def general(mydf, mo_coeffs, kpts=None, compact=True):
if isinstance(mo_coeffs, numpy.ndarray) and mo_coeffs.ndim == 2:
mo_coeffs = (mo_coeffs,) * 4
eri = mydf.get_eri(kpts)
####################
# gamma point, the integral is real and with s4 symmetry
if eri.dtype == numpy.float64:
return ao2mo.general(eri, mo_coeffs, compact=compact)
else:
mokl, klslice = ao2mo.incore._conc_mos(mo_coeffs[2], mo_coeffs[3],
False)[2:]
if mokl.dtype == numpy.float64:
mokl = mokl + 0j
nao = mo_coeffs[0].shape[0]
nmoi = mo_coeffs[0].shape[1]
nmoj = mo_coeffs[1].shape[1]
nmok = mo_coeffs[2].shape[1]
nmol = mo_coeffs[3].shape[1]
moi = numpy.asarray(mo_coeffs[0], order='F')
moj = numpy.asarray(mo_coeffs[1], order='F')
tao = [0]
ao_loc = None
pqkl = _ao2mo.r_e2(eri.reshape(-1,nao**2), mokl, klslice, tao, ao_loc, aosym='s1')
pqkl = pqkl.reshape(nao,nao,nmok*nmol)
pjkl = numpy.empty((nao,nmoj,nmok*nmol), dtype=numpy.complex128)
for i in range(nao):
lib.dot(moj.T, pqkl[i], 1, pjkl[i], 0)
pqkl = None
eri_mo = lib.dot(moi.T.conj(), pjkl.reshape(nao,-1))
return eri_mo.reshape(nmoi*nmoj,-1)
def get_eri(self):
nao = self.mol.nao_nr()
nao_pair = nao * (nao+1) // 2
ao_eri = numpy.zeros((nao_pair,nao_pair))
for eri1 in self.loop():
lib.dot(eri1.T, eri1, 1, ao_eri, 1)
return ao2mo.restore(8, ao_eri, nao)
def get_j_kpts(mf, cell, dm_kpts, kpts, kpt_band=None):
coords = gen_grid.gen_uniform_grids(cell)
nkpts = len(kpts)
ngs = len(coords)
dm_kpts = np.asarray(dm_kpts)
nao = dm_kpts.shape[-1]
ni = numint._KNumInt(kpts)
aoR_kpts = ni.eval_ao(cell, coords, kpts)
if kpt_band is not None:
aoR_kband = numint.eval_ao(cell, coords, kpt_band)
dms = dm_kpts.reshape(-1,nkpts,nao,nao)
nset = dms.shape[0]
vjR = [get_vjR(cell, dms[i], aoR_kpts) for i in range(nset)]
if kpt_band is not None:
vj_kpts = [cell.vol/ngs * lib.dot(aoR_kband.T.conj()*vjR[i], aoR_kband)
for i in range(nset)]
if dm_kpts.ndim == 3: # One set of dm_kpts for KRHF
vj_kpts = vj_kpts[0]
return lib.asarray(vj_kpts)
else:
vj_kpts = []
for i in range(nset):
vj = [cell.vol/ngs * lib.dot(aoR_k.T.conj()*vjR[i], aoR_k)
for aoR_k in aoR_kpts]
vj_kpts.append(lib.asarray(vj))
return lib.asarray(vj_kpts).reshape(dm_kpts.shape)
def h_op(x):
x = self.unpack_uniq_var(x)
norb = x.shape[0]
#:hx = numpy.einsum('qp,xjj,xjq->pj', x, dip, dip)
#:hx+= numpy.einsum('qp,xqq,xjq->pj', x, dip, dip)
#:hx+= numpy.einsum('jk,xkk,xkp->pj', x, dip, dip)
#:hx+= numpy.einsum('jk,xpp,xkp->pj', x, dip, dip)
#:hx+= numpy.einsum('qj,xjq,xjp->pj', x, dip, dip)
#:hx+= numpy.einsum('pk,xjp,xkp->pj', x, dip, dip)
#:hx-= numpy.einsum('qp,xpp,xjq->pj', x, dip, dip) * 2
#:hx-= numpy.einsum('qp,xjp,xpq->pj', x, dip, dip) * 2
#:hx+= numpy.einsum('qj,xjp,xjq->pj', x, dip, dip)
#:hx+= numpy.einsum('pk,xkp,xjp->pj', x, dip, dip)
#:hx-= numpy.einsum('jk,xjj,xkp->pj', x, dip, dip) * 2
#:hx-= numpy.einsum('jk,xkj,xjp->pj', x, dip, dip) * 2
#:return -self.pack_uniq_var(hx)
#:hx = numpy.einsum('iq,qp->pi', g0, x)
hx = lib.dot(x.T, g0.T).conj()
#:hx+= numpy.einsum('qi,xiq,xip->pi', x, dip, dip) * 2
hx+= numpy.einsum('xip,xi->pi', dip, numpy.einsum('qi,xiq->xi', x, dip)) * 2
#:hx-= numpy.einsum('qp,xpp,xiq->pi', x, dip, dip) * 2
hx-= numpy.einsum('xpp,xip->pi', dip,
lib.dot(dip.reshape(-1,norb), x).reshape(3,norb,norb)) * 2
#:hx-= numpy.einsum('qp,xip,xpq->pi', x, dip, dip) * 2
hx-= numpy.einsum('xip,xp->pi', dip, numpy.einsum('qp,xpq->xp', x, dip)) * 2
return -self.pack_uniq_var(hx-hx.conj().T)
def gamma1_intermediates(mycc, t1, t2, l1, l2):
nocc, nvir = t1.shape
doo =-numpy.einsum('ja,ia->ij', l1, t1)
dvv = numpy.einsum('ia,ib->ab', l1, t1)
dvo = l1.T
xtv = numpy.einsum('ie,me->im', t1, l1)
dov = t1 - numpy.einsum('im,ma->ia', xtv, t1)
#:doo -= numpy.einsum('jkab,ikab->ij', l2, theta)
#:dvv += numpy.einsum('jica,jicb->ab', l2, theta)
#:xt1 = numpy.einsum('mnef,inef->mi', l2, make_theta(t2))
#:xt2 = numpy.einsum('mnaf,mnef->ea', l2, make_theta(t2))
#:dov += numpy.einsum('imae,me->ia', make_theta(t2), l1)
#:dov -= numpy.einsum('ma,ie,me->ia', t1, t1, l1)
#:dov -= numpy.einsum('mi,ma->ia', xt1, t1)
#:dov -= numpy.einsum('ie,ae->ia', t1, xt2)
max_memory = mycc.max_memory - lib.current_memory()[0]
unit = nocc*nvir**2
blksize = max(ccsd.BLKMIN, int(max_memory*.95e6/8/unit))
for p0, p1 in prange(0, nocc, blksize):
theta = make_theta(t2[p0:p1])
doo[p0:p1] -= lib.dot(theta.reshape(p1-p0,-1), l2.reshape(nocc,-1).T)
dov[p0:p1] += numpy.einsum('imae,me->ia', theta, l1)
xt1 = lib.dot(l2.reshape(nocc,-1), theta.reshape(p1-p0,-1).T)
dov[p0:p1] -= numpy.einsum('mi,ma->ia', xt1, t1)
xt2 = lib.dot(theta.reshape(-1,nvir).T, l2[p0:p1].reshape(-1,nvir))
dov -= numpy.einsum('ie,ae->ia', t1, xt2)
dvv += lib.dot(l2[p0:p1].reshape(-1,nvir).T, theta.reshape(-1,nvir))
return doo, dov, dvo, dvv
def _nao_sub(mol, pre_occ, pre_nao, s=None):
if s is None:
s = mol.intor_symmetric("cint1e_ovlp_sph")
core_lst, val_lst, rydbg_lst = _core_val_ryd_list(mol)
nbf = mol.nao_nr()
cnao = numpy.empty((nbf, nbf))
if core_lst:
c = pre_nao[:, core_lst].copy()
s1 = reduce(lib.dot, (c.T, s, c))
cnao[:, core_lst] = c1 = lib.dot(c, orth.lowdin(s1))
c = pre_nao[:, val_lst].copy()
c -= reduce(lib.dot, (c1, c1.T, s, c))
else:
c = pre_nao[:, val_lst]
if val_lst:
s1 = reduce(lib.dot, (c.T, s, c))
wt = pre_occ[val_lst]
cnao[:, val_lst] = lib.dot(c, orth.weight_orth(s1, wt))
if rydbg_lst:
cvlst = core_lst + val_lst
c1 = cnao[:, cvlst].copy()
c = pre_nao[:, rydbg_lst].copy()
c -= reduce(lib.dot, (c1, c1.T, s, c))
s1 = reduce(lib.dot, (c.T, s, c))
cnao[:, rydbg_lst] = lib.dot(c, orth.lowdin(s1))
snorm = numpy.linalg.norm(reduce(lib.dot, (cnao.T, s, cnao)) - numpy.eye(nbf))
if snorm > 1e-9:
logger.warn(mol, "Weak orthogonality for localized orbitals %s", snorm)
return cnao
def cost_function(self, u=None):
if u is None: u = numpy.eye(self.mo_coeff.shape[1])
mo_coeff = lib.dot(self.mo_coeff, u)
dip = dipole_integral(self.mol, mo_coeff)
r2 = self.mol.intor_symmetric('int1e_r2')
r2 = numpy.einsum('pi,pi->', mo_coeff, lib.dot(r2, mo_coeff))
val = r2 - numpy.einsum('xii,xii->', dip, dip) * 2
return val
def add_k_(vk, dm, pqk, coulG, buf=None):
nG = pqk.shape[-1]
pqk *= numpy.sqrt(coulG)
ipk = lib.dot(dm, pqk.reshape(nao,-1)).reshape(nao,nao,-1)
pik = numpy.ndarray((nao,nao,nG), buffer=buf)
pik[:] = ipk.transpose(1,0,2)
vk += lib.dot(pqk.reshape(nao,-1), pik.reshape(nao,-1).T)
return vk
def transform_ci_for_orbital_rotation(ci, norb, nelec, u):
'''Transform CI coefficients to the representation in new one-particle basis.
