import numpy as np
import oxdnagmin_ as GMIN
from pele.potentials.gminpotential import GMINPotential
import pele.basinhopping as bh
from pele.takestep import displace
from pele.storage.database import Database
# initialize GMIN
GMIN.initialize()
# create a potential which calls GMIN
potential = GMINPotential(GMIN)
# get the initial coorinates
coords = potential.getCoords()
coords = np.random.random(coords.shape)
# create takestep routine
step = displace.RandomDisplacement(stepsize=1.)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = bh.BasinHopping(coords, potential, step, db.minimum_adder())
# run for 100 steps
opt.run(1000)
# now dump all the minima
i = 0
for m in db.minima():
i += 1
GMIN.userpot_dump("lowest_%03d.dat" % (i), m.coords)
rotate=0.),
frequency=50)
step2 = takestep.AdaptiveStepsize(OXDNATakestep(displace=0.,
rotate=parameters.rotate),
frequency=50)
group.addBlock(100, step1)
group.addBlock(100, step2)
# with a generate random configuration
genrandom = OXDNAReseed()
# in a reseeding takestep procedure
reseed = takestep.Reseeding(group, genrandom, maxnoimprove=parameters.reseed)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = BasinHopping(coords,
potential,
reseed,
db.minimum_adder(),
temperature=parameters.temperature)
# run for 100 steps
opt.run(parameters.nsteps)
# now dump all the minima
i = 0
for m in db.minima():
i += 1
GMIN.userpot_dump("lowest_%03d.dat" % (i), m.coords)
class BatchGeneticAlgorithm(object):
'''The Birmingham Cluster Genetic Algorithm.
A new parallel version of the BCGA. The population is stored in a pele database and can be accessed by several processes simultaneously.
Parameters:
natoms- Number of atoms in cluster
minimiser- See bcga.gpaw_interface
Optional parameters:
composition- A list containing the number of atoms of each type
labels- A tuple orblist containing the names of each atom type (e.g. ["Au","Ag"]
pop_size- Number of clusters in population
max_generation- Number of generations to run GA
selector- Selection method for choosing parents (see bcga.selector)
offspring- Number of crossover operations in each generation
mutant_rate- Probability of any cluster producing a mutant
remove_duplicates- Remove identical clusters from population to prevent stagnation
restart- Read population from restart.xyz and continue a search
'''
def __init__(self,
natoms,
minimiser,
composition="default",
labels=("X", ),
pop_size=10,
max_generation=10,
selector=TournamentSelector(3),
offspring=8,
mutant_rate=0.1,
remove_duplicates=False,
mutator=MutateReplace(),
crossover=DeavenHo()):
#Parameters
self.max_generation = max_generation
self.mutant_rate = mutant_rate
self.offspring = offspring
self.pop_size = pop_size
self.remove_duplicates = remove_duplicates
self.selector = selector
self.mutator = mutator
self.crossover = crossover
#Factory
self.factory = ClusterFactory(natoms, minimiser, composition, labels)
#PopulationList
self.population = PopulationList(natoms, self.factory, pop_size)
#Evolutionary progress
self.mean_energy_series = []
self.min_energy_series = []
self.db = Database(db="mydatabase.sqlite")
self.storage = self.db.minimum_adder()
def write_xyz(self, file_name="cluster.xyz"):
'''Open an xyz file and write the current population to it (non-blocking).'''
try:
with open(file_name, 'w') as xyz_file:
self.population.write_xyz(xyz_file)
except IOError as err:
print("File error: " + str(err))
def read_xyz(self, file_name="restart.xyz"):
'''Read population from an xyz file (non-blocking for now).'''
self.population.mass_extinction(0)
try:
with open(file_name) as xyz_file:
self.population.read_xyz(xyz_file)
except:
print("No restart file available.")
def run(self):
'''Run the GA.'''
for generation in range(1, self.max_generation + 1):
print("Generation " + str(generation))
if self.db.number_of_minima() < self.population.max_size:
cluster = self.factory.get_random_cluster()
print("Filling population with random structure.")
else:
self.population.read_db(self.db)
if np.random < self.mutant_rate:
index = np.random.randint(0, len(self.population))
cluster = self.mutator.get_mutant(self.population[index])
print("Generating mutant of cluster " + str(index))
else:
indices = self.selector.select(self.population)
cluster = self.crossover.get_offspring(
self.population[indices[0]],
self.population[indices[1]])
print("Generating offpsring of clusters " +
str(indices[0]) + " and " + str(indices[1]))
cluster.minimise()
#self.read_xyz("restart.xyz")
#self.population.append(cluster)
self.storage(cluster.energy, cluster._coords.flatten())
class BatchGeneticAlgorithm(object):
'''The Birmingham Cluster Genetic Algorithm.
