def testDistanceAVGLocalKappaAveraging(self):
R = numpy.concatenate((numpy.linspace(5.05, 5.95, 20), numpy.linspace(5.05, 5.95, 20), numpy.linspace(5.05, 5.95, 20)))
kappa = [ 1. ] * 20 + [ 1. ] * 20 + [ 0. ] * 20
weights = [ 1. ] * 60
tm = DistanceAVGKappaTransferMatrix(20, 11, 5, self.constant5000Burstgen, 5.475, R, kappa, weights, RRange = (5, 6))
self.assertEqual(tm.getKappaAVG()[2], 2. / 3)
self.assertEqual(tm.RRange[0], 5)
self.assertEqual(tm.RRange[1], 6)
self.assertEqual(tm.getMatrix()[9][5], 1.)
def testDistanceAVG(self):
R = numpy.linspace(5, 6, 1100)
kappa = [ 2. / 3 ] * 1100
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)
self.assertEqual(tm.getMatrix()[9][5], 1.)
def testSamplesOutsideRange(self):
R = numpy.linspace(4.95, 6.05, 22)
kappa = [ 2. / 3 ] * 22
weights = [ 1. ] * 22
tm = DistanceAVGKappaTransferMatrix(20, 11, 200, self.constant5000Burstgen, 5.475, R, kappa, weights, RRange = (5., 6))
tm.generateMatrix()
self.assertEqual(tm.RRange[0], 5)
self.assertEqual(tm.RRange[1], 6)
self.assertAlmostEqual(tm.getMatrix()[9][5], 1., delta = 0.01)
tmref = GlobalAVGKappaTransferMatrix(20, 11, 200, self.constant5000Burstgen, 5.475, (5, 6))
self.assertMatrixAlmostEqual(tm.getMatrix() , tmref.getMatrix(), delta = 0.15)
def testDistanceAVGLocalKappavsGlobal(self):
R = numpy.linspace(5.05, 5.95, 20)
kappa = [ 2. / 3 ] * 20
weights = [ 1. ] * 20
tm = DistanceAVGKappaTransferMatrix(20, 11, 5000, self.constant5000Burstgen, 5.475, R, kappa, weights, RRange = (5, 6))
tm.generateMatrix()
self.assertEqual(tm.RRange[0], 5)
self.assertEqual(tm.RRange[1], 6)
self.assertAlmostEqual(tm.getMatrix()[9][5], 1., delta = 0.01)
tmref = GlobalAVGKappaTransferMatrix(20, 11, 5000, self.constant5000Burstgen, 5.475, [5, 6])
self.assertMatrixAlmostEqual(tm.getMatrix(), tmref.getMatrix(), delta = 0.05)
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 testRandomUncorr(self):
length = 100000
startdist = 4
enddist = 7
rbins = 10
ebins = 20
bursts = 10000
R0 = 5.475
bgen = self.constant50Burstgen
R = numpy.random.random(length) * (enddist - startdist) + startdist
kappa2 = numpy.array(list(getKappa(genRandomVec(), genRandomVec(), genRandomVec()) ** 2 for _ in range(length)))
print "Kappa^2 mean is ", kappa2.mean()
R0mod = modifyR0(R0, kappa2.mean())
prbs = numpy.ones(length)
globaltm = GlobalAVGKappaTransferMatrix(rbins, ebins, bursts, bgen, R0mod, (startdist, enddist))
localtm = DistanceAVGKappaTransferMatrix(rbins, ebins, bursts, bgen, R0, R, kappa2, prbs, RRange = (startdist, enddist))
self.assertMatrixAlmostEqual(globaltm.getMatrix(), localtm.getMatrix(), delta = 0.10)
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)