########################################################################## # # TRIQS: a Toolbox for Research in Interacting Quantum Systems # # 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 . # ########################################################################## from types import * import numpy import pytriqs.utility.dichotomy as dichotomy from pytriqs.gf.local import * import pytriqs.utility.mpi as mpi from pytriqs.archive import * from symmetry import * from sets import Set from itertools import product class SumkDFT: """This class provides a general SumK method for combining ab-initio code and pytriqs.""" 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'): 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. 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 type(hdf_file) == StringType: 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.h_field = h_field # Read input from HDF: 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.value_read = self.read_input_from_hdf( subgrp=self.dft_data, things_to_read=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, range(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, range(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_list in self.gf_struct_sumk[self.inequiv_to_corr[ish]]: self.solver_to_sumk_block[ish][block] = block for inner in inner_list: 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 # Analyse the block structure and determine the smallest gf_struct # blocks and maps, if desired if use_dft_blocks: self.analyse_block_structure() ################ # 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? value_read : boolean Did the reading of requested datasets succeed? """ value_read = True # initialise variables on all nodes to ensure mpi broadcast works at # the end for it in things_to_read: setattr(self, it, 0) subgroup_present = 0 if mpi.is_master_node(): ar = HDFArchive(self.hdf_file, 'r') 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: mpi.report("Loading %s failed!" % it) value_read = False else: if (len(things_to_read) != 0): mpi.report( "Loading failed: No %s subgroup in hdf5!" % subgrp) subgroup_present = False value_read = False del ar # now do the broadcasting: for it in things_to_read: setattr(self, it, mpi.bcast(getattr(self, it))) subgroup_present = mpi.bcast(subgroup_present) value_read = mpi.bcast(value_read) return subgroup_present, value_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 ar = HDFArchive(self.hdf_file, 'a') if not subgrp in ar: ar.create_group(subgrp) for it in things_to_save: try: ar[subgrp][it] = getattr(self, it) except: mpi.report("%s not found, and so not saved." % it) del ar 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 ar = HDFArchive(self.hdf_file, 'r') 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 del ar 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. 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] 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. 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] 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 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.N1 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 = [ 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]], numpy.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): self.put_Sigma(Sigma_imp) def put_Sigma(self, Sigma_imp): r""" Inserts 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. """ 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_inequiv_shells, "put_Sigma: give exactly one Sigma for each inequivalent corr. shell!" # init self.Sigma_imp_(i)w: if all(type(gf) == GfImFreq for bname, gf in Sigma_imp[0]): # Imaginary frequency Sigma: self.Sigma_imp_iw = [BlockGf(name_block_generator=[(block, GfImFreq(indices=inner, mesh=Sigma_imp[0].mesh)) for block, inner in self.gf_struct_sumk[icrsh]], make_copies=False) for icrsh in range(self.n_corr_shells)] SK_Sigma_imp = self.Sigma_imp_iw elif all(type(gf) == GfReFreq for bname, gf in Sigma_imp[0]): # Real frequency Sigma: self.Sigma_imp_w = [BlockGf(name_block_generator=[(block, GfReFreq(indices=inner, mesh=Sigma_imp[0].mesh)) for block, inner in self.gf_struct_sumk[icrsh]], make_copies=False) 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." # 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] for block, inner in self.gf_struct_solver[ish].iteritems(): for ind1 in inner: for ind2 in inner: block_sumk, ind1_sumk = self.solver_to_sumk[ ish][(block, ind1)] block_sumk, ind2_sumk = self.solver_to_sumk[ ish][(block, ind2)] SK_Sigma_imp[icrsh][block_sumk][ ind1_sumk, ind2_sumk] << Sigma_imp[ish][block][ind1, ind2] # rotation from local to global coordinate system: if self.use_rotations: for icrsh in range(self.n_corr_shells): for bname, gf in SK_Sigma_imp[icrsh]: gf << self.rotloc(icrsh, gf, direction='toGlobal') def extract_G_loc(self, mu=None, iw_or_w='iw', with_Sigma=True, with_dc=True, broadening=None): 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. Returns ------- G_loc_inequiv : 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 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(indices=inner, mesh=G_loc[0].mesh)) for block, inner in self.gf_struct_solver[ish].iteritems()], 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(indices=inner, mesh=mesh)) for block, inner in