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https://github.com/QuantumPackage/qp2.git
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cleaning up converter
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c5726abb13
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8411167e90
@ -509,8 +509,74 @@ def print_ao_bi(mf,kconserv=None,outfilename='W.ao.qp',bielec_int_threshold = 1E
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v.real,v.imag)+'\n')
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def print_kpts_unblocked(ints_k,outfilename,thresh):
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'''
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for ints_k of shape (Nk,n1,n2),
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print the elements of the corresponding block-diagonal matrix of shape (Nk*n1,Nk*n2) in file
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'''
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Nk,n1,n2 = ints_k.shape
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with open(outfilename,'w') as outfile:
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for ik in range(Nk):
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shift1 = ik*n1+1
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shift2 = ik*n2+1
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for i1 in range(n1):
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for i2 in range(n2):
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int_ij = ints_k[ik,i1,i2]
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if abs(int_ij) > thresh:
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outfile.write(stri2z(i1+shift1, i2+shift2, int_ij.real, int_ij.imag)+'\n')
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return
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def print_kpts_unblocked_upper(ints_k,outfilename,thresh):
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'''
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for hermitian ints_k of shape (Nk,n1,n1),
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print the elements of the corresponding block-diagonal matrix of shape (Nk*n1,Nk*n1) in file
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(only upper triangle is printed)
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'''
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Nk,n1,n2 = ints_k.shape
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if (n1!=n2):
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raise Exception('print_kpts_unblocked_upper called with non-square matrix')
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with open(outfilename,'w') as outfile:
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for ik in range(Nk):
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shift = ik*n1+1
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for i1 in range(n1):
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for i2 in range(i1,n1):
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int_ij = ints_k[ik,i1,i2]
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if abs(int_ij) > thresh:
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outfile.write(stri2z(i1+shift, i2+shift, int_ij.real, int_ij.imag)+'\n')
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return
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def get_kin_ao(mf):
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nao = mf.cell.nao_nr()
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Nk = len(mf.kpts)
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return np.reshape(mf.cell.pbc_intor('int1e_kin',1,1,kpts=mf.kpts),(Nk,nao,nao))
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def get_ovlp_ao(mf):
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nao = mf.cell.nao_nr()
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Nk = len(mf.kpts)
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return np.reshape(mf.get_ovlp(cell=cell,kpts=kpts),(Nk,nao,nao))
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def get_pot_ao(mf):
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nao = mf.cell.nao_nr()
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Nk = len(mf.kpts)
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if mf.cell.pseudo:
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v_kpts_ao = np.reshape(mf.with_df.get_pp(kpts=kpts),(Nk,nao,nao))
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else:
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v_kpts_ao = np.reshape(mf.with_df.get_nuc(kpts=kpts),(Nk,nao,nao))
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if len(cell._ecpbas) > 0:
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from pyscf.pbc.gto import ecp
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v_kpts_ao += np.reshape(ecp.ecp_int(cell, kpts),(Nk,nao,nao))
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return v_kpts_ao
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def ao_to_mo_1e(ao_kpts,mo_coef):
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return np.einsum('kim,kij,kjn->kmn',mo_coef.conj(),ao_kpts_ao,mo_coef)
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def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
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print_ao_ints_bi=False,
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print_mo_ints_bi=False,
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@ -532,19 +598,23 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
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import h5py
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import scipy
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qph5=h5py.File('qpdat.h5')
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qph5.create_group('nuclei')
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qph5.create_group('electrons')
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qph5.create_group('ao_basis')
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qph5.create_group('mo_basis')
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mo_coef_threshold = int_threshold
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ovlp_threshold = int_threshold
