def fitpred_new(pop, Optimizer):
"""Predator function to identify similar structures based on energy and replace one with new structure.
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR += 'Predator: Removed total of ' + repr(
len(fitlist) - len(nfitlist)) + ' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
if Optimizer.structure == 'Defect' or Optimizer.structure == 'Cluster':
ind = gen_pop_box(Optimizer.atomlist, Optimizer.size)
elif Optimizer.structure == 'Crystal':
outts = gen_pop_box(Optimizer.atomlist, Optimizer.size,
Optimizer.cell_shape_options)
ind = outts[0]
elif Optimizer.structure == 'Surface':
mutopto = Optimizer.mutation_options
Optimizer.mutation_options = ['Lattice_Alteration_rdrd']
topind = random.choice(pop)[0].copy()
ind, scheme = moves_switch(topind, Optimizer)
Optimizer.mutation_options = mutopto
individ = Individual(ind)
#CHECK THIS LATER!! MAY NEED TO ADD MORE PROPERTIES!!
individ.energy = 1000
individ.fitness = 1000
newpop.append(individ)
STR += 'Predator: Adding mutated duplicates to new pop history=' + individ.history_index + '\n'
nindices.append(individ.index)
nindices.sort()
if Optimizer.natural_selection_scheme == 'fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer, ind,
Optimizer.fpbin,
Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [
index for index, n in enumerate(nindices)
if n > Optimizer.nindiv - 1
][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR += str1
else:
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop, len(pop))
return pop, STR
def fitpred_new(pop,Optimizer):
"""Predator function to identify similar structures based on energy and replace one with new structure.
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
if Optimizer.structure=='Defect' or Optimizer.structure=='Cluster':
ind=gen_pop_box(Optimizer.atomlist,Optimizer.size)
elif Optimizer.structure=='Crystal':
outts=gen_pop_box(Optimizer.atomlist,Optimizer.size,Optimizer.cell_shape_options)
ind=outts[0]
elif Optimizer.structure=='Surface':
mutopto=Optimizer.mutation_options
Optimizer.mutation_options=['Lattice_Alteration_rdrd']
topind=random.choice(pop)[0].copy()
ind, scheme = moves_switch(topind,Optimizer)
Optimizer.mutation_options=mutopto
individ=Individual(ind)
#CHECK THIS LATER!! MAY NEED TO ADD MORE PROPERTIES!!
individ.energy=1000
individ.fitness=1000
newpop.append(individ)
STR+='Predator: Adding mutated duplicates to new pop history='+individ.history_index+'\n'
nindices.append(individ.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
return pop, STR
def get_population(Optimizer):
"""
Function to generate a population of structures.
Inputs:
Optimizer = structopt Optimizer class object
Outputs:
pop = List of structopt Individual class objects containing new structures.
"""
index1 = 0
pop = []
for i in range(Optimizer.nindiv):
if Optimizer.structure == 'Defect':
individ = get_defect_indiv(Optimizer)
elif Optimizer.structure == 'Surface':
individ = get_surface_indiv(Optimizer)
elif Optimizer.structure == 'Crystal':
individ = get_crystal_indiv(Optimizer)
else:
if 'sphere' in Optimizer.generate_flag:
ind = gen_pop_sphere(Optimizer.atomlist,Optimizer.size)
else:
ind = gen_pop_box(Optimizer.atomlist,Optimizer.size)
individ = Individual(ind)
individ.index = index1
if Optimizer.genealogy:
individ.history_index = repr(index1)
Optimizer.output.write('Generated cluster individual with natoms = '+
repr(individ[0].get_number_of_atoms())+'\n')
pop.append(individ)
index1=index1+1
# Generate new atomlist concentrations based on cluster+box
if Optimizer.structure == 'Defect':
if Optimizer.alloy:
concents=[]
for ind in pop:
cs=[]
for sym,c,u,m in Optimizer.atomlist:
sylen=[atm for atm in ind[0] if atm.symbol==sym]
cs.append(len(sylen))
concents.append(cs)
natmlist=[0]*len(Optimizer.atomlist)
for i in range(len(concents[0])):
alls=[cs[i] for cs in concents]
avgall=int(sum(alls)/len(alls))
if avgall>=Optimizer.atomlist[i][1]:
natmlist[i]=(Optimizer.atomlist[i][0], avgall,
Optimizer.atomlist[i][2],Optimizer.atomlist[i][3])
else:
natmlist[i]=(Optimizer.atomlist[i][0], Optimizer.atomlist[i][1],
Optimizer.atomlist[i][2],Optimizer.atomlist[i][3])
else:
natmlist=[0]*len(Optimizer.atomlist)
for i in range(len(Optimizer.atomlist)):
atms1=[inds for inds in pop[0][0] if inds.symbol==Optimizer.atomlist[i][0]]
natmlist[i]=(Optimizer.atomlist[i][0], len(atms1),Optimizer.atomlist[i][2],
Optimizer.atomlist[i][3])
Optimizer.atomlist=natmlist
Optimizer.output.write('\n\nNew atomlist concentrations based on cluster+box = '+
repr(Optimizer.atomlist)+'\n')
return pop
def get_crystal_indiv(Optimizer):
"""
Function to generate an structopt Individual class object containing a crystal structure.