self.gf_struct_solver[ish].iteritems()], 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(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': 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) 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') # transform to CTQMC blocks: for ish in range(self.n_inequiv_shells): for block, inner in self.gf_struct_solver[ish].iteritems(): for ind1 in inner: for ind2 in inner: block_sumk, ind1_sumk = self.solver_to_sumk[ ish][(block, ind1)] block_sumk, ind2_sumk = self.solver_to_sumk[ ish][(block, ind2)] G_loc_inequiv[ish][block][ind1, ind2] << G_loc[ self.inequiv_to_corr[ish]][block_sumk][ind1_sumk, ind2_sumk] # return only the inequivalent shells: return G_loc_inequiv def analyse_block_structure(self, threshold=0.00001, include_shells=None, dm=None): r""" Determines the block structure of local Green's functions by analysing the structure of the corresponding density matrices. The resulting block structures for correlated shells are stored in self.gf_struct_solver list. Parameters ---------- threshold : real, optional If the difference between density 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. 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}. If not provided, dm will be calculated from the DFT Hamiltonian by a simple-point BZ integration. """ 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 include_shells is None: include_shells = 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) # 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]: 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), range(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, inner in self.gf_struct_solver[ish].iteritems(): # get dm for the blocks: dm[block] = numpy.zeros( [len(inner), len(inner)], numpy.complex_) for ind1 in inner: for ind2 in inner: 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].iterkeys(): for block2 in self.gf_struct_solver[ish].iterkeys(): 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 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']], numpy.complex_) ikarray = numpy.array(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] unchangedsize = all( [self.n_orbitals[ik, ntoi[sp]] == self.n_orbitals[0, ntoi[sp]] for sp in spn]) if unchangedsize: dim = self.n_orbitals[0, ntoi[sp]] else: dim = self.n_orbitals[ik, ntoi[sp]] MMat = [numpy.zeros([dim, dim], numpy.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_solver like Effective local Hamiltonian :math:`H^{loc}_{m m'}` for each 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'], numpy.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], numpy.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, numpy.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], numpy.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): r""" Calculates and sets 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. """ 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].iteritems(): bl = self.solver_to_sumk_block[ish][block] Ncr[bl] += dens_mat[block].real.trace() Ncrtot = sum(Ncr.itervalues()) for sp in spn: self.dc_imp[icrsh][sp] = numpy.identity(dim, numpy.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]) 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 dccont = numpy.dot(self.rot_mat[icrsh], numpy.dot(self.dc_imp[icrsh][ bname], self.rot_mat[icrsh].conjugate().transpose())) sigma_minus_dc[icrsh][bname] -= dccont return sigma_minus_dc def symm_deg_gf(self, gf_to_symm, orb): 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 GF. orb : int Index of an inequivalent shell. """ for degsh in self.deg_shells[orb]: ss = gf_to_symm[degsh[0]].copy() ss.zero() n_deg = len(degsh) for bl in degsh: ss += gf_to_symm[bl] / (1.0 * n_deg) for bl in degsh: gf_to_symm[bl] << ss 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(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() return dens 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): 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. 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) 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=0.5, max_loops=100, x_name="Chemical Potential", y_name="Total Density", verbosity=3)[0] return self.chemical_potential def calc_density_correction(self, filename='dens_mat.dat'): 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. Returns ------- (deltaN, dens) : tuple Returns a tuple containing the density matrix `deltaN` and the corresponing total charge `dens`. """ assert type( filename) == StringType, "calc_density_correction: filename has to be a string!" ntoi = self.spin_names_to_ind[self.SO] spn = self.spin_block_names[self.SO] dens = {sp: 0.0 for sp in spn} # Set up deltaN: deltaN = {} for sp in spn: deltaN[sp] = [numpy.zeros([self.n_orbitals[ik, ntoi[sp]], self.n_orbitals[ ik, ntoi[sp]]], numpy.complex_) for ik in range(self.n_k)] ikarray = numpy.array(range(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") 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() # 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) mpi.barrier() # now save to file: 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.iterkeys(): 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() return deltaN, dens ################ # 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']], numpy.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