mirror of
https://github.com/triqs/dft_tools
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c4db7e6867
* remove manual module reference files * move hdf5 structure reference to guide * fix various doc strings * remove very old update_archive python script for triqs 1.2 * refine main documentation.rst file -> better overview
1107 lines
51 KiB
Python
1107 lines
51 KiB
Python
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##########################################################################
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#
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# TRIQS: a Toolbox for Research in Interacting Quantum Systems
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#
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# Copyright (C) 2011 by M. Aichhorn, L. Pourovskii, V. Vildosola
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#
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# TRIQS is free software: you can redistribute it and/or modify it under the
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# terms of the GNU General Public License as published by the Free Software
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# Foundation, either version 3 of the License, or (at your option) any later
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# version.
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#
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# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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# details.
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#
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# You should have received a copy of the GNU General Public License along with
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# TRIQS. If not, see <http://www.gnu.org/licenses/>.
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#
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##########################################################################
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###
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# Wannier90 to HDF5 converter for the SumkDFT class of dfttools/TRIQS;
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#
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# written by Gabriele Sclauzero (Materials Theory, ETH Zurich), Dec 2015 -- Jan 2016,
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# updated by Maximilian Merkel (Materials Theory, ETH Zurich), Aug 2020 -- Jan 2021,
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# and by Sophie Beck (Materials Theory, ETH Zurich), Sep 2020 -- Apr 2021,
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# under the supervision of Claude Ederer (Materials Theory).
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# Partially based on previous work by K. Dymkovski and the DFT_tools/TRIQS team.
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#
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# Limitations of the current implementation:
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# - the T rotation matrices are not used in this implementation
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# - in bloch_basis mode, the number of wannier functions per shell has to be equal
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# to the dim of the shell
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#
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# Things to be improved/checked:
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# - the case with SP=1 might work, but was never tested (do we need to define
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# rot_mat_time_inv also if symm_op = 0?)
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# - the calculation of rot_mat in find_rot_mat() relies on the eigenvalues of H(0);
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# this might fail in presence of degenerate eigenvalues (now just prints warning)
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# - make the code more MPI safe (error handling): if we run with more than one process
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# and an error occurs on the masternode, the calculation does not abort
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# - in case of disentanglement, the outer window being close to Kohn-Sham energies
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# can cause a problem in creating the udis_mat in read_wannier90data
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# - add_lambda does not work for multiple impurities
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###
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"""
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Wannier90 converter
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"""
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import numpy
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import os.path
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from itertools import product
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from h5 import HDFArchive
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from .converter_tools import ConverterTools
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import triqs.utility.mpi as mpi
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class Wannier90Converter(ConverterTools):
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"""
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Conversion from Wannier90 output to an hdf5 file that can be used as input for the SumkDFT class.
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"""
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def __init__(self, seedname, hdf_filename=None, dft_subgrp='dft_input',
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symmcorr_subgrp='dft_symmcorr_input', misc_subgrp='dft_misc_input',
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repacking=False, rot_mat_type='hloc_diag', bloch_basis=False, add_lambda=None,
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w90zero=2.e-6):
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"""
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Initialise the class.
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Parameters
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----------
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seedname : string
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Base name of Wannier90 files
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hdf_filename : string, optional
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Name of hdf5 archive to be created
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dft_subgrp : string, optional
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Name of subgroup storing necessary DFT data
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symmcorr_subgrp : string, optional
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Name of subgroup storing correlated-shell symmetry data
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misc_subgrp : string, optional
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Name of subgroup storing miscellaneous DFT data.
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repacking : boolean, optional
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Does the hdf5 archive need to be repacked to save space?
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rot_mat_type : string, optional
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Type of rot_mat used
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Can be 'hloc_diag', 'wannier', 'none'
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bloch_basis : boolean, optional
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Should the Hamiltonian be written in Bloch rather than Wannier basis?
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add_lambda : list of floats, optional
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add local spin-orbit term
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w90zero : float, optional
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threshold on symmetry checks of Hamiltonian and rot_mat
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"""
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self._name = "Wannier90Converter"
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assert isinstance(seedname, str), self._name + \
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": Please provide the DFT files' base name as a string."
