def timescales(T, tau=1, k=None, ncv=None, reversible=False, mu=None):
    r"""Compute implied time scales of given transition matrix.

    Parameters
    ----------
    T : (M, M) ndarray or scipy.sparse matrix
        Transition matrix
    tau : int (optional)
        The time-lag (in elementary time steps of the microstate
        trajectory) at which the given transition matrix was
        constructed.
    k : int (optional)
        Compute the first `k` implied time scales.
    ncv : int (optional, for sparse T only)
        The number of Lanczos vectors generated, `ncv` must be greater than k;
        it is recommended that ncv > 2*k
    reversible : bool, optional
        Indicate that transition matrix is reversible
    mu : (M,) ndarray, optional
        Stationary distribution of T

    Returns
    -------
    ts : (M,) ndarray
        The implied time scales of the transition matrix.  If `k` is
        not None then the shape of `ts` is (k,).

    Notes
    -----
    The implied time scale :math:`t_i` is defined as

    .. math:: t_i=-\frac{\tau}{\log \lvert \lambda_i \rvert}

    If reversible=True the the eigenvalues of the similar symmetric
    matrix `\sqrt(\mu_i / \mu_j) p_{ij}` will be computed.

    The precomputed stationary distribution will only be used if
    reversible=True.

    Examples
    --------

    >>> import numpy as np
    >>> from msmtools.analysis import timescales

    >>> T = np.array([[0.9, 0.1, 0.0], [0.5, 0.0, 0.5], [0.0, 0.1, 0.9]])
    >>> ts = timescales(T)
    >>> ts
    array([        inf,  9.49122158,  0.43429448])

    """
    T = _types.ensure_ndarray_or_sparse(T, ndim=2, uniform=True, kind='numeric')
    if _issparse(T):
        return sparse.decomposition.timescales(T, tau=tau, k=k, ncv=ncv,
                                               reversible=reversible, mu=mu)
    else:
        return dense.decomposition.timescales(T, tau=tau, k=k, reversible=reversible, mu=mu)
def timescales ( T , tau = 1 , k = None , ncv = None , reversible = False , mu = None ) : T = _types . ensure_ndarray_or_sparse ( T , ndim = 2 , uniform = True , kind = 'numeric' ) if _issparse ( T ) : return sparse . decomposition . timescales ( T , tau = tau , k = k , ncv = ncv , reversible = reversible , mu = mu ) else : return dense . decomposition . timescales ( T , tau = tau , k = k , reversible = reversible , mu = mu )
r"""Compute implied time scales of given transition matrix.

    Parameters
    ----------
    T : (M, M) ndarray or scipy.sparse matrix
        Transition matrix
    tau : int (optional)
        The time-lag (in elementary time steps of the microstate
        trajectory) at which the given transition matrix was
        constructed.
    k : int (optional)
        Compute the first `k` implied time scales.
    ncv : int (optional, for sparse T only)
        The number of Lanczos vectors generated, `ncv` must be greater than k;
        it is recommended that ncv > 2*k
    reversible : bool, optional
        Indicate that transition matrix is reversible
    mu : (M,) ndarray, optional
        Stationary distribution of T

    Returns
    -------
    ts : (M,) ndarray
        The implied time scales of the transition matrix.  If `k` is
        not None then the shape of `ts` is (k,).

    Notes
    -----
    The implied time scale :math:`t_i` is defined as

    .. math:: t_i=-\frac{\tau}{\log \lvert \lambda_i \rvert}

    If reversible=True the the eigenvalues of the similar symmetric
    matrix `\sqrt(\mu_i / \mu_j) p_{ij}` will be computed.

    The precomputed stationary distribution will only be used if
    reversible=True.

    Examples
    --------

    >>> import numpy as np
    >>> from msmtools.analysis import timescales

    >>> T = np.array([[0.9, 0.1, 0.0], [0.5, 0.0, 0.5], [0.0, 0.1, 0.9]])
    >>> ts = timescales(T)
    >>> ts
    array([        inf,  9.49122158,  0.43429448])