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from gputhread import *
from Queue import Queue
from sample import uniform_sphere
import numpy as np
from pycuda import gpuarray
import pycuda.driver as cuda
from uncertainties import ufloat, umath
from histogram import Histogram
import math
jobs = Queue()
output = Queue()
def generate_event(detector, position=(0,0,0), nphotons=5000):
jobs.put((gpuarray.vec.make_float3(*position), nphotons))
jobs.join()
earliest_times = output.get()
pmt_times = earliest_times[detector.pmtids]
event_times = [ (i, t) for i, t in zip(detector.pmtids, pmt_times) if t < 1e8 ]
print '%i hit pmts' % len(event_times)
return event_times
def likelihood(detector, event_times, position=(0,0,0), nphotons=5000, neval=100):
for i in range(neval):
jobs.put((gpuarray.vec.make_float3(*position), nphotons))
jobs.join()
t = np.zeros(shape=(neval, max(detector.pmtids)+1), dtype=np.float32)
for i in range(neval):
t[i] = output.get()
log_likelihood = 0.0
log_likelihood_variance = 0.0
for i, time in event_times:
h = Histogram(500, (-0.5e-9, 99.5e-9))
h.fill(t[:,i])
if h.nentries > 0:
h.normalize()
probability = h.ueval(time)
if probability.nominal_value == 0.0:
if h.nentries > 0:
probability = ufloat((0.5/h.nentries, 0.5/h.nentries))
else:
probability = \
ufloat((1.0/len(h.bincenters), 1.0/len(h.bincenters)))
log_likelihood += math.log(probability.nominal_value)
log_likelihood_variance += (probability.std_dev()/probability.nominal_value)**2
return ufloat((-log_likelihood, log_likelihood_variance**0.5))
if __name__ == '__main__':
import sys
import optparse
import time
parser = optparse.OptionParser('%prog')
parser.add_option('-b', type='int', dest='nbits', default=8)
parser.add_option('-j', type='string', dest='devices', default='0')
parser.add_option('-n', type='int', dest='nblocks', default=64)
options, args = parser.parse_args()
from detectors import build_minilbne
detector = build_minilbne()
detector.build(bits=options.nbits)
cuda.init()
gputhreads = []
for i in [int(s) for s in options.devices.split(',')]:
gputhreads.append(GPUThread(i, detector, jobs, output, options.nblocks))
gputhreads[-1].start()
try:
event_times = generate_event(detector)
for z in np.linspace(-1.0, 1.0, 100):
t0 = time.time()
log_likelihood = likelihood(detector, event_times, (z,0,0))
elapsed = time.time() - t0
print 'z = %5.2f, likelihood = %s, elapsed %.2f sec' % (z, log_likelihood, elapsed)
finally:
for gputhread in gputhreads:
gputhread.stop()
for gputhread in gputhreads:
gputhread.join()
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