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_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_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_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))