import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
import random
import copy
##################################################################################################
nx = 300
ny = 300
Temp=1
n_levels=3
fig = plt.figure()
data = np.zeros((nx, ny))
data=[[random.randint(0,n_levels-1) for i in range(nx)] for i in range(ny)]
data=np.array(data)
im = plt.imshow(data)
def init():
global data
im.set_data(data)
def animate(i):
global data
print(i)
data=next_state(data)
im.set_data(data)
return im
def next_state(data):
for i in range(5000):
n=random.randint(0, nx-1)
m=random.randint(0,ny-1)
next=random.randint(0,n_levels-1);
a=(data[m][(n-1+nx)%nx]==next)*2.0-1
b=(data[m][(n+1)%nx]==next)*2.0-1
c=(data[(m-1+ny)%ny][n]==next)*2.0-1
d=(data[(m+1)%ny][n]==next)*2.0-1
d1 = (data[(m + 1) % ny][(n+1)%nx] == next) * 2.0 - 1
d2 = (data[(m + 1) % ny][(n-1+nx)%nx] == next) * 2.0 - 1
d3 = (data[(m - 1+ny) % ny][(n+1)%nx] == next) * 2.0 - 1
d4 = (data[(m - 1+ny) % ny][(n-1+nx)%nx] == next) * 2.0 - 1
E=a+b+c+d+d1+d2+d3+d4
if E > 0:
data[m][n] = next;
else:
r=np.random.uniform();
if E <= 0 and r < np.exp(E) / Temp:
data[m][n] = random.randint(0,n_levels-1);
return data
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=800,interval=1)
plt.show()
Wednesday, 15 July 2020
Potts Model simulation code (Python)
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