def do(self):
cache = context.application.cache
parent = cache.node
unit_cell = None
if isinstance(parent, UnitCell):
unit_cell = parent
Atom = context.application.plugins.get_node("Atom")
atoms = []
coordinates = []
for child in parent.children:
if isinstance(child, Atom):
atoms.append(child)
coordinates.append(child.transformation.t)
coordinates = numpy.array(coordinates)
cf = ClusterFactory()
for i0, i1, delta, distance in PairSearchIntra(
coordinates, periodic.max_radius * 0.4, unit_cell):
atom0 = atoms[i0]
atom1 = atoms[i1]
if atom0.number == atom1.number:
if distance < periodic[atom0.number].vdw_radius * 0.4:
cf.add_related(atom0, atom1)
clusters = cf.get_clusters()
del cf
# define the new singles
singles = []
for cluster in clusters:
number = iter(cluster.items).next().number
single = Atom(name="Single " + periodic[number].symbol)
single.set_number(number)
singles.append((single, list(cluster.items)))
# calculate their positions
for single, overlappers in singles:
# in the following algorithm, we suppose that the cluster of
# atoms is small compared to the parent's periodic sizes
# (if the parent is a periodic system)
first_pos = overlappers[0].transformation.t
delta_to_mean = numpy.zeros(3, float)
for atom in overlappers[1:]:
delta_to_mean += parent.shortest_vector(atom.transformation.t -
first_pos)
delta_to_mean /= float(len(overlappers))
single.set_transformation(Translation(first_pos + delta_to_mean))
# modify the model
for single, overlappers in singles:
lowest_index = min([atom.get_index() for atom in overlappers])
primitive.Add(single, parent, index=lowest_index)
for atom in overlappers:
while len(atom.references) > 0:
primitive.SetTarget(atom.references[0], single)
primitive.Delete(atom)
def do(self):
cache = context.application.cache
parent = cache.node
unit_cell = None
if isinstance(parent, UnitCell):
unit_cell = parent
Atom = context.application.plugins.get_node("Atom")
atoms = []
coordinates = []
for child in parent.children:
if isinstance(child, Atom):
atoms.append(child)
coordinates.append(child.transformation.t)
coordinates = numpy.array(coordinates)
cf = ClusterFactory()
for i0, i1, delta, distance in PairSearchIntra(coordinates, periodic.max_radius*0.4, unit_cell):
atom0 = atoms[i0]
atom1 = atoms[i1]
if atom0.number == atom1.number:
if distance < periodic[atom0.number].vdw_radius*0.4:
cf.add_related(atom0, atom1)
clusters = cf.get_clusters()
del cf
# define the new singles
singles = []
for cluster in clusters:
number = iter(cluster.items).next().number
single = Atom(name="Single " + periodic[number].symbol)
single.set_number(number)
singles.append((single, list(cluster.items)))
# calculate their positions
for single, overlappers in singles:
# in the following algorithm, we suppose that the cluster of
# atoms is small compared to the parent's periodic sizes
# (if the parent is a periodic system)
first_pos = overlappers[0].transformation.t
delta_to_mean = numpy.zeros(3, float)
for atom in overlappers[1:]:
delta_to_mean += parent.shortest_vector(atom.transformation.t - first_pos)
delta_to_mean /= float(len(overlappers))
single.set_transformation(Translation(first_pos + delta_to_mean))
# modify the model
for single, overlappers in singles:
lowest_index = min([atom.get_index() for atom in overlappers])
primitive.Add(single, parent, index=lowest_index)
for atom in overlappers:
while len(atom.references) > 0:
primitive.SetTarget(atom.references[0], single)
primitive.Delete(atom)
def remove_duplicate(self, threshold=0.1):
'''Return a system object in which the duplicate atoms and bonds are removed.
**Optional argument:**
threshold
The minimum distance between two atoms that are supposed to be
different.
When it makes sense, properties of overlapping atoms are averaged
out. In other cases, the atom with the lowest index in a cluster of
overlapping atoms defines the new value of a property.
'''
# compute distances
ndist = (self.natom*(self.natom-1))/2
if ndist == 0: # single atom systems, go home ...