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if hdf_filename is None:
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hdf_filename = seedname + '.h5'
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self.hdf_file = hdf_filename
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# if the w90 output is seedname_hr.dat, the input file for the
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# converter must be called seedname.inp
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self.inp_file = seedname + '.inp'
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self.w90_seed = seedname
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self.dft_subgrp = dft_subgrp
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self.symmcorr_subgrp = symmcorr_subgrp
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self.misc_subgrp = misc_subgrp
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self.fortran_to_replace = {'D': 'E'}
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# threshold below which matrix elements from wannier90 should be
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# considered equal
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self._w90zero = w90zero
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self.rot_mat_type = rot_mat_type
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self.bloch_basis = bloch_basis
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self.add_lambda = add_lambda
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if self.rot_mat_type not in ('hloc_diag', 'wannier', 'none'):
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raise ValueError('Parameter rot_mat_type invalid, should be one of'
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+ '"hloc_diag", "wannier", "none"')
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# Checks if h5 file is there and repacks it if wanted:
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if (os.path.exists(self.hdf_file) and repacking):
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ConverterTools.repack(self)
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def convert_dft_input(self):
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"""
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Reads the appropriate files and stores the data for the
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- dft_subgrp
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- symmcorr_subgrp
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in the hdf5 archive.
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"""
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mpi.report("\nReading input from %s..." % self.inp_file)
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# R is a generator : each R.Next() will return the next number in the
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# file
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R = ConverterTools.read_fortran_file(
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self, self.inp_file, self.fortran_to_replace)
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shell_entries = ['atom', 'sort', 'l', 'dim']
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corr_shell_entries = ['atom', 'sort', 'l', 'dim', 'SO', 'irep']
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# First, let's read the input file with the parameters needed for the
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# conversion
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try:
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# read k - point mesh generation option
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kmesh_mode = int(next(R))
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if kmesh_mode >= 0:
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# read k-point mesh size from input
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nki = [int(next(R)) for idir in range(3)]
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else:
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# some default grid, if everything else fails...
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nki = [8, 8, 8]
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# read the total number of electrons per cell if not in bloch basis
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# in bloch basis, this is later calculated from the partial occupations
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density_required = float(next(R))
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# we do not read shells, because we have no additional shells beyond correlated ones,
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# and the data will be copied from corr_shells into shells (see below)
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# number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
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n_corr_shells = int(next(R))
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# now read the information about the correlated shells (atom, sort,
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# l, dim, SO flag, irep):
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corr_shells = [{name: int(val) for name, val in zip(
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corr_shell_entries, R)} for icrsh in range(n_corr_shells)]
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try:
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self.fermi_energy = float(next(R))
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except:
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self.fermi_energy = 0.
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except StopIteration: # a more explicit error if the file is corrupted.
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mpi.report(self._name + ": reading input file %s failed!" %
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self.inp_file)
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# close the input file
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R.close()
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# Set or derive some quantities
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# Wannier90 does not use symmetries to reduce the k-points
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# the following might change in future versions
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symm_op = 0
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# copy corr_shells into shells (see above)
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n_shells = n_corr_shells
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shells = []
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for ish in range(n_shells):
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shells.append({key: corr_shells[ish].get(
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key, None) for key in shell_entries})
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# Determine if any shell requires SO
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if any([corr_shell['SO']==1 for corr_shell in corr_shells]):
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SO = 1
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SP = 1
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mpi.report('Spin-orbit interaction turned on')
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else:
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SO = 0
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SP = 0
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# Only one block supported - either non-spin-polarized or spin-orbit coupled
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assert SP == SO, 'Spin-polarized calculations not implemented'
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if self.add_lambda:
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assert n_shells == 1, 'add_lambda not implemented for more than one t2g shell'
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assert [sh['dim'] for sh in corr_shells] == [3 for sh in corr_shells], 'add_lambda only implemented for t2g shell'
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assert SO == SP == 0, 'add_lambda not implemented for SO = SP = 1'
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assert self.bloch_basis == False, 'add_lambda not implemented for bloch_basis = True'
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# now setting SO and SP to 1
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SO = SP = 1
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charge_below = 0 # total charge below energy window NOT used for now
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energy_unit = 1.0 # should be understood as eV units
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# this is more general
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n_spin_blocs = SP + 1 - SO
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assert n_spin_blocs > 0, 'Input error, if SO=1, SP must be 1.'