A new parallel version of the BCGA. The population is stored in a pele database and can be accessed by several processes simultaneously.
Parameters:
natoms- Number of atoms in cluster
minimiser- See bcga.gpaw_interface
Optional parameters:
composition- A list containing the number of atoms of each type
labels- A tuple orblist containing the names of each atom type (e.g. ["Au","Ag"]
pop_size- Number of clusters in population
max_generation- Number of generations to run GA
selector- Selection method for choosing parents (see bcga.selector)
offspring- Number of crossover operations in each generation
mutant_rate- Probability of any cluster producing a mutant
remove_duplicates- Remove identical clusters from population to prevent stagnation
restart- Read population from restart.xyz and continue a search
'''
def __init__(self,natoms,minimiser,
composition="default",labels=("X",),
pop_size=10,max_generation=10,
selector=TournamentSelector(3),
offspring=8,mutant_rate=0.1,remove_duplicates=False,
mutator=MutateReplace(),
crossover=DeavenHo()):
#Parameters
self.max_generation = max_generation
self.mutant_rate = mutant_rate
self.offspring=offspring
self.pop_size=pop_size
self.remove_duplicates=remove_duplicates
self.selector=selector
self.mutator = mutator
self.crossover = crossover
#Factory
self.factory=ClusterFactory(natoms,minimiser,composition,labels)
#PopulationList
self.population = PopulationList(natoms,self.factory,pop_size)
#Evolutionary progress
self.mean_energy_series=[]
self.min_energy_series=[]
self.db = Database(db="mydatabase.sqlite")
self.storage = self.db.minimum_adder()
def write_xyz(self,file_name="cluster.xyz"):
'''Open an xyz file and write the current population to it (non-blocking).'''
try:
with open(file_name,'w') as xyz_file:
self.population.write_xyz(xyz_file)
except IOError as err:
print("File error: "+str(err))
def read_xyz(self,file_name="restart.xyz"):
'''Read population from an xyz file (non-blocking for now).'''
self.population.mass_extinction(0)
try:
with open(file_name) as xyz_file:
self.population.read_xyz(xyz_file)
except:
print("No restart file available.")
def run(self):
'''Run the GA.'''
for generation in range(1,self.max_generation+1):
print ("Generation "+str(generation))
if self.db.number_of_minima() < self.population.max_size:
cluster=self.factory.get_random_cluster()
print("Filling population with random structure.")
else:
self.population.read_db(self.db)
if np.random < self.mutant_rate:
index=np.random.randint(0,len(self.population))
cluster=self.mutator.get_mutant(self.population[index])
print("Generating mutant of cluster "+str(index))
else:
indices = self.selector.select(self.population)
cluster=self.crossover.get_offspring(self.population[indices[0]],
self.population[indices[1]])
print("Generating offpsring of clusters "+str(indices[0])+" and "+str(indices[1]))
cluster.minimise()
#self.read_xyz("restart.xyz")
#self.population.append(cluster)
self.storage(cluster.energy,cluster._coords.flatten())
coords=potential.getCoords()
coords=np.random.random(coords.shape)
# create takestep routine
# we combine a normal step taking
group = takestep.BlockMoves()
step1 = takestep.AdaptiveStepsize(OXDNATakestep(displace=parameters.displace, rotate=0.), frequency=50)
step2 = takestep.AdaptiveStepsize(OXDNATakestep(displace=0., rotate=parameters.rotate), frequency=50)
group.addBlock(100, step1)
group.addBlock(100, step2)
# with a generate random configuration
genrandom = OXDNAReseed()
# in a reseeding takestep procedure
reseed = takestep.Reseeding(group, genrandom, maxnoimprove=parameters.reseed)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = BasinHopping(coords, potential, reseed, db.minimum_adder(), temperature=parameters.temperature)
# run for 100 steps
opt.run(parameters.nsteps)
# now dump all the minima
i=0
for m in db.minima():
i+=1
GMIN.userpot_dump("lowest_%03d.dat"%(i), m.coords)
