def runtest(X, pot, natoms = 100, iprint=-1):
from _lbfgs_py import PrintEvent
tol = 1e-5
maxstep = 0.005
Xinit = np.copy(X)
e, g = pot.getEnergyGradient(X)
print "energy", e
lbfgs = LBFGS(X, pot, maxstep = 0.1, nsteps=10000, tol=tol,
iprint=iprint, H0=2.)
printevent = PrintEvent( "debugout.xyz")
lbfgs.attachEvent(printevent)
ret = lbfgs.run()
print ret
print ""
print "now do the same with scipy lbfgs"
from pygmin.optimize import lbfgs_scipy as quench
ret = quench(Xinit, pot, tol = tol)
print ret
#print ret[1], ret[2], ret[3]
if False:
print "now do the same with scipy bfgs"
from pygmin.optimize import bfgs as oldbfgs
ret = oldbfgs(Xinit, pot, tol = tol)
print ret
if False:
print "now do the same with gradient + linesearch"
import _bfgs
gpl = _bfgs.GradientPlusLinesearch(Xinit, pot, maxstep = 0.1)
ret = gpl.run(1000, tol = 1e-6)
print ret
if False:
print "calling from wrapper function"
from pygmin.optimize import lbfgs_py
ret = lbfgs_py(Xinit, pot, tol = tol)
print ret
if True:
print ""
print "now do the same with lbfgs_py"
from pygmin.optimize import lbfgs_py
ret = lbfgs_py(Xinit, pot, tol = tol)
print ret
try:
import pygmin.utils.pymolwrapper as pym
pym.start()
for n, coords in enumerate(printevent.coordslist):
coords=coords.reshape(natoms, 3)
pym.draw_spheres(coords, "A", n)
except ImportError:
print "error loading pymol"
def runtest(X, pot, natoms = 100, iprint=-1):
tol = 1e-5
Xinit = np.copy(X)
e, g = pot.getEnergyGradient(X)
print "energy", e
lbfgs = LBFGS(X, pot, maxstep = 0.1, tol=tol, iprint=iprint, nsteps=10000)
printevent = PrintEvent( "debugout.xyz")
lbfgs.attachEvent(printevent)
ret = lbfgs.run()
print "done", ret
print "now do the same with scipy lbfgs"
from pygmin.optimize import lbfgs_scipy as quench
ret = quench(Xinit, pot, tol = tol)
print ret
if False:
print "now do the same with scipy bfgs"
from pygmin.optimize import bfgs as oldbfgs
ret = oldbfgs(Xinit, pot, tol = tol)
print ret
if False:
print "now do the same with gradient + linesearch"
import _bfgs
gpl = _bfgs.GradientPlusLinesearch(Xinit, pot, maxstep = 0.1)
ret = gpl.run(1000, tol = 1e-6)
print ret
if False:
print "calling from wrapper function"
from pygmin.optimize import lbfgs_py
ret = lbfgs_py(Xinit, pot, tol = tol)
print ret
try:
import pygmin.utils.pymolwrapper as pym
pym.start()
for n, coords in enumerate(printevent.coordslist):
coords=coords.reshape(natoms, 3)
pym.draw_spheres(coords, "A", n)
except ImportError:
print "error loading pymol"
from pygmin.potentials.lj import LJ
from pygmin.optimize import lbfgs_py
from pygmin.landscape import DoubleEndedConnect, smoothPath
from pygmin.mindist import MinPermDistAtomicCluster
from pygmin.transition_states import orthogopt
from pygmin.storage import Database, Minimum
from pygmin.printing import printAtomsXYZ
np.random.seed(0)
#set up the potential
pot = LJ()
#import the starting and ending points and quench them,
coords1 = np.genfromtxt("coords.A")
coords2 = np.genfromtxt("coords.B")
res1 = lbfgs_py(coords1.reshape(-1), pot)
res2 = lbfgs_py(coords2.reshape(-1), pot)
coords1 = res1.coords
coords2 = res2.coords
E1 = res1.energy
E2 = res2.energy
natoms = len(coords1)/3
#add the minima to a database
dbfile = "database.sqlite"
database = Database(dbfile)
database.addMinimum(E1, coords1)
database.addMinimum(E2, coords2)
min1 = database.minima()[0]
min2 = database.minima()[1]
# finalize the rigid body setup
for rb in rb_sites:
rb.finalize_setup()
# define a new rigid body system
rbsystem = rigidbody.RBSystem()
rbsystem.add_sites(rb_sites)
print len(rbsystem.sites), len(rbsystem.indices)
rbcoords = rbsystem.coords_adapter(np.zeros(len(rbsystem.sites)*6))
for site, com in zip(rbsystem.sites, rbcoords.posRigid):
com[:] = ref.coords[site.indices[0]] - site.atom_positions[0]
# for simplicity just use a lj potential here
pot = LJ(sigma=2.)
