openpathsampling.analysis.tis.StandardTISAnalysis

class openpathsampling.analysis.tis.StandardTISAnalysis(network, steps=None, flux_method=None, scheme=None, ctp_method=None, max_lambda_calcs=None, combiners=None)[source]

Standard TIS analysis: flux, TCP, CTP.

This is what we call the “standard” TIS analysis. It splits the rate equation into a flux, a total crossing probability (calculated using ensemble crossing probability functions), and a conditional transition probability from the outermost interface.

Whenever possible, this code allows you to use default values.

For the flux, you must provide either a flux method or a move scheme (which will use the MinusMoveFlux).

For the conditional transition probability, you may optionally provide a ConditionalTransitionProbability object, otherwise the code will create one for the outermost interfaces of each transition.

For the total crossing probability, you must provide a dictionary for the max_lambda_calcs. The keys of this dictionary are the sampling transitions; the values can either be an EnsembleHistogrammer (such as a FullHistogramMaxLambdas) or a dictionary of histogram parameters, in which case the histogram parameters will be passed to FullHistogramMaxLambdas. In addition, you may optionally provide a dictionary for combiners, which maps the interface set within the sampling transitions to a combining function, such as WHAM. The default is to use WHAM.

Parameters
  • network (TISNetwork) – the network to analyze

  • steps (iterable of MCStep) – if given, the analysis is performed immediately using these steps; otherwise, the analysis can be performed later with calculate()

  • flux_method (flux calculation method) – the method to use to calculate the flux; typical classes are MinusMoveFlux and DictFlux. Optional, but if not given then scheme must be given.

  • scheme (MoveScheme) – used to create a MinusMoveFlux if flux_method is not provided. Not used if flux_method is given.

  • ctp_method (ConditionalTransitionProbability) – object for calculating the conditional transition probability (optional)

  • max_lambda_calcs (dict) – determines how the ensemble crossing probability histograms are build. Keys are sampling transitions, and values can be either EnsembleHistogrammer subclasses, or a list of histogram parameters to pass to FullHistogramMaxLambdas.

  • combiners (dict {InterfaceSet: combination method}) – links the interface set to the method that will be used to combine individual ensembles into the total crossing probability function. Default is to use WHAM.

__init__(network, steps=None, flux_method=None, scheme=None, ctp_method=None, max_lambda_calcs=None, combiners=None)[source]

Methods

__init__(network[, steps, flux_method, ...])

args()

Return a list of args of the __init__ function of a class

base()

Return the most parent class actually derived from StorableObject

calculate(steps)

Perform the analysis, using steps as input.

count_weaks()

Return number of objects subclassed from StorableObject still in memory

crossing_probability(ensemble)

Crossing probability function for a given ensemble

descendants()

Return a list of all subclassed objects

fix_name()

Set the objects name to be immutable.

flux(from_state[, through_interface])

Flux from a volume and through and interface.

from_dict(dct)

Reconstruct an object from a dictionary representaiton

from_weighted_trajectories(input_dict)

Calculate results from weighted trajectories dictionary.

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(from_state, to_state)

Rate for the transition between two states

rate_matrix([steps])

Calculate the rate matrix.

reverse_uuid()

ruuid(uid)

set_observer(active)

(De-)Activate observing creation of storable objects

state_fluxes(from_state)

All fluxes associated with a given initial state.

to_dict()

Convert object into a dictionary representation

transition_probability(from_state, to_state)

Transition probability between two states.

Attributes

ACTIVE_LONG

CREATION_COUNT

INSTANCE_UUID

base_cls

Return the base class

base_cls_name

Return the name of the base class

cls

Return the class name as a string

conditional_transition_probability

conditional transition probabilities

default_name

Return the default name.

flux_matrix

float}: keys are (state, interface); values are the associated flux

is_named

True if this object has a custom name.

name

Return the current name of the object.

observe_objects

progress

total_crossing_probability

total crossing probability

transition_probability_matrix

matrix of transition probabilities