########################################################################## # # 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 . # ########################################################################## """ General SumK class and helper functions for combining ab-initio code and triqs """ from types import * import numpy 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 * from .symmetry import * from .block_structure import BlockStructure from itertools import product from warnings import warn from scipy import compress from scipy.optimize import minimize class SumkDFT(object): """This class provides a general SumK method for combining ab-initio code and triqs.""" def __init__(self, hdf_file, h_field=0.0, 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',fs_data='dft_fs_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. 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 ` and manipulated using the SumkDFT.block_structre attribute (see :class:`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.fs_data = fs_data self.h_field = h_field 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'] subgroup_present, self.optional_values_not_read = self.read_input_from_hdf(subgrp=self.dft_data, things_to_read=optional_things_to_read) 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: self.gf_struct_solver = [dict([(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)] # 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] = block for inner in range(inner_dim): self.sumk_to_solver[ish][ (block, inner)] = (block, inner) self.solver_to_sumk[ish][ (block, 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 ################ def read_input_from_hdf(self, subgrp, things_to_read): r""" Reads data from the HDF file. Prints a warning if a requested dataset is not found. Parameters ---------- subgrp : string Name of hdf5 file subgroup from which the data are to be read. things_to_read : list of strings List of datasets to be read from the hdf5 file. Returns ------- subgroup_present : boolean Is the subgrp is present in hdf5 file? values_not_read : list of strings List of things that could not be read """ values_not_read = [] # initialise variables on all nodes to ensure mpi broadcast works at # the end for it in things_to_read: setattr(self, it, None) subgroup_present = 0 if mpi.is_master_node(): with HDFArchive(self.hdf_file, 'r') as ar: if subgrp in ar: subgroup_present = True # first read the necessary things: for it in things_to_read: if it in ar[subgrp]: setattr(self, it, ar[subgrp][it]) else: values_not_read.append(it) else: if (len(things_to_read) != 0): mpi.report( "Loading failed: No %s subgroup in hdf5!" % subgrp) subgroup_present = False values_not_read = things_to_read # now do the broadcasting: for it in things_to_read: setattr(self, it, mpi.bcast(getattr(self, it))) subgroup_present = mpi.bcast(subgroup_present) values_not_read = mpi.bcast(values_not_read) return subgroup_present, values_not_read 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) 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 ################ 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 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 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 def lattice_gf(self, ik, mu=None, iw_or_w="iw", beta=40, 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. iw_or_w : string, optional - `iw_or_w` = 'iw' for a imaginary-frequency self-energy - `iw_or_w` = 'w' for a real-frequency self-energy beta : real, optional Inverse temperature. 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 : list, optional Data defining mesh on which the real-axis GF will be calculated, given in the form (om_min,om_max,n_points), where om_min is the minimum omega, om_max is the maximum omega and n_points is the number of points. 