Solving CI problem for Hamiltonian h1, h2 defined in old basis,
CI_old = fci.kernel(h1, h2, ...)
Given orbital rotation u, the CI problem can be either solved by
transforming the Hamiltonian, or transforming the coefficients.
CI_new = fci.kernel(u^T*h1*u, ...) = transform_ci_for_orbital_rotation(CI_old, u)
Args:
u : 2D array or a list of 2D array
the orbital rotation to transform the old one-particle basis to new
one-particle basis
'''
neleca, nelecb = _unpack(nelec)
strsa = numpy.asarray(cistring.gen_strings4orblist(range(norb), neleca))
strsb = numpy.asarray(cistring.gen_strings4orblist(range(norb), nelecb))
one_particle_strs = numpy.asarray([1<<i for i in range(norb)])
na = len(strsa)
nb = len(strsb)
if isinstance(u, numpy.ndarray) and u.ndim == 2:
ua = ub = u
else:
ua, ub = u
# Unitary transformation array trans_ci is the overlap between two sets of CI basis.
occ_masks = (strsa[:,None] & one_particle_strs) != 0
trans_ci_a = numpy.zeros((na,na))
#for i in range(na): # for old basis
# for j in range(na):
# uij = u[occ_masks[i]][:,occ_masks[j]]
# trans_ci_a[i,j] = numpy.linalg.det(uij)
occ_idx_all_strs = numpy.where(occ_masks)[1]
for i in range(na):
ui = ua[occ_masks[i]].T.copy()
minors = numpy.take(ui, occ_idx_all_strs, axis=0).reshape(na,neleca,neleca)
trans_ci_a[i,:] = numpy.linalg.det(minors)
if neleca == nelecb and numpy.allclose(ua, ub):
trans_ci_b = trans_ci_a
else:
occ_masks = (strsb[:,None] & one_particle_strs) != 0
trans_ci_b = numpy.zeros((nb,nb))
#for i in range(nb):
# for j in range(nb):
# uij = u[occ_masks[i]][:,occ_masks[j]]
# trans_ci_b[i,j] = numpy.linalg.det(uij)
occ_idx_all_strs = numpy.where(occ_masks)[1]
for i in range(nb):
ui = ub[occ_masks[i]].T.copy()
minors = numpy.take(ui, occ_idx_all_strs, axis=0).reshape(nb,nelecb,nelecb)
trans_ci_b[i,:] = numpy.linalg.det(minors)
# Transform old basis to new basis for all alpha-electron excitations
ci = lib.dot(trans_ci_a.T, ci.reshape(na,nb))
# Transform old basis to new basis for all beta-electron excitations
ci = lib.dot(ci.reshape(na,nb), trans_ci_b)
return ci
def h_op(x):
x = self.unpack_uniq_var(x)
norb = x.shape[0]
hx = lib.dot(x.T, g0.T)
hx+= numpy.einsum('xip,xi->pi', pop, numpy.einsum('qi,xiq->xi', x, pop)) * 2
hx-= numpy.einsum('xpp,xip->pi', pop,
lib.dot(pop.reshape(-1,norb), x).reshape(-1,norb,norb)) * 2
hx-= numpy.einsum('xip,xp->pi', pop, numpy.einsum('qp,xpq->xp', x, pop)) * 2
return -self.pack_uniq_var(hx-hx.T)
def gamma1_intermediates(mycc, t1, t2, l1, l2):
nocc, nvir = t1.shape
goo = -numpy.einsum('ja,ia->ij', l1, t1)
gvv = numpy.einsum('ia,ib->ab', l1, t1)
#:goo -= numpy.einsum('jkab,ikab->ij', l2, theta)
#:gvv += numpy.einsum('jica,jicb->ab', l2, theta)
theta = make_theta(t2)
goo -= lib.dot(theta.reshape(nocc,-1), l2.reshape(nocc,-1).T)
gvv += lib.dot(l2.reshape(-1,nvir).T, theta.reshape(-1,nvir))
return goo, gvv
def short_range_k(dm, Lpq, j3c):
Lpq = Lpq.reshape(-1,nao)
j3c = j3c.reshape(-1,nao)
iLp = lib.dot(dm, Lpq.T).reshape(-1,nao)
vk = lib.dot(j3c.T, iLp)
if hermi:
vk = vk + vk.T
else:
iLp = lib.dot(dm.T, Lpq.T).reshape(-1,nao)
vk += lib.dot(iLp.T, j3c)
return vk
def trans(aoi, aoj, fac=1):
if id(aoi) == id(aoj) and iden_coeffs(mo_coeffs[0], mo_coeffs[1]):
moi = moj = numpy.asarray(lib.dot(mo_coeffs[0].T,aoi.T), order='C')
else:
moi = numpy.asarray(lib.dot(mo_coeffs[0].T, aoi.T), order='C')
moj = numpy.asarray(lib.dot(mo_coeffs[1].T, aoj.T), order='C')
mo_pairs_G = numpy.empty((nmoi,nmoj,ngrids), dtype=numpy.complex128)
for i in range(nmoi):
mo_pairs_G[i] = tools.fft(fac * moi[i].conj() * moj, mydf.mesh)
mo_pairs_G = mo_pairs_G.reshape(-1,ngrids).T
return mo_pairs_G
def shift_grids(r):
r_frac = lib.dot(r - origin, b.T)
# Grids on the boundary (r_frac == +/-0.5) of the new cell may lead to
# unbalanced contributions to the dipole moment. Exclude them from the
# dipole and quadrupole
r_frac[r_frac== 0.5] = 0
r_frac[r_frac==-0.5] = 0
r_frac[r_frac > 0.5] -= 1
r_frac[r_frac <-0.5] += 1
r = lib.dot(r_frac, a)
return r
def trans(aoiR, aojR, fac=1):
if id(aoiR) == id(aojR) and ao2mo.incore.iden_coeffs(mo_coeffs[0], mo_coeffs[1]):
moiR = mojR = numpy.asarray(lib.dot(mo_coeffs[0].T,aoiR.T), order='C')
else:
moiR = numpy.asarray(lib.dot(mo_coeffs[0].T, aoiR.T), order='C')
mojR = numpy.asarray(lib.dot(mo_coeffs[1].T, aojR.T), order='C')
mo_pairs_G = numpy.empty((nmoi,nmoj,ngs), dtype=numpy.complex128)
for i in range(nmoi):
mo_pairs_G[i] = tools.fft(fac * moiR[i].conj() * mojR, mydf.gs)
mo_pairs_G = mo_pairs_G.reshape(-1,ngs).T
return mo_pairs_G
def get_j_kpts(mydf, dm_kpts, hermi=1, kpts=np.zeros((1, 3)), kpt_band=None):
"""Get the Coulomb (J) AO matrix at sampled k-points.
Args:
dm_kpts : (nkpts, nao, nao) ndarray or a list of (nkpts,nao,nao) ndarray
Density matrix at each k-point. If a list of k-point DMs, eg,
UHF alpha and beta DM, the alpha and beta DMs are contracted
separately.
kpts : (nkpts, 3) ndarray
Kwargs:
kpt_band : (3,) ndarray
An arbitrary "band" k-point at which to evalute the matrix.
