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)

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)

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