def vxc_pack(self, **kw):
"""
Computes the exchange-correlation matrix elements packed version (upper triangular)
Args:
sv : (System Variables), this must have arrays of coordinates and species, etc
Returns:
vxc,exc
"""
from pyscf.nao.m_xc_scalar_ni import xc_scalar_ni
from pyscf.nao.m_ao_matelem import ao_matelem_c
#sv, dm, xc_code, deriv, kernel=None, ao_log=None, dtype=float64, **kvargs
sv = self
dm = kw['dm'] if 'dm' in kw else self.make_rdm1()
kernel = kw['kernel'] if 'kernel' in kw else None
ao_log = kw['ao_log'] if 'ao_log' in kw else self.ao_log
(xc_code,iskw) = (kw['xc_code'],True) if 'xc_code' in kw else (self.xc_code,False)
dtype = kw['dtype'] if 'dtype' in kw else float64
aome = ao_matelem_c(ao_log.rr, ao_log.pp, sv, dm)
me = aome.init_one_set(ao_log)
atom2s = zeros((sv.natm+1), dtype=int64)
for atom,sp in enumerate(sv.atom2sp): atom2s[atom+1]=atom2s[atom]+me.ao1.sp2norbs[sp]
sp2rcut = array([max(mu2rcut) for mu2rcut in me.ao1.sp_mu2rcut])
norbs = atom2s[-1]
#ind = triu_indices(norbs)
if kernel is None: kernel = zeros(norbs*(norbs+1)//2, dtype=dtype)
if kernel.size != int(norbs*(norbs+1)//2):
print('kernel.size ', kernel.size)
print('norbs*(norbs+1)//2 ', int(norbs*(norbs+1)//2))
raise ValueError("wrong dimension for kernel")
for atom1,[sp1,rv1,s1,f1] in enumerate(zip(sv.atom2sp,sv.atom2coord,atom2s,atom2s[1:])):
for atom2,[sp2,rv2,s2,f2] in enumerate(zip(sv.atom2sp,sv.atom2coord,atom2s,atom2s[1:])):
if atom2>atom1: continue
if (sp2rcut[sp1]+sp2rcut[sp2])**2<=sum((rv1-rv2)**2) : continue
xc = xc_scalar_ni(me,sp1,rv1,sp2,rv2,**kw) if iskw else xc_scalar_ni(me,sp1,rv1,sp2,rv2,xc_code=xc_code,**kw)
if use_numba:
fill_triu_v2(xc, kernel, s1, f1, s2, f2, norbs, add=True)
else:
for i1 in range(s1,f1):
for i2 in range(s2, min(i1+1, f2)):
ind = 0
if i2 > 0:
for beta in range(1, i2+1):
ind += norbs -beta
ind += i1
kernel[ind] += xc[i1-s1,i2-s2]
return kernel
def vxc_lil(sv, dm, xc_code, deriv, ao_log=None, dtype=float64, **kvargs):
"""
Computes the exchange-correlation matrix elements
Args:
sv : (System Variables), this must have arrays of coordinates and species, etc
Returns:
vxc,exc
"""
from pyscf.nao.m_xc_scalar_ni import xc_scalar_ni
from pyscf.nao.m_ao_matelem import ao_matelem_c
from scipy.sparse import lil_matrix
aome = ao_matelem_c(sv.ao_log.rr, sv.ao_log.pp, sv, dm)
me = aome.init_one_set(
sv.ao_log) if ao_log is None else aome.init_one_set(ao_log)
atom2s = zeros((sv.natm + 1), dtype=int64)
for atom, sp in enumerate(sv.atom2sp):
atom2s[atom + 1] = atom2s[atom] + me.ao1.sp2norbs[sp]
sp2rcut = array([max(mu2rcut) for mu2rcut in me.ao1.sp_mu2rcut])
lil = lil_matrix((atom2s[-1], atom2s[-1]), dtype=dtype)
for atom1, [sp1, rv1, s1, f1] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
for atom2, [sp2, rv2, s2, f2] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
if (sp2rcut[sp1] + sp2rcut[sp2])**2 <= sum((rv1 - rv2)**2):
continue
lil[s1:f1, s2:f2] = xc_scalar_ni(me, sp1, rv1, sp2, rv2, xc_code,
deriv, **kvargs)
return lil
def vxc_pack(self, **kw):
"""
Computes the exchange-correlation matrix elements packed version (upper triangular)
Args:
sv : (System Variables), this must have arrays of coordinates and species, etc
Returns:
vxc,exc
"""
from pyscf.nao.m_xc_scalar_ni import xc_scalar_ni
from pyscf.nao.m_ao_matelem import ao_matelem_c
from pyscf.nao.m_pack2den import cp_block_pack_u, pack2den_u
#sv, dm, xc_code, deriv, kernel=None, ao_log=None, dtype=float64, **kvargs
sv = self
dm = kw['dm'] if 'dm' in kw else self.make_rdm1()
ao_log = kw['ao_log'] if 'ao_log' in kw else self.ao_log
xc_code = kw['xc_code'] if 'xc_code' in kw else self.xc_code
kw.pop('xc_code', None)
dtype = kw['dtype'] if 'dtype' in kw else self.dtype
aome = ao_matelem_c(ao_log.rr, ao_log.pp, sv, dm)
me = aome.init_one_set(ao_log)
atom2s = zeros((sv.natm + 1), dtype=int64)
for atom, sp in enumerate(sv.atom2sp):
atom2s[atom + 1] = atom2s[atom] + me.ao1.sp2norbs[sp]
sp2rcut = array([max(mu2rcut) for mu2rcut in me.ao1.sp_mu2rcut])
norbs = atom2s[-1]