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kin_threshold = int_threshold
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ne_threshold = int_threshold
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bielec_int_threshold = int_threshold
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thresh_mono = int_threshold
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qph5path = 'qpdat.h5'
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# create hdf5 file, delete old data if exists
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with h5py.File(qph5path,'w') as qph5:
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qph5.create_group('nuclei')
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qph5.create_group('electrons')
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qph5.create_group('ao_basis')
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qph5.create_group('mo_basis')
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qph5 = h5py.File(qph5path,'a')
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natom = cell.natm
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nelec = cell.nelectron
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neleca,nelecb = cell.nelec
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@ -573,18 +643,19 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
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print("n active Mos per kpt", nmo)
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print("n AOs per kpt", nao)
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naux = mf.with_df.auxcell.nao
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print("n df fitting functions", naux)
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# naux = mf.with_df.auxcell.nao
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# print("n df fitting functions", naux)
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#in old version: param << nelec*Nk, nmo*Nk, natom*Nk
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qph5['electrons'].attrs['elec_alpha_num']=neleca*Nk
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qph5['electrons'].attrs['elec_beta_num']=nelecb*Nk
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qph5['mo_basis'].attrs['mo_num']=Nk*nmo
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qph5['ao_basis'].attrs['ao_num']=Nk*nao
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qph5['nuclei'].attrs['nucl_num']=Nk*natom
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#qph5['nuclei'].attrs['nucl_num']=Nk*natom
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qph5['nuclei'].attrs['nucl_num']=natom
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qph5['nuclei'].attrs['kpt_num']=Nk
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qph5.create_group('ao_two_e_ints')
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qph5['ao_two_e_ints'].attrs['df_num']=naux
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# qph5['ao_two_e_ints'].attrs['df_num']=naux
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qph5['ao_basis'].attrs['ao_basis']=mf.cell.basis
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ao_nucl=[mf.cell.bas_atom(i)+1 for i in range(nao)]
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@ -612,67 +683,52 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
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qph5.create_dataset('mo_basis/mo_coef_imag',data=mo_coef_blocked.imag)
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qph5.create_dataset('mo_basis/mo_coef_kpts_real',data=mo_k.real)
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qph5.create_dataset('mo_basis/mo_coef_kpts_imag',data=mo_k.imag)
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with open('C.qp','w') as outfile:
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c_kpts = np.reshape(mo_k,(Nk,nao,nmo))
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for ik in range(Nk):
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shift1=ik*nao+1
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shift2=ik*nmo+1
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for i in range(nao):
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for j in range(nmo):
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cij = c_kpts[ik,i,j]
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if abs(cij) > mo_coef_threshold:
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outfile.write(stri2z(i+shift1, j+shift2, cij.real, cij.imag)+'\n')
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print_kpts_unblocked(mo_k,'C.qp',mo_coef_threshold)
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# ___
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# | ._ _|_ _ _ ._ _. | _ |\/| _ ._ _
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# _|_ | | |_ (/_ (_| | (_| | _> | | (_) | | (_)
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# _|
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if mf.cell.pseudo:
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v_kpts_ao = np.reshape(mf.with_df.get_pp(kpts=kpts),(Nk,nao,nao))
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else:
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v_kpts_ao = np.reshape(mf.with_df.get_nuc(kpts=kpts),(Nk,nao,nao))
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if len(cell._ecpbas) > 0:
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v_kpts_ao += np.reshape(ecp.ecp_int(cell, kpts),(Nk,nao,nao))
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ne_ao = ('V',v_kpts_ao,ne_threshold)
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ovlp_ao = ('S',np.reshape(mf.get_ovlp(cell=cell,kpts=kpts),(Nk,nao,nao)),ovlp_threshold)
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kin_ao = ('T',np.reshape(cell.pbc_intor('int1e_kin',1,1,kpts=kpts),(Nk,nao,nao)),kin_threshold)
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kin_ao_blocked=scipy.linalg.block_diag(*kin_ao[1])
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ovlp_ao_blocked=scipy.linalg.block_diag(*ovlp_ao[1])
