def test_force_coulomb():
'''Ensure the correctness of Coulomb force calculation by the testing if the potentials from direct calculation and
from numerical integration of force are the same.
'''
n_dim = 3
l_box = np.array([3., 3., 3.])
n_particles = 2
q = np.array([[0., 0., 0.], [1., 0., 0.]])
charge_vector = np.array([-1., 1.])
# simu_config = es.SimuConfig(n_dim=q.shape[1], n_particles=q.shape[0], l_box=l_box, l_cell=l_box[0], neighbour=True)
simu_config = es.SimuConfig(n_dim=q.shape[1], n_particles=q.shape[0], l_box=l_box, l_cell=l_box[0], PBC=True, neighbour=False)
def test_potential_neighbour(x, epsilon_lj, sigma_lj, switch_start_lj,
cutoff_lj, l_box, l_cell):
simu_config = es.SimuConfig(n_dim=x.shape[1],
n_particles=x.shape[0],
l_box=l_box,
l_cell=l_cell,
switch_start_lj=switch_start_lj,
cutoff_lj=cutoff_lj,
neighbour=True)
distance_vectors = es.distances.DistanceVectors(simu_config)
lennard_jones = es.potentials.LennardJones(simu_config)
potential1 = lennard_jones.calc_potential(distance_vectors(x, 0))
distance_vectors.neighbour_flag = False
potential2 = lennard_jones.calc_potential(distance_vectors(x, 0))
np.testing.assert_allclose(potential1, potential2)
def test_potential(x):
test_config = es.SimuConfig(
l_box=[10.] * x.shape[1],
PBC=True,
particle_info=[0] * x.shape[0],
n_steps=2000,
timestep=0.001,
temp=30,
phys_world=dummy_world,
)
pot = es.potentials.LJ(test_config, 2.5, 3.5)
pot.set_positions(x)
potential1 = pot.pot
# legacy potential implementaton, requires sigma, epsilon = 1
# and a switch region of 2.5 to 3.5
potential2 = es.potentials.lj_potential_total(x)
np.testing.assert_allclose(potential1, potential2)
def test_gradient(n_points, start, end, epsilon_lj, sigma_lj, switch_start,
cutoff):
# initiate classes
simu_config = es.SimuConfig(
n_dim=2,
n_particles=2,
cutoff=cutoff,
switch_start=switch_start,
lj_flag=True,
PBC=False,
)
calc_potential = es.potentials.CalcPotential(simu_config, [])
calc_force = es.potentials.CalcForce(simu_config, [])
# define distances array
distances = np.linspace(start, end, n_points)
# calculate lj potential corresponding to one particle at origin and one at
# d for every element in distances
potential = np.zeros(len(distances))
for i in range(len(distances)):
potential[i] = calc_potential(np.array([[distances[i], 0], [0, 0]]))
# calculate the corresponding gradient by interpolation, mult by -1
gradient_np = -1 * np.gradient(potential, distances)
# caculate the force for one particle at origin and one particle at d acting
# on second particle with force methond from lennard_jones.py
gradient_calc = np.zeros(len(distances))
for i in range(len(distances)):
gradient_calc[i] = calc_force(np.array([[0, 0], [distances[i], 0]]))[1,
0]
# test both gradients
np.testing.assert_allclose(gradient_np,
gradient_calc,
rtol=1E-2,
atol=1E-2)
return pot_func, force_func
def StupidInitializer3(box_size, n_particles):
# need to return a tuple of four vectors
# masses, charges, q_0 and p_0
q_0 = np.array([[0., 0.], [0., 1.]]) + box_size / 2
#v_0 = np.array([[0.5, 0.866], [-0.8, 0.6]])
v_0 = np.array([[0., 0.], [0., 0.]])
return q_0, v_0
test_config = es.SimuConfig(l_box=(8., 8.),
PBC=True,
particle_info=[0, 0],
n_steps=10000,
timestep=0.001,
temp=30,
phys_world=dummy_world)
test_md = es.MD(test_config, StupidInitializer3,
es.step_runners.Langevin(damping=0.01))
test_md.add_potential(HarmonicTrap(test_config, 100., [4., 4.]))
