def main():
parser = argparse.ArgumentParser(description="print information about the database")
parser.add_argument("database", type=str, help="Database file name")
parser.add_argument("--write-disconnect",
dest="writeDPS", action="store_true",
help="generate min.dat and ts.dat to use with disconnectDPS")
parser.add_argument("-m",
dest="writeMinima", action="store_true",
help="dump minima to screen")
parser.add_argument("-t",
dest="writeTS", action="store_true",
help="dump transition states to screen")
parser.add_argument("-p",
dest="properties", action="store_true",
help="print system properties")
parser.add_argument("-s",
dest="summary", action="store_true",
help="print summary")
parser.add_argument("-S",
dest="summary_long", action="store_true",
help="print long summary")
args = parser.parse_args()
if args.summary_long:
args.summary = True
db = Database(db=args.database, createdb=False)
if args.properties or args.summary:
print_system_properties(db)
if args.summary:
print "number of minima:", db.number_of_minima()
print "number of transition states:", db.number_of_transition_states()
if args.summary_long:
long_summary(db)
if args.writeMinima:
print "List of minima: energy id fvib pgorder"
print "---------------"
for m in db.minima():
print "%f\t\tid %d %s %s" % (m.energy, m._id, str(m.fvib), str(m.pgorder))
print "END\n"
if args.writeTS:
print "List of transition states:"
print "--------------------------"
for ts in db.transition_states():
print "%d\t<->\t%d\tid %d\tenergies %f %f %f" % \
(ts.minimum1._id, ts.minimum2._id, ts._id, ts.minimum1.energy, ts.energy, ts.minimum2.energy)
print "END\n"
if args.writeDPS:
writeDPS(db)
def main():
parser = argparse.ArgumentParser(description="print information about the database")
parser.add_argument("database", type=str, help="Database file name")
parser.add_argument("--write-pathsample-db",
dest="write_pathsample", action="store_true",
help="generate a pathsample database by writing files min.data, ts.data, points.min, and points.ts")
parser.add_argument("-m",
dest="writeMinima", action="store_true",
help="dump minima to screen")
parser.add_argument("-t",
dest="writeTS", action="store_true",
help="dump transition states to screen")
parser.add_argument("-p",
dest="properties", action="store_true",
help="print system properties")
parser.add_argument("-s",
dest="summary", action="store_true",
help="print summary")
parser.add_argument("-S",
dest="summary_long", action="store_true",
help="print long summary")
args = parser.parse_args()
if args.summary_long:
args.summary = True
db = Database(db=args.database, createdb=False)
if args.properties or args.summary:
print_system_properties(db)
if args.summary:
print "number of minima:", db.number_of_minima()
print "number of transition states:", db.number_of_transition_states()
if args.summary_long:
long_summary(db)
if args.writeMinima:
print "List of minima: energy id fvib pgorder"
print "---------------"
for m in db.minima():
print "%f\t\tid %d %s %s" % (m.energy, m._id, str(m.fvib), str(m.pgorder))
print "END\n"
if args.writeTS:
print "List of transition states:"
print "--------------------------"
for ts in db.transition_states():
print "%d\t<->\t%d\tid %d\tenergies %f %f %f" % \
(ts.minimum1._id, ts.minimum2._id, ts._id, ts.minimum1.energy, ts.energy, ts.minimum2.energy)
print "END\n"
if args.write_pathsample:
write_pathsample_db(db)
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())
def main():
parser = argparse.ArgumentParser(description="print information about the database")
parser.add_argument("database", type=str, help="Database file name")
parser.add_argument("--write-pathsample-db",
dest="write_pathsample", action="store_true",
help="generate a pathsample database by writing files min.data, ts.data, points.min, and points.ts")
parser.add_argument("--write-dummy-db",
dest="write_pathsample_dummy", action="store_true",
help="generate a pathsample database without the points files - min.data and ts.data only")
parser.add_argument("-m",
dest="writeMinima", action="store_true",
help="dump minima to screen")
parser.add_argument("--minimum", type=int, default=0, help="print the coordinates of a particular minimum to the screen")
parser.add_argument("-t",
dest="writeTS", action="store_true",
help="dump transition states to screen")
parser.add_argument("-p",
dest="properties", action="store_true",
help="print system properties")
parser.add_argument("-s",
dest="summary", action="store_true",
help="print summary")
parser.add_argument("-S",
dest="summary_long", action="store_true",
help="print long summary")
parser.add_argument("-l",
dest="long_output", action="store_true",
help="Allow long output to be printed")
args = parser.parse_args()
if args.summary_long:
args.summary = True
suppress = not args.long_output
db = Database(db=args.database, createdb=False)
if args.properties or args.summary:
print_system_properties(db,suppress_long=suppress)
if args.summary:
print "number of minima:", db.number_of_minima()
print "number of transition states:", db.number_of_transition_states()
if args.summary_long:
long_summary(db)
if args.minimum > 0:
m = db.getMinimum(args.minimum)
print m.energy, m._id
x = m.coords.reshape(-1,3)
print x
if args.writeMinima:
print "List of minima: energy id fvib pgorder"
print "---------------"
for m in db.minima():
print "%f\t\tid %d %s %s" % (m.energy, m._id, str(m.fvib), str(m.pgorder))
print "END\n"
if args.writeTS:
print "List of transition states:"
print "--------------------------"
for ts in db.transition_states():
print "%d\t<->\t%d\tid %d\tenergies %f %f %f" % \
(ts.minimum1._id, ts.minimum2._id, ts._id, ts.minimum1.energy, ts.energy, ts.minimum2.energy)
print "END\n"
if args.write_pathsample:
write_pathsample_db(db)
elif args.write_pathsample_dummy:
write_pathsample_db(db, False)
'''
Read structures from the database generated by the batch GA.
Run the run_batch.py script to generate a database.
@author: Mark Oakley
'''
import numpy as np
from pele.storage.database import Database
db = Database(db="mydatabase.sqlite")
minima=db.minima()
print(db.number_of_minima())
for i in minima:
print(i.energy)
print(np.reshape(i.coords,(-1,3)))
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())
'''
Read structures from the database generated by the batch GA.
Run the run_batch.py script to generate a database.
@author: Mark Oakley
'''
import numpy as np
from pele.storage.database import Database
db = Database(db="mydatabase.sqlite")
minima = db.minima()
print(db.number_of_minima())
for i in minima:
print(i.energy)
print(np.reshape(i.coords, (-1, 3)))
