def uniform_displace(stepsize, coords, indices=None):
'''uniform random displacement
Parameters
----------
coords : array like x(:,3)
coordinates
indices : list, optional
list of coordinates to displace, None for all coordinates in array
'''
if(indices):
for i in indices:
coords[i] += np.random.random()*stepsize*rotations.vec_random()
return
for x in coords:
x += stepsize*rotations.vec_random()
def reduced_coordinates_displace(stepsize, lattice_matrix, coords, indices=None):
'''uniform random displacement of reduced coordinates
Parameters
----------
coords : array like x(:,3)
coordinates
indices : list, optional
list of coordinates to displace, None for all coordinates in array
'''
ilattice = vec3.invert3x3(lattice_matrix) # inverse_lattice
if(indices):
for i in indices:
coords[i] += np.dot(ilattice, stepsize*rotations.vec_random())
return
for x in coords:
x += np.dot(ilattice, stepsize*rotations.vec_random())
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 = 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.):
"""
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 __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() * \
np.sqrt(field_disorder) #np.random.uniform(0, field_disorder, [3])
i += 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
#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])
#print np.shape(coords)
coordsinit = np.copy(coords)
#print "fields", pot.fields
print coords
e = pot.getEnergy(coords)
print "energy ", e
print "try a quench"
from pygmin.optimize import mylbfgs
ret = mylbfgs(coords, pot)
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
#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])
#print np.shape(coords)
coordsinit = np.copy(coords)
#print "fields", pot.fields
print coords
e = pot.getEnergy(coords)
print "energy ", e
print "try a quench"
from pygmin.optimize import mylbfgs
ret = mylbfgs(coords, pot)
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 pygmin.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)
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 pygmin.optimize.quench import mylbfgs
ret = mylbfgs(coords, pot.getEnergyGradient)
print "quenched e = ", ret[1], "funcalls", ret[3]
print ret[0]
with open("out.spins", "w") as fout:
s = coords2ToCoords3( ret[0] )
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[0] )
m = np.linalg.norm( coords3.sum(0) ) / nspins
print "magnetization after quench", m
test_basin_hopping(pot, coords)
def takeStep(self, coords, **kwargs):
c = coords[self.srange]
for x in c.reshape(c.size/3,3):
x += self.stepsize*rotations.vec_random()
