1 files changed, 0 insertions, 94 deletions
diff --git a/threadtest.py b/threadtest.py
deleted file mode 100644
index f03f920..0000000
--- a/threadtest.py
+++ /dev/null
@@ -1,94 +0,0 @@
-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()