pyrevs.strategies.ams.extension

An extension class for the AMS strategy.

Attributes

T_Noise
T_State

Classes

AMSDatabaseExtension

An extension class for the AMS strategy.

Module Contents

T_Noise

T_State

class AMSDatabaseExtension

Bases: pyrevs.database.StrategyDatabaseExtension

An extension class for the AMS strategy.

Variables:

• _nsplitting – maximum number of splitting iterations

• _ams_db – an instance of AMSDB, extending the SQL database

initialize(nsplitting: int, tdb: pyrevs.database.Database) → None

Initialize the AMS database extension.

Parameters:

• nsplitting – maximum number of splitting iterations

• tdb – the core trajectory database

initialize_from_database(tdb: pyrevs.database.Database) → None

Initialize the AMS database extension.

Parameters:

tdb – the core trajectory database

serialize() → None

Serialize the extension.

deserialize() → None

Serialize the extension.

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

init_ensemble_done() → bool

Get the initial ensemble status flag.

Returns:

the flag indicating that the initial ensemble is finished

set_init_ensemble_flag(status: bool) → None

Change the initial ensemble status flag.

Parameters:

status – the new status

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

weights() → numpy.typing.NDArray[numpy.number]

Splitting iterations weights.

mark_last_splitting_iteration_as_done() → None

Flag the last splitting iteration as done.

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.

Parameters:

• ksplit – The splitting iteration index

• bias – The number of restarted trajectories, also ref. to as bias

• discarded_ids – The list of discarded trajectory ids

• ancestor_ids – The list of trajectories used to restart (ancestors)

• min_vals – The list of minimum values

• min_max – The score minimum and maximum values

Raises:

ValueError if the provided ksplit is incompatible with the db state –

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.

Parameters:

• ksplit – The splitting iteration index

• bias – The number of restarted trajectories, also ref. to as bias

• discarded_ids – The list of discarded trajectory ids

• ancestor_ids – The list of trajectories used to restart (ancestors)

• min_vals – The list of minimum values

• min_max – The score minimum and maximum values

Raises:

ValueError if the provided ksplit is incompatible with the db state –

update_trajectories_weights() → None

Update the weights of all the trajectories.

Using the the current splitting iteration weight.

get_event_probability() → float

Return the event probability.

plot_min_max_span(fname: str | None = None) → None

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

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.

Parameters:

k_in – the index of the splitting iteration

Returns:

The list of trajectories active at the beginning of iteration k