From Werner KRAUTH

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		+	This page presents the program markov_disks_box.py, a Markov-chain algorithm for four disks in a square box of sides 1.
		+
		+	__FORCETOC__
		+	=Description=
		+
		+	=Program=
		+
		+	import random
		+
		+	L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]
		+	sigma = 0.15
		+	sigma_sq = sigma ** 2
		+	delta = 0.1
		+	n_steps = 1000
		+	for steps in range(n_steps):
		+	a = random.choice(L)
		+	b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)]
		+	min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a)
		+	box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma
		+	if not (box_cond or min_dist < 4.0 * sigma ** 2):
		+	a[:] = b
		+	print L
		+
		+	=Version=
		+	See history for version information.
		+
		+	[[Category:Python]]
		+
		+
	import random, math		import random, math

---

Revision as of 21:37, 22 September 2015

This page presents the program markov_disks_box.py, a Markov-chain algorithm for four disks in a square box of sides 1.

Contents 1 Description 2 Program 3 Version

Description

Program

import random

L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]
sigma = 0.15
sigma_sq = sigma ** 2
delta = 0.1
n_steps = 1000
for steps in range(n_steps):
    a = random.choice(L)
    b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)]
    min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a)
    box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma
    if not (box_cond or min_dist < 4.0 * sigma ** 2):
        a[:] = b
print L

Version

See history for version information.

import random, math

L = 6
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 = 10000000
beta = 1.0
S = [random.choice([-1, 1]) for site in range(N)]
E = -0.5 * sum(S[k] * sum(S[nn] for nn in nbr[k]) \
                               for k in range(N))
E_tot, E2_tot = 0.0, 0.0
random.seed('123456')
for step in range(nsteps):
    k = random.randint(0, N - 1)
    Upsilon = random.uniform(0.0, 1.0)
    h = sum(S[nn] for nn in nbr[k])
    Sk_old = S[k]
    S[k] = -1
    if Upsilon < 1.0 / (1.0 + math.exp(-2.0 * beta * h)):
        S[k] = 1
    if S[k] != Sk_old:
        E -= 2.0 * h * S[k]
    E_tot += E
    E2_tot += E ** 2
E_av  = E_tot / float(nsteps)
E2_av = E2_tot / float(nsteps)
c_V = beta ** 2 * (E2_av - E_av ** 2) / float(N)