bottom=False,
left=False,
# top=False,
labelbottom=False,
labelleft=False,
# length=0,
)
P = np.zeros(len(bonds))
size = np.zeros(len(bonds))
s = np.zeros(len(bonds))
pvec = np.zeros(len(bonds))
j = 0
for i in range(nn):
for bond in bonds[i * step:(i + 1) * step]:
sites.activate([bond])
P[j] = sites.giantComponent
s[j] = sites.averageSquaredSize
size[j] = sites.sizeOfLargestCluster
pvec[j] = j / len(bonds)
j += 1
print("start: {}, stop: {}, index: {}".format(i * step, (i + 1) * step, i))
image = sites.makeImage()
ax.imshow(image,
aspect="equal",
origin="upper",
vmin=0,
vmax=1,
cmap="Purples")
for L in [100, 200]:
N = L*L
nBonds = 2*N
its = 100 # number of iterations
P = np.zeros([nBonds, its])
P2 = np.zeros([nBonds, its]) # P_inf**2
size = np.zeros([nBonds, its])
s = np.zeros([nBonds, its])
p = np.zeros(nBonds)
for j in range(its):
sites = Sites(L, L)
bonds = makeSquareLattice(L, L)
bonds = shuffleList(bonds)
for i in range(nBonds):
sites.activate([bonds[i]])
P[i, j] = sites.giantComponent
P2[i, j] = pow(sites.giantComponent/N, 2)
s[i, j] = sites.averageSquaredSize
size[i, j] = sites.sizeOfLargestCluster
p[i] = i/nBonds
# p[i] = (N - np.sum(sites.sites == -1))/N
P = np.mean(P, axis=1)
P2 = np.mean(P2, axis=1)
s = np.mean(s, axis=1)
size = np.mean(size, axis=1)
