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¶
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Maximum likelihood estimator for MSMs ( |
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Bayesian estimator for MSMs given discrete trajectory statistics. |
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Transition(-based) Reweighting Analysis Method. |
Models¶
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Markov model with a given transition matrix. |
A collection of Markov state models. |
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Bayesian posterior from bayesian MSM sampling. |
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The TRAM model containing the estimated parameters, free energies, and the underlying Markov models for each thermodynamic state. |
Analysis tools¶
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PCCA+ spectral clustering method with optimized memberships. |
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Computes the A->B reactive flux using transition path theory (TPT). |
With output models
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Model for PCCA+ spectral clustering method with optimized memberships. |
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The A->B reactive flux from transition path theory (TPT). |
Utilities and alternatives¶
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Returns the number of states in the given trajectories. |
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Computes a histogram over the visited states in one or multiple discretized trajectories. |
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Computes the connected sets of a count matrix C. |
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Takes discrete trajectories as input and strides these with an effective stride. |
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Computes the effective stride which is an estimate of the striding required to produce uncorrelated samples. |
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Samples trajectory/time indices according to the given probability distributions |
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Generates trajectory/time indices for the given list of states |
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Samples trajectory/time indices according to the given sequence of states. |
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Samples trajectory/time indices according to the given sequence of states |
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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.
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Estimator which produces a |
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Statistics, count matrices, and connectivity from discrete trajectories. |
Special MSM estimators and models¶
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OOM (observable operator model) MSM estimator for MSMs given discrete trajectory statistics. |
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This class belongs to a markov state model which was estimated by Koopman reweighting. |
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Estimator for augmented Markov state models. |
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An augmented Markov state model. |