def runmc(self, length, prob, steps):
# -----------------------------
cis = numpy.arange(0, length-1)
trans = numpy.array([])
rpr = Representation(cis,trans)
z = OneDimChain(rpr)
# -----------------------------
dT = 0.0
dL = 0
old_trans = z.get_cfg().trans.size
adata = []
for i in range(steps):
it = z.get_lifetime(prob)
dT += it
# -----------------------------
disp = z.get_cfg().trans.size - old_trans
if disp in [1, -(z.get_cfg().length-1)]:
dL += 1
elif disp in [-1, z.get_cfg().length-1]:
dL += -1
# -----------------------------
old_trans = z.get_cfg().trans.size
j = dL/dT
adata.append((dT, z.get_cfg().cis.size, dL, j))
#print (dT, z.get_cfg().cis.size, dL, j)
c, n = z.reconfigure(prob)
return adata
def run(self, length, prob, steps):
# -----------------------------
cis = numpy.arange(0, length-1)
trans = numpy.array([])
rpr = Representation(cis,trans)
z = OneDimChain(rpr)
# -----------------------------
dT = 0.0
dL = 0
old_trans = z.get_cfg().trans.size
data = []
for i in range(steps):
it = z.get_lifetime(prob)
dT += it
old_trans = z.get_cfg().cis
print dT, it, '\t'.join(map(str, -z.get_cfg().cis)), '\t'.join(map(str,z.get_cfg().trans))
z.reconfigure(PROB)