Returns:
vj : (nkpts, nao, nao) ndarray
or list of vj if the input dm_kpts is a list of DMs
"""
cell = mydf.cell
gs = mydf.gs
dm_kpts = lib.asarray(dm_kpts, order="C")
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
coulG = tools.get_coulG(cell, gs=gs)
ngs = len(coulG)
vR = rhoR = np.zeros((nset, ngs))
for k, aoR in mydf.aoR_loop(gs, kpts):
for i in range(nset):
rhoR[i] += numint.eval_rho(cell, aoR, dms[i, k])
for i in range(nset):
rhoR[i] *= 1.0 / nkpts
rhoG = tools.fft(rhoR[i], gs)
vG = coulG * rhoG
vR[i] = tools.ifft(vG, gs).real
if kpt_band is not None:
for k, aoR_kband in mydf.aoR_loop(gs, kpts, kpt_band):
pass
vj_kpts = [cell.vol / ngs * lib.dot(aoR_kband.T.conj() * vR[i], aoR_kband) for i in range(nset)]
if dm_kpts.ndim == 3: # One set of dm_kpts for KRHF
vj_kpts = vj_kpts[0]
return lib.asarray(vj_kpts)
else:
vj_kpts = []
weight = cell.vol / ngs
for k, aoR in mydf.aoR_loop(gs, kpts):
for i in range(nset):
vj_kpts.append(weight * lib.dot(aoR.T.conj() * vR[i], aoR))
vj_kpts = lib.asarray(vj_kpts).reshape(nkpts, nset, nao, nao)
return vj_kpts.transpose(1, 0, 2, 3).reshape(dm_kpts.shape)
def general(mydf, mo_coeffs, kpts=None,
compact=getattr(__config__, 'pbc_df_ao2mo_general_compact', True)):
'''General MO integral transformation'''
from pyscf.pbc.df.df_ao2mo import warn_pbc2d_eri
warn_pbc2d_eri(mydf)
cell = mydf.cell
kptijkl = _format_kpts(kpts)
kpti, kptj, kptk, kptl = kptijkl
if isinstance(mo_coeffs, numpy.ndarray) and mo_coeffs.ndim == 2:
mo_coeffs = (mo_coeffs,) * 4
mo_coeffs = [numpy.asarray(mo, order='F') for mo in mo_coeffs]
if not _iskconserv(cell, kptijkl):
lib.logger.warn(cell, 'fft_ao2mo: momentum conservation not found in '
'the given k-points %s', kptijkl)
return numpy.zeros([mo.shape[1] for mo in mo_coeffs])
allreal = not any(numpy.iscomplexobj(mo) for mo in mo_coeffs)
q = kptj - kpti
coulG = tools.get_coulG(cell, q, mesh=mydf.mesh)
coords = cell.gen_uniform_grids(mydf.mesh)
max_memory = mydf.max_memory - lib.current_memory()[0]
if gamma_point(kptijkl) and allreal:
ao = mydf._numint.eval_ao(cell, coords, kpti)[0]
if ((iden_coeffs(mo_coeffs[0], mo_coeffs[1]) and
iden_coeffs(mo_coeffs[0], mo_coeffs[2]) and
iden_coeffs(mo_coeffs[0], mo_coeffs[3]))):
moiT = mojT = numpy.asarray(lib.dot(mo_coeffs[0].T,ao.T), order='C')
ao = None
max_memory = max_memory - moiT.nbytes*1e-6
eri = _contract_compact(mydf, (moiT,mojT), coulG, max_memory=max_memory)
if not compact:
nmo = moiT.shape[0]
eri = ao2mo.restore(1, eri, nmo).reshape(nmo**2,nmo**2)
else:
mos = [numpy.asarray(lib.dot(c.T, ao.T), order='C') for c in mo_coeffs]
ao = None
fac = numpy.array(1.)
max_memory = max_memory - sum([x.nbytes for x in mos])*1e-6
eri = _contract_plain(mydf, mos, coulG, fac, max_memory=max_memory).real
return eri
else:
aos = mydf._numint.eval_ao(cell, coords, kptijkl)
mos = [numpy.asarray(lib.dot(c.T, aos[i].T), order='C')
for i,c in enumerate(mo_coeffs)]
aos = None
fac = numpy.exp(-1j * numpy.dot(coords, q))
max_memory = max_memory - sum([x.nbytes for x in mos])*1e-6
eri = _contract_plain(mydf, mos, coulG, fac, max_memory=max_memory)
return eri
def gamma1_intermediates(mycc, t1, t2, l1, l2, max_memory=2000):
nocc, nvir = t1.shape
goo = -numpy.einsum('ja,ia->ij', l1, t1)
gvv = numpy.einsum('ia,ib->ab', l1, t1)
#:goo -= numpy.einsum('jkab,ikab->ij', l2, theta)
#:gvv += numpy.einsum('jica,jicb->ab', l2, theta)
max_memory = max_memory - lib.current_memory()[0]
unit = nocc*nvir**2
blksize = max(ccsd.BLKMIN, int(max_memory*.95e6/8/unit))
for p0, p1 in prange(0, nocc, blksize):
theta = make_theta(t2[p0:p1])
goo[p0:p1] -= lib.dot(theta.reshape(p1-p0,-1), l2.reshape(nocc,-1).T)
gvv += lib.dot(l2[p0:p1].reshape(-1,nvir).T, theta.reshape(-1,nvir))
return goo, gvv
def make_kpt(kpt): # kpt = kptj - kpti
# search for all possible ki and kj that has ki-kj+kpt=0
kk_match = numpy.einsum('ijx->ij', abs(kk_table + kpt)) < 1e-9
kpti_idx, kptj_idx = numpy.where(kk_todo & kk_match)
nkptj = len(kptj_idx)
log.debug1('kpt = %s', kpt)
log.debug1('kpti_idx = %s', kpti_idx)
log.debug1('kptj_idx = %s', kptj_idx)
kk_todo[kpti_idx,kptj_idx] = False
if swap_2e and abs(kpt).sum() > 1e-9:
kk_todo[kptj_idx,kpti_idx] = False
mydf.exxdiv = exxdiv
vkcoulG = tools.get_coulG(cell, kpt, True, mydf, mydf.gs) / cell.vol
# <r|-G+k_rs|s> = conj(<s|G-k_rs|r>) = conj(<s|G+k_sr|r>)
for k, pqkR, pqkI, p0, p1 \
in mydf.ft_loop(cell, mydf.gs, kpt, kpts[kptj_idx],
max_memory=max_memory):
ki = kpti_idx[k]
kj = kptj_idx[k]
coulG = numpy.sqrt(vkcoulG[p0:p1])
# case 1: k_pq = (pi|iq)
pqkR *= coulG
pqkI *= coulG
rsk =(pqkR.reshape(nao,nao,-1).transpose(1,0,2) -
pqkI.reshape(nao,nao,-1).transpose(1,0,2)*1j)
qpk = rsk.conj()
for i in range(nset):
qsk = lib.dot(dms[i,kj], rsk.reshape(nao,-1)).reshape(nao,nao,-1)
#:vk_kpts[i,ki] += numpy.einsum('qpk,qsk->ps', qpk, qsk)
vk_kpts[i,ki] += lib.dot(qpk.transpose(1,0,2).reshape(nao,-1),
qsk.transpose(1,0,2).reshape(nao,-1).T)
qsk = None
rsk = qpk = None
# case 2: k_pq = (iq|pi)
if swap_2e and abs(kpt).sum() > 1e-9:
srk = pqkR - pqkI*1j
pqk = srk.reshape(nao,nao,-1).conj()
for i in range(nset):
prk = lib.dot(dms[i,ki].T, srk.reshape(nao,-1)).reshape(nao,nao,-1)
#:vk_kpts[i,kj] += numpy.einsum('prk,pqk->rq', prk, pqk)
vk_kpts[i,kj] += lib.dot(prk.transpose(1,0,2).reshape(nao,-1),
pqk.transpose(1,0,2).reshape(nao,-1).T)
prk = None
srk = pqk = None
pqkR = pqkI = coulG = None
return None
def _fftn_blas(f, mesh):
Gx = np.fft.fftfreq(mesh[0])
Gy = np.fft.fftfreq(mesh[1])
Gz = np.fft.fftfreq(mesh[2])
expRGx = np.exp(np.einsum('x,k->xk', -2j * np.pi * np.arange(mesh[0]), Gx))
expRGy = np.exp(np.einsum('x,k->xk', -2j * np.pi * np.arange(mesh[1]), Gy))
expRGz = np.exp(np.einsum('x,k->xk', -2j * np.pi * np.arange(mesh[2]), Gz))
g0 = g = lib.transpose(f.reshape(-1,
mesh[1] * mesh[2])).astype(np.complex128)
g1 = np.empty_like(g0)
g = lib.dot(g.reshape(-1, mesh[0]), expRGx, c=g1.reshape(-1, mesh[0]))
g = lib.dot(g.reshape(mesh[1], -1).T, expRGy, c=g0.reshape(-1, mesh[1]))
g = lib.dot(g.reshape(mesh[2], -1).T, expRGz, c=g1.reshape(-1, mesh[2]))
return g.reshape(-1, *mesh)
def pw_contract(istep, sh_range, j3cR, j3cI):
bstart, bend, ncol = sh_range
if aosym == 's2':
shls_slice = (bstart, bend, 0, bend)
else:
shls_slice = (bstart, bend, 0, cell.nbas)
for p0, p1 in lib.prange(0, ngrids, Gblksize):
dat = ft_ao._ft_aopair_kpts(cell,
Gv[p0:p1],
shls_slice,
aosym,
b,
gxyz[p0:p1],
Gvbase,
kpt,
adapted_kptjs,
out=buf)
nG = p1 - p0
for k, ji in enumerate(adapted_ji_idx):
aoao = dat[k].reshape(nG, ncol)
pqkR = numpy.ndarray((ncol, nG), buffer=pqkRbuf)
pqkI = numpy.ndarray((ncol, nG), buffer=pqkIbuf)
pqkR[:] = aoao.real.T
pqkI[:] = aoao.imag.T
lib.dot(kLR[p0:p1].T, pqkR.T, -1, j3cR[k][naux:], 1)
lib.dot(kLI[p0:p1].T, pqkI.T, -1, j3cR[k][naux:], 1)
if not (is_zero(kpt) and gamma_point(adapted_kptjs[k])):
lib.dot(kLR[p0:p1].T, pqkI.T, -1, j3cI[k][naux:], 1)
lib.dot(kLI[p0:p1].T, pqkR.T, 1, j3cI[k][naux:], 1)
