openpathsampling.ensemble.SequentialEnsemble

class openpathsampling.ensemble.SequentialEnsemble(ensembles, min_overlap=0, max_overlap=0, greedy=False)

Ensemble which satisfies several subensembles in sequence.

ensembles

The ensembles, in time-order of when they should occur in the trajectory.

Type:

tuple of Ensemble

min_overlap

The minimum number of frames that overlap between two ensembles in the sequence. A positive number n indicates that at least n frames must be in both ensembles at the transition between them. A negative number -n indicates that at least n frames in neither ensemble at the transition between them. If given as a list, the list should be of length len(ensembles)-1, with one value for each transition. If given as an integer, that value will be used for all transitions.

Type:

int or tuple of int

max_overlap

The maximum number of frames that overlap between two ensembles in the sequence. A positive number n indicates that no more than n frames can be in both ensembles at the transition between them. A negative number -n indicates no more than n frames in neither ensemble at the transition between them. If given as a list, the list should be of length len(ensembles)-1, with one value for each transition. If given as an integer, that value will be used for all transitions.

Type:

int or list of int

Notes

TODO: Overlap features not implemented because ohmygod this was hard enough already.

__init__(ensembles, min_overlap=0, max_overlap=0, greedy=False)

A path volume defines a set of paths.

Methods

__init__(ensembles[, min_overlap, ...])	A path volume defines a set of paths.
args()	Return a list of args of the __init__ function of a class
assign_frames(cache, ens_num[, ...])
base()	Return the most parent class actually derived from StorableObject
can_append(trajectory[, trusted])	Returns true, if the trajectory so far can still be in the ensemble if it is appended by a frame.
can_prepend(trajectory[, trusted])	Returns true, if the trajectory so far can still be in the ensemble if it is prepended by a frame.
check(trajectory)	Alias for __call__
check_reverse(trajectory[, trusted])	See __call__; same thing, but potentially in reverse frame order
count_weaks()	Return number of objects subclassed from StorableObject still in memory
descendants()	Return a list of all subclassed objects
extend_sample_from_trajectories(...[, ...])	Generate a sample in the ensemble by extending parts of trajectories
find_first_subtrajectory(trajectory)	Return the first sub-trajectory that matches the ensemble
find_last_subtrajectory(trajectory)	Return the last sub-trajectory that matches the ensemble
fix_name()	Set the objects name to be immutable.
from_dict(dct)	Reconstruct an object from a dictionary representaiton
get_sample_from_trajectories(trajectories[, ...])	Generate a sample in the ensemble by testing trajectories
get_uuid()
idx(store)	Return the index which is used for the object in the given store.
iter_extendable_slices(trajectory[, ...])	Return an iterator over maxiaml slices of extendable subtrajectories
iter_split(trajectory[, max_length, ...])	Return iterator over subtrajectories satisfying the given ensemble.
iter_valid_slices(trajectory[, max_length, ...])	Return an iterator over slices of subtrajectories matching the ensemble
named(name)	Name an unnamed object.
objects()	Returns a dictionary of all storable objects
reverse_uuid()
ruuid(uid)
set_observer(active)	(De-)Activate observing creation of storable objects
split(trajectory[, max_length, min_length, ...])	Return list of subtrajectories satisfying the given ensemble.
split_sample_from_trajectories(trajectories)	Generate a sample in the ensemble by searching for sub-parts
strict_can_append(trajectory[, trusted])	Returns true if the trajectory can be the beginning of a trajectory in the ensemble.
strict_can_prepend(trajectory[, trusted])	Returns true if the trajectory can be the end of a trajectory in the ensemble.
to_dict()	Convert object into a dictionary representation
trajectory_summary(trajectory)	Return dict with info on how this ensemble "sees" the trajectory.
trajectory_summary_str(trajectory)	Returns a string with the results of the trajectory_summary function.
transition_frames(trajectory[, trusted])
update_cache(cache, ens_num, ens_from, ...)	Updates the given cache.

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.
extendable_sub_ensembles
is_named	True if this object has a custom name.
name	Return the current name of the object.
observe_objects