def stationary_distribution(T):
    r"""Compute stationary distribution of stochastic matrix T.

    Parameters
    ----------
    T : (M, M) ndarray or scipy.sparse matrix
        Transition matrix

    Returns
    -------
    mu : (M,) ndarray
        Vector of stationary probabilities.

    Notes
    -----
    The stationary distribution :math:`\mu` is the left eigenvector
    corresponding to the non-degenerate eigenvalue :math:`\lambda=1`,

    .. math:: \mu^T T =\mu^T.

    Examples
    --------

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

    >>> T = np.array([[0.9, 0.1, 0.0], [0.4, 0.2, 0.4], [0.0, 0.1, 0.9]])
    >>> mu = stationary_distribution(T)
    >>> mu
    array([ 0.44444444, 0.11111111, 0.44444444])

    """
    # is this a transition matrix?
    if not is_transition_matrix(T):
        raise ValueError("Input matrix is not a transition matrix."
                         "Cannot compute stationary distribution")
    # is the stationary distribution unique?
    if not is_connected(T, directed=False):
        raise ValueError("Input matrix is not weakly connected. "
                         "Therefore it has no unique stationary "
                         "distribution. Separate disconnected components "
                         "and handle them separately")
    # we're good to go...
    if _issparse(T):
        mu = sparse.stationary_vector.stationary_distribution(T)
    else:
        mu = dense.stationary_vector.stationary_distribution(T)
    return mu