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working on converter

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hdf5 outputs c-contiguous numpy arrays
ezfio assumes arrays are fortran-ordered
np.view can be used to get re,im parts as floats with doubling of one dimension
(last for c-contiguous, possibly first for f-contiguous?)

working on changing the converter to minimize transposing, reshaping, taking re/im parts, stacking, etc.
This commit is contained in:
Kevin Gasperich 2020-03-11 17:44:47 -05:00
parent b0bf0c79d6
commit 01360efd84
1 changed files with 68 additions and 27 deletions
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95
src/utils_complex/MolPyscfToQPkpts.py
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@ -434,6 +434,8 @@ def get_j3ao(fname,nao,Nk):
'''
returns padded df AO array
fills in zeros when functions are dropped due to linear dependency
last AO index corresponds to smallest kpt index?
(k, mu, i, j) where i.kpt >= j.kpt
'''
import h5py
with h5py.File(fname,'r') as intfile:
@ -461,8 +463,48 @@ def get_j3ao(fname,nao,Nk):
iaux,dim2 = j3c[kpair+keysub].shape
if (dim2==naosq):
j3arr[i,:iaux,:,:] = j3c[kpair+keysub][()].reshape([iaux,nao,nao]) * nkinvsq
#j3arr[i,:iaux,:,:] = j3c[kpair+keysub][()].reshape([iaux,nao,nao]).transpose((0,2,1)) * nkinvsq
else:
j3arr[i,:iaux,:,:] = makesq3(j3c[kpair+keysub][()],nao) * nkinvsq
#j3arr[i,:iaux,:,:] = makesq3(j3c[kpair+keysub][()].conj(),nao) * nkinvsq
return j3arr
def get_j3ao_new(fname,nao,Nk):
'''
returns padded df AO array
fills in zeros when functions are dropped due to linear dependency
last AO index corresponds to largest kpt index?
(k, mu, j, i) where i.kpt >= j.kpt
'''
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]).transpose((0,2,1)) * nkinvsq
else:
j3arr[i,:iaux,:,:] = makesq3(j3c[kpair+keysub][()].conj(),nao) * nkinvsq
return j3arr
@ -494,6 +536,16 @@ def df_ao_to_mo(j3ao,mo_coef):
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_new(j3ao,mo_coef):
#TODO: fix this (C/F ordering, conj, transpose, view cmplx->float)
from itertools import product
Nk = mo_coef.shape[0]
return np.array([
np.einsum('mji,ik,jl->mlk',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 df_ao_to_mo_test(j3ao,mo_coef):
from itertools import product
Nk = mo_coef.shape[0]
@ -627,13 +679,17 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
# MO Coef #
# #
##########################################
mo_coef_blocked=block_diag(*mo_k)
with h5py.File(qph5path,'a') as qph5:
mo_coef_f = np.array(mo_k.transpose((0,2,1)),order='c')
mo_coef_blocked=block_diag(*mo_k)
mo_coef_blocked_f = block_diag(*mo_coef_f)
qph5.create_dataset('mo_basis/mo_coef_real',data=mo_coef_blocked.real)
qph5.create_dataset('mo_basis/mo_coef_imag',data=mo_coef_blocked.imag)
qph5.create_dataset('mo_basis/mo_coef_kpts_real',data=mo_k.real)
qph5.create_dataset('mo_basis/mo_coef_kpts_imag',data=mo_k.imag)
qph5.create_dataset('mo_basis/mo_coef',data=mo_coef_blocked_f.view(dtype=np.float64).reshape((Nk*nmo,Nk*nao,2)))
qph5.create_dataset('mo_basis/mo_coef_kpts',data=mo_coef_f.view(dtype=np.float64).reshape((Nk,nmo,nao,2)))
print_kpts_unblocked(mo_k,'C.qp',mo_coef_threshold)
@ -714,12 +770,9 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
j3arr = get_j3ao(mf.with_df._cderi,nao,Nk)
# test? should be (Nk*(Nk+1))//2
nkpt_pairs = j3arr.shape[0]
# test? nkpt_pairs should be (Nk*(Nk+1))//2
nkpt_pairs, naux, _, _ = j3arr.shape
# 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:
qph5.create_group('ao_two_e_ints')
@ -727,36 +780,24 @@ def pyscf2QP2(cell,mf, kpts, kmesh=None, cas_idx=None, int_threshold = 1E-8,
if print_ao_ints_df:
print_df(j3arr,'D.qp',bielec_int_threshold)
j3ao_new = get_j3ao_new(mf.with_df._cderi,nao,Nk)
df_ao_tmp = np.zeros((nao,nao,naux,nkpt_pairs),dtype=np.complex128)
for i,di in enumerate(j3arr):
df_ao_tmp[:,:,:di.shape[0],i] = np.transpose(di,(1,2,0))
#df_ao_old = df_pad_ref_test(j3arr,nao,naux,nkpt_pairs)
#assert(abs(df_ao_tmp - df_ao_old).max() <= 1e-12)
with h5py.File(qph5path,'a') as qph5:
qph5.create_dataset('ao_two_e_ints/df_ao_integrals_real',data=df_ao_tmp.real)
qph5.create_dataset('ao_two_e_ints/df_ao_integrals_imag',data=df_ao_tmp.imag)
qph5.create_dataset('ao_two_e_ints/df_ao_integrals_real',data=j3arr.transpose((2,3,1,0)).real)
qph5.create_dataset('ao_two_e_ints/df_ao_integrals_imag',data=j3arr.transpose((2,3,1,0)).imag)
qph5.create_dataset('ao_two_e_ints/df_ao_integrals',data=j3ao_new.view(dtype=np.float64).reshape((nkpt_pairs,naux,nao,nao,2)))
if print_mo_ints_df:
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)]))
j3mo_new = df_ao_to_mo_new(j3ao_new,mo_k)
print_df(j3mo,'D.mo.qp',bielec_int_threshold)
df_mo_tmp = np.zeros((nmo,nmo,naux,nkpt_pairs),dtype=np.complex128)
for i,di in enumerate(j3mo):
df_mo_tmp[:,:,:di.shape[0],i] = np.transpose(di,(1,2,0))
#df_mo_old = df_pad_ref_test(j3mo,nmo,naux,nkpt_pairs)
#assert(abs(df_mo_tmp - df_mo_old).max() <= 1e-12)
with h5py.File(qph5path,'a') as qph5:
qph5.create_dataset('mo_two_e_ints/df_mo_integrals_real',data=df_mo_tmp.real)
qph5.create_dataset('mo_two_e_ints/df_mo_integrals_imag',data=df_mo_tmp.imag)
qph5.create_dataset('mo_two_e_ints/df_mo_integrals_real',data=j3mo.transpose((2,3,1,0)).real)
qph5.create_dataset('mo_two_e_ints/df_mo_integrals_imag',data=j3mo.transpose((2,3,1,0)).imag)
qph5.create_dataset('mo_two_e_ints/df_mo_integrals',data=j3mo_new.view(dtype=np.float64).reshape((nkpt_pairs,naux,nmo,nmo,2)))
if (print_ao_ints_bi):
print_ao_bi(mf,kconserv,'W.qp',bielec_int_threshold)