def takeStep(self, coords, **kwargs):
# ake a new monte carlo class
mc = MonteCarlo(coords,
self.potential,
self.mcstep,
temperature=self.T,
outstream=None)
mc.run(self.nsteps)
coords[:] = mc.coords[:]
def __init__(self,
coords,
potential,
takeStep,
storage=None,
event_after_step=None,
acceptTest=None,
temperature=1.0,
quench=None,
confCheck=None,
outstream=sys.stdout,
insert_rejected=False):
#########################################################################
# initialize MonteCarlo base class
#########################################################################
MonteCarlo.__init__(self,
coords,
potential,
takeStep,
storage=storage,
event_after_step=event_after_step,
acceptTest=acceptTest,
temperature=temperature,
confCheck=confCheck,
outstream=outstream,
store_initial=False)
if quench is None:
quench = lambda coords: mylbfgs(coords, self.potential)
self.quench = quench
#########################################################################
# do initial quench
#########################################################################
self.markovE_old = self.markovE
res = self.quench(self.coords)
self.result.nfev += res.nfev
self.coords = res.coords
self.markovE = res.energy
self.rms = res.rms
self.funcalls = res.nfev
self.insert_rejected = insert_rejected
if self.storage:
self.storage(self.markovE, self.coords)
# print the initial quench
self.acceptstep = True
self.trial_energy = self.markovE
self.printStep()
self.result.energy = self.markovE
self.result.coords = self.coords.copy()
def __init__(self, coords, potential, takeStep, storage=None,
event_after_step=[], acceptTest=None, temperature=1.0,
quench=None, confCheck=[], outstream=sys.stdout,
insert_rejected=False
):
#########################################################################
# initialize MonteCarlo base class
#########################################################################
MonteCarlo.__init__(self, coords, potential, takeStep, storage=storage,
event_after_step=event_after_step,
acceptTest=acceptTest, temperature=temperature,
confCheck=confCheck, outstream=outstream,
store_initial=False)
if quench is None:
quench = lambda coords : mylbfgs(coords, self.potential)
self.quench = quench
#########################################################################
# do initial quench
#########################################################################
self.markovE_old = self.markovE
res = self.quench(self.coords)
self.coords = res.coords
self.markovE = res.energy
self.rms = res.rms
self.funcalls = res.nfev
self.insert_rejected = insert_rejected
if(self.storage):
self.storage(self.markovE, self.coords)
# print the initial quench
self.acceptstep = True
self.trial_energy = self.markovE
self.printStep()
self.result.energy = self.markovE
self.result.coords = self.coords.copy()
def takeStep(self, coords, **kwargs):
# make a new monte carlo class
mc = MonteCarlo(coords, self.potential, self.mcstep,
temperature=self.T, outstream=None)
mc.run(self.nsteps)
coords[:] = mc.coords[:]
natoms = 40
coords = np.random.rand(natoms*3)
lj = LJ()
ret = quench(coords, lj)
coords = ret.coords
takestep = TakeStep(stepsize=0.1 )
#do equilibration steps, adjusting stepsize
tsadapt = AdaptiveStepsize(takestep)
mc = MonteCarlo(coords, lj, tsadapt, temperature = 0.5)
equilout = open("equilibration","w")
#mc.outstream = equilout
mc.setPrinting(equilout, 1)
mc.run(10000)
#fix stepsize and do production run
mc.takeStep = takestep
mcout = open("mc.out", "w")
mc.setPrinting(mcout, 10)
#print coords from time to time
xyzout = open("out.xyz", "w")
printevent = PrintEvent(xyzout, 300)
mc.addEventAfterStep(printevent.printwrapper)
mc.run(10000)
#set up the step taking routine
#Normal basin hopping takes each step from the quenched coords. This modified step taking routine takes a step from the
#last accepted coords, not from the quenched coords
from pele.take_step.random_displacement import takeStep
takestep = takeStep(stepsize=.01)
#pass a function which rejects the step if the system leaved the inital basin.
import do_quenching
dostuff = do_quenching.DoQuenching(pot, coords, quench=quench)
accept_test_list = [dostuff.acceptReject]
#set up basin hopping
from pele.mc import MonteCarlo
temperature = 1.0
event_after_step = []
mc = MonteCarlo(coords, pot, takestep, \
event_after_step = event_after_step, \
acceptTests = accept_test_list, temperature = temperature)
#run basin hopping
mc.run(200)
print mc.naccepted, "steps accepted out of", mc.stepnum
print "quench: ", dostuff.nrejected, "steps rejected out of", dostuff.ntot
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 pele.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
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 pele.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