openpathsampling.pathmover.EnsembleHopMover

class openpathsampling.pathmover.EnsembleHopMover(ensemble, target_ensemble, change_replica=None, bias=None)

__init__(ensemble, target_ensemble, change_replica=None, bias=None)

A Mover that allows the change between ensembles.

Parameters:

• ensemble (openpathsampling.Ensemble) – the initial ensemble to be jumped from

• target_ensemble (openpathsampling.Ensemble) – the final ensemble to be jumped to

• change_replica (int of None) – if None the replica id of the chosen sample will not be changed. Otherwise the replica id will be set to change_replica. This is useful when hoping to ensembles to create a new replica.

• bias (float, dict or None (default)) – gives the bias of accepting (not proposing) a hop. A float will be the acceptance for all possible attempts. If a dict is given, then it contains a list of ensembles and a matrix. None means no bias

Notes

The bias dict has the following form :

{
    'ensembles' : [ens_1, ens_2, ens_n],
    'values' : np.array((n,n))
}

The numpy array contains all the acceptance probabilties. If possible a HopMover should (as all movers) be used for only a specific hop and not multiple ones.

Methods

__init__(ensemble, target_ensemble[, ...])	A Mover that allows the change between ensembles.
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