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 testDistanceAVGAlteredKappa(self):
R = numpy.linspace(5, 6, 1100)
kappa = [ 2. / 3 ] * 200 + [ 1. / 3 ] * 400 + [ 2. / 3 ] * 500
weights = [ 1. ] * 1100
tm = DistanceAVGKappaTransferMatrix(20, 11, 5, self.constant5000Burstgen, 5.475, R, kappa, weights)
tm.generateMatrix()
self.assertEqual(tm.getMatrix().shape, (20, 11))
self.assertEqual(tm.RRange[0], 5)
self.assertEqual(tm.RRange[1], 6)
R0neu = modifyR0(5.475, 1. / 3)
print "New R0 is %f" % R0neu
eff = rToEff(5.475, R0 = R0neu)
effndx = int(eff * 11)
self.assertAlmostEqual(tm.getMatrix()[9][effndx], 1, delta = 0.01)
def testGlobalShotNoise(self):
for sn, sng in ((5, self.constant5Burstgen), (50, self.constant50Burstgen), (500, self.constant500Burstgen), (5000, self.constant5000Burstgen)):
tm = GlobalAVGKappaTransferMatrix(20, 11, 1000, sng, 5.475, (5, 6))
tmx = tm.getMatrix()
print "Burstsizes %d" % sn
for _ in range(7):
mbin = random.choice(range(20))
binmid = generateBinMid(1, mbin, 20, 5)
eff = rToEff(binmid, R0 = 5.475)
print "Testing mbin %d at pos %f with efficiency %f" % (mbin, binmid, eff)
shotnoise = getShotNoise(eff, 11, 1000, sn)
tmxvec = tmx[mbin, :]
tmxvec.shape = (11, 1)
self.assertAlmostEqual((tmxvec - shotnoise).sum(), 0.0, delta = 0.005)
def testDistanceAVGShotNoise(self):
R = numpy.linspace(5, 6, 1100)
kappa = [ 2. / 3 ] * 1100
weights = [ 1. ] * 1100
for sn, sng in ((5, self.constant5Burstgen), (50, self.constant50Burstgen), (500, self.constant500Burstgen), (5000, self.constant5000Burstgen)):
tm = DistanceAVGKappaTransferMatrix(20, 11, 1000, sng, 5.475, R, kappa, weights, RRange = (5, 6))
tmx = tm.getMatrix()
print "Burstsizes %d" % sn
for _ in range(7):
mbin = random.choice(range(20))
binmid = generateBinMid(1, mbin, 20, 5)
eff = rToEff(binmid, R0 = 5.475)
print "Testing mbin %d at pos %f with efficiency %f" % (mbin, binmid, eff)
shotnoise = getShotNoise(eff, 11, 1000, sn)
tmxvec = tmx[mbin, :]
tmxvec.shape = (11, 1)
self.assertAlmostEqual((tmxvec - shotnoise).sum(), 0.0, delta = 0.005)
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)
