def sr_loop(self,
kpti_kptj=numpy.zeros((2, 3)),
max_memory=2000,
compact=True,
blksize=None):
'''Short range part'''
if self._cderi is None:
self.build()
cell = self.cell
kpti, kptj = kpti_kptj
unpack = is_zero(kpti - kptj) and not compact
is_real = is_zero(kpti_kptj)
nao = cell.nao_nr()
if blksize is None:
if is_real:
blksize = max_memory * 1e6 / 8 / (nao**2 * 2)
else:
blksize = max_memory * 1e6 / 16 / (nao**2 * 2)
blksize /= 2 # For prefetch
blksize = max(16, min(int(blksize), self.blockdim))
logger.debug3(self, 'max_memory %d MB, blksize %d', max_memory,
blksize)
def load(aux_slice):
b0, b1 = aux_slice
if is_real:
LpqR = numpy.asarray(j3c[b0:b1])
if unpack:
LpqR = lib.unpack_tril(LpqR).reshape(-1, nao**2)
LpqI = numpy.zeros_like(LpqR)
else:
Lpq = numpy.asarray(j3c[b0:b1])
LpqR = numpy.asarray(Lpq.real, order='C')
LpqI = numpy.asarray(Lpq.imag, order='C')
Lpq = None
if unpack:
LpqR = lib.unpack_tril(LpqR).reshape(-1, nao**2)
LpqI = lib.unpack_tril(LpqI,
lib.ANTIHERMI).reshape(-1, nao**2)
return LpqR, LpqI
with _load3c(self._cderi, 'j3c', kpti_kptj, 'j3c-kptij') as j3c:
slices = lib.prange(0, j3c.shape[0], blksize)
for LpqR, LpqI in lib.map_with_prefetch(load, slices):
yield LpqR, LpqI, 1
LpqR = LpqI = None
if cell.dimension == 2 and cell.low_dim_ft_type != 'inf_vacuum':
# Truncated Coulomb operator is not postive definite. Load the
# CDERI tensor of negative part.
with _load3c(self._cderi,
'j3c-',
kpti_kptj,
'j3c-kptij',
ignore_key_error=True) as j3c:
slices = lib.prange(0, j3c.shape[0], blksize)
for LpqR, LpqI in lib.map_with_prefetch(load, slices):
yield LpqR, LpqI, -1
LpqR = LpqI = None
def sr_loop(self,
kpti_kptj=numpy.zeros((2, 3)),
max_memory=2000,
compact=True,
blksize=None):
'''Short range part'''
if self._cderi is None:
self.build()
kpti, kptj = kpti_kptj
unpack = is_zero(kpti - kptj) and not compact
is_real = is_zero(kpti_kptj)
nao = self.cell.nao_nr()
if blksize is None:
if is_real:
if unpack:
blksize = max_memory * 1e6 / 8 / (nao *
(nao + 1) // 2 + nao**2)
else:
blksize = max_memory * 1e6 / 8 / (nao * (nao + 1))
else:
blksize = max_memory * 1e6 / 16 / (nao**2 * 2)
blksize = max(16, min(int(blksize), self.blockdim))
logger.debug3(self, 'max_memory %d MB, blksize %d', max_memory,
blksize)
if unpack:
buf = numpy.empty((blksize, nao * (nao + 1) // 2))
def load(Lpq, b0, b1, bufR, bufI):
Lpq = numpy.asarray(Lpq[b0:b1])
if is_real:
if unpack:
LpqR = lib.unpack_tril(Lpq, out=bufR).reshape(-1, nao**2)
else:
LpqR = Lpq
LpqI = numpy.zeros_like(LpqR)
else:
shape = Lpq.shape
if unpack:
tmp = numpy.ndarray(shape, buffer=buf)
tmp[:] = Lpq.real
LpqR = lib.unpack_tril(tmp, out=bufR).reshape(-1, nao**2)
tmp[:] = Lpq.imag
LpqI = lib.unpack_tril(tmp, lib.ANTIHERMI,
out=bufI).reshape(-1, nao**2)
else:
LpqR = numpy.ndarray(shape, buffer=bufR)
LpqR[:] = Lpq.real
LpqI = numpy.ndarray(shape, buffer=bufI)
LpqI[:] = Lpq.imag
return LpqR, LpqI
LpqR = LpqI = None
with _load3c(self._cderi, 'j3c', kpti_kptj) as j3c:
naux = j3c.shape[0]
for b0, b1 in lib.prange(0, naux, blksize):
LpqR, LpqI = load(j3c, b0, b1, LpqR, LpqI)
yield LpqR, LpqI
def __init__(self, cell, kpts=numpy.zeros((1, 3))):
