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)
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)
class ComputeGR():
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)
def run(self):
self.mc.set_print_progress()
if not self.verbose:
self.mc.disable_input_warnings()
self.mc.run()
if self.verbose:
print ("adapted MC stepsize")
print self.step.get_stepsize()
def show_result(self):
r = self.gr.get_hist_r()
number_density = self.nr_particles / np.prod(self.box_vector)
gr = self.gr.get_hist_gr(number_density, self.nr_particles)
grq = self.gr_quench.get_hist_gr(number_density, self.nr_particles)
plt.plot(r, gr, "o-", label="Equilibrium")
plt.plot(r, grq, "x-", label="Quench")
plt.xlabel(r"Distance $r$")
plt.ylabel(r"Radial distr. function $g(r)$")
plt.legend()
plt.show()
class ComputeGR():
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)
def run(self):
self.mc.set_print_progress()
if not self.verbose:
self.mc.disable_input_warnings()
self.mc.run()
if self.verbose:
print("adapted MC stepsize")
print self.step.get_stepsize()
def show_result(self):
r = self.gr.get_hist_r()
number_density = self.nr_particles / np.prod(self.box_vector)
gr = self.gr.get_hist_gr(number_density, self.nr_particles)
grq = self.gr_quench.get_hist_gr(number_density, self.nr_particles)
plt.plot(r, gr, "o-", label="Equilibrium")
plt.plot(r, grq, "x-", label="Quench")
plt.xlabel(r"Distance $r$")
plt.ylabel(r"Radial distr. function $g(r)$")
plt.legend()
plt.show()