Inputs:
Optimizer = structopt Optimizer class
Outputs:
individ = structopt Individual class object containing crystal structure data
"""
if 'sphere' in Optimizer.generate_flag:
outts = gen_pop_sphere(Optimizer.atomlist,Optimizer.size,Optimizer.cell_shape_options)
else:
outts = gen_pop_box(Optimizer.atomlist,Optimizer.size,Optimizer.cell_shape_options)
ind = outts[0]
Optimizer.output.write(outts[1])
individ = Individual(ind)
return individ
def get_defect_indiv_random(Optimizer):
"""
Function to generate a structopt Individual class structure with a defect structure.
Inputs:
Optimizer = structopt Optimizer class object
Outputs:
individ = structopt Individual class object containing defect structure data
"""
#Initialize Bulk - Generate or load positions of bulk solid
if not Optimizer.solidbulk:
if 'Island_Method' not in Optimizer.algorithm_type:
outfilename = os.path.join(
os.path.join(os.getcwd(), Optimizer.filename), 'Bulkfile.xyz')
else:
from mpi4py import MPI
rank = MPI.COMM_WORLD.Get_rank()
outfilename = os.path.join(
os.path.join(os.getcwd(),
Optimizer.filename + '-rank' + repr(rank)),
'Bulkfile.xyz')
if Optimizer.evalsolid:
if Optimizer.parallel:
from MAST.structopt.tools.setup_calculator import setup_calculator
Optimizer.calc = setup_calculator(Optimizer)
bulk1, PureBulkEnpa, stro = gen_solid(Optimizer.solidfile,
Optimizer.solidcell,
outfilename, Optimizer.calc,
Optimizer.calc_method)
Optimizer.output.write(stro)
else:
bulk1 = gen_solid(Optimizer.solidfile, Optimizer.solidcell,
outfilename)
PureBulkEnpa = 0
natomsbulk = len(bulk1)
Optimizer.solidbulk = bulk1.copy()
Optimizer.purebulkenpa = PureBulkEnpa
Optimizer.natomsbulk = natomsbulk
# Identify nearby atoms for region 2 inclusion
bulk = Optimizer.solidbulk.copy()
bulkcom = bulk.get_center_of_mass()
bulk.translate(-bulkcom)
if Optimizer.sf != 0:
bulk.append(Atom(position=[0, 0, 0]))
nbulk = Atoms(pbc=True, cell=bulk.get_cell())
nr2 = Atoms(pbc=True, cell=bulk.get_cell())
for i in range(len(bulk) - 1):
dist = bulk.get_distance(-1, i)
if dist <= Optimizer.sf:
nr2.append(bulk[i])
else:
nbulk.append(bulk[i])
else:
nbulk = bulk.copy()
nr2 = Atoms(pbc=True, cell=bulk.get_cell())
#Update atom list with atoms in region 2
natlist = []
for sym, c, m, u in Optimizer.atomlist:
atsym = [atm for atm in nr2 if atm.symbol == sym]
natlist.append((sym, len(atsym), m, u))
# Generate random individual and region 2
if 'sphere' in Optimizer.generate_flag:
ind = gen_pop_sphere(Optimizer.atomlist, Optimizer.size)
elif 'dumbbell' in Optimizer.generate_flag:
ind = Atoms(cell=[Optimizer.size for i in range(3)], pbc=True)
for sym, c, m, u in Optimizer.atomlist:
if c > 0:
dums = generate_dumbbells(c,
dumbbellsym=sym,
nindiv=1,
solid=Optimizer.solidbulk,
size=Optimizer.size)[0]
ind.extend(dums)
else:
ind = gen_pop_box(Optimizer.atomlist, Optimizer.size)
nnr2 = gen_pop_sphere(natlist, Optimizer.sf * 2.0)
nnr2.translate([-Optimizer.sf, -Optimizer.sf, -Optimizer.sf])
nnr2.set_pbc(True)
nnr2.set_cell(bulk.get_cell())
# Initialize class individual with known values
individ = Individual(ind)
individ.purebulkenpa = Optimizer.purebulkenpa
individ.natomsbulk = Optimizer.natomsbulk
# Combine individual with R2
icom = ind.get_center_of_mass()
ind.translate(-icom)
