name="state1",
backup_trajectory=True,
)
state2 = State(
kT=2.0,
state_dir="./state2",
top_file="start.hoomdxml",
name="state2",
backup_trajectory=True,
)
states = [state0, state1, state2]
# Specify pairs.
indices = list(itertools.combinations(range(1468),
2)) # all-all for 1468 atoms
initial_guess = mie(opt.pot_r, 1.0, 1.0) # 1-D array of potential values.
rdf_targets = [
np.loadtxt("rdfs/rdf.target{0:d}.t1t1.txt".format(i)) for i in range(3)
]
pair0 = Pair("1", "1", initial_guess)
alphas = [1.0, 1.0, 1.0]
# Add targets to pair.
for state, target, alpha in zip(states, rdf_targets, alphas):
pair0.add_state(state, target, alpha, indices)
pairs = [pair0] # optimize() expects a list of pairs
# Do magic.
opt.optimize(states, pairs, n_iterations=5, engine="hoomd")
# Set up global parameters.
rdf_cutoff = 5.0
opt = MSIBI(rdf_cutoff=rdf_cutoff, n_rdf_points=101, pot_cutoff=3.0,
smooth_rdfs=True)
# Specify states.
state0 = State(kT=0.5, state_dir='./state0', top_file='start.hoomdxml',
name='state0', backup_trajectory=True)
state1 = State(kT=1.5, state_dir='./state1', top_file='start.hoomdxml',
name='state1', backup_trajectory=True)
state2 = State(kT=2.0, state_dir='./state2', top_file='start.hoomdxml',
name='state2', backup_trajectory=True)
states = [state0, state1, state2]
# Specify pairs.
indices = list(itertools.combinations(range(1468), 2)) # all-all for 1468 atoms
initial_guess = mie(opt.pot_r, 1.0, 1.0) # 1-D array of potential values.
rdf_targets = [np.loadtxt('rdfs/rdf.target{0:d}.t1t1.txt'.format(i))
for i in range(3)]
pair0 = Pair('1', '1', initial_guess)
alphas = [1.0, 1.0, 1.0]
# Add targets to pair.
for state, target, alpha in zip(states, rdf_targets, alphas):
pair0.add_state(state, target, alpha, indices)
pairs = [pair0] # optimize() expects a list of pairs
# Do magic.
opt.optimize(states, pairs, n_iterations=5, engine='hoomd')
def pair(self):
return Pair("0", "1", potential=mie(r, 1.0, 1.0))
# Clear out the temp files
os.system('rm state*/_* rdfs/pair* potentials/* f_fits.log state*/log.txt')
os.system('rm state*/err.txt')
# Set up global parameters.
rdf_cutoff = 1.0
opt = MSIBI(engine='lammps', rdf_cutoff=rdf_cutoff, n_rdf_points=100, pot_cutoff=1.0,
smooth_rdfs=True, max_frames=50)
# Specify states.
state0 = State(k=1, T=0.5, state_dir='./state_lmp', top_file='target.pdb',
traj_file='query.dcd', name='state0', backup_trajectory=True)
states = [state0]
# Specify pairs.
indices = list(itertools.combinations(range(3000), 2)) # all-all for 1468 atoms
initial_guess = mie(opt.pot_r, 0.01, 0.2) # 1-D array of potential values.
#rdf_target = np.loadtxt('rdfs/lmp.rdf')
rdf_target = np.loadtxt('rdfs/lmp.target.txt')
pair0 = Pair('1', '1', initial_guess, head_correction_form='linear')
alphas = [1.0]
# Add targets to pair.
#for state, target, alpha in zip(states, rdf_targets, alphas):
pair0.add_state(states[0], rdf_target, alphas[0], indices)
pairs = [pair0] # optimize() expects a list of pairs
# Do magic.
opt.optimize(states, pairs, n_iterations=10)
def test_save_table_potential(self, tmp_path):
pair = Pair("A", "B", potential=mie(r, 1.0, 1.0))
pair.potential_file = os.path.join(tmp_path, "pot.txt")
pair.save_table_potential(r, dr)
assert os.path.isfile(pair.potential_file)