m = T.shape[0]
percentiles = list(arange(0.5, 100.0, 0.5))
seed(0)
seeds = random_integers(0, 2 ** 31 - 2, size=EX_NUM)
for tr in trends:
results = zeros((len(percentiles), len(T), EX_NUM)) * nan
filename = 'adf_z_' + tr + '.npz'
for i in range(EX_NUM):
print("Experiment Number {0} for Trend {1}".format(i + 1, tr))
# Non parallel version
# out = lmap(wrapper, T, [tr] * m, [EX_SIZE] * m, [seeds[i]] * m))
now = datetime.datetime.now()
out = lview.map_sync(wrapper,
T,
[tr] * m, [EX_SIZE] * m,
[seeds[i]] * m)
# Prevent unnecessary results from accumulating
lview.purge_results('all')
rc.purge_everything()
print(datetime.datetime.now() - now)
quantiles = lmap(lambda x: percentile(x, percentiles), out)
results[:, :, i] = array(quantiles).T
if i % 50 == 0:
savez(filename, trend=tr, results=results,
percentiles=percentiles, T=T)
savez(filename, trend=tr, results=results, percentiles=percentiles, T=T)
180, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900,
1000, 1200, 1400, 2000))
T = T[::-1]
m = T.shape[0]
percentiles = list(arange(0.5, 100.0, 0.5))
seed(0)
seeds = random_integers(0,2**31-2,size=EX_NUM)
for tr in trends:
results = zeros((len(percentiles), len(T), EX_NUM)) * nan
filename = 'adf_z_' + tr + '.npz'
for i in range(EX_NUM):
print("Experiment Number {0} for Trend {1}".format(i + 1, tr))
# Non parallel version
# out = lmap(wrapper, T, [tr] * m, [EX_SIZE] * m, [seeds[i]] * m))
now = datetime.datetime.now()
out = lview.map_sync(wrapper, T, [tr] * m, [EX_SIZE] * m, [seeds[i]] * m)
# Prevent unnecessary results from accumulating
lview.purge_results('all')
rc.purge_everything()
print(datetime.datetime.now() - now)
q = lambda x: percentile(x, percentiles)
quantiles = lmap(q, out)
results[:, :, i] = array(quantiles).T
if i % 50 == 0:
savez(filename, trend=tr, results=results,
percentiles=percentiles, T=T)
savez(filename, trend=tr, results=results, percentiles=percentiles, T=T)
import sys
from IPython.parallel import Client, interactive
from util import timer
client = Client()
view = client[:]
view.execute('import numpy')
@interactive # this runs on the engins
def calc_pi_on_engines(n):
x = numpy.random.rand(n)
y = numpy.random.rand(n)
r = numpy.hypot(x, y)
return 4. * (r < 1.).sum() / n
@timer
def calc_pi(n):
"""Estimate pi using IPython.parallel."""
n_engines = n/len(view)
results = view.apply_sync(calc_pi_on_engines, n_engines)
return float(sum(results))/len(results)
if __name__ == "__main__":
N = int(sys.argv[1])
result, time = calc_pi(N)
print('time : %3.4g\nresult: %.7f' % (time, result))
client.purge_everything()
