def gen_partition(mol,
atom_grids_tab,
radii_adjust=None,
atomic_radii=radi.BRAGG_RADII,
becke_scheme=original_becke):
'''Generate the mesh grid coordinates and weights for DFT numerical integration.
We can change radii_adjust, becke_scheme functions to generate different meshgrid.
Returns:
grid_coord and grid_weight arrays. grid_coord array has shape (N,3);
weight 1D array has N elements.
'''
if radii_adjust is not None and atomic_radii is not None:
f_radii_adjust = radii_adjust(mol, atomic_radii)
else:
f_radii_adjust = None
atm_coords = numpy.array([mol.atom_coord(i) for i in range(mol.natm)])
atm_dist = radi._inter_distance(mol)
def gen_grid_partition(coords):
ngrid = coords.shape[0]
grid_dist = numpy.empty((mol.natm, ngrid))
for ia in range(mol.natm):
dc = coords - atm_coords[ia]
grid_dist[ia] = numpy.sqrt(numpy.einsum('ij,ij->i', dc, dc))
pbecke = numpy.ones((mol.natm, ngrid))
for i in range(mol.natm):
for j in range(i):
g = 1 / atm_dist[i, j] * (grid_dist[i] - grid_dist[j])
if f_radii_adjust is not None:
g = f_radii_adjust(i, j, g)
g = becke_scheme(g)
pbecke[i] *= .5 * (1 - g)
pbecke[j] *= .5 * (1 + g)
return pbecke
coords_all = []
weights_all = []
for ia in range(mol.natm):
coords, vol = atom_grids_tab[mol.atom_symbol(ia)]
coords = coords + atm_coords[ia]
pbecke = gen_grid_partition(coords)
weights = vol * pbecke[ia] / pbecke.sum(axis=0)
coords_all.append(coords)
weights_all.append(weights)
return numpy.vstack(coords_all), numpy.hstack(weights_all)
def gen_partition(mol, atom_grids_tab,
radii_adjust=None, atomic_radii=radi.BRAGG_RADII,
becke_scheme=original_becke):
'''Generate the mesh grid coordinates and weights for DFT numerical integration.
We can change radii_adjust, becke_scheme functions to generate different meshgrid.
Returns:
grid_coord and grid_weight arrays. grid_coord array has shape (N,3);
weight 1D array has N elements.
'''
if radii_adjust is not None and atomic_radii is not None:
f_radii_adjust = radii_adjust(mol, atomic_radii)
else:
f_radii_adjust = None
atm_coords = numpy.array([mol.atom_coord(i) for i in range(mol.natm)])
atm_dist = radi._inter_distance(mol)
def gen_grid_partition(coords):
ngrid = coords.shape[0]
grid_dist = numpy.empty((mol.natm,ngrid))
for ia in range(mol.natm):
dc = coords - atm_coords[ia]
grid_dist[ia] = numpy.sqrt(numpy.einsum('ij,ij->i',dc,dc))
pbecke = numpy.ones((mol.natm,ngrid))
for i in range(mol.natm):
for j in range(i):
g = 1/atm_dist[i,j] * (grid_dist[i]-grid_dist[j])
if f_radii_adjust is not None:
g = f_radii_adjust(i, j, g)
g = becke_scheme(g)
pbecke[i] *= .5 * (1-g)
pbecke[j] *= .5 * (1+g)
return pbecke
coords_all = []
weights_all = []
for ia in range(mol.natm):
coords, vol = atom_grids_tab[mol.atom_symbol(ia)]
coords = coords + atm_coords[ia]
pbecke = gen_grid_partition(coords)
weights = vol * pbecke[ia] / pbecke.sum(axis=0)
coords_all.append(coords)
weights_all.append(weights)
return numpy.vstack(coords_all), numpy.hstack(weights_all)
def gen_partition(mol, atom_grids_tab,
radii_adjust=None, atomic_radii=radi.BRAGG_RADII,
becke_scheme=original_becke):
'''Generate the mesh grid coordinates and weights for DFT numerical integration.
We can change radii_adjust, becke_scheme functions to generate different meshgrid.
Returns:
grid_coord and grid_weight arrays. grid_coord array has shape (N,3);
weight 1D array has N elements.