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if SO == 1:
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for shell_list in [shells, corr_shells]:
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for entry in shell_list:
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entry['dim'] *= 2
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if 'SO' in entry.keys() and self.add_lambda: entry['SO'] = 1
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dim_corr_shells = sum([sh['dim'] for sh in corr_shells])
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mpi.report('Total number of WFs expected in the correlated shells: {0:d}'.format(dim_corr_shells))
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# build the k-point mesh, if its size was given on input (kmesh_mode >= 0),
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# otherwise it is built according to the data in the hr file (see below)
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# If output is in bloch_basis, we use k mesh from seedname_u.mat for consistency
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if kmesh_mode >= 0 and not self.bloch_basis:
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n_k, kpts, kpt_weights = self.kmesh_build(nki, kmesh_mode)
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self.n_k = n_k
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self.kpts = kpts
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# determine the number of inequivalent correlated shells and maps,
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# needed for further processing
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n_inequiv_shells, corr_to_inequiv, inequiv_to_corr = ConverterTools.det_shell_equivalence(
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self, corr_shells)
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mpi.report("Number of inequivalent shells: %d" % n_inequiv_shells)
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mpi.report("Shell representatives: " + format(inequiv_to_corr))
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shells_map = [inequiv_to_corr[corr_to_inequiv[ish]]
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for ish in range(n_corr_shells)]
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mpi.report("Mapping: " + format(shells_map))
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# not used in this version: reset to dummy values?
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n_reps = [1 for i in range(n_inequiv_shells)]
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dim_reps = [0 for i in range(n_inequiv_shells)]
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T = []
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for ish in range(n_inequiv_shells):
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ll = 2 * corr_shells[inequiv_to_corr[ish]]['l'] + 1
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lmax = ll * (corr_shells[inequiv_to_corr[ish]]['SO'] + 1)
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T.append(numpy.zeros([lmax, lmax], dtype=complex))
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spin_w90name = ['_up', '_down']
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hamr_full = []
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umat_full = []
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udismat_full = []
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bandmat_full = []
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# TODO: generalise to SP=1 (only partially done)
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rot_mat_time_inv = [0 for i in range(n_corr_shells)]
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# Second, let's read the file containing the Hamiltonian in WF basis
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# produced by Wannier90
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for isp in range(n_spin_blocs):
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# begin loop on isp
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# build filename according to wannier90 conventions
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if SP == 1 and SO == 0:
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mpi.report(
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"Reading information for spin component n. %d" % isp)
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file_seed = self.w90_seed + spin_w90name[isp]
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else:
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file_seed = self.w90_seed
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# now grab the data from the H(R) file
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mpi.report(
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"\nThe Hamiltonian in MLWF basis is extracted from %s files..." % file_seed)
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nr = rvec = rdeg = nw = hamr = u_mat = udis_mat = band_mat = k_mesh_from_umat = None
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if (mpi.is_master_node()):
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(nr, rvec, rdeg, nw, hamr, u_mat, udis_mat,
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band_mat, k_mesh_from_umat) = self.read_wannier90data(file_seed, kmesh_mode)
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mpi.barrier()
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nr = mpi.bcast(nr)
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rvec = mpi.bcast(rvec)
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rdeg = mpi.bcast(rdeg)
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nw = mpi.bcast(nw)
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hamr = mpi.bcast(hamr)
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u_mat = mpi.bcast(u_mat)
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udis_mat = mpi.bcast(udis_mat)
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band_mat = mpi.bcast(band_mat)
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k_mesh_from_umat = mpi.bcast(k_mesh_from_umat)
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# number of R vectors, their indices, their degeneracy, number of WFs, H(R),
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# U matrices, U(dis) matrices, band energies, k_mesh of U matrices
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mpi.report('\n... done: {} R vectors, {} WFs found'.format(nr, nw))
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if self.add_lambda:
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mpi.report('Adding local spin-orbit term to Hamiltonian (assuming dxz, dyz, dxy as orbital order)')
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# upscaling quantities
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nw *= 2
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# scale Hamiltonian by 2 to account for spin DOF
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hamr = [numpy.kron(numpy.eye(2), hamr[ir]) for ir in range(nr)]
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# scale lambda matrix by number of correlated shells to account for shells
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# FIXME: does not give the correct order for multiple impurities!