self.cell = cell
self.stdout = cell.stdout
self.verbose = cell.verbose
self.max_memory = cell.max_memory
self.kpts = kpts # default is gamma point
self.kpts_band = None
self._auxbasis = None
# Search for optimized eta and mesh here.
if cell.dimension == 0:
self.eta = 0.2
self.mesh = cell.mesh
else:
ke_cutoff = tools.mesh_to_cutoff(cell.lattice_vectors(), cell.mesh)
ke_cutoff = ke_cutoff[:cell.dimension].min()
eta_cell = aft.estimate_eta_for_ke_cutoff(cell, ke_cutoff,
cell.precision)
eta_guess = estimate_eta(cell, cell.precision)
logger.debug3(self, 'eta_guess = %g', eta_guess)
if eta_cell < eta_guess:
self.eta = eta_cell
self.mesh = cell.mesh
else:
self.eta = eta_guess
ke_cutoff = aft.estimate_ke_cutoff_for_eta(
cell, self.eta, cell.precision)
self.mesh = tools.cutoff_to_mesh(cell.lattice_vectors(),
ke_cutoff)
if cell.dimension < 2 or cell.low_dim_ft_type == 'inf_vacuum':
self.mesh[cell.dimension:] = cell.mesh[cell.dimension:]
self.mesh = _round_off_to_odd_mesh(self.mesh)
# exp_to_discard to remove diffused fitting functions. The diffused
# fitting functions may cause linear dependency in DF metric. Removing
# the fitting functions whose exponents are smaller than exp_to_discard
# can improve the linear dependency issue. However, this parameter
# affects the quality of the auxiliary basis. The default value of
# this parameter was set to 0.2 in v1.5.1 or older and was changed to
# 0 since v1.5.2.
self.exp_to_discard = cell.exp_to_discard
# The following attributes are not input options.
self.exxdiv = None # to mimic KRHF/KUHF object in function get_coulG
self.auxcell = None
self.blockdim = getattr(__config__, 'pbc_df_df_DF_blockdim', 240)
