## Temperature T=2.4
# variance = 7.80
T = 2.4
doit()
bins = [-800+4*i for i in range(401)]
### Now plot things:
## Uniform start
filename1 = Filename(1, MCcycles, 'Uniform')
filename2 = Filename(2.4, MCcycles, 'Uniform')
# T = 1
data1 = lesfil(filename1)
plt.figure(1)
plt.subplot(211)
plt.hist(data1[3][500:], bins=bins, normed=True, label='$T=1$')
plt.xlim([-800, -770])
plt.ylabel('$P(E)$')
plt.legend()
# T = 2.4
data2 = lesfil(filename2)
plt.figure(1)
plt.subplot(212)
plt.hist(data2[3][500:], bins=bins, normed=True, label='$T=2.4$')
plt.xlim([-700, -300])
plt.legend()
plt.ylabel('$P(E)$')
args = "%f %i %i %s %s" % (T, L, MCcycles, filename, initial_state)
run = "%stask_d %s" % (path, args)
if runAgain:
os.system(run)
path = "../../../build-FYS3150-project-4-OpenMPI-Release/task_d/"
L = 20
MCcycles = 100000
initial_state = "Random"
for T in np.linspace(2, 2.4, 100):
doit()
data = lesfil(Filename(2, MCcycles, initial_state))
bins = [-800 + 4 * i for i in range(401)]
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
hist, bins, patches = ax.hist(data[3][10000:], bins=bins, normed=True, label="$T=2.4$")
plt.draw()
for T in np.linspace(2, 2.4, 100):
filename = Filename(T, MCcycles, initial_state)
data = lesfil(filename)
n, bins = np.histogram(data[3][10000:], bins, normed=True)
for rect, h in zip(patches, n):
rect.set_height(h)
path = "../../../build-FYS3150-project-4-OpenMPI-Release/task_e/"
T_start = 2.0
T_stop = 2.4
T_step = 0.01 # 0.02
MCcycles = 1000000
initial_state = "Random"
for L in [20, 40, 60, 80, 100]:
if doItAgain:
doit()
for L in [20, 40, 60, 80, 100]:
filename = Filename(L, T_start, T_stop, T_step, MCcycles, initial_state)
data = lesfil(filename)
plt.figure(0)
plt.subplot(211)
plotfil(filename, 0, 1, label="$L=%i$" % L)
plt.subplot(212)
plotfil(filename, 0, 2, label="$L=%i$" % L)
plt.figure(2)
plt.subplot(211)
plotfil(filename, 0, 3, label="$L=%i$" % L)
plt.subplot(212)
plotfil(filename, 0, 4, label="$L=%i$" % L)
plt.figure(0)
Maverage = 0
M2average = (32.0 * np.exp(8.0 / T_start) + 8.0) / Z
C_V = (E2average - Eaverage ** 2) / T_start / T_start / 4.0
chi = 1.0 / T_start * (M2average - Maverage * Maverage) / 4.0
absMaverage = (8.0 * np.exp(8.0 / T_start) + 4) / Z / 4.0
chi = 1.0 / T_start * (M2average - absMaverage * absMaverage) / 4.0
for cycle in np.linspace(0, 100000, 1001):
MCcylces = int(cycle)
filename = "task_b_T1_%f_T2_%f_NT_%i_MC_%i_%s.txt" % (T_start, T_stop, N_T, MCcylces, Initial_state)
args = "%f %f %i %i %s %s" % (T_start, T_stop, N_T, MCcylces, filename, Initial_state)
run = "mpirun -n 4 %stask_b %s" % (path, args)
if runAgain:
os.system(run)
Eaverage_numerical = lesfil(filename)[2][0]
relerr.append(abs(Eaverage / 4.0 - Eaverage_numerical) / abs(Eaverage / 4.0) * 100)
print ""
print "\# MC Cycles & $\langle E \\rangle$ & $C_V$ & $\langle |M| \\rangle$ & $\chi$ \\\\ \hline"
print "%s & %.5f & %.5f & %.5f & %.5f \\\\" % ("Analytical", Eaverage / 4.0, C_V, absMaverage, chi)
for MCcycles in [100, 1000, 10000, 100000, 1000000]:
filename = "task_b_T1_%f_T2_%f_NT_%i_MC_%i_%s.txt" % (T_start, T_stop, N_T, MCcycles, Initial_state)
fil = lesfil(filename)
C_V_numerical = float(fil[3][0])
absMaverage_numerical = float(fil[6][0])
chi_numerical = float(fil[5][0])
Eaverage_numerical = float(fil[2][0])
print "%i & %.5f & %.5f & %.5f & %.5f \\\\" % (
fil[0][0],
Eaverage_numerical,
def plotfil(filename, x, y, label=None): data = lesfil(filename) plt.plot(data[x], data[y], label=label)
