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 check_pop(self, pop):
# Gather all the energies/fitnesses
if self.output_format=='totalenergy':
complist = [ind.energy for ind in pop]
elif self.output_format=='formation_energy':
complist = []
for ind in pop:
solid=Atoms()
solid.extend(ind[0])
solid.extend(ind.bulki)
energy=ind.energy
for sym,c,m,u in self.atomlist:
nc=len([atm for atm in solid if atm.symbol==sym])
energy-= float(nc)*float(u)
complist.append(energy)
elif self.output_format=='formation_energy_per_int':
complist = []
for ind in pop:
solid=Atoms()
solid.extend(ind[0])
solid.extend(ind.bulki)
energy=ind.energy
for sym,c,m,u in self.atomlist:
nc=len([atm for atm in solid if atm.symbol==sym])
energy-= float(nc)*float(u)
energy=energy/self.natoms
complist.append(energy)
elif self.output_format=='formation_energy2':
complist = [(ind.energy - ind.purebulkenpa*(ind[0].get_number_of_atoms()+ind.bulki.get_number_of_atoms()))/(ind[0].get_number_of_atoms()+ind.bulki.get_number_of_atoms()-ind.natomsbulk) for ind in pop]
elif self.output_format=='energy_per_atom':
complist = [ind.energy/(ind[0].get_number_of_atoms()+ind.bulki.get_number_of_atoms()) for ind in pop]
else:
complist = [ind.fitness for ind in pop]
# Calcluate and print the Stats
length = len(pop)
mean = sum(complist) / length
complist.sort()
medium = complist[length/2]
sum2 = sum(x*x for x in complist)
std = abs(sum2 / length - mean**2)**0.5
mine = min(complist)
maxe = max(complist)
self.output.write('\n----Stats----\n')
self.output.write(' Min '+repr(mine)+'\n')
self.output.write(' Max '+repr(maxe)+'\n')
self.output.write(' Avg '+repr(mean)+'\n')
self.output.write(' Medium '+repr(medium)+'\n')
self.output.write(' Std '+repr(std)+'\n')
self.output.write(' Genrep '+repr(self.genrep)+'\n')
self.summary.write('{Gen} {Fitmin} {Fitavg} {Fitmed} {Fitmax} {Std} {time}\n'.format(
Gen=str(self.generation),Fitmin=repr(mine),Fitavg=repr(mean),Fitmed=repr(medium),Fitmax=repr(maxe),
Std=repr(std),time=repr(time.asctime( time.localtime(time.time()) ))))
# Set new index values and write population
index1 = 0
for ind in pop:
ind.index=index1
index1+=1
inp_out.write_pop(self,pop)
if self.allenergyfile:
for ind in pop:
self.tenergyfile.write(repr(ind.energy)+' ')
self.tenergyfile.write('\n')
#Check Convergence of population based on fitness
fitnesses = [ind.fitness for ind in pop]
popmin = min(fitnesses)
popmean = sum(fitnesses) / len(fitnesses)
sum2 = sum(x*x for x in fitnesses)
popstd = abs(sum2 / len(fitnesses) - popmean**2)**0.5
convergence = False
if self.convergence_scheme=='gen_rep_min':
if self.generation < self.maxgen:
if self.generation == 0:
self.minfit = popmin
if abs(self.minfit - popmin) < self.tolerance:
self.genrep += 1
else:
self.genrep = 0
self.minfit = popmin
if self.genrep > self.reqrep:
self.convergence = True
else:
self.convergence = True
elif self.convergence_scheme=='gen_rep_avg':
if self.generation < self.maxgen:
if self.generation == 0:
self.minfit = popmean
if abs(self.minfit - popmean) < self.tolerance:
self.genrep += 1
else:
self.genrep = 0
self.minfit = popmean
if self.genrep > self.reqrep:
self.convergence = True
else:
self.convergence = True
elif self.convergence_scheme=='std':
if popstd < self.tolerance:
self.convergence = True
else:#self.convergence_scheme=='max_gen':
if self.generation > self.maxgen:
self.convergence = True
#Flush output to files
self.output.flush()
self.summary.flush()
for ind in self.files:
ind.flush()
if self.indiv_defect_write:
for ind in self.ifiles:
ind.flush()
if self.genealogy: self.Genealogyfile.flush()
if self.allenergyfile: self.tenergyfile.flush()
if 'MA' in self.debug: self.debugfile.flush()
if self.fingerprinting:
self.fpfile.flush()
self.fpminfile.flush()
return self
