bluemira.equilibria.optimisation.problem._minimal_current
=========================================================

.. py:module:: bluemira.equilibria.optimisation.problem._minimal_current


Classes
-------

.. autoapisummary::

   bluemira.equilibria.optimisation.problem._minimal_current.MinimalCurrentCOP


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

.. py:class:: MinimalCurrentCOP(eq: bluemira.equilibria.equilibrium.Equilibrium, max_currents: numpy.typing.ArrayLike | None = None, opt_algorithm: bluemira.optimisation.AlgorithmType = Algorithm.SLSQP, opt_conditions: dict[str, float | int] | None = None, opt_parameters: dict[str, float] | None = None, constraints: list[bluemira.equilibria.optimisation.constraints.UpdateableConstraint] | None = None, *, plot: bool | None = False, reference_eq: bluemira.equilibria.equilibrium.Equilibrium | None = None, diag_ops: bluemira.equilibria.diagnostics.EqDiagnosticOptions | None = None)

   Bases: :py:obj:`bluemira.equilibria.optimisation.problem.base.EqCoilsetOptimisationProblem`

   .. autoapi-inheritance-diagram:: bluemira.equilibria.optimisation.problem._minimal_current.MinimalCurrentCOP
      :parts: 1
      :private-bases:


   Bounded, constrained, minimal current optimisation problem.

   :param eq: Equilibrium object to optimise the currents for
   :param max_currents: Current bounds vector [A]
   :param opt_conditions: Optimiser conditions
   :param opt_algorithm: optimiser algorithm
   :param opt_parameters: Optimiser specific parameters,
                          see https://nlopt.readthedocs.io/en/latest/NLopt_Reference/#algorithm-specific-parameters
                          Otherwise, the parameters can be founded by digging through the source code.
   :param constraints: List of optimisation constraints to apply to the optimisation problem
   :param plot: Whether or not to plot
   :param reference_eq: For plotting only.
                        Equilibrium object to compare to current state of eq during optimisation.
                        Will use initial state if None is chosen.
   :param diag_ops: Diagnostic plotting options for Equilibrium


   .. py:attribute:: plotting_enabled
      :value: False



   .. py:method:: optimise(x0: numpy.typing.NDArray | None = None, *, fixed_coils: bool = True, keep_history: bool = False, check_constraints: bool = False) -> bluemira.equilibria.optimisation.problem.base.CoilsetOptimiserResult

      Run the optimisation problem

      :param fixed_coils: Whether or not to update to coilset response matrices

      :returns: **coilset** -- Optimised CoilSet
      :rtype: CoilSet