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

ReactiveFlux

References