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)

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