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 (iw_or_w != "iw") and (iw_or_w != "w"): raise ValueError("lattice_gf: Implemented only for Re/Im frequency functions.") if not hasattr(self, "Sigma_imp_" + iw_or_w): 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[1] - mesh[0]) / (mesh[2] - 1)) # Are we including Sigma? if with_Sigma: Sigma_imp = getattr(self, "Sigma_imp_" + iw_or_w) sigma_minus_dc = [s.copy() for s in Sigma_imp] if with_dc: sigma_minus_dc = self.add_dc(iw_or_w) if iw_or_w == "iw": # override beta if Sigma_iw is present beta = Sigma_imp[0].mesh.beta mesh = Sigma_imp[0].mesh elif iw_or_w == "w": mesh = Sigma_imp[0].mesh if broadening>0 and mpi.is_master_node(): warn('lattice_gf called with Sigma and broadening > 0 (broadening = {}). You might want to explicitly set the broadening to 0.'.format(broadening)) else: if iw_or_w == "iw": if beta is None: raise ValueError("lattice_gf: Give the beta for the lattice GfReFreq.") # Default number of Matsubara frequencies mesh = MeshImFreq(beta=beta, S='Fermion', n_max=1025) elif iw_or_w == "w": if mesh is None: raise ValueError("lattice_gf: Give the mesh=(om_min,om_max,n_points) for the lattice GfReFreq.") mesh = MeshReFreq(mesh[0], mesh[1], mesh[2]) # Check if G_latt is present set_up_G_latt = False # Assume not if not hasattr(self, "G_latt_" + iw_or_w): # Need to create G_latt_(i)w set_up_G_latt = True else: # Check that existing GF is consistent G_latt = getattr(self, "G_latt_" + iw_or_w) 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 unchangedsize: set_up_G_latt = True if (iw_or_w == "iw") and (self.G_latt_iw.mesh.beta != beta): set_up_G_latt = True # additional check for ImFreq # 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 iw_or_w == "iw": glist = lambda: [GfImFreq(indices=inner, mesh=mesh) for block, inner in gf_struct] elif iw_or_w == "w": glist = lambda: [GfReFreq(indices=inner, mesh=mesh) for block, inner in gf_struct] G_latt = BlockGf(name_list=block_ind_list, block_list=glist(), make_copies=False) G_latt.zero() if iw_or_w == "iw": G_latt << iOmega_n elif iw_or_w == "w": G_latt << Omega + 1j * broadening idmat = [numpy.identity( self.n_orbitals[ik, ntoi[sp]], complex) for sp in spn] M = copy.deepcopy(idmat) for ibl in range(self.n_spin_blocks[self.SO]): ind = ntoi[spn[ibl]] n_orb = self.n_orbitals[ik, ind] M[ibl] = self.hopping[ik, ind, 0:n_orb, 0:n_orb] - \ (idmat[ibl] * mu) - (idmat[ibl] * self.h_field * (1 - 2 * ibl)) G_latt -= M if with_Sigma: for icrsh in range(self.n_corr_shells): for bname, gf in G_latt: gf -= self.upfold(ik, icrsh, bname, sigma_minus_dc[icrsh][bname], gf) G_latt.invert() setattr(self, "G_latt_" + iw_or_w, G_latt) return G_latt def set_Sigma(self, Sigma_imp, transform_to_sumk_blocks=True): self.put_Sigma(Sigma_imp, transform_to_sumk_blocks) 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 all((isinstance(gf, Gf) and isinstance(gf.mesh, MeshImFreq)) for bname, gf in Sigma_imp[0]): # Imaginary frequency Sigma: self.Sigma_imp_iw = [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_iw elif all(isinstance(gf, Gf) and isinstance(gf.mesh, MeshReFreq) for bname, gf in Sigma_imp[0]): # Real frequency Sigma: self.Sigma_imp_w = [self.block_structure.create_gf(ish=icrsh, mesh=Sigma_imp[icrsh].mesh, gf_function=GfReFreq, space='sumk') for icrsh in range(self.n_corr_shells)] SK_Sigma_imp = self.Sigma_imp_w else: raise ValueError("put_Sigma: This type of Sigma is not handled, give either BlockGf of GfReFreq or GfImFreq.") # 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(Sigma_imp[0].mesh, MeshReFreq): if self.min_band_energy is None or self.max_band_energy is None: self.calculate_min_max_band_energies() for gf in Sigma_imp: Sigma_mesh = numpy.array([i for i in gf.mesh.values()]) if Sigma_mesh[0] > (self.min_band_energy - self.chemical_potential) or Sigma_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'%(Sigma_mesh[0], Sigma_mesh[-1], self.min_band_energy, self.max_band_energy)) def transform_to_sumk_blocks(self, Sigma_imp, Sigma_out=None): r""" transform Sigma from solver to sumk space Parameters ---------- Sigma_imp : list of BlockGf (Green's function) objects List containing impurity self-energy for all inequivalent correlated shells. The self-energies can be of the real or imaginary-frequency type. Sigma_out : list of BlockGf list of one BlockGf per