##########################################################################
#
# TRIQS: a Toolbox for Research in Interacting Quantum Systems
#
# Copyright (C) 2018 by G. J. Kraberger
# Copyright (C) 2011 by M. Aichhorn, L. Pourovskii, V. Vildosola
#
# TRIQS is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# TRIQS. If not, see <http://www.gnu.org/licenses/>.
#
##########################################################################
"""
General SumK class and helper functions for combining ab-initio code and triqs
"""
from types import *
import numpy as np
import triqs.utility.dichotomy as dichotomy
from triqs.gf import *
import triqs.utility.mpi as mpi
from triqs.utility.comparison_tests import assert_arrays_are_close
from h5 import HDFArchive
from .symmetry import *
from .block_structure import BlockStructure
from .util import compute_DC_from_density
from itertools import product
from warnings import warn
from numpy import compress
from scipy.optimize import minimize, newton, brenth
[docs]
class SumkDFT(object):
"""This class provides a general SumK method for combining ab-initio code and triqs."""
[docs]
def __init__(self, hdf_file, h_field=0.0, mesh=None, beta=40, n_iw=1025, use_dft_blocks=False,
dft_data='dft_input', symmcorr_data='dft_symmcorr_input', parproj_data='dft_parproj_input',
symmpar_data='dft_symmpar_input', bands_data='dft_bands_input', transp_data='dft_transp_input',
misc_data='dft_misc_input',bc_data='dft_bandchar_input',cont_data='dft_contours_input'):
r"""
Initialises the class from data previously stored into an hdf5 archive.
Parameters
----------
hdf_file : string
Name of hdf5 containing the data.
h_field : scalar, optional
The value of magnetic field to add to the DFT Hamiltonian.
The contribution -h_field*sigma is added to diagonal elements of the Hamiltonian.
It cannot be used with the spin-orbit coupling on; namely h_field is set to 0 if self.SO=True.
mesh: MeshImFreq or MeshReFreq, optional. Frequency mesh of Sigma.
beta : real, optional
Inverse temperature. Used to construct imaginary frequency if mesh is not given.
n_iw : integer, optional
Number of Matsubara frequencies. Used to construct imaginary frequency if mesh is not given.
use_dft_blocks : boolean, optional
If True, the local Green's function matrix for each spin is divided into smaller blocks
with the block structure determined from the DFT density matrix of the corresponding correlated shell.
Alternatively and additionally, the block structure can be analysed using :meth:`analyse_block_structure <dft.sumk_dft.SumkDFT.analyse_block_structure>`
and manipulated using the SumkDFT.block_structre attribute (see :class:`BlockStructure <dft.block_structure.BlockStructure>`).
dft_data : string, optional
Name of hdf5 subgroup in which DFT data for projector and lattice Green's function construction are stored.
symmcorr_data : string, optional
Name of hdf5 subgroup in which DFT data on symmetries of correlated shells
(symmetry operations, permutaion matrices etc.) are stored.
parproj_data : string, optional
Name of hdf5 subgroup in which DFT data on non-normalized projectors for non-correlated
states (used in the partial density of states calculations) are stored.
symmpar_data : string, optional
Name of hdf5 subgroup in which DFT data on symmetries of the non-normalized projectors
are stored.
bands_data : string, optional
Name of hdf5 subgroup in which DFT data necessary for band-structure/k-resolved spectral
function calculations (projectors, DFT Hamiltonian for a chosen path in the Brillouin zone etc.)
are stored.
transp_data : string, optional
Name of hdf5 subgroup in which DFT data necessary for transport calculations are stored.
misc_data : string, optional
Name of hdf5 subgroup in which miscellaneous DFT data are stored.
"""
if not isinstance(hdf_file, str):
mpi.report("Give a string for the hdf5 filename to read the input!")
else:
self.hdf_file = hdf_file
self.dft_data = dft_data
self.symmcorr_data = symmcorr_data
self.parproj_data = parproj_data
self.symmpar_data = symmpar_data
self.bands_data = bands_data
self.transp_data = transp_data
self.misc_data = misc_data
self.bc_data = bc_data
self.cont_data = cont_data
self.h_field = h_field
if mesh is None:
self.mesh = MeshImFreq(beta=beta, S='Fermion', n_max=n_iw)
self.mesh_values = np.linspace(self.mesh(self.mesh.first_index()),
self.mesh(self.mesh.last_index()),
len(self.mesh))
elif isinstance(mesh, MeshImFreq):
self.mesh = mesh
self.mesh_values = np.linspace(self.mesh(self.mesh.first_index()),
self.mesh(self.mesh.last_index()),
len(self.mesh))
elif isinstance(mesh, MeshReFreq):
self.mesh = mesh
self.mesh_values = np.linspace(self.mesh.w_min, self.mesh.w_max, len(self.mesh))
else:
raise ValueError('mesh must be a triqs mesh of type MeshImFreq or MeshReFreq')
self.block_structure = BlockStructure()
# Read input from HDF:
req_things_to_read = ['energy_unit', 'n_k', 'k_dep_projection', 'SP', 'SO', 'charge_below', 'density_required',
'symm_op', 'n_shells', 'shells', 'n_corr_shells', 'corr_shells', 'use_rotations', 'rot_mat',
'rot_mat_time_inv', 'n_reps', 'dim_reps', 'T', 'n_orbitals', 'proj_mat', 'bz_weights', 'hopping',
'n_inequiv_shells', 'corr_to_inequiv', 'inequiv_to_corr']
self.subgroup_present, self.values_not_read = self.read_input_from_hdf(
subgrp=self.dft_data, things_to_read=req_things_to_read)
# test if all required properties have been found
if len(self.values_not_read) > 0 and mpi.is_master_node:
raise ValueError('ERROR: One or more necessary SumK input properties have not been found in the given h5 archive:', self.values_not_read)
# optional properties to load
# soon bz_weights is depraced and replaced by kpt_weights, kpts_basis and kpts will become required to read soon
optional_things_to_read = ['proj_mat_csc', 'proj_or_hk', 'kpt_basis', 'kpts', 'kpt_weights', 'dft_code']
subgroup_present, self.optional_values_not_read = self.read_input_from_hdf(subgrp=self.dft_data, things_to_read=optional_things_to_read)
# warning if dft_code was not read (old h5 structure)
if 'dft_code' in self.optional_values_not_read:
self.dft_code = None
mpi.report('\nWarning: old h5 archive without dft_code input flag detected. Please specify sumk.dft_code manually!\n')
if self.symm_op:
self.symmcorr = Symmetry(hdf_file, subgroup=self.symmcorr_data)
if self.SO and (abs(self.h_field) > 0.000001):
self.h_field = 0.0
mpi.report(
"For SO, the external magnetic field is not implemented, setting it to 0!")
self.spin_block_names = [['up', 'down'], ['ud']]
self.n_spin_blocks = [2, 1]
# Convert spin_block_names to indices -- if spin polarized,
# differentiate up and down blocks
self.spin_names_to_ind = [{}, {}]
for iso in range(2): # SO = 0 or 1
for isp in range(self.n_spin_blocks[iso]):
self.spin_names_to_ind[iso][
self.spin_block_names[iso][isp]] = isp * self.SP
# GF structure used for the local things in the k sums
# Most general form allowing for all hybridisation, i.e. largest
# blocks possible
self.gf_struct_sumk = [[(sp, self.corr_shells[icrsh]['dim']) for sp in self.spin_block_names[self.corr_shells[icrsh]['SO']]]
for icrsh in range(self.n_corr_shells)]
# First set a standard gf_struct solver (add _0 here for consistency with analyse_block_structure):
self.gf_struct_solver = [dict([(sp+'_0', self.corr_shells[self.inequiv_to_corr[ish]]['dim'])
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]])
for ish in range(self.n_inequiv_shells)]
# Set standard (identity) maps from gf_struct_sumk <->
# gf_struct_solver
self.sumk_to_solver = [{} for ish in range(self.n_inequiv_shells)]
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)]
for ish in range(self.n_inequiv_shells):
for block, inner_dim in self.gf_struct_sumk[self.inequiv_to_corr[ish]]:
self.solver_to_sumk_block[ish][block+'_0'] = block
for inner in range(inner_dim):
self.sumk_to_solver[ish][
(block, inner)] = (block+'_0', inner)
self.solver_to_sumk[ish][
(block+'_0', inner)] = (block, inner)
# assume no shells are degenerate
self.deg_shells = [[] for ish in range(self.n_inequiv_shells)]
self.chemical_potential = 0.0 # initialise mu
self.init_dc() # initialise the double counting
# charge mixing parameters
self.charge_mixing = False
# defaults from PRB 90 235103 ("... slow but stable convergence ...")
self.charge_mixing_alpha = 0.1
self.charge_mixing_gamma = 1.0
self.deltaNOld = None
# Analyse the block structure and determine the smallest gf_struct
# blocks and maps, if desired
if use_dft_blocks:
self.analyse_block_structure()
self.min_band_energy = None
self.max_band_energy = None
################
# hdf5 FUNCTIONS
################
[docs]
def save(self, things_to_save, subgrp='user_data'):
r"""
Saves data from a list into the HDF file. Prints a warning if a requested data is not found in SumkDFT object.
Parameters
----------
things_to_save : list of strings
List of datasets to be saved into the hdf5 file.
subgrp : string, optional
Name of hdf5 file subgroup in which the data are to be stored.
"""
if not (mpi.is_master_node()):
return # do nothing on nodes
with HDFArchive(self.hdf_file, 'a') as ar:
if not subgrp in ar: ar.create_group(subgrp)
for it in things_to_save:
if it in [ "gf_struct_sumk", "gf_struct_solver",
"solver_to_sumk", "sumk_to_solver", "solver_to_sumk_block"]:
warn("It is not recommended to save '{}' individually. Save 'block_structure' instead.".format(it))
try:
ar[subgrp][it] = getattr(self, it)
except:
mpi.report("%s not found, and so not saved." % it)
[docs]
def load(self, things_to_load, subgrp='user_data'):
r"""
Loads user data from the HDF file. Raises an exeption if a requested dataset is not found.
Parameters
----------
things_to_read : list of strings
List of datasets to be read from the hdf5 file.
subgrp : string, optional
Name of hdf5 file subgroup from which the data are to be read.
Returns
-------
list_to_return : list
A list containing data read from hdf5.
"""
if not (mpi.is_master_node()):
return # do nothing on nodes
with HDFArchive(self.hdf_file, 'r') as ar:
if not subgrp in ar:
mpi.report("Loading %s failed!" % subgrp)
list_to_return = []
for it in things_to_load:
try:
list_to_return.append(ar[subgrp][it])
except:
raise ValueError("load: %s not found, and so not loaded." % it)
return list_to_return
################
# CORE FUNCTIONS
################
[docs]
def downfold(self, ik, ish, bname, gf_to_downfold, gf_inp, shells='corr', ir=None):
r"""
Downfolds a block of the Green's function for a given shell and k-point using the corresponding projector matrices.
Parameters
----------
ik : integer
k-point index for which the downfolding is to be done.
ish : integer
Shell index of GF to be downfolded.
