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 test__generate_trial_position_redundancy(self):
particle_type = ParticleType(name="Natrium",
mass=2,
charge=2,
lj_epsilon=1.25,
lj_sigma=0.5)
particle_type = np.array([particle_type])
parameters = Parameters(temperature=0,
box=np.array([12., 13., 14.]),
es_sigma=0.5,
update_radius=1,
particle_types=particle_type,
cutoff_radius=0.5,
K_cutoff=1)
particle_1 = Particle(position=np.array([1, 2, 3]), type_index=0)
particle_2 = Particle(position=np.array([1, 2, 3]), type_index=0)
trial_position1 = np.array(
MetropolisMonteCarlo._generate_trial_position(
particle_1.position, parameters))
trial_position2 = np.array(
MetropolisMonteCarlo._generate_trial_position(
particle_1.position, parameters))
x = 0
if np.array_equal(trial_position1, trial_position2):
x = 1
else:
x = 2
npt.assert_equal(x, 2, 'Failed', verbose=True)
def test_longrange(self):
particle_type = ParticleType(name="Natrium", mass=2, charge=2, lj_epsilon=1.25, lj_sigma=0.5)
particle_type = np.array([particle_type])
parameters = Parameters(temperature=0, box=np.array([1, 1, 1]), es_sigma=0.5, update_radius=1,
particle_types=particle_type, cutoff_radius=0.5, K_cutoff=2)
particle_1 = Particle(position=np.array([1, 2, 3]), type_index=0)
particle_2 = Particle(position=np.array([2, 3.5, 6]), type_index=0)
particles = np.array([particle_1, particle_2])
system = System(particles, parameters)
def test_shortrange_potential(self):
reference_potential = -0.000042499
print(reference_potential)
particle_type = ParticleType(name="Hydrogen", mass=3, charge=1, shortrange_epsilon=1.25, shortrange_sigma=0.5)
particle_type = np.array([particle_type])
parameters = Parameters(temperature=0, es_sigma=0, update_radius=1, particle_types=particle_type,
box=np.array([1, 2, 1]), cutoff_radius=1)
particle_1 = Particle(position=np.array([1, 2, 2]), type_index=0)
particle_2 = Particle(position=np.array([2, 3.5, 4]), type_index=0)
shortrange_value = shortrange._calculate_potential(particle_1, particle_2, parameters)
shortrange_value_rounded = shortrange_value.round(decimals=9)
print(shortrange_value_rounded)
npt.assert_equal(reference_potential, shortrange_value_rounded, 'Failed', verbose=True)
def test_calculate_lennardjones_potential(self):
reference_potential = -0.000042499
print(reference_potential)
particle_type = ParticleType(name="Natrium", mass=2, charge=2, lj_epsilon=1.25, lj_sigma=0.5)
particle_type = np.array([particle_type])
parameters = Parameters(temperature=0, es_sigma=0, update_radius=1, particle_types=particle_type,
box=np.array([1, 1, 1]), cutoff_radius=1, K_cutoff= 2)
particle_1 = Particle(position=np.array([1, 2, 3]), type_index=0)
particle_2 = Particle(position=np.array([2, 3.5, 6]), type_index=0)
lg_value = LennardJones._calculate_potential(particle_1, particle_2, parameters)
lg_value_rounded = lg_value.round(decimals=9)
print(lg_value_rounded)
npt.assert_equal(reference_potential, lg_value_rounded, 'Failed', verbose=True)
def get_inputs(file_path):
#Load arrays from .npz file
with np.load(file_path) as fh :
box = fh['box']
particle_positions = fh['positions']
types = fh['types']
readme = fh['readme']
#scalarization (parameters.npy to dictionary)
parameters = fh['parameters'].item()
#fetch values from dictionary and create input ndarrays
prmtr_vals = []
for i in range(len(types)):
prmtr_vals.append(parameters.get(types[i]))
prmtr_vals = np.asarray(prmtr_vals)
name = types
lj_sigmas = prmtr_vals[ : , 0]
lj_epsilons = prmtr_vals[ : , 1]
mass = prmtr_vals[ : , 2]
charges = prmtr_vals[ : , 3]
#Particle object:
particle_list = []
for i in range(len(particle_positions)):
particle_obj = Particle(position=particle_positions[i])
particle_list.append(particle_obj)
