def pool(request):
multimode = 'None'
# multimode = 'Serial'
# multimode = 'Multi'
# multimode = 'MPI'
# setup code
pool = None
if multimode == 'Serial':
from schwimmbad import SerialPool
pool = SerialPool()
elif multimode == 'Multi':
from schwimmbad import MultiPool
pool = MultiPool()
elif multimode == 'MPI':
from schwimmbad import MPIPool
pool = MPIPool()
if not pool.is_master():
pool.wait()
import sys
sys.exit(0)
# inject class variables
request.cls.pool = pool
yield
# tear down
if multimode == 'Multi' or multimode == 'MPI':
pool.close()
def sample(self, n_samples):
if self.pool is None or _GPU_ENABLED:
pool = SerialPool()
else:
if isinstance(self.pool, int):
pool = MultiPool(self.pool)
elif isinstance(self.pool, (SerialPool, MultiPool)):
pool = self.pool
else:
raise TypeError(
"Does not understand the given multiprocessing pool.")
drawn_samples = list(
pool.map(self.draw_one_joint_posterior_sample_map,
range(n_samples)))
pool.close()
drawn_zs = [drawn_samples[i][0] for i in range(n_samples)]
drawn_inference_posteriors = [
drawn_samples[i][1] for i in range(n_samples)
]
drawn_joint_posterior_samples = pd.DataFrame(
drawn_inference_posteriors)
drawn_joint_posterior_samples["redshift"] = drawn_zs
return drawn_joint_posterior_samples
def compute_mean_selection_function(selection_function, N_avg, pool=None):
if pool is None:
pool = SerialPool()
elif isinstance(pool, int):
pool = MultiPool(pool)
elif isinstance(pool, (SerialPool, MultiPool)):
pool = pool
else:
raise TypeError("Does not understand the given multiprocessing pool.")
out = list(
pool.starmap(selection_function.evaluate, [() for _ in range(N_avg)]))
avg = np.average(out)
pool.close()
return avg