- if shells='corr': ish labels all correlated shells (equivalent or not)
- if shells='all': ish labels only representative (inequivalent) non-correlated shells
bname : string
Block name of the target block of the lattice Green's function.
gf_to_downfold : Gf
Block of the Green's function that is to be downfolded.
gf_inp : Gf
FIXME
shells : string, optional
- if shells='corr': orthonormalized projectors for correlated shells are used for the downfolding.
- if shells='all': non-normalized projectors for all included shells are used for the downfolding.
- if shells='csc': orthonormalized projectors for all shells are used for the downfolding. Used for H(k).
ir : integer, optional
Index of equivalent site in the non-correlated shell 'ish', only used if shells='all'.
Returns
-------
gf_downfolded : Gf
Downfolded block of the lattice Green's function.
"""
gf_downfolded = gf_inp.copy()
# get spin index for proj. matrices
isp = self.spin_names_to_ind[self.SO][bname]
n_orb = self.n_orbitals[ik, isp]
if shells == 'corr':
dim = self.corr_shells[ish]['dim']
projmat = self.proj_mat[ik, isp, ish, 0:dim, 0:n_orb]
elif shells == 'all':
if ir is None:
raise ValueError("downfold: provide ir if treating all shells.")
dim = self.shells[ish]['dim']
projmat = self.proj_mat_all[ik, isp, ish, ir, 0:dim, 0:n_orb]
elif shells == 'csc':
projmat = self.proj_mat_csc[ik, isp, :, 0:n_orb]
gf_downfolded.from_L_G_R(
projmat, gf_to_downfold, projmat.conjugate().transpose())
return gf_downfolded
[docs]
def upfold(self, ik, ish, bname, gf_to_upfold, gf_inp, shells='corr', ir=None):
r"""
Upfolds a block of the Green's function for a given shell and k-point using the corresponding projector matrices.
Parameters
----------
ik : integer
k-point index for which the upfolding is to be done.
ish : integer
Shell index of GF to be upfolded.
- if shells='corr': ish labels all correlated shells (equivalent or not)
- if shells='all': ish labels only representative (inequivalent) non-correlated shells
bname : string
Block name of the target block of the lattice Green's function.
gf_to_upfold : Gf
Block of the Green's function that is to be upfolded.
gf_inp : Gf
FIXME
shells : string, optional
- if shells='corr': orthonormalized projectors for correlated shells are used for the upfolding.
- if shells='all': non-normalized projectors for all included shells are used for the upfolding.
- if shells='csc': orthonormalized projectors for all shells are used for the upfolding. Used for H(k).
ir : integer, optional
Index of equivalent site in the non-correlated shell 'ish', only used if shells='all'.
Returns
-------
gf_upfolded : Gf
Upfolded block of the lattice Green's function.
"""
gf_upfolded = gf_inp.copy()
# get spin index for proj. matrices
isp = self.spin_names_to_ind[self.SO][bname]
n_orb = self.n_orbitals[ik, isp]
if shells == 'corr':
dim = self.corr_shells[ish]['dim']
projmat = self.proj_mat[ik, isp, ish, 0:dim, 0:n_orb]
elif shells == 'all':
if ir is None:
raise ValueError("upfold: provide ir if treating all shells.")
dim = self.shells[ish]['dim']
projmat = self.proj_mat_all[ik, isp, ish, ir, 0:dim, 0:n_orb]
elif shells == 'csc':
projmat = self.proj_mat_csc[ik, isp, 0:n_orb, 0:n_orb]
gf_upfolded.from_L_G_R(
projmat.conjugate().transpose(), gf_to_upfold, projmat)
return gf_upfolded
[docs]
def rotloc(self, ish, gf_to_rotate, direction, shells='corr'):
r"""
Rotates a block of the local Green's function from the local frame to the global frame and vice versa.
Parameters
----------
ish : integer
Shell index of GF to be rotated.
- if shells='corr': ish labels all correlated shells (equivalent or not)
- if shells='all': ish labels only representative (inequivalent) non-correlated shells
gf_to_rotate : Gf
Block of the Green's function that is to be rotated.
direction : string
The direction of rotation can be either
- 'toLocal' : global -> local transformation,
- 'toGlobal' : local -> global transformation.
shells : string, optional
- if shells='corr': the rotation matrix for the correlated shell 'ish' is used,
- if shells='all': the rotation matrix for the generic (non-correlated) shell 'ish' is used.
Returns
-------
gf_rotated : Gf
Rotated block of the local Green's function.
"""
assert ((direction == 'toLocal') or (direction == 'toGlobal')
), "rotloc: Give direction 'toLocal' or 'toGlobal'."
gf_rotated = gf_to_rotate.copy()
if shells == 'corr':
rot_mat_time_inv = self.rot_mat_time_inv
rot_mat = self.rot_mat
elif shells == 'all':
rot_mat_time_inv = self.rot_mat_all_time_inv
rot_mat = self.rot_mat_all
if direction == 'toGlobal':
if (rot_mat_time_inv[ish] == 1) and self.SO:
gf_rotated << gf_rotated.transpose()
gf_rotated.from_L_G_R(rot_mat[ish].conjugate(
), gf_rotated, rot_mat[ish].transpose())
else:
gf_rotated.from_L_G_R(rot_mat[ish], gf_rotated, rot_mat[
ish].conjugate().transpose())
elif direction == 'toLocal':
if (rot_mat_time_inv[ish] == 1) and self.SO:
gf_rotated << gf_rotated.transpose()
gf_rotated.from_L_G_R(rot_mat[ish].transpose(
), gf_rotated, rot_mat[ish].conjugate())
else:
gf_rotated.from_L_G_R(rot_mat[ish].conjugate(
).transpose(), gf_rotated, rot_mat[ish])
return gf_rotated
[docs]
def lattice_gf(self, ik, mu=None, broadening=None, mesh=None, with_Sigma=True, with_dc=True):
r"""
Calculates the lattice Green function for a given k-point from the DFT Hamiltonian and the self energy.
Parameters
----------
ik : integer
k-point index.
mu : real, optional
Chemical potential for which the Green's function is to be calculated.
If not provided, self.chemical_potential is used for mu.
broadening : real, optional
Imaginary shift for the axis along which the real-axis GF is calculated.
If not provided, broadening will be set to double of the distance between mesh points in 'mesh'.
mesh : MeshReFreq or MeshImFreq, optional
Mesh to be used if with_Sigma=False. If with Sigma=False and mesh is none then self.mesh is used.
with_Sigma : boolean, optional
If True the GF will be calculated with the self-energy stored in self.Sigmaimp_(w/iw), for real/Matsubara GF, respectively.
In this case the mesh is taken from the self.Sigma_imp object.
If with_Sigma=True but self.Sigmaimp_(w/iw) is not present, with_Sigma is reset to False.
with_dc : boolean, optional
if True and with_Sigma=True, the dc correction is substracted from the self-energy before it is included into GF.
Returns
-------
G_latt : BlockGf
Lattice Green's function.
"""
if mu is None:
mu = self.chemical_potential
ntoi = self.spin_names_to_ind[self.SO]
spn = self.spin_block_names[self.SO]
if not hasattr(self, "Sigma_imp"):
with_Sigma = False
if broadening is None:
if mesh is None:
broadening = 0.01
else: # broadening = 2 * \Delta omega, where \Delta omega is the spacing of omega points
broadening = 2.0 * ((mesh.w_max - mesh.w_min) / (len(mesh) - 1))
# Check if G_latt is present
set_up_G_latt = False # Assume not
if not hasattr(self, "G_latt" ):
# Need to create G_latt_(i)w
set_up_G_latt = True
else: # Check that existing GF is consistent
G_latt = self.G_latt
GFsize = [gf.target_shape[0] for bname, gf in G_latt]
unchangedsize = all([self.n_orbitals[ik, ntoi[spn[isp]]] == GFsize[
isp] for isp in range(self.n_spin_blocks[self.SO])])
if (not mesh is None) or (not unchangedsize):
set_up_G_latt = True
# Are we including Sigma?
if with_Sigma:
Sigma_imp = self.Sigma_imp
if with_dc:
sigma_minus_dc = self.add_dc()
else:
sigma_minus_dc = [s for s in Sigma_imp]
if not mesh is None:
warn('lattice_gf called with Sigma and given mesh. Mesh will be taken from Sigma.')
if self.mesh != Sigma_imp[0].mesh:
mesh = Sigma_imp[0].mesh
if isinstance(mesh, MeshImFreq):
mesh_values = np.linspace(mesh(mesh.first_index()), mesh(mesh.last_index()), len(mesh))
else:
mesh_values = np.linspace(mesh.w_min, mesh.w_max, len(mesh))
else:
mesh = self.mesh
mesh_values = self.mesh_values
elif not mesh is None:
assert isinstance(mesh, MeshReFreq) or isinstance(mesh, MeshImFreq), "mesh must be a triqs MeshReFreq or MeshImFreq"
if isinstance(mesh, MeshImFreq):
mesh_values = np.linspace(mesh(mesh.first_index()), mesh(mesh.last_index()), len(mesh))
else:
mesh_values = np.linspace(mesh.w_min, mesh.w_max, len(mesh))
else:
mesh = self.mesh
mesh_values = self.mesh_values
# Set up G_latt
if set_up_G_latt:
block_structure = [
list(range(self.n_orbitals[ik, ntoi[sp]])) for sp in spn]
gf_struct = [(spn[isp], block_structure[isp])
for isp in range(self.n_spin_blocks[self.SO])]
block_ind_list = [block for block, inner in gf_struct]
if isinstance(mesh, MeshImFreq):
glist = lambda: [Gf(mesh=mesh, target_shape=[len(inner),len(inner)])
for block, inner in gf_struct]
else:
glist = lambda: [Gf(mesh=mesh, target_shape=[len(inner),len(inner)])
for block, inner in gf_struct]
G_latt = BlockGf(name_list=block_ind_list,
block_list=glist(), make_copies=False)
G_latt.zero()
idmat = [np.identity(
self.n_orbitals[ik, ntoi[sp]], complex) for sp in spn]
# fill Glatt
for ibl, (block, gf) in enumerate(G_latt):
ind = ntoi[spn[ibl]]
n_orb = self.n_orbitals[ik, ind]
if isinstance(mesh, MeshImFreq):
gf.data[:, :, :] = (idmat[ibl] * (mesh_values[:, None, None] + mu + self.h_field*(1-2*ibl))
- self.hopping[ik, ind, 0:n_orb, 0:n_orb])
else:
gf.data[:, :, :] = (idmat[ibl] *
(mesh_values[:, None, None] + mu + self.h_field*(1-2*ibl) + 1j*broadening)
- self.hopping[ik, ind, 0:n_orb, 0:n_orb])
if with_Sigma:
for icrsh in range(self.n_corr_shells):
gf -= self.upfold(ik, icrsh, block,
sigma_minus_dc[icrsh][block], gf)
G_latt.invert()
self.G_latt = G_latt
return G_latt
[docs]
def set_Sigma(self, Sigma_imp, transform_to_sumk_blocks=True):
self.put_Sigma(Sigma_imp, transform_to_sumk_blocks)
[docs]
def put_Sigma(self, Sigma_imp, transform_to_sumk_blocks=True):
r"""
Insert the impurity self-energies into the sumk_dft class.