nq, npk = (self.nspin - 1) * 2 + 1, norbs * (norbs + 1) // 2
kernel = kw['kernel'].reshape((nq, npk)) if 'kernel' in kw else zeros(
(nq, npk), dtype=dtype)
for atom1, [sp1, rv1, s1, f1] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
for atom2, [sp2, rv2, s2, f2] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
if atom2 > atom1: continue
if (sp2rcut[sp1] + sp2rcut[sp2])**2 <= sum((rv1 - rv2)**2):
continue
iab2block = xc_scalar_ni(me,
sp1,
rv1,
sp2,
rv2,
xc_code=xc_code,
**kw)
for i, ab2v in enumerate(iab2block):
cp_block_pack_u(ab2v, s1, f1, s2, f2, kernel[i], add=True)
return kernel
def vxc_lil(self, **kw):
"""
Computes the exchange-correlation matrix elements
Args:
sv : (System Variables), this must have arrays of coordinates and species, etc
Returns:
fxc,vxc,exc
"""
from pyscf.nao.m_xc_scalar_ni import xc_scalar_ni
from pyscf.nao.m_ao_matelem import ao_matelem_c
from scipy.sparse import lil_matrix
#dm, xc_code, deriv, ao_log=None, dtype=float64, **kvargs
dm = kw['dm'] if 'dm' in kw else self.make_rdm1()
kernel = kw['kernel'] if 'kernel' in kw else None
ao_log = kw['ao_log'] if 'ao_log' in kw else self.ao_log
xc_code = kw['xc_code'] if 'xc_code' in kw else self.xc_code
kw.pop('xc_code', None)
dtype = kw['dtype'] if 'dtype' in kw else self.dtype
aome = ao_matelem_c(self.ao_log.rr, self.ao_log.pp, self, dm)
me = aome.init_one_set(
self.ao_log) if ao_log is None else aome.init_one_set(ao_log)
atom2s = zeros((self.natm + 1), dtype=int64)
for atom, sp in enumerate(self.atom2sp):
atom2s[atom + 1] = atom2s[atom] + me.ao1.sp2norbs[sp]
lil = [
lil_matrix((atom2s[-1], atom2s[-1]), dtype=dtype)
for i in range((self.nspin - 1) * 2 + 1)
]
for atom1, [sp1, rv1, s1, f1] in enumerate(
zip(self.atom2sp, self.atom2coord, atom2s, atom2s[1:])):
for atom2, [sp2, rv2, s2, f2] in enumerate(
zip(self.atom2sp, self.atom2coord, atom2s, atom2s[1:])):
blk = xc_scalar_ni(me, sp1, rv1, sp2, rv2, xc_code=xc_code, **kw)
for i, b in enumerate(blk):
lil[i][s1:f1, s2:f2] = b[:, :]
return lil
def vxc_pack(sv,
dm,
xc_code,
deriv,
kernel=None,
ao_log=None,
dtype=float64,
**kvargs):
"""
Computes the exchange-correlation matrix elements packed version (upper triangular)
Args:
sv : (System Variables), this must have arrays of coordinates and species, etc
Returns:
vxc,exc
"""
from pyscf.nao.m_xc_scalar_ni import xc_scalar_ni
from pyscf.nao.m_ao_matelem import ao_matelem_c
from pyscf.nao.m_pack2den import triu_indices
aome = ao_matelem_c(sv.ao_log.rr, sv.ao_log.pp, sv, dm)
me = aome.init_one_set(
sv.ao_log) if ao_log is None else aome.init_one_set(ao_log)
atom2s = zeros((sv.natm + 1), dtype=int64)
for atom, sp in enumerate(sv.atom2sp):
atom2s[atom + 1] = atom2s[atom] + me.ao1.sp2norbs[sp]
sp2rcut = array([max(mu2rcut) for mu2rcut in me.ao1.sp_mu2rcut])
norbs = atom2s[-1]
ind = triu_indices(norbs)
if kernel is None:
kernel = zeros(norbs * (norbs + 1) // 2, dtype=dtype)
for atom1, [sp1, rv1, s1, f1] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
for atom2, [sp2, rv2, s2, f2] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
if (sp2rcut[sp1] + sp2rcut[sp2])**2 <= sum((rv1 - rv2)**2):
continue
xc = xc_scalar_ni(me, sp1, rv1, sp2, rv2, xc_code, deriv,
**kvargs)
if use_numba:
fill_triu(xc, ind, kernel, s1, f1, s2, f2)
else:
for i1 in range(s1, f1):
for i2 in range(s2, f2):
if ind[i1, i2] >= 0:
kernel[ind[i1, i2]] = xc[i1 - s1, i2 - s2]
return kernel
else:
if kernel.size != int(norbs * (norbs + 1) // 2):
raise ValueError("wrong dimension for kernel")
for atom1, [sp1, rv1, s1, f1] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
for atom2, [sp2, rv2, s2, f2] in enumerate(
zip(sv.atom2sp, sv.atom2coord, atom2s, atom2s[1:])):
if (sp2rcut[sp1] + sp2rcut[sp2])**2 <= sum((rv1 - rv2)**2):
continue
xc = xc_scalar_ni(me, sp1, rv1, sp2, rv2, xc_code, deriv,
**kvargs)
if use_numba:
fill_triu(xc, ind, kernel, s1, f1, s2, f2, add=True)
else:
for i1 in range(s1, f1):
for i2 in range(s2, f2):
if ind[i1, i2] >= 0:
kernel[ind[i1, i2]] += xc[i1 - s1, i2 - s2]
