def entry_point():
args, metric = parser.parse_args()
if hasattr(args, 'sclarans_parallel') and args.sclarans_parallel == 'dtm':
from deap import dtm
dtm.start(main, args)
else:
main(args, metric)
def entry_point():
args, metric = parser.parse_args()
if hasattr(args, 'sclarans_parallel') and args.sclarans_parallel == 'dtm':
from deap import dtm
dtm.start(main, args)
else:
main(args, metric)
outf.write("%s\n" % (tree[2]))
outf.close()
# compute bootreps on genetrees from above
if args.run == 'both':
sys.stdout.write("Running boostraps of genetrees...\n")
# get models for each locus based on genetrees in-memory
models = dict([[tree[0], tree[1]] for tree in genetrees])
boostrap_all_loci(args, models, alns)
# compute bootreps on genetrees from a file
if args.run == 'bootstraps':
sys.stdout.write("Running boostraps...\n")
# get models for each locus based on genetrees in genetree file
models = get_models_from_genetrees(args.genetrees)
boostrap_all_loci(args, models, alns)
if __name__ == '__main__':
args = get_args()
if args.parallelism == 'mpi':
from deap.dtm import map as mmap
from deap.dtm import start
start(main)
elif args.parallelism == 'multiprocessing':
from multiprocessing import Pool
pool = Pool(args.cores)
mmap = pool.map
main()
elif args.parallelism == 'single':
mmap = map
main()
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
"""
A very simple example of recursive nested tasks.
Each task maps 2 others tasks, each of these 2 tasks maps 2 others, etc.,
up to RECURSIVITY_LEVEL.
Setting RECURSIVITY_LEVEL to a high value (typically more than 16) may
produce some strange OS errors (as the kernel will probably complain about
the number of active threads...
"""
from deap import dtm
RECURSIVITY_LEVEL = 12
def recursiveFunc(level):
if level == 1:
return 1
else:
args = [level-1] * 2
s = sum(dtm.map(recursiveFunc, args))
if level == RECURSIVITY_LEVEL:
print("2^"+str(level)+" = " + str(s))
return s
dtm.start(recursiveFunc, RECURSIVITY_LEVEL)
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
"""
Distributed computation of the prime numbers below 45,000 using DTM.
"""
import time
import math
from deap import dtm
def primaryTest(nbr):
if nbr <= 2:
return True
for i in range(2, int(math.sqrt(nbr))+1):
if nbr % i == 0:
return False
return True
def main(upTo):
beginTime = time.time()
listNbr = range(3,upTo,2)
print("Computation begin...")
listPrimes = dtm.filter(primaryTest, listNbr)
print("Found " + str(len(listPrimes)) + " prime numbers between 3 and " + str(upTo))
print("Computation time : " + str(time.time()-beginTime))
dtm.start(main, 45003)
outf.write("%s\n" % (tree[2]))
outf.close()
# compute bootreps on genetrees from above
if args.run == 'both':
sys.stdout.write("Running bootstraps of genetrees...\n")
# get models for each locus based on genetrees in-memory
models = dict([[tree[0], tree[1]] for tree in genetrees])
boostrap_all_loci(args, models, alns)
# compute bootreps on genetrees from a file
if args.run == 'bootstraps':
sys.stdout.write("Running boostraps...\n")
# get models for each locus based on genetrees in genetree file
models = get_models_from_genetrees(args.genetrees)
boostrap_all_loci(args, models, alns)
if __name__ == '__main__':
args = get_args()
if args.parallelism == 'mpi':
from deap.dtm import map as mmap
from deap.dtm import start
start(main)
elif args.parallelism == 'multiprocessing':
from multiprocessing import Pool
pool = Pool(args.cores)
mmap = pool.map
main()
elif args.parallelism == 'single':
mmap = map
main()
parser.add_option("--job_name", dest="job_name", default="fold", help="name for submitted job. default:%default", metavar="NAME")
parser.add_option("--processors", dest="np", default=4, help="number of processors to use. default:%default", type="int", metavar="NP")
parser.add_option("--notify", dest="notify", default=None, help="email address to notify of start, errors and completion", metavar="EMAIL")
parser.add_option("--foldparams", dest="fp", type="str", action="store", default="-W_80_-L_40_-u_1", help="folding parameters default=%default", metavar="PARAMETER STRING")
parser.add_option("--start", dest="start", default=False, action="store_true", help=SUPPRESS_HELP) #private, don't use
(options, args) = parser.parse_args()
options.prefix = os.getcwd()
#main(options)
#exit()
if options.start is True:
dtm.start(main, options)
#main(options)
else:
scriptName = os.path.join(options.prefix, options.job_name+".RUNFOLD.sh")
runerr = os.path.join(options.prefix, options.job_name+ ".err")
runout = os.path.join(options.prefix, options.job_name+ ".out")
shScript = open(scriptName, 'w')
host = Popen(["hostname"], stdout=PIPE).communicate()[0].strip()
if "optiputer" in host or "compute" in host:
