pyrevs.strategies.ams.extension
===============================

.. py:module:: pyrevs.strategies.ams.extension

.. autoapi-nested-parse::

   An extension class for the AMS strategy.



Attributes
----------

.. autoapisummary::

   pyrevs.strategies.ams.extension.T_Noise
   pyrevs.strategies.ams.extension.T_State


Classes
-------

.. autoapisummary::

   pyrevs.strategies.ams.extension.AMSDatabaseExtension


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

.. py:data:: T_Noise

.. py:data:: T_State

.. py:class:: AMSDatabaseExtension

   Bases: :py:obj:`pyrevs.database.StrategyDatabaseExtension`


   An extension class for the AMS strategy.

   :ivar _nsplitting: maximum number of splitting iterations
   :ivar _ams_db: an instance of AMSDB, extending the SQL database



   .. py:method:: initialize(nsplitting: int, tdb: pyrevs.database.Database) -> None

      Initialize the AMS database extension.

      :param nsplitting: maximum number of splitting iterations
      :param tdb: the core trajectory database



   .. py:method:: initialize_from_database(tdb: pyrevs.database.Database) -> None

      Initialize the AMS database extension.

      :param tdb: the core trajectory database



   .. py:method:: serialize() -> None

      Serialize the extension.



   .. py:method:: deserialize() -> None

      Serialize the extension.



   .. py:method:: k_split() -> int

      Get the current splitting iteration index.

      The current splitting iteration index is equal to the
      ksplit + bias (number of branching event in the last iteration)
      entries of last entry in the SQL db table

      :returns: Internal splitting iteration index



   .. py:method:: init_ensemble_done() -> bool

      Get the initial ensemble status flag.

      :returns: the flag indicating that the initial ensemble is finished



   .. py:method:: set_init_ensemble_flag(status: bool) -> None

      Change the initial ensemble status flag.

      :param status: the new status



   .. py:method:: get_ongoing() -> list[int] | None

      Get the list of ongoing trajectories if any.

      :returns: Either a list trajectories or None if nothing was left to do



   .. py:method:: weights() -> numpy.typing.NDArray[numpy.number]

      Splitting iterations weights.



   .. py:method:: mark_last_splitting_iteration_as_done() -> None

      Flag the last splitting iteration as done.



   .. py:method:: append_splitting_iteration_data(ksplit: int, bias: int, discarded_ids: list[int], ancestor_ids: list[int], min_vals: list[float], min_max: list[float]) -> None

      Append a set of splitting data to internal list.

      :param ksplit: The splitting iteration index
      :param bias: The number of restarted trajectories, also ref. to as bias
      :param discarded_ids: The list of discarded trajectory ids
      :param ancestor_ids: The list of trajectories used to restart (ancestors)
      :param min_vals: The list of minimum values
      :param min_max: The score minimum and maximum values

      :raises ValueError if the provided ksplit is incompatible with the db state:



   .. py:method:: update_splitting_iteration_data(ksplit: int, bias: int, discarded_ids: list[int], ancestor_ids: list[int], min_vals: list[float], min_max: list[float]) -> None

      Update the last set of splitting data to internal list.

      :param ksplit: The splitting iteration index
      :param bias: The number of restarted trajectories, also ref. to as bias
      :param discarded_ids: The list of discarded trajectory ids
      :param ancestor_ids: The list of trajectories used to restart (ancestors)
      :param min_vals: The list of minimum values
      :param min_max: The score minimum and maximum values

      :raises ValueError if the provided ksplit is incompatible with the db state:



   .. py:method:: update_trajectories_weights() -> None

      Update the weights of all the trajectories.

      Using the the current splitting iteration weight.



   .. py:method:: get_event_probability() -> float

      Return the event probability.



   .. py:method:: plot_min_max_span(fname: str | None = None) -> None

      Plot the evolution of the ensemble min/max during iterations.



   .. py:method:: get_trajectory_active_at_k(k_in: int) -> list[pyrevs.trajectory.Trajectory[T_Noise, T_State]]

      Return the list of trajectory active at a given splitting iteration.

      To explore the ensemble evolution during splitting iterations, it is
      useful to reconstruct the list of active trajectories at the beginning
      of any given splitting iteration.

      Note that k here is not the splitting index, but the iteration index.
      Since more than one child can be spawned at each splitting iteration,
      the two might differ.

      :param k_in: the index of the splitting iteration

      :returns: The list of trajectories active at the beginning of iteration k