Parameters
----------
Sigma_imp : list of BlockGf (Green's function) objects
List containing impurity self-energy for all (inequivalent) correlated shells.
Self-energies for equivalent shells are then automatically set by this function.
The self-energies can be of the real or imaginary-frequency type.
transform_to_sumk_blocks : bool, optional
If True (default), the input Sigma_imp will be transformed to the block structure ``gf_struct_sumk``,
else it has to be given in ``gf_struct_sumk``.
"""
if transform_to_sumk_blocks:
Sigma_imp = self.transform_to_sumk_blocks(Sigma_imp)
assert isinstance(Sigma_imp, list),\
"put_Sigma: Sigma_imp has to be a list of Sigmas for the correlated shells, even if it is of length 1!"
assert len(Sigma_imp) == self.n_corr_shells,\
"put_Sigma: give exactly one Sigma for each corr. shell!"
if isinstance(self.mesh, MeshImFreq) and all(isinstance(gf.mesh, MeshImFreq) and isinstance(gf, Gf) and gf.mesh == self.mesh for bname, gf in Sigma_imp[0]):
# Imaginary frequency Sigma:
self.Sigma_imp = [self.block_structure.create_gf(ish=icrsh, mesh=Sigma_imp[icrsh].mesh, space='sumk')
for icrsh in range(self.n_corr_shells)]
SK_Sigma_imp = self.Sigma_imp
elif isinstance(self.mesh, MeshReFreq) and all(isinstance(gf, Gf) and isinstance(gf.mesh, MeshReFreq) and gf.mesh == self.mesh for bname, gf in Sigma_imp[0]):
# Real frequency Sigma:
self.Sigma_imp = [self.block_structure.create_gf(ish=icrsh, mesh=Sigma_imp[icrsh].mesh, gf_function=Gf, space='sumk')
for icrsh in range(self.n_corr_shells)]
SK_Sigma_imp = self.Sigma_imp
else:
raise ValueError("put_Sigma: Sigma_imp must have the same mesh as SumKDFT.mesh.")
# rotation from local to global coordinate system:
for icrsh in range(self.n_corr_shells):
for bname, gf in SK_Sigma_imp[icrsh]:
if self.use_rotations:
gf << self.rotloc(icrsh,
Sigma_imp[icrsh][bname],
direction='toGlobal')
else:
gf << Sigma_imp[icrsh][bname]
#warning if real frequency self energy is within the bounds of the band energies
if isinstance(self.mesh, MeshReFreq):
if self.min_band_energy is None or self.max_band_energy is None:
self.calculate_min_max_band_energies()
mesh = np.linspace(self.mesh.w_min, self.mesh.w_max, len(self.mesh))
if mesh[0] > (self.min_band_energy - self.chemical_potential) or mesh[-1] < (self.max_band_energy - self.chemical_potential):
warn('The given Sigma is on a mesh which does not cover the band energy range. The Sigma MeshReFreq runs from %f to %f, while the band energy (minus the chemical potential) runs from %f to %f'%(mesh[0], mesh[-1], self.min_band_energy, self.max_band_energy))
[docs]
def analyse_block_structure(self, threshold=0.00001, include_shells=None, dm=None, hloc=None):
r"""
Determines the block structure of local Green's functions by analysing the structure of
the corresponding density matrices and the local Hamiltonian. The resulting block structures
for correlated shells are stored in the :class:`SumkDFT.block_structure <dft.block_structure.BlockStructure>` attribute.
Parameters
----------
threshold : real, optional
If the difference between density matrix / hloc elements is below threshold,
they are considered to be equal.
include_shells : list of integers, optional
List of inequivalent shells to be analysed.
If include_shells is not provided all inequivalent shells will be analysed.
dm : list of dict, optional
List of density matrices from which block stuctures are to be analysed.
Each density matrix is a dict {block names: 2d numpy arrays} for each correlated shell.
If not provided, dm will be calculated from the DFT Hamiltonian by a simple-point BZ integration.
hloc : list of dict, optional
List of local Hamiltonian matrices from which block stuctures are to be analysed
Each Hamiltonian is a dict {block names: 2d numpy arrays} for each inequivalent shell.
If not provided, it will be calculated using eff_atomic_levels.
"""
if dm is None:
warn("WARNING: No density matrix given. Calculating density matrix with default parameters. This will be deprecated in future releases.")
dm = self.density_matrix(method='using_gf', transform_to_solver_blocks=False)
assert len(dm) == self.n_corr_shells, "The number of density matrices must be equal to the number of correlated shells."
dens_mat = [dm[self.inequiv_to_corr[ish]]
for ish in range(self.n_inequiv_shells)]
if hloc is None:
hloc = self.eff_atomic_levels()
if include_shells is None:
include_shells = list(range(self.n_inequiv_shells))
for ish in include_shells:
self.gf_struct_solver[ish] = {}
self.sumk_to_solver[ish] = {}
self.solver_to_sumk[ish] = {}
self.solver_to_sumk_block[ish] = {}
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]:
assert sp in dens_mat[ish], f"The density matrix does not contain the block {sp}. Is the input dm given in sumk block structure?"
n_orb = self.corr_shells[self.inequiv_to_corr[ish]]['dim']
# gives an index list of entries larger that threshold
dmbool = (abs(dens_mat[ish][sp]) > threshold)
hlocbool = (abs(hloc[ish][sp]) > threshold)
# Determine off-diagonal entries in upper triangular part of
# density matrix
offdiag = set([])
for i in range(n_orb):
for j in range(i + 1, n_orb):
if dmbool[i, j] or hlocbool[i, j]:
offdiag.add((i, j))
# Determine the number of non-hybridising blocks in the gf
blocs = [[i] for i in range(n_orb)]
while len(offdiag) != 0:
pair = offdiag.pop()
for b1, b2 in product(blocs, blocs):
if (pair[0] in b1) and (pair[1] in b2):
if blocs.index(b1) != blocs.index(b2): # In separate blocks?
# Merge two blocks
b1.extend(blocs.pop(blocs.index(b2)))
break # Move on to next pair in offdiag
# Set the gf_struct for the solver accordingly
num_blocs = len(blocs)
for i in range(num_blocs):
blocs[i].sort()
self.gf_struct_solver[ish].update(
[('%s_%s' % (sp, i), len(blocs[i]))])
# Construct sumk_to_solver taking (sumk_block, sumk_index) --> (solver_block, solver_inner)
# and solver_to_sumk taking (solver_block, solver_inner) -->
# (sumk_block, sumk_index)
for i in range(num_blocs):
for j in range(len(blocs[i])):
block_sumk = sp
inner_sumk = blocs[i][j]
block_solv = '%s_%s' % (sp, i)
inner_solv = j
self.sumk_to_solver[ish][(block_sumk, inner_sumk)] = (
block_solv, inner_solv)
self.solver_to_sumk[ish][(block_solv, inner_solv)] = (
block_sumk, inner_sumk)
self.solver_to_sumk_block[ish][block_solv] = block_sumk
# Now calculate degeneracies of orbitals
dm = {}
for block, block_dim in self.gf_struct_solver[ish].items():
# get dm for the blocks:
dm[block] = np.zeros(
[block_dim, block_dim], complex)
for ind1 in range(block_dim):
for ind2 in range(block_dim):
block_sumk, ind1_sumk = self.solver_to_sumk[
ish][(block, ind1)]
block_sumk, ind2_sumk = self.solver_to_sumk[
ish][(block, ind2)]
dm[block][ind1, ind2] = dens_mat[ish][
block_sumk][ind1_sumk, ind2_sumk]
for block1 in self.gf_struct_solver[ish].keys():
for block2 in self.gf_struct_solver[ish].keys():
if dm[block1].shape == dm[block2].shape:
if ((abs(dm[block1] - dm[block2]) < threshold).all()) and (block1 != block2):
ind1 = -1
ind2 = -2
# check if it was already there:
for n, ind in enumerate(self.deg_shells[ish]):
if block1 in ind:
ind1 = n
if block2 in ind:
ind2 = n
if (ind1 < 0) and (ind2 >= 0):
self.deg_shells[ish][ind2].append(block1)
elif (ind1 >= 0) and (ind2 < 0):
self.deg_shells[ish][ind1].append(block2)
elif (ind1 < 0) and (ind2 < 0):
self.deg_shells[ish].append([block1, block2])
def _get_hermitian_quantity_from_gf(self, G):
""" Convert G to a Hermitian quantity
For G(tau) and G(iw), G(tau) is returned.
For G(t) and G(w), the spectral function is returned.
Parameters
----------
G : list of BlockGf of GfImFreq, GfImTime, GfReFreq or GfReTime
the input Green's function
Returns
-------
gf : list of BlockGf of GfImTime or GfReFreq
the output G(tau) or A(w)
"""
# make a GfImTime from the supplied GfImFreq
if all(isinstance(g_sh.mesh, MeshImFreq) for g_sh in G):
gf = [BlockGf(name_block_generator = [(name, GfImTime(beta=block.mesh.beta,
indices=block.indices,n_points=len(block.mesh)+1)) for name, block in g_sh],
make_copies=False) for g_sh in G]
for ish in range(len(gf)):
for name, g in gf[ish]:
g.set_from_fourier(G[ish][name])
# keep a GfImTime from the supplied GfImTime
elif all(isinstance(g_sh.mesh, MeshImTime) for g_sh in G):
gf = G
# make a spectral function from the supplied GfReFreq
elif all(isinstance(g_sh.mesh, MeshReFreq) for g_sh in G):
gf = [g_sh.copy() for g_sh in G]
for ish in range(len(gf)):
for name, g in gf[ish]:
g << 1.0j*(g-g.conjugate().transpose())/2.0/np.pi
elif all(isinstance(g_sh.mesh, MeshReTime) for g_sh in G):
def get_delta_from_mesh(mesh):
w0 = None
for w in mesh:
if w0 is None:
w0 = w
else:
return w-w0
gf = [BlockGf(name_block_generator = [(name, GfReFreq(
window=(-np.pi*(len(block.mesh)-1) / (len(block.mesh)*get_delta_from_mesh(block.mesh)),
np.pi*(len(block.mesh)-1) / (len(block.mesh)*get_delta_from_mesh(block.mesh))),
n_points=len(block.mesh), indices=block.indices)) for name, block in g_sh], make_copies=False)
for g_sh in G]
for ish in range(len(gf)):
for name, g in gf[ish]:
g.set_from_fourier(G[ish][name])
g << 1.0j*(g-g.conjugate().transpose())/2.0/np.pi
else:
raise Exception("G must be a list of BlockGf of either GfImFreq, GfImTime, GfReFreq or GfReTime")
return gf
[docs]
def analyse_block_structure_from_gf(self, G, threshold=1.e-5, include_shells=None, analyse_deg_shells = True):
r"""
Determines the block structure of local Green's functions by analysing
the structure of the corresponding non-interacting Green's function.
The resulting block structures for correlated shells are
stored in the :class:`SumkDFT.block_structure <dft.block_structure.BlockStructure>`
attribute.
This is a safer alternative to analyse_block_structure, because
the full non-interacting Green's function is taken into account
and not just the density matrix and Hloc.
