def test():
pi = np.pi
L = 3
nspins = L**2
#phases = np.zeros(nspins)
pot = XYModel( dim = [L,L], phi = np.pi) #, phases=phases)
angles = np.random.uniform(-pi, pi, nspins)
print angles
e = pot.getEnergy(angles)
print "energy ", e
print "numerical gradient"
ret = pot.getEnergyGradientNumerical(angles)
print ret[1]
print "analytical gradient"
ret2 = pot.getEnergyGradient(angles)
print ret2[1]
print ret[0]
print ret2[0]
#try a quench
from pygmin.optimize.quench import mylbfgs
ret = mylbfgs(angles, pot.getEnergyGradient)
print "quenched e = ", ret[1]
print ret[0]
test_basin_hopping(pot, angles)
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)
help="outfile for results")
(options, args) = parser.parse_args()
configurations = pickle.load(open("quench_benchmark.dat"))
GMIN.initialize()
pot = GMINPotential(GMIN)
#fl = open("quenched.xyz", "w")
res = open("results.txt", "w")
t0 = time.time()
print "# energy nsteps rms"
res.write("# energy nsteps rms\n")
for coords in configurations[0:5]:
ret = mylbfgs(coords,
pot.getEnergyGradient,
tol=options.tol,
M=options.M,
maxstep=options.maxstep,
maxErise=options.maxErise)
energy = ret[1]
fcalls = ret[3]
#oxdna.export_xyz(fl, ret[0])
print energy, fcalls, ret[2]
res.write("%f %d %g\n" % (energy, fcalls, ret[2]))
print "total runtime: ", time.time() - t0
#fl.close()
res.close()
parser.add_option("--maxErise", dest="maxErise", type=float, default=1e-4,
help="maximum energy increase for lbfgs")
parser.add_option("--maxstep", dest="maxstep", type=float, default=0.1,
help="maximum stepsize for minimizer")
parser.add_option("-o", dest="outfile", default="results.txt",
help="outfile for results")
(options, args) = parser.parse_args()
configurations = pickle.load(open("quench_benchmark.dat"))
GMIN.initialize()
pot = GMINPotential(GMIN)
#fl = open("quenched.xyz", "w")
res = open("results.txt", "w")
t0 = time.time()
print "# energy nsteps rms"
res.write("# energy nsteps rms\n")
for coords in configurations[0:5]:
ret = mylbfgs(coords, pot.getEnergyGradient, tol=options.tol, M=options.M, maxstep=options.maxstep, maxErise=options.maxErise)
energy = ret[1]
fcalls = ret[3]
#oxdna.export_xyz(fl, ret[0])
print energy, fcalls, ret[2]
res.write("%f %d %g\n"%(energy, fcalls, ret[2]))
print "total runtime: ", time.time()-t0
#fl.close()
res.close()
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.quench import mylbfgs
ret = mylbfgs(coords, pot.getEnergyGradient)
print "quenched e = ", ret[1], "funcalls", ret[3]
print ret[0]
m = getm( ret[0] )
print "magnetization after quench", m
#do basin hopping
from pygmin.basinhopping import BasinHopping
from pygmin.takestep.displace import RandomDisplacement
from pygmin.takestep.adaptive import AdaptiveStepsize
from pygmin.storage import savenlowest
#phases = np.zeros(nspins)
pot = XYModel( dim = [L,L], phi = np.pi) #, phases=phases)
angles = np.random.uniform(-pi, pi, nspins)
print angles
e = pot.getEnergy(angles)
print "energy ", e
#try a quench
if False:
from pygmin.optimize.quench import mylbfgs
ret = mylbfgs(angles, pot.getEnergyGradient)
print "quenched e = ", ret[1]
print ret[0]
#set up and run basin hopping
from pygmin.basinhopping import BasinHopping
from pygmin.takestep.displace import RandomDisplacement
from pygmin.takestep.adaptive import AdaptiveStepsize
from pygmin.storage import savenlowest
#should probably use a different take step routine which takes into account
#the cyclical periodicity of angles
takestep = RandomDisplacement(stepsize = np.pi/4)
