def test_generate_trial_position_2(self):
position = np.array([0.5, 0.5, 0.5])
# 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)
distances = []
for i in range(10000):
trial_position = MetropolisMonteCarlo._generate_trial_position(
position, para)
distances.append(np.linalg.norm(position - trial_position))
distances = np.array(distances)
npt.assert_approx_equal(distances.sum() / 10000, 0.5, significant=2)
def test_shift_position(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., 14.]),
es_sigma=0.5,
update_radius=1,
particle_types=particle_type,
cutoff_radius=3,
K_cutoff=1)
reference_pos1 = np.array([1.5, 1.0, 1.5])
reference_pos2 = np.array([10.5, 12.0, 5.5])
particle_position_1 = np.array([13.5, 14., 15.5])
shifted_position_1 = MetropolisMonteCarlo._shift_position(
particle_position_1, parameters)
particle_position_2 = np.array([-1.5, -1., 5.5])
shifted_position_2 = MetropolisMonteCarlo._shift_position(
particle_position_2, parameters)
npt.assert_equal(reference_pos1,
shifted_position_1,
'Failed',
verbose=True)
npt.assert_equal(reference_pos2,
shifted_position_2,
'Failed',
verbose=True)
def test_generate_trial_position_1(self):
position = np.array([0.5, 0.5, 0.5])
# 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)
for i in range(1000):
trial_position = MetropolisMonteCarlo._generate_trial_position(
position, para)
distance = np.linalg.norm(position - trial_position)
if distance > para.update_radius:
raise AssertionError("Distance greater than update radius")
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_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_kvector(self):
Parameter = Parameters(temperature=1,
box=[1, 1, 1],
es_sigma=2,
update_radius=0.5,
cutoff_radius=1,
K_cutoff=1,
particle_types=[0])
print(Parameter.k_vector)
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 test_1Dkvector(self):
# setting up 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)
parameters = Parameters(temperature=1,
box=np.array([1]),
update_radius=0.5,
accuracy=0.5,
charges=charges,
lj_sigmas=lj_sigmas,
lj_epsilons=lj_epsilons,
update_probability=0.5)
reference = [[-2], [-1]]
npt.assert_array_equal(parameters.k_vector, reference)
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 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_shift_position_1(self):
position = np.array([0.5, 0.5, 0.5])
# 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]),
update_radius=1,
charges=charges,
lj_sigmas=lj_sigmas,
lj_epsilons=lj_epsilons,
update_probability=0.5,
accuracy=1)
actual = MetropolisMonteCarlo._shift_position(position, para)
npt.assert_array_equal(actual, position)
def test_shift_position_4(self):
position = np.array([-1.3, 2.4, -3.4])
# 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)
actual = MetropolisMonteCarlo._shift_position(position, para)
reference = np.array([0.7, 0.4, 0.6])
npt.assert_array_almost_equal(actual, reference)
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)
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_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)
# 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)
print("Update radius: ", parameters.update_radius)
print("ES sigma: ", parameters.es_sigma)
initial_system = System(particles, parameters)
# ToolBox.plot_system(initial_system, parameters)
steps_opt = 5000
from Particle_Simulation.ToolBox import ToolBox
from Particle_Simulation.Parameters import Parameters
from Particle_Simulation.System import System
from Particle_Simulation.Simulation import Simulation
data = np.load('/home/max/Downloads/sodium-chloride-example.npz')
print(data['parameters'])
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)
lj_epsilons = lj_epsilons * 1000 / avogadro_constant
lj_sigmas = lj_sigmas / 10
parameters = Parameters(temperature=800, box=box, charges=charges, lj_epsilons=lj_epsilons, lj_sigmas=lj_sigmas,
accuracy=15, update_radius=0.01)
initial_system = System(particles, parameters)
simulation = Simulation(initial_system, parameters)
simulation.optimize_annealing(6000)
simulation.parameters.temperature = 300
simulation.simulate(2500)
i = 0
j = 0
ToolBox.plot_overall_energy_trend(simulation.opt_systems)
ToolBox.plot_overall_energy_trend(simulation.sim_systems)
ToolBox.plot_system(simulation.opt_systems[-1], simulation.parameters)