def get_D_square_min(atoms_now,
atoms_old,
i_now,
j_now,
delta_plus_epsilon=None):
"""
Calculate the D^2_min norm of Falk and Langer
"""
nat = len(atoms_now)
assert len(atoms_now) == len(atoms_old)
pos_now = atoms_now.positions
pos_old = atoms_old.positions
# Compute current and old distance vectors. Note that current distance
# vectors cannot be taken from the neighbor calculation, because neighbors
# are calculated from the sheared cell while these distance need to come
# from the unsheared cell. Taking the distance from the unsheared cell
# make periodic boundary conditions (and flipping of cell) a lot easier.
dr_now = mic(pos_now[i_now] - pos_now[j_now], atoms_now.cell)
dr_old = mic(pos_old[i_now] - pos_old[j_now], atoms_old.cell)
# Sanity check: Shape needs to be identical!
assert dr_now.shape == dr_old.shape
if delta_plus_epsilon is None:
# Get minimum strain tensor
delta_plus_epsilon = get_delta_plus_epsilon(nat, i_now, dr_now, dr_old)
# Spread epsilon out for each neighbor index
delta_plus_epsilon_n = delta_plus_epsilon[i_now]
# Compute D^2_min
d_sq_n = np.sum((dr_now - np.sum(
delta_plus_epsilon_n.reshape(-1, 3, 3) * dr_old.reshape(-1, 1, 3),
axis=2))**2,
axis=1)
# For each atom, sum over all neighbors
d_sq = np.bincount(i_now, weights=d_sq_n)
return delta_plus_epsilon, d_sq
def get_D_square_min(atoms_now, atoms_old, i_now, j_now, delta_plus_epsilon=None):
"""
Calculate the D^2_min norm of Falk and Langer
"""
nat = len(atoms_now)
assert len(atoms_now) == len(atoms_old)
pos_now = atoms_now.positions
pos_old = atoms_old.positions
# Compute current and old distance vectors. Note that current distance
# vectors cannot be taken from the neighbor calculation, because neighbors
# are calculated from the sheared cell while these distance need to come
# from the unsheared cell. Taking the distance from the unsheared cell
# make periodic boundary conditions (and flipping of cell) a lot easier.
dr_now = mic(pos_now[i_now] - pos_now[j_now], atoms_now.cell)
dr_old = mic(pos_old[i_now] - pos_old[j_now], atoms_old.cell)
# Sanity check: Shape needs to be identical!
assert dr_now.shape == dr_old.shape
if delta_plus_epsilon is None:
# Get minimum strain tensor
delta_plus_epsilon = get_delta_plus_epsilon(nat, i_now, dr_now, dr_old)
# Spread epsilon out for each neighbor index
delta_plus_epsilon_n = delta_plus_epsilon[i_now]
# Compute D^2_min
d_sq_n = np.sum(
(
dr_now-
np.sum(delta_plus_epsilon_n.reshape(-1,3,3)*dr_old.reshape(-1,1,3),
axis=2)
)**2,
axis=1)
# For each atom, sum over all neighbors
d_sq = np.bincount(i_now, weights=d_sq_n)
return delta_plus_epsilon, d_sq
def test_neighbor_list(self):
a = io.read('aC.cfg')
an = native.from_atoms(a)
nl = native.Neighbors(100)
nl.request_interaction_range(5.0)
i, j, abs_dr_no_vec = nl.get_neighbors(an)
i, j, dr, abs_dr = nl.get_neighbors(an, vec=True)
self.assertTrue(np.all(np.abs(abs_dr_no_vec-abs_dr) < 1e-12))
r = a.get_positions()
dr_direct = mic(r[i]-r[j], a.cell)
abs_dr_from_dr = np.sqrt(np.sum(dr*dr, axis=1))
abs_dr_direct = np.sqrt(np.sum(dr_direct*dr_direct, axis=1))
self.assertTrue(np.all(np.abs(abs_dr-abs_dr_from_dr) < 1e-12))
self.assertTrue(np.all(np.abs(abs_dr-abs_dr_direct) < 1e-12))
self.assertTrue(np.all(np.abs(dr-dr_direct) < 1e-12))