def test_o(self):
"""test orthogopt"""
from pele.transition_states import zeroEV_cluster
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(vec, coords)
zeroev = zeroEV_cluster(coords)
for u in zeroev:
#print np.dot(u, vec)
self.assertAlmostEqual(0., np.dot(u, vec), 5)
def test_o(self):
# test orthogopt
from pele.transition_states import zeroEV_cluster
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(vec, coords)
zeroev = zeroEV_cluster(coords)
for u in zeroev:
# print np.dot(u, vec)
self.assertAlmostEqual(0., np.dot(u, vec), 5)
natoms = 1000
system = LJCluster(natoms)
#system.params.structural_quench_params.debug = True
#system.params.structural_quench_params.iprint = 100
db = system.create_database()
bh = system.get_basinhopping(db)
bh.run(1)
m = db.minima()[0]
coords = m.coords
potential = system.get_potential()
energy, gradient, hessian = potential.getEnergyGradientHessian(coords)
dummy_vec = ts.gramm_schmidt(ts.zeroEV_cluster(coords))
shifted_hess = hessian.copy()
for i in range(6):
shifted_hess += np.outer(dummy_vec[i], dummy_vec[i])
shifted_eval, shifted_evec = get_sorted_eig(shifted_hess)
print "First log sum: ", np.sum(np.log(shifted_eval[6:]))
sparse_hess = scipy.sparse.csc_matrix(shifted_hess)
factor = cholmod.cholesky(sparse_hess)
diagonal = np.diagonal(factor.L().todense())
natoms = 1000
system = LJCluster(natoms)
#system.params.structural_quench_params.debug = True
#system.params.structural_quench_params.iprint = 100
db = system.create_database()
bh = system.get_basinhopping(db)
bh.run(1)
m = db.minima()[0]
coords = m.coords
potential = system.get_potential()
energy, gradient, hessian = potential.getEnergyGradientHessian(coords)
dummy_vec = ts.gramm_schmidt(ts.zeroEV_cluster(coords))
shifted_hess = hessian.copy()
for i in range(6):
shifted_hess += np.outer(dummy_vec[i], dummy_vec[i])
shifted_eval, shifted_evec = get_sorted_eig(shifted_hess)
print("First log sum: ", np.sum(np.log(shifted_eval[6:])))
sparse_hess = scipy.sparse.csc_matrix(shifted_hess)
factor = cholmod.cholesky(sparse_hess)
diagonal = np.diagonal(factor.L().todense())
