code: def distances_within(coords_a, coords_b, cutoff,
                       periodic=False, method="simple"):
      
      mat = distance_matrix(coords_a, coords_b, cutoff, periodic, method)
      return mat[mat.nonzero()]
docs: Calculate distances between the array of coordinates *coord_a*
      and *coord_b* within a certain cutoff.
      
      This function is a wrapper around different routines and data structures
      for distance searches. It return a np.ndarray containing the distances.
      
      **Parameters**
  
      coords_a: np.ndarray((N, 3), dtype=float)
         First coordinate array
      coords_b: np.ndarray((N, 3), dtype=float)
         Second coordinate array
      cutoff: float
         Maximum distance to search for
      periodic: False or np.ndarray((3,), dtype=float)
         If False, don't consider periodic images. Otherwise
         periodic is an array containing the periodicity in the
         3 dimensions.
      method: "simple" | "cell-lists"
         The method to use. *simple* is a brute-force 
         distance search, *kdtree* uses scipy ``ckdtree`` module
         (periodic not available) and *cell-lists* uses the cell
         linked list method.