class TestOTPExplicit(unittest.TestCase):
def setUp(self):
nrigid = 3
self.topology = RBTopology()
self.topology.add_sites([make_otp() for i in range(nrigid)])
self.topology.finalize_setup()
cartesian_potential = LJ()
self.pot = RBPotentialWrapper(self.topology, cartesian_potential)
self.x0 = _x03
self.x0 = np.array(self.x0)
self.e0 = -17.3387670023
assert nrigid * 6 == self.x0.size
self.x0atomistic = _x03_atomistic
self.nrigid = nrigid
def test_energy(self):
e = self.pot.getEnergy(self.x0)
self.assertAlmostEqual(e, self.e0, delta=1e-4)
def test_energy_gradient(self):
e = self.pot.getEnergy(self.x0)
gnum = self.pot.NumericalDerivative(self.x0)
e2, g = self.pot.getEnergyGradient(self.x0)
self.assertAlmostEqual(e, e2, delta=1e-4)
for i in range(g.size):
self.assertAlmostEqual(g[i], gnum[i], 2)
def test_to_atomistic(self):
xatom = self.topology.to_atomistic(self.x0).flatten()
for i in range(xatom.size):
self.assertAlmostEqual(xatom[i], self.x0atomistic[i], 2)
def test_site_to_atomistic(self):
rf = make_otp()
p = np.array([1., 2, 3])
p /= np.linalg.norm(p)
com = np.array([4., 5, 6])
print("otp to atomistic")
print(rf.to_atomistic(com, p))
print("otp transform grad")
g = np.array(list(range(9)), dtype=float).reshape([-1, 3])
print(g.reshape(-1))
print(rf.transform_grad(p, g))
def test_to_atomistic2(self):
x0 = np.array(list(range(self.nrigid * 6)), dtype=float)
x2 = x0.reshape([-1, 3])
for p in x2[self.nrigid:, :]:
p /= np.linalg.norm(p)
atomistic = self.topology.to_atomistic(x0).flatten()
from pele.potentials import LJ
lj = LJ()
e, g = lj.getEnergyGradient(atomistic.reshape(-1))
grb = self.topology.transform_gradient(x0, g)
rbpot = RBPotentialWrapper(self.topology, lj)
print(rbpot.getEnergy(x0))
class TestOTPExplicit(unittest.TestCase):
def make_otp(self):
"""this constructs a single OTP molecule"""
otp = RigidFragment()
otp.add_atom("O", np.array([0.0, -2. / 3 * np.sin(7. * pi / 24.),
0.0]), 1.)
otp.add_atom(
"O",
np.array([cos(7. * pi / 24.), 1. / 3. * sin(7. * pi / 24.), 0.0]),
1.)
otp.add_atom(
"O",
np.array([-cos(7. * pi / 24.), 1. / 3. * sin(7. * pi / 24), 0.0]),
1.)
otp.finalize_setup()
return otp
def setUp(self):
nrigid = 3
self.topology = RBTopology()
self.topology.add_sites([self.make_otp() for i in xrange(nrigid)])
self.topology.finalize_setup()
cartesian_potential = LJ()
self.pot = RBPotentialWrapper(self.topology, cartesian_potential)
self.x0 = _x03
self.x0 = np.array(self.x0)
self.e0 = -17.3387670023
assert nrigid * 6 == self.x0.size
self.x0atomistic = _x03_atomistic
self.nrigid = nrigid
def test_energy(self):
e = self.pot.getEnergy(self.x0)
self.assertAlmostEqual(e, self.e0, delta=1e-4)
def test_energy_gradient(self):
e = self.pot.getEnergy(self.x0)
gnum = self.pot.NumericalDerivative(self.x0)
e2, g = self.pot.getEnergyGradient(self.x0)
self.assertAlmostEqual(e, e2, delta=1e-4)
for i in xrange(g.size):
self.assertAlmostEqual(g[i], gnum[i], 2)
def test_to_atomistic(self):
xatom = self.topology.to_atomistic(self.x0).flatten()
for i in xrange(xatom.size):
self.assertAlmostEqual(xatom[i], self.x0atomistic[i], 2)
def test_site_to_atomistic(self):
rf = self.make_otp()
p = np.array([1., 2, 3])
p /= np.linalg.norm(p)
com = np.array([4., 5, 6])
print "otp to atomistic"
print rf.to_atomistic(com, p)
print "otp transform grad"
g = np.array(range(9), dtype=float).reshape([-1, 3])
print g.reshape(-1)
print rf.transform_grad(p, g)
def test_to_atomistic2(self):
x0 = np.array(range(self.nrigid * 6), dtype=float)
x2 = x0.reshape([-1, 3])
for p in x2[self.nrigid:, :]:
p /= np.linalg.norm(p)
atomistic = self.topology.to_atomistic(x0).flatten()
from pele.potentials import LJ
lj = LJ()
e, g = lj.getEnergyGradient(atomistic.reshape(-1))
grb = self.topology.transform_gradient(x0, g)
rbpot = RBPotentialWrapper(self.topology, lj)
print rbpot.getEnergy(x0)