deeptime.markov

The markov package contains algorithms which can be used to estimate markov state models and apply analysis tools like PCCA+, TPT, bayesian sampling for confidence intervals.

Estimators

msm.MaximumLikelihoodMSM([reversible, ...])	Maximum likelihood estimator for MSMs (MarkovStateModel) given discrete trajectories or statistics thereof.
msm.BayesianMSM([n_samples, n_steps, ...])	Bayesian estimator for MSMs given discrete trajectory statistics.
msm.TRAM([lagtime, count_mode, maxiter, ...])	Transition(-based) Reweighting Analysis Method.

Models

msm.MarkovStateModel(transition_matrix[, ...])	Markov model with a given transition matrix.
msm.MarkovStateModelCollection(...)	A collection of Markov state models.
msm.BayesianMSMPosterior([prior, samples])	Bayesian posterior from bayesian MSM sampling.
msm.TRAMModel(count_models, ...[, ...])	The TRAM model containing the estimated parameters, free energies, and the underlying Markov models for each thermodynamic state.

Analysis tools

pcca(P, m[, stationary_distribution])	PCCA+ spectral clustering method with optimized memberships.
reactive_flux(transition_matrix, ...[, ...])	Computes the A->B reactive flux using transition path theory (TPT).

With output models

PCCAModel(transition_matrix_coarse, ...)	Model for PCCA+ spectral clustering method with optimized memberships.
ReactiveFlux(source_states, target_states, ...)	The A->B reactive flux from transition path theory (TPT).

Utilities and alternatives

number_of_states(dtrajs[, only_used])	Returns the number of states in the given trajectories.
count_states(dtrajs[, ignore_negative])	Computes a histogram over the visited states in one or multiple discretized trajectories.
compute_connected_sets(count_matrix[, ...])	Computes the connected sets of a count matrix C.
compute_dtrajs_effective(dtrajs, lagtime, ...)	Takes discrete trajectories as input and strides these with an effective stride.
compute_effective_stride(dtrajs, lagtime, ...)	Computes the effective stride which is an estimate of the striding required to produce uncorrelated samples.
sample.indices_by_distribution(indices, ...)	Samples trajectory/time indices according to the given probability distributions
sample.compute_index_states(dtrajs[, subset])	Generates trajectory/time indices for the given list of states
sample.indices_by_sequence(indices, sequence)	Samples trajectory/time indices according to the given sequence of states.
sample.indices_by_state(indices, nsample[, ...])	Samples trajectory/time indices according to the given sequence of states
msm.TRAMDataset(dtrajs, bias_matrices[, ...])	Dataset for organizing data and obtaining properties from data that are needed for TRAM.

Transition counting

An alternative to estimating Markov state models directly from discrete timeseries is to first estimate (and potentially subselect) a count matrix and then use that for estimation.

TransitionCountEstimator(lagtime, count_mode)	Estimator which produces a TransitionCountModel given discretized trajectories.
TransitionCountModel(count_matrix[, ...])	Statistics, count matrices, and connectivity from discrete trajectories.

Special MSM estimators and models

msm.OOMReweightedMSM(lagtime[, reversible, ...])	OOM (observable operator model) MSM estimator for MSMs given discrete trajectory statistics.
msm.KoopmanReweightedMSM(transition_matrix)	This class belongs to a markov state model which was estimated by Koopman reweighting.
msm.AugmentedMSMEstimator(...[, eps, ...])	Estimator for augmented Markov state models.
msm.AugmentedMSM(transition_matrix[, ...])	An augmented Markov state model.