def select_atoms(self, atoms):
subset = None
subset_map = {}
subset_weights = []
k = 0
ids = Mixer.get_atom_ids(atoms)
for i in range(len(atoms)):
if ids[i] not in self._atom_ids:
continue
if not subset:
subset = Atoms(atoms[i:i+1])
else:
subset.extend(atoms[i:i+1])
subset_map[k] = i
subset_weights.append(self._weights[ids[i]])
k += 1
wa = np.zeros((len(subset_weights), 3))
for i in range(len(subset_weights)):
wa[i] += subset_weights[i]
return subset, subset_map, wa
def select_atoms(self, atoms):
subset = None
subset_map = {}
subset_weights = []
k = 0
ids = Mixer.get_atom_ids(atoms)
for i in range(len(atoms)):
if ids[i] not in self._atom_ids:
continue
if not subset:
subset = Atoms(atoms[i:i + 1])
else:
subset.extend(atoms[i:i + 1])
subset_map[k] = i
subset_weights.append(self._weights[ids[i]])
k += 1
wa = np.zeros((len(subset_weights), 3))
for i in range(len(subset_weights)):
wa[i] += subset_weights[i]
return subset, subset_map, wa
def select_atoms(self, atoms):
"""
Return an Atoms object containing only those from atoms that
are within the CalcRegion and that don't have an interaction-
chain to an atom outside the CalcRegion. This interaction-chain
is a chain of atoms within cutoff distance from each other.
@type atoms: Atoms
@param atoms: the normal ASE Atoms object
@rtype: (Atoms, dict, list)
@return: Atoms provides an ASE Atoms object with the
selected atoms. Dict contains a map, where map[i]
gives the original index of Atoms[i]. List provides
force calculation weights for each atom in Atoms.
"""
subset = None
subset_map = {}
weights = []
cutoff2 = self._cutoff**2 # use square for comparison with length2()
p, d = self.get_bounding_box() # get the rectangular bounding box
# create Octree with atoms inside or within cutoff distance from
# CalcRegion
root = OctreeNode(pos=p,
dim=(d[0] + 2*self._cutoff,
d[1] + 2*self._cutoff,
d[2] + 2*self._cutoff),
res=max(self._cutoff/2.0, 0.1))
atom_ids = Mixer.get_atom_ids(atoms) # extract atomids from atoms
rev_map = {} # reverse map used to get from atom_ids to atoms[] indexes
for i in range(len(atoms)):
root.add_object(atom_ids[i], atoms[i].position)
rev_map[atom_ids[i]] = i
black_list = {} # blacklist of atoms with interaction-chain to outside
for k in root.get_objects():
black_list[k] = False
wrk = [] # stack for holding blacklisted atoms whose neighbours
# need to be checked
# populate wrk with those atoms outside CalcRegion that are still
# within cutoff distance from it
for atom_id in root.get_objects():
if self.atom_inside(atoms[rev_map[atom_id]].position):
continue
wrk.append(atom_id)
black_list[atom_id] = True
# loop until wrk is empty, any not-blacklisted neighbours within
# cutoff distance from atoms that are blacklisted will be blacklisted
# and pushed to wrk
while len(wrk) > 0:
atom_id = wrk.pop()
# find neighbours approximately within cutoff
ns = root.find_objects(atoms[rev_map[atom_id]].position,
self._cutoff)
for n in ns:
if black_list[n]: # already blacklisted, ignore
continue
if (self.length2(atoms[rev_map[n]].position,
atoms[rev_map[atom_id]].position) > cutoff2):
# outside cutoff distance, ignore
continue
# not blacklisted, within cutoff, add to wrk
# and blacklist
wrk.append(n)
black_list[n] = True
k = 0
subset = Atoms()
# construct cleaned up subset of atoms within the CalcRegion
for atom_id in root.get_objects():
if black_list[atom_id]: # exclude blacklisted atom
if self._debug > 2:
print("excluding atom: %i" % atom_id)
continue
subset.extend(atoms[rev_map[atom_id]:rev_map[atom_id]+1])
weights.append(
self.get_weight(atoms[rev_map[atom_id]].position))
subset_map[k] = rev_map[atom_id]
k += 1
# create weights array that matches subset
wa = np.zeros((len(weights), 3))
for i in range(len(weights)):
wa[i] += weights[i]
# set periodic boundary condition
if self._pbc:
subset.set_pbc(self._pbc)
# dump CalcRegion contents to a trajectory file if debugging
if self._debug_file != None:
ase.io.pdb.write_pdb(self._debug_file, subset)
return subset, subset_map, wa
def select_atoms(self, atoms):
"""
Return an Atoms object containing only those from atoms that
are within the CalcRegion and that don't have an interaction-
chain to an atom outside the CalcRegion. This interaction-chain
is a chain of atoms within cutoff distance from each other.
