openpathsampling.high_level.network.MSTISNetwork

class openpathsampling.high_level.network.MSTISNetwork(trans_info, ms_outers=None)

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)

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)	param flux_dictionary: keys are in the form (state, interface), and values are the
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