Markov ising.py
From Werner KRAUTH
(Difference between revisions)
Revision as of 21:31, 22 September 2015
import random, math
L = 16
N = L * L
nbr = {i : ((i // L) * L + (i + 1) % L, (i + L) % N,
(i // L) * L + (i - 1) % L, (i - L) % N) \
for i in range(N)}
nsteps = 1000000
T = 2.0
beta = 1.0 / T
S = [random.choice([1, -1]) for k in range(N)]
for step in range(nsteps):
k = random.randint(0, N - 1)
delta_E = 2.0 * S[k] * sum(S[nn] for nn in nbr[k])
if random.uniform(0.0, 1.0) < math.exp(-beta * delta_E):
S[k] *= -1
print S, sum(S)
