code: def find_ref_centers(mds):
      
      # Computing the centers of each of the reference clusters
      ceu_mds = mds[mds["pop"] == "CEU"]
      yri_mds = mds[mds["pop"] == "YRI"]
      asn_mds = mds[mds["pop"] == "JPT-CHB"]
  
      # Computing the centers
      centers = [[np.mean(ceu_mds["c1"]), np.mean(ceu_mds["c2"])],
                 [np.mean(yri_mds["c1"]), np.mean(yri_mds["c2"])],
                 [np.mean(asn_mds["c1"]), np.mean(asn_mds["c2"])]]
  
      return np.array(centers), {"CEU": 0, "YRI": 1, "JPT-CHB": 2}
docs: Finds the center of the three reference clusters.
  
      :param mds: the ``mds`` information about each samples.
  
      :type mds: numpy.recarray
  
      :returns: a tuple with a :py:class:`numpy.array` containing the centers of
                the three reference population cluster as first element, and a
                :py:class:`dict` containing the label of each of the three
                reference population clusters.
  
      First, we extract the ``mds`` values of each of the three reference
      populations. The, we compute the center of each of those clusters by
      computing the means.
  
      .. math::
          \\textrm{Cluster}_\\textrm{pop} = \\left(
              \\frac{\\sum_{i=1}^n x_i}{n}, \\frac{\\sum_{i=1}^n y_i}{n}
          \\right)