for k, ji in enumerate(adapted_ji_idx):
if is_zero(kpt) and gamma_point(adapted_kptjs[k]):
v = fuse(j3cR[k])
else:
v = fuse(j3cR[k] + j3cI[k] * 1j)
if j2ctag == 'CD':
v = scipy.linalg.solve_triangular(j2c,
v,
lower=True,
overwrite_b=True)
feri['j3c/%d/%d' % (ji, istep)] = v
else:
feri['j3c/%d/%d' % (ji, istep)] = lib.dot(j2c, v)
# low-dimension systems
if j2c_negative is not None:
feri['j3c-/%d/%d' % (ji, istep)] = lib.dot(j2c_negative, v)
def _ifftn_blas(g, mesh):
Gx = np.fft.fftfreq(mesh[0])
Gy = np.fft.fftfreq(mesh[1])
Gz = np.fft.fftfreq(mesh[2])
expRGx = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[0]), Gx))
expRGy = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[1]), Gy))
expRGz = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[2]), Gz))
out = np.empty(g.shape, dtype=np.complex128)
buf = np.empty(mesh, dtype=np.complex128)
for i, gi in enumerate(g):
buf[:] = gi.reshape(mesh)
f = lib.dot(buf.reshape(mesh[0],-1).T, expRGx, 1./mesh[0], c=out[i].reshape(-1,mesh[0]))
f = lib.dot(f.reshape(mesh[1],-1).T, expRGy, 1./mesh[1], c=buf.reshape(-1,mesh[1]))
f = lib.dot(f.reshape(mesh[2],-1).T, expRGz, 1./mesh[2], c=out[i].reshape(-1,mesh[2]))
return out.reshape(-1, *mesh)
def _fftn_blas(f, mesh):
Gx = np.fft.fftfreq(mesh[0])
Gy = np.fft.fftfreq(mesh[1])
Gz = np.fft.fftfreq(mesh[2])
expRGx = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[0]), Gx))
expRGy = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[1]), Gy))
expRGz = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[2]), Gz))
out = np.empty(f.shape, dtype=np.complex128)
buf = np.empty(mesh, dtype=np.complex128)
for i, fi in enumerate(f):
buf[:] = fi.reshape(mesh)
g = lib.dot(buf.reshape(mesh[0],-1).T, expRGx, c=out[i].reshape(-1,mesh[0]))
g = lib.dot(g.reshape(mesh[1],-1).T, expRGy, c=buf.reshape(-1,mesh[1]))
g = lib.dot(g.reshape(mesh[2],-1).T, expRGz, c=out[i].reshape(-1,mesh[2]))
return out.reshape(-1, *mesh)
def h_op(x):
x = self.unpack_uniq_var(x)
norb = x.shape[0]
hx = lib.dot(x.T, g0.T).conj() * 2
pop2 = numpy.einsum('xii->xi', pop)**2
pop3 = numpy.einsum('xii->xi', pop)**3
tmp = numpy.einsum('qi,xiq->xi', x, pop) * pop2
hx += numpy.einsum('xip,xi->pi', pop, tmp) * 12
hx -= numpy.einsum(
'xp,xip->pi', pop3,
lib.dot(pop.reshape(-1, norb), x).reshape(-1, norb,
norb)) * 4
tmp = numpy.einsum('qp,xpq->xp', x, pop) * pop2
hx -= numpy.einsum('xip,xp->pi', pop, tmp) * 12
return -self.pack_uniq_var(hx - hx.conj().T)
def ao2mo(self, mo_coeffs):
from pyscf.ao2mo import _ao2mo
nmoi, nmoj, nmok, nmol = [x.shape[1] for x in mo_coeffs]
mo_eri = numpy.zeros((nmoi * nmoj, nmok * nmol))
moij = numpy.asarray(numpy.hstack((mo_coeffs[0], mo_coeffs[1])),
order='F')
ijshape = (0, nmoi, nmoi, nmoi + nmoj)
mokl = numpy.asarray(numpy.hstack((mo_coeffs[2], mo_coeffs[3])),
order='F')
klshape = (0, nmok, nmok, nmok + nmol)
for eri1 in self.loop():
buf1 = _ao2mo.nr_e2(eri1, moij, ijshape, 's2', 's1')
buf2 = _ao2mo.nr_e2(eri1, mokl, klshape, 's2', 's1')
lib.dot(buf1.T, buf2, 1, mo_eri, 1)
return mo_eri
def _ifftn_blas(g, mesh):
Gx = np.fft.fftfreq(mesh[0])
Gy = np.fft.fftfreq(mesh[1])
Gz = np.fft.fftfreq(mesh[2])
expRGx = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[0]), Gx))
expRGy = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[1]), Gy))
expRGz = np.exp(np.einsum('x,k->xk', 2j*np.pi*np.arange(mesh[2]), Gz))
out = np.empty(g.shape, dtype=np.complex128)
buf = np.empty(mesh, dtype=np.complex128)
for i, gi in enumerate(g):
buf[:] = gi.reshape(mesh)
f = lib.dot(buf.reshape(mesh[0],-1).T, expRGx, 1./mesh[0], c=out[i].reshape(-1,mesh[0]))
f = lib.dot(f.reshape(mesh[1],-1).T, expRGy, 1./mesh[1], c=buf.reshape(-1,mesh[1]))
f = lib.dot(f.reshape(mesh[2],-1).T, expRGz, 1./mesh[2], c=out[i].reshape(-1,mesh[2]))
return out.reshape(-1, *mesh)
def _fftn_blas(f, mesh):
Gx = np.fft.fftfreq(mesh[0])
Gy = np.fft.fftfreq(mesh[1])
Gz = np.fft.fftfreq(mesh[2])
expRGx = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[0]), Gx))
expRGy = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[1]), Gy))
expRGz = np.exp(np.einsum('x,k->xk', -2j*np.pi*np.arange(mesh[2]), Gz))
out = np.empty(f.shape, dtype=np.complex128)
buf = np.empty(mesh, dtype=np.complex128)
for i, fi in enumerate(f):
buf[:] = fi.reshape(mesh)
g = lib.dot(buf.reshape(mesh[0],-1).T, expRGx, c=out[i].reshape(-1,mesh[0]))
g = lib.dot(g.reshape(mesh[1],-1).T, expRGy, c=buf.reshape(-1,mesh[1]))
g = lib.dot(g.reshape(mesh[2],-1).T, expRGz, c=out[i].reshape(-1,mesh[2]))
return out.reshape(-1, *mesh)
def get_eri(mydf, kpts=None, compact=False):
cell = mydf.cell
if kpts is None:
kptijkl = numpy.zeros((4,3))
elif numpy.shape(kpts) == (3,):
kptijkl = numpy.vstack([kpts]*4)
else:
kptijkl = numpy.reshape(kpts, (4,3))
kpti, kptj, kptk, kptl = kptijkl
nao = cell.nao_nr()
nao_pair = nao * (nao+1) // 2
coulG = tools.get_coulG(cell, kptj-kpti, gs=mydf.gs)
ngs = len(coulG)
####################
# gamma point, the integral is real and with s4 symmetry
if abs(kptijkl).sum() < 1e-9:
ao_pairs_G = get_ao_pairs_G(mydf, kptijkl[:2], compact)
ao_pairs_G *= numpy.sqrt(coulG).reshape(-1,1)
aoijR = ao_pairs_G.real.copy()
aoijI = ao_pairs_G.imag.copy()
ao_pairs_G = None
eri = lib.dot(aoijR.T, aoijR, cell.vol/ngs**2)
eri = lib.dot(aoijI.T, aoijI, cell.vol/ngs**2, eri, 1)
return eri
####################
# (kpt) i == j == k == l != 0
# (kpt) i == l && j == k && i != j && j != k =>
#
# complex integrals, N^4 elements
elif (abs(kpti-kptl).sum() < 1e-9) and (abs(kptj-kptk).sum() < 1e-9):
ao_pairs_G = get_ao_pairs_G(mydf, kptijkl[:2], False)
ao_pairs_G *= numpy.sqrt(coulG).reshape(-1,1)
ao_pairs_invG = ao_pairs_G.T.reshape(nao,nao,-1).transpose(1,0,2).conj()
ao_pairs_invG = ao_pairs_invG.reshape(-1,ngs)
return lib.dot(ao_pairs_G.T, ao_pairs_invG.T, cell.vol/ngs**2)
####################
# aosym = s1, complex integrals
#
else:
ao_pairs_G = get_ao_pairs_G(mydf, kptijkl[:2], False)
# ao_pairs_invG = rho_rs(-G+k_rs) = conj(rho_sr(G+k_sr)).swap(r,s)
ao_pairs_invG = get_ao_pairs_G(mydf, -kptijkl[2:], False).conj()
ao_pairs_G *= coulG.reshape(-1,1)
return lib.dot(ao_pairs_G.T, ao_pairs_invG, cell.vol/ngs**2)
def test_dot_ao_ao(self):
non0tab = dft.numint.make_mask(mol, mf.grids.coords)
ao = dft.numint.eval_ao(mol, mf.grids.coords, deriv=1)
res0 = lib.dot(ao[0].T, ao[1])
res1 = dft.numint._dot_ao_ao(mol, ao[0], ao[1], nao,
mf.grids.weights.size, non0tab)
self.assertTrue(numpy.allclose(res0, res1))
def test_dot_ao_ao(self):
non0tab = dft.numint.make_mask(mol, mf.grids.coords)
ao = dft.numint.eval_ao(mol, mf.grids.coords, deriv=1)
res0 = lib.dot(ao[0].T, ao[1])
res1 = dft.numint._dot_ao_ao(mol, ao[0], ao[1], non0tab,
shls_slice=(0,mol.nbas), ao_loc=ao_loc)
self.assertTrue(numpy.allclose(res0, res1))
def make_natorbs(self, rdm1_mo=None, relaxed=False):
'''
Calculate natural orbitals.
Note: the most occupied orbitals come first (left)
and the least occupied orbitals last (right).
