def __init__(self,
potential,
coords,
temperature,
stepsize,
niter,
hEmin=0,
hEmax=100,
hbinsize=0.01,
radius=2.5,
acceptance=0.5,
adjustf=0.9,
adjustf_niter=1e4,
adjustf_navg=100,
bdim=3,
single=False,
seeds=None):
#construct base class
super(Metropolis_MCrunner, self).__init__(potential, coords,
temperature, niter)
#get/set seeds
if not seeds:
i32max = np.iinfo(np.int32).max
seeds = dict(takestep=np.random.randint(i32max),
metropolis=np.random.randint(i32max))
self.seeds = seeds
#construct takestep: random step
self.step = RandomCoordsDisplacement(self.seeds['takestep'],
stepsize,
report_interval=adjustf_navg,
factor=adjustf,
min_acc_ratio=acceptance,
max_acc_ratio=acceptance,
single=single,
nparticles=int(
len(coords) / bdim),
bdim=bdim)
#construct early configuration test: check within spherical container
self.conftest = CheckSphericalContainer(radius, bdim)
#construct accept test: Metropolis
self.metropolis = MetropolisTest(self.seeds['metropolis'])
#construct action: energy histogram
self.binsize = hbinsize
self.histogram = RecordEnergyHistogram(hEmin, hEmax, self.binsize,
adjustf_niter)
#set up pele:MC
self.set_takestep(self.step)
self.set_report_steps(
adjustf_niter
) #set number of iterations for which steps are adapted
self.add_accept_test(self.metropolis)
self.add_conf_test(self.conftest)
self.add_action(self.histogram)
def __init__(self,
boxdim=2,
nr_particles=100,
hard_phi=0.4,
nr_steps=1e6,
epsilon=1,
alpha=0.1,
verbose=False):
# Settings.
np.random.seed(42)
# Input parameters.
self.boxdim = boxdim
self.nr_particles = nr_particles
self.hard_phi = hard_phi
self.nr_steps = nr_steps
self.epsilon = epsilon
self.alpha = alpha
self.verbose = verbose
# Derived quantities.
self.hard_radii = np.ones(self.nr_particles)
def volume_nball(radius, n):
return np.power(np.pi, n / 2) * np.power(radius,
n) / gamma(n / 2 + 1)
self.box_length = np.power(
np.sum(
np.asarray(
[volume_nball(r, self.boxdim)
for r in self.hard_radii])) / self.hard_phi,
1 / self.boxdim)
self.box_vector = np.ones(self.boxdim) * self.box_length
# HS-WCA potential.
self.potential = HS_WCA(use_periodic=True,
use_cell_lists=True,
ndim=self.boxdim,
eps=self.epsilon,
sca=self.alpha,
radii=self.hard_radii,
boxvec=self.box_vector)
# Initial configuration by minimization.
self.nr_dof = self.boxdim * self.nr_particles
self.x = np.random.uniform(-0.5 * self.box_length,
0.5 * self.box_length, self.nr_dof)
optimizer = LBFGS_CPP(self.x, self.potential)
optimizer.run()
if not optimizer.get_result().success:
print("warning: minimization has not converged")
self.x = optimizer.get_result().coords.copy()
# Potential and MC rules.
self.temperature = 1
self.mc = MC(self.potential, self.x, self.temperature, self.nr_steps)
self.step = RandomCoordsDisplacement(42,
1,
single=True,
nparticles=self.nr_particles,
bdim=self.boxdim)
if self.verbose:
print("initial MC stepsize")
print self.step.get_stepsize()
self.mc.set_takestep(self.step)
self.eq_steps = self.nr_steps / 2
self.mc.set_report_steps(self.eq_steps)
self.gr_quench = RecordPairDistHistogram(self.box_vector,
50,
self.eq_steps,
self.nr_particles,
optimizer=optimizer)
self.gr = RecordPairDistHistogram(self.box_vector, 50, self.eq_steps,
self.nr_particles)
self.mc.add_action(self.gr_quench)
self.mc.add_action(self.gr)
self.test = MetropolisTest(44)
self.mc.add_accept_test(self.test)
