mirror of
https://github.com/QuantumPackage/qp2.git
synced 2024-06-17 01:55:17 +02:00
removed unused functions from converter
This commit is contained in:
Kevin Gasperich
parent
f07bdee9cd
commit
b0bf0c79d6
|
|
@ -218,203 +218,6 @@ def qp2rename():
|
|||
shutil.move(old,new)
|
||||
shutil.copy('e_nuc','E.qp')
|
||||
|
||||
def pyscf2QP(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
|
||||
print_ao_ints_bi=False,
|
||||
print_mo_ints_bi=False,
|
||||
print_ao_ints_df=True,
|
||||
print_mo_ints_df=False,
|
||||
print_ao_ints_mono=True,
|
||||
print_mo_ints_mono=False):
|
||||
'''
|
||||
kpts = List of kpoints coordinates. Cannot be null, for gamma is other script
|
||||
kmesh = Mesh of kpoints (optional)
|
||||
cas_idx = List of active MOs. If not specified all MOs are actives
|
||||
int_threshold = The integral will be not printed in they are bellow that
|
||||
'''
|
||||
|
||||
from pyscf.pbc import ao2mo
|
||||
from pyscf.pbc import tools
|
||||
from pyscf.pbc.gto import ecp
|
||||
import h5py
|
||||
|
||||
mo_coef_threshold = int_threshold
|
||||
ovlp_threshold = int_threshold
|
||||
kin_threshold = int_threshold
|
||||
ne_threshold = int_threshold
|
||||
bielec_int_threshold = int_threshold
|
||||
|
||||
natom = len(cell.atom_coords())
|
||||
print('n_atom per kpt', natom)
|
||||
print('num_elec per kpt', cell.nelectron)
|
||||
|
||||
mo_coeff = mf.mo_coeff
|
||||
# Mo_coeff actif
|
||||
mo_k = np.array([c[:,cas_idx] for c in mo_coeff] if cas_idx is not None else mo_coeff)
|
||||
e_k = np.array([e[cas_idx] for e in mf.mo_energy] if cas_idx is not None else mf.mo_energy)
|
||||
|
||||
Nk, nao, nmo = mo_k.shape
|
||||
print("n Kpts", Nk)
|
||||
print("n active Mos per kpt", nmo)
|
||||
print("n AOs per kpt", nao)
|
||||
|
||||
naux = mf.with_df.auxcell.nao
|
||||
print("n df fitting functions", naux)
|
||||
with open('num_df','w') as f:
|
||||
f.write(str(naux))
|
||||
|
||||
# Write all the parameter need to creat a dummy EZFIO folder who will containt the integral after.
|
||||
# More an implentation detail than a real thing
|
||||
with open('param','w') as f:
|
||||
# Note the use of nmo_tot
|
||||
f.write(' '.join(map(str,(cell.nelectron*Nk, Nk*nmo, natom*Nk))))
|
||||
|
||||
with open('num_ao','w') as f:
|
||||
f.write(str(nao*Nk))
|
||||
with open('kpt_num','w') as f:
|
||||
f.write(str(Nk))
|
||||
# _
|
||||
# |\ | _ | _ _. ._ |_) _ ._ | _ o _ ._
|
||||
# | \| |_| (_ | (/_ (_| | | \ (/_ |_) |_| | _> | (_) | |
|
||||
# |
|
||||
|
||||
#Total energy shift due to Ewald probe charge = -1/2 * Nelec*madelung/cell.vol =
|
||||
shift = tools.pbc.madelung(cell, kpts)*cell.nelectron * -.5
|
||||
e_nuc = (cell.energy_nuc() + shift)*Nk
|
||||
|
||||
print('nucl_repul', e_nuc)
|
||||
