def run_trajectories_remotely(ts, processors, walltime, memory, output_dir, trj_file, config_file):
# List to hold the PBS job ids
ids = []
# Go through each trajectory
for i, t in enumerate(ts):
# Create the directory to hold all the outputs
ext = str(i + 1)
trd = output_dir + "/Trajectory" + ext
os.makedirs(trd)
# Determine the names for the pbs script and the individual trajectory file
pbsfile = trd + "/runTrajectory" + str(i + 1) + ".pbs"
tfile = trd + "/" + trj_file + "." + str(i + 1)
# Create a file containing the current trajectory, located in the newly created directory
io.write_trajectories([t], ptv, [], tfile)
# Create a pbs script to run the trajectory
write_pbs(pbsfile, processors, walltime, memory, config_file, ext)
# Submit the script
ids.append(sp.check_output(["qsub", "-o", trd, "-e", trd, pbsfile]).strip())
# Wait for each of the processes to finish
while ids != []:
queue = sp.check_output(["qstat"]).strip()
print "polling: " + str(len(ids)) + " trajectories remaining"
for id_string in ids:
if not re.search(id_string, queue):
ids.remove(id_string)
time.sleep(60)
def run_trajectories_remotely(ts, processors, walltime, memory, output_dir, trj_file, config_file): #List to hold the PBS job ids ids = [] #Go through each trajectory for i, t in enumerate(ts): #Create the directory to hold all the outputs ext = str(i + 1) trd = output_dir + "/Trajectory" + ext os.makedirs(trd) #Determine the names for the pbs script and the individual trajectory file pbsfile = trd + "/runTrajectory" + str(i + 1) + ".pbs" tfile = trd + "/" + trj_file + "." + str(i + 1) #Create a file containing the current trajectory, located in the newly created directory io.write_trajectories([t], ptv, [], tfile) #Create a pbs script to run the trajectory write_pbs(pbsfile, processors, walltime, memory, config_file, ext) #Submit the script ids.append(sp.check_output(["qsub", "-o", trd, "-e", trd, pbsfile]).strip()) #Wait for each of the processes to finish while ids != []: queue = sp.check_output(["qstat"]).strip() print "polling: " + str(len(ids)) + " trajectories remaining" for id_string in ids: if not re.search(id_string, queue): ids.remove(id_string) time.sleep(60)
import sys as sys
import itertools as itertools
sys.path.append("./Modules")
import gmbiIO as io
if __name__ == "__main__":
output_file = sys.argv[-1]
for (t1, ptv, sentinels), (t2, _, _) in itertools.izip(
io.read_trajectories_gen(sys.argv[1]),
io.read_trajectories_gen(sys.argv[2])):
t1.merge(t2)
print "merged"
io.write_trajectories([t1], ptv, sentinels, sys.argv[-1], append=True)
x[index,:] = point.independents()
if np.all(x[index, :] == x[index - 1,:]):
x[index, :] += x[index, :] / 1000.
y[index, :] = y[index - 1, :]
else:
y[index,:] = point.mean()
return x, y
if __name__ == "__main__" :
parser = ap.ArgumentParser()
parser.add_argument('--config', default="config.py")
parser.add_argument('--task', required=True)
parser.add_argument('--design', default="oat")
args = parser.parse_args()
#Load the configuration file
config = os.path.realpath(args.config)
execfile(args.config)
if "generate" == args.task:
ps = pms.parameter_set_from_file(input_dir + "/" + organism_file)
ts = _generate_trajectories(ps, r, levels, jumps, perc, ptv, args.design)
io.write_trajectories(ts, ptv, [], output_dir + "/" + trajectory_file)
elif "analyse":
ts, ptv, sentinels = io.read_all_trajectories(output_dir + "/" + results_file)
ees = _calculate_elementary_effects(ts, ptv, "oat")
io.plot_ees(ees, ptv, output_dir)
# Construct the parameter set from the organism file specified in config
ps = params.parameter_set_from_file(input_dir + "/" + organism_file)
# Construct the landscape from the landscape file specified in config
landscape = lscp.landscape_from_file(input_dir + "/" + landscape_file)
ps.landscape_size = landscape.shape
# If a single run is being performed
if args.task == "run":
ps.convert_to_age_classes()
Ns = run_parrallel_iterations(ps, landscape, config)
elif args.task == "run_trajectories":
# Read in the trajectories from file, if ext is set, the original trajectory file has been split into multiple
# files and each trajectory has been assigned a working sub-directory under the output_directory
if args.ext == "":
ts, ptv, _ = io.read_all_trajectories(input_dir + "/" + trajectory_file)
else:
output_dir += "/Trajectory" + args.ext
ts, ptv, _ = io.read_all_trajectories(output_dir + "/" + trajectory_file + "." + args.ext)
if args.remote:
run_trajectories_remotely(ts, num_processors, walltime, memory, output_dir, trajectory_file, config_file)
io.cat_trajectories(len(ts), ptv, ps.sentinels, output_dir)
else:
# If a specific trajectory is to be run
if args.trajectory > 0:
ts = [run_trajectory(ts[int(args.trajectory)], ps, landscape, ptv, num_iterations, pool)]
else:
ts = [run_trajectory(t, ps, landscape, ptv, num_iterations, pool) for t in ts]
io.write_trajectories(ts, ptv, ps.sentinels, output_dir + "/results.txt")
import sys as sys
import itertools as itertools
sys.path.append("./Modules")
import gmbiIO as io
if __name__ == "__main__":
output_file = sys.argv[-1]
for (t1, ptv, sentinels), (t2, _, _) in itertools.izip(io.read_trajectories_gen(sys.argv[1]), io.read_trajectories_gen(sys.argv[2])):
t1.merge(t2)
print "merged"
io.write_trajectories([t1], ptv, sentinels, sys.argv[-1], append=True)
ps = params.parameter_set_from_file(input_dir + "/" + organism_file) #Construct the landscape from the landscape file specified in config landscape = lscp.landscape_from_file(input_dir + "/" + landscape_file) ps.landscape_size = landscape.shape #If a single run is being performed if args.task == "run": ps.convert_to_age_classes() Ns = run_parrallel_iterations(ps, landscape, config) elif args.task == "run_trajectories": #Read in the trajectories from file, if ext is set, the original trajectory file has been split into multiple #files and each trajectory has been assigned a working sub-directory under the output_directory if args.ext == "": ts, ptv, _ = io.read_all_trajectories(input_dir + "/" + trajectory_file) else: output_dir += "/Trajectory" + args.ext ts, ptv, _ = io.read_all_trajectories(output_dir + "/" + trajectory_file + "." + args.ext) if args.remote: run_trajectories_remotely( ts, num_processors, walltime, memory, output_dir, trajectory_file, config_file) io.cat_trajectories(len(ts), ptv, ps.sentinels, output_dir) else: #If a specific trajectory is to be run if args.trajectory > 0: ts = [run_trajectory(ts[int(args.trajectory)], ps, landscape, ptv, num_iterations, pool)] else: ts = [run_trajectory(t, ps, landscape, ptv, num_iterations, pool) for t in ts] io.write_trajectories(ts, ptv, ps.sentinels, output_dir + "/results.txt")