ind.extend(nnr2)
ind.set_pbc(True)
ind.set_cell(bulk.get_cell())
# Recenter structure
nbulk.translate(bulkcom)
ind.translate(bulkcom)
individ[0] = ind.copy()
individ.bulki = nbulk.copy()
individ.bulko = nbulk.copy()
bulk = nbulk.copy()
bul = bulk.copy()
for atm in individ[0]:
bul.append(atm)
indices = []
for sym, c, m, u in Optimizer.atomlist:
if c < 0:
if Optimizer.randvacst:
alist = [one for one in bul if one.symbol == sym]
count = abs(c)
while count > 0:
indices.append(random.choice(alist).index)
count -= 1
else:
pos = individ[0][0:Optimizer.natoms].get_center_of_mass()
count = abs(c)
bul.append(Atom(position=pos))
alist = [one for one in bul if one.symbol == sym]
alistd = [(bul.get_distance(len(bul) - 1,
one.index), one.index)
for one in alist]
alistd.sort(reverse=True)
bul.pop()
while count > 0:
idx = alistd.pop()[1]
indices.append(idx)
count -= 1
if len(indices) != 0:
nbulklist = [
at for at in bul
if at.index not in indices and at.index < len(bulk)
]
nalist = [
at for at in bul
if at.index not in indices and at.index >= len(bulk)
]
bulkn = Atoms(cell=bulk.get_cell(), pbc=True)
for atm in nbulklist:
bulkn.append(atm)
individ.bulki = bulkn.copy()
individ.bulko = bulkn.copy()
newind = Atoms()
for atm in nalist:
newind.append(atm)
newind.set_cell(individ[0].get_cell())
newind.set_pbc(True)
individ[0] = newind
return individ
def random_replacement(indiv, Optimizer):
"""Move function to replace selection of atoms with randomly generated group
Inputs:
indiv = Individual class object to be altered
Optimizer = Optimizer class object with needed parameters
Outputs:
indiv = Altered Individual class object
"""
if 'MU' in Optimizer.debug:
debug = True
else:
debug = False
if Optimizer.structure == 'Defect':
if Optimizer.isolate_mutation:
atms, indb, vacant, swap, stro = find_defects(
indiv[0], Optimizer.solidbulk, 0)
else:
atms = indiv[0].copy()
else:
atms = indiv[0].copy()
nat = len(atms)
if nat != 0:
#Select number of atoms to replace
if nat <= 1:
natrep2 = 1
natrep1 = 0
elif nat <= 5:
natrep2 = 2
natrep1 = 0
else:
natrep1 = random.randint(1, nat / 2)
while True:
natrep2 = random.randint(2, nat / 2)
if natrep2 != natrep1:
break
natrep = abs(natrep2 - natrep1)
#Select random position in cluster
maxcell = numpy.maximum.reduce(atms.get_positions())
mincell = numpy.minimum.reduce(atms.get_positions())
pt = [
random.uniform(mincell[0], maxcell[0]),
random.uniform(mincell[1], maxcell[1]),
random.uniform(mincell[2], maxcell[2])
]
#Get distance of atoms from random point
atpt = Atom(position=pt)
atms.append(atpt)
dist = []
for i in range(len(atms) - 1):
dist.append(atms.get_distance(i, len(atms) - 1))
atms.pop()
dlist = zip(dist, atms)
dlist = sorted(dlist, key=lambda one: one[0], reverse=True)
# Select atoms closest to random point
atmsr = Atoms()
indexlist = []
for i in range(natrep):
atmsr.append(dlist[i][1])
indexlist.append(dlist[i][1].index)
natomlist = [0] * len(Optimizer.atomlist)
for i in range(len(Optimizer.atomlist)):
atms1 = [
inds for inds in atmsr
if inds.symbol == Optimizer.atomlist[i][0]
]
natomlist[i] = (Optimizer.atomlist[i][0], len(atms1),
Optimizer.atomlist[i][2], Optimizer.atomlist[i][3])
nsize = max(
numpy.maximum.reduce(atmsr.get_positions()) -
numpy.minimum.reduce(atmsr.get_positions()))