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hamr[nr//2] += numpy.kron(numpy.eye(n_corr_shells), self.lambda_matrix_w90_t2g())
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with numpy.printoptions(linewidth=100, formatter={'complexfloat': '{:+.3f}'.format}):
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mpi.report('Local Hamiltonian including spin-orbit coupling:')
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mpi.report(hamr[nr//2])
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if isp == 0:
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# set or check some quantities that must be the same for both
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# spins
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self.nrpt = nr
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# k-point grid: (if not defined before)
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if self.bloch_basis:
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mpi.report('Reading k mesh from seedname_u.mat file')
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kpts = k_mesh_from_umat
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n_k = len(kpts)
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kpt_weights = numpy.full(n_k, 1/n_k)
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self.n_k = n_k
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self.kpts = kpts
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elif kmesh_mode == -1:
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# the size of the k-point mesh is determined from the
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# largest R vector
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nki = [2 * rvec[:, idir].max() + 1 for idir in range(3)]
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# it will be the same as in the win only when nki is odd, because of the
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# wannier90 convention: if we have nki k-points along the i-th direction,
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# then we should get 2*(nki/2)+nki%2 R points along that
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# direction
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n_k, kpts, kpt_weights = self.kmesh_build(nki)
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self.n_k = n_k
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self.kpts = kpts
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# set the R vectors and their degeneracy
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self.rvec = rvec
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self.rdeg = rdeg
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self.nwfs = nw
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# check that the total number of WFs makes sense
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if self.nwfs < dim_corr_shells:
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mpi.report(
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"ERROR: number of WFs in the file smaller than number of correlated orbitals!")
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elif self.nwfs > dim_corr_shells:
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# NOTE: correlated shells must appear before uncorrelated
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# ones inside the file
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mpi.report("Number of WFs larger than correlated orbitals:\n" +
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"WFs from %d to %d treated as uncorrelated" % (dim_corr_shells + 1, self.nwfs))
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else:
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mpi.report(
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"Number of WFs equal to number of correlated orbitals")
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# we assume spin up and spin down always have same total number
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# of WFs
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# get second dimension of udis_mat which corresponds to number of bands in window
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# n_bands_max corresponds to numpy.max(n_orbitals)
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n_bands_max = udis_mat.shape[1] if not self.add_lambda else 2*udis_mat.shape[1]
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n_orbitals = numpy.full([self.n_k, n_spin_blocs], n_bands_max)
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else:
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# consistency check between the _up and _down file contents
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if nr != self.nrpt:
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mpi.report(
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"Different number of R vectors for spin-up/spin-down!")
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if nw != self.nwfs:
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mpi.report(
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"Different number of WFs for spin-up/spin-down!")
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hamr_full.append(hamr)
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umat_full.append(u_mat)
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udismat_full.append(udis_mat)
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bandmat_full.append(band_mat)
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for ir in range(nr):
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# checks if the Hamiltonian is real (it should, if
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# wannierisation worked fine)
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if numpy.abs((hamr[ir].imag.max()).max()) > self._w90zero:
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mpi.report(
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"H(R) has large complex components at R %d" % ir)
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# copy the R=0 block corresponding to the correlated shells
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# into another variable (needed later for finding rot_mat)
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if rvec[ir, 0] == 0 and rvec[ir, 1] == 0 and rvec[ir, 2] == 0:
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ham_corr0 = hamr[ir][0:dim_corr_shells, 0:dim_corr_shells]
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# checks if ham0 is Hermitian
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if not numpy.allclose(ham_corr0.transpose().conjugate(), ham_corr0, atol=self._w90zero, rtol=0):
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raise ValueError("H(R=0) matrix is not Hermitian!")