Parameters
----------
G : list of BlockGf of GfImFreq, GfImTime, GfReFreq or GfReTime
the non-interacting Green's function for each inequivalent correlated shell
threshold : real, optional
If the difference between matrix elements is below threshold,
they are considered to be equal.
include_shells : list of integers, optional
List of inequivalent shells to be analysed.
If include_shells is not provided all inequivalent shells will be analysed.
analyse_deg_shells : bool
Whether to call the analyse_deg_shells function
after having finished the block structure analysis
Returns
-------
G : list of BlockGf of GfImFreq or GfImTime
the Green's function transformed into the new block structure
"""
assert isinstance(G, list), "G must be a list (with elements for each correlated shell)"
assert len(G) == self.n_corr_shells, "G must have one element for each correlated shell, run extract_G_loc with transform_to_solver_blocks=False to get the correct G"
gf = self._get_hermitian_quantity_from_gf(G)
if include_shells is None:
# include all shells
include_shells = list(range(self.n_inequiv_shells))
for ish in include_shells:
self.gf_struct_solver[ish] = {}
self.sumk_to_solver[ish] = {}
self.solver_to_sumk[ish] = {}
self.solver_to_sumk_block[ish] = {}
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]:
assert sp in gf[self.inequiv_to_corr[ish]].indices, f"The Green's function does not contain the block {sp}. Is the input G given in sumk block structure?"
n_orb = self.corr_shells[self.inequiv_to_corr[ish]]['dim']
# gives an index list of entries larger that threshold
max_gf = np.max(np.abs(gf[self.inequiv_to_corr[ish]][sp].data),0)
maxgf_bool = (max_gf > threshold)
# Determine off-diagonal entries in upper triangular part of the
# Green's function
offdiag = set([])
for i in range(n_orb):
for j in range(i + 1, n_orb):
if maxgf_bool[i, j]:
offdiag.add((i, j))
# Determine the number of non-hybridising blocks in the gf
blocs = [[i] for i in range(n_orb)]
while len(offdiag) != 0:
pair = offdiag.pop()
for b1, b2 in product(blocs, blocs):
if (pair[0] in b1) and (pair[1] in b2):
if blocs.index(b1) != blocs.index(b2): # In separate blocks?
# Merge two blocks
b1.extend(blocs.pop(blocs.index(b2)))
break # Move on to next pair in offdiag
# Set the gf_struct for the solver accordingly
num_blocs = len(blocs)
for i in range(num_blocs):
blocs[i].sort()
self.gf_struct_solver[ish].update(
[('%s_%s' % (sp, i), len(blocs[i]))])
# Construct sumk_to_solver taking (sumk_block, sumk_index) --> (solver_block, solver_inner)
# and solver_to_sumk taking (solver_block, solver_inner) -->
# (sumk_block, sumk_index)
for i in range(num_blocs):
for j in range(len(blocs[i])):
block_sumk = sp
inner_sumk = blocs[i][j]
block_solv = '%s_%s' % (sp, i)
inner_solv = j
self.sumk_to_solver[ish][(block_sumk, inner_sumk)] = (
block_solv, inner_solv)
self.solver_to_sumk[ish][(block_solv, inner_solv)] = (
block_sumk, inner_sumk)
self.solver_to_sumk_block[ish][block_solv] = block_sumk
# transform G to the new structure
full_structure = BlockStructure.full_structure(
[{sp:self.corr_shells[self.inequiv_to_corr[ish]]['dim']
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]}
for ish in range(self.n_inequiv_shells)],self.corr_to_inequiv)
G_transformed = [
self.block_structure.convert_gf(G[ish],
full_structure, ish, mesh=G[ish].mesh.copy(), show_warnings=threshold,
gf_function=type(G[ish]._first()), space_from='sumk', space_to='solver')
for ish in range(self.n_inequiv_shells)]
if analyse_deg_shells:
self.analyse_deg_shells(G_transformed, threshold, include_shells)
return G_transformed
[docs]
def analyse_deg_shells(self, G, threshold=1.e-5, include_shells=None):
r"""
Determines the degenerate shells of local Green's functions by analysing
the structure of the corresponding non-interacting Green's function.
The results are stored in the
:class:`SumkDFT.block_structure <dft.block_structure.BlockStructure>`
attribute.
Due to the implementation and numerics, the maximum difference between
two matrix elements that are detected as equal can be a bit higher
(e.g. a factor of two) than the actual threshold.
Parameters
----------
G : list of BlockGf of GfImFreq or GfImTime
the non-interacting Green's function for each inequivalent correlated shell
threshold : real, optional
If the difference between matrix elements is below threshold,
they are considered to be equal.
include_shells : list of integers, optional
List of correlated shells to be analysed.
If include_shells is not provided all correlated shells will be analysed.
"""
# initialize
self.deg_shells = [[] for ish in range(self.n_inequiv_shells)]
# helper function
def null(A, eps=1e-15):
""" Calculate the null-space of matrix A """
u, s, vh = np.linalg.svd(A)
null_mask = (s <= eps)
null_space = compress(null_mask, vh, axis=0)
return null_space.conjugate().transpose()
gf = self._get_hermitian_quantity_from_gf(G)
if include_shells is None:
# include all shells
include_shells = list(range(self.n_inequiv_shells))
# We consider two blocks equal, if their Green's functions obey
# maybe_conjugate1( v1^dagger G1 v1 ) = maybe_conjugate2( v2^dagger G2 v2 )
# where maybe_conjugate is a function that conjugates the Green's
# function if the flag 'conjugate' is set and the v are unitary
# matrices
#
# for each pair of blocks, we check whether there is a transformation
# maybe_conjugate( T G1 T^dagger ) = G2
# where our goal is to find T
# we just try whether there is such a T with and without conjugation
for ish in include_shells:
for block1 in self.gf_struct_solver[ish].keys():
for block2 in self.gf_struct_solver[ish].keys():
if block1==block2: continue
# check if the blocks are already present in the deg_shells
ind1 = -1
ind2 = -2
for n, ind in enumerate(self.deg_shells[ish]):
if block1 in ind:
ind1 = n
v1 = ind[block1]
if block2 in ind:
ind2 = n
v2 = ind[block2]
# if both are already present, go to the next pair of blocks
if ind1 >= 0 and ind2 >= 0:
continue
gf1 = gf[ish][block1]
gf2 = gf[ish][block2]
# the two blocks have to have the same shape
if gf1.target_shape != gf2.target_shape:
continue
# Instead of directly comparing the two blocks, we
# compare its eigenvalues. As G(tau) is Hermitian,
# they are real and the eigenvector matrix is unitary.
# Thus, if the eigenvalues are equal we can transform
# one block to make it equal to the other (at least
# for tau=0).
e1 = np.linalg.eigvalsh(gf1.data[0])
e2 = np.linalg.eigvalsh(gf2.data[0])
if np.any(abs(e1-e2) > threshold): continue
for conjugate in [False,True]:
if conjugate:
gf2 = gf2.conjugate()
# we want T gf1 T^dagger = gf2
# while for a given tau, T could be calculated
# by diagonalizing gf1 and gf2, this does not
# work for all taus simultaneously because of
# numerical imprecisions
# rather, we rewrite the equation to
# T gf1 = gf2 T
# which is the Sylvester equation.
# For that equation, one can use the Kronecker
# product to get a linear problem, which consists
# of finding the null space of M vec T = 0.
M = np.kron(np.eye(*gf1.target_shape),gf2.data[0])-np.kron(gf1.data[0].transpose(),np.eye(*gf1.target_shape))
N = null(M, threshold)
# now we get the intersection of the null spaces
# of all values of tau
for i in range(1,len(gf1.data)):
M = np.kron(np.eye(*gf1.target_shape),gf2.data[i])-np.kron(gf1.data[i].transpose(),np.eye(*gf1.target_shape))
# transform M into current null space
M = np.dot(M, N)
N = np.dot(N, null(M, threshold))
if np.size(N) == 0:
break
# no intersection of the null spaces -> no symmetry
if np.size(N) == 0: continue
# reshape N: it then has the indices matrix, matrix, number of basis vectors of the null space
N = N.reshape(gf1.target_shape[0], gf1.target_shape[1], -1).transpose([1, 0, 2])
"""
any matrix in the null space can now be constructed as
M = 0
for i in range(N.shape[-1]):
M += y[i]*N[:,:,i]
with coefficients (complex numbers) y[i].
We want to get a set of coefficients y so that M is unitary.
Unitary means M M^dagger = 1.
Thus,
sum y[i] N[:,:,i] y[j].conjugate() N[:,:,j].conjugate().transpose() = eye.
The object N[:,:,i] N[:,:,j] is a four-index object which we call Z.
"""
Z = np.einsum('aci,bcj->abij', N, N.conjugate())
"""
function chi2
This function takes a real parameter vector y and reinterprets it as complex.
Then, it calculates the chi2 of
sum y[i] N[:,:,i] y[j].conjugate() N[:,:,j].conjugate().transpose() - eye.
"""
def chi2(y):
# reinterpret y as complex number
y = y.view(complex)
ret = 0.0
for a in range(Z.shape[0]):
for b in range(Z.shape[1]):
ret += np.abs(np.dot(y, np.dot(Z[a, b], y.conjugate()))
- (1.0 if a == b else 0.0))**2
return ret
# use the minimization routine from scipy
res = minimize(chi2, np.ones(2 * N.shape[-1]))
# if the minimization fails, there is probably no symmetry
if not res.success: continue
# check if the minimization returned zero within the tolerance
if res.fun > threshold: continue
# reinterpret the solution as a complex number
y = res.x.view(complex)
# reconstruct the T matrix
T = np.zeros(N.shape[:-1], dtype=complex)
for i in range(len(y)):
T += N[:, :, i] * y[i]
# transform gf1 using T
G_transformed = gf1.copy()
G_transformed.from_L_G_R(T, gf1, T.conjugate().transpose())
# it does not make sense to check the tails for an
# absolute error because it will usually not hold;
# we could just check the relative error
# (here, we ignore it, reasoning that if the data
# is the same, the tails have to coincide as well)
try:
assert_arrays_are_close(G_transformed.data, gf2.data, threshold)
except (RuntimeError, AssertionError):
# the symmetry does not hold
continue
# Now that we have found a valid T, we have to
# rewrite it to match the convention that
# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2),
# where C conjugates if the flag is True
# For each group of degenerate shells, the list
# SK.deg_shells[ish] contains a dict. The keys
# of the dict are the block names, the values
# are tuples. The first entry of the tuple is
# the transformation matrix v, the second entry
# is the conjugation flag
# the second block is already present
# set v1 and C1 so that they are compatible with
# C(T gf1 T^dagger) = gf2
# and with
# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2)
if (ind1 < 0) and (ind2 >= 0):
if conjugate:
self.deg_shells[ish][ind2][block1] = np.dot(T.conjugate().transpose(), v2[0].conjugate()), not v2[1]
else:
self.deg_shells[ish][ind2][block1] = np.dot(T.conjugate().transpose(), v2[0]), v2[1]
# the first block is already present
# set v2 and C2 so that they are compatible with
# C(T gf1 T^dagger) = gf2
# and with
# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2)
elif (ind1 >= 0) and (ind2 < 0):
if conjugate:
self.deg_shells[ish][ind1][block2] = np.dot(T.conjugate(), v1[0].conjugate()), not v1[1]
else:
self.deg_shells[ish][ind1][block2] = np.dot(T, v1[0]), v1[1]
# the blocks are not already present
# we arbitrarily choose v1=eye and C1=False and
# set v2 and C2 so that they are compatible with
# C(T gf1 T^dagger) = gf2
# and with
# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2)
elif (ind1 < 0) and (ind2 < 0):
d = dict()
d[block1] = np.eye(*gf1.target_shape), False
if conjugate:
d[block2] = T.conjugate(), True
else:
d[block2] = T, False
self.deg_shells[ish].append(d)
# a block was found, break out of the loop
break
[docs]
def calculate_diagonalization_matrix(self, prop_to_be_diagonal='eal', calc_in_solver_blocks=True, write_to_blockstructure = True, shells=None):
"""
Calculates the diagonalisation matrix, and (optionally) stores it in the BlockStructure.
