Mover Stubs

class ops_piggybacker.ShootingStub(ensemble, selector=None, engine=None, pre_joined=True)

Bases: openpathsampling.pathmover.PathMover

Stub to mimic a shooting move.

Parameters:

• ensemble (paths.Ensemble) – the ensemble for the shooting mover
• selector (paths.ShootingPointSelector or None) – the selector for the shooting point. Default None creates a UniformSelector. Currently, only UniformSelector is supported.
• engine (paths.engines.DynamicsEngine) – the engine to report as the source of the dynamics
• pre_joined (bool) – whether the input trial trajectories are pre-joined into complete trajectories, or take partial one-way segments which should by dynamically joined. Currently defaults to pre_joined=True (likely to change soon, though).

mimic

paths.OneWayShootingMover – the mover that this stub mimics

__delattr__

x.__delattr__(‘name’) <==> del x.name

__format__()

default object formatter

__getattribute__

x.__getattribute__(‘name’) <==> x.name

__reduce__()

helper for pickle

__reduce_ex__()

helper for pickle

__repr__

__setattr__

x.__setattr__(‘name’, value) <==> x.name = value

__sizeof__() → int

size of object in memory, in bytes

classmethod args()

Return a list of args of the __init__ function of a class

Returns:the list of argument names. No information about defaults is included. Return type:list of str

classmethod base()

Return the most parent class actually derived from StorableObject

Important to determine which store should be used for storage

Returns:the base class Return type:type

base_cls

Return the base class

Returns:the base class Return type:type

See also

base()

base_cls_name

Return the name of the base class

Returns:the string representation of the base class Return type:str

cls

Return the class name as a string

Returns:the class name Return type:str

static count_weaks()

Return number of objects subclassed from StorableObject still in memory

This includes objects not yet recycled by the garbage collector.

Returns:dict of str – the dictionary which assigns the base class name of each references objects the integer number of objects still present Return type:int

depth_post_order(fnc, level=0, **kwargs)

Traverse the tree in post-order applying a function with depth

This traverses the underlying tree and applies the given function at each node returning a list of the results. Post-order means that subnodes are called BEFORE the node itself is evaluated.

Parameters:

• fnc (function(node, **kwargs)) – the function run at each node. It is given the node and the optional (fixed) parameters
• level (int) – the initial level
• kwargs (named arguments) – optional arguments added to the function

Returns:

flattened list of tuples of results of the map. First part of the tuple is the level, second part is the function result.

Return type:

list of tuple(level, func(node, **kwargs))

See also

map_pre_order(), map_post_order(), level_pre_order(), level_post_order()

depth_pre_order(fnc, level=0, only_canonical=False, **kwargs)

Traverse the tree of node in pre-order applying a function

This traverses the underlying tree applies the given function at each node returning a list of the results. Pre-order means that subnodes are called AFTER the node itself is evaluated.

Parameters:

• fnc (function(node, **kwargs)) – the function run at each node. It is given the node and the optional parameters
• level (int) – the initial level
• only_canonical (bool, default: False) – if True the recursion stops at canonical movers and will hence be more compact
• kwargs (named arguments) – optional arguments added to the function

Returns:

flattened list of tuples of results of the map. First part of the tuple is the level, second part is the function result.

Return type:

list of tuple(level, fnc(node, **kwargs))

See also

map_pre_order(), map_post_order(), level_pre_order(), level_post_order()

classmethod descendants()

Return a list of all subclassed objects

Returns:list of subclasses of a storable object Return type:list of type

fix_name()

Set the objects name to be immutable.

Usually called after load and save to fix the stored state.

classmethod from_dict(dct)

Reconstruct an object from a dictionary representaiton

Parameters:dct (dict) – the dictionary containing a state representaion of the class. Returns:the reconstructed storable object Return type:openpathsampling.netcdfplus.StorableObject

idx(store)

Return the index which is used for the object in the given store.

Once you store a storable object in a store it gets assigned a unique number that can be used to retrieve the object back from the store. This function will ask the given store if the object is stored if so what the used index is.

Parameters:store (openpathsampling.netcdfplus.ObjectStore) – the store in which to ask for the index Returns:the integer index for the object of it exists or None else Return type:int or None

in_out

List the input -> output relation for ensembles

A mover will pick one or more replicas from specific ensembles. Alter them (or not) and place these (or additional ones) in specific ensembles. This relation can be visualized as a mapping of input to output ensembles. Like

ReplicaExchange ens1 -> ens2 ens2 -> ens1

EnsembleHop (A sample in ens1 will disappear and appear in ens2) ens1 -> ens2

DuplicateMover (create a copy with a new replica number) Not used yet! ens1 -> ens1 None -> ens1

Returns:

• list of list of tuple ((openpathsampling.Ensemble,)
• openpathsampling.Ensemble) – a list of possible lists of tuples of ensembles.

Notes

The default implementation will (1) in case of a single input and output connect the two, (2) return nothing if there are no out_ensembles and (3) for more then two require implementation

input_ensembles

Return a list of possible used ensembles for this mover

This list contains all Ensembles from which this mover might pick samples. This is very useful to determine on which ensembles a mover acts for analysis and sanity checking.

Returns:the list of input ensembles Return type:list of openpathsampling.Ensemble

is_named

True if this object has a custom name.

