def angles(struct, pbc=False, mask_val=999.0, deg=True):
"""Python implementation of angles."""
nang = struct.natoms*(struct.natoms-1)*(struct.natoms-2)
norm = np.linalg.norm
anglesijk = np.ones((struct.natoms,)*3, dtype=float)*mask_val
angleidx = np.array([x for x in permutations(range(struct.natoms),3)])
for ijk in angleidx:
ii,jj,kk = ijk
ci = struct.coords_frac[ii,:]
cj = struct.coords_frac[jj,:]
ck = struct.coords_frac[kk,:]
dvij = ci - cj
dvik = ci - ck
if pbc:
dvij = np.dot(crys.min_image_convention(dvij), struct.cell)
dvik = np.dot(crys.min_image_convention(dvik), struct.cell)
else:
dvij = np.dot(dvij, struct.cell)
dvik = np.dot(dvik, struct.cell)
cang = np.dot(dvij, dvik) / norm(dvij) / norm(dvik)
ceps = 1.0-2.2e-16
if cang > ceps:
cang = 1.0
elif cang < -ceps:
cang = -1.0
if deg:
anglesijk[ii,jj,kk] = np.arccos(cang) * 180.0 / np.pi
else:
anglesijk[ii,jj,kk] = cang
return anglesijk, angleidx
def angles(struct, pbc=False, mask_val=999.0, deg=True):
"""Python implementation of angles."""
nang = struct.natoms * (struct.natoms - 1) * (struct.natoms - 2)
norm = np.linalg.norm
anglesijk = np.ones((struct.natoms, ) * 3, dtype=float) * mask_val
angleidx = np.array(
[x for x in permutations(list(range(struct.natoms)), 3)])
for ijk in angleidx:
ii, jj, kk = ijk
ci = struct.coords_frac[ii, :]
cj = struct.coords_frac[jj, :]
ck = struct.coords_frac[kk, :]
dvij = ci - cj
dvik = ci - ck
if pbc:
dvij = np.dot(crys.min_image_convention(dvij), struct.cell)
dvik = np.dot(crys.min_image_convention(dvik), struct.cell)
else:
dvij = np.dot(dvij, struct.cell)
dvik = np.dot(dvik, struct.cell)
cang = np.dot(dvij, dvik) / norm(dvij) / norm(dvik)
ceps = 1.0 - 2.2e-16
if cang > ceps:
cang = 1.0
elif cang < -ceps:
cang = -1.0
if deg:
anglesijk[ii, jj, kk] = np.arccos(cang) * 180.0 / np.pi
else:
anglesijk[ii, jj, kk] = cang
return anglesijk, angleidx
def pydist(arr, cell, pbc=0):
distvecs_frac = arr[:,None,:] - arr[None,...]
if pbc == 1:
distvecs_frac = crys.min_image_convention(distvecs_frac)
distvecs = np.dot(distvecs_frac, cell)
distsq = (distvecs**2.0).sum(axis=2)
return distsq, distvecs, distvecs_frac
def pydist(coords_frac, cell, pbc=0):
distvecs_frac = coords_frac[:,None,:] - coords_frac[None,...]
if pbc == 1:
distvecs_frac = crys.min_image_convention(distvecs_frac)
distvecs = np.dot(distvecs_frac, cell)
distsq = (distvecs**2.0).sum(axis=2)
return distsq, distvecs, distvecs_frac
def pydist_bigmem(traj, pbc=True):
# Pure numpy version w/ big temp arrays. Also slowest.
#
# (nstep, natoms, natoms, 3)
distvecs_frac = traj.coords_frac[:,:,None,:] - \
traj.coords_frac[:,None,:,:]
if pbc:
distvecs_frac = crys.min_image_convention(distvecs_frac)
distvecs = np.empty((nstep, natoms, natoms, 3))
for ii in range(traj.nstep):
distvecs[ii,...] = np.dot(distvecs_frac[ii,...], traj.cell[ii,...])