@type atoms: Atoms
@param atoms: the normal ASE Atoms object
@rtype: (Atoms, dict, list)
@return: Atoms provides an ASE Atoms object with the
selected atoms. Dict contains a map, where map[i]
gives the original index of Atoms[i]. List provides
force calculation weights for each atom in Atoms.
"""
subset = None
subset_map = {}
weights = []
cutoff2 = self._cutoff**2 # use square for comparison with length2()
p, d = self.get_bounding_box() # get the rectangular bounding box
# create Octree with atoms inside or within cutoff distance from
# CalcRegion
root = OctreeNode(pos=p,
dim=(d[0] + 2 * self._cutoff,
d[1] + 2 * self._cutoff,
d[2] + 2 * self._cutoff),
res=max(self._cutoff / 2.0, 0.1))
atom_ids = Mixer.get_atom_ids(atoms) # extract atomids from atoms
rev_map = {
} # reverse map used to get from atom_ids to atoms[] indexes
for i in range(len(atoms)):
root.add_object(atom_ids[i], atoms[i].position)
rev_map[atom_ids[i]] = i
black_list = {} # blacklist of atoms with interaction-chain to outside
for k in root.get_objects():
black_list[k] = False
wrk = [] # stack for holding blacklisted atoms whose neighbours
# need to be checked
# populate wrk with those atoms outside CalcRegion that are still
# within cutoff distance from it
for atom_id in root.get_objects():
if self.atom_inside(atoms[rev_map[atom_id]].position):
continue
wrk.append(atom_id)
black_list[atom_id] = True
# loop until wrk is empty, any not-blacklisted neighbours within
# cutoff distance from atoms that are blacklisted will be blacklisted
# and pushed to wrk
while len(wrk) > 0:
atom_id = wrk.pop()
# find neighbours approximately within cutoff
ns = root.find_objects(atoms[rev_map[atom_id]].position,
self._cutoff)
for n in ns:
if black_list[n]: # already blacklisted, ignore
continue
if (self.length2(atoms[rev_map[n]].position,
atoms[rev_map[atom_id]].position) > cutoff2):
# outside cutoff distance, ignore
continue
# not blacklisted, within cutoff, add to wrk
# and blacklist
wrk.append(n)
black_list[n] = True
k = 0
subset = Atoms()
# construct cleaned up subset of atoms within the CalcRegion
for atom_id in root.get_objects():
if black_list[atom_id]: # exclude blacklisted atom
if self._debug > 2:
print("excluding atom: %i" % atom_id)
continue
subset.extend(atoms[rev_map[atom_id]:rev_map[atom_id] + 1])
weights.append(self.get_weight(atoms[rev_map[atom_id]].position))
subset_map[k] = rev_map[atom_id]
k += 1
# create weights array that matches subset
wa = np.zeros((len(weights), 3))
for i in range(len(weights)):
wa[i] += weights[i]
# set periodic boundary condition
if self._pbc:
subset.set_pbc(self._pbc)
# dump CalcRegion contents to a trajectory file if debugging
if self._debug_file != None:
ase.io.pdb.write_pdb(self._debug_file, subset)
return subset, subset_map, wa
