docs: def qualNormGaussian(data, qualitative):
      """
      Generates starting points using binarized data. If qualitative data is missing for a given gene, all of its entries should be -1 in the qualitative matrix.
  
      Args:
          data (array): 2d array of genes x cells
          qualitative (array): 2d array of numerical data - genes x clusters
  
      Returns:
          Array of starting positions for state estimation or
          clustering, with shape genes x clusters
      """
      genes, cells = data.shape
      clusters = qualitative.shape[1]
      output = np.zeros((genes, clusters))
      missing_indices = []
      qual_indices = []
      for i in range(genes):
          if qualitative[i,:].max() == -1 and qualitative[i,:].min() == -1:
              missing_indices.append(i)
              continue
          qual_indices.append(i)
          threshold = (qualitative[i,:].max() - qualitative[i,:].min())/2.0
          kmeans = KMeans(n_clusters = 2).fit(data[i,:].reshape((1, cells)))
          assignments = kmeans.labels_
          means = kmeans.cluster_centers_
          high_mean = means.max()
          low_mean = means.min()
          for k in range(clusters):
              if qualitative[i,k]>threshold:
                  output[i,k] = high_mean
              else:
                  output[i,k] = low_mean
      if missing_indices:
          #generating centers for missing indices 
          M_init = output[qual_indices, :]
          kmeans = KMeans(n_clusters = 2, init = M_init, max_iter = 1).fit(data[qual_indices, :])
          assignments = kmeans.labels_
          #assignments, means = poisson_cluster(data[qual_indices, :], clusters, output[qual_indices, :], max_iters=1)
          for ind in missing_indices:
              for k in range(clusters):
                  output[ind, k] = np.mean(data[ind, assignments==k])
      # TODO: assign to closest
      return output