def _analyze(self):
"""Guesses the mmtypes, bonds and pointgroup"""
dummies = ase.Atoms([x for x in self.atoms if x.symbol == "X"])
if len(dummies) > 0:
pg = symmetry.PointGroup(mol=dummies.copy(), tol=0.1)
max_order = min(8, len(dummies))
shape = symmetry.get_symmetry_elements(mol=dummies.copy(),
max_order=max_order)
self.shape = shape
self.pg = pg.schoenflies
bonds, mmtypes = analyze_mm(self.get_atoms())
self.bonds = bonds
self.mmtypes = mmtypes
return None
def _analyze(self):
"""Analyze the topology to cut the fragments out."""
# separate the dummies from the rest
logger.debug("Analyzing fragments of topology {0}.".format(self.name))
numbers = numpy.asarray(self.atoms.get_atomic_numbers())
Xis = numpy.where(numbers == 0)[0]
Ais = numpy.where(numbers > 0)[0]
# setup the tags
tags = numpy.zeros(len(self.atoms))
tags[Xis] = Xis + 1
self.atoms.set_tags(tags)
tags = self.atoms.get_tags()
# analyze
# first build the neighborlist
cutoffs = self._get_cutoffs(Xis=Xis, Ais=Ais)
neighborlist = NeighborList(cutoffs=cutoffs,
bothways=True,
self_interaction=False,
skin=0.0)
neighborlist.update(self.atoms)
# iterate over non-dummies to find dummy neighbors
for ai in Ais:
# get indices and offsets of dummies only!
ni, no = neighborlist.get_neighbors(ai)
ni, no = zip(*[(idx, off) for idx, off in list(zip(ni, no))
if idx in Xis])
ni = numpy.asarray(ni)
no = numpy.asarray(no)
# get absolute positions, no offsets
positions = self.atoms.positions[ni] + no.dot(self.atoms.cell)
# create the Atoms object
fragment = Atoms("X" * len(ni), positions, tags=tags[ni])
# calculate the point group properties
max_order = min(8, len(ni))
shape = symmetry.get_symmetry_elements(mol=fragment.copy(),
max_order=max_order)
pg = symmetry.PointGroup(mol=fragment.copy(), tol=0.1)
# save that info
self.fragments[ai] = fragment
self.shapes[ai] = shape
self.pointgroups[ai] = pg.schoenflies
# now getting the equivalent sites using the Spacegroup object
sg = self.atoms.info["spacegroup"]
if not isinstance(sg, Spacegroup):
sg = Spacegroup(sg)
scaled_positions = self.atoms.get_scaled_positions()
seen_indices = []
symbols = numpy.array(self.atoms.get_chemical_symbols())
for ai in Ais:
if ai in seen_indices:
continue
sites, _ = sg.equivalent_sites(scaled_positions[ai])
these_indices = []
for site in sites:
norms = numpy.linalg.norm(scaled_positions - site, axis=1)
if norms.min() < 1e-6:
these_indices.append(norms.argmin())
# take pbc into account
norms = numpy.abs(norms - 1.0)
if norms.min() < 1e-6:
these_indices.append(norms.argmin())
these_indices = [idx for idx in these_indices if idx in Ais]
seen_indices += these_indices
self.equivalent_sites.append(these_indices)
logger.info("{es} equivalent sites kinds.".format(
es=len(self.equivalent_sites)))
return None