def getSetOfMinLJ(natoms = 32): #for testing purposes
from pygmin.potentials.lj import LJ
pot = LJ()
coords = np.random.uniform(-1,1,natoms*3)
from pygmin.basinhopping import BasinHopping
from pygmin.takestep.displace import RandomDisplacement
from pygmin.takestep.adaptive import AdaptiveStepsize
from pygmin.storage.database import Database
import os
#dbfile = "test.db"
#os.remove(dbfile)
#saveit = Database(db=dbfile)
saveit = Database()
takestep1 = RandomDisplacement()
takestep = AdaptiveStepsize(takestep1, frequency=15)
bh = BasinHopping(coords, pot, takestep, storage=saveit.minimum_adder(), outstream=None)
bh.run(100)
return pot, saveit
import numpy as np
import oxdnagmin_ as GMIN
from pygmin.potentials.gminpotential import GMINPotential
import pygmin.basinhopping as bh
from pygmin.takestep import displace
from pygmin.storage.database import Database
# initialize GMIN
GMIN.initialize()
# create a potential which calls GMIN
potential = GMINPotential(GMIN)
# get the initial coorinates
coords = potential.getCoords()
coords = np.random.random(coords.shape)
# create takestep routine
step = displace.RandomDisplacement(stepsize=1.)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = bh.BasinHopping(coords, potential, step, db.minimum_adder())
# run for 100 steps
opt.run(1000)
# now dump all the minima
i = 0
for m in db.minima():
i += 1
GMIN.userpot_dump("lowest_%03d.dat" % (i), m.coords)
coords=potential.getCoords()
coords=np.random.random(coords.shape)
# create takestep routine
# we combine a normal step taking
group = takestep.BlockMoves()
step1 = takestep.AdaptiveStepsize(OXDNATakestep(displace=parameters.displace, rotate=0.), frequency=50)
step2 = takestep.AdaptiveStepsize(OXDNATakestep(displace=0., rotate=parameters.rotate), frequency=50)
group.addBlock(100, step1)
group.addBlock(100, step2)
# with a generate random configuration
genrandom = OXDNAReseed()
# in a reseeding takestep procedure
reseed = takestep.Reseeding(group, genrandom, maxnoimprove=parameters.reseed)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = BasinHopping(coords, potential, reseed, db.minimum_adder(), temperature=parameters.temperature)
# run for 100 steps
opt.run(parameters.nsteps)
# now dump all the minima
i=0
for m in db.minima():
i+=1
GMIN.userpot_dump("lowest_%03d.dat"%(i), m.coords)
import oxdnagmin_ as GMIN
from pygmin.potentials.gminpotential import GMINPotential
import pygmin.basinhopping as bh
from pygmin.takestep import displace
from pygmin.storage.database import Database
# initialize GMIN
GMIN.initialize()
# create a potential which calls GMIN
potential = GMINPotential(GMIN)
# get the initial coorinates
coords=potential.getCoords()
coords=np.random.random(coords.shape)
# create takestep routine
step = displace.RandomDisplacement(stepsize=1.)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = bh.BasinHopping(coords, potential, step, db.minimum_adder())
# run for 100 steps
opt.run(1000)
# now dump all the minima
i=0
for m in db.minima():
i+=1
GMIN.userpot_dump("lowest_%03d.dat"%(i), m.coords)
rotate=0.),
frequency=50)
step2 = takestep.AdaptiveStepsize(OXDNATakestep(displace=0.,
rotate=parameters.rotate),
frequency=50)
group.addBlock(100, step1)
group.addBlock(100, step2)
# with a generate random configuration
genrandom = OXDNAReseed()
# in a reseeding takestep procedure
reseed = takestep.Reseeding(group, genrandom, maxnoimprove=parameters.reseed)
# store all minima in a database
db = Database(db="storage.sqlite", accuracy=1e-2)
# create Basinhopping object
opt = BasinHopping(coords,
potential,
reseed,
db.minimum_adder(),
temperature=parameters.temperature)
# run for 100 steps
opt.run(parameters.nsteps)
# now dump all the minima
i = 0
for m in db.minima():
i += 1
GMIN.userpot_dump("lowest_%03d.dat" % (i), m.coords)
