def test_epi(direction, cartesian):
from numpy import abs
from pylada.vff.functional import Functional
vff = functional()
vff.cartesian = cartesian
vff.direction = direction
structure = crystal()
# gets functions for current structure
x, jacobian, energy, make_structure \
= Functional(copy=vff)._getfuncs_epi(structure)
stress, forces = vff.jacobian(structure)
jac0 = jacobian(x)
epsilon = 1e-7
for i in xrange(0, len(x)):
xminus = x.copy()
xminus[i] = xminus[i] - epsilon * 0.5
eminus = energy(xminus)
xplus = x.copy()
xplus[i] = xplus[i] + epsilon * 0.5
eplus = energy(xplus)
assert abs(jac0[i] - (eplus - eminus) / epsilon ) < 1e-6
def test_relaxall(cartesian):
""" Tests gradients as implemented in the functional. """
from numpy import abs
from pylada.vff.functional import Functional
vff = functional()
vff.cartesian = cartesian
structure = crystal()
epsilon = 1e-5
# gets functions for current structure
x, jacobian, energy, make_structure = Functional(copy=vff)._getfuncs_relaxall(structure)
stress, forces = vff.jacobian(structure)
jac0 = jacobian(x)
for i in xrange(6):
xminus = x.copy()
xminus[i] = xminus[i] - epsilon * 0.5
eminus = energy(xminus)
xplus = x.copy()
xplus[i] = xplus[i] + epsilon * 0.5
eplus = energy(xplus)
assert abs(jac0[i] - (eplus - eminus) / epsilon) < 1e-6
epsilon = 1e-7
for i in xrange(6, len(x)):
xminus = x.copy()
xminus[i] = xminus[i] - epsilon * 0.5
eminus = energy(xminus)
xplus = x.copy()
xplus[i] = xplus[i] + epsilon * 0.5
eplus = energy(xplus)
assert abs(jac0[i] - (eplus - eminus) / epsilon) < 1e-6