def generate_traj(P, N, start=None, stop=None, dt=1):
    """
    Generates a realization of the Markov chain with transition matrix P.

    Parameters
    ----------
    P : (n, n) ndarray
        transition matrix
    N : int
        trajectory length
    start : int, optional, default = None
        starting state. If not given, will sample from the stationary distribution of P
    stop : int or int-array-like, optional, default = None
        stopping set. If given, the trajectory will be stopped before N steps
        once a state of the stop set is reached
    dt : int
        trajectory will be saved every dt time steps.
        Internally, the dt'th power of P is taken to ensure a more efficient simulation.

    Returns
    -------
    traj_sliced : (N/dt, ) ndarray
        A discrete trajectory with length N/dt

    """
    sampler = MarkovChainSampler(P, dt=dt)
    return sampler.trajectory(N, start=start, stop=stop)
def generate_traj ( P , N , start = None , stop = None , dt = 1 ) : sampler = MarkovChainSampler ( P , dt = dt ) return sampler . trajectory ( N , start = start , stop = stop )
Generates a realization of the Markov chain with transition matrix P.

    Parameters
    ----------
    P : (n, n) ndarray
        transition matrix
    N : int
        trajectory length
    start : int, optional, default = None
        starting state. If not given, will sample from the stationary distribution of P
    stop : int or int-array-like, optional, default = None
        stopping set. If given, the trajectory will be stopped before N steps
        once a state of the stop set is reached
    dt : int
        trajectory will be saved every dt time steps.
        Internally, the dt'th power of P is taken to ensure a more efficient simulation.

    Returns
    -------
    traj_sliced : (N/dt, ) ndarray
        A discrete trajectory with length N/dt