# get the flattened coordinate array
print pot.getEnergy(ref.coords.flatten())
rbpot = rigidbody.RBPotentialWrapper(rbsystem, pot)
print rbpot.getEnergy(rbcoords.coords)
e, g = rbpot.getEnergyGradient(rbcoords.coords)
g_n = rbpot.NumericalDerivative(rbcoords.coords, eps=1e-4)
cg = rbsystem.coords_adapter(g-g_n)
print cg.posRigid
print cg.rotRigid
ret = lbfgs_py(rbcoords.coords, rbpot.getEnergyGradient)
print ret[1]
xyz.write_xyz(open("quenched.xyz", "w"), rbsystem.to_atomistic(ret[0]), atomtypes=ref.atomtypes)
# finalize the rigid body setup
for rb in rb_sites:
rb.finalize_setup()
# define a new rigid body system
rbsystem = rigidbody.RBSystem()
rbsystem.add_sites(rb_sites)
print len(rbsystem.sites), len(rbsystem.indices)
rbcoords = rbsystem.coords_adapter(np.zeros(len(rbsystem.sites)*6))
for site, com in zip(rbsystem.sites, rbcoords.posRigid):
com[:] = ref.coords[site.indices[0]] - site.atom_positions[0]
# for simplicity just use a lj potential here
pot = LJ(sigma=2.)
# get the flattened coordinate array
print pot.getEnergy(ref.coords.flatten())
rbpot = rigidbody.RBPotentialWrapper(rbsystem, pot)
print rbpot.getEnergy(rbcoords.coords)
e, g = rbpot.getEnergyGradient(rbcoords.coords)
g_n = rbpot.NumericalDerivative(rbcoords.coords, eps=1e-4)
cg = rbsystem.coords_adapter(g-g_n)
print cg.posRigid
print cg.rotRigid
ret = lbfgs_py(rbcoords.coords, rbpot)
print ret.energy
xyz.write_xyz(open("quenched.xyz", "w"), rbsystem.to_atomistic(ret.coords), atomtypes=ref.atomtypes)
from pygmin.potentials.lj import LJ
from pygmin.optimize import lbfgs_py
from pygmin.landscape import DoubleEndedConnect, smoothPath
from pygmin.mindist import minPermDistStochastic, MinDistWrapper
from pygmin.transition_states import orthogopt
from pygmin.storage import Database, Minimum
from pygmin.printing import printAtomsXYZ
np.random.seed(0)
#set up the potential
pot = LJ()
#import the starting and ending points and quench them,
coords1 = np.genfromtxt("coords.A")
coords2 = np.genfromtxt("coords.B")
res1 = lbfgs_py(coords1.reshape(-1), pot.getEnergyGradient)
res2 = lbfgs_py(coords2.reshape(-1), pot.getEnergyGradient)
coords1 = res1[0]
coords2 = res2[0]
E1 = res1[1]
E2 = res2[1]
natoms = len(coords1)/3
#add the minima to a database
dbfile = "database.sqlite"
database = Database(dbfile)
database.addMinimum(E1, coords1)
database.addMinimum(E2, coords2)
min1 = database.minima()[0]
min2 = database.minima()[1]
"""
an example for finding the minimum distance and best alignment
between two lennard jones clusters
"""
import numpy as np
from pygmin.potentials.lj import LJ
from pygmin.optimize import lbfgs_py
from pygmin.mindist import minPermDistStochastic
pot = LJ()
natoms = 40
#get two random quenched structures to compare
coords1 = np.random.rand(natoms*3)*natoms**(1./3)*1.5
coords2 = np.random.rand(natoms*3)*natoms**(1./3)*1.5
