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Merge simple_point_dens_mat and density_gf into a single function density_matrix

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density_matrix takes as argument method == using_gf or using_point_integration
This commit is contained in:
Priyanka Seth 2014-11-16 18:02:04 +01:00
parent 4cb0d67e02
commit 5eaa27a946
4 changed files with 42 additions and 72 deletions
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1
python/TODOFIX
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@ -58,3 +58,4 @@ setattr(self,it,mpi.bcast(getattr(self,it))
* replaced long archive saves in converters by setattr construction
* removed G_upfolded_id -- looked redundant
* write corr_to_inequiv, inequiv_to_corr, n_inequiv_shells (shellmap, invshellmap, n_inequiv_corr_shells) in converter
* merge simple_point_dens_mat and density_gf into a single function density_matrix

91
python/sumk_lda.py
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@ -295,27 +295,38 @@ class SumkLDA:
return self.G_upfold
def simple_point_dens_mat(self):
ntoi = self.spin_names_to_ind[self.SO]
bln = self.spin_block_names[self.SO]
MMat = [numpy.zeros( [self.n_orbitals[0,ntoi[bl]],self.n_orbitals[0,ntoi[bl]]], numpy.complex_) for bl in bln]
def density_matrix(self, method = 'using_gf', beta=40.0):
"""Calculate density matrices in one of two ways:
if 'using_gf': First get upfolded gf (g_loc is not set up), then density matrix.
It is useful for Hubbard I, and very quick.
No assumption on the hopping structure is made (ie diagonal or not).
if 'using_point_integration': Only works for diagonal hopping matrix (true in wien2k).
"""
dens_mat = [ {} for icrsh in range(self.n_corr_shells)]
for icrsh in range(self.n_corr_shells):
for bl in self.spin_block_names[self.corr_shells[icrsh][4]]:
dens_mat[icrsh][bl] = numpy.zeros([self.corr_shells[icrsh][3],self.corr_shells[icrsh][3]], numpy.complex_)
ikarray = numpy.array(range(self.n_k))
for ik in mpi.slice_array(ikarray):
unchangedsize = all( [ self.n_orbitals[ik,ntoi[bln[ibl]]] == len(MMat[ibl])
for ibl in range(self.n_spin_blocks[self.SO]) ] )
if method == "using_gf":
if not unchangedsize:
MMat = [numpy.zeros( [self.n_orbitals[ik,ntoi[bl]],self.n_orbitals[ik,ntoi[bl]]], numpy.complex_) for bl in bln]
G_upfold = self.lattice_gf_matsubara(ik=ik, beta=beta, mu=self.chemical_potential)
G_upfold *= self.bz_weights[ik]
dm = G_upfold.density()
MMat = [dm[bl] for bl in self.spin_block_names[self.SO]]
elif method == "using_point_integration":
ntoi = self.spin_names_to_ind[self.SO]
bln = self.spin_block_names[self.SO]
unchangedsize = all( [self.n_orbitals[ik,ntoi[bl]] == self.n_orbitals[0,ntoi[bl]] for bl in bln] )
if unchangedsize:
dim = self.n_orbitals[0,ntoi[bl]]
else:
dim = self.n_orbitals[ik,ntoi[bl]]
MMat = [numpy.zeros( [dim,dim], numpy.complex_) for bl in bln]
for ibl, bl in enumerate(bln):
ind = ntoi[bl]
@ -331,6 +342,10 @@ class SumkLDA:
dim = self.corr_shells[icrsh][3]
n_orb = self.n_orbitals[ik,isp]
projmat = self.proj_mat[ik,isp,icrsh,0:dim,0:n_orb]
if method == "using_gf":
dens_mat[icrsh][bn] += numpy.dot( numpy.dot(projmat,MMat[ibl]),
projmat.transpose().conjugate() )
elif method == "using_point_integration":
dens_mat[icrsh][bn] += self.bz_weights[ik] * numpy.dot( numpy.dot(projmat,MMat[ibl]) ,
projmat.transpose().conjugate() )
@ -347,57 +362,11 @@ class SumkLDA:
for icrsh in range(self.n_corr_shells):
for bn in dens_mat[icrsh]:
if self.rot_mat_time_inv[icrsh] == 1: dens_mat[icrsh][bn] = dens_mat[icrsh][bn].conjugate()
dens_mat[icrsh][bn] = numpy.dot( numpy.dot(self.rot_mat[icrsh].conjugate().transpose(),dens_mat[icrsh][bn]) ,