Args:
rdm1_mo : 1-RDM in MO basis
the function calculates a density matrix if none is provided
relaxed : calculated relaxed or unrelaxed density matrix
Returns:
natural occupation numbers, natural orbitals
'''
if rdm1_mo is None:
dm = self.make_rdm1(relaxed=relaxed, ao_repr=False)
elif isinstance(rdm1_mo, np.ndarray):
dm = rdm1_mo
else:
raise TypeError('rdm1_mo must be a 2-D array')
eigval, eigvec = np.linalg.eigh(dm)
natocc = np.flip(eigval)
natorb = lib.dot(self.mo_coeff, np.fliplr(eigvec))
return natocc, natorb
def get_nuc(mydf, kpts):
mydf = _sync_mydf(mydf)
cell = mydf.cell
if kpts is None:
kpts_lst = numpy.zeros((1, 3))
else:
kpts_lst = numpy.reshape(kpts, (-1, 3))
if abs(kpts_lst).sum() < 1e-9: # gamma_point
dtype = numpy.float64
else:
dtype = numpy.complex128
mesh = mydf.mesh
charge = -cell.atom_charges()
Gv = cell.get_Gv(mesh)
SI = cell.get_SI(Gv)
rhoG = numpy.dot(charge, SI)
coulG = tools.get_coulG(cell, mesh=mesh, Gv=Gv)
vneG = rhoG * coulG
vneR = tools.ifft(vneG, mydf.mesh).real
vne = [0] * len(kpts_lst)
for ao_ks_etc, p0, p1 in mydf.mpi_aoR_loop(mydf.grids, kpts_lst):
ao_ks = ao_ks_etc[0]
for k, ao in enumerate(ao_ks):
vne[k] += lib.dot(ao.T.conj() * vneR[p0:p1], ao)
ao = ao_ks = None
vne = mpi.reduce(lib.asarray(vne))
if rank == 0:
if kpts is None or numpy.shape(kpts) == (3, ):
vne = vne[0]
return vne
def get_grad(self, u=None):
if u is None: u = numpy.eye(self.mo_coeff.shape[1])
mo_coeff = lib.dot(self.mo_coeff, u)
pop = atomic_pops(self.mol, mo_coeff, self.pop_method)
g0 = numpy.einsum('xii,xip->pi', pop, pop)
g = -self.pack_uniq_var(g0 - g0.T) * 2
return g
def get_grad(self, u=None):
if u is None: u = numpy.eye(self.mo_coeff.shape[1])
mo_coeff = lib.dot(self.mo_coeff, u)
dip = dipole_integral(self.mol, mo_coeff)
g0 = numpy.einsum('xii,xip->pi', dip, dip)
g = -self.pack_uniq_var(g0 - g0.T) * 2
return g
def gen_g_hop(self, u):
mo_coeff = lib.dot(self.mo_coeff, u)
pop = atomic_pops(self.mol, mo_coeff, self.pop_method)
g0 = numpy.einsum('xii,xip->pi', pop, pop)
g = -self.pack_uniq_var(g0 - g0.T) * 2
h_diag = numpy.einsum('xii,xpp->pi', pop, pop) * 2
g_diag = g0.diagonal()
h_diag -= g_diag + g_diag.reshape(-1, 1)
h_diag += numpy.einsum('xip,xip->pi', pop, pop) * 2
h_diag += numpy.einsum('xip,xpi->pi', pop, pop) * 2
h_diag = -self.pack_uniq_var(h_diag) * 2
g0 = g0 + g0.T
def h_op(x):
x = self.unpack_uniq_var(x)
norb = x.shape[0]
hx = lib.dot(x.T, g0.T)
hx += numpy.einsum('xip,xi->pi', pop,
numpy.einsum('qi,xiq->xi', x, pop)) * 2
hx -= numpy.einsum(
'xpp,xip->pi', pop,
lib.dot(pop.reshape(-1, norb), x).reshape(-1, norb, norb)) * 2
hx -= numpy.einsum('xip,xp->pi', pop,
numpy.einsum('qp,xpq->xp', x, pop)) * 2
return -self.pack_uniq_var(hx - hx.T)
return g, h_op, h_diag
def gen_g_hop(self, u):
mo_coeff = lib.dot(self.mo_coeff, u)
pop = atomic_pops(self.mol, mo_coeff, self.pop_method)
g0 = numpy.einsum('xii,xip->pi', pop, pop)
g = -self.pack_uniq_var(g0-g0.T) * 2
h_diag = numpy.einsum('xii,xpp->pi', pop, pop) * 2
g_diag = g0.diagonal()
h_diag-= g_diag + g_diag.reshape(-1,1)
h_diag+= numpy.einsum('xip,xip->pi', pop, pop) * 2
h_diag+= numpy.einsum('xip,xpi->pi', pop, pop) * 2
h_diag = -self.pack_uniq_var(h_diag) * 2
g0 = g0 + g0.T
def h_op(x):
x = self.unpack_uniq_var(x)
norb = x.shape[0]
hx = lib.dot(x.T, g0.T)
hx+= numpy.einsum('xip,xi->pi', pop, numpy.einsum('qi,xiq->xi', x, pop)) * 2
hx-= numpy.einsum('xpp,xip->pi', pop,
lib.dot(pop.reshape(-1,norb), x).reshape(-1,norb,norb)) * 2
hx-= numpy.einsum('xip,xp->pi', pop, numpy.einsum('qp,xpq->xp', x, pop)) * 2
return -self.pack_uniq_var(hx-hx.T)
return g, h_op, h_diag
def get_nuc(mydf, kpts=None):
cell = mydf.cell
if kpts is None:
kpts_lst = numpy.zeros((1, 3))
else:
kpts_lst = numpy.reshape(kpts, (-1, 3))
gs = mydf.gs
charge = -cell.atom_charges()
Gv = cell.get_Gv(gs)
SI = cell.get_SI(Gv)
rhoG = numpy.dot(charge, SI)
coulG = tools.get_coulG(cell, gs=gs, Gv=Gv)
vneG = rhoG * coulG
vneR = tools.ifft(vneG, mydf.gs).real
vne = [
lib.dot(aoR.T.conj() * vneR, aoR)
for k, aoR in mydf.aoR_loop(gs, kpts_lst)
]
if kpts is None or numpy.shape(kpts) == (3, ):
vne = vne[0]
return numpy.asarray(vne)
def make_rdm12e(fcivec, nsite, nelec):
'''1-electron and 2-electron density matrices
dm_pq = <|p^+ q|>
dm_{pqrs} = <|p^+ r^+ q s|> (note 2pdm is ordered in chemist notation)
'''
neleca, nelecb = _unpack_nelec(nelec)
link_indexa = cistring.gen_linkstr_index(range(nsite), neleca)
link_indexb = cistring.gen_linkstr_index(range(nsite), nelecb)
na = cistring.num_strings(nsite, neleca)
nb = cistring.num_strings(nsite, nelecb)
ci0 = fcivec.reshape(na,nb,-1)
rdm1 = numpy.zeros((nsite,nsite))
rdm2 = numpy.zeros((nsite,nsite,nsite,nsite))
for str0 in range(na):
t1 = numpy.zeros((nsite,nsite,nb)+ci0.shape[2:])
for a, i, str1, sign in link_indexa[str0]:
t1[i,a,:] += sign * ci0[str1,:]
for k, tab in enumerate(link_indexb):
for a, i, str1, sign in tab:
t1[i,a,k] += sign * ci0[str0,str1]
rdm1 += numpy.einsum('mp,ijmp->ij', ci0[str0], t1)
# i^+ j|0> => <0|j^+ i, so swap i and j
#:rdm2 += numpy.einsum('ijmp,klmp->jikl', t1, t1)
tmp = lib.dot(t1.reshape(nsite**2,-1), t1.reshape(nsite**2,-1).T)
rdm2 += tmp.reshape((nsite,)*4).transpose(1,0,2,3)
rdm1, rdm2 = rdm.reorder_rdm(rdm1, rdm2, True)
return rdm1, rdm2
def make_rdm12e(fcivec, nsite, nelec):
'''1-electron and 2-electron density matrices
dm_pq = <|p^+ q|>
dm_{pqrs} = <|p^+ r^+ q s|> (note 2pdm is ordered in chemist notation)
'''
neleca, nelecb = _unpack_nelec(nelec)
link_indexa = cistring.gen_linkstr_index(range(nsite), neleca)
link_indexb = cistring.gen_linkstr_index(range(nsite), nelecb)
na = cistring.num_strings(nsite, neleca)
nb = cistring.num_strings(nsite, nelecb)
ci0 = fcivec.reshape(na, nb, -1)
rdm1 = numpy.zeros((nsite, nsite))
rdm2 = numpy.zeros((nsite, nsite, nsite, nsite))
for str0 in range(na):
t1 = numpy.zeros((nsite, nsite, nb) + ci0.shape[2:])
for a, i, str1, sign in link_indexa[str0]:
t1[i, a, :] += sign * ci0[str1, :]
for k, tab in enumerate(link_indexb):
for a, i, str1, sign in tab:
