pyrevs.strategies.montecarlo
============================

.. py:module:: pyrevs.strategies.montecarlo


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/pyrevs/strategies/montecarlo/config/index
   /autoapi/pyrevs/strategies/montecarlo/extension/index
   /autoapi/pyrevs/strategies/montecarlo/montecarlo/index


Classes
-------

.. autoapisummary::

   pyrevs.strategies.montecarlo.MCConfig
   pyrevs.strategies.montecarlo.MCDatabaseExtension
   pyrevs.strategies.montecarlo.MonteCarlo


Functions
---------

.. autoapisummary::

   pyrevs.strategies.montecarlo.load_database_extension


Package Contents
----------------

.. py:class:: MCConfig

   MC strategy configuration.


   .. py:attribute:: ntrajectories
      :type:  int
      :value: -1



   .. py:attribute:: end_time
      :type:  float | None
      :value: None



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

      Validate MC configuration.



.. py:class:: MCDatabaseExtension

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


   An extension class for the MonteCarlo strategy.

   The Monte-Carlo strategy has minimal extension requirements.
   It mostly provides a concrete implementation of the
   event probability computation method.

   It only holds a reference to the core trajectory database.

   :ivar _tdb: the core trajectory database



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

      Initialize the AMS database extension.

      :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:: get_event_probability() -> float

      Return the event probability.



.. py:class:: MonteCarlo(fmodel_t: Any, runtime_cfg: pyrevs.core.RuntimeConfig, runner_cfg: pyrevs.runner.RunnerConfig, strategy_cfg: pyrevs.strategies.montecarlo.config.MCConfig, deterministic: bool)

   Bases: :py:obj:`pyrevs.strategies.base.BaseSamplingStrategy`


   A strategy class implementing MonteCarlo.

   Monte-Carlo or Direct Numerical Simulation (DNS) is not per-se
   a sampling strategy tailored for rare events but it provides
   a baseline for comparison with other sampling strategies.

   An ensemble of size n_traj is constructed and the rare-event probability
   is simply computed as the ratio of the number of converged trajectories
   to the total number of trajectories in the ensemble n_traj.

   In practice, this is the first step of a TAMS or AMS run (depending
   on the termination condition), such that this class is a lightweight
   version of these other strategies.

   .. rubric:: Notes

   This strategy relies on time management provided by
   BaseSamplingStrategy (e.g. ``self._end_date``, ``elapsed_time()``).


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

      Schedule the generation of an ensemble of stochastic trajectories.

      Loop over all the trajectories in the database and schedule
      advancing them to either end time or convergence with the
      runner.

      The runner will use the number of workers specified in the
      input file under the runner section.

      :raises Error if the runner fails:



   .. py:method:: compute_probability(tdb: pyrevs.database.Database) -> float

      Compute the rare-event probability using MonteCarlo.

      :returns: the rare-event probability



   .. py:method:: initialize_database_schema(database: pyrevs.database.Database, diag_configs: dict[str, pyrevs.core.Config] | None) -> None

      Initialize database core state.



.. py:function:: load_database_extension(tdb: pyrevs.database.Database) -> extension.MCDatabaseExtension

   A factory function to instanciate the extension from the database.