def tpt(T, A, B, mu=None, qminus=None, qplus=None, rate_matrix=False):
    r""" Computes the A->B reactive flux using transition path theory (TPT)

    Parameters
    ----------
    T : (M, M) ndarray or scipy.sparse matrix
        Transition matrix (default) or Rate matrix (if rate_matrix=True)
    A : array_like
        List of integer state labels for set A
    B : array_like
        List of integer state labels for set B
    mu : (M,) ndarray (optional)
        Stationary vector
    qminus : (M,) ndarray (optional)
        Backward committor for A->B reaction
    qplus : (M,) ndarray (optional)
        Forward committor for A-> B reaction
    rate_matrix = False : boolean
        By default (False), T is a transition matrix.
        If set to True, T is a rate matrix.

    Returns
    -------
    tpt: msmtools.flux.ReactiveFlux object
        A python object containing the reactive A->B flux network
        and several additional quantities, such as stationary probability,
        committors and set definitions.

    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
    --------
    msmtools.analysis.committor, ReactiveFlux

    References
    ----------
    .. [1] W. E and E. Vanden-Eijnden.
        Towards a theory of transition paths.
        J. Stat. Phys. 123: 503-523 (2006)
    .. [2] P. Metzner, C. Schuette and E. Vanden-Eijnden.
        Transition Path Theory for Markov Jump Processes.
        Multiscale Model Simul 7: 1192-1219 (2009)
    .. [3] F. Noe, Ch. Schuette, E. Vanden-Eijnden, L. Reich and T. Weikl:
        Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations.
        Proc. Natl. Acad. Sci. USA, 106, 19011-19016 (2009)

    """
    import msmtools.analysis as msmana

    if len(A) == 0 or len(B) == 0:
        raise ValueError('set A or B is empty')
    n = T.shape[0]
    if len(A) > n or len(B) > n or max(A) > n or max(B) > n:
        raise ValueError('set A or B defines more states, than given transition matrix.')
    if (rate_matrix is False) and (not msmana.is_transition_matrix(T)):
        raise ValueError('given matrix T is not a transition matrix')
    if (rate_matrix is True):
        raise NotImplementedError(
            'TPT with rate matrix is not yet implemented - But it is very simple, so feel free to do it.')

    # we can compute the following properties from either dense or sparse T
    # stationary dist
    if mu is None:
        mu = msmana.stationary_distribution(T)
    # forward committor
    if qplus is None:
        qplus = msmana.committor(T, A, B, forward=True)
    # backward committor
    if qminus is None:
        if msmana.is_reversible(T, mu=mu):
            qminus = 1.0 - qplus
        else:
            qminus = msmana.committor(T, A, B, forward=False, mu=mu)
    # gross flux
    grossflux = flux_matrix(T, mu, qminus, qplus, netflux=False)
    # net flux
    netflux = to_netflux(grossflux)

    # construct flux object
    from .reactive_flux import ReactiveFlux

    F = ReactiveFlux(A, B, netflux, mu=mu, qminus=qminus, qplus=qplus, gross_flux=grossflux)
    # done
    return F
def tpt ( T , A , B , mu = None , qminus = None , qplus = None , rate_matrix = False ) : import msmtools . analysis as msmana if len ( A ) == 0 or len ( B ) == 0 : raise ValueError ( 'set A or B is empty' ) n = T . shape [ 0 ] if len ( A ) > n or len ( B ) > n or max ( A ) > n or max ( B ) > n : raise ValueError ( 'set A or B defines more states, than given transition matrix.' ) if ( rate_matrix is False ) and ( not msmana . is_transition_matrix ( T ) ) : raise ValueError ( 'given matrix T is not a transition matrix' ) if ( rate_matrix is True ) : raise NotImplementedError ( 'TPT with rate matrix is not yet implemented - But it is very simple, so feel free to do it.' ) if mu is None : mu = msmana . stationary_distribution ( T ) if qplus is None : qplus = msmana . committor ( T , A , B , forward = True ) if qminus is None : if msmana . is_reversible ( T , mu = mu ) : qminus = 1.0 - qplus else : qminus = msmana . committor ( T , A , B , forward = False , mu = mu ) grossflux = flux_matrix ( T , mu , qminus , qplus , netflux = False ) netflux = to_netflux ( grossflux ) from . reactive_flux import ReactiveFlux F = ReactiveFlux ( A , B , netflux , mu = mu , qminus = qminus , qplus = qplus , gross_flux = grossflux ) return F
r""" Computes the A->B reactive flux using transition path theory (TPT)

    Parameters
    ----------
    T : (M, M) ndarray or scipy.sparse matrix
        Transition matrix (default) or Rate matrix (if rate_matrix=True)
    A : array_like
        List of integer state labels for set A
    B : array_like
        List of integer state labels for set B
    mu : (M,) ndarray (optional)
        Stationary vector
    qminus : (M,) ndarray (optional)
        Backward committor for A->B reaction
    qplus : (M,) ndarray (optional)
        Forward committor for A-> B reaction
    rate_matrix = False : boolean
        By default (False), T is a transition matrix.
        If set to True, T is a rate matrix.

    Returns
    -------
    tpt: msmtools.flux.ReactiveFlux object
        A python object containing the reactive A->B flux network
        and several additional quantities, such as stationary probability,
        committors and set definitions.

    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
    --------
    msmtools.analysis.committor, ReactiveFlux

    References
    ----------
    .. [1] W. E and E. Vanden-Eijnden.
        Towards a theory of transition paths.
        J. Stat. Phys. 123: 503-523 (2006)
    .. [2] P. Metzner, C. Schuette and E. Vanden-Eijnden.
        Transition Path Theory for Markov Jump Processes.
        Multiscale Model Simul 7: 1192-1219 (2009)
    .. [3] F. Noe, Ch. Schuette, E. Vanden-Eijnden, L. Reich and T. Weikl:
        Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations.
        Proc. Natl. Acad. Sci. USA, 106, 19011-19016 (2009)