t1[i, a, k] += sign * ci0[str0, str1]
rdm1 += numpy.einsum('mp,ijmp->ij', ci0[str0], t1)
# i^+ j|0> => <0|j^+ i, so swap i and j
#:rdm2 += numpy.einsum('ijmp,klmp->jikl', t1, t1)
tmp = lib.dot(t1.reshape(nsite**2, -1), t1.reshape(nsite**2, -1).T)
rdm2 += tmp.reshape((nsite, ) * 4).transpose(1, 0, 2, 3)
rdm1, rdm2 = rdm.reorder_rdm(rdm1, rdm2, True)
return rdm1, rdm2
def make_rdm12e(fcivec, nsite, nelec):
if isinstance(nelec, (int, numpy.number)):
nelecb = nelec//2
neleca = nelec - nelecb
else:
neleca, nelecb = nelec
link_indexa = cistring.gen_linkstr_index(range(nsite), neleca)
link_indexb = cistring.gen_linkstr_index(range(nsite), nelecb)
na = cistring.num_strings(nsite, neleca)
nb = cistring.num_strings(nsite, nelecb)
ci0 = fcivec.reshape(na,nb,-1)
rdm1 = numpy.zeros((nsite,nsite))
rdm2 = numpy.zeros((nsite,nsite,nsite,nsite))
for str0 in range(na):
t1 = numpy.zeros((nsite,nsite,nb)+ci0.shape[2:])
for a, i, str1, sign in link_indexa[str0]:
t1[i,a,:] += sign * ci0[str1,:]
for k, tab in enumerate(link_indexb):
for a, i, str1, sign in tab:
t1[i,a,k] += sign * ci0[str0,str1]
rdm1 += numpy.einsum('mp,ijmp->ij', ci0[str0], t1)
# i^+ j|0> => <0|j^+ i, so swap i and j
#:rdm2 += numpy.einsum('ijmp,klmp->jikl', t1, t1)
tmp = lib.dot(t1.reshape(nsite**2,-1), t1.reshape(nsite**2,-1).T)
rdm2 += tmp.reshape((nsite,)*4).transpose(1,0,2,3)
rdm1, rdm2 = pyscf.fci.rdm.reorder_rdm(rdm1, rdm2, True)
return rdm1, rdm2
def madd_admn_mnbc(a, b, c, fac=1):
m0, m1, m2, m3 = a.shape
n0, n1, n2, n3 = b.shape
assert(c.shape == (m0,n2,n3,m1) and m2 == n0 and m3 == n1)
tmp = lib.dot(a.reshape(m0*m1,m2*m3), b.reshape(n0*n1,n2*n3), fac)
c += tmp.reshape(m0,m1,n2,n3).transpose(0,2,3,1)
return c
def get_grad(self, u=None):
if u is None: u = numpy.eye(self.mo_coeff.shape[1])
mo_coeff = lib.dot(self.mo_coeff, u)
dip = dipole_integral(self.mol, mo_coeff)
g0 = numpy.einsum('xii,xip->pi', dip, dip)
g = -self.pack_uniq_var(g0-g0.conj().T) * 2
return g
def _contract_plain(mydf, mos, coulG, phase, max_memory):
cell = mydf.cell
moiT, mojT, mokT, molT = mos
nmoi, nmoj, nmok, nmol = [x.shape[0] for x in mos]
ngrids = moiT.shape[1]
wcoulG = coulG * (cell.vol/ngrids)
dtype = numpy.result_type(phase, *mos)
eri = numpy.empty((nmoi*nmoj,nmok*nmol), dtype=dtype)
blksize = int(min(max(nmoi,nmok), (max_memory*1e6/16 - eri.size)/2/ngrids/max(nmoj,nmol)+1))
assert blksize > 0
buf0 = numpy.empty((blksize,max(nmoj,nmol),ngrids), dtype=dtype)
buf1 = numpy.ndarray((blksize,nmoj,ngrids), dtype=dtype, buffer=buf0)
buf2 = numpy.ndarray((blksize,nmol,ngrids), dtype=dtype, buffer=buf0)
for p0, p1 in lib.prange(0, nmoi, blksize):
mo_pairs = numpy.einsum('ig,jg->ijg', moiT[p0:p1].conj()*phase,
mojT, out=buf1[:p1-p0])
mo_pairs_G = tools.fft(mo_pairs.reshape(-1,ngrids), mydf.mesh)
mo_pairs = None
mo_pairs_G*= wcoulG
v = tools.ifft(mo_pairs_G, mydf.mesh)
mo_pairs_G = None
v *= phase.conj()
if dtype == numpy.double:
v = numpy.asarray(v.real, order='C')
for q0, q1 in lib.prange(0, nmok, blksize):
mo_pairs = numpy.einsum('ig,jg->ijg', mokT[q0:q1].conj(),
molT, out=buf2[:q1-q0])
eri[p0*nmoj:p1*nmoj,q0*nmol:q1*nmol] = lib.dot(v, mo_pairs.reshape(-1,ngrids).T)
v = None
return eri
def make_natorbs(self, rdm1_mo=None, relaxed=False):
'''
Calculate natural orbitals.
Note: the most occupied orbitals come first (left)
and the least occupied orbitals last (right).
Args:
rdm1_mo : 1-RDM in MO basis
the function calculates a density matrix if none is provided
relaxed : calculated relaxed or unrelaxed density matrix
Returns:
natural occupation numbers, natural orbitals
'''
if rdm1_mo is None:
dm = self.make_rdm1(ao_repr=False, relaxed=relaxed)
elif isinstance(rdm1_mo, np.ndarray):
dm = rdm1_mo
else:
raise TypeError('rdm1_mo must be a 3-D array')
# Transform the beta component to the alpha basis and sum both together.
SAO = self.mol.intor_symmetric('int1e_ovlp')
Sab = lib.einsum('xp,xy,yq->pq', self.mo_coeff[0, :, :], SAO,
self.mo_coeff[1, :, :])
rdm1_abas = dm[0, :, :] + lib.einsum('pr,rs,qs->pq', Sab, dm[1, :, :],
Sab)
# Diagonalize the spin-traced 1-RDM in alpha basis to get the natural orbitals.
eigval, eigvec = np.linalg.eigh(rdm1_abas)
natocc = np.flip(eigval)
natorb = lib.dot(self.mo_coeff[0, :, :], np.fliplr(eigvec))
return natocc, natorb
def get_mo_pairs_G(mydf, mo_coeffs, kpts=numpy.zeros((2, 3)), compact=False):
'''Calculate forward (G|ij) FFT of all MO pairs.
Args:
mo_coeff: length-2 list of (nao,nmo) ndarrays
The two sets of MO coefficients to use in calculating the
product |ij).
Returns:
mo_pairs_G : (ngs, nmoi*nmoj) ndarray
The FFT of the real-space MO pairs.
'''
if kpts is None: kpts = mydf.kpts
cell = mydf.cell
kpts = numpy.asarray(kpts)
coords = pdft.gen_grid.gen_uniform_grids(cell, mydf.gs)
nmoi = mo_coeffs[0].shape[1]
nmoj = mo_coeffs[1].shape[1]
ngs = len(coords)
def trans(aoiR, aojR, fac=1):
if id(aoiR) == id(aojR) and ao2mo.incore.iden_coeffs(
mo_coeffs[0], mo_coeffs[1]):
moiR = mojR = numpy.asarray(lib.dot(mo_coeffs[0].T, aoiR.T),
order='C')
else:
moiR = numpy.asarray(lib.dot(mo_coeffs[0].T, aoiR.T), order='C')
mojR = numpy.asarray(lib.dot(mo_coeffs[1].T, aojR.T), order='C')
mo_pairs_G = numpy.empty((nmoi, nmoj, ngs), dtype=numpy.complex128)
for i in range(nmoi):
mo_pairs_G[i] = tools.fft(fac * moiR[i].conj() * mojR, mydf.gs)
mo_pairs_G = mo_pairs_G.reshape(-1, ngs).T
return mo_pairs_G
if abs(kpts).sum() < 1e-9: # gamma point, real
aoR = mydf._numint.eval_ao(cell, coords, kpts[:1])[0]
if compact and ao2mo.incore.iden_coeffs(mo_coeffs[0], mo_coeffs[1]):
moR = numpy.asarray(lib.dot(mo_coeffs[0].T, aoR.T), order='C')
npair = nmoi * (nmoi + 1) // 2
mo_pairs_G = numpy.empty((npair, ngs), dtype=numpy.complex128)
ij = 0
for i in range(nmoi):
mo_pairs_G[ij:ij + i + 1] = tools.fft(
moR[i].conj() * moR[:i + 1], mydf.gs)
ij += i + 1
mo_pairs_G = mo_pairs_G.T
else:
mo_pairs_G = trans(aoR, aoR)
elif abs(kpts[0] - kpts[1]).sum() < 1e-9:
aoR = mydf._numint.eval_ao(cell, coords, kpts[:1])[0]
mo_pairs_G = trans(aoR, aoR)
else:
