def __detect_branching_haghverdi16(self, Dseg, tips):
        """Detect branching on given segment.

        Compute point that maximizes kendall tau correlation of the sequences of
        distances to the second and the third tip, respectively, when 'moving
        away' from the first tip: tips[0]. 'Moving away' means moving in the
        direction of increasing distance from the first tip.

        Parameters
        ----------
        Dseg : np.ndarray
            Dchosen distance matrix restricted to segment.
        tips : np.ndarray
            The three tip points. They form a 'triangle' that contains the data.

        Returns
        -------
        ssegs : list of np.ndarray
            List of segments obtained from "splitting away the first tip cell".
        """
        # sort distance from first tip point
        # then the sequence of distances Dseg[tips[0]][idcs] increases
        idcs = np.argsort(Dseg[tips[0]])
        # consider now the sequence of distances from the other
        # two tip points, which only increase when being close to `tips[0]`
        # where they become correlated
        # at the point where this happens, we define a branching point
        if True:
            imax = self.kendall_tau_split(Dseg[tips[1]][idcs],
                                          Dseg[tips[2]][idcs])
        if False:
            # if we were in euclidian space, the following should work
            # as well, but here, it doesn't because the scales in Dseg are
            # highly different, one would need to write the following equation
            # in terms of an ordering, such as exploited by the kendall
            # correlation method above
            imax = np.argmin(Dseg[tips[0]][idcs]
                             + Dseg[tips[1]][idcs]
                             + Dseg[tips[2]][idcs])
        # init list to store new segments
        ssegs = []
        # first new segment: all points until, but excluding the branching point
        # increasing the following slightly from imax is a more conservative choice
        # as the criterion based on normalized distances, which follows below,
        # is less stable
        if imax > 0.95 * len(idcs) and self.allow_kendall_tau_shift:
            # if "everything" is correlated (very large value of imax), a more
            # conservative choice amounts to reducing this
            logg.warn('shifting branching point away from maximal kendall-tau correlation (suppress this with `allow_kendall_tau_shift=False`)')
            ibranch = int(0.95 * imax)
        else:
            # otherwise, a more conservative choice is the following
            ibranch = imax + 1
        return idcs[:ibranch]