def get_random_configuration(self):
nspins = self.nspins
if self.one_frozen:
nspins -= 1
coords = np.zeros([nspins, 2])
for i in range(nspins):
vec = vec_random()
coords[i,:] = hs.make2dVector(vec)
return coords.reshape(-1)
def get_random_configuration(self):
nspins = self.nspins
if self.one_frozen:
nspins -= 1
coords = np.zeros([nspins, 2])
for i in range(nspins):
vec = vec_random()
coords[i,:] = hs.make2dVector(vec)
return coords.reshape(-1)
for node in pot.G.nodes():
i = pot.indices[node]
fout.write("%g %g %g %g %g %g %g %g\n" % (node[0], node[1],
s[i, 0], s[i, 1], s[i, 2], h[i, 0], h[i, 1], h[i, 2] ))
pi = np.pi
L = 4
nspins = L ** 2
pot = HeisenbergModelRA(dim=[L, L], field_disorder=2.)
coords = np.zeros([nspins, 2])
for i in range(nspins):
vec = rotations.vec_random()
coords[i, :] = make2dVector(vec)
coords = np.reshape(coords, [nspins * 2])
coordsinit = np.copy(coords)
print coords
e = pot.getEnergy(coords)
print "energy ", e
print "try a quench"
from pele.optimize import mylbfgs
ret = mylbfgs(coords, pot)
print "quenched e = ", ret.energy, "funcalls", ret.nfev
print ret.coords
def normalize_2dspins(coords):
"""there is probably a better way to do this."""
return hs.make2dVector(hs.make3dVector(coords))
def normalize_2dspins(coords):
"""there is probably a better way to do this."""
return hs.make2dVector(hs.make3dVector(coords))
def test():
pi = np.pi
L = 4
nspins = L**2
#phases = np.zeros(nspins)
pot = HeisenbergModelRA( dim = [L,L], field_disorder = 2. ) #, phases=phases)
coords = np.zeros([nspins, 2])
for i in range(nspins):
vec = rotations.vec_random()
coords[i,:] = make2dVector(vec)
coords = np.reshape(coords, [nspins*2])
if False:
normfields = np.copy(pot.fields)
for i in range(nspins): normfields[i,:] /= np.linalg.norm(normfields[i,:])
coords = coords3ToCoords2( np.reshape(normfields, [nspins*3] ) )
coords = np.reshape(coords, nspins*2)
#print np.shape(coords)
coordsinit = np.copy(coords)
#print "fields", pot.fields
print coords
if False:
coords3 = coords2ToCoords3(coords)
coords2 = coords3ToCoords2(coords3)
print np.reshape(coords, [nspins,2])
print coords2
coords3new = coords2ToCoords3(coords2)
print coords3
print coords3new
e = pot.getEnergy(coords)
print "energy ", e
if True:
print "numerical gradient"
ret = pot.getEnergyGradientNumerical(coords)
print ret[1]
if True:
print "analytical gradient"
ret2 = pot.getEnergyGradient(coords)
print ret2[1]
print ret[0]
print ret2[0]
#print "ratio"
#print ret2[1] / ret[1]
#print "inverse sin"
#print 1./sin(coords)
#print cos(coords)
print "try a quench"
from pele.optimize import mylbfgs
ret = mylbfgs(coords, pot)
print "quenched e = ", ret.energy, "funcalls", ret.nfev
print ret.coords
with open("out.spins", "w") as fout:
s = coords2ToCoords3( ret.coords )
h = pot.fields
c = coords2ToCoords3( coordsinit )
for node in pot.G.nodes():
i = pot.indices[node]
fout.write( "%g %g %g %g %g %g %g %g %g %g %g\n" % (node[0], node[1], \
s[i,0], s[i,1], s[i,2], h[i,0], h[i,1], h[i,2], c[i,0], c[i,1], c[i,2] ) )
coords3 = coords2ToCoords3( ret.coords )
m = np.linalg.norm( coords3.sum(0) ) / nspins
print "magnetization after quench", m
test_basin_hopping(pot, coords)
i = pot.indices[node]
fout.write("%g %g %g %g %g %g %g %g\n" %
(node[0], node[1], s[i, 0], s[i, 1], s[i, 2], h[i, 0],
h[i, 1], h[i, 2]))
pi = np.pi
L = 4
nspins = L**2
pot = HeisenbergModelRA(dim=[L, L], field_disorder=2.)
coords = np.zeros([nspins, 2])
for i in range(nspins):
vec = rotations.vec_random()
coords[i, :] = make2dVector(vec)
coords = np.reshape(coords, [nspins * 2])
coordsinit = np.copy(coords)
print coords
e = pot.getEnergy(coords)
print "energy ", e
print "try a quench"
from pele.optimize import mylbfgs
ret = mylbfgs(coords, pot)
print "quenched e = ", ret.energy, "funcalls", ret.nfev
print ret.coords
