Replace calculation of likelihood variance from uncertainties because

it is too slow.  Now we do it directly.

1 files changed, 7 insertions, 4 deletions

diff --git a/threadtest.py b/threadtest.py
index d76224f..c9854a7 100644
--- a/threadtest.py
+++ b/threadtest.py
@@ -6,6 +6,7 @@ from pycuda import gpuarray
 import pycuda.driver as cuda
 from uncertainties import ufloat, umath
 from histogram import Histogram
+import math
 
 jobs = Queue()
 output = Queue()
@@ -43,9 +44,10 @@ def likelihood(detector, event_times, position=(0,0,0), nphotons=5000, neval=100
         job = output.get()
         t[i] = job.earliest_times
 
-    log_likelihood = ufloat((0,0))
+    log_likelihood = 0.0
+    log_likelihood_variance = 0.0
     for i, time in event_times:
-        h = Histogram(100, (-0.5e-9, 99.5e-9))
+        h = Histogram(500, (-0.5e-9, 99.5e-9))
         h.fill(t[:,i])
 
         if h.nentries > 0:
@@ -60,9 +62,10 @@ def likelihood(detector, event_times, position=(0,0,0), nphotons=5000, neval=100
                 probability = \
                     ufloat((1.0/len(h.bincenters), 1.0/len(h.bincenters)))
 
-        log_likelihood += umath.log(probability)
+        log_likelihood += math.log(probability.nominal_value)
+        log_likelihood_variance += (probability.std_dev()/probability.nominal_value)**2
 
-    return -log_likelihood
+    return ufloat((-log_likelihood, log_likelihood_variance**0.5))
 
 if __name__ == '__main__':
     import sys