def get_vector(self, atom0, atom1, distance):
if atom0 in atom1.iter_neighbors():
return None
bond_type = bonds.bonded(atom0.number, atom1.number, distance)
if bond_type is None:
return None
else:
return Bond(bond_type=bond_type, targets=[atom0, atom1])
def detect_bonds(self, exceptions=None):
"""Initialize the ``bonds`` attribute based on inter-atomic distances
**Optional argument:**
exceptions:
Specify custom threshold for certain pairs of elements. This
must be a dictionary with ((num0, num1), threshold) as items.
For each pair of elements, a distance threshold is used to detect
bonded atoms. The distance threshold is based on a database of known
bond lengths. If the database does not contain a record for the given
element pair, the threshold is based on the sum of covalent radii.
"""
with log.section('SYS'):
from molmod.bonds import bonds
if self.bonds is not None:
if log.do_warning:
log.warn('Overwriting existing bonds.')
work = np.zeros((self.natom*(self.natom-1))/2, float)
self.cell.compute_distances(work, self.pos)
ishort = (work < bonds.max_length*1.01).nonzero()[0]
new_bonds = []
for i in ishort:
i0, i1 = _unravel_triangular(i)
n0 = self.numbers[i0]
n1 = self.numbers[i1]
if exceptions is not None:
threshold = exceptions.get((n0, n1))
if threshold is None and n0!=n1:
threshold = exceptions.get((n1, n0))
if threshold is not None:
if work[i] < threshold:
new_bonds.append([i0, i1])
continue
if bonds.bonded(n0, n1, work[i]):
new_bonds.append([i0, i1])
self.bonds = np.array(new_bonds)
self._init_derived_bonds()
def detect_bonds(self, exceptions=None):
"""Initialize the ``bonds`` attribute based on inter-atomic distances
**Optional argument:**
exceptions:
Specify custom threshold for certain pairs of elements. This
must be a dictionary with ((num0, num1), threshold) as items.
For each pair of elements, a distance threshold is used to detect
bonded atoms. The distance threshold is based on a database of known
bond lengths. If the database does not contain a record for the given
element pair, the threshold is based on the sum of covalent radii.
"""
with log.section('SYS'):
from molmod.bonds import bonds
if self.bonds is not None:
if log.do_warning:
log.warn('Overwriting existing bonds.')
work = np.zeros((self.natom * (self.natom - 1)) // 2, float)
self.cell.compute_distances(work, self.pos)
ishort = (work < bonds.max_length * 1.01).nonzero()[0]
new_bonds = []
for i in ishort:
i0, i1 = _unravel_triangular(i)
n0 = self.numbers[i0]
n1 = self.numbers[i1]
if exceptions is not None:
threshold = exceptions.get((n0, n1))
if threshold is None and n0 != n1:
threshold = exceptions.get((n1, n0))
if threshold is not None:
if work[i] < threshold:
new_bonds.append([i0, i1])
continue
if bonds.bonded(n0, n1, work[i]):
new_bonds.append([i0, i1])
self.bonds = np.array(new_bonds)
self._init_derived_bonds()
def from_geometry(cls, molecule, do_orders=False, scaling=1.0):
"""Construct a MolecularGraph object based on interatomic distances
All short distances are computed with the binning module and compared
with a database of bond lengths. Based on this comparison, bonded
atoms are detected.
Argument:
| ``molecule`` -- The molecule to derive the graph from
Optional arguments:
| ``do_orders`` -- set to True to estimate the bond order
| ``scaling`` -- scale the threshold for the connectivity. increase
this to 1.5 in case of transition states when a
fully connected topology is required.