repcenter = atmsr.get_center_of_mass()
atmsn = gen_pop_box(natomlist, nsize)
atmsn.translate(repcenter)
#Update individual with new atom positions
for i in range(len(indexlist)):
index = indexlist[i]
atms[index].position = atmsn[i].position
if Optimizer.structure == 'Defect':
if Optimizer.isolate_mutation:
atms.extend(indb)
indiv[0] = atms.copy()
else:
natrep = 0
pt = 0
Optimizer.output.write(
'Random Group Replacement Mutation performed on individual\n')
Optimizer.output.write('Index = ' + repr(indiv.index) + '\n')
Optimizer.output.write('Number of atoms replaced = ' + repr(natrep) + '\n')
Optimizer.output.write('Geometry point = ' + repr(pt) + '\n')
Optimizer.output.write(repr(indiv[0]) + '\n')
muttype = 'RGR' + repr(natrep)
if indiv.energy == 0:
indiv.history_index = indiv.history_index + 'm' + muttype
else:
indiv.history_index = repr(indiv.index) + 'm' + muttype
return indiv
def get_defect_indiv_random(Optimizer):
"""
Function to generate a structopt Individual class structure with a defect structure.
Inputs:
Optimizer = structopt Optimizer class object
Outputs:
individ = structopt Individual class object containing defect structure data
"""
#Initialize Bulk - Generate or load positions of bulk solid
if not Optimizer.solidbulk:
if 'Island_Method' not in Optimizer.algorithm_type:
outfilename = os.path.join(os.path.join(os.getcwd(),Optimizer.filename),'Bulkfile.xyz')
else:
from mpi4py import MPI
rank = MPI.COMM_WORLD.Get_rank()
outfilename = os.path.join(os.path.join(os.getcwd(),Optimizer.filename+'-rank'+repr(rank)),'Bulkfile.xyz')
if Optimizer.evalsolid:
if Optimizer.parallel:
from MAST.structopt.tools.setup_calculator import setup_calculator
Optimizer.calc = setup_calculator(Optimizer)
bulk1, PureBulkEnpa, stro = gen_solid(Optimizer.solidfile,
Optimizer.solidcell,outfilename,Optimizer.calc,Optimizer.calc_method)
Optimizer.output.write(stro)
else:
bulk1 = gen_solid(Optimizer.solidfile,Optimizer.solidcell,outfilename)
PureBulkEnpa = 0
natomsbulk = len(bulk1)
Optimizer.solidbulk = bulk1.copy()
Optimizer.purebulkenpa = PureBulkEnpa
Optimizer.natomsbulk = natomsbulk
# Identify nearby atoms for region 2 inclusion
bulk = Optimizer.solidbulk.copy()
bulkcom = bulk.get_center_of_mass()
bulk.translate(-bulkcom)
if Optimizer.sf != 0:
bulk.append(Atom(position=[0,0,0]))
nbulk = Atoms(pbc=True, cell=bulk.get_cell())
nr2 = Atoms(pbc=True, cell=bulk.get_cell())
for i in range(len(bulk)-1):
dist = bulk.get_distance(-1,i)
if dist <= Optimizer.sf:
nr2.append(bulk[i])
else:
nbulk.append(bulk[i])
else:
nbulk = bulk.copy()
nr2 = Atoms(pbc=True, cell=bulk.get_cell())
#Update atom list with atoms in region 2
natlist = []
for sym,c,m,u in Optimizer.atomlist:
atsym = [atm for atm in nr2 if atm.symbol==sym]
natlist.append((sym,len(atsym),m,u))
# Generate random individual and region 2
if 'sphere' in Optimizer.generate_flag:
ind = gen_pop_sphere(Optimizer.atomlist,Optimizer.size)
elif 'dumbbell' in Optimizer.generate_flag:
ind = Atoms(cell=[Optimizer.size for i in range(3)], pbc=True)
for sym,c,m,u in Optimizer.atomlist:
if c > 0:
dums = generate_dumbbells(c, dumbbellsym=sym, nindiv=1, solid = Optimizer.solidbulk, size=Optimizer.size)[0]
ind.extend(dums)
else:
ind = gen_pop_box(Optimizer.atomlist,Optimizer.size)
