def test_nerf_long():
PROTEIN_XYZ = np.loadtxt('test_protein.xyz', dtype=np.dtype('f8'))
dof = iNeRF(PROTEIN_XYZ)
xyz = NeRF(dof)
assert np.all(np.absolute(xyz - PROTEIN_XYZ) < 0.001)
def test_nerf_vectorized_abc():
DOFS = perturb_dofs(np.repeat(DOF[np.newaxis], repeats, axis=0),
bond_length_factor=0.01 * np.ones(1),
bond_angle_factor=0.1 * np.ones(len(DOF)),
bond_torsion_factor=1.0 * np.ones((repeats, len(DOF))))
xyzs = NeRF(DOFS, abcs=ABC, deps=DEP)
dofs = iNeRF(xyzs, deps=DEP)
xyzs_delta = xyzs - XYZ
dofs_delta = dofs - DOF
dofs_delta[np.where(dofs_delta > 180.0)] -= 360.0
dofs_delta[np.where(dofs_delta < -180.0)] += 360.0
assert np.all(np.absolute(np.mean(xyzs_delta, axis=0)) < 0.05)
assert np.all(np.absolute(np.mean(dofs_delta, axis=0)) < 0.05)
])
dep = np.array([
[0, 0, 0], #
[0, 0, 0], #
[1, 0, 0], #
[1, 0, 2], #
[3, 1, 0], # <- Perturb SG1-CB1-CA1-N1
[4, 3, 1], # <- Perturb SG2-SG1-CB1-CA1
[5, 4, 3], # <- Perturb CB2-SG2-SG1-CB1
[6, 5, 4], # <- Perturb CA2-CB2-SG2-SG1
[7, 6, 5], # <- Perturb N2-CA2-CB2-SG2
[7, 6, 8], #
])
dof = nerf.iNeRF(xyz, dependency=dep)
repeats = 100
dofs = np.repeat(dof[np.newaxis], repeats, axis=0)
for i in [4, 5, 6, 7, 8]: # [CA1-CB1, CB1-SG1, SG1-SG2, SG2-CB2, CB2-CA2]
dofs[:, i, 2] += np.random.uniform(-5, 5, repeats)
dofs[:, i,
2] = (dofs[:, i, 2] + (180.0 + chi_resolution / 2.0)) % 360.0 - (
180.0 + chi_resolution / 2.0)
xyzs = nerf.NeRF(dofs, dependency=dep)
stubs_a = homog.hstub(xyzs[:, 0, :], xyzs[:, 1, :],
xyzs[:, 2, :]) # N1 CA1 C1
stubs_b = homog.hstub(xyzs[:, -2, :], xyzs[:, -3, :],
xyzs[:, -1, :]) # N2 CA2 C2
def test_nerf_abc():
xyz = NeRF(DOF, abcs=ABC, deps=DEP)
dof = iNeRF(xyz, deps=DEP)
assert np.all(np.absolute(xyz - XYZ) < 0.001)
assert np.all(np.absolute(dof - DOF) < 0.001)
def test_nerf():
xyz = NeRF(DOF, deps=DEP)
dof = iNeRF(xyz, deps=DEP)
assert np.all(np.absolute(xyz - ORIGIN_XYZ) < 0.001)
assert np.all(np.absolute(dof - DOF) < 0.001)