particle = np.array(particle_list)
return particle, box, particle_positions, types, name, lj_sigmas, lj_epsilons, mass, charges, readme
def calculate_shortranged_energy(self, system):
lj_energy = 0
short_ranged_energy = 0
neighbour_cell_number = 3**system.neighbourlist.dim
for i in range(system.neighbourlist.total_cell_number):
particle_index_1 = system.neighbourlist.cell_list[i]
while particle_index_1 != -1:
for k in range(neighbour_cell_number):
cell_index = System.cell_neighbour_list[k][i][0]
particle_index_2 = system.neighbourlist.cell_list[
cell_index]
while particle_index_2 != -1:
particle_1 = system.particles[particle_index_1]
particle_2 = system.particles[particle_index_2]
if particle_index_1 != particle_index_2:
if System.cell_neighbour_list[k][i][1] == 0:
if particle_index_1 < particle_index_2:
particle_distance = np.linalg.norm(
particle_1.position -
particle_2.position)
if particle_distance < self.parameters.cutoff_radius:
lj_energy += LennardJones.calculate_potential(
particle_1, particle_2,
self.parameters)
short_ranged_energy += EwaldSummation.calculate_shortranged_potential(
particle_1, particle_2,
self.parameters)
elif System.cell_neighbour_list[k][i][1] != 0:
box_shift = self._determine_box_shift(i, k)
particle_2 = Particle(
type_index=particle_2.type_index,
position=particle_2.position + box_shift)
particle_distance = np.linalg.norm(
particle_1.position - particle_2.position)
if particle_distance < self.parameters.cutoff_radius:
lj_energy += LennardJones.calculate_potential(
particle_1, particle_2,
self.parameters)
short_ranged_energy += EwaldSummation.calculate_shortranged_potential(
particle_1, particle_2,
self.parameters)
particle_index_2 = system.neighbourlist.particle_neighbour_list[
particle_index_2]
particle_index_1 = system.neighbourlist.particle_neighbour_list[
particle_index_1]
short_ranged_energy *= 1 / (8 * np.pi * Parameters.VACUUM_PERMITTIVITY)
return [lj_energy, short_ranged_energy]
def test__generate_trial_position_3(self):
particle_type = ParticleType(name="Hydrogen",
mass=1.008,
charge=1.602,
lj_epsilon=0.21,
lj_sigma=2.5)
particle_type = np.array([particle_type])
parameters = Parameters(temperature=0,
box=np.array([12., 13., 13.]),
es_sigma=0.5,
update_radius=1,
particle_types=particle_type,
cutoff_radius=0.5)
particle_1 = Particle(position=np.array([1, 2, 4]), type_index=1)
trial_position1 = np.array(
MetropolisMonteCarlo._generate_trial_position(
particle_1.position, parameters))
def generate_trial_configuration(system, parameters):
n_particles = len(system.particles)
update_probability = parameters.update_probability
trial_particles = []
for i in range(n_particles):
trial_particles.append(
Particle(np.zeros(len(system.particles[0].position))))
for i in range(0, n_particles):
random_number = np.random.rand(1)[0]
if random_number <= update_probability:
trial_particles[
i].position = MetropolisMonteCarlo._generate_trial_position(
system.particles[i].position, parameters)
else:
trial_particles[i].position = system.particles[i].position
return System(trial_particles, parameters)
def setup_random_simulation(box_length):
cr = []
for i in range(box_length):
cr.append(i + 0.5)
x, y, z = np.meshgrid(cr, cr, cr)
xyz = np.vstack((x.flat, y.flat, z.flat))
xyz = np.ascontiguousarray(xyz)
particles = []
for i in range(len(xyz[0, :])):
particles.append(Particle(np.array(xyz[:, i])))
charges = np.zeros(len(particles))
lj_sigmas = np.zeros(len(particles))
lj_epsilons = np.zeros(len(particles))
avogadro_constant = 6.02214085774 * 10**(23)
for i in range(len(particles)):
if i % 2 == 0:
charges[i] = 1
lj_sigmas[i] = 0.33
lj_epsilons[i] = 11.6 / avogadro_constant
elif i % 2 == 1:
charges[i] = -1
lj_sigmas[i] = 0.44
lj_epsilons[i] = 418.4 / avogadro_constant
