def test_constrained_dihedral_minimization(minkey):
minimizer = MINIMIZERS[minkey]
mol = mdt.from_smiles('C=C')
assert mol.atoms[0].atnum == mol.atoms[
1].atnum == 6 # make sure we're picking the right atoms
dihedral = mdt.DihedralMonitor(mol.atoms[0], mol.atoms[1])
assert dihedral.value == 0.0
dihedral.value = 45 * u.degrees
constraint = dihedral.constrain()
mol.set_energy_model(mdt.models.OpenBabelPotential, forcefield='mmff94s')
e0_1 = mol.calculate_potential_energy()
p0_1 = mol.positions.copy()
if minkey == 'bfgs': # BFGS expected to fail here
with pytest.raises(mdt.exceptions.NotSupportedError):
minimizer(mol)
return
traj = minimizer(mol, nsteps=100)
assert_something_resembling_minimization_happened(p0_1, e0_1, traj, mol)
assert constraint.error() <= 1.0 * u.degree
traj_twists = traj.dihedral(mol.atoms[0], mol.atoms[1])
assert (abs(traj_twists - 45 * u.degrees) <= 1.0 * u.degree).all()
e0_2 = mol.potential_energy
p0_2 = mol.positions.copy()
mol.clear_constraints()
traj2 = minimizer(mol, nsteps=100)
assert_something_resembling_minimization_happened(p0_2, e0_2, traj2, mol)
assert dihedral.value <= 5.0 * u.degrees
def test_dihedral_gradient_sign_convention(four_particle_45_twist):
mol = four_particle_45_twist
mol.atoms[-1].y += 0.4 * u.nm
dihe = mdt.DihedralMonitor(*mol.atoms)
calc_grad = dihe.gradient()
num_grad = helpers.num_grad(mol, lambda: dihe.value)
np.testing.assert_allclose(calc_grad,
num_grad,
atol=5.0 * helpers.DEFSTEP.defunits_value())
def test_dihedral_gradient(four_particle_45_twist):
mol = four_particle_45_twist
dihe = mdt.DihedralMonitor(*mol.atoms)
calc_grad = dihe.gradient()
num_grad = helpers.num_grad(mol, lambda: dihe.value)
np.testing.assert_allclose(calc_grad.defunits_value(),
num_grad.defunits_value(),
atol=5.0 * helpers.DEFSTEP.defunits_value())