shScript.write("#!/bin/bash\n#$ -N %s\n#$ -S /bin/bash\n#$ -V\n#$ -pe mpi %d\n#$ -cwd\n#$ -o %s\n#$ -e %s\n#$ -l bigmem\n" %(options.job_name, options.np, runout, runerr))
if options.notify is not None:
shScript.write("#$ -notify\n#$ -m abe\n#$ -M %s\n" %(options.notify))
shScript.write("/opt/openmpi/bin/mpirun -np $NSLOTS -machinefile $TMPDIR/machines python %s --start\n" %(" ".join(sys.argv)))
######################################################################################
_logger.info("[%s] Testing parameters and exceptions handling...", time.time()-beginTime)
applyParamPassr = dtm.apply(applyFunc4, 1, 2, "abc", bb={'a':2, 'b':3, 'c':4}, cc=range(10), dd=13.37)
applyParamPasst = applyFunc4(1, 2, "abc", bb={'a':2, 'b':3, 'c':4}, cc=range(10), dd=13.37)
if applyParamPassr == applyParamPasst:
_logger.info("[%s] DTM parameters passing test successful", time.time()-beginTime)
else:
_logger.warning("[%s] DTM parameters passing test FAILED", time.time()-beginTime)
countFailed += 1
try:
applyExceptTestr = dtm.map(applyFunc3, [-2,-1,0])
except ZeroDivisionError:
_logger.info("[%s] DTM exception catch test successful", time.time()-beginTime)
else:
_logger.warning("[%s] DTM exception catch test FAILED", time.time()-beginTime)
countFailed += 1
_logger.info("[%s] DTM test suite done with %i errors", time.time()-beginTime, countFailed)
return 0
#dtm.setOptions(setTraceMode=True)
dtm.start(main)
could stride a little at the begining, but its not recommended.""")
sys.exit(1)
trajs = load_trajectories(args.project, args.stride)
logger.info('Loaded %d trajs', len(trajs))
clusterer = cluster(metric, trajs, args)
if not isinstance(clusterer, clustering.Hierarchical):
generators = clusterer.get_generators_as_traj()
logger.info('Saving %s', args.generators)
generators.save_to_lhdf(args.generators)
if args.stride == 1:
assignments = clusterer.get_assignments()
distances = clusterer.get_distances()
logger.info('Since stride=1, Saving %s', args.assignments)
logger.info('Since stride=1, Saving %s', args.distances)
io.saveh(args.assignments, assignments)
io.saveh(args.distances, distances)
if __name__ == '__main__':
args, metric = parser.parse_args()
if hasattr(args, 'sclarans_parallel') and args.sclarans_parallel == 'dtm':
from deap import dtm
dtm.start(main, args)
else:
main(args, metric)
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
"""
Calculation of Pi using a Monte Carlo method.
"""
from math import hypot
from random import random
from deap import dtm
def test(tries):
return sum(hypot(random(), random()) < 1 for i in range(tries))
def calcPi(n, t):
expr = dtm.repeat(test, n, t)
pi_value = 4. * sum(expr) / float(n*t)
print("pi = " + str(pi_value))
return pi_value
dtm.setOptions(setTraceMode=True, taskGranularity=0.01)
dataPi = dtm.start(calcPi, 3000, 5000)
_logger.info("Lauching %s on worker %s", taskCmd, dtm.getWorkerId())
return subprocess.call(taskCmd, stdout=sys.stdout, stderr=sys.stderr)
def main(fTasks):
tasksList = []
with open(fTasks) as f:
for line in f:
line = line.strip()
if len(line) > 0:
tasksList.append(line)
retVals = dtm.map(executor, tasksList)
if sum(retVals) > 0:
_logger.warning("Some tasks did not run well (return code > 0) :")
for i in range(len(retVals)):
if retVals[i] > 0:
_logger.warning("%s returns %s", tasksList[i], retVals[i])
_logger.info("Done")
if __name__ == '__main__':
cmdLine = optparse.OptionParser(description = "Simple script scheduler (sssched) for remote job management")
cmdLine.add_option("-c", "--commands",
dest = "commandsFileName",
action = "store",
help = "File with list of commands to perform (default:\"commands.lst\")",
type = "string")
(options, args) = cmdLine.parse_args()
dtm.start(main, options.commandsFileName)
# -*- coding: utf-8 -*-
from Tree import *
import sys
import time
from deap import dtm
def main():
return executeTree()
importTree(sys.argv[1] if len(sys.argv) > 1 else "tree.txt")
registerMap(dtm.map)
if __name__=="__main__":
bt = time.time()
dtm.start(main)
totalTime = time.time() - bt
print("total time : {}".format(totalTime))
clusterer = cluster(metric, trajs, args)
if not isinstance(clusterer, clustering.Hierarchical):
generators = clusterer.get_generators_as_traj()
logger.info('Saving %s', args.generators)
generators.save_to_lhdf(args.generators)
if args.stride == 1:
assignments = clusterer.get_assignments()
distances = clusterer.get_distances()
logger.info('Since stride=1, Saving %s', args.assignments)
logger.info('Since stride=1, Saving %s', args.distances)
io.saveh(args.assignments, assignments)
io.saveh(args.distances, distances)
if __name__ == '__main__':
args, metric = parser.parse_args()
if hasattr(args, 'sclarans_parallel') and args.sclarans_parallel == 'dtm':
from deap import dtm
dtm.start(main, args)
else:
main(args, metric)
