docs: def draw_sparse_matrix(
      array_filename,
      output_image,
      vmax=DEFAULT_SATURATION_THRESHOLD,
      max_size_matrix=DEFAULT_MAX_SIZE_MATRIX,
  ):
      """Draw a quick preview of a sparse matrix with automated
      binning and normalization.
      """
  
      matrix = np.loadtxt(array_filename, dtype=np.int32, skiprows=1)
      try:
          row, col, data = matrix.T
      except ValueError:
          row, col, data = matrix
      size = max(np.amax(row), np.amax(col)) + 1
      S = sparse.coo_matrix((data, (row, col)), shape=(size, size))
      if max_size_matrix <= 0:
          binning = 1
      else:
          binning = (size // max_size_matrix) + 1
      binned_S = hcs.bin_sparse(S, subsampling_factor=binning)
      dense_S = binned_S.todense()
      dense_S = dense_S + dense_S.T - np.diag(np.diag(dense_S))
      normed_S = hcs.normalize_dense(dense_S)
      spaceless_pdf_plot_maker(normed_S, output_image, vmax=vmax)