def optimize(self, n_steps):
# set up initial system
current_system = System(self.system.particles, self.parameters)
# pass particle positions and neighbourlist to the energy calculator class
self.energy_calculator.set_system(
current_system.neighbourlist.particle_positions,
current_system.neighbourlist.cell_list,
current_system.neighbourlist.particle_neighbour_list)
# calculate energy of initial system
current_system.energy = self._calculate_overall_energy()
# append to optimize trajectory
self.opt_systems.append(current_system)
# crude optimization
for i in range(n_steps):
# generate trial system
trial_system = MetropolisMonteCarlo.generate_trial_configuration(
self.opt_systems[-1], self.parameters)
# update particle positions and neighbourlist
self.energy_calculator.set_system(
trial_system.neighbourlist.particle_positions,
trial_system.neighbourlist.cell_list,
trial_system.neighbourlist.particle_neighbour_list)
# calculate energy of trial system
trial_system.energy = self._calculate_overall_energy()
# evaluate system and trial system and append the accepted system to the trajectory
self.opt_systems.append(
MetropolisMonteCarlo.evaluate_trial_configuration_greedy(
self.opt_systems[-1], trial_system))
# interim update_radius
update_radius = self.parameters.update_radius
self.parameters.update_radius = update_radius / 10
# fine optimization
for i in range(100):
# generate trial system
trial_system = MetropolisMonteCarlo.generate_trial_configuration(
self.opt_systems[-1], self.parameters)
# update particle positions and neighbourlist
self.energy_calculator.set_system(
trial_system.neighbourlist.particle_positions,
trial_system.neighbourlist.cell_list,
trial_system.neighbourlist.particle_neighbour_list)
# calculate energy of trial system
trial_system.energy = self._calculate_overall_energy()
# evaluate system and trial system and append the accepted system to the trajectory
self.opt_systems.append(
MetropolisMonteCarlo.evaluate_trial_configuration_greedy(
self.opt_systems[-1], trial_system))
# resetting update_radius to desired value
self.parameters.update_radius = update_radius
def test_evaluate_trial_configuration_greedy_3(self):
# set test parameters
charges = np.ones(10).astype(np.float32)
lj_sigmas = np.ones(10).astype(np.float32)
lj_epsilons = np.ones(10).astype(np.float32)
para = Parameters(temperature=1,
box=np.array([1, 1, 1]),
es_sigma=1,
update_radius=1,
charges=charges,
lj_sigmas=lj_sigmas,
lj_epsilons=lj_epsilons,
update_probability=0.5,
accuracy=1)
particle1 = Particle(np.array([0.5, 0.5, 0.5]))
particle2 = Particle(np.array([0.5, 0.5, 0.1]))
particles = [particle1, particle2]
system = System(particles=particles, parameters=para)
system.energy.overall_energy = 1.1
trial_system = System(particles=particles, parameters=para)
trial_system.energy.overall_energy = 1
actual = MetropolisMonteCarlo.evaluate_trial_configuration_greedy(
system, trial_system)
npt.assert_equal(actual, trial_system)
def optimize(self, n_steps):
raise NotImplementedError()
current_system = System(self.system.particles, self.parameters)
current_system.energy = calculate_energy() # not implemented yet
for i in range(n_steps):
trial_system = MetropolisMonteCarlo.generate_trial_configuration(
current_system, self.parameters)
trial_system.energy = calculate_energy() # not implemented yet
current_system = MetropolisMonteCarlo.evaluate_trial_configuration_greedy(
current_system, trial_system)
self.opt_system = current_system