Analyze the void space in the input structure using high accuracy
    voronoi decomposition.
    Calls Zeo++ for Voronoi decomposition.

    Args:
        structure: pymatgen.core.structure.Structure
        rad_dict (optional): Dictionary of radii of elements in structure.
            If not given, Zeo++ default values are used.
            Note: Zeo++ uses atomic radii of elements.
            For ionic structures, pass rad_dict with ionic radii
        probe_rad (optional): Sampling probe radius in Angstroms.
            Default is 0.1 A

    Returns:
        voronoi nodes as pymatgen.core.structure.Strucutre within the
        unit cell defined by the lattice of input structure
        voronoi face centers as pymatgen.core.structure.Strucutre within the
        unit cell defined by the lattice of input structure

def get_high_accuracy_voronoi_nodes(structure, rad_dict, probe_rad=0.1):
    """
    Analyze the void space in the input structure using high accuracy
    voronoi decomposition.
    Calls Zeo++ for Voronoi decomposition.

    Args:
        structure: pymatgen.core.structure.Structure
        rad_dict (optional): Dictionary of radii of elements in structure.
            If not given, Zeo++ default values are used.
            Note: Zeo++ uses atomic radii of elements.
            For ionic structures, pass rad_dict with ionic radii
        probe_rad (optional): Sampling probe radius in Angstroms.
            Default is 0.1 A

    Returns:
        voronoi nodes as pymatgen.core.structure.Strucutre within the
        unit cell defined by the lattice of input structure
        voronoi face centers as pymatgen.core.structure.Strucutre within the
        unit cell defined by the lattice of input structure
    """

    with ScratchDir('.'):
        name = "temp_zeo1"
        zeo_inp_filename = name + ".cssr"
        ZeoCssr(structure).write_file(zeo_inp_filename)
        rad_flag = True
        rad_file = name + ".rad"
        with open(rad_file, 'w+') as fp:
            for el in rad_dict.keys():
                print("{} {}".format(el, rad_dict[el].real), file=fp)

        atmnet = AtomNetwork.read_from_CSSR(
            zeo_inp_filename, rad_flag=rad_flag, rad_file=rad_file)
        # vornet, vor_edge_centers, vor_face_centers = \
        #        atmnet.perform_voronoi_decomposition()
        red_ha_vornet = \
            prune_voronoi_network_close_node(atmnet)
        # generate_simplified_highaccuracy_voronoi_network(atmnet)
        # get_nearest_largest_diameter_highaccuracy_vornode(atmnet)
        red_ha_vornet.analyze_writeto_XYZ(name, probe_rad, atmnet)
        voro_out_filename = name + '_voro.xyz'
        voro_node_mol = ZeoVoronoiXYZ.from_file(voro_out_filename).molecule

    species = ["X"] * len(voro_node_mol.sites)
    coords = []
    prop = []
    for site in voro_node_mol.sites:
        coords.append(list(site.coords))
        prop.append(site.properties['voronoi_radius'])

    lattice = Lattice.from_lengths_and_angles(
        structure.lattice.abc, structure.lattice.angles)
    vor_node_struct = Structure(
        lattice, species, coords, coords_are_cartesian=True,
        to_unit_cell=True, site_properties={"voronoi_radius": prop})

    return vor_node_struct