with open('e_nuc','w') as f:
|
||||
f.write(str(e_nuc))
|
||||
|
||||
|
||||
|
||||
# __ __ _
|
||||
# |\/| | | | _ _ |_ _
|
||||
# | | |__| |__ (_) (/_ | _>
|
||||
#
|
||||
with open('mo_coef_complex','w') as outfile:
|
||||
c_kpts = np.reshape(mo_k,(Nk,nao,nmo))
|
||||
|
||||
for ik in range(Nk):
|
||||
shift1=ik*nao+1
|
||||
shift2=ik*nmo+1
|
||||
for i in range(nao):
|
||||
for j in range(nmo):
|
||||
cij = c_kpts[ik,i,j]
|
||||
if abs(cij) > mo_coef_threshold:
|
||||
outfile.write('%s %s %s %s\n' % (i+shift1, j+shift2, cij.real, cij.imag))
|
||||
|
||||
# ___
|
||||
# | ._ _|_ _ _ ._ _. | _ |\/| _ ._ _
|
||||
# _|_ | | |_ (/_ (_| | (_| | _> | | (_) | | (_)
|
||||
# _|
|
||||
|
||||
if mf.cell.pseudo:
|
||||
v_kpts_ao = np.reshape(mf.with_df.get_pp(kpts=kpts),(Nk,nao,nao))
|
||||
else:
|
||||
v_kpts_ao = np.reshape(mf.with_df.get_nuc(kpts=kpts),(Nk,nao,nao))
|
||||
if len(cell._ecpbas) > 0:
|
||||
v_kpts_ao += np.reshape(ecp.ecp_int(cell, kpts),(Nk,nao,nao))
|
||||
|
||||
ne_ao = ('ne',v_kpts_ao,ne_threshold)
|
||||
ovlp_ao = ('overlap',np.reshape(mf.get_ovlp(cell=cell,kpts=kpts),(Nk,nao,nao)),ovlp_threshold)
|
||||
kin_ao = ('kinetic',np.reshape(cell.pbc_intor('int1e_kin',1,1,kpts=kpts),(Nk,nao,nao)),kin_threshold)
|
||||
|
||||
for name, intval_kpts_ao, thresh in (ne_ao, ovlp_ao, kin_ao):
|
||||
if print_ao_ints_mono:
|
||||
with open('%s_ao_complex' % name,'w') as outfile:
|
||||
for ik in range(Nk):
|
||||
shift=ik*nao+1
|
||||
for i in range(nao):
|
||||
for j in range(i,nao):
|
||||
int_ij = intval_kpts_ao[ik,i,j]
|
||||
if abs(int_ij) > thresh:
|
||||
outfile.write(stri2z(i+shift, j+shift, int_ij.real, int_ij.imag)+'\n')
|
||||
if print_mo_ints_mono:
|
||||
intval_kpts_mo = np.einsum('kim,kij,kjn->kmn',mo_k.conj(),intval_kpts_ao,mo_k)
|
||||
with open('%s_mo_complex' % name,'w') as outfile:
|
||||
for ik in range(Nk):
|
||||
shift=ik*nmo+1
|
||||
for i in range(nmo):
|
||||
for j in range(i,nmo):
|
||||
int_ij = intval_kpts_mo[ik,i,j]
|
||||
if abs(int_ij) > thresh:
|
||||
outfile.write(stri2z(i+shift, j+shift, int_ij.real, int_ij.imag)+'\n')
|
||||
|
||||
|
||||
# ___ _
|
||||
# | ._ _|_ _ _ ._ _. | _ |_) o
|
||||
# _|_ | | |_ (/_ (_| | (_| | _> |_) |
|
||||
# _|
|
||||
#
|
||||
kconserv = tools.get_kconserv(cell, kpts)
|
||||
|
||||
with open('kconserv_complex','w') as outfile:
|
||||
for a in range(Nk):
|
||||
for b in range(Nk):
|
||||
for c in range(Nk):
|
||||
d = kconserv[a,b,c]
|
||||
outfile.write('%s %s %s %s\n' % (a+1,c+1,b+1,d+1))
|
||||
|
||||
|
||||
intfile=h5py.File(mf.with_df._cderi,'r')
|
||||
|
||||
j3c = intfile.get('j3c')
|
||||
naosq = nao*nao
|
||||
naotri = (nao*(nao+1))//2
|
||||
j3ckeys = list(j3c.keys())
|
||||
j3ckeys.sort(key=lambda strkey:int(strkey))
