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
for stat in output:
stats_dict[stat].append(output[stat], os.path.basename(name1),
os.path.basename(name2))
print("Finished designs at {}".format(datetime.now()))
# Convert to normal lists
if multiprocess:
for stat in stats_dict:
stats_dict[stat] = list(stats_dict[stat])
# Add the filename columns. The list order should be preserved since we're not
# using asynchronous mapping
for i, stat in enumerate(stats_dict.keys()):
results_arr = np.array(stats_dict[stat]).T
if i == 0:
stats_dict["Data 1"] = results_arr[1]
stats_dict["Data 2"] = results_arr[2]
stats_dict[stat] = results_arr[0].astype(np.float)
# Now save the results
df = DataFrame(stats_dict)
df.to_csv(os.path.join(results_dir,
"view_angle_comparison_{}.csv".format(comp)))
if multiprocess:
pool.close()
print("Done at {}".format(datetime.now()))