defaults.NEBquenchParams["maxErise"] = 0.1
defaults.NEBquenchParams["maxstep"] = 0.001
defaults.NEBquenchRoutine = lbfgs_py
#defaults.NEBquenchParams["M"] = 1
k = 100.
nimages = 20
dneb=True
#set up the potential
pot = LJ()
#import the starting and ending points and quench them,
coords1 = np.genfromtxt("coords.A").flatten()
coords2 = np.genfromtxt("coords.B").flatten()
dist, coords1, coords2 = minPermDistStochastic(coords1, coords2,
niter=100, verbose=False)
print "The distance is:", dist
neb = NEB(InterpolatedPath(coords1, coords2, nimages), pot, k=k, dneb=dneb)
neb.optimize()
energies1 = copy(neb.energies)
distances1 = []
for i in xrange(1,len(neb.energies)):
x1 = neb.coords[i-1]
x2 = neb.coords[i]
distances1.append(np.linalg.norm(x2-x1))
neb.optimize()
energies2 = copy(neb.energies)
distances2 = []
"""
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