Parameters
----------
prop_to_be_diagonal : string, optional
Defines the property to be diagonalized.
- 'eal' : local hamiltonian (i.e. crystal field)
- 'dm' : local density matrix
calc_in_solver_blocks : bool, optional
Whether the property shall be diagonalized in the
full sumk structure, or just in the solver structure.
write_to_blockstructure : bool, optional
Whether the diagonalization matrix shall be written to
the BlockStructure directly.
shells : list of int, optional
Indices of correlated shells to be diagonalized.
None: all shells
Returns
-------
trafo : dict
The transformation matrix for each spin-block in the correlated shell
"""
if self.block_structure.transformation:
mpi.report(
"calculate_diagonalization_matrix: requires block_structure.transformation = None.")
return 0
# Use all shells
if shells is None:
shells = range(self.n_corr_shells)
elif max(shells) >= self.n_corr_shells: # Check if the shell indices are present
mpi.report("calculate_diagonalization_matrix: shells not correct.")
return 0
if prop_to_be_diagonal == 'eal':
prop = [self.eff_atomic_levels()[self.corr_to_inequiv[ish]]
for ish in range(self.n_corr_shells)]
elif prop_to_be_diagonal == 'dm':
prop = self.density_matrix(method='using_point_integration')
else:
mpi.report(
"calculate_diagonalization_matrix: Choices for prop_to_be_diagonal are 'eal' or 'dm'.")
return 0
trans = [{block: np.eye(block_dim) for block, block_dim in gfs} for gfs in self.gf_struct_sumk]
for ish in shells:
trafo = {}
# Transform to solver basis if desired, blocks of prop change in this step!
if calc_in_solver_blocks:
prop[ish] = self.block_structure.convert_matrix(prop[ish], space_from='sumk', space_to='solver')
# Get diagonalisation matrix, if not already diagonal
for name in prop[ish]:
if np.sum(abs(prop[ish][name]-np.diag(np.diagonal(prop[ish][name])))) > 1e-13:
trafo[name] = np.linalg.eigh(prop[ish][name])[1].conj().T
else:
trafo[name] = np.identity(np.shape(prop[ish][name])[0])
# Transform back to sumk if necessay, blocks change in this step!
if calc_in_solver_blocks:
trafo = self.block_structure.convert_matrix(trafo, space_from='solver', space_to='sumk')
trans[ish] = trafo
# Write to block_structure object
if write_to_blockstructure:
self.block_structure.transformation = trans
return trans
[docs]
def density_matrix_using_point_integration(self):
"""
Calculate density matrices using point integration: Only works for
diagonal hopping matrix (true in wien2k). Consider using extract_G_loc
together with [G.density() for G in Gloc] instead. Returned density
matrix is always given in SumK block structure.
Returns
-------
dens_mat : list of dicts
Density matrix for each spin in each correlated shell.
"""
dens_mat = [{} for icrsh in range(self.n_corr_shells)]
for icrsh in range(self.n_corr_shells):
for sp in self.spin_block_names[self.corr_shells[icrsh]['SO']]:
dens_mat[icrsh][sp] = np.zeros(
[self.corr_shells[icrsh]['dim'], self.corr_shells[icrsh]['dim']], complex)
ikarray = np.array(list(range(self.n_k)))
ntoi = self.spin_names_to_ind[self.SO]
spn = self.spin_block_names[self.SO]
for ik in mpi.slice_array(ikarray):
dims = {sp:self.n_orbitals[ik, ntoi[sp]] for sp in spn}
MMat = [np.zeros([dims[sp], dims[sp]], complex) for sp in spn]
for isp, sp in enumerate(spn):
ind = ntoi[sp]
for inu in range(self.n_orbitals[ik, ind]):
# only works for diagonal hopping matrix (true in
# wien2k)
if (self.hopping[ik, ind, inu, inu] - self.h_field * (1 - 2 * isp)) < 0.0:
MMat[isp][inu, inu] = 1.0
else:
MMat[isp][inu, inu] = 0.0
for icrsh in range(self.n_corr_shells):
for isp, sp in enumerate(self.spin_block_names[self.corr_shells[icrsh]['SO']]):
ind = self.spin_names_to_ind[
self.corr_shells[icrsh]['SO']][sp]
dim = self.corr_shells[icrsh]['dim']
n_orb = self.n_orbitals[ik, ind]
projmat = self.proj_mat[ik, ind, icrsh, 0:dim, 0:n_orb]
dens_mat[icrsh][sp] += self.bz_weights[ik] * np.dot(np.dot(projmat, MMat[isp]),
projmat.transpose().conjugate())
# get data from nodes:
for icrsh in range(self.n_corr_shells):
for sp in dens_mat[icrsh]:
dens_mat[icrsh][sp] = mpi.all_reduce(dens_mat[icrsh][sp])
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 sp in dens_mat[icrsh]:
if self.rot_mat_time_inv[icrsh] == 1:
dens_mat[icrsh][sp] = dens_mat[icrsh][sp].conjugate()
dens_mat[icrsh][sp] = np.dot(np.dot(self.rot_mat[icrsh].conjugate().transpose(), dens_mat[icrsh][sp]),
self.rot_mat[icrsh])
return dens_mat
[docs]
def density_matrix(self, method='using_gf', mu=None, with_Sigma=True, with_dc=True, broadening=None,
transform_to_solver_blocks=True, show_warnings=True):
"""Calculate density matrices in one of two ways.
Parameters
----------
method : string, optional
- if 'using_gf': First get lattice 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).
mu : real, optional
Input chemical potential. If not provided the value of self.chemical_potential is used as mu.
with_Sigma : boolean, optional
If True then the local GF is calculated with the self-energy self.Sigma_imp.
with_dc : boolean, optional
If True then the double-counting correction is subtracted from the self-energy in calculating the GF.
broadening : float, optional
Imaginary shift for the axis along which the real-axis GF is calculated.
If not provided, broadening will be set to double of the distance between mesh points in 'mesh'.
Only relevant for real-frequency GF.
transform_to_solver_blocks : bool, optional
If True (default), the returned G_loc will be transformed to the block structure ``gf_struct_solver``,
else it will be in ``gf_struct_sumk``.
show_warnings : bool, optional
Displays warning messages during transformation
(Only effective if transform_to_solver_blocks = True
Returns
-------
dens_mat : list of dicts
Density matrix for each spin in each correlated shell.
"""
if method == "using_gf":
warn("WARNING: density_matrix: method 'using_gf' is deprecated. Use 'extract_G_loc' instead.")
Gloc = self.extract_G_loc(mu, with_Sigma, with_dc, broadening,
transform_to_solver_blocks, show_warnings)
dens_mat = [G.density() for G in Gloc]
elif method == "using_point_integration":
warn("WARNING: density_matrix: method 'using_point_integration' is deprecated. Use 'density_matrix_using_point_integration' instead. All additionally provided arguments are ignored.")
dens_mat = self.density_matrix_using_point_integration()
else:
raise ValueError("density_matrix: the method '%s' is not supported." % method)
return dens_mat
# For simple dft input, get crystal field splittings.
[docs]
def eff_atomic_levels(self):
r"""
Calculates the effective local Hamiltonian required as an input for
the Hubbard I Solver.
The local Hamiltonian (effective atomic levels) is calculated by
projecting the on-site Bloch Hamiltonian:
.. math:: H^{loc}_{m m'} = \sum_{k} P_{m \nu}(k) H_{\nu\nu'}(k) P^{*}_{\nu' m'}(k),
where
.. math:: H_{\nu\nu'}(k) = [\epsilon_{\nu k} - h_{z} \sigma_{z}] \delta_{\nu\nu'}.
Parameters
----------
None
Returns
-------
eff_atlevels : gf_struct_sumk like
Effective local Hamiltonian :math:`H^{loc}_{m m'}` for each
inequivalent correlated shell.
"""
# define matrices for inequivalent shells:
eff_atlevels = [{} for ish in range(self.n_inequiv_shells)]
for ish in range(self.n_inequiv_shells):
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]:
eff_atlevels[ish][sp] = np.identity(
self.corr_shells[self.inequiv_to_corr[ish]]['dim'], complex)
eff_atlevels[ish][sp] *= -self.chemical_potential
eff_atlevels[ish][
sp] -= self.dc_imp[self.inequiv_to_corr[ish]][sp]
# sum over k:
if not hasattr(self, "Hsumk"):
# calculate the sum over k. Does not depend on mu, so do it only
# once:
self.Hsumk = [{} for icrsh in range(self.n_corr_shells)]
for icrsh in range(self.n_corr_shells):
dim = self.corr_shells[icrsh]['dim']
for sp in self.spin_block_names[self.corr_shells[icrsh]['SO']]:
self.Hsumk[icrsh][sp] = np.zeros(
[dim, dim], complex)
for isp, sp in enumerate(self.spin_block_names[self.corr_shells[icrsh]['SO']]):
ind = self.spin_names_to_ind[
self.corr_shells[icrsh]['SO']][sp]
for ik in range(self.n_k):
n_orb = self.n_orbitals[ik, ind]
MMat = np.identity(n_orb, complex)
MMat = self.hopping[
ik, ind, 0:n_orb, 0:n_orb] - (1 - 2 * isp) * self.h_field * MMat
projmat = self.proj_mat[ik, ind, icrsh, 0:dim, 0:n_orb]
self.Hsumk[icrsh][sp] += self.bz_weights[ik] * np.dot(np.dot(projmat, MMat),
projmat.conjugate().transpose())
# symmetrisation:
if self.symm_op != 0:
self.Hsumk = self.symmcorr.symmetrize(self.Hsumk)
# Rotate to local coordinate system:
if self.use_rotations:
for icrsh in range(self.n_corr_shells):
for sp in self.Hsumk[icrsh]:
if self.rot_mat_time_inv[icrsh] == 1:
self.Hsumk[icrsh][sp] = self.Hsumk[
icrsh][sp].conjugate()
self.Hsumk[icrsh][sp] = np.dot(np.dot(self.rot_mat[icrsh].conjugate().transpose(), self.Hsumk[icrsh][sp]),
self.rot_mat[icrsh])
# add to matrix:
for ish in range(self.n_inequiv_shells):
for sp in eff_atlevels[ish]:
eff_atlevels[ish][
sp] += self.Hsumk[self.inequiv_to_corr[ish]][sp]
return eff_atlevels
[docs]
def init_dc(self):
r"""
Initializes the double counting terms.