self.linear_dep_threshold = LINEAR_DEP_THR
self._j_only = False
# If _cderi_to_save is specified, the 3C-integral tensor will be saved in this file.
self._cderi_to_save = tempfile.NamedTemporaryFile(dir=lib.param.TMPDIR)
# If _cderi is specified, the 3C-integral tensor will be read from this file
self._cderi = None
self._rsh_df = {} # Range separated Coulomb DF objects
self._keys = set(self.__dict__.keys())
def sr_loop(self, kpti_kptj=numpy.zeros((2,3)), max_memory=2000,
compact=True, blksize=None):
'''Short range part'''
kpti, kptj = kpti_kptj
unpack = is_zero(kpti-kptj) and not compact
is_real = is_zero(kpti_kptj)
nao = self.cell.nao_nr()
if blksize is None:
if is_real:
if unpack:
blksize = max_memory*1e6/8/(nao*(nao+1)//2+nao**2)
else:
blksize = max_memory*1e6/8/(nao*(nao+1))
else:
blksize = max_memory*1e6/16/(nao**2*2)
blksize = max(16, min(int(blksize), self.blockdim))
logger.debug3(self, 'max_memory %d MB, blksize %d', max_memory, blksize)
if unpack:
buf = numpy.empty((blksize,nao*(nao+1)//2))
def load(Lpq, b0, b1, bufR, bufI):
Lpq = numpy.asarray(Lpq[b0:b1])
if is_real:
if unpack:
LpqR = lib.unpack_tril(Lpq, out=bufR).reshape(-1,nao**2)
else:
LpqR = Lpq
LpqI = numpy.zeros_like(LpqR)
else:
shape = Lpq.shape
if unpack:
tmp = numpy.ndarray(shape, buffer=buf)
tmp[:] = Lpq.real
LpqR = lib.unpack_tril(tmp, out=bufR).reshape(-1,nao**2)
tmp[:] = Lpq.imag
LpqI = lib.unpack_tril(tmp, lib.ANTIHERMI, out=bufI).reshape(-1,nao**2)
else:
LpqR = numpy.ndarray(shape, buffer=bufR)
LpqR[:] = Lpq.real
LpqI = numpy.ndarray(shape, buffer=bufI)
LpqI[:] = Lpq.imag
return LpqR, LpqI
LpqR = LpqI = None
with _load3c(self._cderi, 'j3c', kpti_kptj) as j3c:
naux = j3c.shape[0]
for b0, b1 in lib.prange(0, naux, blksize):
LpqR, LpqI = load(j3c, b0, b1, LpqR, LpqI)
yield LpqR, LpqI
def aux_e2(cell,
auxcell,
erifile,
intor='cint3c2e_sph',
aosym='s1',
comp=1,
kptij_lst=None,
dataname='eri_mo',
max_memory=2000,
verbose=0):
'''3-center AO integrals (ij|L) with double lattice sum:
\sum_{lm} (i[l]j[m]|L[0]), where L is the auxiliary basis.
On diks, the integrals are stored as (kptij_idx, naux, nao_pair)
Args:
kptij_lst : (*,2,3) array
A list of (kpti, kptj)
'''
if comp > 1:
raise NotImplementedError('comp = %d' % comp)
if h5py.is_hdf5(erifile):
feri = h5py.File(erifile)
if dataname in feri:
del (feri[dataname])
if dataname + '-kptij' in feri:
del (feri[dataname + '-kptij'])
else:
feri = h5py.File(erifile, 'w')
