function reactive_flux¶
- deeptime.markov.reactive_flux(transition_matrix: ndarray, source_states: Iterable[int], target_states: Iterable[int], stationary_distribution=None, qminus=None, qplus=None, transition_matrix_tolerance: Optional[float] = None) ReactiveFlux ¶
Computes the A->B reactive flux using transition path theory (TPT).
- Parameters:
transition_matrix ((M, M) ndarray or scipy.sparse matrix) – The transition matrix.
source_states (array_like) – List of integer state labels for set A
target_states (array_like) – List of integer state labels for set B
stationary_distribution ((M,) ndarray, optional, default=None) – Stationary vector. If None is computed from the transition matrix internally.
qminus ((M,) ndarray (optional)) – Backward committor for A->B reaction
qplus ((M,) ndarray (optional)) – Forward committor for A-> B reaction
transition_matrix_tolerance (float, optional, default=None) – Tolerance with which is checked whether the input is actually a transition matrix. If None (default), no check is performed.
- Returns:
tpt – A python object containing the reactive A->B flux network and several additional quantities, such as stationary probability, committors and set definitions.
- Return type:
deeptime.markov.tools.flux.ReactiveFlux object
Notes
The central object used in transition path theory is the forward and backward comittor function.
TPT (originally introduced in [1]) for continous systems has a discrete version outlined in [2]. Here, we use the transition matrix formulation described in [3].
See also
References