def main():
# Initialize the MPI pool
pool = MPIPool()
# Make sure only we run map() on the master process
if not pool.is_master():
pool.wait()
sys.exit(0)
# create some random input data
x = np.random.uniform(size=10000)
y = np.random.uniform(size=10000)
tasks = np.vstack((x,y)).T
vals = pool.map(worker, tasks)
pool.close()
# create the pool
pool = MPIPool()
# only run map() on the master process, all other processes wait for their work
if not pool.is_master():
pool.wait()
# worker processes exit after they have done their work
sys.exit(0)
# the following code is executed by the master process only
# create some random input data
x = np.random.uniform(size=10)
y = np.random.uniform(size=10)
tasks = list(zip(x, y))
# crate a callback function
def cb(x):
print x
# map the function worker to tasks
# and execute them parallel by processes other than the master
results = pool.map(worker, tasks, callback=cb)
# close the pool
pool.close()
print 'results:', results
if multiprocess:
stats_dict[stat] = manager.list([])
else:
stats_dict[stat] = []
for fid in fiducials[int(comp[0])]:
print("On fiducial {0} of {1}".format(fid, len(fiducials[int(comp[0])])))
print(str(datetime.now()))
if multiprocess:
iterat = ((fiducials[int(comp[0])][fid][i],
fiducials[int(comp[-1])][fid][i], comp, stats_dict)
for i in xrange(len(fiducials[int(comp[0])][fid])))
pool.map(runner, iterat)
else:
for i in range(len(fiducials[int(comp[0])][fid])):
name1 = fiducials[int(comp[0])][fid][i]
name2 = fiducials[int(comp[-1])][fid][i]
print("On {0} {1}".format(name1, name2))
dataset1 = load_and_reduce(name1)
dataset2 = load_and_reduce(name2)
output = stats_wrapper(dataset1, dataset2, statistics=stats)[0]
for stat in output:
stats_dict[stat].append(output[stat], os.path.basename(name1),
os.path.basename(name2))
