def getBinEfficiencies(self, binnr):
binmid = generateBinMid(self.myrange, binnr, self.RBins,
self.RRange[0])
myR0 = self.getR0(binnr)
reff = rToEff(binmid, myR0)
print "Bin efficiency is %6.4f" % reff
return [reff] * self.BurstCount, getBurstSizes(self.BurstCount,
self.burstGenerator)
def calculateBursts(traj, eprob, conf, nbursts, randseed):
"""calculates efficiencies from trajectories with given probabilities and given configuration, here the burst sizes are determined"""
pyrand.seed(randseed)
nprand.seed(pyrand.randint(0, sys.maxint))
verbose = conf.get("System", "verbose")
burstGenerator = getBurstSizeGenerator(conf, verbose)
if verbose:
print "Calculating bursts sizes."
burstsizelist = getBurstSizes(nbursts, burstGenerator)
return getBursts(traj, eprob, conf, burstsizelist)
def getDistanceBursts(trajectories, probabilities, config):
print "Generating Bursts"
burstGenerator = getBurstSizeGenerator(config, 1)
nbursts = config.get("Burst Size Distribution", "nbursts")
burstsizelist = getBurstSizes(nbursts, burstGenerator)
burstdists = []
for bs in burstsizelist:
burstdists.append(generateBurst(trajectories, probabilities, config, bs))
if (len(burstdists) % 100 == 0):
print "\r %d of %d completed." % (len(burstdists), len(burstsizelist)),
return numpy.array(burstdists)
def getDistanceBursts(trajectories, probabilities, config):
print "Generating Bursts"
burstGenerator = getBurstSizeGenerator(config, 1)
nbursts = config.get("Burst Size Distribution", "nbursts")
burstsizelist = getBurstSizes(nbursts, burstGenerator)
burstdists = []
for bs in burstsizelist:
burstdists.append(
generateBurst(trajectories, probabilities, config, bs))
if (len(burstdists) % 100 == 0):
print "\r %d of %d completed." % (len(burstdists),
len(burstsizelist)),
return numpy.array(burstdists)
def getBinEfficiencies(self, binnr):
if not self.kappaBinned:
self.binKappa()
bursts = getBurstSizes(self.BurstCount, self.burstGenerator)
beffs = []
rkprb = np.array(self.rkappaBinned[binnr])
Rarr = rkprb[:, 0]
Kappaarr = rkprb[:, 1]
Prbarr = rkprb[:, 2]**0
# R0s_mod = modifyR0(self.R0, Kappaarr)
#
# cumulprb = Prbarr.cumsum()
# cumulprb /= cumulprb[-1]
# for burst in bursts:
# randnrs = np.random.random(burst)
# ndxchoice = cumulprb.searchsorted(randnrs)
# effs = rToEff(Rarr[ndxchoice], R0s_mod[ndxchoice])
# beffs.append(effs)
cumulprb = Prbarr.cumsum()
cumulprb /= cumulprb[-1]
for burst in bursts:
randnrs = np.random.random(burst)
ndxchoice = cumulprb.searchsorted(randnrs)
Ravg = Rarr[ndxchoice].mean()
Kavg = Kappaarr[ndxchoice].mean()
R0_mod = modifyR0(self.R0, Kavg)
effs = rToEff(Ravg, R0_mod)
beffs.append(effs)
print "%d Efficiencies for bursts in bin %d calculated from %d R-K samples" % (
len(beffs), binnr, R0_mod)
return beffs, bursts
def getBinEfficiencies(self, binnr):
if not self.kappaBinned:
self.binKappa()
bursts = getBurstSizes(self.BurstCount, self.burstGenerator)
beffs = []
rkprb = np.array(self.rkappaBinned[binnr])
Rarr = rkprb[:, 0]
Kappaarr = rkprb[:, 1]
Prbarr = rkprb[:, 2] ** 0
# R0s_mod = modifyR0(self.R0, Kappaarr)
#
# cumulprb = Prbarr.cumsum()
# cumulprb /= cumulprb[-1]
# for burst in bursts:
# randnrs = np.random.random(burst)
# ndxchoice = cumulprb.searchsorted(randnrs)
# effs = rToEff(Rarr[ndxchoice], R0s_mod[ndxchoice])
# beffs.append(effs)
cumulprb = Prbarr.cumsum()
cumulprb /= cumulprb[-1]
for burst in bursts:
randnrs = np.random.random(burst)
ndxchoice = cumulprb.searchsorted(randnrs)
Ravg = Rarr[ndxchoice].mean()
Kavg = Kappaarr[ndxchoice].mean()
R0_mod = modifyR0(self.R0, Kavg)
effs = rToEff(Ravg, R0_mod)
beffs.append(effs)
print "%d Efficiencies for bursts in bin %d calculated from %d R-K samples" % (len(beffs), binnr, R0_mod)
return beffs, bursts
def getBinEfficiencies(self, binnr):
binmid = generateBinMid(self.myrange, binnr, self.RBins, self.RRange[0])
myR0 = self.getR0(binnr)
reff = rToEff(binmid, myR0)
print "Bin efficiency is %6.4f" % reff
return [reff] * self.BurstCount, getBurstSizes(self.BurstCount, self.burstGenerator)