nnr2 = gen_pop_sphere(natlist, Optimizer.sf*2.0)
nnr2.translate([-Optimizer.sf,-Optimizer.sf,-Optimizer.sf])
nnr2.set_pbc(True)
nnr2.set_cell(bulk.get_cell())
# Initialize class individual with known values
individ = Individual(ind)
individ.purebulkenpa = Optimizer.purebulkenpa
individ.natomsbulk = Optimizer.natomsbulk
# Combine individual with R2
icom = ind.get_center_of_mass()
ind.translate(-icom)
ind.extend(nnr2)
ind.set_pbc(True)
ind.set_cell(bulk.get_cell())
# Recenter structure
nbulk.translate(bulkcom)
ind.translate(bulkcom)
individ[0] = ind.copy()
individ.bulki = nbulk.copy()
individ.bulko = nbulk.copy()
bulk = nbulk.copy()
bul = bulk.copy()
for atm in individ[0]:
bul.append(atm)
indices = []
for sym,c,m,u in Optimizer.atomlist:
if c < 0:
if Optimizer.randvacst:
alist = [one for one in bul if one.symbol==sym]
count = abs(c)
while count > 0:
indices.append(random.choice(alist).index)
count -= 1
else:
pos = individ[0][0:Optimizer.natoms].get_center_of_mass()
count = abs(c)
bul.append(Atom(position=pos))
alist = [one for one in bul if one.symbol==sym]
alistd = [(bul.get_distance(len(bul)-1,one.index),one.index)
for one in alist]
alistd.sort(reverse=True)
bul.pop()
while count > 0:
idx = alistd.pop()[1]
indices.append(idx)
count-=1
if len(indices) !=0:
nbulklist = [at for at in bul if at.index not in indices and at.index<len(bulk)]
nalist = [at for at in bul if at.index not in indices and at.index>=len(bulk)]
bulkn = Atoms(cell=bulk.get_cell(),pbc=True)
for atm in nbulklist:
bulkn.append(atm)
individ.bulki = bulkn.copy()
individ.bulko = bulkn.copy()
newind = Atoms()
for atm in nalist:
newind.append(atm)
newind.set_cell(individ[0].get_cell())
newind.set_pbc(True)
individ[0] = newind
return individ
def get_population(Optimizer):
"""
Function to generate a population of structures.
Inputs:
Optimizer = structopt Optimizer class object
Outputs:
pop = List of structopt Individual class objects containing new structures.
"""
index1 = 0
pop = []
for i in range(Optimizer.nindiv):
if Optimizer.structure == 'Defect':
individ = get_defect_indiv(Optimizer)
elif Optimizer.structure == 'Surface':
individ = get_surface_indiv(Optimizer)
elif Optimizer.structure == 'Crystal':
individ = get_crystal_indiv(Optimizer)
else:
if 'sphere' in Optimizer.generate_flag:
ind = gen_pop_sphere(Optimizer.atomlist, Optimizer.size)
else:
ind = gen_pop_box(Optimizer.atomlist, Optimizer.size)
individ = Individual(ind)
individ.index = index1
if Optimizer.genealogy:
individ.history_index = repr(index1)
Optimizer.output.write('Generated cluster individual with natoms = ' +
repr(individ[0].get_number_of_atoms()) + '\n')
pop.append(individ)
index1 = index1 + 1
# Generate new atomlist concentrations based on cluster+box
if Optimizer.structure == 'Defect':
if Optimizer.alloy:
concents = []
for ind in pop:
cs = []
for sym, c, u, m in Optimizer.atomlist:
sylen = [atm for atm in ind[0] if atm.symbol == sym]
cs.append(len(sylen))
concents.append(cs)
natmlist = [0] * len(Optimizer.atomlist)
for i in range(len(concents[0])):
alls = [cs[i] for cs in concents]
avgall = int(sum(alls) / len(alls))
if avgall >= Optimizer.atomlist[i][1]:
natmlist[i] = (Optimizer.atomlist[i][0], avgall,
Optimizer.atomlist[i][2],
