def get_vind(self, mf):
'''Compute Ax'''
singlet = self.singlet
cell = mf.cell
kpts = mf.kpts
mo_coeff = mf.mo_coeff
mo_energy = mf.mo_energy
mo_occ = mf.mo_occ
nkpts = len(mo_occ)
nao, nmo = mo_coeff[0][0].shape
occidxa = [numpy.where(mo_occ[0][k]> 0)[0] for k in range(nkpts)]
occidxb = [numpy.where(mo_occ[1][k]> 0)[0] for k in range(nkpts)]
viridxa = [numpy.where(mo_occ[0][k]==0)[0] for k in range(nkpts)]
viridxb = [numpy.where(mo_occ[1][k]==0)[0] for k in range(nkpts)]
orboa = [mo_coeff[0][k][:,occidxa[k]] for k in range(nkpts)]
orbob = [mo_coeff[1][k][:,occidxb[k]] for k in range(nkpts)]
orbva = [mo_coeff[0][k][:,viridxa[k]] for k in range(nkpts)]
orbvb = [mo_coeff[1][k][:,viridxb[k]] for k in range(nkpts)]
e_ai_a = _get_eai(mo_energy[0], mo_occ[0])
e_ai_b = _get_eai(mo_energy[1], mo_occ[1])
hdiag = numpy.hstack([x.ravel() for x in (e_ai_a + e_ai_b)])
tot_x_a = sum(x.size for x in e_ai_a)
tot_x_b = sum(x.size for x in e_ai_b)
mem_now = lib.current_memory()[0]
max_memory = max(2000, self.max_memory*.8-mem_now)
vresp = _gen_uhf_response(mf, hermi=0, max_memory=max_memory)
def vind(zs):
nz = len(zs)
zs = [_unpack(z, mo_occ) for z in zs]
dmvo = numpy.empty((2,nz,nkpts,nao,nao), dtype=numpy.complex128)
for i in range(nz):
dm1a, dm1b = zs[i]
for k in range(nkpts):
dmvo[0,i,k] = reduce(numpy.dot, (orbva[k], dm1a[k], orboa[k].T.conj()))
dmvo[1,i,k] = reduce(numpy.dot, (orbvb[k], dm1b[k], orbob[k].T.conj()))
v1ao = vresp(dmvo)
v1s = []
for i in range(nz):
dm1a, dm1b = zs[i]
v1as = []
v1bs = []
for k in range(nkpts):
v1a = reduce(numpy.dot, (orbva[k].T.conj(), v1ao[0,i,k], orboa[k]))
v1b = reduce(numpy.dot, (orbvb[k].T.conj(), v1ao[1,i,k], orbob[k]))
v1a += e_ai_a[k] * dm1a[k]
v1b += e_ai_b[k] * dm1b[k]
v1as.append(v1a.ravel())
v1bs.append(v1b.ravel())
v1s += v1as + v1bs
return numpy.hstack(v1s).reshape(nz,-1)
return vind, hdiag
def init_guess(self, mf, nstates=None):
if nstates is None: nstates = self.nstates
mo_energy = mf.mo_energy
mo_occ = mf.mo_occ
e_ai_a = _get_eai(mo_energy[0], mo_occ[0])
e_ai_b = _get_eai(mo_energy[1], mo_occ[1])
eai = numpy.hstack([x.ravel() for x in (e_ai_a + e_ai_b)])
nov = eai.size
nroot = min(nstates, nov)
x0 = numpy.zeros((nroot, nov))
idx = numpy.argsort(eai)
for i in range(nroot):
x0[i, idx[i]] = 1 # lowest excitations
return x0
def get_vind(self, mf):
singlet = self.singlet
cell = mf.cell
kpts = mf.kpts
mo_coeff = mf.mo_coeff
mo_energy = mf.mo_energy
mo_occ = mf.mo_occ
nkpts = len(mo_occ)
nao, nmo = mo_coeff[0][0].shape
occidxa = [numpy.where(mo_occ[0][k] > 0)[0] for k in range(nkpts)]
occidxb = [numpy.where(mo_occ[1][k] > 0)[0] for k in range(nkpts)]
viridxa = [numpy.where(mo_occ[0][k] == 0)[0] for k in range(nkpts)]
viridxb = [numpy.where(mo_occ[1][k] == 0)[0] for k in range(nkpts)]
orboa = [mo_coeff[0][k][:, occidxa[k]] for k in range(nkpts)]
orbob = [mo_coeff[1][k][:, occidxb[k]] for k in range(nkpts)]
orbva = [mo_coeff[0][k][:, viridxa[k]] for k in range(nkpts)]
orbvb = [mo_coeff[1][k][:, viridxb[k]] for k in range(nkpts)]
e_ai_a = _get_eai(mo_energy[0], mo_occ[0])
e_ai_b = _get_eai(mo_energy[1], mo_occ[1])
hdiag = numpy.hstack([x.ravel() for x in (e_ai_a + e_ai_b)])
hdiag = numpy.hstack((hdiag, hdiag))
tot_x_a = sum(x.size for x in e_ai_a)
tot_x_b = sum(x.size for x in e_ai_b)
tot_x = tot_x_a + tot_x_b
mem_now = lib.current_memory()[0]
max_memory = max(2000, self.max_memory * .8 - mem_now)
vresp = _gen_uhf_response(mf, hermi=0, max_memory=max_memory)
def vind(xys):
nz = len(xys)
x1s = [_unpack(x[:tot_x], mo_occ) for x in xys]
y1s = [_unpack(x[tot_x:], mo_occ) for x in xys]
dmvo = numpy.empty((2, nz, nkpts, nao, nao),
dtype=numpy.complex128)
for i in range(nz):
xa, xb = x1s[i]
ya, yb = y1s[i]
for k in range(nkpts):
dmx = reduce(numpy.dot,
(orbva[k], xa[k], orboa[k].T.conj()))
dmy = reduce(numpy.dot,
(orboa[k], ya[k].T, orbva[k].T.conj()))
dmvo[0, i, k] = dmx + dmy # AX + BY
dmx = reduce(numpy.dot,
(orbvb[k], xb[k], orbob[k].T.conj()))
dmy = reduce(numpy.dot,
(orbob[k], yb[k].T, orbvb[k].T.conj()))
dmvo[1, i, k] = dmx + dmy # AX + BY
v1ao = vresp(dmvo)
v1s = []
for i in range(nz):
xa, xb = x1s[i]
ya, yb = y1s[i]
v1xsa = []
v1xsb = []
v1ysa = []
v1ysb = []
for k in range(nkpts):
v1xa = reduce(numpy.dot,
(orbva[k].T.conj(), v1ao[0, i, k], orboa[k]))
v1xb = reduce(numpy.dot,
(orbvb[k].T.conj(), v1ao[1, i, k], orbob[k]))
v1ya = reduce(
numpy.dot,
(orboa[k].T.conj(), v1ao[0, i, k], orbva[k])).T
v1yb = reduce(
numpy.dot,
(orbob[k].T.conj(), v1ao[1, i, k], orbvb[k])).T
v1xa += e_ai_a[k] * xa[k]
v1xb += e_ai_b[k] * xb[k]
v1ya += e_ai_a[k] * ya[k]
v1yb += e_ai_b[k] * yb[k]
v1xsa.append(v1xa.ravel())
v1xsb.append(v1xb.ravel())
v1ysa.append(-v1ya.ravel())
v1ysb.append(-v1yb.ravel())
v1s += v1xsa + v1xsb + v1ysa + v1ysb
return numpy.hstack(v1s).reshape(nz, -1)
return vind, hdiag