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_bests(pop,Optimizer):
"""Predator function to identify similar structures based on energy and replace one
with structure from BESTS List.
"""
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])
count = 0
while len(newpop) < Optimizer.nindiv:
try:
Optimizer.BESTS
except:
Optimizer.BESTS=[]
if len(Optimizer.BESTS) > 0:
idx = random.choice(range(len(Optimizer.BESTS)))
newpop.append(Optimizer.BESTS[idx])
STR+='Predator: Adding in structure from Best List from position = {0} with fitness = {1}\n'.format(idx,Optimizer.BESTS[idx].fitness)
newindices.append(len(pop)+count)
count+=1
else:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.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 mutation_dups_adapt_stem(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
"""
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:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.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))
indiv = pop[0]
if (indiv.fitness/indiv.energy <2.0):
from MAST.structopt.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(Optimizer,indiv)
ind = outs[1]
Optimizer.stem_coeff = outs[0]
STR+='Readjusting STEM Coeff = {0}'.format(Optimizer.stem_coeff))
return pop, STR
def initialize_structures(self):
global logger
self.output.write('\n----Initialize Structures----\n')
#self.logger.info('Initializing Structures')
# Initialize Population - Generate a list of ncluster individuals
# Set bulk and index atributes
if self.restart:
logger.info('Loading previous population')
pop = generate.get_restart_population(self)
else:
logger.info('Generating new population')
pop = generate.get_population(self)
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,pop[0][0],'First Generated Individual')
#Use if concentration of interstitials is unknown
if self.swaplist:
mutlist=self.mutation_options
self.mutation_options=['IntSwapLocal']
for i in range(len(pop)):
one = pop[i]
one.swaplist=copy.deepcopy(swaplist)
if random.random() < 0.25:
pop[i], opt = switches.moves_switch(one,self)
self.mutation_options=mutlist
# Write Initial structures to files
self.output.write('\n---Starting Structures---\n')
inp_out.write_pop(self, pop)
#Print number of atoms to Summary file
if self.structure=='Defect' or self.structure=='Surface':
natstart=len(pop[0][0])+len(pop[0].bulki)
else:
natstart=len(pop[0][0])
if not self.restart:
if self.structure=='Defect':
self.summary.write('Defect Run Pure Bulk Energy per Atom : '+repr(self.purebulkenpa)+'\n')
else:
self.summary.write(self.structure+' Run : '+repr(0)+'\n')
self.summary.write('Natoms '+repr(natstart)+ '\n')
#Print data headers to summary file
self.summary.write('Generation Fitmin Fitavg Fitmedium Fitmax std time \n')
offspring=pop
#pop=[]
#BESTS=[]
return offspring
def mutation_dups_zp(pop, Optimizer):
"""Predator function that selects individuals that are too similar based fitness and
replaces them with a zero point rotation of the 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:
indiv = random.choice(otherlist).duplicate()
mutopts = Optimizer.mutation_options
Optimizer.mutation_options = ['ZP_Rotation']
indiv = moves_switch(indiv, Optimizer)
Optimizer.mutation_options = mutopts
newpop.append(indiv)
nindices.append(indiv.index)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
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 mutation_dups_energy(pop, Optimizer):
"""Predator function that removes duplicates based on energy and replaces with mutations
"""
fitlist = [one.energy 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:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.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_surface_indiv(Optimizer):
"""
Function to generate an structopt Individual class object containing as surface structure.
Inputs:
Optimizer = structopt Optimizer class
Outputs:
individ = structopt Individual class object containing surface structure data
"""
#Load surface structure
surfs = read_xyz(Optimizer.surfacefile)
cells = Optimizer.surfacecell
surfs.set_cell(cells)
surf.set_pbc([True, True, False])
#Find top layer
top, bulks = find_top_layer(surfs, Optimizer.surftopthick)
mutopto = Optimizer.mutation_options
Optimizer.mutation_options = ['lattice_alteration_rdrd']
topind = top.copy()
ind = moves_switch(topind, Optimizer)
Optimizer.mutation_options = mutopto
individ = Individual(ind)
individ.bulki = bulks.copy()
individ.bulko = bulks.copy()
return individ
def get_surface_indiv(Optimizer):
"""
Function to generate an structopt Individual class object containing as surface structure.
