def __init__(self, atoms, constraints):
self.atoms = atoms
natoms = len(self.atoms)
nconst = sum([len(c) for c in constraints])
b = N.zeros((nconst, natoms), N.Float)
c = N.zeros((nconst, ), N.Float)
i = 0
for cons in constraints:
cons.setCoefficients(self.atoms, b, c, i)
i = i + len(cons)
u, s, vt = LA.singular_value_decomposition(b)
self.rank = 0
for i in range(min(natoms, nconst)):
if s[i] > 0.:
self.rank = self.rank + 1
self.b = b
self.bi = LA.generalized_inverse(b)
self.p = N.identity(natoms) - N.dot(self.bi, self.b)
self.c = c
self.bi_c = N.dot(self.bi, c)
c_test = N.dot(self.b, self.bi_c)
if N.add.reduce((c_test - c)**2) / nconst > 1.e-12:
Utility.warning("The charge constraints are inconsistent."
" They will be applied as a least-squares"
" condition.")
def __init__(self, atoms, constraints):
self.atoms = atoms
natoms = len(self.atoms)
nconst = sum([len(c) for c in constraints])
b = N.zeros((nconst, natoms), N.Float)
c = N.zeros((nconst,), N.Float)
i = 0
for cons in constraints:
cons.setCoefficients(self.atoms, b, c, i)
i = i + len(cons)
u, s, vt = LA.singular_value_decomposition(b)
self.rank = 0
for i in range(min(natoms, nconst)):
if s[i] > 0.:
self.rank = self.rank + 1
self.b = b
self.bi = LA.generalized_inverse(b)
self.p = N.identity(natoms)-N.dot(self.bi, self.b)
self.c = c
self.bi_c = N.dot(self.bi, c)
c_test = N.dot(self.b, self.bi_c)
if N.add.reduce((c_test-c)**2)/nconst > 1.e-12:
Utility.warning("The charge constraints are inconsistent."
" They will be applied as a least-squares"
" condition.")
def rigidMovement(atoms, vector):
a = N.zeros((len(atoms), 3, 2, 3), N.Float)
b = N.zeros((len(atoms), 3), N.Float)
for i in range(len(atoms)):
a[i, :, 0, :] = delta.array
a[i, :, 1, :] = (epsilon * atoms[i].position()).array
b[i] = vector[atoms[i]].array
a.shape = (3 * len(atoms), 6)
b.shape = (3 * len(atoms), )
vo = N.dot(LA.generalized_inverse(a), b)
return Vector(vo[:3]), Vector(vo[3:])