openpathsampling.pathmover.StateSwapMover

class openpathsampling.pathmover.StateSwapMover(ensemble1, ensemble2, bias=None)[source]
__init__(ensemble1, ensemble2, bias=None)[source]

A move to swap states for state changing samples

This does a replica exchange with prededing PathReversal and will only succeed if initial and final state are different

Parameters
  • ensemble1 (openpathsampling.Ensemble) – one of the ensemble between to make the swap move

  • ensemble2 (openpathsampling.Ensemble) – one of the ensemble between to make the swap move

  • bias (list of float) – bias is not used yet

Notes

So, if ensemble1 goes from A to B, then ensemble2 must go from B to A.

Methods

__init__(ensemble1, ensemble2[, bias])

A move to swap states for state changing samples

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