Inputs:
Optimizer = structopt Optimizer class
Outputs:
individ = structopt Individual class object containing surface structure data
"""
#Load surface structure
surfs = read_xyz(Optimizer.surfacefile)
cells = Optimizer.surfacecell
surfs.set_cell(cells)
surf.set_pbc([True,True,False])
#Find top layer
top,bulks=find_top_layer(surfs,Optimizer.surftopthick)
mutopto = Optimizer.mutation_options
Optimizer.mutation_options = ['lattice_alteration_rdrd']
topind = top.copy()
ind = moves_switch(topind,Optimizer)
Optimizer.mutation_options = mutopto
individ = Individual(ind)
individ.bulki = bulks.copy()
individ.bulko = bulks.copy()
return individ
def generation_set(self,pop):
global logger
self.calc = tools.setup_calculator(self) #Set up calculator for atomic structures
#Set up calculator for fixed region calculations
if self.fixed_region:
self.static_calc = self.calc #May need to copy this
self.calc = tools.setup_fixed_region_calculator(self)
self.output.write('\n-------- Generation '+repr(self.generation)+' --------\n')
self.files[self.nindiv].write('Generation '+str(self.generation)+'\n')
if len(pop) == 0:
logger.info('Initializing structures')
offspring = self.initialize_structures()
self.population = offspring
else:
for i in range(len(pop)):
# Reset History index
pop[i].history_index=repr(pop[i].index)
# Select the next generation individuals
offspring = switches.selection_switch(pop, self.nindiv,
self.selection_scheme, self)
# Clone the selected individuals
offspring=[off1.duplicate() for off1 in offspring]
# Apply crossover to the offspring
self.output.write('\n--Applying Crossover--\n')
cxattempts = 0
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < self.cxpb:
child1,child2 = switches.crossover_switch(child1, child2, self)
cxattempts+=2
self.cxattempts=cxattempts
#DEBUG: Write first child
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,offspring[0][0],'First Child '+
repr(offspring[0].history_index))
# Apply mutation to the offspring
self.output.write('\n--Applying Mutation--\n')
mutattempts = []
muts = []
for mutant in offspring:
if random.random() < self.mutpb:
if self.mutant_add:
mutant = mutant.duplicate()
mutant, optsel = switches.moves_switch(mutant,self)
mutattempts.append([mutant.history_index,optsel])
if self.mutant_add:
muts.append(mutant)
if self.mutant_add:
offspring.extend(muts)
self.mutattempts=mutattempts
#DEBUG: Write first offspring
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,muts[0][0],'First Mutant '+\
repr(muts[0].history_index))
if 'stem' in self.fitness_scheme:
if self.stem_coeff==None:
logger.info('Setting STEM coeff (alpha)')
ind = offspring.pop()
from MAST.structopt.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(self,ind)
ind = outs[1]
ind.fitness = 0
ind.energy = 0
self.stem_coeff = outs[0]
logger.info('STEM Coeff = {0}'.format(self.stem_coeff))
self.output.write('stem_coeff Calculated to be: '+repr(self.stem_coeff)+'\n')
offspring.append(ind)
return offspring
def mutation_dups_quench(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
Also quenches top individuals
"""
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:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.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))
if Optimizer.genrep >10:
from MAST.structopt.moves.quench import quench
import os
olammpsvar = os.environ['LAMMPS_COMMAND']
try:
from mpi4py import MPI
if '-n' in olammpsvar:
lcommand = olammpsvar.split('-n')
lcommand[1]=lcommand[1].split()
nproc = MPI.COMM_WORLD.Get_size()
os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(lcommand[0],nproc,lcommand[1][1])
except:
pass
oqns2 = Optimizer.quench_n_steps_2
Optimizer.quench_n_steps_2 = 100000
opar = Optimizer.parallel
Optimizer.parallel = False
for i in range(3):
pop[i] = quench(pop[i],Optimizer)
Optimizer.quench_n_steps_2 = oqns2
os.environ['LAMMPS_COMMAND'] = olammpsvar
Optimizer.parallel = opar
return pop, STR
def mutation_dups_quench(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
Also quenches top individuals
"""
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:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv, Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n'
nindices.append(indiv.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))
if Optimizer.genrep > 10:
from MAST.structopt.moves.quench import quench
import os
olammpsvar = os.environ['LAMMPS_COMMAND']
try:
from mpi4py import MPI
if '-n' in olammpsvar:
lcommand = olammpsvar.split('-n')
lcommand[1] = lcommand[1].split()
nproc = MPI.COMM_WORLD.Get_size()
os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(
lcommand[0], nproc, lcommand[1][1])
except:
pass
oqns2 = Optimizer.quench_n_steps_2
Optimizer.quench_n_steps_2 = 100000
opar = Optimizer.parallel
Optimizer.parallel = False
for i in range(3):
pop[i] = quench(pop[i], Optimizer)
Optimizer.quench_n_steps_2 = oqns2
os.environ['LAMMPS_COMMAND'] = olammpsvar
Optimizer.parallel = opar
return pop, STR
