From Werner KRAUTH

Revision as of 21:37, 22 September 2015 Werner (Talk | contribs) ← Previous diff		Revision as of 21:50, 22 September 2015 Werner (Talk | contribs) Next diff →
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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.	+	This page presents the program cluster_ising.py, the Wolff cluster algorithm for the Ising model on an LxL square lattice in two dimensions
	__FORCETOC__		__FORCETOC__
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	=Program=		=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]]
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	for j in Cluster:		for j in Cluster:
	S[j] *= -1		S[j] *= -1
		+
		+
		+	=Version=
		+	See history for version information.
		+
		+	[[Category:Python]] [[Category:Honnef_2015]] [[Category:MOOC_SMAC]]

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Revision as of 21:50, 22 September 2015

This page presents the program cluster_ising.py, the Wolff cluster algorithm for the Ising model on an LxL square lattice in two dimensions

Contents 1 Description 2 Program 3 Version

Description

Program

import random, math

L = 100
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)}
T = 2.5
p  = 1.0 - math.exp(-2.0 / T)
nsteps = 10000
S = [random.choice([1, -1]) for k in range(N)]
for step in range(nsteps):
    k = random.randint(0, N - 1)
    Pocket, Cluster = [k], [k]
    while Pocket != []:
        j = random.choice(Pocket)
        for l in nbr[j]:
            if S[l] == S[j] and l not in Cluster \
                   and random.uniform(0.0, 1.0) < p:
                Pocket.append(l)
                Cluster.append(l)
        Pocket.remove(j)
    for j in Cluster:
        S[j] *= -1

Version

See history for version information.