def zeroEV(self, x):
zev = []
ca = self.coords_adapter(x)
cv = self.coords_adapter(np.zeros(x.shape))
translate_rigid = zeroev.zeroEV_translation(ca.posRigid)
for v in translate_rigid:
cv.posRigid[:] = v
zev.append(cv.coords.copy())
#rotate_r = zeroev.zeroEV_rotation(ca.posRigid)
#rotate_aa =
transform = TransformAngleAxisCluster(self)
d = 1e-5
dx = x.copy()
transform.rotate(dx, rotations.aa2mx(np.array([d, 0, 0])))
self.align_path([x, dx])
dx -= x
dx /= np.linalg.norm(dx)
dy = x.copy()
transform.rotate(dy, rotations.aa2mx(np.array([0, d, 0])))
self.align_path([x, dy])
dy -= x
dy /= np.linalg.norm(dy)
dz = x.copy()
transform.rotate(dz, rotations.aa2mx(np.array([0, 0, d])))
self.align_path([x, dz])
dz -= x
dz /= np.linalg.norm(dz)
#print "Zero eigenvectors", zev
return zev + [dx, dy, dz]
def test_translation_only(self):
# test orthogopt with translations only
natoms = 13
vec = np.random.uniform(-1, 1, natoms * 3)
vec /= np.linalg.norm(vec)
coords = np.random.uniform(-1, 1, natoms * 3)
# orthogonalize vec
vec = orthogopt_translation_only(vec, coords)
zeroev = zeroEV_translation(coords)
for u in zeroev:
# print np.dot(u, vec)
self.assertAlmostEqual(0., np.dot(u, vec), 5)
rotev = zeroEV_rotation(coords)
for u in rotev:
# print np.dot(u, vec)
self.assertNotAlmostEqual(0., np.dot(u, vec), 5)
def _zeroEV_python(self, x):
"""return a list of zero eigenvectors
This does both translational and rotational eigenvectors
"""
zev = []
ca = self.coords_adapter(x)
cv = self.coords_adapter(np.zeros(x.shape))
# get the zero eigenvectors corresponding to translation
translate_rigid = zeroev.zeroEV_translation(ca.posRigid)
for v in translate_rigid:
cv.posRigid[:] = v
zev.append(cv.coords.copy())
# get the zero eigenvectors corresponding to rotation
#rotate_r = zeroev.zeroEV_rotation(ca.posRigid)
#rotate_aa =
transform = TransformAngleAxisCluster(self)
d = 1e-5
dx = x.copy()
transform.rotate(dx, rotations.aa2mx(np.array([d, 0, 0])))
self.align_path([x, dx])
dx -= x
dx /= np.linalg.norm(dx)
dy = x.copy()
transform.rotate(dy, rotations.aa2mx(np.array([0, d, 0])))
self.align_path([x, dy])
dy -= x
dy /= np.linalg.norm(dy)
dz = x.copy()
transform.rotate(dz, rotations.aa2mx(np.array([0, 0, d])))
self.align_path([x, dz])
dz -= x
dz /= np.linalg.norm(dz)
#print "Zero eigenvectors", zev
return zev + [dx, dy, dz]