|
||||
|
||||
# in new(?) version of PySCF, there is an extra layer of groups before the datasets
|
||||
# datasets used to be [/j3c/0, /j3c/1, /j3c/2, ...]
|
||||
# datasets now are [/j3c/0/0, /j3c/1/0, /j3c/2/0, ...]
|
||||
j3clist = [j3c.get(i+'/0') for i in j3ckeys]
|
||||
if j3clist==[None]*len(j3clist):
|
||||
# if using older version, stop before last level
|
||||
j3clist = [j3c.get(i) for i in j3ckeys]
|
||||
|
||||
nkinvsq = 1./np.sqrt(Nk)
|
||||
|
||||
# dimensions are (kikj,iaux,jao,kao), where kikj is compound index of kpts i and j
|
||||
# output dimensions should be reversed (nao, nao, naux, nkptpairs)
|
||||
j3arr=np.array([(i.value.reshape([-1,nao,nao]) if (i.shape[1] == naosq) else makesq3(i.value,nao)) * nkinvsq for i in j3clist])
|
||||
|
||||
nkpt_pairs = j3arr.shape[0]
|
||||
|
||||
if print_ao_ints_df:
|
||||
with open('df_ao_integral_array','w') as outfile:
|
||||
pass
|
||||
with open('df_ao_integral_array','a') as outfile:
|
||||
for k,kpt_pair in enumerate(j3arr):
|
||||
for iaux,dfbasfunc in enumerate(kpt_pair):
|
||||
for i,i0 in enumerate(dfbasfunc):
|
||||
for j,v in enumerate(i0):
|
||||
if (abs(v) > bielec_int_threshold):
|
||||
outfile.write(stri4z(i+1,j+1,iaux+1,k+1,v.real,v.imag)+'\n')
|
||||
|
||||
if print_mo_ints_df:
|
||||
kpair_list=[]
|
||||
for i in range(Nk):
|
||||
for j in range(Nk):
|
||||
if(i>=j):
|
||||
kpair_list.append((i,j,idx2_tri((i,j))))
|
||||
j3mo = np.array([np.einsum('mij,ik,jl->mkl',j3arr[kij],mo_k[ki].conj(),mo_k[kj]) for ki,kj,kij in kpair_list])
|
||||
with open('df_mo_integral_array','w') as outfile:
|
||||
pass
|
||||
with open('df_mo_integral_array','a') as outfile:
|
||||
for k,kpt_pair in enumerate(j3mo):
|
||||
for iaux,dfbasfunc in enumerate(kpt_pair):
|
||||
for i,i0 in enumerate(dfbasfunc):
|
||||
for j,v in enumerate(i0):
|
||||
if (abs(v) > bielec_int_threshold):
|
||||
outfile.write(stri4z(i+1,j+1,iaux+1,k+1,v.real,v.imag)+'\n')
|
||||
|
||||
|
||||
if (print_ao_ints_bi):
|
||||
print_ao_bi(mf,kconserv,'bielec_ao_complex',bielec_int_threshold)
|
||||
if (print_mo_ints_bi):
|
||||
print_mo_bi(mf,kconserv,'bielec_mo_complex',cas_idx,bielec_int_threshold)
|
||||
|
||||
|
||||
def print_mo_bi(mf,kconserv=None,outfilename='W.mo.qp',cas_idx=None,bielec_int_threshold = 1E-8):
|
||||
|
||||
cell = mf.cell
|
||||
|
|
@ -474,8 +277,8 @@ def print_ao_bi(mf,kconserv=None,outfilename='W.ao.qp',bielec_int_threshold = 1E
|
|||
Nk = kpts.shape[0]
|
||||
|
||||
if (kconserv is None):
|
||||
from pyscf.pbc import tools
|
||||
kconserv = tools.get_kconserv(cell, kpts)
|
||||
from pyscf.pbc.tools import get_kconserv