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ne_ao_blocked=scipy.linalg.block_diag(*v_kpts_ao)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_kinetic_real',data=kin_ao_blocked.real)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_kinetic_imag',data=kin_ao_blocked.imag)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_overlap_real',data=ovlp_ao_blocked.real)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_overlap_imag',data=ovlp_ao_blocked.imag)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_n_e_real', data=ne_ao_blocked.real)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_n_e_imag', data=ne_ao_blocked.imag)
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ne_ao = get_pot_ao(mf)
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kin_ao = get_kin_ao(mf)
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ovlp_ao = get_ovlp_ao(mf)
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if print_ao_ints_mono:
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kin_ao_blocked=scipy.linalg.block_diag(*kin_ao)
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ovlp_ao_blocked=scipy.linalg.block_diag(*ovlp_ao)
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ne_ao_blocked=scipy.linalg.block_diag(*ne_ao)
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for name, intval_kpts_ao, thresh in (ne_ao, ovlp_ao, kin_ao):
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if print_ao_ints_mono:
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with open('%s.qp' % name,'w') as outfile:
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for ik in range(Nk):
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shift=ik*nao+1
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for i in range(nao):
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for j in range(i,nao):
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int_ij = intval_kpts_ao[ik,i,j]
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if abs(int_ij) > thresh:
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outfile.write(stri2z(i+shift, j+shift, int_ij.real, int_ij.imag)+'\n')
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if print_mo_ints_mono:
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intval_kpts_mo = np.einsum('kim,kij,kjn->kmn',mo_k.conj(),intval_kpts_ao,mo_k)
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with open('%s_mo.qp' % name,'w') as outfile:
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for ik in range(Nk):
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shift=ik*nmo+1
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for i in range(nmo):
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for j in range(i,nmo):
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int_ij = intval_kpts_mo[ik,i,j]
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if abs(int_ij) > thresh:
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outfile.write(stri2z(i+shift, j+shift, int_ij.real, int_ij.imag)+'\n')
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qph5.create_dataset('ao_one_e_ints/ao_integrals_kinetic_real',data=kin_ao_blocked.real)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_kinetic_imag',data=kin_ao_blocked.imag)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_overlap_real',data=ovlp_ao_blocked.real)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_overlap_imag',data=ovlp_ao_blocked.imag)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_n_e_real', data=ne_ao_blocked.real)
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qph5.create_dataset('ao_one_e_ints/ao_integrals_n_e_imag', data=ne_ao_blocked.imag)
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for fname,ints in zip(('S.qp','V.qp','T.qp'),
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(ovlp_ao, ne_ao, kin_ao)):
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print_kpts_unblocked_upper(ints,fname,thresh_mono)
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if print_mo_ints_mono:
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kin_mo = ao_to_mo_1e(kin_ao,mo_k)
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ovlp_mo = ao_to_mo_1e(ovlp_ao,mo_k)
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ne_mo = ao_to_mo_1e(ne_ao,mo_k)
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kin_mo_blocked=scipy.linalg.block_diag(*kin_mo)
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ovlp_mo_blocked=scipy.linalg.block_diag(*ovlp_mo)
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ne_mo_blocked=scipy.linalg.block_diag(*ne_mo)
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qph5.create_dataset('mo_one_e_ints/mo_integrals_kinetic_real',data=kin_mo_blocked.real)
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qph5.create_dataset('mo_one_e_ints/mo_integrals_kinetic_imag',data=kin_mo_blocked.imag)
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qph5.create_dataset('mo_one_e_ints/mo_integrals_overlap_real',data=ovlp_mo_blocked.real)
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qph5.create_dataset('mo_one_e_ints/mo_integrals_overlap_imag',data=ovlp_mo_blocked.imag)
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qph5.create_dataset('mo_one_e_ints/mo_integrals_n_e_real', data=ne_mo_blocked.real)
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qph5.create_dataset('mo_one_e_ints/mo_integrals_n_e_imag', data=ne_mo_blocked.imag)
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for fname,ints in zip(('S.mo.qp','V.mo.qp','T.mo.qp'),
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(ovlp_mo, ne_mo, kin_mo)):
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print_kpts_unblocked_upper(ints,fname,thresh_mono)
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# ___ _
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@ -721,6 +777,9 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
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j3arr=np.array([(i.value.reshape([-1,nao,nao]) if (i.shape[1] == naosq) else makesq3(i.value,nao)) * nkinvsq for i in j3clist])
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nkpt_pairs = j3arr.shape[0]
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naux = max(i.shape[0] for i in j3arr)
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print("n df fitting functions", naux)
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qph5['ao_two_e_ints'].attrs['df_num']=naux
