code: def to_hdf5(self, filename):
          
          import h5py
          with h5py.File(filename, "w") as f:
              ds = f.create_dataset("lattice", (3, 3), dtype='float')
              ds[...] = self.structure.lattice.matrix
              ds = f.create_dataset("Z", (len(self.structure.species),),
                                    dtype="i")
              ds[...] = np.array([sp.Z for sp in self.structure.species])
              ds = f.create_dataset("fcoords", self.structure.frac_coords.shape,
                                    dtype='float')
              ds[...] = self.structure.frac_coords
              dt = h5py.special_dtype(vlen=str)
              ds = f.create_dataset("species", (len(self.structure.species),),
                                    dtype=dt)
              ds[...] = [str(sp) for sp in self.structure.species]
              grp = f.create_group("vdata")
              for k, v in self.data.items():
                  ds = grp.create_dataset(k, self.data[k].shape, dtype='float')
                  ds[...] = self.data[k]
              f.attrs["name"] = self.name
              f.attrs["structure_json"] = json.dumps(self.structure.as_dict())
docs: Writes the VolumetricData to a HDF5 format, which is a highly optimized
          format for reading storing large data. The mapping of the VolumetricData
          to this file format is as follows:
  
          VolumetricData.data -> f["vdata"]
          VolumetricData.structure ->
              f["Z"]: Sequence of atomic numbers
              f["fcoords"]: Fractional coords
              f["lattice"]: Lattice in the pymatgen.core.lattice.Lattice matrix
                  format
              f.attrs["structure_json"]: String of json representation
  
          Args:
              filename (str): Filename to output to.