=None, **kwargs)

Plot evolution of additional objectives.

Parameters:

• objectives (DataFrame)

• simulation_output_cols (list[str])

• subplots (bool, default: False)

• logy (bool | Literal['sym'] | None, default: None)

• post_treat (Literal['relative difference', 'difference'] | None, default: None)

Return type:

Axis | ndarray | list

main(folder)

Provide an example of complete study.

Parameters:

folder (Path)

Return type:

DataFrame