if kptij_lst is None:
kptij_lst = numpy.zeros((1, 2, 3))
feri[dataname + '-kptij'] = kptij_lst
nkptij = len(kptij_lst)
# sum over largest number of images in either cell or auxcell
nimgs = numpy.max((cell.nimgs, auxcell.nimgs), axis=0)
Ls = cell.get_lattice_Ls(nimgs)
logger.debug1(cell, "pbc.df.outcore.Images %s", nimgs)
logger.debug3(cell, "Ls = %s", Ls)
nao = cell.nao_nr()
#naux = auxcell.nao_nr('ssc' in intor)
naux = auxcell.nao_nr()
aosym_s2 = numpy.zeros(nkptij, dtype=bool)
for k, kptij in enumerate(kptij_lst):
key = '%s/%d' % (dataname, k)
if abs(kptij).sum() < 1e-9: # gamma_point:
dtype = 'f8'
else:
dtype = 'c16'
aosym_s2[k] = abs(kptij[0] - kptij[1]).sum() < 1e-9
if aosym_s2[k]:
nao_pair = nao * (nao + 1) // 2
else:
nao_pair = nao * nao
if comp == 1:
shape = (naux, nao_pair)
else:
shape = (comp, naux, nao_pair)
chunks = (min(256, naux), min(256, nao_pair)) # 512 KB
feri.create_dataset(key, shape, dtype, chunks=chunks)
if naux == 0:
feri.close()
return erifile
aux_loc = auxcell.ao_loc_nr('ssc' in intor)
buflen = max(8, int(max_memory * 1e6 / 16 / (nkptij * nao**2 * comp)))
auxranges = balance_segs(aux_loc[1:] - aux_loc[:-1], buflen)
buflen = max([x[2] for x in auxranges])
buf = [
numpy.zeros(nao * nao * buflen * comp, dtype=numpy.complex128)
for k in range(nkptij)
]
ints = incore._wrap_int3c(cell, auxcell, intor, comp, Ls, buf)
atm, bas, env = ints._envs[:3]
xyz = numpy.asarray(cell.atom_coords(), order='C')
ptr_coordL = atm[:cell.natm, PTR_COORD]
ptr_coordL = numpy.vstack(
(ptr_coordL, ptr_coordL + 1, ptr_coordL + 2)).T.copy('C')
kpti = kptij_lst[:, 0]
kptj = kptij_lst[:, 1]
if numpy.all(aosym_s2):
def ccsum_or_reorder(Lpq):
tmp = numpy.asarray(Lpq.transpose(0, 2, 1).conj(), order='C')
tmp += Lpq
return tmp
else:
def ccsum_or_reorder(Lpq):
return numpy.asarray(Lpq, order='C')
naux0 = 0
for istep, auxrange in enumerate(auxranges):
sh0, sh1, nrow = auxrange
c_shls_slice = (ctypes.c_int * 6)(0, cell.nbas, cell.nbas,
cell.nbas * 2, cell.nbas * 2 + sh0,
cell.nbas * 2 + sh1)
if numpy.all(aosym_s2):
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
e = numpy.dot(
Ls[:l + 1] - L1,
kptj.T) # Lattice sum over half of the images {1..l}
exp_Lk = numpy.exp(1j * numpy.asarray(e, order='C'))
exp_Lk[l] = .5
ints(exp_Lk, c_shls_slice)
else:
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
e = numpy.dot(Ls, kptj.T) - numpy.dot(L1, kpti.T)
exp_Lk = numpy.exp(1j * numpy.asarray(e, order='C'))