return
dists = np.zeros(ndist)
self.cell.compute_distances(dists, self.pos)
# find clusters of overlapping atoms
from molmod import ClusterFactory
cf = ClusterFactory()
counter = 0
for i0 in xrange(self.natom):
for i1 in xrange(i0):
if dists[counter] < threshold:
cf.add_related(i0, i1)
counter += 1
clusters = [c.items for c in cf.get_clusters()]
# make a mapping from new to old atoms
newold = {}
oldnew = {}
counter = 0
for cluster in clusters: # all merged atoms come first
newold[counter] = sorted(cluster)
for item in cluster:
oldnew[item] = counter
counter += 1
if len(clusters) > 0:
old_reduced = set.union(*clusters)
else:
old_reduced = []
for item in xrange(self.natom): # all remaining atoms follow
if item not in old_reduced:
newold[counter] = [item]
oldnew[item] = counter
counter += 1
natom = len(newold)
def reduce_int_array(old):
if old is None:
return None
else:
new = np.zeros(natom, old.dtype)
for inew, iolds in newold.iteritems():
new[inew] = old[iolds[0]]
return new
def reduce_float_array(old):
if old is None:
return None
else:
new = np.zeros(natom, old.dtype)
for inew, iolds in newold.iteritems():
new[inew] = old[iolds].mean()
return new
def reduce_float_matrix(old):
'''Reduce array with dim=2'''
if old is None:
return None
else:
new = np.zeros((natom,np.shape(old)[1]), old.dtype)
for inew, iolds in newold.iteritems():
new[inew] = old[iolds].mean(axis=0)
return new
# trivial cases
numbers = reduce_int_array(self.numbers)
scope_ids = reduce_int_array(self.scope_ids)
ffatype_ids = reduce_int_array(self.ffatype_ids)
charges = reduce_float_array(self.charges)
radii = reduce_float_array(self.radii)
dipoles = reduce_float_matrix(self.dipoles)
radii2 = reduce_float_array(self.radii2)
masses = reduce_float_array(self.masses)
# create averaged positions
pos = np.zeros((natom, 3), float)
for inew, iolds in newold.iteritems():
# move to the same image
oldposs = self.pos[iolds].copy()
assert oldposs.ndim == 2
ref = oldposs[0]
for oldpos in oldposs[1:]:
delta = oldpos-ref
self.cell.mic(delta)
oldpos[:] = delta+ref
# compute mean position
pos[inew] = oldposs.mean(axis=0)
# create reduced list of bonds
if self.bonds is None:
bonds = None
else:
bonds = set((oldnew[ia], oldnew[ib]) for ia, ib in self.bonds)
bonds = np.array([bond for bond in bonds])
return self.__class__(numbers, pos, self.scopes, scope_ids, self.ffatypes, ffatype_ids, bonds, self.cell.rvecs, charges, radii, dipoles, radii2, masses)
def remove_duplicate(self, threshold=0.1):
'''Return a system object in which the duplicate atoms and bonds are removed.
**Optional argument:**
threshold
The minimum distance between two atoms that are supposed to be
different.
When it makes sense, properties of overlapping atoms are averaged
out. In other cases, the atom with the lowest index in a cluster of
overlapping atoms defines the new value of a property.
'''
# compute distances
ndist = (self.natom * (self.natom - 1)) // 2
if ndist == 0: # single atom systems, go home ...
return
dists = np.zeros(ndist)
self.cell.compute_distances(dists, self.pos)
# find clusters of overlapping atoms
from molmod import ClusterFactory
cf = ClusterFactory()
counter = 0
for i0 in range(self.natom):
for i1 in range(i0):
if dists[counter] < threshold:
cf.add_related(i0, i1)
counter += 1
clusters = [c.items for c in cf.get_clusters()]
# make a mapping from new to old atoms
newold = {}
oldnew = {}
counter = 0
for cluster in clusters: # all merged atoms come first
newold[counter] = sorted(cluster)
for item in cluster:
oldnew[item] = counter
counter += 1
if len(clusters) > 0:
old_reduced = set.union(*clusters)
else:
old_reduced = []
for item in range(self.natom): # all remaining atoms follow
if item not in old_reduced:
newold[counter] = [item]
oldnew[item] = counter
counter += 1
natom = len(newold)
def reduce_int_array(old):
if old is None:
return None
else:
new = np.zeros(natom, old.dtype)
for inew, iolds in newold.items():
new[inew] = old[iolds[0]]
return new
def reduce_float_array(old):
if old is None:
return None
else:
new = np.zeros(natom, old.dtype)
for inew, iolds in newold.items():
new[inew] = old[iolds].mean()
return new
def reduce_float_matrix(old):
'''Reduce array with dim=2'''
if old is None:
return None
else:
new = np.zeros((natom, np.shape(old)[1]), old.dtype)
for inew, iolds in newold.items():
new[inew] = old[iolds].mean(axis=0)
return new
# trivial cases
numbers = reduce_int_array(self.numbers)
scope_ids = reduce_int_array(self.scope_ids)
ffatype_ids = reduce_int_array(self.ffatype_ids)
charges = reduce_float_array(self.charges)
radii = reduce_float_array(self.radii)
valence_charges = reduce_float_array(self.valence_charges)
dipoles = reduce_float_matrix(self.dipoles)
radii2 = reduce_float_array(self.radii2)
masses = reduce_float_array(self.masses)
# create averaged positions
pos = np.zeros((natom, 3), float)
for inew, iolds in newold.items():
# move to the same image
oldposs = self.pos[iolds].copy()
assert oldposs.ndim == 2
ref = oldposs[0]
for oldpos in oldposs[1:]:
delta = oldpos - ref
self.cell.mic(delta)
oldpos[:] = delta + ref
# compute mean position
pos[inew] = oldposs.mean(axis=0)
# create reduced list of bonds
if self.bonds is None:
bonds = None
else:
bonds = set((oldnew[ia], oldnew[ib]) for ia, ib in self.bonds)
bonds = np.array([bond for bond in bonds])
return self.__class__(numbers, pos, self.scopes, scope_ids,
self.ffatypes, ffatype_ids, bonds,
self.cell.rvecs, charges, radii, valence_charges,
dipoles, radii2, masses)