ret1 = lbfgs_py(coords1, pot.getEnergyGradient)
ret2 = lbfgs_py(coords2, pot.getEnergyGradient)
coords1 = ret1[0]
coords2 = ret2[0]
#all the atoms are permutable
permlist = [range(natoms)]
dist, newcoords1, newcoords2 = minPermDistStochastic(coords1, coords2,
niter=100, permlist=permlist, verbose=False)
print ""
print "dist =", dist
""" an example for finding the minimum distance and best alignment between two lennard jones clusters """ import numpy as np from pygmin.potentials.lj import LJ from pygmin.optimize import lbfgs_py from pygmin.mindist import MinPermDistAtomicCluster pot = LJ() natoms = 40 #get two random quenched structures to compare coords1 = np.random.rand(natoms * 3) * natoms**(1. / 3) * 1.5 coords2 = np.random.rand(natoms * 3) * natoms**(1. / 3) * 1.5 ret1 = lbfgs_py(coords1, pot) ret2 = lbfgs_py(coords2, pot) coords1 = ret1.coords coords2 = ret2.coords #all the atoms are permutable permlist = [range(natoms)] mindist = MinPermDistAtomicCluster(niter=100, permlist=permlist, verbose=False) dist, newcoords1, newcoords2 = mindist(coords1, coords2) print "" print "dist =", dist
# finalize the rigid body setup
for rb in rb_sites:
rb.finalize_setup()
# define a new rigid body system
rbsystem = rigidbody.RBSystem()
rbsystem.add_sites(rb_sites)
print len(rbsystem.sites), len(rbsystem.indices)
rbcoords = rbsystem.coords_adapter(np.zeros(len(rbsystem.sites) * 6))
for site, com in zip(rbsystem.sites, rbcoords.posRigid):
com[:] = ref.coords[site.indices[0]] - site.atom_positions[0]
# for simplicity just use a lj potential here
pot = LJ(sigma=2.0)
# get the flattened coordinate array
print pot.getEnergy(ref.coords.flatten())
rbpot = rigidbody.RBPotentialWrapper(rbsystem, pot)
print rbpot.getEnergy(rbcoords.coords)
e, g = rbpot.getEnergyGradient(rbcoords.coords)
g_n = rbpot.NumericalDerivative(rbcoords.coords, eps=1e-4)
cg = rbsystem.coords_adapter(g - g_n)
print cg.posRigid
print cg.rotRigid
ret = lbfgs_py(rbcoords.coords, rbpot)
print ret.energy
xyz.write_xyz(open("quenched.xyz", "w"), rbsystem.to_atomistic(ret.coords), atomtypes=ref.atomtypes)
""" an example for finding the minimum distance and best alignment between two lennard jones clusters """ import numpy as np from pygmin.potentials.lj import LJ from pygmin.optimize import lbfgs_py from pygmin.mindist import MinPermDistAtomicCluster pot = LJ() natoms = 40 #get two random quenched structures to compare coords1 = np.random.rand(natoms*3)*natoms**(1./3)*1.5 coords2 = np.random.rand(natoms*3)*natoms**(1./3)*1.5 ret1 = lbfgs_py(coords1, pot) ret2 = lbfgs_py(coords2, pot) coords1 = ret1.coords coords2 = ret2.coords #all the atoms are permutable permlist = [range(natoms)] mindist = MinPermDistAtomicCluster(niter=100, permlist=permlist, verbose=False) dist, newcoords1, newcoords2 = mindist(coords1, coords2) print "" print "dist =", dist