|
||||
kconserv = get_kconserv(cell, kpts)
|
||||
|
||||
with open(outfilename,'w') as outfile:
|
||||
for d, kd in enumerate(kpts):
|
||||
|
|
@ -598,7 +401,12 @@ def get_pot_ao(mf):
|
|||
def ao_to_mo_1e(ao_kpts,mo_coef):
|
||||
return np.einsum('kim,kij,kjn->kmn',mo_coef.conj(),ao_kpts_ao,mo_coef)
|
||||
|
||||
def get_j3ao(fname,nao,Nk):
|
||||
def get_j3ao_old(fname,nao,Nk):
|
||||
'''
|
||||
returns list of Nk_pair arrays of shape (naux,nao,nao)
|
||||
if naux is the same for each pair, returns numpy array
|
||||
if naux is not the same for each pair, returns array of arrays
|
||||
'''
|
||||
import h5py
|
||||
with h5py.File(fname,'r') as intfile:
|
||||
j3c = intfile.get('j3c')
|
||||
|
|
@ -622,6 +430,42 @@ def get_j3ao(fname,nao,Nk):
|
|||
# output dimensions should be reversed (nao, nao, naux, nkptpairs)
|
||||
return np.array([(i.value.reshape([-1,nao,nao]) if (i.shape[1] == naosq) else makesq3(i.value,nao)) * nkinvsq for i in j3clist])
|
||||
|
||||
def get_j3ao(fname,nao,Nk):
|
||||
'''
|
||||
returns padded df AO array
|
||||
fills in zeros when functions are dropped due to linear dependency
|
||||
'''
|
||||
import h5py
|
||||
with h5py.File(fname,'r') as intfile:
|
||||
j3c = intfile.get('j3c')
|
||||
j3ckeys = list(j3c.keys())
|
||||
nkpairs = len(j3ckeys)
|
||||
|
||||
# get num order instead of lex order
|
||||
j3ckeys.sort(key=lambda strkey:int(strkey))
|
||||
|
||||
# in new(?) version of PySCF, there is an extra layer of groups before the datasets
|
||||
# datasets used to be [/j3c/0, /j3c/1, /j3c/2, ...]
|
||||
# datasets now are [/j3c/0/0, /j3c/1/0, /j3c/2/0, ...]
|
||||
keysub = '/0' if bool(j3c.get('0/0',getclass=True)) else ''
|
||||
|
||||
naux = max(map(lambda k: j3c[k+keysub].shape[0],j3c.keys()))
|
||||
|
||||
naosq = nao*nao
|
||||
naotri = (nao*(nao+1))//2
|
||||
nkinvsq = 1./np.sqrt(Nk)
|
||||
|
||||
j3arr = np.zeros((nkpairs,naux,nao,nao),dtype=np.complex128)
|
||||
|
||||
for i,kpair in enumerate(j3ckeys):
|
||||
iaux,dim2 = j3c[kpair+keysub].shape
|
||||
if (dim2==naosq):
|
||||
j3arr[i,:iaux,:,:] = j3c[kpair+keysub][()].reshape([iaux,nao,nao]) * nkinvsq
|
||||
else:
|
||||
j3arr[i,:iaux,:,:] = makesq3(j3c[kpair+keysub][()],nao) * nkinvsq
|
||||
|
||||
return j3arr
|
||||
|
||||
def print_df(j3arr,fname,thresh):
|
||||
with open(fname,'w') as outfile:
|
||||
for k,kpt_pair in enumerate(j3arr):
|
||||
|
|
@ -642,6 +486,21 @@ def df_pad_ref_test(j3arr,nao,naux,nkpt_pairs):
|
|||
return df_ao_tmp
|
||||
|
||||
|
||||
def df_ao_to_mo(j3ao,mo_coef):
|
||||
from itertools import product
|
||||
Nk = mo_coef.shape[0]