Parameters
----------
None
"""
self.dc_imp = [{} for icrsh in range(self.n_corr_shells)]
self.dc_imp_dyn = [{} for icrsh in range(self.n_corr_shells)]
for icrsh in range(self.n_corr_shells):
dim = self.corr_shells[icrsh]['dim']
spn = self.spin_block_names[self.corr_shells[icrsh]['SO']]
for sp in spn:
self.dc_imp[icrsh][sp] = np.zeros([dim, dim], float)
self.dc_imp_dyn[icrsh][sp] = None
self.dc_energ = [0.0 for icrsh in range(self.n_corr_shells)]
[docs]
def set_dc(self, dc_imp, dc_energ):
r"""
Sets double counting corrections to given values.
Parameters
----------
dc_imp : gf_struct_sumk like
Double-counting self-energy term.
dc_energ : list of floats
Double-counting energy corrections for each correlated shell.
"""
self.dc_imp = dc_imp
self.dc_energ = dc_energ
[docs]
def calc_dc(self, dens_mat, orb=0, U_interact=None, J_hund=None,
use_dc_formula=0, use_dc_value=None, transform=True):
r"""
Calculate and set the double counting corrections.
If 'use_dc_value' is provided the double-counting term is uniformly initialized
with this constant and 'U_interact' and 'J_hund' are ignored.
If 'use_dc_value' is None the correction is evaluated according to
one of the following formulae:
* use_dc_formula = 0: fully-localised limit (FLL)
* use_dc_formula = 1: Held's formula, i.e. mean-field formula for the Kanamori
type of the interaction Hamiltonian
* use_dc_formula = 2: around mean-field (AMF)
Note that FLL and AMF formulae were derived assuming a full Slater-type interaction
term and should be thus used accordingly. For the Kanamori-type interaction
one should use formula 1.
The double-counting self-energy term is stored in `self.dc_imp` and the energy
correction in `self.dc_energ`.
Parameters
----------
dens_mat : gf_struct_solver like
Density matrix for the specified correlated shell.
orb : int, optional
Index of an inequivalent shell.
U_interact : float, optional
Value of interaction parameter `U`.
J_hund : float, optional
Value of interaction parameter `J`.
use_dc_formula : int or string, optional
Type of double-counting correction (see description of `compute_DC_from_density` above).
There is an interface with the legacy implementation which allows for the old convention:
* 0 -> 'sFLL' spin dependent fully localized limit
* 1 -> 'cHeld' spin independent Held formula
* 2 -> 'sAMF' spin dependent around-mean field approximation
use_dc_value : float, optional
Value of the double-counting correction. If specified
`U_interact`, `J_hund` and `use_dc_formula` are ignored.
transform : bool
whether or not to use the transformation in block_structure
to transform the dc
"""
for icrsh in range(self.n_corr_shells):
# ish is the index of the inequivalent shell corresponding to icrsh
ish = self.corr_to_inequiv[icrsh]
if ish != orb:
continue # ignore this orbital
# *(1+self.corr_shells[icrsh]['SO'])
dim = self.corr_shells[icrsh]['dim']
spn = self.spin_block_names[self.corr_shells[icrsh]['SO']]
Ncr = {sp: 0.0 for sp in spn}
for block, inner in self.gf_struct_solver[ish].items():
bl = self.solver_to_sumk_block[ish][block]
Ncr[bl] += dens_mat[block].real.trace()
Ncrtot = sum(Ncr.values())
for sp in spn:
self.dc_imp[icrsh][sp] = np.identity(dim, float)
if self.SP == 0: # average the densities if there is no SP:
Ncr[sp] = Ncrtot / len(spn)
# correction for SO: we have only one block in this case, but
# in DC we need N/2
elif self.SP == 1 and self.SO == 1:
Ncr[sp] = Ncrtot / 2.0
# Uses "orbital" dimension with SO for double counting
if self.SP == 1 and self.SO == 1:
assert dim % 2 == 0
dim //= 2
if use_dc_value is None:
#For legacy compatibility
if use_dc_formula == 0:
mpi.report(f"Detected {use_dc_formula=}, changing to sFLL")
use_dc_formula = "sFLL"
if use_dc_formula == 1:
mpi.report(f"Detected {use_dc_formula=}, changing to cHeld")
use_dc_formula = "cHeld"
if use_dc_formula == 2:
mpi.report(f"Detected {use_dc_formula=}, changing to sAMF")
use_dc_formula = "sAMF"
for sp in spn:
DC_val, E_val = compute_DC_from_density(N_tot=Ncrtot,U=U_interact, J=J_hund, n_orbitals=dim, N_spin=Ncr[sp], method=use_dc_formula)
self.dc_imp[icrsh][sp] *= DC_val
self.dc_energ[icrsh] = E_val
else: # use value provided for user to determine dc_energ and dc_imp
self.dc_energ[icrsh] = use_dc_value * Ncrtot
for sp in spn:
self.dc_imp[icrsh][sp] *= use_dc_value
mpi.report(
"DC for shell %(icrsh)i = %(use_dc_value)f" % locals())
mpi.report("DC energy = %s" % self.dc_energ[icrsh])
if transform:
for sp in spn:
T = self.block_structure.effective_transformation_sumk[icrsh][sp]
self.dc_imp[icrsh][sp] = np.dot(T.conjugate().transpose(),
np.dot(self.dc_imp[icrsh][sp], T))
[docs]
def add_dc(self):
r"""
Subtracts the double counting term from the impurity self energy.
Parameters
----------
Returns
-------
sigma_minus_dc : gf_struct_sumk like
Self-energy with a subtracted double-counting term.
"""
# Be careful: Sigma_imp is already in the global coordinate system!!
sigma_minus_dc = [s.copy() for s in self.Sigma_imp]
for icrsh in range(self.n_corr_shells):
for bname, gf in sigma_minus_dc[icrsh]:
# Transform dc_imp to global coordinate system
if self.use_rotations:
gf -= np.dot(self.rot_mat[icrsh], np.dot(self.dc_imp[icrsh][bname], self.rot_mat[icrsh].conjugate().transpose()))
else:
gf -= self.dc_imp[icrsh][bname]
if self.dc_imp_dyn[icrsh][bname] is not None:
if self.use_rotations:
gf -= self.rotloc(icrsh, self.dc_imp_dyn[icrsh][bname], direction='toGlobal')
else:
gf -= self.dc_imp_dyn[icrsh][bname]
return sigma_minus_dc
[docs]
def symm_deg_gf(self, gf_to_symm, ish=0):
r"""
Averages a GF or a dict of np.ndarrays over degenerate shells.
Degenerate shells of an inequivalent correlated shell are defined by
`self.deg_shells`. This function enforces corresponding degeneracies
in the input GF.
Parameters
----------
gf_to_symm : gf_struct_solver like
Input and output GF (i.e., it gets overwritten)
or dict of np.ndarrays.
ish : int
Index of an inequivalent shell. (default value 0)
"""
# when reading block_structures written with older versions from
# an h5 file, self.deg_shells might be None
if self.deg_shells is None: return
if not isinstance(gf_to_symm, BlockGf) and isinstance(gf_to_symm[list(gf_to_symm.keys())[0]], np.ndarray):
blockgf = False
elif isinstance(gf_to_symm, BlockGf):
blockgf = True
else:
raise ValueError("gf_to_symm should be either a BlockGf or a dict of numpy arrays")
for degsh in self.deg_shells[ish]:
# ss will hold the averaged orbitals in the basis where the
# blocks are all equal
# i.e. maybe_conjugate(v^dagger gf v)
ss = None
n_deg = len(degsh)
for key in degsh:
if ss is None:
if blockgf:
ss = gf_to_symm[key].copy()
ss.zero()
helper = ss.copy()
else:
ss = np.zeros_like(gf_to_symm[key])
helper = np.zeros_like(gf_to_symm[key])
# get the transformation matrix
if isinstance(degsh, dict):
v, C = degsh[key]
else:
# for backward compatibility, allow degsh to be a list
if blockgf:
v = np.eye(*ss.target_shape)
else:
v = np.eye(*ss.shape)
C = False
# the helper is in the basis where the blocks are all equal
if blockgf:
helper.from_L_G_R(v.conjugate().transpose(), gf_to_symm[key], v)
else:
helper = np.dot(v.conjugate().transpose(), np.dot(gf_to_symm[key], v))
if C:
helper << helper.transpose()
# average over all shells
ss += helper / (1.0 * n_deg)
# now put back the averaged gf to all shells
for key in degsh:
if isinstance(degsh, dict):
v, C = degsh[key]
else:
# for backward compatibility, allow degsh to be a list
if blockgf:
v = np.eye(*ss.target_shape)
else:
v = np.eye(*ss.shape)
C = False
if blockgf and C:
gf_to_symm[key].from_L_G_R(v, ss.transpose().copy(), v.conjugate().transpose())
elif blockgf and not C:
gf_to_symm[key].from_L_G_R(v, ss, v.conjugate().transpose())
elif not blockgf and C:
gf_to_symm[key] = np.dot(v, np.dot(ss.transpose().copy(), v.conjugate().transpose()))
elif not blockgf and not C:
gf_to_symm[key] = np.dot(v, np.dot(ss, v.conjugate().transpose()))
[docs]
def total_density(self, mu=None, with_Sigma=True, with_dc=True, broadening=None, beta=None):
r"""
Calculates the total charge within the energy window for a given chemical potential.
The chemical potential is either given by parameter `mu` or, if it is not specified,
taken from `self.chemical_potential`.
The total charge is calculated from the trace of the GF in the Bloch basis.
By default, a full interacting GF is used. To use the non-interacting GF, set
parameter `with_Sigma = False`.
The number of bands within the energy windows generally depends on `k`. The trace is
therefore calculated separately for each `k`-point.
Since in general n_orbitals depends on k, the calculation is done in the following order:
.. math:: n_{tot} = \sum_{k} n(k),
with
.. math:: n(k) = Tr G_{\nu\nu'}(k, i\omega_{n}).
The calculation is done in the global coordinate system, if distinction is made between local/global.
Parameters
----------
mu : float, optional
Input chemical potential. If not specified, `self.chemical_potential` is used instead.
with_Sigma : boolean, optional
If `True` the full interacing GF is evaluated, otherwise the self-energy is not
included and the charge would correspond to a non-interacting system.
with_dc : boolean, optional
Whether or not to subtract the double-counting term from the self-energy.
broadening : float, optional
Imaginary shift for the axis along which the real-axis GF is calculated.
If not provided, broadening will be set to double of the distance between mesh points in 'mesh'.
Only relevant for real-frequency GF.
beta : float, optional, default = broadening
when using MeshReFreq this determines the temperature for the Fermi function
smearing when integrating G(w). If not given broadening will be used
(converted to beta)
Returns
-------
dens : float
Total charge :math:`n_{tot}`.