Optimizer.atomlist[i][3])
else:
natmlist[i] = (Optimizer.atomlist[i][0],
Optimizer.atomlist[i][1],
Optimizer.atomlist[i][2],
Optimizer.atomlist[i][3])
else:
natmlist = [0] * len(Optimizer.atomlist)
for i in range(len(Optimizer.atomlist)):
atms1 = [
inds for inds in pop[0][0]
if inds.symbol == Optimizer.atomlist[i][0]
]
natmlist[i] = (Optimizer.atomlist[i][0], len(atms1),
Optimizer.atomlist[i][2],
Optimizer.atomlist[i][3])
Optimizer.atomlist = natmlist
Optimizer.output.write(
'\n\nNew atomlist concentrations based on cluster+box = ' +
repr(Optimizer.atomlist) + '\n')
return pop
def random_replacement(indiv, Optimizer):
"""Move function to replace selection of atoms with randomly generated group
Inputs:
indiv = Individual class object to be altered
Optimizer = Optimizer class object with needed parameters
Outputs:
indiv = Altered Individual class object
"""
if 'MU' in Optimizer.debug:
debug = True
else:
debug = False
if Optimizer.structure=='Defect':
if Optimizer.isolate_mutation:
atms,indb,vacant,swap,stro = find_defects(indiv[0],Optimizer.solidbulk,0)
else:
atms = indiv[0].copy()
else:
atms=indiv[0].copy()
nat=len(atms)
if nat != 0:
#Select number of atoms to replace
if nat<=1:
natrep2=1
natrep1=0
elif nat<=5:
natrep2=2
natrep1=0
else:
natrep1=random.randint(1,nat/2)
while True:
natrep2=random.randint(2,nat/2)
if natrep2 != natrep1:
break
natrep=abs(natrep2 - natrep1)
#Select random position in cluster
maxcell = numpy.maximum.reduce(atms.get_positions())
mincell = numpy.minimum.reduce(atms.get_positions())
pt=[random.uniform(mincell[0],maxcell[0]),random.uniform(mincell[1],maxcell[1]),random.uniform(mincell[2],maxcell[2])]
#Get distance of atoms from random point
atpt=Atom(position=pt)
atms.append(atpt)
dist=[]
for i in range(len(atms)-1):
dist.append(atms.get_distance(i,len(atms)-1))
atms.pop()
dlist=zip(dist,atms)
dlist=sorted(dlist, key=lambda one: one[0], reverse=True)
# Select atoms closest to random point
atmsr=Atoms()
indexlist=[]
for i in range(natrep):
atmsr.append(dlist[i][1])
indexlist.append(dlist[i][1].index)
natomlist=[0]*len(Optimizer.atomlist)
for i in range(len(Optimizer.atomlist)):
atms1=[inds for inds in atmsr if inds.symbol==Optimizer.atomlist[i][0]]
natomlist[i]=(Optimizer.atomlist[i][0], len(atms1),Optimizer.atomlist[i][2],Optimizer.atomlist[i][3])
nsize = max(numpy.maximum.reduce(atmsr.get_positions())-numpy.minimum.reduce(atmsr.get_positions()))
repcenter = atmsr.get_center_of_mass()
atmsn = gen_pop_box(natomlist,nsize)
atmsn.translate(repcenter)
#Update individual with new atom positions
for i in range(len(indexlist)):
index=indexlist[i]
atms[index].position=atmsn[i].position
if Optimizer.structure=='Defect':
if Optimizer.isolate_mutation:
atms.extend(indb)
indiv[0]=atms.copy()
else:
natrep=0
pt=0
Optimizer.output.write('Random Group Replacement Mutation performed on individual\n')
Optimizer.output.write('Index = '+repr(indiv.index)+'\n')
Optimizer.output.write('Number of atoms replaced = '+repr(natrep)+'\n')
Optimizer.output.write('Geometry point = '+repr(pt)+'\n')
Optimizer.output.write(repr(indiv[0])+'\n')
muttype='RGR'+repr(natrep)
if indiv.energy==0:
indiv.history_index=indiv.history_index+'m'+muttype
else:
indiv.history_index=repr(indiv.index)+'m'+muttype
return indiv