correlated shell with the block structure according to ``gf_struct_sumk``; if None, it will be created """ assert isinstance(Sigma_imp, list),\ "transform_to_sumk_blocks: Sigma_imp has to be a list of Sigmas for the inequivalent correlated shells, even if it is of length 1!" assert len(Sigma_imp) == self.n_inequiv_shells,\ "transform_to_sumk_blocks: give exactly one Sigma for each inequivalent corr. shell!" if Sigma_out is None: Sigma_out = [self.block_structure.create_gf(ish=icrsh, mesh=Sigma_imp[self.corr_to_inequiv[icrsh]].mesh, space='sumk') for icrsh in range(self.n_corr_shells)] else: for icrsh in range(self.n_corr_shells): self.block_structure.check_gf(Sigma_out, ish=icrsh, space='sumk') # transform the CTQMC blocks to the full matrix: 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] self.block_structure.convert_gf( G=Sigma_imp[ish], G_struct=None, space_from='solver', space_to='sumk', ish_from=ish, ish_to=icrsh, G_out=Sigma_out[icrsh]) return Sigma_out def extract_G_loc(self, mu=None, iw_or_w='iw', with_Sigma=True, with_dc=True, broadening=None, transform_to_solver_blocks=True, show_warnings=True): r""" Extracts the local downfolded Green function by the Brillouin-zone integration of the lattice Green's function. Parameters ---------- 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 ------- G_loc : list of BlockGf (Green's function) objects List of the local Green's functions for all (inequivalent) correlated shells, rotated into the corresponding local frames. If ``transform_to_solver_blocks`` is True, it will be one per inequivalent correlated shell, else one per correlated shell. """ if mu is None: mu = self.chemical_potential if iw_or_w == "iw": G_loc = [self.Sigma_imp_iw[icrsh].copy() for icrsh in range( self.n_corr_shells)] # this list will be returned beta = G_loc[0].mesh.beta G_loc_inequiv = [BlockGf(name_block_generator=[(block, GfImFreq(target_shape=(block_dim, block_dim), mesh=G_loc[0].mesh)) for block, block_dim in self.gf_struct_solver[ish].items()], make_copies=False) for ish in range(self.n_inequiv_shells)] elif iw_or_w == "w": G_loc = [self.Sigma_imp_w[icrsh].copy() for icrsh in range( self.n_corr_shells)] # this list will be returned mesh = G_loc[0].mesh G_loc_inequiv = [BlockGf(name_block_generator=[(block, GfReFreq(target_shape=(block_dim, block_dim), mesh=mesh)) for block, block_dim in self.gf_struct_solver[ish].items()], make_copies=False) for ish in range(self.n_inequiv_shells)] for icrsh in range(self.n_corr_shells): G_loc[icrsh].zero() # initialize to zero ikarray = numpy.array(list(range(self.n_k))) for ik in mpi.slice_array(ikarray): if iw_or_w == 'iw': G_latt = self.lattice_gf( ik=ik, mu=mu, iw_or_w=iw_or_w, with_Sigma=with_Sigma, with_dc=with_dc, beta=beta) elif iw_or_w == 'w': mesh_parameters = (G_loc[0].mesh.omega_min,G_loc[0].mesh.omega_max,len(G_loc[0].mesh)) G_latt = self.lattice_gf( ik=ik, mu=mu, iw_or_w=iw_or_w, with_Sigma=with_Sigma, with_dc=with_dc, broadening=broadening, mesh=mesh_parameters) G_latt *= self.bz_weights[ik] for icrsh in range(self.n_corr_shells): # init temporary storage tmp = G_loc[icrsh].copy() for bname, gf in tmp: tmp[bname] << self.downfold( ik, icrsh, bname, G_latt[bname], gf) G_loc[icrsh] += tmp # Collect data from mpi for icrsh in range(self.n_corr_shells): G_loc[icrsh] << mpi.all_reduce( mpi.world, G_loc[icrsh], lambda x, y: x + y) mpi.barrier() # G_loc[:] is now the sum over k projected to the local orbitals. # here comes the symmetrisation, if needed: if self.symm_op != 0: G_loc = self.symmcorr.symmetrize(G_loc) # G_loc is rotated to the local coordinate system: if self.use_rotations: for icrsh in range(self.n_corr_shells): for bname, gf in G_loc[icrsh]: G_loc[icrsh][bname] << self.rotloc( icrsh, gf, direction='toLocal') if transform_to_solver_blocks: return self.transform_to_solver_blocks(G_loc, show_warnings=show_warnings) return G_loc def transform_to_solver_blocks(self, G_loc, G_out=None, show_warnings = True): """ transform G_loc from sumk to solver space Parameters ---------- G_loc : list of BlockGf a list of one BlockGf per correlated