ints(exp_Lk, c_shls_slice)
for k, kptij in enumerate(kptij_lst):
h5dat = feri['%s/%d' % (dataname, k)]
# transpose 3201 as (comp,L,i,j)
mat = numpy.ndarray((nao, nao, nrow, comp),
order='F',
dtype=numpy.complex128,
buffer=buf[k])
for icomp, vi in enumerate(mat.transpose(3, 2, 0, 1)):
v = ccsum_or_reorder(vi)
if abs(kptij).sum() < 1e-9: # gamma_point:
v = v.real
if aosym_s2[k]:
v = lib.pack_tril(v)
else:
v = v.reshape(nrow, -1)
if comp == 1:
h5dat[naux0:naux0 + nrow] = v
else:
h5dat[icomp, naux0:naux0 + nrow] = v
mat[:] = 0
naux0 += nrow
feri.close()
return erifile
def sr_loop(self,
kpti_kptj=numpy.zeros((2, 3)),
max_memory=2000,
compact=True,
blksize=None):
'''Short range part'''
if self._cderi is None:
self.build()
cell = self.cell
kpti, kptj = kpti_kptj
unpack = is_zero(kpti - kptj) and not compact
is_real = is_zero(kpti_kptj)
nao = cell.nao_nr()
if blksize is None:
if is_real:
if unpack:
blksize = max_memory * 1e6 / 8 / (nao *
(nao + 1) // 2 + nao**2)
else:
blksize = max_memory * 1e6 / 8 / (nao * (nao + 1))
else:
blksize = max_memory * 1e6 / 16 / (nao**2 * 2)
blksize = max(16, min(int(blksize), self.blockdim))
logger.debug3(self, 'max_memory %d MB, blksize %d', max_memory,
blksize)
def load(Lpq, b0, b1, bufR, bufI):
Lpq = numpy.asarray(Lpq[b0:b1])
if is_real:
if unpack:
LpqR = lib.unpack_tril(Lpq, out=bufR).reshape(-1, nao**2)
else:
LpqR = Lpq
LpqI = numpy.zeros_like(LpqR)
else:
shape = Lpq.shape
if unpack:
tmp = numpy.ndarray(shape, buffer=buf)
tmp[:] = Lpq.real
LpqR = lib.unpack_tril(tmp, out=bufR).reshape(-1, nao**2)
tmp[:] = Lpq.imag
LpqI = lib.unpack_tril(tmp, lib.ANTIHERMI,
out=bufI).reshape(-1, nao**2)
else:
LpqR = numpy.ndarray(shape, buffer=bufR)
LpqR[:] = Lpq.real
LpqI = numpy.ndarray(shape, buffer=bufI)
LpqI[:] = Lpq.imag
return LpqR, LpqI
LpqR = LpqI = None
with _load3c(self._cderi, 'j3c', kpti_kptj, 'j3c-kptij') as j3c:
naux = j3c.shape[0]
if unpack:
buf = numpy.empty((min(blksize, naux), nao * (nao + 1) // 2))
for b0, b1 in lib.prange(0, naux, blksize):
LpqR, LpqI = load(j3c, b0, b1, LpqR, LpqI)
yield LpqR, LpqI, 1
if cell.dimension == 2 and cell.low_dim_ft_type != 'inf_vacuum':
# Truncated Coulomb operator is not postive definite. Load the
# CDERI tensor of negative part.
LpqR = LpqI = None
with _load3c(self._cderi,
'j3c-',
kpti_kptj,
'j3c-kptij',
ignore_key_error=True) as j3c:
naux = j3c.shape[0]
if unpack:
buf = numpy.empty((min(blksize,
