openpathsampling.analysis.network.MISTISNetwork¶

class openpathsampling.analysis.network.MISTISNetwork(trans_info, strict_sampling=False)[source]¶

Multiple interface set TIS network.

Input is given as a list of 4-tuples. Each 4-tuple represents a transition, and is in the order:

(initial_state, interfaces, order_parameter, final_states)

This will create the input_transitions objects.

input_transitions¶: list of TISTransition – the transitions given as input

sampling_transitions¶: list of TISTransition – the transitions used in sampling

transitions¶: list of TISTransition – the transitions used in analysis

Note

The distinction between the three types of transitions in the object are a bit subtle, but important. The input_transitions are, of course, the transitions given in the input. These are A->B transitions, but would allow any other state. The sampling_transitions are what are used in sampling. These are A->any transitions if strict sampling is off, or “A->B & not_others” if strict sampling is on. Finally, the regular transitions are the transitions that are used for analysis (use the sampling ensembles for the interfaces, but also A->B).

Parameters:

trans_info (list of tuple) – Details of each interface set. 4-tuple in the order (initial_state, interfaces, orderparameter, final_state) where initial_state and final_state are Volumes, interfaces is a list of Volumes, and orderparameter is a CollectiveVariable
strict_sampling (bool) – whether the final state from the tuple is the only allowed final state in the sampling; default False

__init__(trans_info, strict_sampling=False)[source]¶

Methods

`__init__`(trans_info[, strict_sampling])
`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
`build_analysis_transitions`()
`build_sampling_transitions`(transitions)
`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)
`from_transitions`(transitions[, interfaces])
`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
`rate_matrix`(steps[, force])
`save`(store)	Save the object in the given store (or storage)
`set_observer`(active)	(De-)Activate observing creation of storable objects
`to_dict`()

Attributes

`all_ensembles`	All ensembles in the sampling transitions, including special ensembles.
`all_states`
`analysis_ensembles`	Ensembles from the analysis transitions, excluding special ensembles.
`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.
`is_named`	True if this object has a custom name.
`minus_ensembles`
`ms_outers`
`name`	Return the current name of the object.
`observe_objects`
`sampling_ensembles`	Ensembles from the sampling transitions, excluding special ensembles.
`sampling_transitions`	The transitions used in sampling

__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__¶

all_ensembles¶: All ensembles in the sampling transitions, including special ensembles.

analysis_ensembles¶: Ensembles from the analysis transitions, excluding special ensembles.

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

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.

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

sampling_ensembles¶: Ensembles from the sampling transitions, excluding special ensembles.

sampling_transitions: The transitions used in sampling

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.