'batch_to_feed_dict'

class DTNNGraphTopology(object):

  """Manages placeholders associated with batch of graphs and their topology"""

  def __init__(self, max_n_atoms, n_distance, name='DTNN_topology'):

  def __init__(self, max_n_atoms=30, n_distance=100, name='DTNN_topology'):

    """

    Note that batch size is not specified in a GraphTopology object. A batch

    of molecules must be combined into a disconnected graph and fed to topology

    for molecule in atom_number:

        ZiZj.append(np.outer(molecule, molecule))

    ZiZj = np.asarray(ZiZj)

    distance_matrix = batch[:]

    distance_matrix = np.expand_dims(batch[:], axis=3)

    distance_matrix = np.concatenate([distance_matrix]*self.n_distance, axis=3)

    distance_matrix_mask = batch[:]

    for im, molecule in enumerate(batch):

      for ir, row in enumerate(molecule):

        for ie, element in enumerate(row):

          if element>0 and ir != ie:

            distance_matrix[im, ir, ie] = ZiZj[im, ir,ie]/element

            distance_matrix[im, ir, ie, :] = self.gauss_expand(ZiZj[im, ir, ie]/element, 

                                                               self.n_distance)

            distance_matrix_mask[im, ir, ie] = 1

          else:

            distance_matrix[im, ir, ie] = 0

    distance_matrix_mask = np.asarray(np.asarray(distance_matrix, dtype=bool), dtype=float)

    distance_matrix = self.gauss_expand(distance_matrix)

            distance_matrix[im, ir, ie, :] = 0

            distance_matrix_mask[im, ir, ie] = 0

    # Generate dicts

    dict_DTNN = {

        self.atom_number_placeholder: atom_number,

    return dict_DTNN

  @staticmethod

  def gauss_expand(distance_matrix):

    for im, molecule in enumerate(distance_matrix):

      for ir, row in enumerate(molecule):

        for ie, element in enumerate(row):

          if element>0

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  def gauss_expand(distance, n_distance, distance_min=-1., distance_max=18.):

    step_size = (distance_max - distance_min)/n_distance

    steps = np.array([distance_min+i*step_size for i in range(n_distance)])

    distance_vector = np.exp(-np.square(distance - steps)/(2*step_size**2))

    return distance_vector

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