deeptime.markov.tools¶
Package containing various low-level tools that are used for estimating and analyzing Markov state models. This package is a fork of MSMTools.
MSM estimation¶
This module (deeptime.markov.tools.estimation
) contains utility functions dealing with MSM estimation from data.
Countmatrix¶
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Generate a count matrix from given microstate trajectory. |
Connectivity¶
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Compute connected sets of microstates. |
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Largest connected component for a directed graph with edge-weights given by the count matrix. |
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Compute the count matrix on the largest connected set. |
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Check connectivity of the given matrix. |
Estimation¶
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Estimate the transition matrix from the given countmatrix. |
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Estimate a reversible rate matrix from a count matrix. |
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Log-likelihood of the count matrix given a transition matrix. |
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Covariance tensor for non-reversible transition matrix posterior. |
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Error perturbation for given sensitivity matrix. |
Sampling¶
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Generate transition matrix sampler object. |
Bootstrap¶
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Generates a randomly resampled count matrix given the input coordinates. |
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Generates a randomly resampled trajectory segments. |
Priors¶
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Neighbor prior for the given count matrix. |
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Constant prior for given count matrix. |
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Prior counts for sampling of reversible transition matrices. |
MSM analysis¶
This module (deeptime.markov.tools.analysis
) contains functions to analyze a created Markov model, which is
specified with a transition matrix T.
Validation¶
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Check if the given matrix is a transition matrix. |
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Check if the given matrix is a rate matrix. |
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Check connectivity of the given matrix. |
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Check reversibility of the given transition matrix. |
Decomposition¶
Decomposition routines use the scipy LAPACK bindings for dense numpy-arrays and the ARPACK bindings for scipy sparse matrices.
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Compute stationary distribution of stochastic matrix T. |
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Find eigenvalues of the transition matrix. |
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Compute eigenvectors of given transition matrix. |
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Compute the decomposition into eigenvalues, left and right eigenvectors. |
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Compute implied time scales of given transition matrix. |
Expected counts¶
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Compute expected transition counts for Markov chain with n steps. |
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Expected transition counts for Markov chain in equilibrium. |
Passage times¶
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Mean first passage times (from a set of starting states - optional) to a set of target states. |
Committors and PCCA¶
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Compute the committor between sets of microstates. |
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Compute meta-stable sets using PCCA++. |
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Computes the hitting probabilities for all states to the target states. |
Fingerprints¶
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Dynamical fingerprint for equilibrium correlation experiment. |
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Dynamical fingerprint for relaxation experiment. |
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Equilibrium expectation value of a given observable. |
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Time-correlation for equilibrium experiment. |
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Relaxation experiment. |
Sensitivity analysis¶
Sensitivity matrix of a stationary distribution element. |
|
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Sensitivity matrix of a specified eigenvalue. |
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Sensitivity matrix of a specified time-scale. |
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Sensitivity matrix of a selected eigenvector element. |
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Sensitivity matrix of the mean first-passage time from specified state. |
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Sensitivity matrix of a specified committor entry. |
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Sensitivity of expectation value of observable A=(a_i). |
Transition path theory tools¶
This module (deeptime.markov.tools.flux
) contains functions to compute reactive flux networks and
find dominant reaction pathways in such networks.
Reactive flux¶
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Compute the TPT flux network for the reaction A-->B. |
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Compute the netflux from the gross flux. |
Returns the net flux production for all states |
|
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Return indexes of states that are net flux producers. |
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Return indexes of states that are net flux producers. |
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Coarse-grains the flux to the given sets. |
Reaction rates and fluxes¶
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Compute the total flux, or turnover flux, that is produced by |
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Transition rate for reaction A to B. |
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Mean first passage time for reaction A to B. |
Pathway decomposition¶
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Decompose flux network into dominant reaction paths. |