openpathsampling.dynamics_engine.DynamicsEngine¶

class openpathsampling.dynamics_engine.DynamicsEngine(options=None, template=None)[source]¶

Wraps simulation tool (parameters, storage, etc.)

Notes

Should be considered an abstract class: only its subclasses can be instantiated.

__init__(options=None, template=None)[source]¶

Create an empty DynamicsEngine object

Notes

The purpose of an engine is to create trajectories and keep track of the results. The main method is ‘generate’ to create a trajectory, which is a list of snapshots and then can store the in the associated storage. In the initialization this storage is created as well as the related Trajectory and Snapshot classes are initialized.

Methods

`__init__`([options, template])	Create an empty DynamicsEngine object
`args`()	Return a list of args of the __init__ function of a class
`base`()	Return the most parent class that is actually derived from Storable(Named)Object
`check_snapshot_type`(snapshot)
`count_weaks`()	Return the counts of how many objects of storable type are still in memory
`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
`generate`(snapshot[, running, direction])	Generate a trajectory consisting of ntau segments of tau_steps in between storage of Snapshots.
`generate_backward`(snapshot, ensemble)	Generate a potential trajectory in ensemble simulating forward in time
`generate_forward`(snapshot, ensemble)	Generate a potential trajectory in ensemble simulating forward in time
`generate_n_frames`([n_frames])	Generates n_frames, from but not including the current snapshot.
`generate_next_frame`()
`idx`(store)	Return the index which is used for the object in the given store.
`named`(name)	Name an unnamed object.
`objects`()	Returns a dictionary of all storable objects
`save`(store)	Save the object in the given store (or storage)
`set_as_default`()
`set_observer`(active)	(De-)Activate observing creation of storable objects
`start`([snapshot])
`stop`(trajectory)	Nothing special needs to be done for direct-control simulations when you hit a stop condition.
`stop_conditions`(trajectory[, ...])	Test whether we can continue; called by generate a couple of times, so the logic is separated here.
`to_dict`()

Attributes

`BACKWARD`
`FORWARD`
`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.
`default_options`
`is_named`	True if this object has a custom name.
`n_atoms`
`n_spatial`
`name`	Return the current name of the object.
`observe_objects`
`topology`
`units`

__delattr__¶: x.__delattr__(‘name’) <==> del x.name

__format__()¶: default object formatter

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

__hash__¶

__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

__str__¶

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

base()¶

Return the most parent class that is actually derived from Storable(Named)Object

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

base_snapshot_type¶: alias of BaseSnapshot

cls¶

Return the class name as a string

Returns:	the class name
Return type:	str

count_weaks()¶

Return the counts of how many objects of storable type are 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

default_name¶

Return the default name.

Usually derived from the objects class

Returns:	the default name
Return type:	str

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.

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`

generate(snapshot, running=None, direction=1)[source]¶

Generate a trajectory consisting of ntau segments of tau_steps in between storage of Snapshots.

Parameters:	snapshot (`openpathsampling.snapshot.Snapshot`) – initial coordinates and velocities in form of a Snapshot object running ((list of) function(`openpathsampling.trajectory.Trajectory`)) – callable function of a ‘Trajectory’ that returns True or False. If one of these returns False the simulation is stopped. direction (-1 or +1 (DynamicsEngine.FORWARD or DynamicsEngine.BACKWARD)) – If +1 then this will integrate forward, if -1 it will reversed the momenta of the given snapshot and then prepending generated snapshots with reversed momenta. This will generate a _reversed_ trajectory that effectively ends in the initial snapshot
Returns:	trajectory – generated trajectory of initial conditions, including initial coordinate set
Return type:	`openpathsampling.trajectory.Trajectory`

Notes

If the returned trajectory has length n_frames_max it can still happen that it stopped because of the stopping criterion. You need to check in that case.

generate_backward(snapshot, ensemble)[source]¶: Generate a potential trajectory in ensemble simulating forward in time

generate_forward(snapshot, ensemble)[source]¶: Generate a potential trajectory in ensemble simulating forward in time

generate_n_frames(n_frames=1)[source]¶

Generates n_frames, from but not including the current snapshot.

This generates a fixed number of frames at once. If you desire the reversed trajectory, you can reverse the returned trajectory.

Parameters:	n_frames (integer) – number of frames to generate
Returns:	the n_frames of the trajectory following (and not including) the initial current_snapshot
Return type:	paths.Trajectory()

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.objects.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

is_named¶

True if this object has a custom name.

This distinguishes default algorithmic names from assigned names.

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.

Examples

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

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

save(store)¶

Save the object in the given store (or storage)

Parameters:	store (`openpathsampling.netcdfplus.objects.ObjectStore` or `openpathsampling.netcdfplus.netcdfplus.NetCDFStorage`) – the store or storage to be saved in. if a storage is given then the default store for the given object base type is determined and the appropriate store is used.
Returns:	the integer index used to save the object or None if the object has already been saved.
Return type:	int or None

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.