Markov State Models package (pyemma.msm)

The msm package provides functions to estimate, analyze and generate discrete-state Markov models. All public functions accept dense NumPy and sparse SciPy matrices and automatically choose the corresponding implementation.

User API

its(dtrajs[, lags, nits, reversible, connected]) Calculate implied timescales for a series of lag times.
markov_model(P[, dt]) Markov model with a given transition matrix
estimate_markov_model(dtrajs, lag[, ...]) Estimates a Markov model from discrete trajectories
tpt(msmobj, A, B) A->B reactive flux from transition path theory (TPT)
cktest(msmobj, K[, nsets, sets, full_output]) Chapman-Kolmogorov test for the given MSM

MSM classes encapsulating complex functionality. You don’t need to construct these classes yourself, as this is done by the user API functions above. Find here a documentation how to extract features from them.

ui.MSM(T[, dt]) Markov model with a given transition matrix
ui.EstimatedMSM(dtrajs, lag[, reversible, ...]) Estimates a Markov model from discrete trajectories.
ui.ImpliedTimescales(dtrajs[, lags, nits, ...]) Implied timescales for a series of lag times.
flux.ReactiveFlux(A, B, flux[, mu, qminus, ...]) A->B reactive flux from transition path theory (TPT)