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# find rot_mat symmetries by diagonalising the on-site Hamiltonian
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# of the first spin
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if isp == 0:
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use_rotations, rot_mat = self.find_rot_mat(
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n_corr_shells, corr_shells, shells_map, ham_corr0)
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else:
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# consistency check
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use_rotations_, rot_mat_ = self.find_rot_mat(
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n_corr_shells, corr_shells, shells_map, ham_corr0)
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if (use_rotations and not use_rotations_):
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mpi.report(
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"Rotations cannot be used for spin component n. %d" % isp)
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for icrsh in range(n_corr_shells):
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if not numpy.allclose(rot_mat_[icrsh], rot_mat[icrsh], atol=self._w90zero, rtol=0):
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mpi.report(
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"Rotations for spin component n. %d do not match!" % isp)
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# end loop on isp
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# Reads misc input needed for CSC calculations
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if self.bloch_basis:
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if os.path.isfile(self.w90_seed + '.nscf.out'):
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fermi_weight_file = self.w90_seed + '.nscf.out'
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mpi.report('Reading DFT band occupations from Quantum Espresso output {}'.format(fermi_weight_file))
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elif os.path.isfile('LOCPROJ'):
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assert os.path.isfile('OUTCAR')
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fermi_weight_file = 'LOCPROJ'
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mpi.report('Reading DFT band occupations from Vasp output {}'.format(fermi_weight_file))
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else:
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raise IOError('seedname.nscf.out or LOCPROJ required in bloch_basis mode')
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f_weights = band_window = self.fermi_energy = kpt_basis = None
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if (mpi.is_master_node()):
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f_weights, band_window, self.fermi_energy, kpt_basis = self.convert_misc_input(fermi_weight_file,
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self.w90_seed + '.nnkp', n_spin_blocs)
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mpi.barrier()
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f_weights = mpi.bcast(f_weights)
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band_window = mpi.bcast(band_window)
|
|
self.fermi_energy = mpi.bcast(self.fermi_energy)
|
|
kpt_basis = mpi.bcast(kpt_basis)
|
|
# Get density from k-point weighted average and sum over all spins and bands
|
|
density_required = numpy.sum(f_weights.T * kpt_weights) * (2 - SP)
|
|
mpi.report('Overwriting required density with DFT result {:.5f}'.format(density_required))
|
|
mpi.report('and using the DFT Fermi energy {:.5f} eV\n'.format(self.fermi_energy))
|
|
|
|
if not self.bloch_basis:
|
|
mpi.report("The k-point grid has dimensions: %d, %d, %d" % tuple(nki))
|
|
|
|
# if calculations are spin-polarized, then renormalize k-point weights
|
|
if SP == 1 and SO == 0:
|
|
kpt_weights *= 0.5
|
|
|
|
# Third, initialise the projectors
|
|
k_dep_projection = 0 # at the moment not really used, but might get important
|
|
proj_mat = numpy.zeros([self.n_k, n_spin_blocs, n_corr_shells, max(
|
|
[crsh['dim'] for crsh in corr_shells]), numpy.max(n_orbitals)], dtype=complex)