aoiR, aojR = mydf._numint.eval_ao(cell, coords, kpts)
q = kpts[1] - kpts[0]
fac = numpy.exp(-1j * numpy.dot(coords, q))
mo_pairs_G = trans(aoiR, aojR, fac)
return mo_pairs_G
def gen_g_hop(self, u):
mo_coeff = lib.dot(self.mo_coeff, u)
dip = dipole_integral(self.mol, mo_coeff)
g0 = numpy.einsum('xii,xip->pi', dip, dip)
g = -self.pack_uniq_var(g0 - g0.T) * 2
h_diag = numpy.einsum('xii,xpp->pi', dip, dip) * 2
h_diag -= g0.diagonal() + g0.diagonal().reshape(-1, 1)
h_diag += numpy.einsum('xip,xip->pi', dip, dip) * 2
h_diag += numpy.einsum('xip,xpi->pi', dip, dip) * 2
h_diag = -self.pack_uniq_var(h_diag) * 2
#:nmo = mo_coeff.shape[1]
#:h = numpy.einsum('xjj,xjq,pk->pjqk', dip, dip, numpy.eye(nmo))
#:h+= numpy.einsum('xqq,xjq,pk->pjqk', dip, dip, numpy.eye(nmo))
#:h+= numpy.einsum('xjq,xjp,jk->pjqk', dip, dip, numpy.eye(nmo))
#:h+= numpy.einsum('xjp,xkp,pq->pjqk', dip, dip, numpy.eye(nmo))
#:h-= numpy.einsum('xjj,xkp,jq->pjqk', dip, dip, numpy.eye(nmo))
#:h-= numpy.einsum('xpp,xjq,pk->pjqk', dip, dip, numpy.eye(nmo))
#:h-= numpy.einsum('xjp,xpq,pk->pjqk', dip, dip, numpy.eye(nmo))*2
#:h = h - h.transpose(0,1,3,2)
#:h = h - h.transpose(1,0,2,3)
#:h = h + h.transpose(2,3,0,1)
#:h *= -.5
#:idx = numpy.tril_indices(nmo, -1)
#:h = h[idx][:,idx[0],idx[1]]
g0 = g0 + g0.T
def h_op(x):
x = self.unpack_uniq_var(x)
norb = x.shape[0]
#:hx = numpy.einsum('qp,xjj,xjq->pj', x, dip, dip)
#:hx+= numpy.einsum('qp,xqq,xjq->pj', x, dip, dip)
#:hx+= numpy.einsum('jk,xkk,xkp->pj', x, dip, dip)
#:hx+= numpy.einsum('jk,xpp,xkp->pj', x, dip, dip)
#:hx+= numpy.einsum('qj,xjq,xjp->pj', x, dip, dip)
#:hx+= numpy.einsum('pk,xjp,xkp->pj', x, dip, dip)
#:hx-= numpy.einsum('qp,xpp,xjq->pj', x, dip, dip) * 2
#:hx-= numpy.einsum('qp,xjp,xpq->pj', x, dip, dip) * 2
#:hx+= numpy.einsum('qj,xjp,xjq->pj', x, dip, dip)
#:hx+= numpy.einsum('pk,xkp,xjp->pj', x, dip, dip)
#:hx-= numpy.einsum('jk,xjj,xkp->pj', x, dip, dip) * 2
#:hx-= numpy.einsum('jk,xkj,xjp->pj', x, dip, dip) * 2
#:return -self.pack_uniq_var(hx)
#:hx = numpy.einsum('iq,qp->pi', g0, x)
hx = lib.dot(x.T, g0.T)
#:hx+= numpy.einsum('qi,xiq,xip->pi', x, dip, dip) * 2
hx += numpy.einsum('xip,xi->pi', dip,
numpy.einsum('qi,xiq->xi', x, dip)) * 2
#:hx-= numpy.einsum('qp,xpp,xiq->pi', x, dip, dip) * 2
hx -= numpy.einsum(
'xpp,xip->pi', dip,
lib.dot(dip.reshape(-1, norb), x).reshape(3, norb, norb)) * 2
#:hx-= numpy.einsum('qp,xip,xpq->pi', x, dip, dip) * 2
hx -= numpy.einsum('xip,xp->pi', dip,
numpy.einsum('qp,xpq->xp', x, dip)) * 2
return -self.pack_uniq_var(hx - hx.T)
return g, h_op, h_diag
def get_nuc(mydf, kpts):
mydf = _sync_mydf(mydf)
cell = mydf.cell
if kpts is None:
kpts_lst = numpy.zeros((1,3))
else:
kpts_lst = numpy.reshape(kpts, (-1,3))
if abs(kpts_lst).sum() < 1e-9: # gamma_point
dtype = numpy.float64
else:
dtype = numpy.complex128
gs = mydf.gs
charge = -cell.atom_charges()
Gv = cell.get_Gv(gs)
SI = cell.get_SI(Gv)
rhoG = numpy.dot(charge, SI)
coulG = tools.get_coulG(cell, gs=gs, Gv=Gv)
vneG = rhoG * coulG
vneR = tools.ifft(vneG, mydf.gs).real
vne = [lib.dot(aoR.T.conj()*vneR, aoR)
for k, aoR in mydf.mpi_aoR_loop(gs, kpts_lst)]
vne = mpi.gather(lib.asarray(vne, dtype=dtype))
if rank == 0:
if kpts is None or numpy.shape(kpts) == (3,):
vne = vne[0]
return vne
def get_nuc(mydf, kpts=None):
if kpts is None:
kpts_lst = numpy.zeros((1, 3))
else:
kpts_lst = numpy.reshape(kpts, (-1, 3))
cell = mydf.cell
mesh = mydf.mesh
charge = -cell.atom_charges()
Gv = cell.get_Gv(mesh)
SI = cell.get_SI(Gv)
rhoG = numpy.dot(charge, SI)
coulG = tools.get_coulG(cell, mesh=mesh, Gv=Gv)
vneG = rhoG * coulG
vneR = tools.ifft(vneG, mesh).real
vne = [0] * len(kpts_lst)
for ao_ks_etc, p0, p1 in mydf.aoR_loop(mydf.grids, kpts_lst):
ao_ks = ao_ks_etc[0]
for k, ao in enumerate(ao_ks):
vne[k] += lib.dot(ao.T.conj() * vneR[p0:p1], ao)
ao = ao_ks = None
if kpts is None or numpy.shape(kpts) == (3, ):
vne = vne[0]
return numpy.asarray(vne)
def pw_contract(istep, sh_range, j3cR, j3cI):
bstart, bend, ncol = sh_range
if aosym == 's2':
shls_slice = (bstart, bend, 0, bend)
else:
shls_slice = (bstart, bend, 0, cell.nbas)
for p0, p1 in lib.prange(0, ngrids, Gblksize):
dat = ft_ao._ft_aopair_kpts(cell, Gv[p0:p1], shls_slice, aosym,
b, gxyz[p0:p1], Gvbase, kpt,
adapted_kptjs, out=buf)
if (cell.dimension == 1 or cell.dimension == 2) and is_zero(kpt):
G0idx, SI_on_z = pbcgto.cell._SI_for_uniform_model_charge(cell, Gv[p0:p1])
if SI_on_z.size > 0:
for k, aoao in enumerate(dat):
aoao[G0idx] -= numpy.einsum('g,i->gi', SI_on_z, ovlp[k])
aux = fuse(ft_ao.ft_ao(fused_cell, Gv[p0:p1][G0idx]).T)
vG_mod = numpy.einsum('ig,g,g->i', aux.conj(),
wcoulG[p0:p1][G0idx], SI_on_z)
if gamma_point(adapted_kptjs[k]):
j3cR[k][:naux] -= vG_mod[:,None].real * ovlp[k]
else:
tmp = vG_mod[:,None] * ovlp[k]
j3cR[k][:naux] -= tmp.real
j3cI[k][:naux] -= tmp.imag
tmp = aux = vG_mod
nG = p1 - p0
for k, ji in enumerate(adapted_ji_idx):
aoao = dat[k].reshape(nG,ncol)
pqkR = numpy.ndarray((ncol,nG), buffer=pqkRbuf)
pqkI = numpy.ndarray((ncol,nG), buffer=pqkIbuf)
pqkR[:] = aoao.real.T
pqkI[:] = aoao.imag.T
lib.dot(kLR[p0:p1].T, pqkR.T, -1, j3cR[k][naux:], 1)
lib.dot(kLI[p0:p1].T, pqkI.T, -1, j3cR[k][naux:], 1)
if not (is_zero(kpt) and gamma_point(adapted_kptjs[k])):
lib.dot(kLR[p0:p1].T, pqkI.T, -1, j3cI[k][naux:], 1)
lib.dot(kLI[p0:p1].T, pqkR.T, 1, j3cI[k][naux:], 1)
for k, ji in enumerate(adapted_ji_idx):
if is_zero(kpt) and gamma_point(adapted_kptjs[k]):
v = fuse(j3cR[k])
else:
v = fuse(j3cR[k] + j3cI[k] * 1j)
if j2ctag == 'CD':
v = scipy.linalg.solve_triangular(j2c, v, lower=True, overwrite_b=True)
else:
v = lib.dot(j2c, v)
feri['j3c/%d/%d'%(ji,istep)] = v
def get_mo_pairs_G(mydf, mo_coeffs, kpts=numpy.zeros((2,3)), q=None,
compact=getattr(__config__, 'pbc_df_mo_pairs_compact', False)):
'''Calculate forward (G|ij) FFT of all MO pairs.
Args:
mo_coeff: length-2 list of (nao,nmo) ndarrays
The two sets of MO coefficients to use in calculating the
product |ij).
Returns:
mo_pairs_G : (ngrids, nmoi*nmoj) ndarray
The FFT of the real-space MO pairs.
