docs: def qualNorm(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 = []
      thresholds = qualitative.min(1) + (qualitative.max(1) - qualitative.min(1))/2.0
      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 = thresholds[i]
          data_i = data[i,:]
          if sparse.issparse(data):
              data_i = data_i.toarray().flatten()
          assignments, means = poisson_cluster(data_i.reshape((1, cells)), 2)
          means = means.flatten()
          high_i = 1
          low_i = 0
          if means[0]>means[1]:
              high_i = 0
              low_i = 1
          high_mean = np.median(data_i[assignments==high_i])
          low_mean = np.median(data_i[assignments==low_i])
          for k in range(clusters):
              if qualitative[i,k]>threshold:
                  output[i,k] = high_mean
              else:
                  output[i,k] = low_mean
      if missing_indices:
          assignments, means = poisson_cluster(data[qual_indices, :], clusters, output[qual_indices, :], max_iters=1)
          for ind in missing_indices:
              for k in range(clusters):
                  if len(assignments==k)==0:
                      output[ind, k] = data[ind,:].mean()
                  else:
                      output[ind, k] = data[ind, assignments==k].mean()
      return output