from random import randint
from random import random
import numpy as np
import matplotlib.pyplot as plt
A=[]
K=[]
temp_analitico=[]

def my_range(start, end, step):
    while start <= end:
        yield start
        start += step

def cc(k,tam):
    if k > tam-1:
        return 0
    if k < 0:
        return tam-1
    else:
        return k

def energia(sistema, N, M, tam):
    t=cc(N-1,tam)
    u=cc(N+1,tam)
    v=cc(M-1,tam)
    x=cc(M+1,tam)
    a=sistema[N,M] * (sistema[t, M]+ sistema[u, M] + sistema[N, v] + sistema[N, x])
    return a


#######magnteizaçao###########3
def magntotal(sistema,tam):
    M=0

    for i in range(tam):
        for j in range(tam):
            M+=sistema[i,j]
    return M

def main(T, tam, sweep):

    system=[]
    sistema=[]
    autocorrelacao=[]

    for x in range(tam):
        m=[]

        for y in range(tam):
            m.append(1)

        system.append(m)

    sistema=np.array(system)



    for i in range(sweep):

        M = randint(0,tam-1)
        N = randint(0,tam-1)

        deltaE =2*energia(sistema, N, M, tam)

        if deltaE <= 0.:
            sistema[N,M] *= -1

        elif np.exp((-1/T)*deltaE) > random():
            sistema[N,M] *= -1

    Mag=magntotal(sistema,tam)

    for i in range(sweep):
        for j in range(tam**2):

            M = randint(0,tam-1)
            N = randint(0,tam-1)

            deltaE =2*energia(sistema, N, M, tam)



            if deltaE <= 0.:

                sistema[N,M] *= -1
                Mag+=2*sistema[N,M]/tam**2

            elif np.exp((-1/T)*deltaE) > random():

                sistema[N,M] *= -1
                Mag+=2*sistema[N,M]/tam**2

        autocorrelacao.append(Mag)

    return autocorrelacao

def my_range(start, end, step):
    while start <= end:
        yield start
        start += step

################## início ##################
k=0
tau=[]
t=[]

for tam in my_range(20,20,5):

    correlacao=[]
    T=2.4
    res=main(T,tam,100)
    length=np.size(res)
    L=[]
    L.append(tam)

    for i in range (np.size(res)):
        k=0

        for j in range (np.size(res)-i):
            k+=res[j]*res[j+i]
            t.append(i)
            correlacao.append(k/((res[0])*(length-i)))

    tau.append(np.trapz(correlacao))

    plt.plot(t,correlacao,'r')
    plt.show()

plt.plot(L,tau,'b')
plt.xscale('log')
plt.yscale('log')
plt.show()