|
|
iorb = 0
|
|
# Projectors are either identity matrix blocks to use with Wannier basis
|
|
# OR correspond to the overlap between Kohn-Sham and Wannier orbitals as
|
|
# P_{nu,alpha](k) = <w_{alpha,k}|psi_{nu,k}>
|
|
# NOTE: we assume that the correlated orbitals appear at the beginning of the H(R)
|
|
# file and that the ordering of MLWFs matches the corr_shell info from
|
|
# the input.
|
|
for isp in range(n_spin_blocs):
|
|
# now combine udismat and umat
|
|
u_total = numpy.einsum('abc,acd->abd',udismat_full[isp],umat_full[isp])
|
|
# transpose and write into proj_mat
|
|
u_temp = numpy.transpose(u_total.conj(),(0,2,1))
|
|
# scale unitary U by 2 to account for spin DOF
|
|
if self.add_lambda: u_temp = numpy.kron(numpy.eye(2), u_temp)
|
|
for icrsh in range(n_corr_shells):
|
|
dim = corr_shells[icrsh]['dim']
|
|
proj_mat[:, isp, icrsh, 0:dim, :] = u_temp[:,iorb:iorb+dim,:]
|
|
iorb += dim
|
|
|
|
# Then, compute the hoppings in reciprocal space
|
|
hopping = numpy.zeros([self.n_k, n_spin_blocs, numpy.max(n_orbitals), numpy.max(n_orbitals)], dtype=complex)
|
|
for isp in range(n_spin_blocs):
|
|
# if bloch_basis is True, use Kohn-Sham eigenvalues as hamk
|
|
# this ensures that the calculation of the band-correlation energy
|
|
# is consistent with SumkDFT's calc_density_correction
|
|
if self.bloch_basis:
|
|
# diagonal Kohn-Sham bands
|
|
# TODO: test for system with self.nwfs > dim_corr_shells
|
|
hamk = [numpy.diag(bandmat_full[isp][ik]) for ik in range(self.n_k)]
|
|
|
|
# Sanity check if the local Hamiltonian with the projector method
|
|
# corresponds to W90 result
|
|
wannier_ham = self.fourier_ham(hamr_full[isp])
|
|
for ik in range(self.n_k):
|
|
proj_mat_flattened = numpy.zeros((numpy.max(n_orbitals), numpy.max(n_orbitals)), dtype=complex)
|
|
iorb = 0
|
|
for icrsh in range(n_corr_shells):
|
|
dim = corr_shells[icrsh]['dim']
|
|
proj_mat_flattened[iorb:iorb+dim,:] = proj_mat[ik, isp][icrsh,0:dim,:].reshape(dim, numpy.max(n_orbitals))
|
|
iorb += dim
|
|
downfolded_ham = proj_mat_flattened.dot(hamk[ik].dot(proj_mat_flattened.conj().T))
|
|
if dim_corr_shells < numpy.max(n_orbitals):
|
|
downfolded_ham = downfolded_ham[:dim_corr_shells,:dim_corr_shells]
|
|
wannier_ham[ik] = wannier_ham[ik][:dim_corr_shells,:dim_corr_shells]
|
|
|
|
if not numpy.allclose(downfolded_ham, wannier_ham[ik], atol=1e-4, rtol=0):
|
|
mpi.report('WARNING: mismatch between downfolded Hamiltonian and '
|
|
+ f'Fourier transformed H(R). First occurred at kpt {ik}:')
|
|
|
|
with numpy.printoptions(formatter={'complexfloat': '{:+.4f}'.format}):
|
|
mpi.report('Downfolded Hamiltonian, P H_eig P')
|
|
mpi.report(downfolded_ham)
|
|
mpi.report('\nWannier Hamiltonian, Fourier(H(r))')
|
|
mpi.report(wannier_ham[ik])
|
|
break
|
|
# else for an isolated set of bands use fourier transform of H(R)
|
|
else:
|
|
# make Fourier transform H(R) -> H(k) : it can be done one spin at a time
|
|
hamk = self.fourier_ham(hamr_full[isp])