dens_mat[icrsh][bn] = numpy.dot( numpy.dot(self.rot_mat[icrsh].conjugate().transpose(),dens_mat[icrsh][bn]),
self.rot_mat[icrsh] )
return dens_mat
# Calculate upfolded gf, then density matrix. No assumption on the structure made here (ie diagonal or not).
def density_gf(self,beta):
"""Calculates the density without setting up Gloc. It is useful for Hubbard I, and very quick."""
dens_mat = [ {} for icrsh in range(self.n_corr_shells)]
for icrsh in range(self.n_corr_shells):
for bl in self.spin_block_names[self.corr_shells[icrsh][4]]:
dens_mat[icrsh][bl] = numpy.zeros([self.corr_shells[icrsh][3],self.corr_shells[icrsh][3]], numpy.complex_)
ikarray = numpy.array(range(self.n_k))
for ik in mpi.slice_array(ikarray):
G_upfold = self.lattice_gf_matsubara(ik=ik, beta=beta, mu=self.chemical_potential)
G_upfold *= self.bz_weights[ik]
dm = G_upfold.density()
MMat = [dm[bl] for bl in self.spin_block_names[self.SO]]
for icrsh in range(self.n_corr_shells):
for ibl, bn in enumerate(self.spin_block_names[self.corr_shells[icrsh][4]]):
isp = self.spin_names_to_ind[self.corr_shells[icrsh][4]][bn]
dim = self.corr_shells[icrsh][3]
n_orb = self.n_orbitals[ik,isp]
projmat = self.proj_mat[ik,isp,icrsh,0:dim,0:n_orb]
dens_mat[icrsh][bn] += numpy.dot( numpy.dot(projmat,MMat[ibl]),
projmat.transpose().conjugate() )
# get data from nodes:
for icrsh in range(self.n_corr_shells):
for bname in dens_mat[icrsh]:
dens_mat[icrsh][bname] = mpi.all_reduce(mpi.world, dens_mat[icrsh][bname], lambda x,y : x+y)
mpi.barrier()
if self.symm_op != 0: dens_mat = self.symmcorr.symmetrize(dens_mat)
# Rotate to local coordinate system:
if self.use_rotations:
for icrsh in range(self.n_corr_shells):
for bn in dens_mat[icrsh]:
if self.rot_mat_time_inv[icrsh] == 1: dens_mat[icrsh][bn] = dens_mat[icrsh][bn].conjugate()
dens_mat[icrsh][bn] = numpy.dot( numpy.dot(self.rot_mat[icrsh].conjugate().transpose(),dens_mat[icrsh][bn]) ,
self.rot_mat[icrsh] )
return dens_mat
def analyse_block_structure(self, threshold = 0.00001, include_shells = None, dm = None):
""" Determines the Green function block structure from simple point integration."""
@ -407,7 +376,7 @@ class SumkLDA:
self.solver_to_sumk = [ {} for ish in range(self.n_inequiv_shells) ]
self.solver_to_sumk_block = [ {} for ish in range(self.n_inequiv_shells) ]
if dm is None: dm = self.simple_point_dens_mat()
if dm is None: dm = self.density_matrix(method = 'using_point_integration')
dens_mat = [ dm[self.inequiv_to_corr[ish]] for ish in range(self.n_inequiv_shells) ]
if include_shells is None: include_shells = range(self.n_inequiv_shells)
@ -499,7 +468,7 @@ class SumkLDA:
for bl in degsh: ss += gf_to_symm[bl] / (1.0*Ndeg)
for bl in degsh: gf_to_symm[bl] << ss
# for simple dft input, get crystal field splittings.
# For simple dft input, get crystal field splittings.
def eff_atomic_levels(self):
"""Calculates the effective atomic levels needed as input for the Hubbard I Solver."""

2
python/trans_basis.py
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@ -37,7 +37,7 @@ class TransBasis:
if prop_to_be_diagonal == 'eal':
prop = self.SK.eff_atomic_levels()[0]
elif prop_to_be_diagonal == 'dm':
prop = self.SK.simple_point_dens_mat()[0]
prop = self.SK.density_matrix(method = 'using_point_integration')[0]
else:
mpi.report("trans_basis: not a valid quantitiy to be diagonal. Choices are 'eal' or 'dm'.")
return 0

2
test/sumklda_basic.py
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@ -26,7 +26,7 @@ from pytriqs.applications.dft.sumk_lda_tools import SumkLDATools
SK = SumkLDATools(hdf_file = 'SrVO3.h5')
dm = SK.density_gf(40)
dm = SK.density_matrix(method = 'using_gf', beta = 40)
dm_pc = SK.partial_charges(40)
ar = HDFArchive('sumklda_basic.output.h5','w')