# Build radius array:
build = np.sort([2**x for x in range(20)]+[3*2**x for x in range(20)])
radius = build[build <= L//2]
#radius = np.arange(1,28)
#radius = radius[ radius <= L//2 ]
#two_point:
result = np.zeros( (samples,len(radius)) )
#result = 0
ker,norm = randomsurf.kernel(L,alpha)
start1 = time.time()
for s in range(samples):
#start=time.time()
surface = ( randomsurf.gaussian_field(L, alpha,ker,norm) > h )*1
#surface = randomsurf.percolation(L)
#surface = randomsurf.berry(L)
surface = hoshen.get_clusters( surface )
#result += np.array(two_point_full(surface))
result[s,:] = np.array([two_point(surface, r) for r in radius])/(2*L**2)
#del surface #dump memory
print(f'Total elapsed time = {round(time.time() - start1, 3)}')
twopt = [np.mean(result[:,r]) for r in range(len(radius))]
error = [np.std(result[:,r])/np.sqrt(samples) for r in range(len(radius))]
#np.savetxt(f'data/two_point/L={L}-alpha={alpha}-s={samples}.txt',(radius,twopt,error), fmt='%1.4f',)
#np.savetxt(f'data/two_point/L={L}-Percolation-s={samples}.txt',(radius,twopt,error), fmt='%1.4f',)
def fractal_dim_measurement(L, alpha, height, ker, norm):
'''Gather here all the calculations of the fractal dimension of a surface'''
surface = 1 * (randomsurf.gaussian_field(L, alpha, ker, norm) > height)
masses = hoshen.get_masses(surface)
return np.max(masses) * L**2
ax = plt.axes()
print(parameters)
for L in sizes:
ker, ker_norm = randomsurf.kernel(L, alpha)
data = []
error = []
for i, h in enumerate(parameters):
print(
f'Binder Cumulants: Size {L}, measure {i+1} of {len(parameters)}...'
)
m2 = np.zeros(samples)
m4 = np.zeros(samples)
for s in range(samples):
surface = 1 * (randomsurf.gaussian_field(L, alpha, ker, ker_norm) >
h)
#surface = randomsurf.percolation(L,p=h)
m4[s], m2[s] = stats(surface)
mean_m2 = np.mean(m2)
mean_m4 = np.mean(m4)
std_m2 = np.std(m2)
std_m4 = np.std(m4)
data.append(mean_m4 / mean_m2**2)
error.append(
(std_m4 / mean_m2**2 + 2 * mean_m4 * std_m2 / mean_m2**3) /
np.sqrt(samples))
#np.savetxt(f'data/binder/L={L}-alpha={alpha}-s={samples}.txt',(parameters,np.round(data, 5),np.round(error, 5)), fmt='%1.5f,')
# plot results
@njit
def measure_field(field, radii):
size = field.shape[0]
ans = np.zeros(len(radii))
for x in range(size // 2):
for y in range(size // 2):
ans += np.array([field[y, x] * field[y, x + r] for r in radii])
return ans / (size * size / 4)
corrs = np.zeros((samples, len(radii)))
for i, alpha in enumerate(alphas):
ker = randomsurf.kernel(L, alpha)
for s in range(samples):
surf = 1 * (randomsurf.gaussian_field(L, alpha, ker) > crit_level[i])
corrs[s, :] = measure_field(surf, radii) - (np.sum(surf) / (L**2))**2
result = [np.mean(corrs[:, i]) for i in range(len(radii))]
error = [np.std(corrs[:, i]) / np.sqrt(samples) for i in range(len(radii))]
plt.figure()
# plot results
ax = plt.axes()
ax.set_title(f'Alpha = {alpha}')
ax.set_xscale("log")
ax.set_yscale("log")
ax.errorbar(radii,
result,
yerr=error,
capsize=2,
sample_set = [10**7,10**6,10**6,10**5,10**5,10**5,10**5,10**4,10**4]
start = time.time()
for i,alpha in enumerate(alphas):
print(f'Alpha = {alpha}')
Levels = []
Err = []
for j,L in enumerate(sizes):
print(f'Currently on size {L}')
samples = sample_set[j]
ker = randomsurf.kernel(L, alpha)
levels = []
for _ in range(samples):
rsurf = randomsurf.gaussian_field(L, alpha, ker)
crit_level = find_critical(rsurf)
levels.append(crit_level)
Levels.append(np.mean(levels))
Err.append(np.std(levels)/np.sqrt(samples))
print(Levels[-1],Err[-1])
print(Levels)
print(Err)
print(f'elapsed time = {time.time() - start}')
plt.errorbar(
x = sizes,
y = Levels,
yerr = Err,