explorator module

Define Explorator, a module to explore the design space.

In order to be consistent with the ABC OptimisationAlgorithm, it also returns the solution with the lowest residue value – hence it is also a “brute-force” optimisation algorithm.

Todo

Make this class more robust. In particular: save all objectives (not just the norm), handle export when there is more than two variables, also save complementary data (e.g.: always save phi_s even it is not in the constraints nor variables).

Todo

Allow for different number of points according to variable.

class Explorator(*, compensating_elements: Collection[Element], elts: ListOfElements, objectives: Collection[Objective], variables: Collection[Variable], compute_beam_propagation: Callable[[SetOfCavitySettings], SimulationOutput], compute_residuals: Callable[[SimulationOutput], Any], cavity_settings_factory: CavitySettingsFactory, reference_simulation_output: SimulationOutput, constraints: Collection[Constraint] | None = None, compute_constraints: Callable[[SimulationOutput], ndarray] | None = None, optimisation_algorithm_kwargs: dict[str, Any] | None = None, history_kwargs: dict[str, Any] | None = None, **kwargs)

Bases: OptimisationAlgorithm

Method that tries all the possible solutions.

Notes

Very inefficient for optimization. It is however useful to study a specific case.

All the attributes but solution are inherited from the Abstract Base Class OptimisationAlgorithm.

supports_constraints: bool = True
compute_constraints: Callable[[SimulationOutput], ndarray]
optimize() → OptiSol

Set up the optimization and solve the problem.

Returns:

opti_sol – Gives list of solutions, corresponding objective, convergence violation if applicable, etc.

Return type:

OptiSol

_algorithm_parameters() → dict

Create the kwargs for the optimisation.

_generate_combinations(n_points: int = 10, **kwargs) → tuple[ndarray, ndarray]

Generate all the possible combinations of the variables.

_array_of_values_to_mesh(objectives_values: ndarray, n_points: int = 10, **kwargs) → ndarray

Reformat the results for plotting purposes.

_generate_opti_sol(variables_values: ndarray, objectives_values: ndarray, criterion: Literal['minimize norm of objective']) → OptiSol

Create the dictionary holding all relatable information.

_take_best_solution(variable_comb: ndarray, objectives_values: ndarray, criterion: Literal['minimize norm of objective']) → tuple[ndarray | None, ndarray | None]

Take the “best” of the calculated solutions.

Parameters:

  • variable_comb (numpy.ndarray) – All the set of variables (cavity parameters) that were tried.

  • objectives_values (numpy.ndarray) – The values of the objective corresponding to variable_comb.

  • criterion (Literal['minimize norm of objective']) – Name of the criterion that will determine which solution is the “best”. Only one is implemented for now, may add others in the future.

Returns:

  • best_solution (numpy.ndarray | None) – “Best” solution.

  • best_objective (numpy.ndarray | None) – Objective values corresponding to best_solution.

_abc_impl = <_abc._abc_data object at 0x7f36f75b1a40>