#test_md.add_potential(es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs() % 8.
plt.plot(qs[:, 0, 0], qs[:, 0, 1])
plt.plot(qs[:, 1, 0], qs[:, 1, 1])
plt.show()
q_opt = np.load("optimized.npy")
def langevin_init_N_108_l_box_6(box_size, n_particles):
n_dim = len(box_size)
q_0 = q_opt
v_0 = np.zeros((n_particles, n_dim))
return q_0, v_0
test_config = es.SimuConfig(l_box=(6., 6., 6.),
PBC=True,
neighbor_list=False,
particle_info=[0] * 108,
n_steps=8000,
timestep=0.001,
temp=0.5,
phys_world=dummy_world)
test_md = es.MD(test_config, random_initiliazer, es.step_runners.MMC(step=0.1))
test_md.add_potential(
es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
test_md.run_all()
print("simu_done")
qs = test_md.traj.get_qs()
#np.save("optimized.npy",qs[-1])
Gr = es.observables.RadDistFunc(test_config, 40, [108, 0, 0])
g_r, radii = Gr.calc_radial_dist(qs, 500, 1)
# 3.104 is for correct liquid water density at 300 K
l_box = 7. * np.array([2 * n, n, n])
grid=_grid([n, n, n])
q_0 = 7. * grid
#init_directions = []
q = (q_0[:, None, :] + water_shape[None, :, :]).reshape(-1, 3)
v = np.zeros((3 * n_mol, n_dim))
mol_list = []
for i in range(n_mol):
mol_list.append((0, [3 * i, 3 * i + 1, 3 * i + 2]))
particle_info = np.tile([1, 2, 2], n_mol)
return q, v, particle_info, l_box, mol_list
q, v, particle_info, l_box, mol_list = _intializer_WaterBox(3)
steps = 5000
test_config = es.SimuConfig(l_box=l_box, PBC=True, neighbor_list=False, particle_info=particle_info, mol_list=mol_list, n_steps=steps, timestep=1e-3, temp=500)
init = lambda x,y: (q, v)
test_md = es.MD(test_config, init, es.step_runners.GradientDescent())
test_md.add_potential(es.potentials.Water(test_config))
test_md.add_potential(es.potentials.LJ(test_config, switch_start=5., cutoff=7.))
test_md.add_potential(es.potentials.Coulomb(test_config))
print(es.potentials.Coulomb(test_config))
#test_md.add_potential(HarmonicTrap(test_config, 100., [4., 4.]))
#test_md.add_potential(es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs()
pdb = es.observables.PdbWriter(test_config, 'water_opt2.pdb')
for i in range(int(steps/100)):
pdb.write_frame(qs[i * 100])
q_opt = np.load("optimized.npy")
def langevin_init_N_108_l_box_6(box_size, n_particles):
n_dim = len(box_size)
q_0 = q_opt
v_0 = np.zeros((n_particles, n_dim))
return q_0, v_0
test_config = es.SimuConfig(l_box=(17.5, 17.5, 17.5),
PBC=True,
neighbor_list=True,
particle_info=np.asarray([0] * 100),
n_steps=100000,
timestep=0.001,
temp=100)
test_md = es.MD(test_config, random_initiliazer, es.step_runners.MMC(step=0.5))
test_md.add_potential(
es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
#test_md.add_potential(es.potentials.Coulomb(test_config))
test_md.run_all()
print("simu_done")
qs = test_md.traj.get_qs()
#np.save("optimized.npy",qs[-1])
Gr = es.observables.RadDistFunc(test_config, 100)
g_r, radii, name_list = Gr.calc_radial_dist(qs, 100, 100000, 50)
def grid_initializer_2d(l_box, n_particles):
x = np.linspace(0., l_box[0], 3, endpoint=False)
y = np.linspace(0., l_box[1], 3, endpoint=False)
masses = np.array([1.] * 9)
charges = np.array([0.] * 9)
q_0 = np.array(np.meshgrid(x, x)).T.reshape(9,2)
v_0 = np.array([[0., 0.]] * 9)
return masses, charges, q_0, v_0 * masses[:, None]
N_particles = 9
l_box = (8., 8.)
