def runGenTraj(options):
donor = genRandomVec()
acceptor = genRandomVec()
distance = genRandomVec()
with open(options.outputfile, "w") as fh:
R = float(options.distexpr.split(";")[0])
print "Starting distance is %f" % R
for time in numpy.arange(0, options.timestep * options.samples, options.timestep):
donor = alterVec(options, donor)
acceptor = alterVec(options, acceptor)
distance = alterVec(options, distance)
kappa2 = getKappa(donor, acceptor, distance) ** 2
t = float(time)
R = eval(options.distexpr.split(";")[1])
kappa2 = eval(options.kappacorr)
fh.write("%f %f %f\n" % (time, R, kappa2))
print "Trajectory generated."
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 runGenTraj(options):
donor = genRandomVec()
acceptor = genRandomVec()
distance = genRandomVec()
with open(options.outputfile, "w") as fh:
R = float(options.distexpr.split(";")[0])
print "Starting distance is %f" % R
for time in numpy.arange(0, options.timestep * options.samples,
options.timestep):
donor = alterVec(options, donor)
acceptor = alterVec(options, acceptor)
distance = alterVec(options, distance)
kappa2 = getKappa(donor, acceptor, distance)**2
t = float(time)
R = eval(options.distexpr.split(";")[1])
kappa2 = eval(options.kappacorr)
fh.write("%f %f %f\n" % (time, R, kappa2))
print "Trajectory generated."
def alterVec(options, vec):
vec = (1 - options.decay) * vec + options.decay * genRandomVec()
vec /= sqrt((vec ** 2).sum())
return vec
def alterVec(options, vec):
vec = (1 - options.decay) * vec + options.decay * genRandomVec()
vec /= sqrt((vec**2).sum())
return vec