"""
from molmod.bonds import bonds
unit_cell = molecule.unit_cell
pair_search = PairSearchIntra(
molecule.coordinates,
bonds.max_length*bonds.bond_tolerance,
unit_cell
)
orders = []
lengths = []
edges = []
for i0, i1, delta, distance in pair_search:
bond_order = bonds.bonded(molecule.numbers[i0], molecule.numbers[i1], distance/scaling)
if bond_order is not None:
if do_orders:
orders.append(bond_order)
lengths.append(distance)
edges.append((i0,i1))
if do_orders:
result = cls(edges, molecule.numbers, orders, symbols=molecule.symbols)
else:
result = cls(edges, molecule.numbers, symbols=molecule.symbols)
# run a check on all neighbors. if two bonds point in a direction that
# differs only by 45 deg. the longest of the two is discarded. the
# double loop over the neighbors is done such that the longest bonds
# are eliminated first
slated_for_removal = set([])
threshold = 0.5**0.5
for c, ns in result.neighbors.iteritems():
lengths_ns = []
for n in ns:
delta = molecule.coordinates[n] - molecule.coordinates[c]
if unit_cell is not None:
delta = unit_cell.shortest_vector(delta)
length = numpy.linalg.norm(delta)
lengths_ns.append([length, delta, n])
lengths_ns.sort(reverse=True, cmp=(lambda r0, r1: cmp(r0[0], r1[0])))
for i0, (length0, delta0, n0) in enumerate(lengths_ns):
for i1, (length1, delta1, n1) in enumerate(lengths_ns[:i0]):
if length1 == 0.0:
continue
cosine = numpy.dot(delta0, delta1)/length0/length1
if cosine > threshold:
# length1 > length0
slated_for_removal.add((c,n1))
lengths_ns[i1][0] = 0.0
# construct a mask
mask = numpy.ones(len(edges), bool)
for i0, i1 in slated_for_removal:
edge_index = result.edge_index.get(frozenset([i0,i1]))
if edge_index is None:
raise ValueError('Could not find edge that has to be removed: %i %i' % (i0, i1))
mask[edge_index] = False
# actual removal
edges = [edges[i] for i in xrange(len(edges)) if mask[i]]
if do_orders:
bond_order = [bond_order[i] for i in xrange(len(bond_order)) if mask[i]]
result = cls(edges, molecule.numbers, orders)
else:
result = cls(edges, molecule.numbers)
lengths = [lengths[i] for i in xrange(len(lengths)) if mask[i]]
result.bond_lengths = numpy.array(lengths)
return result
def from_geometry(cls, molecule, do_orders=False, scaling=1.0):
"""Construct a MolecularGraph object based on interatomic distances
All short distances are computed with the binning module and compared
with a database of bond lengths. Based on this comparison, bonded
atoms are detected.
Argument:
| ``molecule`` -- The molecule to derive the graph from
Optional arguments:
| ``do_orders`` -- set to True to estimate the bond order
| ``scaling`` -- scale the threshold for the connectivity. increase
this to 1.5 in case of transition states when a
fully connected topology is required.
"""
from molmod.bonds import bonds
unit_cell = molecule.unit_cell
pair_search = PairSearchIntra(molecule.coordinates,
bonds.max_length * bonds.bond_tolerance,
unit_cell)
orders = []
lengths = []
edges = []
for i0, i1, delta, distance in pair_search:
bond_order = bonds.bonded(molecule.numbers[i0],
molecule.numbers[i1], distance / scaling)
if bond_order is not None:
if do_orders:
orders.append(bond_order)
lengths.append(distance)
edges.append((i0, i1))
if do_orders:
result = cls(edges,
molecule.numbers,
orders,
symbols=molecule.symbols)
else:
result = cls(edges, molecule.numbers, symbols=molecule.symbols)
# run a check on all neighbors. if two bonds point in a direction that
# differs only by 45 deg. the longest of the two is discarded. the
# double loop over the neighbors is done such that the longest bonds
# are eliminated first
slated_for_removal = set([])
threshold = 0.5**0.5
for c, ns in result.neighbors.items():
lengths_ns = []
for n in ns:
delta = molecule.coordinates[n] - molecule.coordinates[c]
if unit_cell is not None:
delta = unit_cell.shortest_vector(delta)
length = np.linalg.norm(delta)
lengths_ns.append([length, delta, n])
lengths_ns.sort(reverse=True, key=(lambda r: r[0]))
for i0, (length0, delta0, n0) in enumerate(lengths_ns):
for i1, (length1, delta1, n1) in enumerate(lengths_ns[:i0]):
if length1 == 0.0:
continue
cosine = np.dot(delta0, delta1) / length0 / length1
if cosine > threshold:
# length1 > length0
slated_for_removal.add((c, n1))
lengths_ns[i1][0] = 0.0
# construct a mask
mask = np.ones(len(edges), bool)
for i0, i1 in slated_for_removal:
edge_index = result.edge_index.get(frozenset([i0, i1]))
if edge_index is None:
raise ValueError(
'Could not find edge that has to be removed: %i %i' %
(i0, i1))
mask[edge_index] = False
# actual removal
edges = [edges[i] for i in range(len(edges)) if mask[i]]
if do_orders:
bond_order = [
bond_order[i] for i in range(len(bond_order)) if mask[i]
]
result = cls(edges, molecule.numbers, orders)
else:
result = cls(edges, molecule.numbers)
lengths = [lengths[i] for i in range(len(lengths)) if mask[i]]
result.bond_lengths = np.array(lengths)
return result