本文整理汇总了Python中pymatgen.symmetry.analyzer.SpacegroupAnalyzer.from_sites方法的典型用法代码示例。如果您正苦于以下问题：Python SpacegroupAnalyzer.from_sites方法的具体用法？Python SpacegroupAnalyzer.from_sites怎么用？Python SpacegroupAnalyzer.from_sites使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pymatgen.symmetry.analyzer.SpacegroupAnalyzer的用法示例。
在下文中一共展示了SpacegroupAnalyzer.from_sites方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_structure_type
# 需要导入模块: from pymatgen.symmetry.analyzer import SpacegroupAnalyzer [as 别名]
# 或者: from pymatgen.symmetry.analyzer.SpacegroupAnalyzer import from_sites [as 别名]
#.........这里部分代码省略.........
type = "noble gas"
else:
# make 2x2x2 supercell to ensure sufficient number of atoms
# for cluster building.
s.make_supercell(2)
# Distance matrix (rowA, columnB) shows distance between
# atoms A and B, taking PBCs into account.
distance_matrix = s.distance_matrix
# Fill diagonal with a large number, so the code knows that
# each atom is not bonded to itself.
np.fill_diagonal(distance_matrix, 100)
# Rows (`radii`) and columns (`radiiT`) of radii.
radii = [ELEMENT_RADII[site.species_string] for site in s.sites]
radiiT = np.array(radii)[np.newaxis].T
radii_matrix = radii + radiiT*(1+tol)
# elements of temp that have value less than 0 are bonded.
temp = distance_matrix - radii_matrix
# True (1) is placed where temp < 0, and False (0) where
# it is not.
binary_matrix = (temp < 0).astype(int)
# list of atoms bonded to the seed atom of a cluster
seed = set((np.where(binary_matrix[seed_index]==1))[0])
cluster = seed
NEW = seed
while True:
temp_set = set()
for n in NEW:
# temp_set will have all atoms, without duplicates,
# that are connected to all atoms in NEW.
temp_set.update(set(np.where(binary_matrix[n]==1)[0]))
if temp_set.issubset(cluster):
# if temp_set has no new atoms, the search is done.
break
else:
NEW = temp_set - cluster # List of newly discovered atoms
cluster.update(temp_set) # cluster is updated with new atoms
if len(cluster) == 0: # i.e. the cluster is a single atom.
cluster = [seed_index] # Make sure it's not empty to write POSCAR.
type = "molecular"
elif len(cluster) == len(s.sites): # i.e. all atoms are bonded.
type = "conventional"
else:
cmp = Composition.from_dict(Counter([s[l].specie.name for l in
list(cluster)]))
if cmp.reduced_formula != s.composition.reduced_formula:
# i.e. the cluster does not have the same composition
# as the overall crystal; therefore there are other
# clusters of varying composition.
heterogeneous = True
old_cluster_size = len(cluster)
# Increase structure to determine whether it is
# layered or molecular, then perform the same kind
# of cluster search as before.
s.make_supercell(2)
distance_matrix = s.distance_matrix
np.fill_diagonal(distance_matrix,100)
radii = [ELEMENT_RADII[site.species_string] for site in s.sites]
radiiT = np.array(radii)[np.newaxis].T
radii_matrix = radii + radiiT*(1+tol)
temp = distance_matrix-radii_matrix
binary_matrix = (temp < 0).astype(int)
seed = set((np.where(binary_matrix[seed_index]==1))[0])
cluster = seed
NEW = seed
check = True
while check:
temp_set = set()
for n in NEW:
temp_set.update(set(np.where(binary_matrix[n]==1)[0]))
if temp_set.issubset(cluster):
check = False
else:
NEW = temp_set - cluster
cluster.update(temp_set)
if len(cluster) != 4 * old_cluster_size:
type = "molecular"
else:
type = "layered"
if heterogeneous:
type += " heterogeneous"
cluster_sites = [s.sites[n] for n in cluster]
if write_poscar_from_cluster:
s.from_sites(cluster_sites).get_primitive_structure().to("POSCAR",
"POSCAR")
return type