def test_volume():
coordinates = mc_lj_potential.generate_initial_state(method='random',
num_particles=100,
box_length=5.0)
mcs = mc_lj_potential.Box(coordinates=coordinates, box_length=5.0)
assert mcs.volume == 125
def test_lennard_jones_potential():
"""
Test the definition of lennard_jones_potential() function. It will pass when the calculated potential is equal to actual potential defined by lennard jones.
"""
coordinates = [np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 1.0])]
box_length = 10.0
box = mc_lj_potential.Box(box_length=box_length, coordinates=coordinates)
mcs = mc_lj_potential.MCState(box, cutoff=3.0)
expected_vaule = 224.0
calculated_value = mcs.lennard_jones_potential(0.5)
assert np.isclose(expected_vaule, calculated_value)
def test_get_particle_energy_equi():
"""
Test the get_paricle_energy() function when it is at the equilibrated point.
"""
coordinates = [np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 1.0])]
box_length = 10.0
box = mc_lj_potential.Box(box_length=box_length, coordinates=coordinates)
mcs = mc_lj_potential.MCState(box, cutoff=3.0)
expected_vaule = 0.0
calculated_value = mcs.get_particle_energy(0)
assert np.isclose(expected_vaule, calculated_value)
def test_get_particle_energy_cutoff():
"""
Test the get_particle_energy() function when the particle is exceed the cutoff.
"""
coordinates = [np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 4.0])]
box_length = 10.0
box = mc_lj_potential.Box(box_length=box_length, coordinates=coordinates)
mcs = mc_lj_potential.MCState(box, cutoff=3.0)
expected_vaule = 0.0
calculated_value = mcs.get_particle_energy(0)
assert np.isclose(expected_vaule, calculated_value)
def test_num_particles():
"""
Test if the property of num_particles is true.
"""
coordinates = mc_lj_potential.generate_initial_state(method='random',
num_particles=100,
box_length=10.0)
mcs = mc_lj_potential.Box(coordinates=coordinates, box_length=10.0)
assert mcs.num_particles == 100
def mcs():
"""
Set up the fixture to have a general MCState that can be callable for all tests of different energy functions.
"""
current_directory = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(current_directory, "sample_config.xyz")
coordinates = mc_lj_potential.generate_initial_state(method = "file", file_name=file_path)
box_length = 10.0
cutoff = 3.0
box = mc_lj_potential.Box(box_length=box_length, coordinates=coordinates)
mcs = mc_lj_potential.MCState(box, cutoff = cutoff)
return mcs
def test_volume_2():
"""Test the result of the volume function[property]- State 2
Parameters
----------
box_length : float
One side of the cubic simulation box.
coordinates : np.array
A random set of coordinates in the box.
Returns
-------
volume : float
Volume of the cubic simulation box.
"""
box_length = 12
mcs = mc_lj_potential.Box(coordinates=[[24, 36]], box_length=box_length)
expected_volume = 1728
calculated_volume = mcs.volume
assert expected_volume == calculated_volume
def test_wrap_2():
"""Tests the result of the wrap function- State 2
Parameters
----------
coordinates : np.array(num_of_particles, 3)
A numpy array with the s, y and z coordinates of each atom in the simulation box.
box_length : float
One side of the cubic simulation box.
Returns
-------
coordinates : np.array
Determines whether the expected coordinates and calculated coordinates from the wrap function match.
"""
coordinates = np.array([-5, 12, 1.5])
box_length = 6.0
mcs = mc_lj_potential.Box(coordinates=coordinates, box_length=box_length)
expected_coordinates = [1, 0, 1.5]
mcs.wrap(coordinates=coordinates, box_length=box_length)
assert np.array_equal(expected_coordinates, mcs.coordinates)
def test_minimum_image_distance_2():
"""Test the result of the minimum_image_distance function- State 2
Parameters
----------
r_a : float
Position of particle a
r_b : float
Position of particle b
box_length : float
One side of the cubic simulation box.
Returns
-------
rij2 : float
Square of the minimum image distance between atom pair a and b.
"""
r_a = np.array([0, 0, 0])
r_b = np.array([5, 12, 2])
box_length = 5
mcs = mc_lj_potential.Box(coordinates=[r_a, r_b], box_length=box_length)
expected_distance = 8.0
calculated_distance = mcs.minimum_image_distance(r_i=r_a,
r_j=r_b,
box_length=box_length)
assert np.isclose(expected_distance, calculated_distance)
import mc_lj_potential as mc
import numpy as np
np.random.seed(123)
num_particles = 100
box_length = 10.0
coordinates = mc.generate_initial_state(method='random',
num_particles=num_particles,
box_length=box_length)
#print(coordinates)
box = mc.Box(coordinates=coordinates, box_length=box_length)
#print(box.coordinates)
mcs = mc.MCState(box1=box, cutoff=3.0)
total_pair_energy = mcs.calculate_total_pair_energy()
#print(total_pair_energy)
#print(mcs.calculate_tail_correction())
#print(mcs.calculate_unit_energy())
#print(mcs.get_particle_energy(0))