'''
if radii_adjust is not None and atomic_radii is not None:
f_radii_adjust = radii_adjust(mol, atomic_radii)
else:
f_radii_adjust = None
atm_coords = numpy.array([mol.atom_coord(i) for i in range(mol.natm)])
atm_dist = radi._inter_distance(mol)
if (becke_scheme == original_becke and
(f_radii_adjust is None or
radii_adjust in (radi.treutler_atomic_radii_adjust,
radi.becke_atomic_radii_adjust))):
if f_radii_adjust is None:
p_radii_table = pyscf.lib.c_null_ptr()
else:
f_radii_table = numpy.asarray([f_radii_adjust(i, j, 0)
for i in range(mol.natm)
for j in range(mol.natm)])
p_radii_table = f_radii_table.ctypes.data_as(ctypes.c_void_p)
atm_coords = numpy.asarray(atm_coords, order='C')
def gen_grid_partition(coords):
coords = numpy.asarray(coords, order='C')
ngrids = coords.shape[0]
pbecke = numpy.empty((mol.natm,ngrids))
libdft.VXCgen_grid(pbecke.ctypes.data_as(ctypes.c_void_p),
coords.ctypes.data_as(ctypes.c_void_p),
atm_coords.ctypes.data_as(ctypes.c_void_p),
p_radii_table,
ctypes.c_int(mol.natm), ctypes.c_int(ngrids))
return pbecke
else:
def gen_grid_partition(coords):
ngrids = coords.shape[0]
grid_dist = numpy.empty((mol.natm,ngrids))
for ia in range(mol.natm):
dc = coords - atm_coords[ia]
grid_dist[ia] = numpy.sqrt(numpy.einsum('ij,ij->i',dc,dc))
pbecke = numpy.ones((mol.natm,ngrids))
for i in range(mol.natm):
for j in range(i):
g = 1/atm_dist[i,j] * (grid_dist[i]-grid_dist[j])
if f_radii_adjust is not None:
g = f_radii_adjust(i, j, g)
g = becke_scheme(g)
pbecke[i] *= .5 * (1-g)
pbecke[j] *= .5 * (1+g)
return pbecke
coords_all = []
weights_all = []
for ia in range(mol.natm):
coords, vol = atom_grids_tab[mol.atom_symbol(ia)]
coords = coords + atm_coords[ia]
pbecke = gen_grid_partition(coords)
weights = vol * pbecke[ia] * (1./pbecke.sum(axis=0))
coords_all.append(coords)
weights_all.append(weights)
return numpy.vstack(coords_all), numpy.hstack(weights_all)
def gen_partition(mol,
atom_grids_tab,
radii_adjust=None,
atomic_radii=radi.BRAGG_RADII,
becke_scheme=original_becke):
'''Generate the mesh grid coordinates and weights for DFT numerical integration.
We can change radii_adjust, becke_scheme functions to generate different meshgrid.
Returns:
grid_coord and grid_weight arrays. grid_coord array has shape (N,3);
weight 1D array has N elements.
'''
if radii_adjust is not None and atomic_radii is not None:
f_radii_adjust = radii_adjust(mol, atomic_radii)
else:
f_radii_adjust = None
atm_coords = numpy.array([mol.atom_coord(i) for i in range(mol.natm)])
atm_dist = radi._inter_distance(mol)
if (becke_scheme == original_becke and
(f_radii_adjust is None or radii_adjust in
(radi.treutler_atomic_radii_adjust, radi.becke_atomic_radii_adjust))):
if f_radii_adjust is None:
p_radii_table = pyscf.lib.c_null_ptr()
else:
f_radii_table = numpy.asarray([
f_radii_adjust(i, j, 0) for i in range(mol.natm)
for j in range(mol.natm)
])
p_radii_table = f_radii_table.ctypes.data_as(ctypes.c_void_p)
atm_coords = numpy.asarray(atm_coords, order='C')
def gen_grid_partition(coords):
coords = numpy.asarray(coords, order='F')
ngrids = coords.shape[0]
pbecke = numpy.empty((mol.natm, ngrids))
libdft.VXCgen_grid(pbecke.ctypes.data_as(ctypes.c_void_p),
coords.ctypes.data_as(ctypes.c_void_p),
atm_coords.ctypes.data_as(ctypes.c_void_p),
p_radii_table, ctypes.c_int(mol.natm),
ctypes.c_int(ngrids))
return pbecke
else:
def gen_grid_partition(coords):
ngrids = coords.shape[0]
grid_dist = numpy.empty((mol.natm, ngrids))
for ia in range(mol.natm):
dc = coords - atm_coords[ia]
grid_dist[ia] = numpy.sqrt(numpy.einsum('ij,ij->i', dc, dc))
pbecke = numpy.ones((mol.natm, ngrids))
for i in range(mol.natm):
for j in range(i):
g = 1 / atm_dist[i, j] * (grid_dist[i] - grid_dist[j])
if f_radii_adjust is not None:
g = f_radii_adjust(i, j, g)
g = becke_scheme(g)
pbecke[i] *= .5 * (1 - g)
pbecke[j] *= .5 * (1 + g)
return pbecke
coords_all = []
weights_all = []
for ia in range(mol.natm):
coords, vol = atom_grids_tab[mol.atom_symbol(ia)]
coords = coords + atm_coords[ia]
pbecke = gen_grid_partition(coords)
weights = vol * pbecke[ia] * (1. / pbecke.sum(axis=0))
coords_all.append(coords)
weights_all.append(weights)
return numpy.vstack(coords_all), numpy.hstack(weights_all)