|
||||
kpair_list = ((i,j,idx2_tri((i,j))) for (i,j) in product(range(Nk),repeat=2) if (i>=j))
|
||||
return np.array([
|
||||
np.einsum('mij,ik,jl->mkl',j3ao[kij],mo_coef[ki].conj(),mo_coef[kj])
|
||||
for ki,kj,kij in kpair_list])
|
||||
|
||||
def df_ao_to_mo_test(j3ao,mo_coef):
|
||||
from itertools import product
|
||||
Nk = mo_coef.shape[0]
|
||||
return np.array([
|
||||
np.einsum('mij,ik,jl->mkl',j3ao[idx2_tri((ki,kj))],mo_coef[ki].conj(),mo_coef[kj])
|
||||
for ki,kj in product(range(Nk),repeat=2) if (ki>=kj)])
|
||||
|
||||
|
||||
def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
|
||||
print_ao_ints_bi=False,
|
||||
|
|
@ -853,11 +712,13 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
|
|||
# #
|
||||
##########################################
|
||||
|
||||
# qph5['ao_two_e_ints'].attrs['df_num']=naux
|
||||
|
||||
j3arr = get_j3ao(mf.with_df._cderi,nao,Nk)
|
||||
|
||||
# test? should be (Nk*(Nk+1))//2
|
||||
nkpt_pairs = j3arr.shape[0]
|
||||
|
||||
# mf.with_df.get_naoaux() gives correct naux if no linear dependency in auxbasis
|
||||
# this should work even with linear dependency
|
||||
naux = max(i.shape[0] for i in j3arr)
|
||||
print("n df fitting functions", naux)
|
||||
with h5py.File(qph5path,'a') as qph5:
|
||||
|
|
@ -879,10 +740,11 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
|
|||
qph5.create_dataset('ao_two_e_ints/df_ao_integrals_imag',data=df_ao_tmp.imag)
|
||||
|
||||
if print_mo_ints_df:
|
||||
from itertools import product
|
||||
# WARNING: this is a generator, not a list; don't use it more than once
|
||||
kpair_list = ((i,j,idx2_tri((i,j))) for (i,j) in product(range(Nk),repeat=2) if (i>=j))
|
||||
j3mo = np.array([np.einsum('mij,ik,jl->mkl',j3arr[kij],mo_k[ki].conj(),mo_k[kj]) for ki,kj,kij in kpair_list])
|
||||
|
||||
j3mo = df_ao_to_mo(j3arr,mo_k)
|
||||
#j3mo_test = df_ao_to_mo_test(j3arr,mo_k)
|
||||
#assert(all([abs(i-j).max() <= 1e-12 for (i,j) in zip(j3mo,j3mo_test)]))
|
||||
|
||||
print_df(j3mo,'D.mo.qp',bielec_int_threshold)
|
||||
|
||||
df_mo_tmp = np.zeros((nmo,nmo,naux,nkpt_pairs),dtype=np.complex128)
|
||||
|
|
@ -900,224 +762,4 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
|
|||
print_ao_bi(mf,kconserv,'W.qp',bielec_int_threshold)
|
||||
if (print_mo_ints_bi):
|
||||
print_mo_bi(mf,kconserv,'W.mo.qp',cas_idx,bielec_int_threshold)
|
||||
|
||||
|
||||
def getj3ao(cell,mf, kpts, cas_idx=None, int_threshold = 1E-8):
|
||||
'''
|
||||
kpts = List of kpoints coordinates. Cannot be null, for gamma is other script
|
||||
kmesh = Mesh of kpoints (optional)
|
||||
