def genHRfacSpatialQt(pindx,nsite,isite,int1e,qpts,maxslc=1,model_u=0.):
p,ip = pindx
iop = 1
isym = 2
status = 'L'
isz = p%2
site = mpo_dmrg_opers1e.genHRfacSpatial(pindx,nsite,isite,int1e,qpts,model_u)
# Site physical indices
qu,qd = qtensor_opers.genQphys(isym,isite)
# Orbital dependent part
ql1e,qr1e = qtensor_opers.genElemQnums(p,2*isite ,iop,status)
ql2e,qr2e = qtensor_opers.genElemQnums(p,2*isite+1,iop,status)
ql1 = ql1e
qr1 = qr2e
ql1w,qr1w = genWfacQnums(nsite,2*isite ,isz,status)
ql2w,qr2w = genWfacQnums(nsite,2*isite+1,isz,status)
ql2 = ql1w
qr2 = qr2w
ql = [q1+q2 for q1,q2 in itertools.product(ql1,ql2)]
qr = [q1+q2 for q1,q2 in itertools.product(qr1,qr2)]
if abs(model_u)>1.e-12:
if isite == 0:
qr += [[0.,0.]]
elif isite == nsite//2-1:
ql += [[0.,0.]]
else:
ql += [[0.,0.]]
qr += [[0.,0.]]
# Reduce symmetry: local spin rotation do not change particle number
if ip is not None:
ql = qtensor_util.reduceQnumsToN(ql)
qr = qtensor_util.reduceQnumsToN(qr)
qu = qtensor_util.reduceQnumsToN(qu)
qd = qtensor_util.reduceQnumsToN(qd)
#
# We sort the internal quantum number first
#
nql = len(ql)
nqr = len(qr)
idxl = qtensor_opers.sortQnums(ql)
idxr = qtensor_opers.sortQnums(qr)
ql = numpy.array(ql)[idxl]
qr = numpy.array(qr)[idxr]
site = site[numpy.ix_(idxl,idxr)].copy()
# Slice operators if necessary
qts = qtensor.Qt(2)
qts.maxslc[0] = min(maxslc,nql)
qts.maxslc[1] = min(maxslc,nqr)
qts.genDic()
for islc in range(qts.maxslc[0]):
slc0 = parallel_util.partitionSites(nql,qts.maxslc[0],islc)
for jslc in range(qts.maxslc[1]):
slc1 = parallel_util.partitionSites(nqr,qts.maxslc[1],jslc)
ijdx = qts.ravel([islc,jslc])
qts.dic[ijdx] = qtensor.qtensor([False,True,False,True])
# Note that the qsyms for the last two dimensions are not collected
qts.dic[ijdx].fromDenseTensor(site[numpy.ix_(slc0,slc1)],[ql[slc0],qr[slc1],qu,qd],ifcollect=[1,1,0,0])
qts.size[ijdx] = qts.dic[ijdx].size_allowed
return qts
def genGlobalSpatialQt(nsite, isite, key):
isym = 2
# (D,D,4,4) [D<=2]
site = mpo_dmrg_spinopers.genGlobalSpatial(nsite, isite, key)
# Status
qt = qtensor.qtensor([False, True, False, True])
# Site physical indices
qu, qd = qtensor_opers.genQphys(isym, isite)
ql, qr = genSpinOpersQnums(nsite, isite, key)
qt.fromDenseTensor(site, [ql, qr, qu, qd])
return qt
def genLocal2SpatialQt(nsite, isite, ig, jg, ikey, jkey, fac):
isym = 2
# (D,D,4,4) [D<=2]
site = mpo_dmrg_spinopers.genLocal2Spatial(nsite, isite, ig, jg, ikey,
jkey, fac)
# Status
qt = qtensor.qtensor([False, True, False, True])
# Site physical indices
qu, qd = qtensor_opers.genQphys(isym, isite)
qli, qri = genSpinOpersQnums(nsite, isite, ikey)
qlj, qrj = genSpinOpersQnums(nsite, isite, jkey)
# Direct product of quantum numbers
ql = [q1 + q2 for q1, q2 in itertools.product(qli, qlj)]
qr = [q1 + q2 for q1, q2 in itertools.product(qri, qrj)]
qt.fromDenseTensor(site, [ql, qr, qu, qd])
return qt
def genLocalRSpatialQt(nsite, isite, ig, key, phi):
isym = 2
# (D,D,4,4) [D<=2]
cop = mpo_dmrg_spinopers.genLocalSpatial(nsite, isite, ig, key)
rop = mpo_dmrg_opers.genExpISyPhiMat(phi)
site = numpy.tensordot(cop, rop, axes=([3], [0]))
# Status
qt = qtensor.qtensor([False, True, False, True])
# Site physical indices
qu, qd = qtensor_opers.genQphys(isym, isite)
ql, qr = genSpinOpersQnums(nsite, isite, key)
# Reduce symmetry
qu = qtensor_util.reduceQnumsToN(qu)
qd = qtensor_util.reduceQnumsToN(qd)
ql = qtensor_util.reduceQnumsToN(ql)
qr = qtensor_util.reduceQnumsToN(qr)
qt.fromDenseTensor(site, [ql, qr, qu, qd], ifcollect=[0, 0, 0, 0])
return qt
def genLocal2RSpatialQt(nsite, isite, ig, jg, ikey, jkey, fac, phi):
isym = 2
# (D,D,4,4) [D<=2]
cop = mpo_dmrg_spinopers.genLocal2Spatial(nsite, isite, ig, jg, ikey, jkey,
fac)
rop = mpo_dmrg_opers.genExpISyPhiMat(phi)
site = numpy.tensordot(cop, rop, axes=([3], [0]))
# Status
qt = qtensor.qtensor([False, True, False, True])
# Site physical indices
qu, qd = qtensor_opers.genQphys(isym, isite)
qli, qri = genSpinOpersQnums(nsite, isite, ikey)
qlj, qrj = genSpinOpersQnums(nsite, isite, jkey)
# Direct product of quantum numbers
ql = [q1 + q2 for q1, q2 in itertools.product(qli, qlj)]
qr = [q1 + q2 for q1, q2 in itertools.product(qri, qrj)]
# Reduce symmetry
qu = qtensor_util.reduceQnumsToN(qu)
qd = qtensor_util.reduceQnumsToN(qd)
ql = qtensor_util.reduceQnumsToN(ql)
qr = qtensor_util.reduceQnumsToN(qr)
qt.fromDenseTensor(site, [ql, qr, qu, qd], ifcollect=[0, 0, 0, 0])
return qt