leap_c.parameters.utils
=======================

.. py:module:: leap_c.parameters.utils

.. autoapi-nested-parse::

   Parameter type alias and numpy helpers for stage-varying MPC parameters.



Attributes
----------

.. autoapisummary::

   leap_c.parameters.utils.ParamSplits


Functions
---------

.. autoapisummary::

   leap_c.parameters.utils.broadcast_default_param
   leap_c.parameters.utils.n_segments


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

.. py:function:: broadcast_default_param(default: numpy.ndarray | dict[str, numpy.ndarray], obs: numpy.ndarray | torch.Tensor | None = None) -> numpy.ndarray | dict[str, numpy.ndarray]

   Broadcast a per-stage default parameter to the batch shape implied by ``obs``.

   For a batched ``obs`` of shape ``(B, obs_dim)``, the default is broadcast to
   ``(B, *param_shape)``. Without ``obs`` (or a 1-D ``obs``) the unbatched default
   is returned. Handles both ``np.ndarray`` and ``dict[str, np.ndarray]`` defaults.


.. py:function:: n_segments(splits: ParamSplits, N_horizon: int) -> int

   Return the number of independent segments a ``splits`` specification produces.

   :param splits: The split specification (see :data:`ParamSplits`).
   :param N_horizon: The MPC horizon length (number of shooting intervals).

   :returns: ``1`` for ``"global"``, ``N_horizon + 1`` for
             ``"stagewise"``, ``splits`` for an ``int``, and ``len(splits)`` for a list.
   :rtype: The number of segments


.. py:data:: ParamSplits

   How a differentiable parameter varies across the MPC horizon.

   - ``"global"``: one value shared across all stages.
   - ``"stagewise"``: one independent value per stage (``N+1`` values total).
   - ``int``: number of equal-sized stage segments.
   - ``list[int]``: explicit stage boundaries (ascending), e.g. ``[4, 9]``.