for m in self.minimizer:
pl.loglog(np.array(m[3]), label=m[0])
pl.legend()
#pl.semilogy()
pl.xlabel("energy evaluations")
pl.ylabel("energy")
pl.show()
if __name__ == "__main__":
import pygmin.potentials.lj as lj
import scipy.optimize
from pygmin.optimize import _quench as quench
print "Running benchmark with lennard jones potential"
pot = lj.LJ()
natoms = 36
coords = np.random.random(3 * natoms) * 10.0
res = quench.lbfgs_scipy(coords, pot, tol=1e-3)
coords = res.coords
coords = coords + np.random.random(coords.shape) * 0.1
res = quench.lbfgs_scipy(coords, pot, tol=1e-3)
Emin = res.energy
bench = QuenchBenchmark(pot)
bench.addMinimizer("lbfgs", quench.lbfgs_scipy)
bench.addMinimizer("mylbfgs", quench.mylbfgs)
bench.addMinimizer("lbfgs_py", quench.lbfgs_py)
#bench.addMinimizer("lbfgs_ase", quench.lbfgs_ase)
self.potential,
self.mcstep,
temperature=self.T,
outstream=None)
mc.run(self.nsteps)
coords[:] = mc.coords[:]
def updateStep(self, acc, **kwargs):
pass
natoms = 12
# random initial coordinates
coords = np.random.random(3 * natoms)
potential = lj.LJ()
reseed = TakeStepMonteCarlo(potential, T=100, nsteps=1000)
takestep = displace.RandomDisplacement(stepsize=0.5)
stepGroup = group.Reseeding(takestep, reseed, maxnoimprove=20)
opt = bh.BasinHopping(coords, potential, takeStep=stepGroup)
opt.run(100)
# some visualization
try:
import pygmin.utils.pymolwrapper as pym
pym.start()
pym.draw_spheres(opt.coords, "A", 1)
except:
def runptmc(nsteps_tot=100000):
natoms = 31
nreplicas = 4
Tmin = 0.2
Tmax = 0.4
nsteps_equil = 10000
nsteps_tot = 100000
histiprint = nsteps_tot / 10
exchange_frq = 100 * nreplicas
coords = np.random.random(3 * natoms)
#quench the coords so we start from a reasonable location
mypot = lj.LJ()
ret = quench(coords, mypot)
coords = ret.coords
Tlist = getTemps(Tmin, Tmax, nreplicas)
replicas = []
ostreams = []
histograms = []
takesteplist = []
radius = 2.5
# create all the replicas which will be passed to PTMC
for i in range(nreplicas):
T = Tlist[i]
potential = lj.LJ()
takestep = RandomDisplacement(stepsize=0.01)
adaptive = AdaptiveStepsize(takestep, last_step=nsteps_equil)
takesteplist.append(adaptive)
file = "mcout." + str(i + 1)
ostream = open(file, "w")
hist = EnergyHistogram(-134., 10., 1000)
histograms.append(hist)
event_after_step = [hist]
radiustest = SphericalContainer(radius)
accept_tests = [radiustest]
mc = MonteCarlo(coords, potential, takeStep=takestep, temperature=T, \
outstream=ostream, event_after_step = event_after_step, \
confCheck = accept_tests)
mc.histogram = hist #for convienence
mc.printfrq = 1
replicas.append(mc)
#is it possible to pickle a mc object?
#cp = copy.deepcopy(replicas[0])
#import pickle
#with open("mc.pickle", "w") as fout:
#pickle.dump(takesteplist[0], fout)
#attach an event to print xyz coords
from pygmin.printing.print_atoms_xyz import PrintEvent
printxyzlist = []
for n, rep in enumerate(replicas):
outf = "dumpstruct.%d.xyz" % (n + 1)
printxyz = PrintEvent(outf, frq=500)
printxyzlist.append(printxyz)
rep.addEventAfterStep(printxyz)
#attach an event to print histograms
for n, rep in enumerate(replicas):
outf = "hist.%d" % (n + 1)
histprint = PrintHistogram(outf, rep.histogram, histiprint)
rep.addEventAfterStep(histprint)
ptmc = PTMC(replicas)
ptmc.use_independent_exchange = True
ptmc.exchange_frq = exchange_frq
ptmc.run(nsteps_tot)
#do production run
#fix the step sizes
#for takestep in takesteplist:
# takestep.useFixedStep()
#ptmc.run(30000)
if False: #this doesn't work
print "final energies"
for rep in ptmc.replicas:
print rep.temperature, rep.markovE
for rep in ptmc.replicas_par:
print rep.mcsys.markovE
for k in range(nreplicas):
e, T = ptmc.getRepEnergyT(k)
print T, e
if False: #this doesn't work
print "histograms"
for i, hist in enumerate(histograms):
fname = "hist." + str(i)
print fname
with open(fname, "w") as fout:
for (e, visits) in hist:
fout.write("%g %d\n" % (e, visits))
ptmc.end() #close the open threads