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df_ao_tmp = np.zeros((nao,nao,naux,nkpt_pairs),dtype=np.complex128)
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if print_ao_ints_df:
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@ -764,6 +823,88 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
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if (print_mo_ints_bi):
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print_mo_bi(mf,kconserv,'W.mo.qp',cas_idx,bielec_int_threshold)
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def getj3ao(cell,mf, kpts, cas_idx=None, int_threshold = 1E-8):
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'''
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kpts = List of kpoints coordinates. Cannot be null, for gamma is other script
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kmesh = Mesh of kpoints (optional)
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cas_idx = List of active MOs. If not specified all MOs are actives
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int_threshold = The integral will be not printed in they are bellow that
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'''
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from pyscf.pbc import ao2mo
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from pyscf.pbc import tools
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from pyscf.pbc.gto import ecp
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from pyscf.data import nist
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import h5py
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import scipy
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mo_coef_threshold = int_threshold
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ovlp_threshold = int_threshold
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kin_threshold = int_threshold
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ne_threshold = int_threshold
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bielec_int_threshold = int_threshold
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mo_coeff = mf.mo_coeff
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# Mo_coeff actif
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mo_k = np.array([c[:,cas_idx] for c in mo_coeff] if cas_idx is not None else mo_coeff)
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e_k = np.array([e[cas_idx] for e in mf.mo_energy] if cas_idx is not None else mf.mo_energy)
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Nk, nao, nmo = mo_k.shape
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print("n Kpts", Nk)
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print("n active Mos per kpt", nmo)
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print("n AOs per kpt", nao)
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# naux = mf.with_df.auxcell.nao
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# print("n df fitting functions", naux)
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with h5py.File(mf.with_df._cderi) as intfile:
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# intfile=h5py.File(mf.with_df._cderi,'r')
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j3c = intfile.get('j3c')
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naosq = nao*nao
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naotri = (nao*(nao+1))//2
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j3ckeys = list(j3c.keys())
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j3ckeys.sort(key=lambda strkey:int(strkey))
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# in new(?) version of PySCF, there is an extra layer of groups before the datasets
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# datasets used to be [/j3c/0, /j3c/1, /j3c/2, ...]
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# datasets now are [/j3c/0/0, /j3c/1/0, /j3c/2/0, ...]
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j3clist = [j3c.get(i+'/0') for i in j3ckeys]
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if j3clist==[None]*len(j3clist):
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# if using older version, stop before last level
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j3clist = [j3c.get(i) for i in j3ckeys]
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nkinvsq = 1./np.sqrt(Nk)
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# dimensions are (kikj,iaux,jao,kao), where kikj is compound index of kpts i and j
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# output dimensions should be reversed (nao, nao, naux, nkptpairs)
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j3arr=np.array([(i.value.reshape([-1,nao,nao]) if (i.shape[1] == naosq) else makesq3(i.value,nao)) * nkinvsq for i in j3clist])
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return j3arr
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#nkpt_pairs = j3arr.shape[0]
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#df_ao_tmp = np.zeros((nao,nao,naux,nkpt_pairs),dtype=np.complex128)
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#if print_ao_ints_df:
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# with open('D.qp','w') as outfile:
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# pass
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# with open('D.qp','a') as outfile:
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# for k,kpt_pair in enumerate(j3arr):
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# for iaux,dfbasfunc in enumerate(kpt_pair):
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# for i,i0 in enumerate(dfbasfunc):
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# for j,v in enumerate(i0):
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# if (abs(v) > bielec_int_threshold):
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# outfile.write(stri4z(i+1,j+1,iaux+1,k+1,v.real,v.imag)+'\n')
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# df_ao_tmp[i,j,iaux,k]=v
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#def testpyscf2QP(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8):
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