def gen_offspring_box(self, mutRate=0.3, xyzLims=[], bondRejList = None, constrainTop=False, rattleOn=True, growOn=True, leachOn=True, permuteOn = True, transOn = True, transVec=[[-2,2],[-2,2]]):
kid, parent = None, None
mater, pater = 0, 0
while kid is None:
mater, pater = self.choose_parents()
a1 = self.gadb.get(id=mater).toatoms()
a2 = self.gadb.get(id=pater).toatoms()
surf1 = Interface(a1, self.substrate, zLim=self.zLim)
surf2 = Interface(a2, self.substrate, zLim=self.zLim)
kid = crossover_snsSurf_2d_GC(surf1, surf2, tolerance=0.75)
parent = surf1.copy()
print('PARENTS: %i and %i'%(mater, pater))
myMutate = ''
if srtDist_similar_zz(a1, a2)\
or srtDist_similar_zz(a1, kid.get_allAtoms())\
or srtDist_similar_zz(a2, kid.get_allAtoms()):
print(' |- TOO SIMILAR!')
mutRate = 1
if np.random.rand() < mutRate:
mutType = np.random.choice([0,1,2,3,4], size=1)[0]
if mutType == 0 and rattleOn:
myMutate = 'rattle'
kid.rattleMut()
if mutType == 1 and growOn:
myMutate = 'grow'
kid.growMut_box([l for l in self.chemPotDict], xyzLims=xyzLims, bondRejList=bondRejList, constrainTop=constrainTop)
if mutType == 2 and leachOn:
myMutate = 'leach'
kid.leachMut([l for l in self.chemPotDict])
if mutType == 3 and permuteOn:
myMutate = 'permute'
kid.permuteMut()
if mutType == 4 and transOn:
myMutate = 'translate'
kid.transMut(transVec=transVec)
if len(kid.get_adsList()) <= 1:
myMutate = 'init'
print(' |- Bare substrate, BAD!')
kid = parent.copy()
kid.rattleMut()
kid.growMut([l for l in self.chemPotDict])
open('label', 'w').write('%i %i %s'%(mater, pater, myMutate))
self.gadb.update(mater, mated=self.gadb.get(id=mater).mated+1)
self.gadb.update(pater, mated=self.gadb.get(id=pater).mated+1)
return kid
def is_uniqueInPop(self, atoms):
'''
Check similarity against the current population
'''
aliveList = self.get_ID('alive=1')
aliveList = [self.gadb.get(id=n).toatoms() for n in aliveList]
isUnique = True
for a in aliveList:
if srtDist_similar_zz(atoms, a):
isUnique = False
break
return isUnique
def gen_offspring(self, mutRate=0.4):
kid = None
mater, pater = 0, 0
while kid is None:
mater, pater = self.choose_parents()
a1 = self.gadb.get(id=mater).toatoms()
a2 = self.gadb.get(id=pater).toatoms()
surf1 = Interface(a1, self.substrate, zLim=self.zLim)
surf2 = Interface(a2, self.substrate, zLim=self.zLim)
kid = crossover_snsSurf_2d(surf1, surf2, tolerance=0.75)
print('PARENTS: %i and %i' % (mater, pater))
if srtDist_similar_zz(a1, a2)\
or srtDist_similar_zz(a1, kid.get_allAtoms())\
or srtDist_similar_zz(a2, kid.get_allAtoms()):
print(' |- TOO SIMILAR!')
mutRate = 1
if np.random.rand() < mutRate:
print(' |- MUTATION!')
kid.transMut()
kid.rattleMut()
self.gadb.update(mater, mated=self.gadb.get(id=mater).mated + 1)
self.gadb.update(pater, mated=self.gadb.get(id=pater).mated + 1)
return kid
def is_uniqueInPop(self, atoms, grandPot):
'''
Check similarity against the current population
'''
eneCut = 0.05
aliveList = self.get_ID('alive=1')
grandPotList = self.get_valueOf('grandPot', aliveList)
isUnique = True
for ii in range(len(aliveList)):
a = self.gadb.get(id=aliveList[ii]).toatoms()
if srtDist_similar_zz(atoms, a):
if -eneCut < grandPotList[ii] - grandPot < eneCut:
isUnique = False
break
return isUnique
def clusterIsomerAbs(
trajAtoms, # Must containg energy information
eneToler=0.05,
geomToler1=5e-4,
geomToler2=0.5,
outName='sort'):
import gocia.ensemble.comparator as comp
# Handle the bad or other-format outputs
if 'mag' in trajAtoms[0].info.keys():
magArray = [a.info['mag'] for a in trajAtoms]
else:
magArray = [0] * len(trajAtoms)
if 'eV' in trajAtoms[0].info.keys():
eneArray = [a.info['eV'] for a in trajAtoms]
else:
eneArray = [a.get_potential_energy() for a in trajAtoms]
print(' * Detecting unique isomers...')
nStates = len(trajAtoms)
isomerLabel = [-1] * nStates
currIso, currInd = [], 0
while -1 in isomerLabel:
if currInd >= nStates:
break
if isomerLabel[currInd] != -1:
currInd += 1
continue
print(' |-Isomer %i' % len(list(set(isomerLabel))), end='\t:')
for i in range(nStates):
if isomerLabel[i] != -1:
continue
# if simMat[currInd][i] == 1:
# isomerLabel[i] = currInd
if abs(eneArray[i] - eneArray[currInd]) < eneToler and\
comp.srtDist_similar_zz(trajAtoms[i],trajAtoms[currInd],\
delta_rel=geomToler1, d_max=geomToler2):
isomerLabel[i] = currInd
print(i, end=',')
currInd += 1
print('')
isoUniq = set(isomerLabel)
eneUniq = [eneArray[i] for i in isoUniq]
magUniq = [magArray[i] for i in isoUniq]
countUniq = [isomerLabel.count(i) for i in isoUniq]
# print(countUniq)
print('-' * 76)
print(' %i\tisomers found from %s\tsamples. Oversampling = %s'%\
(len(isoUniq), nStates, '{:.2%}'.format(nStates/len(isoUniq)-1)))
sortedTraj = []
for v in sorted(eneUniq):
sortedTraj.append(trajAtoms[list(eneArray).index(v)])
with connect('%s.db' % outName, append=False) as srtDB:
for i in range(len(sortedTraj)):
srtDB.write(
sortedTraj[i],
done=1,
eV=sorted(eneUniq)[i],
eV2GM=sorted(eneUniq)[i] - min(eneUniq),
mag=magUniq[eneUniq.index(sorted(eneUniq)[i])],
counts=countUniq[eneUniq.index(sorted(eneUniq)[i])],
)