openpathsampling.high_level.network.MSTISNetwork

class openpathsampling.high_level.network.MSTISNetwork(trans_info, ms_outers=None)[source]

Multiple state transition interface sampling network.

The way this works is that it sees two effective sets of transitions. First, there are sampling transitions. These are based on ensembles which go to any final state. Second, there are analysis transitions. These are based on ensembles which go to a specific final state.

Sampling is done using the sampling transitions. Sampling transitions are stored in the from_state[state] dictionary. For MSTIS, the flux and total crossing probabilities are independent of the final state, and so the analysis calculates them in the sampling transitions, and copies the results into the analysis transitions. This way flux and total crossing probably are only calculated once per interface set.

The conditional transition probability depends on the final state, so it (and the rate) are calculated using the analysis transitions. The analysis transitions are obtained using .transition[(stateA, stateB)].

__init__(trans_info, ms_outers=None)[source]

Creates MSTISNetwork, including interfaces.

Parameters:

trans_info (list of tuple) – Details of each state-based ensemble set. 2-tuple in the order (state, interface_set) where state is a Volume, and interface_set is an InterfaceSet (with associated CollectiveVariable)
ms_outers (MSOuterTISInterface or list of MSOuterTISInterface) – mutliple state outer interfaces for this network

Methods

`__init__`(trans_info[, ms_outers])	Creates MSTISNetwork, including interfaces.
`add_ms_outer_interface`(ms_outer, transitions)
`args`()	Return a list of args of the __init__ function of a class
`base`()	Return the most parent class actually derived from StorableObject
`build_one_state_sampling_transition`(state, ...)
`count_weaks`()	Return number of objects subclassed from StorableObject 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
`get_state`(snapshot)	Find which core state a snapshot is in, if any
`get_uuid`()
`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])	Calculate the matrix of all rates.
`reverse_uuid`()
`ruuid`(uid)
`set_fluxes`(flux_dictionary)
`set_observer`(active)	(De-)Activate observing creation of storable objects
`to_dict`()	Convert object into a dictionary representation

Attributes

`ACTIVE_LONG`
`CREATION_COUNT`
`INSTANCE_UUID`
`all_ensembles`	All ensembles in the sampling transitions, including special ensembles.
`all_states`
`analysis_ensembles`	Ensembles from the analysis transitions, excluding special ensembles.
`analysis_to_sampling`	dict mapping analysis transitions to sampling transitions
`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_ensemble_for`	dict mapping ensembles (incl.
`sampling_ensembles`	Ensembles from the sampling transitions, excluding special ensembles.
`sampling_to_analysis`	dict mapping sampling transitions to analysis transitions
`sampling_transitions`	The transitions used in sampling