def testtrackHistory():
"""
"""
geo = Geometry.Geometry(10,[[-1,1]])
xs = CrossSection.CrossSection()
mark = Markov.Markov(geo,xs, histories=1000)
n = particle.neutron()
bank = fissionBank.fissionBank()
bank.append(n,5)
newbank = fissionBank.fissionBank()
print "xs: %s" %xs
for neut in bank[:1]:
neut.setRandDirection()
mark.trackHistory(neut, newbank, 1.0)
def testtrackHistory():
"""
"""
geo = Geometry.Geometry(10, [[-1, 1]])
xs = CrossSection.CrossSection()
mark = Markov.Markov(geo, xs, histories=1000)
n = particle.neutron()
bank = fissionBank.fissionBank()
bank.append(n, 5)
newbank = fissionBank.fissionBank()
print "xs: %s" % xs
for neut in bank[:1]:
neut.setRandDirection()
mark.trackHistory(neut, newbank, 1.0)
def makeFBank(parts):
"""
"""
n = particle.neutron()
bank = fissionBank.fissionBank()
bank.append(n,parts)
return bank
def power(self, source):
"""
power is the main method for this algorithm
source: Initial guess of fission source
"""
# Initialize
self.k = 1
self.cycle_k = [] # list of eigenvalues per iteration
self.convergence = []
self.sd = [] # list of standard deviaiton per iterations
self.k_inactive = []
self.vectorStorage = []
self.source = source
start = time.time()
elapsed = 0
totaltime = 0
for i in xrange(1, self.inactive + 1):
self.nextBank = fissionBank.fissionBank()
self.transport(self.source)
self.k = self.k * len(self.nextBank) / float(self.histories)
self.k_inactive.append(self.k)
totaltime = time.time() - start
print "iteration: %5i, eigenvalue = %8.6f," % (i, self.k),
print " time: %8.3f sec" % (totaltime)
self.source = self.nextBank
print "------- Starting active cycles -------"
for self.i in xrange(1, self.active + 1):
self.nextBank = fissionBank.fissionBank()
self.transport(self.source)
self.k = (self.k * len(self.nextBank) / float(self.histories))
self.cycle_k.append(self.k)
self.convergence.append(scipy.mean(self.cycle_k))
self.sd.append((1 / math.sqrt(self.i)) * scipy.std(self.cycle_k))
totaltime = time.time() - start
print "iteration: %5i, eigenvalue = %8.6f," % (self.i, self.k),
print " std.dev = %6.4f, time: %8.3f sec" % (scipy.std(
self.convergence), totaltime)
self.source = self.nextBank
Y = fissionSource.histogramSource(self.source, self.geo)
Y = Y / sum(Y)
self.vectorStorage.append(Y)
def testRoulette():
"""
"""
geo = Geometry.Geometry(10,[[-1,1]])
xs = CrossSection.CrossSection()
mark = Markov.Markov(geo,xs, histories=1000)
n = particle.neutron()
bank = fissionBank.fissionBank()
bank.append(n,1000000)
print "weight-in: %s" %(len(bank))
for i in xrange(len(bank)):
bank[i] = mark.russianRoulette(bank[i])
print "\nweight-out: %s, +- %.4f" %(bankWeight(bank),math.sqrt(len(bank)))
def main(self):
bins = 50
halfwidth=0.5
histories = 10000
iterations = 50
restarts = 5
geo = Geometry.Geometry(bins, [[-halfwidth,halfwidth]])
xs = CrossSection.CrossSection(xS=0.5,nu=1.0,xF=0.5,xG=0)
self.mark = Markov.Markov(geo, xs, histories)
self.mark.score = self.score
self.Q = scipy.zeros((bins,0))
for i in xrange(bins):
print "I am at %i" %i
point = scipy.zeros((bins))
point[i] = 1