This distinguishes default algorithmic names from assigned names.

static join_one_way(input_trajectory, partial_trial, shooting_point, direction)

Create a one-way trial trajectory

Parameters:

• input_trajectory (paths.Trajectory) – the previous complete trajectory
• partial_trial (paths.Trajectory) – The partial (one-way) trial trajectory. Must not include the shooting point.
• shooting_point (paths.Snapshot) – the snapshot for the shooting point – must be a member of the input trajectory
• direction (+1 or -1) – if positive, treat as forward shooting; if negative, treat as backward shooting

Returns:

the complete trial trajectory

Return type:

paths.Trajectory

keylist()

Return a list of key : subtree tuples

Returns:A list of all subtrees with their respective keys Return type:list of tuple(key, subtree)

static legal_sample_set(sample_set, ensembles=None, replicas='all')

This returns all the samples from sample_set which are in both self.replicas and the parameter ensembles. If ensembles is None, we use self.ensembles. If you want all ensembles allowed, pass ensembles=’all’.

Parameters:

• sample_set (openpathsampling.SampleSet) – the sampleset from which to pick specific samples matching certain criteria
• ensembles (list of openpathsampling.Ensembles) – the ensembles to pick from
• replicas (list of int or all) – the replicas to pick or ‘all’ for all

map_post_order(fnc, **kwargs)

Traverse the tree in post-order applying a function

This traverses the underlying tree and applies the given function at each node returning a list of the results. Post-order means that subnodes are called BEFORE the node itself is evaluated.

Parameters:

• fnc (function(node, kwargs)) – the function run at each node. It is given the node and the optional (fixed) parameters
• kwargs (named arguments) – optional arguments added to the function

Returns:

flattened list of the results of the map

Return type:

list (fnc(node, **kwargs))

Notes

This uses the same order as reversed()

See also

map_pre_order(), map_post_order(), level_pre_order(), level_post_order()

map_pre_order(fnc, **kwargs)

Traverse the tree in pre-order applying a function

This traverses the underlying tree applies the given function at each node returning a list of the results. Pre-order means that subnodes are called AFTER the node itself is evaluated.

Parameters:

• fnc (function(node, **kwargs)) – the function run at each node. It is given the node and the optional parameters
• kwargs (named arguments) – optional arguments added to the function

Returns:

flattened list of the results of the map

Return type:

list (fnc(node, **kwargs))

Notes

This uses the same order as iter()

See also

map_pre_order(), map_post_order(), level_pre_order(), level_post_order()

map_tree(fnc)

Apply a function to each node and return a nested tree of results

Parameters:

• fnc (function(node, args, kwargs)) – the function run at each node node. It is given the node and the optional (fixed) parameters
• kwargs (named arguments) – optional arguments added to the function

Returns:

nested list of the results of the map

Return type:

tree (fnc(node, **kwargs))

move(input_sample, trial_trajectory, shooting_point, accepted, direction=None)

Fake a move.

Parameters:

• input_sample (paths.Sample) – the input sample for this shooting move
• trial_trajectory (paths.Trajectory) – the trial trajectory generated by this move
• shooting_point (paths.Snapshot) – the shooting point snapshot for this trial
• accepted (bool) – whether the trial was accepted
• direction (+1, -1, or None) – direction of the shooting move (positive is forward, negative is backward). If self.pre_joined is True, the trial trajectory is reconstructed from the parts. To use the exact input trial trajectory with self.pre_joined == True, set direction=None

name

Return the current name of the object.

If no name has been set a default generated name is returned.

Returns:the name of the object Return type:str

named(name)

Name an unnamed object.

This only renames the object if it does not yet have a name. It can be used to chain the naming onto the object creation. It should also be used when naming things algorithmically: directly setting the .name attribute could override a user-defined name.

Parameters:name (str) – the name to be used for the object. Can only be set once

Examples

>>> import openpathsampling as p
>>> full = p.FullVolume().named('myFullVolume')

static objects()

Returns a dictionary of all storable objects

Returns:dict of str – a dictionary of all subclassed objects from StorableObject. The name points to the class Return type:type

output_ensembles

Return a list of possible returned ensembles for this mover

This list contains all Ensembles for which this mover might return samples. This is very useful to determine on which ensembles a mover affects in later steps for analysis and sanity checking.

Returns:the list of output ensembles Return type:list of Ensemble

static select_sample(sample_set, ensembles=None, replicas=None)

Returns one of the legal samples given self.replica and the ensemble set in ensembles.

Parameters:

• sample_set (openpathsampling.SampleSet) – the sampleset from which to pick specific samples matching certain criteria
• ensembles (list of openpathsampling.Ensembles or None) – the ensembles to pick from or None for all
• replicas (list of int or None) – the replicas to pick or None for all

static set_observer(active)

(De-)Activate observing creation of storable objects

This can be used to track which storable objects are still alive and hence look for memory leaks and inspect caching. Use openpathsampling.netcdfplus.base.StorableObject.count_weaks() to get the current summary of created objects

Parameters:active (bool) – if True then observing is enabled. False disables observing. Per default observing is disabled.

See also

openpathsampling.netcdfplus.base.StorableObject.count_weaks()

sub_replica_state(replica_states)

Return set of replica states that a submover might be called with

Parameters:replica_states (set of openpathsampling.pathmover_inout.ReplicaState) – Returns: Return type:list of set of ReplicaState

submovers

Returns a list of submovers

Returns:the list of sub-movers Return type:list of openpathsampling.PathMover

to_dict()

Convert object into a dictionary representation

Used to convert the dictionary into JSON string for serialization

Returns:the dictionary representing the (immutable) state of the object Return type:dict

tree()

Return the object as a tree structure of nested lists of nodes

Returns:the tree in nested list format Return type:nested list of nodes