Пример #1
0
 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)
Пример #2
0
 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)