test_config = es.SimuConfig(n_dim = 2,
l_box = l_box,
n_particles = N_particles,
n_steps = 3000,
timestep = 0.001,
temp = 100,
PBC = True,
neighbour = False,
lj_flag = True,
)
test_md = es.MD(es.PhysWorld(), test_config, grid_initializer_2d, es.step_runners.Langevin(damping=0.1))
#test_md.add_global_potential(HarmonicPotential(1.))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs()
#plt.plot(qs[:, 0, 0]%3., qs[:, 0, 1]%3.)
#plt.plot(qs[:, 1, 0]%3., qs[:, 1, 1]%3.)
#plt.show()
import matplotlib.animation as manimation
q_0 = np.array([[0., 0., -0.064609], [0., -0.81649, 0.51275],
[0., 0.81649, 0.51275]])
q = np.vstack((q_0, q_0 + box_size / 5 * 2))
#v_0 = np.array([[0.5, 0.866], [-0.8, 0.6]])
v_0 = np.array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]])
v = np.vstack((v_0, v_0))
return q, v
particle_info = [1, 2, 2, 1, 2, 2]
mol_list = [(0, [0, 1, 2]), (0, [3, 4, 5])]
test_config = es.SimuConfig(l_box=(8., 8., 8.),
PBC=True,
particle_info=particle_info,
mol_list=mol_list,
n_steps=1000,
timestep=0.02,
temp=300)
test_md = es.MD(test_config, StupidInitializerWater,
es.step_runners.Langevin(damping=0.05))
test_md.add_potential(es.potentials.Water(test_config))
test_md.add_potential(es.potentials.LJ(test_config, switch_start=5.,
cutoff=7.))
test_md.add_potential(es.potentials.Coulomb(test_config))
#test_md.add_potential(HarmonicTrap(test_config, 100., [4., 4.]))
#test_md.add_potential(es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs()
pdb = es.observables.PdbWriter(test_config, 'water_dimer.pdb')
#init_directions = []
q = (q_0[:, None, :] + water_shape[None, :, :]).reshape(-1, 3)
v = np.zeros((3 * n_mol, n_dim))
mol_list = []
for i in range(n_mol):
mol_list.append((0, [3 * i, 3 * i + 1, 3 * i + 2]))
particle_info = np.tile([1, 2, 2], n_mol)
return q, v, particle_info, l_box, mol_list
q, v, particle_info, l_box, mol_list = _intializer_WaterBox(6)
steps = 2000
test_config = es.SimuConfig(l_box=l_box,
PBC=True,
particle_info=particle_info,
mol_list=mol_list,
n_steps=steps,
timestep=0.01,
temp=500)
init = lambda x, y: (q, v)
test_md = es.MD(test_config, init, es.step_runners.Langevin(damping=0.05))
test_md.add_potential(es.potentials.Water(test_config))
test_md.add_potential(es.potentials.LJ(test_config, switch_start=5.,
cutoff=7.))
test_md.add_potential(es.potentials.Coulomb(test_config))
#test_md.add_potential(HarmonicTrap(test_config, 100., [4., 4.]))
#test_md.add_potential(es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs()
pdb = es.observables.PdbWriter(test_config, 'water_gas.pdb')
'''
@njit
def func(pair, dv):
return dv[1]
'''
def func(Coeff):
@njit
def pot_func(pair, dv):
return Coeff * exp(-dv[1])
@njit
def force_func(pair, dv):
return dv[0] * Coeff
return pot_func, force_func
q, charge_vector, l_box = _intializer_NaCl(3)
simu_config = es.SimuConfig(n_dim=q.shape[1], n_particles=q.shape[0], l_box=l_box, PBC=True)
# simu_config.charges = charge_vector
distance_vectors = PWNumba(simu_config, func(2.), 1.)