shell with a structure according to ``gf_struct_sumk``, e.g. as returned by :py:meth:`.extract_G_loc` with ``transform_to_solver_blocks=False``. G_out : list of BlockGf a list of one BlockGf per *inequivalent* correlated shell with a structure according to ``gf_struct_solver``. The output Green's function (if not given, a new one is created) Returns ------- G_out """ assert isinstance(G_loc, list), "G_loc must be a list (with elements for each correlated shell)" if G_out is None: G_out = [self.block_structure.create_gf(ish=ish, mesh=G_loc[self.inequiv_to_corr[ish]].mesh) for ish in range(self.n_inequiv_shells)] else: for ish in range(self.n_inequiv_shells): self.block_structure.check_gf(G_out, ish=ish) # transform to CTQMC blocks: for ish in range(self.n_inequiv_shells): self.block_structure.convert_gf( G=G_loc[self.inequiv_to_corr[ish]], G_struct=None, ish_from=self.inequiv_to_corr[ish], ish_to=ish, space_from='sumk', G_out=G_out[ish], show_warnings = show_warnings) # return only the inequivalent shells: return G_out 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 ` 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 correlated shells to be analysed. If include_shells is not provided all correlated 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. """ self.gf_struct_solver = [{} for ish in range(self.n_inequiv_shells)] 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)] 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 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: for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]: 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] = numpy.zeros( [block_dim, block_dim], numpy.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/numpy.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=(-numpy.pi*(len(block.mesh)-1) / (len(block.mesh)*get_delta_from_mesh(block.mesh)), numpy.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/numpy.pi else: raise Exception("G must be a list of BlockGf of either GfImFreq, GfImTime, GfReFreq or GfReTime") return gf 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 ` 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 correlated shells to be analysed. If include_shells is not provided all correlated 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)" gf = self._get_hermitian_quantity_from_gf(G) # initialize the variables self.gf_struct_solver = [{} for ish in range(self.n_inequiv_shells)] 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)] # the maximum value of each matrix element of each block and shell max_gf = [{name:numpy.max(numpy.abs(g.data),0) for name, g in gf[ish]} for ish in range(self.n_inequiv_shells)] if include_shells is None: # include all shells include_shells = list(range(self.n_inequiv_shells)) for ish in include_shells: for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]: n_orb = self.corr_shells[self.inequiv_to_corr[ish]]['dim'] # gives an index list of entries larger that threshold maxgf_bool = (abs(max_gf[ish][sp]) > 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 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 ` 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 = numpy.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 = numpy.linalg.eigvalsh(gf1.data[0]) e2 = numpy.linalg.eigvalsh(gf2.data[0]) if numpy.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 = numpy.kron(numpy.eye(*gf1.target_shape),gf2.data[0])-numpy.kron(gf1.data[0].transpose(),numpy.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 = numpy.kron(numpy.eye(*gf1.target_shape),gf2.data[i])-numpy.kron(gf1.data[i].transpose(),numpy.eye(*gf1.target_shape)) # transform M into current null space M = numpy.dot(M, N) N = numpy.dot(N, null(M, threshold)) if numpy.size(N) == 0: break # no intersection of the null spaces -> no symmetry if numpy.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 = numpy.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(numpy.complex_) ret = 0.0 for a in range(Z.shape[0]): for b in range(Z.shape[1]): ret += numpy.abs(numpy.dot(y, numpy.