cas_idx = List of active MOs. If not specified all MOs are actives
|
||||
int_threshold = The integral will be not printed in they are bellow that
|
||||
'''
|
||||
|
||||
from pyscf.pbc import ao2mo
|
||||
from pyscf.pbc import tools
|
||||
from pyscf.pbc.gto import ecp
|
||||
import h5py
|
||||
import scipy
|
||||
|
||||
|
||||
|
||||
mo_coef_threshold = int_threshold
|
||||
ovlp_threshold = int_threshold
|
||||
kin_threshold = int_threshold
|
||||
ne_threshold = int_threshold
|
||||
bielec_int_threshold = int_threshold
|
||||
|
||||
mo_coeff = mf.mo_coeff
|
||||
# Mo_coeff actif
|
||||
mo_k = np.array([c[:,cas_idx] for c in mo_coeff] if cas_idx is not None else mo_coeff)
|
||||
e_k = np.array([e[cas_idx] for e in mf.mo_energy] if cas_idx is not None else mf.mo_energy)
|
||||
|
||||
Nk, nao, nmo = mo_k.shape
|
||||
print("n Kpts", Nk)
|
||||
print("n active Mos per kpt", nmo)
|
||||
print("n AOs per kpt", nao)
|
||||
|
||||
# naux = mf.with_df.auxcell.nao
|
||||
# print("n df fitting functions", naux)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
with h5py.File(mf.with_df._cderi) as intfile:
|
||||
# intfile=h5py.File(mf.with_df._cderi,'r')
|
||||
|
||||
j3c = intfile.get('j3c')
|
||||
naosq = nao*nao
|
||||
naotri = (nao*(nao+1))//2
|
||||
j3ckeys = list(j3c.keys())
|
||||
j3ckeys.sort(key=lambda strkey:int(strkey))
|
||||
|
||||
# in new(?) version of PySCF, there is an extra layer of groups before the datasets
|
||||
# datasets used to be [/j3c/0, /j3c/1, /j3c/2, ...]
|
||||
# datasets now are [/j3c/0/0, /j3c/1/0, /j3c/2/0, ...]
|
||||
j3clist = [j3c.get(i+'/0') for i in j3ckeys]
|
||||
if j3clist==[None]*len(j3clist):
|
||||
# if using older version, stop before last level
|
||||
j3clist = [j3c.get(i) for i in j3ckeys]
|
||||
|
||||
nkinvsq = 1./np.sqrt(Nk)
|
||||
|
||||
# dimensions are (kikj,iaux,jao,kao), where kikj is compound index of kpts i and j
|
||||
# output dimensions should be reversed (nao, nao, naux, nkptpairs)
|
||||
j3arr=np.array([(i.value.reshape([-1,nao,nao]) if (i.shape[1] == naosq) else makesq3(i.value,nao)) * nkinvsq for i in j3clist])
|
||||
|
||||
return j3arr
|
||||
#nkpt_pairs = j3arr.shape[0]
|
||||
#df_ao_tmp = np.zeros((nao,nao,naux,nkpt_pairs),dtype=np.complex128)
|
||||
|
||||
#if print_ao_ints_df:
|
||||
# with open('D.qp','w') as outfile:
|
||||
# pass
|
||||
# with open('D.qp','a') as outfile:
|
||||
# for k,kpt_pair in enumerate(j3arr):
|
||||
# for iaux,dfbasfunc in enumerate(kpt_pair):
|
||||
# for i,i0 in enumerate(dfbasfunc):
|
||||
# for j,v in enumerate(i0):
|
||||
# if (abs(v) > bielec_int_threshold):
|
||||