|
|
|
|
# Sanity check if imaginary diagonal elements are zero, otherwise instabilties in lattice Gf!
|
|
diag_iterator = range(0,dim_corr_shells)
|
|
for ik in range(self.n_k):
|
|
if not numpy.allclose(hamk[ik][diag_iterator, diag_iterator].imag, 0, atol=1e-10):
|
|
mpi.report('ERROR: Wannier Hamiltonian has complex diagonal entries. '
|
|
+ f'First occurred at kpt {ik}:')
|
|
with numpy.printoptions(formatter={'float': '{:+.10f}'.format}):
|
|
mpi.report('\nWannier Hamiltonian diagonal, Fourier(H(r)), imaginary')
|
|
mpi.report(hamk[ik][diag_iterator, diag_iterator].imag)
|
|
mpi.MPI.COMM_WORLD.Abort(1)
|
|
# set imaginary part to zero
|
|
hamk[ik][diag_iterator, diag_iterator] = hamk[ik][diag_iterator, diag_iterator].real + 0*1j
|
|
# finally write hamk into hoppings
|
|
for ik in range(self.n_k):
|
|
hopping[ik, isp] = hamk[ik] - numpy.identity(numpy.max(n_orbitals)) * self.fermi_energy
|
|
hopping *= energy_unit
|
|
mpi.report("Subtracting {:.5f} eV from the Fermi level.".format(self.fermi_energy))
|
|
|
|
# bz_weights required by triqs h5 standard but soon to be replaced by kpt_weights
|
|
bz_weights = kpt_weights
|
|
|
|
# Finally, save all required data into the HDF archive:
|
|
# use_rotations is supposed to be an int = 0, 1, no bool
|
|
use_rotations = int(use_rotations)
|
|
if mpi.is_master_node():
|
|
with HDFArchive(self.hdf_file, 'a') as ar:
|
|
if not (self.dft_subgrp in ar):
|
|
ar.create_group(self.dft_subgrp)
|
|
# The subgroup containing the data. If it does not exist, it is
|
|
# created. If it exists, the data is overwritten!
|
|
things_to_save = ['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', 'kpt_weights', 'kpts']
|
|
if self.bloch_basis: numpy.append(things_to_save, 'kpt_basis')
|
|
for it in things_to_save:
|
|
ar[self.dft_subgrp][it] = locals()[it]
|
|
|
|
# Store Fermi weights to 'dft_misc_input'
|
|
if not (self.misc_subgrp in ar):
|
|
ar.create_group(self.misc_subgrp)
|
|
ar[self.misc_subgrp]['dft_fermi_energy'] = self.fermi_energy
|
|
if self.bloch_basis:
|
|
ar[self.misc_subgrp]['dft_fermi_weights'] = f_weights
|
|
ar[self.misc_subgrp]['band_window'] = band_window+1 # Change to 1-based index
|
|
ar[self.misc_subgrp]['kpts_cart'] = numpy.dot(kpts, kpt_basis.T)
|
|
mpi.barrier()
|
|
|
|
def read_wannier90data(self, wannier_seed="wannier", kmesh_mode=0):
|
|
"""
|
|
Method for reading the seedname_hr.dat file produced by Wannier90 (http://wannier.org)
|
|
|
|
Parameters
|
|
----------
|
|
wannier_seed : string
|
|
seedname to read H(R) file produced by Wannier90 (usually seedname_hr.dat)
|
|
|
|
Returns
|
|
-------
|
|
nrpt : integer
|
|
number of R vectors found in the file
|
|
rvec_idx : numpy.array of integers
|
|
Miller indices of the R vectors
|
|
rvec_deg : numpy.array of floats
|
|
weight of the R vectors
|
|
num_wf : integer
|
|
number of Wannier functions found
|
|
h_of_r : list of numpy.array
|
|
<w_i|H(R)|w_j> = Hamilonian matrix elements in the Wannier basis
|
|
u_mat : numpy.array
|
|
U_mn^k = unitary matrix elements which mix the Kohn-Sham states
|
|
udis_mat : numpy.array
|
|
U^dis(k) = rectangular matrix for entangled bands
|
|
band_mat : numpy.array
|
|
\epsilon_nk = Kohn-Sham eigenvalues (in eV) needed for entangled bands
|
|
If not self.bloch_basis unused and therefore None
|
|
k_mesh : numpy.array
|
|
The k mesh read from the seedname_u.mat file to ensure consistency
|
|
If not self.bloch_basis unused and therefore None
|
|
"""
|
|
|
|
hr_filename = wannier_seed + '_hr.dat'
|
|
try:
|
|