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, numpy.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 = numpy.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] = numpy.dot(T.conjugate().transpose(), v2[0].conjugate()), not v2[1] else: self.deg_shells[ish][ind2][block1] = numpy.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] = numpy.dot(T.conjugate(), v1[0].conjugate()), not v1[1] else: self.deg_shells[ish][ind1][block2] = numpy.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] = numpy.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 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: numpy.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 numpy.sum(abs(prop[ish][name]-numpy.diag(numpy.diagonal(prop[ish][name])))) > 1e-13: trafo[name] = numpy.linalg.eigh(prop[ish][name])[1].conj().T else: trafo[name] = numpy.identity(numpy.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 def density_matrix(self, method='using_gf', beta=40.0): """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). beta : float, optional Inverse temperature. 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] = numpy.zeros( [self.corr_shells[icrsh]['dim'], self.corr_shells[icrsh]['dim']], complex) ikarray = numpy.array(list(range(self.n_k))) for ik in mpi.slice_array(ikarray): if method == "using_gf": G_latt_iw = self.lattice_gf( ik=ik, mu=self.chemical_potential, iw_or_w="iw", beta=beta) G_latt_iw *= self.bz_weights[ik] dm = G_latt_iw.density() MMat = [dm[sp] for sp in self.spin_block_names[self.SO]] elif method == "using_point_integration": ntoi = self.spin_names_to_ind[self.SO] spn = self.spin_block_names[self.SO] dims = {sp:self.n_orbitals[ik, ntoi[sp]] for sp in spn} MMat = [numpy.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 else: raise ValueError("density_matrix: the method '%s' is not supported." % method) 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] if method == "using_gf": dens_mat[icrsh][sp] += numpy.dot(numpy.dot(projmat, MMat[isp]), projmat.transpose().conjugate()) elif method == "using_point_integration": dens_mat[icrsh][sp] += self.bz_weights[ik] * numpy.dot(numpy.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( mpi.world, dens_mat[icrsh][sp], 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 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] = numpy.dot(numpy.dot(self.rot_mat[icrsh].conjugate().transpose(), dens_mat[icrsh][sp]), self.rot_mat[icrsh]) return dens_mat # For simple dft input, get crystal field splittings. 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] = numpy.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] = numpy.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 = numpy.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] * numpy.dot(numpy.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] = numpy.dot(numpy.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 def init_dc(self): r""" Initializes the double counting terms. Parameters ---------- None """ self.dc_imp = [{} 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] = numpy.zeros([dim, dim], float) self.dc_energ = [0.0 for icrsh in range(self.n_corr_shells)] 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 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, optional Type of double-counting correction (see description). 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] = numpy.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 if use_dc_value is None: if U_interact is None and J_hund is None: raise ValueError("set_dc: either provide U_interact and J_hund or set use_dc_value to dc value.") if use_dc_formula == 0: # FLL self.dc_energ[icrsh] = U_interact / \ 2.0 * Ncrtot * (Ncrtot - 1.0) for sp in spn: Uav = U_interact * (Ncrtot - 0.5) - \ J_hund * (Ncr[sp] - 0.5) self.dc_imp[icrsh][sp] *= Uav self.dc_energ[icrsh] -= J_hund / \ 2.0 * (Ncr[sp]) * (Ncr[sp] - 1.0) mpi.report( "DC for shell %(icrsh)i and block %(sp)s = %(Uav)f" % locals()) elif use_dc_formula == 1: # Held's formula, with U_interact the interorbital onsite interaction self.dc_energ[icrsh] = (U_interact + (dim - 1) * (U_interact - 2.0 * J_hund) + ( dim - 1) * (U_interact - 3.0 * J_hund)) / (2 * dim - 1) / 2.0 * Ncrtot * (Ncrtot - 1.0) for sp in spn: Uav = (U_interact + (dim - 1) * (U_interact - 2.0 * J_hund) + (dim - 1) * (U_interact - 3.0 * J_hund)) / (2 * dim - 1) * (Ncrtot - 0.5) self.dc_imp[icrsh][sp] *= Uav mpi.report( "DC for shell %(icrsh)i and block %(sp)s = %(Uav)f" % locals()) elif use_dc_formula == 2: # AMF self.dc_energ[icrsh] = 0.5 * U_interact * Ncrtot * Ncrtot for sp in spn: Uav = U_interact * \ (Ncrtot - Ncr[sp] / dim) - \ J_hund * (Ncr[sp] - Ncr[sp] / dim) self.dc_imp[icrsh][sp] *= Uav self.dc_energ[ icrsh] -= (U_interact + (dim - 1) * J_hund) / dim * 0.5 * Ncr[sp] * Ncr[sp] mpi.report( "DC for shell %(icrsh)i and block %(sp)s = %(Uav)f" % locals()) mpi.report("DC energy for shell %s = %s" % (icrsh, self.dc_energ[icrsh])) 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] = numpy.dot(T.conjugate().transpose(), numpy.dot(self.dc_imp[icrsh][sp], T)) def add_dc(self, iw_or_w="iw"): r""" Subtracts the double counting term from the impurity self energy. Parameters ---------- iw_or_w : string, optional - `iw_or_w` = 'iw' for a imaginary-frequency self-energy - `iw_or_w` = 'w' for a real-frequency self-energy 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 getattr(self, "Sigma_imp_" + iw_or_w)] 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: dccont = numpy.dot(self.rot_mat[icrsh], numpy.dot(self.dc_imp[icrsh][ bname], self.rot_mat[icrsh].conjugate().transpose())) else: dccont = self.dc_imp[icrsh][bname] sigma_minus_dc[icrsh][bname] -= dccont return sigma_minus_dc def symm_deg_gf(self, gf_to_symm, ish=0): r""" Averages a GF 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) 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 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: ss = gf_to_symm[key].copy() ss.zero() helper = ss.copy() # get the transformation matrix if isinstance(degsh, dict): v, C = degsh[key] else: # for backward compatibility, allow degsh to be a list v = numpy.eye(*ss.target_shape) C = False # the helper is in the basis where the blocks are all equal helper.from_L_G_R(v.conjugate().transpose(), 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 v = numpy.eye(*ss.target_shape) C = False if C: gf_to_symm[key].from_L_G_R(v, ss.transpose().copy(), v.conjugate().transpose()) else: gf_to_symm[key].from_L_G_R(v, ss, v.conjugate().transpose()) def total_density(self, mu=None, iw_or_w="iw", with_Sigma=True, with_dc=True, broadening=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. iw_or_w : string, optional - `iw_or_w` = 'iw' for a imaginary-frequency self-energy - `iw_or_w` = 'w' for a real-frequency self-energy 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. Returns ------- dens : float Total charge :math:`n_{tot}`. """ if mu is None: mu = self.chemical_potential dens = 0.0 ikarray = numpy.array(list(range(self.n_k))) for ik in mpi.slice_array(ikarray): G_latt = self.lattice_gf( ik=ik, mu=mu, iw_or_w=iw_or_w, with_Sigma=with_Sigma, with_dc=with_dc, broadening=broadening) dens += self.bz_weights[ik] * G_latt.total_density() # collect data from mpi: dens = mpi.all_reduce(mpi.world, dens, lambda x, y: x + y) 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 def set_mu(self, mu): r""" Sets a new chemical potential. Parameters ---------- mu : float New value of the chemical potential. """ self.chemical_potential = mu def calc_mu(self, precision=0.01, iw_or_w='iw', broadening=None, delta=0.5, max_loops=100): r""" Searches for the chemical potential that gives the DFT total charge. A simple bisection method is used. Parameters ---------- precision : float, optional A desired precision of the resulting total charge. iw_or_w : string, optional - `iw_or_w` = 'iw' for a imaginary-frequency self-energy - `iw_or_w` = 'w' for a real-frequency 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. max_loops : int, optional Number of dichotomy loops maximally performed. Returns ------- mu : float Value of the chemical potential giving the DFT total charge within specified