# (nstep, natoms, natoms)
dists = np.sqrt((distvecs**2.0).sum(axis=-1))
return dists
def pydist_bigmem(traj, pbc=True):
# Pure numpy version w/ big temp arrays. Also slowest.
#
# (nstep, natoms, natoms, 3)
distvecs_frac = traj.coords_frac[:,:,None,:] - \
traj.coords_frac[:,None,:,:]
if pbc:
distvecs_frac = crys.min_image_convention(distvecs_frac)
distvecs = np.empty((nstep, natoms, natoms, 3))
for ii in range(traj.nstep):
distvecs[ii, ...] = np.dot(distvecs_frac[ii, ...], traj.cell[ii, ...])
# (nstep, natoms, natoms)
dists = np.sqrt((distvecs**2.0).sum(axis=-1))
return dists
def test_dist_traj():
natoms = 10
nstep = 100
cell = rand(nstep, 3, 3)
stress = rand(nstep, 3, 3)
forces = rand(nstep, natoms, 3)
etot = rand(nstep)
cryst_const = crys.cell2cc3d(cell, axis=0)
coords_frac = np.random.rand(nstep, natoms, 3)
coords = crys.coord_trans3d(coords=coords_frac,
old=cell,
new=num.extend_array(np.identity(3),
nstep,
axis=0),
axis=1,
timeaxis=0)
assert cryst_const.shape == (nstep, 6)
assert coords.shape == (nstep, natoms, 3)
symbols = ['H'] * natoms
traj = Trajectory(
coords_frac=coords_frac,
cell=cell,
symbols=symbols,
forces=forces,
stress=stress,
etot=etot,
timestep=1,
)
for pbc in [True, False]:
# (nstep, natoms, natoms, 3)
distvecs_frac = traj.coords_frac[:,:,None,:] - \
traj.coords_frac[:,None,:,:]
assert distvecs_frac.shape == (nstep, natoms, natoms, 3)
if pbc:
distvecs_frac = crys.min_image_convention(distvecs_frac)
distvecs = np.empty((nstep, natoms, natoms, 3))
for ii in range(traj.nstep):
distvecs[ii, ...] = np.dot(distvecs_frac[ii, ...], traj.cell[ii,
...])
# (nstep, natoms, natoms)
dists = np.sqrt((distvecs**2.0).sum(axis=-1))
assert np.allclose(dists, crys.distances_traj(traj, pbc=pbc))
def test_dist_traj():
natoms = 10
nstep = 100
cell = rand(nstep,3,3)
stress = rand(nstep,3,3)
forces = rand(nstep,natoms,3)
etot=rand(nstep)
cryst_const = crys.cell2cc3d(cell, axis=0)
coords_frac = np.random.rand(nstep,natoms,3)
coords = crys.coord_trans3d(coords=coords_frac,
old=cell,
new=num.extend_array(np.identity(3),
nstep,axis=0),
axis=1,
timeaxis=0)
assert cryst_const.shape == (nstep, 6)
assert coords.shape == (nstep,natoms,3)
symbols = ['H']*natoms
traj = Trajectory(coords_frac=coords_frac,
cell=cell,
symbols=symbols,
forces=forces,
stress=stress,
etot=etot,
timestep=1,
)
for pbc in [True, False]:
# (nstep, natoms, natoms, 3)
distvecs_frac = traj.coords_frac[:,:,None,:] - \
traj.coords_frac[:,None,:,:]
assert distvecs_frac.shape == (nstep, natoms, natoms, 3)
if pbc:
distvecs_frac = crys.min_image_convention(distvecs_frac)
distvecs = np.empty((nstep, natoms, natoms, 3))
for ii in range(traj.nstep):
distvecs[ii,...] = np.dot(distvecs_frac[ii,...], traj.cell[ii,...])
# (nstep, natoms, natoms)
dists = np.sqrt((distvecs**2.0).sum(axis=-1))
assert np.allclose(dists, crys.distances_traj(traj, pbc=pbc))