def generator(dbfile='godb.db'):
''' This function will be called to generate the initial batch
of random structures, which will then be partially relaxed
using the reference method (usually DFT). Here, we simply
use the random structure generator which will also be
used in the genetic algorithm searches. We wish to have
twenty of such random structures (N=20):
'''
prepare_ga(dbfile=dbfile, N=10)
return
def run(args):
rundir, maxiter = args
if not os.path.exists(rundir):
os.mkdir(rundir)
os.chdir(rundir)
sys.stdout = open('out.txt', 'a')
sys.stderr = open('err.txt', 'w')
try:
if not os.path.exists('godb.db'):
prepare_ga(splits={(2, 2): 1, (2, ): 2, (1, ): 1}, N=N)
run_ga(maxiter, kptdensity=kptdensity)
except:
traceback.print_exc()
sys.stderr.flush()
raise
os.chdir('..')
return
def run(args):
''' This method specifies how to conduct a global optimization
search, accepting a (run_directory, maximum_iterations)
tuple as required by TANGO.
'''
rundir, maxiter = args
# Create the run directory if it doesn't already exist:
if not os.path.exists(rundir):
os.mkdir(rundir)
os.chdir(rundir)
# Redirect the standard output and error to local files:
sys.stdout = open('out.txt', 'a')
sys.stderr = open('err.txt', 'w')
try:
# Create a database with N initial random structures
# (N is here a global variable set in the __main__
# function):
if not os.path.exists('godb.db'):
prepare_ga(N=N)
#prepare_ga(splits={(2, 2): 1, (2,): 2, (1,): 1}, N=N)
# Run the genetic algorithm (kptdensity is another
# global variable set in the __main__ function):
run_ga(maxiter, kptdensity=kptdensity)
except:
# When using the multiprocessing module
# we need to explicitly print the error
# traceback:
traceback.print_exc()
sys.stderr.flush()
raise
os.chdir('..')
return
def generator(dbfile='godb.db'):
prepare_ga(dbfile=dbfile, splits={(2, 2): 1, (2, ): 2, (1, ): 1}, N=20)
return