def get_chemical_ordering_parameters(self):
'''
Computes the mean chemical ordering parameter and chemical ordering
parameter variance for all elements in the crystal using
Voronoi polyhedra
returns:
[mean coordination number, coordination number variance]
'''
if len(self._crystal.composition) == 1:
return [0] * len(self._shells)
# Get a list of types
elems, fracs = zip(*self._crystal.composition.element_composition.
fractional_composition.items())
# Precompute the list of NNs in the structure
weight = 'area'
voro = VoronoiNN(weight=weight)
all_nn = self._get_all_nearest_neighbors(voro, self._crystal)
# Evaluate each shell
output = []
for shell in self._shells:
# Initialize an array to store the ordering parameters
ordering = np.zeros((len(self._crystal), len(elems)))
# Get the ordering of each type of each atom
for site_idx in range(len(self._crystal)):
nns = voro._get_nn_shell_info(self._crystal, all_nn, site_idx,
shell)
# Sum up the weights
total_weight = sum(x['weight'] for x in nns)
# Get weight by type
for nn in nns:
site_elem = nn['site'].specie.element \
if isinstance(nn['site'].specie, Specie) else \
nn['site'].specie
elem_idx = elems.index(site_elem)
ordering[site_idx, elem_idx] += nn['weight']
# Compute the ordering parameter
ordering[site_idx, :] = 1 - ordering[site_idx, :] / \
total_weight / np.array(fracs)
# Compute the average ordering for the entire structure
output.append(np.abs(ordering).mean())
return output
def featurize(self, strc):
# Shortcut: Return 0 if there is only 1 type of atom
if len(strc.composition) == 1:
return [0] * len(self.shells)
# Get a list of types
elems, fracs = zip(*strc.composition.element_composition.
fractional_composition.items())
# Precompute the list of NNs in the structure
voro = VoronoiNN(weight=self.weight)
all_nn = get_all_nearest_neighbors(voro, strc)
# Evaluate each shell
output = []
for shell in self.shells:
# Initialize an array to store the ordering parameters
ordering = np.zeros((len(strc), len(elems)))
# Get the ordering of each type of each atom
for site_idx in range(len(strc)):
nns = voro._get_nn_shell_info(strc, all_nn, site_idx, shell)
# Sum up the weights
total_weight = sum(x['weight'] for x in nns)
# Get weight by type
for nn in nns:
site_elem = nn['site'].specie.element \
if isinstance(nn['site'].specie, Specie) else \
nn['site'].specie
elem_idx = elems.index(site_elem)
ordering[site_idx, elem_idx] += nn['weight']
# Compute the ordering parameter
ordering[site_idx, :] = 1 - ordering[site_idx, :] / \
total_weight / np.array(fracs)
# Compute the average ordering for the entire structure
output.append(np.abs(ordering).mean())
return output