parameters = Parameters(temperature=300,
box=np.array(
[box_length, box_length, box_length]),
charges=charges,
lj_sigmas=lj_sigmas,
lj_epsilons=lj_epsilons,
accuracy=10)
system = System(particles, parameters)
simulation = Simulation(system, parameters)
return simulation
def test_selfinteraction_potential(self):
particle_1 = Particle(position=np.array([1, 0, 0]), type_index=1)
particle_2 = Particle(position=np.array([12, 0, 0]), type_index=1)
parameters = Parameters(temperature=0, es_sigma=0, update_radius=1, particle_types=particle_type,
box=np.array([2, 1, 1]), cutoff_radius=1)
box=np.array([1, 2, 1]), cutoff_radius=1)
particle_1 = Particle(position=np.array([1, 2, 2]), type_index=0)
particle_2 = Particle(position=np.array([2, 3.5, 4]), type_index=0)
shortrange_value = shortrange._calculate_potential(particle_1, particle_2, parameters)
shortrange_value_rounded = shortrange_value.round(decimals=9)
print(shortrange_value_rounded)
npt.assert_equal(reference_potential, shortrange_value_rounded, 'Failed', verbose=True)
def test_wrapped_shortrange_potential(self):
reference_potential = -0.000042499
print(reference_potential)
particle_type = ParticleType(name="Natgerium", mass=1, charge=3, shortrange_epsilon=1.25, shortrange_sigma=0.5)
particle_type = np.array([particle_type])
parameters = Parameters(temperature=0, es_sigma=0, update_radius=1, particle_types=particle_type,
box=np.array([2, 1, 1]), cutoff_radius=1)
particle_1 = Particle(position=np.array([1, 2, 2]), type_index=0)
particle_2 = Particle(position=np.array([2, 3.5, 2]), type_index=0)
shortrange_value = shortrange. _calculate_potential(particle_1, particle_2, parameters)
shortrange_value_rounded = shortrange_value.round(decimals=9)
print(shortrange_value_rounded)
npt.assert_equal(reference_potential, shortrange_value_rounded, 'Failed', verbose=True)
distance = shortrange._calculate_distance(particle_1, particle_2)
npt.assert_equal(reference_distance, distance, 'Failed', verbose=True)
def test_selfinteraction_potential(self):
particle_1 = Particle(position=np.array([1, 0, 0]), type_index=1)
particle_2 = Particle(position=np.array([12, 0, 0]), type_index=1)
parameters = Parameters(temperature=0, es_sigma=0, update_radius=1, particle_types=particle_type,
box=np.array([2, 1, 1]), cutoff_radius=1)
npt.assert_equal(selfinteraction_potential, 'Failed', verbose=True)
def test_calculate_distance_2(self):
reference_distance = 3.5
particle_1 = Particle(position=np.array([1, 2, 3]), type_index=1)
particle_2 = Particle(position=np.array([2, 3.5, 6]), type_index=1)
distance = LennardJones._calculate_distance(particle_1, particle_2)
npt.assert_equal(reference_distance, distance, 'Failed', verbose=True)
particles, box, particle_positions, types, name, lj_sigmas, lj_epsilons, mass, charges, readme = ToolBox.get_inputs(
'/home/max/Downloads/sodium-chloride-example.npz')
avogadro_constant = 6.02214085774 * 10 ** (23)
# scale parameters
lj_epsilons = lj_epsilons * 1000 / avogadro_constant
lj_sigmas = lj_sigmas / 10
particle_positions = particle_positions / 10
box = box / 10
particles = []
# create particle array
for i in range(len(particle_positions)):
particle_obj = Particle(position=particle_positions[i])
particles.append(particle_obj)
particles = np.array(particles)
# initialize parameters object
parameters = Parameters(temperature=100, box=box, charges=charges, lj_epsilons=lj_epsilons, lj_sigmas=lj_sigmas,
accuracy=2)
print("Starting simulation with the following parameters:")
print("Temperature: ", parameters.temperature)
print("Accuracy: ", parameters.accuracy)
print("Cutoff radius: ", parameters.cutoff_radius)
print("K cutoff: ", parameters.K_cutoff)
print("Update probability: ", parameters.update_probability)