# outfile.write(stri4z(i+1,j+1,iaux+1,k+1,v.real,v.imag)+'\n')
|
||||
# df_ao_tmp[i,j,iaux,k]=v
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#def testpyscf2QP(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8):
|
||||
# '''
|
||||
# kpts = List of kpoints coordinates. Cannot be null, for gamma is other script
|
||||
# kmesh = Mesh of kpoints (optional)
|
||||
# cas_idx = List of active MOs. If not specified all MOs are actives
|
||||
# int_threshold = The integral will be not printed in they are bellow that
|
||||
# '''
|
||||
#
|
||||
# from pyscf.pbc import ao2mo
|
||||
# from pyscf.pbc import tools
|
||||
# from pyscf.pbc.gto import ecp
|
||||
#
|
||||
# mo_coef_threshold = int_threshold
|
||||
# ovlp_threshold = int_threshold
|
||||
# kin_threshold = int_threshold
|
||||
# ne_threshold = int_threshold
|
||||
# bielec_int_threshold = int_threshold
|
||||
#
|
||||
# natom = len(cell.atom_coords())
|
||||
# print('n_atom per kpt', natom)
|
||||
# print('num_elec per kpt', cell.nelectron)
|
||||
#
|
||||
# mo_coeff = mf.mo_coeff
|
||||
# # Mo_coeff actif
|
||||
# mo_k = np.array([c[:,cas_idx] for c in mo_coeff] if cas_idx is not None else mo_coeff)
|
||||
# e_k = np.array([e[cas_idx] for e in mf.mo_energy] if cas_idx is not None else mf.mo_energy)
|
||||
#
|
||||
# Nk, nao, nmo = mo_k.shape
|
||||
# print("n Kpts", Nk)
|
||||
# print("n active Mos per kpt", nmo)
|
||||
# print("n AOs per kpt", nao)
|
||||
#
|
||||
# naux = mf.with_df.get_naoaux()
|
||||
# print("n df fitting functions", naux)
|
||||
#
|
||||
# # _
|
||||
# # |\ | _ | _ _. ._ |_) _ ._ | _ o _ ._
|
||||
# # | \| |_| (_ | (/_ (_| | | \ (/_ |_) |_| | _> | (_) | |
|
||||
# # |
|
||||
#
|
||||
# #Total energy shift due to Ewald probe charge = -1/2 * Nelec*madelung/cell.vol =
|
||||
# shift = tools.pbc.madelung(cell, kpts)*cell.nelectron * -.5
|
||||
# e_nuc = (cell.energy_nuc() + shift)*Nk
|
||||
#
|
||||
# print('nucl_repul', e_nuc)
|
||||
#
|
||||
#
|
||||
# # ___
|
||||
# # | ._ _|_ _ _ ._ _. | _ |\/| _ ._ _
|
||||
# # _|_ | | |_ (/_ (_| | (_| | _> | | (_) | | (_)
|
||||
# # _|
|
||||
#
|
||||
# if mf.cell.pseudo:
|
||||
# v_kpts_ao = np.reshape(mf.with_df.get_pp(kpts=kpts),(Nk,nao,nao))
|
||||
# else:
|
||||
# v_kpts_ao = np.reshape(mf.with_df.get_nuc(kpts=kpts),(Nk,nao,nao))
|
||||
# if len(cell._ecpbas) > 0:
|
||||
# v_kpts_ao += np.reshape(ecp.ecp_int(cell, kpts),(Nk,nao,nao))
|
||||
#
|
||||
# ne_ao = ('ne',v_kpts_ao,ne_threshold)
|
||||
# ovlp_ao = ('overlap',np.reshape(mf.get_ovlp(cell=cell,kpts=kpts),(Nk,nao,nao)),ovlp_threshold)
|
||||
