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].

See also

ReactiveFlux(): Reactive Flux object

References

[1]	W. E and E. Vanden-Eijnden. Towards a theory of transition paths. J. Stat. Phys. 123: 503-523 (2006)

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

[3]	F. Noe, Ch. Schuette, E. Vanden-Eijnden, L. Reich and T. Weikl: Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations. Proc. Natl. Acad. Sci. USA, 106, 19011-19016 (2009)