pyemma.msm.tpt¶

pyemma.msm.tpt(msmobj, A, B)¶

A->B reactive flux from transition path theory (TPT)

The returned ReactiveFlux object can be used to extract various quantities of the flux, as well as to compute A -> B transition pathways, their weights, and to coarse-grain the flux onto sets of states.

Parameters:	msmobj (`MSM` or `EstimatedMSM` object) – Markov state model (MSM) object A (array_like) – List of integer state labels for set A B (array_like) – List of integer state labels for set B
Returns:	tptobj – A python object containing the reactive A->B flux network and several additional quantities, such as stationary probability, committors and set definitions.
Return type:	`ReactiveFlux` object

Notes

The central object used in transition path theory is the forward and backward committor function.

TPT (originally introduced in [1]_) for continuous systems has a discrete version outlined in [2]. Here, we use the transition matrix formulation described in [3].

class pyemma.msm.flux.reactive_flux.ReactiveFlux(A, B, flux, mu=None, qminus=None, qplus=None, gross_flux=None)¶

Methods

`coarse_grain`(user_sets)	Coarse-grains the flux onto user-defined sets.
`major_flux`([fraction])	Returns the main pathway part of the net flux comprising at most the requested fraction of the full flux.
`pathways`([fraction, maxiter])	Decompose flux network into dominant reaction paths.

Attributes

`A`	Returns the set of reactant (source) states.
`B`	Returns the set of product (target) states
`I`	Returns the set of intermediate states
`backward_committor`	Returns the backward committor probability
`committor`	Returns the forward committor probability
`flux`	Returns the effective or net flux
`forward_committor`	Returns the forward committor probability
`gross_flux`	Returns the gross A–>B flux
`mfpt`	Returns the rate (inverse mfpt) of A–>B transitions
`net_flux`	Returns the effective or net flux
`nstates`	Returns the number of states.
`rate`	Returns the rate (inverse mfpt) of A–>B transitions
`stationary_distribution`	Returns the stationary distribution
`total_flux`	Returns the total flux

A¶: Returns the set of reactant (source) states.

B¶: Returns the set of product (target) states

I¶: Returns the set of intermediate states

backward_committor¶: Returns the backward committor probability

coarse_grain(user_sets)¶

Coarse-grains the flux onto user-defined sets.

Parameters:	user_sets (list of int-iterables) – sets of states that shall be distinguished in the coarse-grained flux.
Returns:	(sets, tpt) – sets contains the sets tpt is computed on. The tpt states of the new tpt object correspond to these sets of states in this order. Sets might be identical, if the user has already provided a complete partition that respects the boundary between A, B and the intermediates. If not, Sets will have more members than provided by the user, containing the “remainder” states and reflecting the splitting at the A and B boundaries. tpt contains a new tpt object for the coarse-grained flux. All its quantities (gross_flux, net_flux, A, B, committor, backward_committor) are coarse-grained to sets.
Return type:	(list of int-iterables, tpt-object)

Notes

All user-specified sets will be split (if necessary) to preserve the boundary between A, B and the intermediate states.

committor¶: Returns the forward committor probability

flux¶: Returns the effective or net flux

forward_committor¶: Returns the forward committor probability

gross_flux¶: Returns the gross A–>B flux

major_flux(fraction=0.9)¶: Returns the main pathway part of the net flux comprising at most the requested fraction of the full flux.

mfpt¶: Returns the rate (inverse mfpt) of A–>B transitions

net_flux¶: Returns the effective or net flux

nstates¶: Returns the number of states.

pathways(fraction=1.0, maxiter=1000)¶

Decompose flux network into dominant reaction paths.

Parameters:

fraction (float, optional) – Fraction of total flux to assemble in pathway decomposition
maxiter (int, optional) – Maximum number of pathways for decomposition

Returns:

paths (list) – List of dominant reaction pathways
capacities (list) – List of capacities corresponding to each reactions pathway in paths

References

[1]	P. Metzner, C. Schuette and E. Vanden-Eijnden. Transition Path Theory for Markov Jump Processes. Multiscale Model Simul 7: 1192-1219 (2009)

rate¶: Returns the rate (inverse mfpt) of A–>B transitions

stationary_distribution¶: Returns the stationary distribution

total_flux¶: Returns the total flux