def test_SW_nrj_2C_3C(self):
size = 400
shape = (int(size**.5), int(size**.5))
mask = _np.ones(shape, dtype=int) # full mask
g = graph_from_lattice(mask, kerMask=kerMask2D_4n)
betas = _np.arange(0, 2.7, .2)
nitMC = 100
mU2C = _np.zeros(len(betas))
vU2C = _np.zeros(len(betas))
mU3C = _np.zeros(len(betas))
vU3C = _np.zeros(len(betas))
nrjCalc = field_energy_calculator(g)
# print "nbClasses = 2"
for ib, b in enumerate(betas):
# print ' MC for beta ', b
pottsGen = potts_generator(graph=g,
beta=b,
nbLabels=2,
method='SW')
mU2C[ib], vU2C[ib] = montecarlo(pottsGen, nrjCalc, nbit=nitMC)
# print ' mu2C=',mU2C
# print ' vU2C=',vU2C
# print "nbClasses = 3"
for ib, b in enumerate(betas):
# print ' MC for beta ', b
pottsGen = potts_generator(graph=g,
beta=b,
nbLabels=3,
method='SW')
mU3C[ib], vU3C[ib] = montecarlo(pottsGen, nrjCalc, nbit=nitMC)
# print ' mu3C=',mU3C
# print ' vU3C=',vU3C
if config.savePlots:
import matplotlib.pyplot as plt
plt.plot(betas, mU2C, 'b-', label="2C")
plt.errorbar(betas, mU2C, vU2C**.5, fmt=None, ecolor='b')
plt.plot(betas, mU3C, 'r-', label="3C")
plt.errorbar(betas, mU3C, vU3C**.5, fmt=None, ecolor='r')
plt.legend(loc='upper right')
plt.title(
'Mean energy in terms of beta \n for 2-color and 3-color Potts (SW sampling)'
)
plt.xlabel('beta')
plt.ylabel('mean U per site')
plt.xlim(betas[0] - .1, betas[-1] * 1.05)
figFn = os.path.join(self.outDir, figfn('potts_energy_2C_3C'))
# print figFn
plt.savefig(figFn)
def test_SW_nrj_2C_3C(self):
size = 400
shape = (int(size ** .5), int(size ** .5))
mask = _np.ones(shape, dtype=int) # full mask
g = graph_from_lattice(mask, kerMask=kerMask2D_4n)
betas = _np.arange(0, 2.7, .2)
nitMC = 100
mU2C = _np.zeros(len(betas))
vU2C = _np.zeros(len(betas))
mU3C = _np.zeros(len(betas))
vU3C = _np.zeros(len(betas))
nrjCalc = field_energy_calculator(g)
# print "nbClasses = 2"
for ib, b in enumerate(betas):
# print ' MC for beta ', b
pottsGen = potts_generator(graph=g, beta=b, nbLabels=2,
method='SW')
mU2C[ib], vU2C[ib] = montecarlo(pottsGen, nrjCalc, nbit=nitMC)
# print ' mu2C=',mU2C
# print ' vU2C=',vU2C
# print "nbClasses = 3"
for ib, b in enumerate(betas):
# print ' MC for beta ', b
pottsGen = potts_generator(graph=g, beta=b, nbLabels=3,
method='SW')
mU3C[ib], vU3C[ib] = montecarlo(pottsGen, nrjCalc, nbit=nitMC)
# print ' mu3C=',mU3C
# print ' vU3C=',vU3C
if config.savePlots:
import matplotlib.pyplot as plt
plt.plot(betas, mU2C, 'b-', label="2C")
plt.errorbar(betas, mU2C, vU2C ** .5, fmt=None, ecolor='b')
plt.plot(betas, mU3C, 'r-', label="3C")
plt.errorbar(betas, mU3C, vU3C ** .5, fmt=None, ecolor='r')
plt.legend(loc='upper right')
plt.title(
'Mean energy in terms of beta \n for 2-color and 3-color Potts (SW sampling)')
plt.xlabel('beta')
plt.ylabel('mean U per site')
plt.xlim(betas[0] - .1, betas[-1] * 1.05)
figFn = os.path.join(self.outDir, figfn('potts_energy_2C_3C'))
# print figFn
plt.savefig(figFn)
def test_sw_nrj(self):
size = 100
shape = (int(size**.5), int(size**.5))
mask = _np.ones(shape, dtype=int) #full mask
g = graph_from_lattice(mask, kerMask=kerMask2D_4n)
nc = 2
betas = _np.arange(0, 1.4, .2)
mU = _np.zeros(len(betas))
vU = _np.zeros(len(betas))
nrjCalc = field_energy_calculator(g)
for ib, b in enumerate(betas):
#print 'MC for beta ', b
pottsGen = potts_generator(graph=g, beta=b, nbLabels=nc,
method='SW')
mU[ib], vU[ib] = montecarlo(pottsGen, nrjCalc, nbit=40)
if config.savePlots:
import matplotlib.pyplot as plt
plt.plot(betas, mU)
plt.errorbar(betas, mU, vU**.5)
plt.xlabel('beta')
plt.ylabel('mean U per site')
plt.show()
def test_sw_nrj(self):
size = 100
shape = (int(size**.5), int(size**.5))
mask = _np.ones(shape, dtype=int) # full mask
g = graph_from_lattice(mask, kerMask=kerMask2D_4n)
nc = 2
betas = _np.arange(0, 1.4, .2)
mU = _np.zeros(len(betas))
vU = _np.zeros(len(betas))
nrjCalc = field_energy_calculator(g)
for ib, b in enumerate(betas):
pottsGen = potts_generator(graph=g,
beta=b,
nbLabels=nc,
method='SW')
mU[ib], vU[ib] = montecarlo(pottsGen, nrjCalc, nbit=40)
if config.savePlots:
import matplotlib.pyplot as plt
plt.plot(betas, mU)
plt.errorbar(betas, mU, vU**.5)
plt.xlabel('beta')
plt.ylabel('mean U per site')
plt.show()
def test_SW_nrj(self):
size = 100
shape = (int(size**.5), int(size**.5))
mask = _np.ones(shape, dtype=int) # full mask
g = graph_from_lattice(mask, kerMask=kerMask2D_4n)
nc = 2
betas = _np.arange(0, 2.5, .2)
mU = _np.zeros(len(betas))
vU = _np.zeros(len(betas))
nrjCalc = field_energy_calculator(g)
for ib, b in enumerate(betas):
# print 'MC for beta ', b
pottsGen = potts_generator(graph=g,
beta=b,
nbLabels=nc,
method='SW')
mU[ib], vU[ib] = montecarlo(pottsGen, nrjCalc, nbit=5)
if 0:
import matplotlib.pyplot as plt
plt.plot(betas, mU, 'b-')
plt.errorbar(betas, mU, vU**.5, fmt=None, ecolor='b')
for ib, b in enumerate(betas):
# print 'MC for beta ', b
pottsGen = potts_generator(graph=g,
beta=b,
nbLabels=3,
method='SW')
mU[ib], vU[ib] = montecarlo(pottsGen, nrjCalc, nbit=5)
if config.savePlots:
plt.plot(betas, mU, 'r-')
plt.errorbar(betas, mU, vU**.5, fmt=None, ecolor='r')
plt.xlabel('beta')
plt.ylabel('mean U per site')
plt.xlim(betas[0] - .1, betas[-1] * 1.05)
plt.show()
