How to use the pyemma.util.annotators.alias function in pyEMMA

    @alias('dtrajs_full')
    def discrete_trajectories_full(self):
        """
        A list of integer arrays with the original (unmapped) discrete trajectories:

        """
        self._check_is_estimated()
        return self._dtrajs_full

    @alias('dtrajs_active')
    def discrete_trajectories_active(self):
        """
        A list of integer arrays with the discrete trajectories mapped to the connectivity mode used.
        For example, for connectivity='largest', the indexes will be given within the connected set.
        Frames that are not in the connected set will be -1.

        """
        self._check_is_estimated()
        # compute connected dtrajs
        self._dtrajs_active = []
        for dtraj in self._dtrajs_full:
            self._dtrajs_active.append(self._full2active[dtraj])

        return self._dtrajs_active

    @alias('mu')
    def stationary_distribution(self):
        r"""Returns the stationary distribution
        """
        return self._mu

    @_alias('stationary_distribution')
    def pi(self):
        r"""The stationary distribution on the configuration states."""
        return self._pi

    @alias('hist_lagged')
    def histogram_lagged(self, connected_set=None, subset=None, effective=False):
        r""" Histogram of discrete state counts

        """
        C = self.count_matrix(connected_set=connected_set, subset=subset, effective=effective)
        return C.sum(axis=1)

    @alias('forward_committor', 'qplus')
    def committor(self):
        r"""Returns the forward committor probability
        """
        return self._qplus

    @alias('dtrajs')
    def discrete_trajectories(self):
        """
        A list of integer arrays with the original (unmapped) discrete trajectories:

        """
        return self._dtrajs

    @alias('cluster_centers_')  # sk-learn compat.
    def clustercenters(self):
        """ Array containing the coordinates of the calculated cluster centers. """
        return self._clustercenters

    @_alias('free_energies')
    def f(self):
        r"""The free energies (in units of kT) on the configuration states."""
        return self._f

    @alias('number_of_timescales')
    def nits(self):
        """Return the number of timescales."""
        return self._nits