naux), nao * (nao + 1) // 2))
for b0, b1 in lib.prange(0, naux, blksize):
LpqR, LpqI = load(j3c, b0, b1, LpqR, LpqI)
yield LpqR, LpqI, -1
def aux_e2(cell, auxcell, intor='cint3c2e_sph', aosym='s1', comp=1,
kpti_kptj=numpy.zeros((2,3))):
'''3-center AO integrals (ij|L) with double lattice sum:
\sum_{lm} (i[l]j[m]|L[0]), where L is the auxiliary basis.
Returns:
(nao_pair, naux) array
'''
assert(comp == 1)
# sum over largest number of images in either cell or auxcell
rcut = max(cell.rcut, auxcell.rcut)
Ls = cell.get_lattice_Ls(rcut=rcut)
logger.debug1(cell, "rcut %s", rcut)
logger.debug3(cell, "Ls = %s", Ls)
kpti, kptj = kpti_kptj
expkL = numpy.exp(1j*numpy.asarray(numpy.dot(Ls, numpy.reshape(kpti, (1,3)).T), order='C'))
expkR = numpy.exp(1j*numpy.asarray(numpy.dot(Ls, numpy.reshape(kptj, (1,3)).T), order='C'))
gamma_point = abs(kpti).sum() < 1e-9 and abs(kptj).sum() < 1e-9
nao = cell.nao_nr()
#naux = auxcell.nao_nr('ssc' in intor)
naux = auxcell.nao_nr()
if naux == 0:
if aosym == 's1' or abs(kpti-kptj).sum() > 1e-9:
nao_pair = nao * (nao+1) // 2
else:
nao_pair = nao * nao
mat = numpy.zeros((comp,nao_pair,naux))
return mat
buf = [numpy.zeros((nao,nao,naux,comp), order='F', dtype=numpy.complex128)]
ints = _wrap_int3c(cell, auxcell, intor, comp, Ls, buf)
atm, bas, env = ints._envs[:3]
shls_slice = (0, cell.nbas, cell.nbas, cell.nbas*2,
cell.nbas*2, cell.nbas*2+auxcell.nbas)
c_shls_slice = (ctypes.c_int*6)(*(shls_slice[:6]))
xyz = cell.atom_coords().copy('C')
ptr_coordL = atm[:cell.natm,pyscf.gto.PTR_COORD]
ptr_coordL = numpy.vstack((ptr_coordL,ptr_coordL+1,ptr_coordL+2)).T.copy('C')
if aosym == 's1' or abs(kpti-kptj).sum() > 1e-9:
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
exp_Lk = expkL[l].conj() * expkR
ints(exp_Lk, c_shls_slice)
mat, buf = buf[0], None
else:
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
exp_Lk = expkL[l].conj() * expkR[:l+1]
exp_Lk[l] = .5
ints(exp_Lk, c_shls_slice)
mat, buf = buf[0], None
if gamma_point:
mat = mat.real + mat.real.swapaxes(0,1)
else:
mat = mat + mat.swapaxes(0,1).conj()
mat = mat[numpy.tril_indices(nao)]
if comp == 1:
mat = mat.reshape(-1,naux)
else:
mat = numpy.rollaxis(mat, -1, 0).reshape(comp,-1,naux)
return mat
def aux_e2(cell,
auxcell,
intor='cint3c2e_sph',
aosym='s1',
comp=1,
kpti_kptj=numpy.zeros((2, 3))):
'''3-center AO integrals (ij|L) with double lattice sum:
\sum_{lm} (i[l]j[m]|L[0]), where L is the auxiliary basis.
