bluemira.optimisation._tools
============================

.. py:module:: bluemira.optimisation._tools

.. autoapi-nested-parse::

   Collection of utility functions for the module.



Attributes
----------

.. autoapisummary::

   bluemira.optimisation._tools._FloatOrArray
   bluemira.optimisation._tools.NO_CODE


Functions
---------

.. autoapisummary::

   bluemira.optimisation._tools.approx_derivative
   bluemira.optimisation._tools._log_and_raise
   bluemira.optimisation._tools._process_slsqp
   bluemira.optimisation._tools._process_cobyla
   bluemira.optimisation._tools._process_cobyqa
   bluemira.optimisation._tools.process_scipy_result
   bluemira.optimisation._tools._check_bounds
   bluemira.optimisation._tools._initial_guess_from_bounds


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

.. py:data:: _FloatOrArray

.. py:data:: NO_CODE
   :value: 'Unknown termination code'


.. py:function:: approx_derivative(func: collections.abc.Callable[[numpy.ndarray], _FloatOrArray], x0: numpy.ndarray, method: str = '3-point', rel_step: _FloatOrArray | None = None, f0: _FloatOrArray | None = None, bounds: collections.abc.Iterable[_FloatOrArray] | None = (-np.inf, np.inf), args: tuple[Any, Ellipsis] | None = ()) -> numpy.ndarray

   Approximate the gradient of a function about a point.

   :param func: Function for which to calculate the gradient.
   :param x0: Point about which to calculate the gradient.
   :param method: Finite difference method to use.
   :param rel_step: Relative step size to use.
   :param f0: Result of func(x0). If None, this is recomputed.
   :param bounds: Lower and upper bounds on individual variables.
   :param args: Additional positional arguments to ``func``.

   :returns: approximate gradient of the function about a point.


.. py:function:: _log_and_raise(alg: str, msg: str) -> NoReturn

.. py:function:: _process_slsqp(res: scipy.optimize._optimize.OptimizeResult) -> numpy.ndarray

.. py:function:: _process_cobyla(res: scipy.optimize._optimize.OptimizeResult) -> numpy.ndarray

.. py:function:: _process_cobyqa(res: scipy.optimize._optimize.OptimizeResult) -> numpy.ndarray

.. py:function:: process_scipy_result(res: scipy.optimize._optimize.OptimizeResult, alg: str) -> numpy.ndarray

   Handle a scipy.minimize OptimizeResult object. Process error codes, if any.

   :param res: Scipy optimise result

   :returns: **x** -- The optimal set of parameters (result of the optimisation)
   :rtype: np.array

   :raises OptimisationError: if an error code returned without a usable result.


.. py:function:: _check_bounds(n_dims: int, new_bounds: numpy.ndarray) -> None

   Validate that the bounds have the correct dimensions.

   :raises ValueError: New bounds in not 1D and does not have a size of n_dims


.. py:function:: _initial_guess_from_bounds(lower: numpy.ndarray, upper: numpy.ndarray) -> numpy.ndarray

   Derive an initial guess for the optimiser.

   Takes the center of the bounds for each parameter.

   :returns: Initial guess based on the midpoint of the provided bounds.