openpathsampling.pathmover.SubtrajectorySelectMover

class openpathsampling.pathmover.SubtrajectorySelectMover(ensemble, sub_ensemble, n_l=None)

Picks a subtrajectory satisfying the given subensemble.

If there are no subtrajectories which satisfy the subensemble, this returns the zero-length trajectory.

ensemble

the set of allows samples to chose from

Type:

openpathsampling.Ensemble

sub_ensemble

the subensemble to be searched for

Type:

openpathsampling.Ensemble

n_l

the number of subtrajectories that need to be found. If None every number of subtrajectories > 0 is okay. Otherwise the move is only accepted if exactly n_l subtrajectories are found.

Type:

int or None

__init__(ensemble, sub_ensemble, n_l=None)

Methods

__init__(ensemble, sub_ensemble[, n_l])
args()	Return a list of args of the __init__ function of a class
base()	Return the most parent class actually derived from StorableObject
count_weaks()	Return number of objects subclassed from StorableObject still in memory
depth_post_order(fnc[, level])	Traverse the tree in post-order applying a function with depth
depth_pre_order(fnc[, level, only_canonical])	Traverse the tree of node in pre-order applying a function
descendants()	Return a list of all subclassed objects
fix_name()	Set the objects name to be immutable.
from_dict(dct)	Reconstruct an object from a dictionary representaiton
get_samples_from_sample_set(sample_set)	Select samples to use as input to the move core.
get_uuid()
idx(store)	Return the index which is used for the object in the given store.
key(change)
keylist()	Return a list of key : subtree tuples
legal_sample_set(sample_set[, ensembles, ...])	This returns all the samples from sample_set which are in both self.replicas and the parameter ensembles.
map_post_order(fnc, **kwargs)	Traverse the tree in post-order applying a function
map_pre_order(fnc, **kwargs)	Traverse the tree in pre-order applying a function
map_tree(fnc)	Apply a function to each node and return a nested tree of results
metropolis(trials)	Implements the Metropolis acceptance for a list of trial samples
move(sample_set)	Run the generation starting with the initial sample_set specified.
move_core(samples)	Core of the Monte Carlo move.
move_replica_state(replica_states)
named(name)	Name an unnamed object.
objects()	Returns a dictionary of all storable objects
reverse_uuid()
ruuid(uid)
select_sample(sample_set[, ensembles, replicas])	Returns one of the legal samples given self.replica and the ensemble set in ensembles.
set_observer(active)	(De-)Activate observing creation of storable objects
sub_replica_state(replica_states)	Return set of replica states that a submover might be called with
to_dict()	Convert object into a dictionary representation
tree()	Return the object as a tree structure of nested lists of nodes

Attributes

ACTIVE_LONG
CREATION_COUNT
INSTANCE_UUID
base_cls	Return the base class
base_cls_name	Return the name of the base class
cls	Return the class name as a string
default_name	Return the default name.
ensemble_signature
ensemble_signature_set
identifier	A unique identifier to build the unique key for a position in a tree
in_out	List the input -> output relation for ensembles
input_ensembles	Return a list of possible used ensembles for this mover
is_canonical
is_ensemble_change_mover
is_named	True if this object has a custom name.
name	Return the current name of the object.
observe_objects
output_ensembles	Return a list of possible returned ensembles for this mover
submovers	Returns a list of submovers