precision. """ F = lambda mu: self.total_density( mu=mu, iw_or_w=iw_or_w, broadening=broadening).real density = self.density_required - self.charge_below self.chemical_potential = dichotomy.dichotomy(function=F, 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] return self.chemical_potential def calc_density_correction(self, filename=None, dm_type='wien2k', spinave=False, kpts_to_write=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' 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' Returns ------- (deltaN, dens) : tuple Returns a tuple containing the density matrix `deltaN` and the corresponing total charge `dens`. """ 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] = [numpy.zeros([self.n_orbitals[ik, ntoi[sp]], self.n_orbitals[ ik, ntoi[sp]]], complex) for ik in range(self.n_k)] ikarray = numpy.arange(self.n_k) for ik in mpi.slice_array(ikarray): G_latt_iw = self.lattice_gf( ik=ik, mu=self.chemical_potential, iw_or_w="iw") 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_iw: G_latt_rot_iw = gf.copy() G_latt_rot_iw << self.upfold( ik, 0, bname, G_latt_iw[bname], gf,shells='csc') G_latt_iw[bname] = G_latt_rot_iw.copy() for bname, gf in G_latt_iw: deltaN[bname][ik] = G_latt_iw[bname].density() dens[bname] += self.bz_weights[ik] * G_latt_iw[bname].total_density() if dm_type in ['vasp','qe']: # In 'vasp'-mode subtract the DFT density matrix nb = self.n_orbitals[ik, ntoi[bname]] diag_inds = numpy.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 = numpy.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 += numpy.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( mpi.world, deltaN[bname][ik], lambda x, y: x + y) dens[bname] = mpi.all_reduce( mpi.world, dens[bname], lambda x, y: x + y) self.deltaNOld = copy.copy(deltaN) mpi.barrier() band_en_correction = mpi.all_reduce(mpi.world, band_en_correction, lambda x,y : x+y) # 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" % (G_latt_iw.mesh.beta * self.energy_unit)) if self.SP != 0: f1.write("%.14f\n" % (G_latt_iw.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 = numpy.arange(self.n_k) else: assert numpy.min(kpts_to_write) >= 0 and numpy.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 = numpy.max(self.n_orbitals) # output beta and mu in Hartrees beta = G_latt_iw.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': assert self.SP == 0, "Spin-polarized density matrix is not implemented" subgrp = 'dft_update' delta_N = numpy.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 not subgrp 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 def calculate_min_max_band_energies(self): hop = self.hopping diag_hop = numpy.zeros(hop.shape[:-1]) hop_slice = mpi.slice_array(hop) diag_hop_slice = mpi.slice_array(diag_hop) diag_hop_slice[:] = numpy.linalg.eigvalsh(hop_slice) diag_hop = mpi.all_reduce(mpi.world, diag_hop, lambda x, y: x + y) 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 ################ def calc_dc_for_density(self, orb, dc_init, dens_mat, density=None, precision=0.01): """Searches for DC in order to fulfill charge neutrality. If density is given, then DC is set such that the LOCAL charge of orbital orb coincides with the given density.""" def F(dc): self.calc_dc(dens_mat=dens_mat, U_interact=0, J_hund=0, orb=orb, use_dc_value=dc) if dens_req is None: return self.total_density(mu=mu) else: return self.extract_G_loc()[orb].total_density() if density is None: density = self.density_required - self.charge_below dc = dichotomy.dichotomy(function=F, x_init=dc_init, y_value=density, precision_on_y=precision, delta_x=0.5, max_loops=100, x_name="Double Counting", y_name="Total Density", verbosity=3)[0] return dc 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 = [numpy.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][ :, :] += numpy.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] = numpy.dot(numpy.dot(self.rot_mat[icrsh].conjugate().transpose(), dens_mat[icrsh]), self.rot_mat[icrsh]) return dens_mat 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 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)