"""
if mu is None:
mu = self.chemical_potential
if isinstance(self.mesh, MeshReFreq) and beta == None:
assert broadening and broadening > 0.0, 'beta and broadening were not specified. Aborting. Specifiy at least broadening (or better both) to correctly call density(beta) for MeshReFreq'
beta = 1 / broadening
if isinstance(self.mesh, MeshReFreq):
def tot_den(bgf): return bgf.total_density(beta)
else:
def tot_den(bgf): return bgf.total_density()
dens = 0.0
ikarray = np.array(list(range(self.n_k)))
for ik in mpi.slice_array(ikarray):
G_latt = self.lattice_gf(
ik=ik, mu=mu, with_Sigma=with_Sigma, with_dc=with_dc, broadening=broadening)
dens += self.bz_weights[ik] * tot_den(G_latt)
# collect data from mpi:
dens = mpi.all_reduce(dens)
mpi.barrier()
if abs(dens.imag) > 1e-20:
mpi.report("Warning: Imaginary part in density will be ignored ({})".format(str(abs(dens.imag))))
return dens.real
[docs]
def set_mu(self, mu):
r"""
Sets a new chemical potential.
Parameters
----------
mu : float
New value of the chemical potential.
"""
self.chemical_potential = mu
[docs]
def calc_mu(self, precision=0.01, broadening=None, delta=0.5, max_loops=100, method="dichotomy", beta=None):
r"""
Searches for the chemical potential that gives the DFT total charge.
Parameters
----------
precision : float, optional
A desired precision of the resulting total charge.
broadening : float, optional
Imaginary shift for the axis along which the real-axis GF is calculated.
If not provided, broadening will be set to double of the distance between mesh points in 'mesh'.
Only relevant for real-frequency GF.
max_loops : int, optional
Number of dichotomy loops maximally performed.
method : string, optional
Type of optimization used:
* dichotomy: usual bisection algorithm from the TRIQS library
* newton: newton method, faster convergence but more unstable
* brent: finds bounds and proceeds with hyperbolic brent method, a compromise between speed and ensuring convergence
beta : float, optional, default = broadening
when using MeshReFreq this determines the temperature for the Fermi function
smearing when integrating G(w). If not given broadening will be used
(converted to beta)
Returns
-------
mu : float
Value of the chemical potential giving the DFT total charge
within specified precision.
"""
def find_bounds(function, x_init, delta_x, max_loops=1000):
mpi.report("Finding bounds on chemical potential")
x = x_init
# First find the bounds
y1 = function(x)
eps = np.sign(y1)
x1 = x
x2 = x1
y2 = y1
nbre_loop = 0
# abort the loop after maxiter is reached or when y1 and y2 have different sign
while (nbre_loop <= max_loops) and (y2*y1) > 0:
nbre_loop += 1
x1 = x2
y1 = y2
x2 -= eps*delta_x
y2 = function(x2)
if nbre_loop > (max_loops):
raise ValueError("The bounds could not be found")
# Make sure that x2 > x1
if x1 > x2:
x1, x2 = x2, x1
y1, y2 = y2, y1
mpi.report(f"mu_interval: [ {x1:.4f} ; {x2:.4f} ]")
mpi.report(f"delta to target density interval: [ {y1:.4f} ; {y2:.4f} ]")
return x1, x2
# previous implementation
def F_bisection(mu): return self.total_density(mu=mu, broadening=broadening, beta=beta).real
density = self.density_required - self.charge_below
# using scipy.optimize
def F_optimize(mu):
mpi.report("Trying out mu = {}".format(str(mu)))
calc_dens = self.total_density(mu=mu, broadening=broadening, beta=beta).real - density
mpi.report(f"Target density = {density}; Delta to target = {calc_dens}")
return calc_dens
# check for lowercase matching for the method variable
if method.lower() == "dichotomy":
mpi.report("\nsumk calc_mu: Using dichtomy adjustment to find chemical potential\n")
self.chemical_potential = dichotomy.dichotomy(function=F_bisection,
x_init=self.chemical_potential, y_value=density,
precision_on_y=precision, delta_x=delta, max_loops=max_loops,
x_name="Chemical Potential", y_name="Total Density",
verbosity=3)[0]
elif method.lower() == "newton":
mpi.report("\nsumk calc_mu: Using Newton method to find chemical potential\n")
self.chemical_potential = newton(func=F_optimize,
x0=self.chemical_potential,
tol=precision, maxiter=max_loops,
)
elif method.lower() == "brent":
mpi.report("\nsumk calc_mu: Using Brent method to find chemical potential")
mpi.report("sumk calc_mu: Finding bounds \n")
mu_guess_0, mu_guess_1 = find_bounds(function=F_optimize,
x_init=self.chemical_potential,
delta_x=delta, max_loops=max_loops,
)
mu_guess_1 += 0.01 # scrambles higher lying interval to avoid getting stuck
mpi.report("\nsumk calc_mu: Searching root with Brent method\n")
self.chemical_potential = brenth(f=F_optimize,
a=mu_guess_0,
b=mu_guess_1,
xtol=precision, maxiter=max_loops,
)
else:
raise ValueError(
f"sumk calc_mu: The selected method: {method}, is not implemented\n",
"""
Please check for typos or select one of the following:
* dichotomy: usual bisection algorithm from the TRIQS library
* newton: newton method, fastest convergence but more unstable
* brent: finds bounds and proceeds with hyperbolic brent method, a compromise between speed and ensuring convergence
"""
)
return self.chemical_potential
[docs]
def calc_density_correction(self, filename=None, dm_type=None, spinave=False, kpts_to_write=None, broadening=None, beta=None):
r"""
Calculates the charge density correction and stores it into a file.
The charge density correction is needed for charge-self-consistent DFT+DMFT calculations.
It represents a density matrix of the interacting system defined in Bloch basis
and it is calculated from the sum over Matsubara frequecies of the full GF,
..math:: N_{\nu\nu'}(k) = \sum_{i\omega_{n}} G_{\nu\nu'}(k, i\omega_{n})
The density matrix for every `k`-point is stored into a file.
Parameters
----------
filename : string
Name of the file to store the charge density correction.
dm_type : string
DFT code to write the density correction for. Options:
'vasp', 'wien2k', 'elk' or 'qe'. Needs to be set for 'qe'
spinave : logical
Elk specific and for magnetic calculations in DMFT only.
It averages the spin to keep the DFT part non-magnetic.
kpts_to_write : iterable of int
Indices of k points that are written to file. If None (default),
all k points are written. Only implemented for dm_type 'vasp'
broadening : float, optional
Imaginary shift for the axis along which the real-axis GF is calculated.
If not provided, broadening will be set to double of the distance between mesh points in 'mesh'.
Only relevant for real-frequency GF.
beta : float, optional, default = broadening
when using MeshReFreq this determines the temperature for the Fermi function
smearing when integrating G(w). If not given broadening will be used
(converted to beta)
Returns
-------
(deltaN, dens) : tuple
Returns a tuple containing the density matrix `deltaN` and
the corresponing total charge `dens`.
"""
#automatically set dm_type if required
if dm_type is None:
dm_type = self.dft_code
assert dm_type in ('vasp', 'wien2k','elk', 'qe'), "'dm_type' must be either 'vasp', 'wienk', 'elk' or 'qe'"
#default file names
if filename is None:
if dm_type == 'wien2k':
filename = 'dens_mat.dat'
elif dm_type == 'vasp':
filename = 'GAMMA'
elif dm_type == 'elk':
filename = 'DMATDMFT.OUT'
elif dm_type == 'qe':
filename = self.hdf_file
assert isinstance(filename, str), ("calc_density_correction: "
"filename has to be a string!")
assert kpts_to_write is None or dm_type == 'vasp', ('Selecting k-points only'
+'implemented for vasp')
ntoi = self.spin_names_to_ind[self.SO]
spn = self.spin_block_names[self.SO]
dens = {sp: 0.0 for sp in spn}
band_en_correction = 0.0
# Fetch Fermi weights and energy window band indices
if dm_type in ['vasp','qe']:
fermi_weights = 0
band_window = 0
if mpi.is_master_node():
with HDFArchive(self.hdf_file,'r') as ar:
fermi_weights = ar['dft_misc_input']['dft_fermi_weights']
band_window = ar['dft_misc_input']['band_window']
fermi_weights = mpi.bcast(fermi_weights)
band_window = mpi.bcast(band_window)
# Convert Fermi weights to a density matrix
dens_mat_dft = {}
for sp in spn:
dens_mat_dft[sp] = [fermi_weights[ik, ntoi[sp], :].astype(complex) for ik in range(self.n_k)]
# Set up deltaN:
deltaN = {}
for sp in spn:
deltaN[sp] = [np.zeros([self.n_orbitals[ik, ntoi[sp]], self.n_orbitals[
ik, ntoi[sp]]], complex) for ik in range(self.n_k)]
ikarray = np.arange(self.n_k)
for ik in mpi.slice_array(ikarray):
G_latt = self.lattice_gf(
ik=ik, mu=self.chemical_potential, broadening=broadening)
if dm_type == 'vasp' and self.proj_or_hk == 'hk':
# rotate the Green function into the DFT band basis
for bname, gf in G_latt:
G_latt_rot = gf.copy()
G_latt_rot << self.upfold(
ik, 0, bname, G_latt[bname], gf,shells='csc')
G_latt[bname] = G_latt_rot.copy()
for bname, gf in G_latt:
deltaN[bname][ik] = G_latt[bname].density()
if isinstance(self.mesh, MeshImFreq):
dens[bname] += self.bz_weights[ik] * G_latt[bname].total_density()
else:
dens[bname] += self.bz_weights[ik] * G_latt[bname].total_density(beta)
if dm_type in ['vasp','qe']:
# In 'vasp'-mode subtract the DFT density matrix
nb = self.n_orbitals[ik, ntoi[bname]]
diag_inds = np.diag_indices(nb)
deltaN[bname][ik][diag_inds] -= dens_mat_dft[bname][ik][:nb]
if self.charge_mixing and self.deltaNOld is not None:
G2 = np.sum(self.kpts_cart[ik,:]**2)
# Kerker mixing
mix_fac = self.charge_mixing_alpha * G2 / (G2 + self.charge_mixing_gamma**2)
deltaN[bname][ik][diag_inds] = (1.0 - mix_fac) * self.deltaNOld[bname][ik][diag_inds] + mix_fac * deltaN[bname][ik][diag_inds]
dens[bname] -= self.bz_weights[ik] * dens_mat_dft[bname][ik].sum().real
isp = ntoi[bname]
b1, b2 = band_window[isp][ik, :2]
nb = b2 - b1 + 1
assert nb == self.n_orbitals[ik, ntoi[bname]], "Number of bands is inconsistent at ik = %s"%(ik)