distance_vectors.set_positions(q)
# print(distance_vectors.get_particles_in_cutoff(0))
print("MULTI:", distance_vectors.MULTI)
#for pair in distance_vectors.pairs:
# print(pair)
print(distance_vectors.pot)
print(distance_vectors.forces)
print()
print(distance_vectors.pots)
print(distance_vectors.forces)
print()
distance_vectors.set_positions(q)
print(distance_vectors.pots)
X = np.vstack([v.reshape(-1) for v in xx]).T
return X
def _intializer_NaCl(n):
n_particles = n * n * n
n_dim = 3
l_box = 10. * np.array([n, n, n])
grid=_grid([n, n, n])
q = 10. * grid
v = np.zeros((n_particles, n_dim))
particle_info = grid.sum(axis=1)%2+3
return q, v, particle_info, l_box
q, v, particle_info, l_box = _intializer_NaCl(4)
steps = 2000
test_config = es.SimuConfig(l_box=l_box, PBC=True, particle_info=particle_info, n_steps=steps, timestep=0.2, temp=1400)
init = lambda x,y: (q, v)
test_md = es.MD(test_config, init, es.step_runners.Langevin(damping=0.1))
#test_md.add_potential(es.potentials.Water(test_config))
test_md.add_potential(es.potentials.LJ(test_config, switch_start=5., cutoff=7.))
test_md.add_potential(es.potentials.Coulomb(test_config))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs()
Gr = es.observables.RadDistFunc(test_config, 100)
g_r, radii, name_list = Gr.calc_radial_dist(qs, 1000,2000, 1)
print(np.asarray(g_r).shape,np.asarray(radii).shape, np.asarray(name_list).shape)
import matplotlib.pyplot as plt
n_particles_along_axis = 8
n_particles = n_particles_along_axis**3
n_dim = 3
resolution = 6
l_box = np.array([n_particles_along_axis] * 3)
# grid=np.array(list(product(range(0,3), repeat=n_dim)))
# x = 1. * grid
x = particle_init_regular_grid_2_kinds_3d(1, n_particles_along_axis)
charge_vector = x.sum(axis=1) % 2 * 2 - 1
# Madelung = -1.74756459463
# pot_ref = -447.37653622528
# pot_real_ref = -159.04160026039025
simu_config = es.SimuConfig(n_dim=x.shape[1],
n_particles=x.shape[0],
l_box=l_box,
l_cell=l_box[0],
neighbour=True)
simu_config.charges = charge_vector
distance_vectors = es.distances.DistanceVectors(simu_config)
coulomb = es.potentials.Coulomb(simu_config)
force_calc = coulomb.calc_force(x, distance_vectors(x, 0))
print(force_calc)
simu_config = es.SimuConfig(
n_dim=x.shape[1],
n_particles=x.shape[0],
l_box=l_box,
l_cell=l_box[0],
neighbour=False,
def _intializer_NaCl(n):
n_particles = n * n * n
n_dim = 3
l_box = 10. * np.array([2 * n, n, n])
grid = _grid([n, n, n])
q = 10. * grid
v = np.zeros((n_particles, n_dim))
particle_info = grid.sum(axis=1) % 2 + 3
return q, v, particle_info, l_box
q, v, particle_info, l_box = _intializer_NaCl(4)
steps = 1000
test_config = es.SimuConfig(l_box=l_box,
PBC=True,
particle_info=particle_info[1:-1],
n_steps=steps,
timestep=200,
temp=2000)
init = lambda x, y: (q[1:-1], v[1:-1])
test_md = es.MD(test_config, init, es.step_runners.Langevin(damping=0.1))
#test_md.add_potential(es.potentials.Water(test_config))
test_md.add_potential(es.potentials.LJ(test_config, switch_start=5.,
cutoff=7.))
test_md.add_potential(es.potentials.Coulomb(test_config))
#test_md.add_potential(HarmonicTrap(test_config, 100., [4., 4.]))
#test_md.add_potential(es.potentials.LJ(test_config, switch_start=2.5, cutoff=3.5))
test_md.run_all()
#print(test_md.traj.get_qs())
qs = test_md.traj.get_qs()
pdb = es.observables.PdbWriter(test_config, 'NaCl7.pdb', put_in_box=True)
for i in range(steps // 8):