pSource = fissionSource.histogramSource(point,geo)
self.response = fissionBank.fissionBank()
self.mark.transport(pSource)
q = fissionSource.histogramSource(self.response,geo)
q = q*(1.0/self.mark.histories)
self.printVector(q)
self.Q = scipy.column_stack((self.Q,q))
q = scipy.ones(bins)
q = q/scipy.linalg.norm(q,2)
print "Calling Deterministic Arnoldi"
adtm = ArnoldiDtm.Arnoldi(self.Q, iterations, restarts)
eValues, eVectors = adtm.Arnoldi(q)
print "Eigenvalues: "
self.printVector(eValues)
print "Dominant eigenvector: "
self.printVector(eVectors[:,-1])
print "\nAll eigenvectors: "
self.printM(eVectors)
Chart = Gnuplot.Gnuplot()
Chart.title("Histories per 'vector': %i, bins = %i" %(histories, bins))
length = len(eValues)-1
for i in xrange(5):
data = Gnuplot.Data(eVectors[:,length-i],with='lines',
title='vector %i' %i)
Chart.replot(data)
def testRoulette():
"""
"""
geo = Geometry.Geometry(10, [[-1, 1]])
xs = CrossSection.CrossSection()
mark = Markov.Markov(geo, xs, histories=1000)
n = particle.neutron()
bank = fissionBank.fissionBank()
bank.append(n, 1000000)
print "weight-in: %s" % (len(bank))
for i in xrange(len(bank)):
bank[i] = mark.russianRoulette(bank[i])
print "\nweight-out: %s, +- %.4f" % (bankWeight(bank), math.sqrt(
len(bank)))
def testtransport():
"""
"""
geo = Geometry.Geometry(10, [[-1, 1]])
xs = CrossSection.CrossSection()
mark = Markov.Markov(geo, xs, histories=1000)
bank = fissionBank.fissionBank()
n = particle.neutron()
bank.append(n, 10)
for neut in bank:
neut.setRandDirection()
newbank = mark.transport(bank, 1)
print "leak-left: %.4f, leak-right: %.4f, weight-killed: %.4f" % (
mark.minLeakage, mark.maxLeakage, mark.wgtKilled)
def testtransport():
"""
"""
geo = Geometry.Geometry(10,[[-1,1]])
xs = CrossSection.CrossSection()
mark = Markov.Markov(geo,xs, histories=1000)
bank = fissionBank.fissionBank()
n = particle.neutron()
bank.append(n,10)
for neut in bank:
neut.setRandDirection()
newbank = mark.transport(bank, 1)
print "leak-left: %.4f, leak-right: %.4f, weight-killed: %.4f" %(
mark.minLeakage, mark.maxLeakage, mark.wgtKilled)
def sample(self, histories):
"""
sample will return a fissionBank object where the neutrons in the bank
are sampled from self.
histories: The number of neutron desired in the fissionBank
"""
n = particle.neutron()
pdf = self/sum(abs(self))
fb = fissionBank.fissionBank()
for j in Alias.gen_discrete(abs(pdf), histories):
x = random.uniform(self.geo.edges[j], self.geo.edges[j+1])
if pdf[j] < 0:
nWeight = -1
else:
nWeight = 1
n.weight = nWeight
n.position = (x, 0, 0)
n.setRandDirection()
fb.append(n)
return fb
def sample(self, histories):
"""
sample will return a fissionBank object where the neutrons in the bank
are sampled from self.
histories: The number of neutron desired in the fissionBank
"""
n = particle.neutron()
pdf = self / sum(abs(self))
fb = fissionBank.fissionBank()
for j in Alias.gen_discrete(abs(pdf), histories):
x = random.uniform(self.geo.edges[j], self.geo.edges[j + 1])
if pdf[j] < 0:
nWeight = -1
else:
nWeight = 1
n.weight = nWeight
n.position = (x, 0, 0)
n.setRandDirection()
fb.append(n)
return fb