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)