'''
if kpts is None: kpts = numpy.zeros((2,3))
cell = mydf.cell
kpts = numpy.asarray(kpts)
coords = cell.gen_uniform_grids(mydf.mesh)
nmoi = mo_coeffs[0].shape[1]
nmoj = mo_coeffs[1].shape[1]
ngrids = len(coords)
def trans(aoi, aoj, fac=1):
if id(aoi) == id(aoj) and iden_coeffs(mo_coeffs[0], mo_coeffs[1]):
moi = moj = numpy.asarray(lib.dot(mo_coeffs[0].T,aoi.T), order='C')
else:
moi = numpy.asarray(lib.dot(mo_coeffs[0].T, aoi.T), order='C')
moj = numpy.asarray(lib.dot(mo_coeffs[1].T, aoj.T), order='C')
mo_pairs_G = numpy.empty((nmoi,nmoj,ngrids), dtype=numpy.complex128)
for i in range(nmoi):
mo_pairs_G[i] = tools.fft(fac * moi[i].conj() * moj, mydf.mesh)
mo_pairs_G = mo_pairs_G.reshape(-1,ngrids).T
return mo_pairs_G
if gamma_point(kpts): # gamma point, real
ao = mydf._numint.eval_ao(cell, coords, kpts[:1])[0]
if compact and iden_coeffs(mo_coeffs[0], mo_coeffs[1]):
mo = numpy.asarray(lib.dot(mo_coeffs[0].T, ao.T), order='C')
npair = nmoi*(nmoi+1)//2
mo_pairs_G = numpy.empty((npair,ngrids), dtype=numpy.complex128)
ij = 0
for i in range(nmoi):
mo_pairs_G[ij:ij+i+1] = tools.fft(mo[i].conj() * mo[:i+1], mydf.mesh)
ij += i + 1
mo_pairs_G = mo_pairs_G.T
else:
mo_pairs_G = trans(ao, ao)
elif is_zero(kpts[0]-kpts[1]):
ao = mydf._numint.eval_ao(cell, coords, kpts[:1])[0]
mo_pairs_G = trans(ao, ao)
else:
if q is None:
q = kpts[1] - kpts[0]
aoi, aoj = mydf._numint.eval_ao(cell, coords, kpts)
fac = numpy.exp(-1j * numpy.dot(coords, q))
mo_pairs_G = trans(aoi, aoj, fac)
return mo_pairs_G
def get_eri(mydf, kpts=None, compact=False):
cell = mydf.cell
kptijkl = _format_kpts(kpts)
kpti, kptj, kptk, kptl = kptijkl
nao = cell.nao_nr()
nao_pair = nao * (nao + 1) // 2
q = kptj - kpti
coulG = tools.get_coulG(cell, q, gs=mydf.gs)
ngs = len(coulG)
####################
# gamma point, the integral is real and with s4 symmetry
if abs(kptijkl).sum() < 1e-9:
ao_pairs_G = get_ao_pairs_G(mydf, kptijkl[:2], q, compact=compact)
ao_pairs_G *= numpy.sqrt(coulG).reshape(-1, 1)
aoijR = ao_pairs_G.real.copy()
aoijI = ao_pairs_G.imag.copy()
ao_pairs_G = None
eri = lib.dot(aoijR.T, aoijR, cell.vol / ngs**2)
eri = lib.dot(aoijI.T, aoijI, cell.vol / ngs**2, eri, 1)
return eri
####################
# (kpt) i == j == k == l != 0
# (kpt) i == l && j == k && i != j && j != k =>
#
# complex integrals, N^4 elements
elif (abs(kpti - kptl).sum() < 1e-9) and (abs(kptj - kptk).sum() < 1e-9):
ao_pairs_G = get_ao_pairs_G(mydf, kptijkl[:2], q, compact=False)
ao_pairs_G *= numpy.sqrt(coulG).reshape(-1, 1)
ao_pairs_invG = ao_pairs_G.T.reshape(nao, nao, -1).transpose(1, 0,
2).conj()
ao_pairs_invG = ao_pairs_invG.reshape(-1, ngs)
return lib.dot(ao_pairs_G.T, ao_pairs_invG.T, cell.vol / ngs**2)
####################
# aosym = s1, complex integrals
#
else:
ao_pairs_G = get_ao_pairs_G(mydf, kptijkl[:2], q, compact=False)
# ao_pairs_invG = rho_rs(-G+k_rs) = conj(rho_sr(G+k_sr)).swap(r,s)
ao_pairs_invG = get_ao_pairs_G(mydf, -kptijkl[2:], q,
compact=False).conj()
ao_pairs_G *= coulG.reshape(-1, 1)
return lib.dot(ao_pairs_G.T, ao_pairs_invG, cell.vol / ngs**2)
def _Lij_to_Lmn(Lij, basis, ki, kj):
if len(basis.shape) == 3:
basis = basis[np.newaxis, ...]
if len(Lij.shape) == 3:
Lij = Lij[np.newaxis, ...]
spin, ncells, nlo, nemb = basis.shape
nL = Lij.shape[-3]
Lmn = np.zeros((spin, nL, nemb, nemb), dtype=np.complex128)
Li_n = np.empty((nL * nlo, nemb), dtype=np.complex128)
Ln_m = np.empty((nL * nemb, nemb), dtype=np.complex128)
for s in range(spin):
lib.dot(Lij[s].reshape((nL * nlo, nlo)), basis[s, kj], c=Li_n)
lib.dot(np.ascontiguousarray(np.swapaxes(Li_n.reshape((nL, nlo, nemb)), 1, 2).reshape((nL*nemb, nlo))), \
basis[s, ki].conj(), c=Ln_m)
Lmn[s] = np.swapaxes(Ln_m.reshape((nL, nemb, nemb)), 1, 2)
return Lmn
def get_j_kpts(mydf, dm_kpts, hermi=1, kpts=np.zeros((1, 3)), kpts_band=None):
'''Get the Coulomb (J) AO matrix at sampled k-points.
Args:
dm_kpts : (nkpts, nao, nao) ndarray or a list of (nkpts,nao,nao) ndarray
Density matrix at each k-point. If a list of k-point DMs, eg,
UHF alpha and beta DM, the alpha and beta DMs are contracted
separately.
kpts : (nkpts, 3) ndarray
Kwargs:
kpts_band : (3,) ndarray or (*,3) ndarray
A list of arbitrary "band" k-points at which to evalute the matrix.
Returns:
vj : (nkpts, nao, nao) ndarray
or list of vj if the input dm_kpts is a list of DMs
'''
cell = mydf.cell
mesh = mydf.mesh
low_dim_ft_type = mydf.low_dim_ft_type
dm_kpts = lib.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
coulG = tools.get_coulG(cell, mesh=mesh, low_dim_ft_type=low_dim_ft_type)
ngrids = len(coulG)
vR = rhoR = np.zeros((nset, ngrids))
for ao_ks_etc, p0, p1 in mydf.aoR_loop(mydf.grids, kpts):
ao_ks = ao_ks_etc[0]
for k, ao in enumerate(ao_ks):
for i in range(nset):
rhoR[i, p0:p1] += numint.eval_rho(cell, ao, dms[i, k])
ao = ao_ks = None
for i in range(nset):
rhoR[i] *= 1. / nkpts
rhoG = tools.fft(rhoR[i], mesh)
vG = coulG * rhoG
vR[i] = tools.ifft(vG, mesh).real
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
nband = len(kpts_band)
weight = cell.vol / ngrids
vR *= weight
if gamma_point(kpts_band):
vj_kpts = np.zeros((nset, nband, nao, nao))
else:
vj_kpts = np.zeros((nset, nband, nao, nao), dtype=np.complex128)
for ao_ks_etc, p0, p1 in mydf.aoR_loop(mydf.grids, kpts_band):
ao_ks = ao_ks_etc[0]
for k, ao in enumerate(ao_ks):
for i in range(nset):
vj_kpts[i, k] += lib.dot(ao.T.conj() * vR[i, p0:p1], ao)
return _format_jks(vj_kpts, dm_kpts, input_band, kpts)
def cost_function(self, u=None):
if u is None: u = numpy.eye(self.mo_coeff.shape[1])
mo_coeff = lib.dot(self.mo_coeff, u)
pop = self.atomic_pops(self.mol, mo_coeff, self.pop_method)
if self.exponent == 2:
return numpy.einsum('xii,xii->', pop, pop)
else:
pop2 = numpy.einsum('xii->xi', pop)**2
return numpy.einsum('xi,xi', pop2, pop2)
def ao2mo(self, mo_coeffs,
compact=getattr(__config__, 'df_df_DF_ao2mo_compact', True)):
if isinstance(mo_coeffs, numpy.ndarray) and mo_coeffs.ndim == 2:
mo_coeffs = (mo_coeffs,) * 4
ijmosym, nij_pair, moij, ijslice = _conc_mos(mo_coeffs[0], mo_coeffs[1], compact)
klmosym, nkl_pair, mokl, klslice = _conc_mos(mo_coeffs[2], mo_coeffs[3], compact)
mo_eri = numpy.zeros((nij_pair,nkl_pair))
sym = (iden_coeffs(mo_coeffs[0], mo_coeffs[2]) and
iden_coeffs(mo_coeffs[1], mo_coeffs[3]))
Lij = Lkl = None
for eri1 in self.loop():
Lij = _ao2mo.nr_e2(eri1, moij, ijslice, aosym='s2', mosym=ijmosym, out=Lij)
if sym:
Lkl = Lij
else:
Lkl = _ao2mo.nr_e2(eri1, mokl, klslice, aosym='s2', mosym=klmosym, out=Lkl)
lib.dot(Lij.T, Lkl, 1, mo_eri, 1)
return mo_eri
def get_eri_2c2e(mydf):
from pyscf.gto.ft_ao import ft_ao
eriR = 0
kptijkl = numpy.zeros((4, 3))
q = numpy.zeros(3)
coulG = mydf.weighted_coulG(q, False, mydf.mesh)
Gv, Gvbase, kws = cell.get_Gv_weights(mydf.mesh)
ao = ft_ao(cell, Gv)
return lib.dot(ao.T * coulG, ao.conj())