Returns:
(nao_pair, naux) array
'''
assert (comp == 1)
# sum over largest number of images in either cell or auxcell
nimgs = numpy.max((cell.nimgs, auxcell.nimgs), axis=0)
Ls = cell.get_lattice_Ls(nimgs)
logger.debug1(cell, "Images %s", nimgs)
logger.debug3(cell, "Ls = %s", Ls)
kpti, kptj = kpti_kptj
expkL = numpy.exp(
1j *
numpy.asarray(numpy.dot(Ls,
numpy.reshape(kpti, (1, 3)).T), order='C'))
expkR = numpy.exp(
1j *
numpy.asarray(numpy.dot(Ls,
numpy.reshape(kptj, (1, 3)).T), order='C'))
gamma_point = abs(kpti).sum() < 1e-9 and abs(kptj).sum() < 1e-9
nao = cell.nao_nr()
#naux = auxcell.nao_nr('ssc' in intor)
naux = auxcell.nao_nr()
if naux == 0:
if aosym == 's1' or abs(kpti - kptj).sum() > 1e-9:
nao_pair = nao * (nao + 1) // 2
else:
nao_pair = nao * nao
mat = numpy.zeros((comp, nao_pair, naux))
return mat
buf = [
numpy.zeros((nao, nao, naux, comp), order='F', dtype=numpy.complex128)
]
ints = _wrap_int3c(cell, auxcell, intor, comp, Ls, buf)
atm, bas, env = ints._envs[:3]
shls_slice = (0, cell.nbas, cell.nbas, cell.nbas * 2, cell.nbas * 2,
cell.nbas * 2 + auxcell.nbas)
c_shls_slice = (ctypes.c_int * 6)(*(shls_slice[:6]))
xyz = cell.atom_coords().copy('C')
ptr_coordL = atm[:cell.natm, pyscf.gto.PTR_COORD]
ptr_coordL = numpy.vstack(
(ptr_coordL, ptr_coordL + 1, ptr_coordL + 2)).T.copy('C')
if aosym == 's1' or abs(kpti - kptj).sum() > 1e-9:
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
exp_Lk = expkL[l].conj() * expkR
ints(exp_Lk, c_shls_slice)
mat, buf = buf[0], None
else:
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
exp_Lk = expkL[l].conj() * expkR[:l + 1]
exp_Lk[l] = .5
ints(exp_Lk, c_shls_slice)
mat, buf = buf[0], None
if gamma_point:
mat = mat.real + mat.real.swapaxes(0, 1)
else:
mat = mat + mat.swapaxes(0, 1).conj()
mat = mat[numpy.tril_indices(nao)]
if comp == 1:
mat = mat.reshape(-1, naux)
else:
mat = numpy.rollaxis(mat, -1, 0).reshape(comp, -1, naux)
return mat
def sr_loop(self, kpti_kptj=numpy.zeros((2,3)), max_memory=2000,
compact=True, blksize=None):
'''Short range part'''
if self._cderi is None:
self.build()
cell = self.cell
kpti, kptj = kpti_kptj
unpack = is_zero(kpti-kptj) and not compact
is_real = is_zero(kpti_kptj)
nao = cell.nao_nr()
if blksize is None:
if is_real:
if unpack:
blksize = max_memory*1e6/8/(nao*(nao+1)//2+nao**2)
else:
blksize = max_memory*1e6/8/(nao*(nao+1))
else:
blksize = max_memory*1e6/16/(nao**2*2)
blksize = max(16, min(int(blksize), self.blockdim))
logger.debug3(self, 'max_memory %d MB, blksize %d', max_memory, blksize)
if unpack:
buf = numpy.empty((blksize,nao*(nao+1)//2))
def load(Lpq, b0, b1, bufR, bufI):
Lpq = numpy.asarray(Lpq[b0:b1])
if is_real:
if unpack:
LpqR = lib.unpack_tril(Lpq, out=bufR).reshape(-1,nao**2)
else:
LpqR = Lpq
LpqI = numpy.zeros_like(LpqR)
else:
shape = Lpq.shape
if unpack:
tmp = numpy.ndarray(shape, buffer=buf)
tmp[:] = Lpq.real
LpqR = lib.unpack_tril(tmp, out=bufR).reshape(-1,nao**2)
tmp[:] = Lpq.imag
LpqI = lib.unpack_tril(tmp, lib.ANTIHERMI, out=bufI).reshape(-1,nao**2)
else:
LpqR = numpy.ndarray(shape, buffer=bufR)
LpqR[:] = Lpq.real
LpqI = numpy.ndarray(shape, buffer=bufI)
LpqI[:] = Lpq.imag
return LpqR, LpqI
LpqR = LpqI = None
with _load3c(self._cderi, 'j3c', kpti_kptj, 'j3c-kptij') as j3c:
naux = j3c.shape[0]
for b0, b1 in lib.prange(0, naux, blksize):
LpqR, LpqI = load(j3c, b0, b1, LpqR, LpqI)
yield LpqR, LpqI, 1
if cell.dimension == 2 and cell.low_dim_ft_type != 'inf_vacuum':
# Truncated Coulomb operator is not postive definite. Load the
# CDERI tensor of negative part.
LpqR = LpqI = None
with _load3c(self._cderi, 'j3c-', kpti_kptj, 'j3c-kptij',
ignore_key_error=True) as j3c:
naux = j3c.shape[0]
for b0, b1 in lib.prange(0, naux, blksize):
LpqR, LpqI = load(j3c, b0, b1, LpqR, LpqI)
yield LpqR, LpqI, -1
def aux_e2(cell, auxcell, erifile, intor='cint3c2e_sph', aosym='s1', comp=1,
kptij_lst=None, dataname='eri_mo', max_memory=2000, verbose=0):
'''3-center AO integrals (ij|L) with double lattice sum:
\sum_{lm} (i[l]j[m]|L[0]), where L is the auxiliary basis.
