def decisionTimes(N,
NN,
MCS,
plotting=False): #pass an object instead? or dont
decisionTime = []
adt = []
sim = Simulation(N, NN, MCS, plotting)
sim.run()
#for j in range(sim.time):
#decisionTime[j] = (sim.dT[j])
decisionTime.append(sim.dT)
adt.append(sim.adT)
lst = []
for i in decisionTime:
for j in i:
lst.append(j)
lst2 = []
for i in adt:
for j in i:
lst2.append(j)
dt = np.asarray(lst).ravel()
adt = np.asarray(lst2).ravel()
return dt, adt
def decisionProbas():
cB = 0
switch_probas = [2e-4, 2e-3, 2e-2, 2e-1]
for i in switch_probas:
sim = Simulation(500,
10000,
2000000,
cB=cB,
clusterd=True,
random=False,
plotting=False,
switch_proba=i)
sim.run()
adt = sim.adT
adt = np.asarray(adt)
plt.loglog(range(len(adt)), 3 / adt, '+', label='p: %g ' % (i))
plt.xlabel('$\\tau$')
plt.ylabel('$P(\\tau)$')
plt.title('Decision times for different noises p')
plt.legend()
plt.show()
def probas(N, NN, MCS, switch_proba):
cB = 0
sim = Simulation(N,
NN,
MCS,
cB=cB,
clusterd=True,
random=False,
plotting=False,
switch_proba=switch_proba)
A, B, C, M = sim.run()
plt.figure()
plt.title('Magnetization with a noise p=%g' % (switch_proba))
plt.xlabel('Monte Carlo Cycles')
plt.ylabel('Magnetization m')
plt.plot(range(len(M)), M)
plt.ylim([-1.05, 1.05])
plt.ticklabel_format(axis='x', style='sci', scilimits=[0, 0])
plt.show()
def partABC(N, NN, MCS, plotting=True):
sim = Simulation(N, NN, MCS, plotting=plotting)
A, B, C, M = sim.run()
M = np.asarray(M)
plt.figure()
plt.plot(range(len(M)), M)
plt.xlabel('Monte Carlo Cycles')
plt.ylabel('Magnetization m')
plt.title('The magnetization of Monte Carlo cycles')
plt.ylim([-1.05, 1.05])
plt.ticklabel_format(axis='x', style='sci', scilimits=[0, 0])
plt.show()
dtlen = int(len(M) / 2)
dts = np.linspace(1, dtlen, dtlen)
g = G(dts, M)
plt.figure()
plt.plot(dts, g)
plt.title('Auto correlation')
plt.xlabel('$\Delta t$')
plt.ylabel('$G(\Delta t)$')
plt.ticklabel_format(axis='x', style='sci', scilimits=[0, 0])
plt.show()
def clustered(cB, N, NN, MCS, random=False, plotting=False):
sim = Simulation(N,
NN,
MCS,
cB,
clusterd=True,
random=random,
plotting=plotting)
A, B, C, M = sim.run()
plt.figure()
plt.plot(range(len(M)), M)
plt.xlabel('Monte Carlo Cycles')
plt.ylabel('Magnetization m')
plt.ylim([-1.05, 1.05])
plt.ticklabel_format(axis='x', style='sci', scilimits=[0, 0])
plt.show()
dt = sim.dT
dt = np.asarray(dt)
adt = sim.adT
adt = np.asarray(adt)
return dt, adt