With minor tweaks in comments and other errors

  structure(Data Point or diffrent condifuration of adsorbate atoms) is passed

  for featurization.

  The permuted neighbors are calculated using the Primitive cells i.e periodic cells

  in all the data points are built via lattice transformation of the primitive cell.

  [1] The Primitive Template file must be passed as raw text string or path file

  [2] The datapoint must be passed as raw text string.

  [bx][by][bz][pbc]

  [cx][cy][cz][pbc]

  [number of spectator site type][number of active site type]

  [os1][os2][os3]...

  [os1][os2][os3]

  [number sites]

  [site1a][site1b][site1c][site type]

  [site2a][site2b][site2c][site type]

  - property value indicates the trained value. It must start with #y=...

  ...

  Example:

  #y=-1.209352

  2.81859800e+00  0.00000000e+00  0.00000000e+00

  -1.40929900e+00  2.44097800e+00  0.00000000e+00

  0.00000000e+00  0.00000000e+00  2.55082550e+01

  >>> print(graph_ob)

  """

  def __init__(self, cutoff, template=None):

  def __init__(self, cutoff, template):

    """

    Parameters

    ----------

    cutoff: cutoff of radius for getting local environment.Only

        used down to 2 digits.

    input_file_path: Template primitive stucture file path

    template: Template primitive stucture file path

    """

    self.cutoff = np.around(cutoff, 2)

    self.setup_env = SiteEnvironments.Load(template, cutoff)

def InputReader(text, template=False):

  """

  Read Input Files

  Read Input Files in a format which can produces the coordinate dimensions

  and axes. If it is a primitive cell , it return the lattice cell , coordinates

  , site types and occupation state . Else if it is a primitive cell , it returns

  the additional periodic boundary and type of occupation state.

  Parameters

  ----------

        s += path

      raise ValueError(s)

    GM = iso.GraphMatcher(self.G, G, self._nm, self._em)

    # ####################### Most Time Consuming Part #####################

    # Gets the isomorphic mapping. Also the most time consuming part of the code

    ams = list(GM.isomorphisms_iter())

    # Perhaps parallelize it?

    # ####################### Most Time Consuming Part #####################

    if not ams:

      s = 'No isomorphism found.\n'

      s += "- Is the data point's cell a redefined lattice of primitive cell?\n"

  @classmethod

  def Load(cls, path, cutoff, eigen_tol=1e-5):

    """

    Load Primitive cell and return SiteEnvironments

    This loads the primitive cell, along with all the permutations

    required for creating a neighbor. This produces the site environments of

    the primitive cell.

    Parameters

    ----------

                           get_permutations=True,

                           eigen_tol=1e-5):

    """

    Extract local environments from primitive cell

    Used to extract information about both primitve cells and data points.

    Extract local environments from primitive cell. Using the two diffrent types

    site types ,

    Parameters

    ----------

        from scipy.spatial.distance import cdist

    except:

        raise ImportError("This class requires scipy to be installed.")

    # %% Check error

    assert isinstance(coord, (list, np.ndarray))

    assert isinstance(cell, (list, np.ndarray))

    assert len(coord) == len(SiteTypes)

    # %% Initialize

    # TODO: Technically, user doesn't even have to supply site index, because

    #       pymatgen can be used to automatically categorize sites..

    coord = np.mod(coord, 1)

    pbc = np.array(pbc)

    # %% Map sites to other elements..

    # TODO: Available pymatgne functions are very limited when DummySpecie is

    # Available pymatgne functions are very limited when DummySpecie is

    # involved. This may be perhaps fixed in the future. Until then, we

    # simply bypass this by mapping site to an element

    # Find available atomic number to map site to it

        symbols.append(symbol)

        if 'A' in SiteType:

            site_idxs.append(i)

    # %% Get local environments of each site

    # Find neighbors and permutations using pymatgen

    lattice = Lattice(cell)

    structure = Structure(lattice, symbols, coord)

    assert np.all(data['X_NSs'] == check2)

    assert data['X_Sites'].shape == (4, 3)

    assert data['X_NSs'].shape == (1, 4, 6, 19)

if __name__ == "__main__":

    test_LCNNFeaturizer()