On diks, the integrals are stored as (kptij_idx, naux, nao_pair)
Args:
kptij_lst : (*,2,3) array
A list of (kpti, kptj)
'''
if comp > 1:
raise NotImplementedError('comp = %d' % comp)
if h5py.is_hdf5(erifile):
feri = h5py.File(erifile)
if dataname in feri:
del(feri[dataname])
if dataname+'-kptij' in feri:
del(feri[dataname+'-kptij'])
else:
feri = h5py.File(erifile, 'w')
if kptij_lst is None:
kptij_lst = numpy.zeros((1,2,3))
feri[dataname+'-kptij'] = kptij_lst
nkptij = len(kptij_lst)
# sum over largest number of images in either cell or auxcell
nimgs = numpy.max((cell.nimgs, auxcell.nimgs), axis=0)
Ls = cell.get_lattice_Ls(nimgs)
logger.debug1(cell, "pbc.df.outcore.Images %s", nimgs)
logger.debug3(cell, "Ls = %s", Ls)
nao = cell.nao_nr()
#naux = auxcell.nao_nr('ssc' in intor)
naux = auxcell.nao_nr()
aosym_s2 = numpy.zeros(nkptij, dtype=bool)
for k, kptij in enumerate(kptij_lst):
key = '%s/%d' % (dataname, k)
if abs(kptij).sum() < 1e-9: # gamma_point:
dtype = 'f8'
else:
dtype = 'c16'
aosym_s2[k] = abs(kptij[0]-kptij[1]).sum() < 1e-9
if aosym_s2[k]:
nao_pair = nao * (nao+1) // 2
else:
nao_pair = nao * nao
if comp == 1:
shape = (naux,nao_pair)
else:
shape = (comp,naux,nao_pair)
chunks = (min(256,naux), min(256,nao_pair)) # 512 KB
feri.create_dataset(key, shape, dtype, chunks=chunks)
if naux == 0:
feri.close()
return erifile
aux_loc = auxcell.ao_loc_nr('ssc' in intor)
buflen = max(8, int(max_memory*1e6/16/(nkptij*nao**2*comp)))
auxranges = balance_segs(aux_loc[1:]-aux_loc[:-1], buflen)
buflen = max([x[2] for x in auxranges])
buf = [numpy.zeros(nao*nao*buflen*comp, dtype=numpy.complex128)
for k in range(nkptij)]
ints = incore._wrap_int3c(cell, auxcell, intor, comp, Ls, buf)
atm, bas, env = ints._envs[:3]
xyz = numpy.asarray(cell.atom_coords(), order='C')
ptr_coordL = atm[:cell.natm,PTR_COORD]
ptr_coordL = numpy.vstack((ptr_coordL,ptr_coordL+1,ptr_coordL+2)).T.copy('C')
kpti = kptij_lst[:,0]
kptj = kptij_lst[:,1]
if numpy.all(aosym_s2):
def ccsum_or_reorder(Lpq):
tmp = numpy.asarray(Lpq.transpose(0,2,1).conj(), order='C')
tmp += Lpq
return tmp
else:
def ccsum_or_reorder(Lpq):
return numpy.asarray(Lpq, order='C')
naux0 = 0
for istep, auxrange in enumerate(auxranges):
sh0, sh1, nrow = auxrange
c_shls_slice = (ctypes.c_int*6)(0, cell.nbas, cell.nbas, cell.nbas*2,
cell.nbas*2+sh0, cell.nbas*2+sh1)
if numpy.all(aosym_s2):
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
e = numpy.dot(Ls[:l+1]-L1, kptj.T) # Lattice sum over half of the images {1..l}
exp_Lk = numpy.exp(1j * numpy.asarray(e, order='C'))
exp_Lk[l] = .5
ints(exp_Lk, c_shls_slice)
else:
for l, L1 in enumerate(Ls):
env[ptr_coordL] = xyz + L1
e = numpy.dot(Ls, kptj.T) - numpy.dot(L1, kpti.T)
exp_Lk = numpy.exp(1j * numpy.asarray(e, order='C'))
ints(exp_Lk, c_shls_slice)
for k, kptij in enumerate(kptij_lst):
h5dat = feri['%s/%d'%(dataname,k)]
# transpose 3201 as (comp,L,i,j)
mat = numpy.ndarray((nao,nao,nrow,comp), order='F',
dtype=numpy.complex128, buffer=buf[k])
for icomp, vi in enumerate(mat.transpose(3,2,0,1)):
v = ccsum_or_reorder(vi)
if abs(kptij).sum() < 1e-9: # gamma_point:
v = v.real
if aosym_s2[k]:
v = lib.pack_tril(v)
else:
v = v.reshape(nrow,-1)
if comp == 1:
h5dat[naux0:naux0+nrow] = v
else:
h5dat[icomp,naux0:naux0+nrow] = v
mat[:] = 0
naux0 += nrow
feri.close()
return erifile