band_en_correction += np.dot(deltaN[bname][ik], self.hopping[ik, isp, :nb, :nb]).trace().real * self.bz_weights[ik]
# mpi reduce:
for bname in deltaN:
for ik in range(self.n_k):
deltaN[bname][ik] = mpi.all_reduce(deltaN[bname][ik])
dens[bname] = mpi.all_reduce(dens[bname])
self.deltaNOld = copy.copy(deltaN)
mpi.barrier()
band_en_correction = mpi.all_reduce(band_en_correction)
# now save to file:
if dm_type == 'wien2k':
if mpi.is_master_node():
if self.SP == 0:
f = open(filename, 'w')
else:
f = open(filename + 'up', 'w')
f1 = open(filename + 'dn', 'w')
# write chemical potential (in Rydberg):
f.write("%.14f\n" % (self.chemical_potential / self.energy_unit))
if self.SP != 0:
f1.write("%.14f\n" %
(self.chemical_potential / self.energy_unit))
# write beta in rydberg-1
f.write("%.14f\n" % (self.mesh.beta * self.energy_unit))
if self.SP != 0:
f1.write("%.14f\n" % (self.mesh.beta * self.energy_unit))
if self.SP == 0: # no spin-polarization
for ik in range(self.n_k):
f.write("%s\n" % self.n_orbitals[ik, 0])
for inu in range(self.n_orbitals[ik, 0]):
for imu in range(self.n_orbitals[ik, 0]):
valre = (deltaN['up'][ik][
inu, imu].real + deltaN['down'][ik][inu, imu].real) / 2.0
valim = (deltaN['up'][ik][
inu, imu].imag + deltaN['down'][ik][inu, imu].imag) / 2.0
f.write("%.14f %.14f " % (valre, valim))
f.write("\n")
f.write("\n")
f.close()
elif self.SP == 1: # with spin-polarization
# dict of filename: (spin index, block_name)
if self.SO == 0:
to_write = {f: (0, 'up'), f1: (1, 'down')}
if self.SO == 1:
to_write = {f: (0, 'ud'), f1: (0, 'ud')}
for fout in to_write.keys():
isp, sp = to_write[fout]
for ik in range(self.n_k):
fout.write("%s\n" % self.n_orbitals[ik, isp])
for inu in range(self.n_orbitals[ik, isp]):
for imu in range(self.n_orbitals[ik, isp]):
fout.write("%.14f %.14f " % (deltaN[sp][ik][
inu, imu].real, deltaN[sp][ik][inu, imu].imag))
fout.write("\n")
fout.write("\n")
fout.close()
elif dm_type == 'vasp':
if kpts_to_write is None:
kpts_to_write = np.arange(self.n_k)
else:
assert np.min(kpts_to_write) >= 0 and np.max(kpts_to_write) < self.n_k
assert self.SP == 0, "Spin-polarized density matrix is not implemented"
if mpi.is_master_node():
with open(filename, 'w') as f:
f.write(" %i -1 ! Number of k-points, default number of bands\n"%len(kpts_to_write))
for index, ik in enumerate(kpts_to_write):
ib1 = band_window[0][ik, 0]
ib2 = band_window[0][ik, 1]
f.write(" %i %i %i\n"%(index + 1, ib1, ib2))
for inu in range(self.n_orbitals[ik, 0]):
for imu in range(self.n_orbitals[ik, 0]):
valre = (deltaN['up'][ik][inu, imu].real + deltaN['down'][ik][inu, imu].real) / 2.0
valim = (deltaN['up'][ik][inu, imu].imag + deltaN['down'][ik][inu, imu].imag) / 2.0
f.write(" %.14f %.14f"%(valre, valim))
f.write("\n")
elif dm_type == 'elk':
# output each k-point density matrix for Elk
if mpi.is_master_node():
# read in misc data from .h5 file
things_to_read = ['band_window','vkl','nstsv']
self.subgroup_present, self.value_read = self.read_input_from_hdf(
subgrp=self.misc_data, things_to_read=things_to_read)
# open file
with open(filename, 'w') as f:
# determine the number of spin blocks
n_spin_blocks = self.SP + 1 - self.SO
nbmax = np.max(self.n_orbitals)
# output beta and mu in Hartrees
beta = self.mesh.beta * self.energy_unit
mu = self.chemical_potential/self.energy_unit
# ouput n_k, nspin and max orbitals - a check
f.write(" %d %d %d %.14f %.14f ! nkpt, nspin, nstmax, beta, mu\n"%(self.n_k, n_spin_blocks, nbmax, beta, mu))
for ik in range(self.n_k):
for ispn in range(n_spin_blocks):
#Determine the SO density matrix band indices from the spinor band indices
if(self.SO==1):
band0=[self.band_window[0][ik, 0],self.band_window[1][ik, 0]]
band1=[self.band_window[0][ik, 1],self.band_window[1][ik, 1]]
ib1=int(min(band0))
ib2=int(max(band1))
else:
#Determine the density matrix band indices from the spinor band indices
ib1 = self.band_window[ispn][ik, 0]
ib2 = self.band_window[ispn][ik, 1]
f.write(" %d %d %d %d ! ik, ispn, minist, maxist\n"%(ik + 1, ispn + 1, ib1, ib2))
for inu in range(self.n_orbitals[ik, ispn]):
for imu in range(self.n_orbitals[ik, ispn]):
#output non-magnetic or spin-averaged density matrix
if((self.SP==0) or (spinave)):
valre = (deltaN['up'][ik][inu, imu].real + deltaN['down'][ik][inu, imu].real) / 2.0
valim = (deltaN['up'][ik][inu, imu].imag + deltaN['down'][ik][inu, imu].imag) / 2.0
else:
valre = deltaN[spn[ispn]][ik][inu, imu].real
valim = deltaN[spn[ispn]][ik][inu, imu].imag
f.write(" %.14f %.14f"%(valre, valim))
f.write("\n")
elif dm_type == 'qe':
if self.SP == 0:
mpi.report("SUMK calc_density_correction: WARNING! Averaging out spin-polarized correction in the density channel")
subgrp = 'dft_update'
delta_N = np.zeros([self.n_k, max(self.n_orbitals[:,0]), max(self.n_orbitals[:,0])], dtype=complex)
mpi.report(" %i -1 ! Number of k-points, default number of bands\n"%(self.n_k))
for ik in range(self.n_k):
ib1 = band_window[0][ik, 0]
ib2 = band_window[0][ik, 1]
for inu in range(self.n_orbitals[ik, 0]):
for imu in range(self.n_orbitals[ik, 0]):
valre = (deltaN['up'][ik][inu, imu].real + deltaN['down'][ik][inu, imu].real) / 2.0
valim = (deltaN['up'][ik][inu, imu].imag + deltaN['down'][ik][inu, imu].imag) / 2.0
# write into delta_N
delta_N[ik, inu, imu] = valre + 1j*valim
if mpi.is_master_node():
with HDFArchive(self.hdf_file, 'a') as ar:
if subgrp not in ar:
ar.create_group(subgrp)
things_to_save = ['delta_N']
for it in things_to_save:
ar[subgrp][it] = locals()[it]
else:
raise NotImplementedError("Unknown density matrix type: '%s'"%(dm_type))
res = deltaN, dens
if dm_type in ['vasp', 'qe']:
res += (band_en_correction,)
return res
[docs]
def calculate_min_max_band_energies(self):
hop = self.hopping
diag_hop = np.zeros(hop.shape[:-1])
hop_slice = mpi.slice_array(hop)
diag_hop_slice = mpi.slice_array(diag_hop)
diag_hop_slice[:] = np.linalg.eigvalsh(hop_slice)
diag_hop = mpi.all_reduce(diag_hop)
min_band_energy = diag_hop.min().real
max_band_energy = diag_hop.max().real
self.min_band_energy = min_band_energy
self.max_band_energy = max_band_energy
return min_band_energy, max_band_energy
################
# FIXME LEAVE UNDOCUMENTED
################
[docs]
def check_projectors(self):
"""Calculated the density matrix from projectors (DM = P Pdagger) to check that it is correct and
specifically that it matches DFT."""
dens_mat = [np.zeros([self.corr_shells[icrsh]['dim'], self.corr_shells[icrsh]['dim']], complex)
for icrsh in range(self.n_corr_shells)]
for ik in range(self.n_k):
for icrsh in range(self.n_corr_shells):
dim = self.corr_shells[icrsh]['dim']
n_orb = self.n_orbitals[ik, 0]
projmat = self.proj_mat[ik, 0, icrsh, 0:dim, 0:n_orb]
dens_mat[icrsh][
:, :] += np.dot(projmat, projmat.transpose().conjugate()) * self.bz_weights[ik]
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):
if self.rot_mat_time_inv[icrsh] == 1:
dens_mat[icrsh] = dens_mat[icrsh].conjugate()
dens_mat[icrsh] = np.dot(np.dot(self.rot_mat[icrsh].conjugate().transpose(), dens_mat[icrsh]),
self.rot_mat[icrsh])
return dens_mat
[docs]
def sorts_of_atoms(self, shells):
"""
Determine the number of inequivalent sorts.
"""
sortlst = [shells[i]['sort'] for i in range(len(shells))]
n_sorts = len(set(sortlst))
return n_sorts
[docs]
def number_of_atoms(self, shells):
"""
Determine the number of inequivalent atoms.
"""
atomlst = [shells[i]['atom'] for i in range(len(shells))]
n_atoms = len(set(atomlst))
return n_atoms
# The following methods are here to ensure backward-compatibility
# after introducing the block_structure class
def __get_gf_struct_sumk(self):
return self.block_structure.gf_struct_sumk
def __set_gf_struct_sumk(self,value):
self.block_structure.gf_struct_sumk = value
gf_struct_sumk = property(__get_gf_struct_sumk,__set_gf_struct_sumk)
def __get_gf_struct_solver(self):
return self.block_structure.gf_struct_solver
def __set_gf_struct_solver(self,value):
self.block_structure.gf_struct_solver = value
gf_struct_solver = property(__get_gf_struct_solver,__set_gf_struct_solver)
def __get_solver_to_sumk(self):
return self.block_structure.solver_to_sumk
def __set_solver_to_sumk(self,value):
self.block_structure.solver_to_sumk = value
solver_to_sumk = property(__get_solver_to_sumk,__set_solver_to_sumk)
def __get_sumk_to_solver(self):
return self.block_structure.sumk_to_solver
def __set_sumk_to_solver(self,value):
self.block_structure.sumk_to_solver = value
sumk_to_solver = property(__get_sumk_to_solver,__set_sumk_to_solver)
def __get_solver_to_sumk_block(self):
return self.block_structure.solver_to_sumk_block
def __set_solver_to_sumk_block(self,value):
self.block_structure.solver_to_sumk_block = value
solver_to_sumk_block = property(__get_solver_to_sumk_block,__set_solver_to_sumk_block)
def __get_deg_shells(self):
return self.block_structure.deg_shells
def __set_deg_shells(self,value):
self.block_structure.deg_shells = value
deg_shells = property(__get_deg_shells,__set_deg_shells)
@property
def gf_struct_solver_list(self):
return self.block_structure.gf_struct_solver_list
@property
def gf_struct_sumk_list(self):
return self.block_structure.gf_struct_sumk_list
@property
def gf_struct_solver_dict(self):
return self.block_structure.gf_struct_solver_dict
@property
def gf_struct_sumk_dict(self):
return self.block_structure.gf_struct_sumk_dict
def __get_corr_to_inequiv(self):
return self.block_structure.corr_to_inequiv
def __set_corr_to_inequiv(self, value):
self.block_structure.corr_to_inequiv = value
corr_to_inequiv = property(__get_corr_to_inequiv, __set_corr_to_inequiv)
def __get_inequiv_to_corr(self):
return self.block_structure.inequiv_to_corr
def __set_inequiv_to_corr(self, value):
self.block_structure.inequiv_to_corr = value
inequiv_to_corr = property(__get_inequiv_to_corr, __set_inequiv_to_corr)