# kin_ao = ('kinetic',np.reshape(cell.pbc_intor('int1e_kin',1,1,kpts=kpts),(Nk,nao,nao)),kin_threshold)
|
||||
#
|
||||
#
|
||||
# # ___ _
|
||||
# # | ._ _|_ _ _ ._ _. | _ |_) o
|
||||
# # _|_ | | |_ (/_ (_| | (_| | _> |_) |
|
||||
# # _|
|
||||
# #
|
||||
# kconserv = tools.get_kconserv(cell, kpts)
|
||||
#
|
||||
#
|
||||
# import h5py
|
||||
#
|
||||
# intfile=h5py.File(mf.with_df._cderi,'r')
|
||||
#
|
||||
# j3c = intfile.get('j3c')
|
||||
# naosq = nao*nao
|
||||
# naotri = (nao*(nao+1))//2
|
||||
# j3keys = list(j3c.keys())
|
||||
# j3keys.sort(key=lambda x:int(x))
|
||||
# j3clist = [j3c.get(i) for i in j3keys]
|
||||
# nkinvsq = 1./np.sqrt(Nk)
|
||||
#
|
||||
# # dimensions are (kikj,iaux,jao,kao), where kikj is compound index of kpts i and j
|
||||
# # output dimensions should be reversed (nao, nao, naux, nkptpairs)
|
||||
# j3arr=np.array([(pad(i.value.reshape([-1,nao,nao]),[naux,nao,nao]) if (i.shape[1] == naosq) else makesq(i.value,naux,nao)) * nkinvsq for i in j3clist])
|
||||
#
|
||||
# nkpt_pairs = j3arr.shape[0]
|
||||
#
|
||||
# kpair_list=[]
|
||||
# for i in range(Nk):
|
||||
# for j in range(Nk):
|
||||
# if(i>=j):
|
||||
# kpair_list.append((i,j,idx2_tri((i,j))))
|
||||
# j3mo = np.array([np.einsum('mij,ik,jl->mkl',j3arr[kij,:,:,:],mo_k[ki,:,:].conj(),mo_k[kj,:,:]) for ki,kj,kij in kpair_list])
|
||||
#
|
||||
#
|
||||
#
|
||||
# eri_mo = np.zeros(4*[nmo*Nk],dtype=np.complex128)
|
||||
# eri_ao = np.zeros(4*[nao*Nk],dtype=np.complex128)
|
||||
#
|
||||
# for d, kd in enumerate(kpts):
|
||||
# for c, kc in enumerate(kpts):
|
||||
# for b, kb in enumerate(kpts):
|
||||
# a = kconserv[b,c,d]
|
||||
# ka = kpts[a]
|
||||
# eri_4d_ao_kpt = mf.with_df.get_ao_eri(kpts=[ka,kb,kc,kd],compact=False).reshape((nao,)*4)
|
||||
# eri_4d_ao_kpt *= 1./Nk
|
||||
# for l in range(nao):
|
||||
# ll=l+d*nao
|
||||
# for j in range(nao):
|
||||
# jj=j+c*nao
|
||||
# for k in range(nao):
|
||||
# kk=k+b*nao
|
||||
# for i in range(nao):
|
||||
# ii=i+a*nao
|
||||
# v=eri_4d_ao_kpt[i,k,j,l]
|
||||
# eri_ao[ii,kk,jj,ll]=v
|
||||
#
|
||||
# eri_4d_mo_kpt = mf.with_df.ao2mo([mo_k[a], mo_k[b], mo_k[c], mo_k[d]],
|
||||
# [ka,kb,kc,kd],compact=False).reshape((nmo,)*4)
|
||||
# eri_4d_mo_kpt *= 1./Nk
|
||||
# for l in range(nmo):
|
||||
# ll=l+d*nmo
|
||||
# for j in range(nmo):
|
||||
# jj=j+c*nmo
|
||||
# for k in range(nmo):
|
||||
# kk=k+b*nmo
|
||||
# for i in range(nmo):
|
||||
# ii=i+a*nmo
|
||||
# v=eri_4d_mo_kpt[i,k,j,l]
|
||||
# eri_mo[ii,kk,jj,ll]=v
|
||||
#
|
||||
# return (mo_k,j3arr,j3mo,eri_ao,eri_mo,kpair_list)
|
||||
|
||||
|
||||
return
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user