def test_sitedist(self):
drij = np.random.uniform(-1,1,3)
p1 = vec_random() * 0.5
p2 = vec_random() * 0.5
W = 1.3
S = np.random.uniform(-1,1,[3,3])
cog = np.random.uniform(-1,1,3)
dist = sitedist(drij, p1, p2, S, W, cog)
dist2 = _sitedist(drij, p1, p2, S, W, cog)
self.assertAlmostEqual(dist, dist2, places=4)
def test1(self):
drij = np.random.uniform(-1,1,3)
p1 = vec_random() * 0.5
p2 = vec_random() * 0.5
W = 1.3
S = np.random.uniform(-1,1,[3,3])
cog = np.random.uniform(-1,1,3)
g_M, g_P = sitedist_grad(drij, p1, p2, S, W, cog)
g_Mp, g_Pp = _sitedist_grad(drij, p1, p2, S, W, cog)
self.assert_array_almost_equal(g_M, g_Mp, places=4)
self.assert_array_almost_equal(g_P, g_Pp, places=4)
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)
def test_potential_constrained():
L=4
nspins=L*L
pot = HeisenbergModelConstraint( dim = [L,L], field_disorder = 1.) #, phases=phases)
coords = np.zeros([nspins, 3])
for i in range(nspins):
coords[i,:] = rotations.vec_random()
coords = coords.reshape(-1)
pot.test_potential(coords)
def test_big_small(self):
P = vec_random()
P1 = P * 1.1e-6
P2 = P * 0.9e-6
# print P1.dot(P1) > 1e-12, P2.dot(P2) > 1e-12
rm, drm1, drm2, drm3 = rmdrvt(P1, True)
rmp, drm1p, drm2p, drm3p = rmdrvt(P2, True)
# print rm
# print rmp
self.assert_array_almost_equal(rm, rmp, places=4)
self.assert_array_almost_equal(drm1, drm1p, places=4)
self.assert_array_almost_equal(drm2, drm2p, places=4)
self.assert_array_almost_equal(drm3, drm3p, places=4)
def test_potential():
L=4
nspins=L*L
pot = HeisenbergModel( dim = [L,L], field_disorder = 1.) #, 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])
coords[1] = 2.*np.pi - coords[1]
pot.test_potential(coords)
def takeStep(self, coords, **kwargs):
# easy access to coordinates
ca = CoordsAdapter(nrigid=coords.size / 6, coords=coords)
backbone = np.zeros(3)
# random rotation for angle-axis vectors
for pos, rot in zip(ca.posRigid, ca.rotRigid):
backbone += rotations.vec_random() * 0.7525
# choose a random rotation
rot[:] = rotations.random_aa()
# calcualte center of base from backgone
a1 = np.dot(rotations.aa2mx(rot), np.array([1., 0., 0.]))
pos[:] = backbone + 0.4 * a1
def __init__(self, dim=[4, 4], field_disorder=1., fields=None):
self.dim = copy(dim)
self.nspins = np.prod(dim)
self.G = nx.grid_graph(dim, periodic=True)
self.fields = np.zeros([self.nspins, 3])
self.indices = dict()
nodes = sorted(self.G.nodes())
for i, node in enumerate(nodes):
self.indices[node] = i
if fields is None:
self.fields[i,:] = rotations.vec_random() * np.sqrt(field_disorder)
else:
self.fields[i,:] = fields[i,:]
def takeStep(self, coords, **kwargs):
# easy access to coordinates
ca = CoordsAdapter(nrigid=old_div(coords.size, 6), coords=coords)
backbone = np.zeros(3)
# random rotation for angle-axis vectors
for pos, rot in zip(ca.posRigid, ca.rotRigid):
backbone += rotations.vec_random() * 0.7525
# choose a random rotation
rot[:] = rotations.random_aa()
# calcualte center of base from backgone
a1 = np.dot(rotations.aa2mx(rot), np.array([1., 0., 0.]))
pos[:] = backbone + 0.4 * a1
def __init__(self, dim=None, field_disorder=1., fields=None):
if dim is None: dim = [4, 4]
self.dim = copy(dim)
self.nspins = np.prod(dim)
self.G = nx.grid_graph(dim, periodic=True)
self.fields = np.zeros([self.nspins, 3])
self.indices = dict()
nodes = sorted(self.G.nodes())
for i, node in enumerate(nodes):
self.indices[node] = i
if fields is None:
self.fields[i, :] = rotations.vec_random() * field_disorder
else:
self.fields[i, :] = fields[i, :]
def __init__(self, dim = [4, 4], field_disorder = 1.):
"""
dim is an array giving the dimensions of the lattice
phi is the magnitude of the randomness in the fields
"""
self.dim = copy(dim)
self.nspins = np.prod(dim)
self.G = nx.grid_graph(dim, periodic=True)
self.fields = np.zeros([self.nspins, 3])
self.indices = dict()
i = 0
for node in self.G.nodes():
self.indices[node] = i
self.fields[i,:] = rotations.vec_random() * \
field_disorder#np.random.uniform(0, field_disorder, [3])
i += 1
def test_small_theta(self):
P = vec_random() * np.random.uniform(0,1e-7)
print P
self.check(P, True)
def test1(self):
P = vec_random() * np.random.uniform(1e-5,1)
self.check(P, True)
def test():
pi = np.pi
L = 8
nspins = L**2
#phases = np.zeros(nspins)
pot = HeisenbergModel( dim = [L,L], field_disorder = 1.) #, 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 False:
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, iprint=1)
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)
h = pot.fields
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
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
