MSM Construction: msmbuilder.MSMLib¶
Functions for building MSMs
Notes¶
- Assignments typically refer to a numpy array of integers such that Assignments[i,j] gives the state of trajectory i, frame j.
- Transition and Count matrices are typically stored in scipy.sparse.csr_matrix format.
- Some functionality from this module was moved into msmanalysis in version2.6
Mapping¶
| apply_mapping_to_assignments(assignments, ...) | Remap the states in an assignments file according to a mapping. |
| apply_mapping_to_vector(vector, mapping) | Remap an observable vector after ergodic trimming |
| renumber_states(assignments) | Renumber states to be consecutive integers (0, 1, ... |
| invert_assignments(assignments) | Invert an assignments array – that is, produce a mapping |
Trimming¶
| tarjan(graph) | Find the strongly connected components in a graph using Tarjan’s algorithm. |
| ergodic_trim(counts[, assignments]) | Use Tarjan’s Algorithm to find maximal strongly connected subgraph. |
Model Building¶
| build_msm(counts[, symmetrize, ergodic_trimming]) | Estimates the transition probability matrix from the counts matrix. |
| build_msm_from_counts | |
| estimate_rate_matrix(count_matrix, assignments) | MLE Rate Matrix given transition counts and dwell times |
| mle_reversible_count_matrix(count_matrix) | Maximum likelihood estimate for a reversible count matrix |
| estimate_transition_matrix(count_matrix) | Simple Maximum Likelihood estimator of transition matrix. |
| get_count_matrix_from_assignments(assignments) | Calculate counts matrix from assignments. |
| get_counts_from_traj(states[, n_states, ...]) | Computes the transition count matrix for a sequence of states (single trajectory). |
| log_likelihood(count_matrix, transition_matrix) | log of the likelihood of an observed count matrix given a transition matrix |