with open(hr_filename, 'r') as hr_filedesc:
|
|
hr_data = hr_filedesc.readlines()
|
|
except IOError:
|
|
mpi.report("The file %s could not be read!" % hr_filename)
|
|
|
|
mpi.report('reading {:20}...{}'.format(hr_filename,hr_data[0].strip('\n')))
|
|
|
|
try:
|
|
# reads number of Wannier functions per spin
|
|
num_wf = int(hr_data[1])
|
|
nrpt = int(hr_data[2])
|
|
except ValueError:
|
|
mpi.report("Could not read number of WFs or R vectors")
|
|
|
|
k_mesh = None
|
|
|
|
if not self.bloch_basis:
|
|
# For kmesh_mode == -1, size of automatic k mesh known when rvec_idx
|
|
# have been read. For kmesh_mode >= 0, kpts have been determined already
|
|
if kmesh_mode >= 0:
|
|
n_k = self.n_k
|
|
else:
|
|
# first, read u matrices from 'seedname_u.mat'
|
|
u_filename = wannier_seed + '_u.mat'
|
|
with open(u_filename,'r') as u_file:
|
|
u_data = u_file.readlines()
|
|
# reads number of kpoints and number of wannier functions
|
|
n_k, num_wf_u, _ = map(int, u_data[1].split())
|
|
assert num_wf_u == num_wf, '#WFs must be identical for *_u.mat and *_hr.dat'
|
|
mpi.report('reading {:20}...{}'.format(u_filename,u_data[0].strip('\n')))
|
|
|
|
# Reads k mesh from all lines with 3 floats
|
|
k_mesh = numpy.loadtxt((line for line in u_data if line.count('.') == 3))
|
|
assert k_mesh.shape == (n_k, 3)
|
|
# Reads u matrices from all lines with 2 floats
|
|
u_mat = numpy.loadtxt((line for line in u_data if line.count('.') == 2))
|
|
assert u_mat.shape == (n_k*num_wf*num_wf, 2)
|
|
|
|
mpi.report('Writing h5 archive in projector formalism: H(k) defined in KS Bloch basis')
|
|
|
|
try:
|
|
# read 'seedname_u_dis.mat'
|
|
udis_filename = wannier_seed + '_u_dis.mat'
|
|
# if it exists the Kohn-Sham eigenvalues and the window are needed
|
|
band_filename = wannier_seed + '.eig'
|
|
wout_filename = wannier_seed + '.wout'
|
|
|
|
with open(udis_filename,'r') as udis_file:
|
|
udis_data = udis_file.readlines()
|
|
disentangle = True
|
|
except IOError:
|
|
disentangle = False
|
|
mpi.report('WARNING: File {} missing.'.format(udis_filename))
|
|
mpi.report('Assuming an isolated set of bands. Check if this is what you want!')
|
|
|
|
# read Kohn-Sham eigenvalues from 'seedname.eig'
|
|
mpi.report('Reading {}'.format(band_filename))
|
|
band_data = numpy.loadtxt(band_filename, usecols=2)
|
|
|
|
if disentangle:
|
|
# reads number of kpoints, number of wannier functions and bands
|
|
num_k_udis, num_wf_udis, num_ks_bands = map(int, udis_data[1].split())
|
|
|
|
assert num_k_udis == n_k, '#k points must be identical for *.inp and *_u_dis.mat'
|
|
assert num_wf_udis == num_wf, '#WFs must be identical for *_u.mat and *_hr.dat'
|
|
|
|
mpi.report('Found {:22}...{}, '.format(udis_filename,udis_data[0].strip('\n')))
|
|
udis_data = numpy.loadtxt(udis_data, usecols=(0, 1), skiprows=2)
|
|
|
|
# read disentanglement window from 'seedname.wout'
|
|
with open(wout_filename) as wout_file:
|
|
for line in wout_file:
|
|
if 'Outer:' in line:
|
|
content = line.split()
|
|
index = content.index('Outer:') + 1
|
|
dis_window_min = float(content[index])
|
|
dis_window_max = float(content[index+2])
|
|
break
|
|
mpi.report('and {} for disentanglement energy window.'.format(wout_filename))
|
|
else:
|
|
num_ks_bands = num_wf
|
|
|
|
# allocate arrays to save the R vector indexes and degeneracies and the
|
|
# Hamiltonian
|
|
rvec_idx = numpy.zeros((nrpt, 3), dtype=int)
|
|
rvec_deg = numpy.zeros(nrpt, dtype=int)
|
|
h_of_r = [numpy.zeros((num_wf, num_wf), dtype=complex)
|
|
for n in range(nrpt)]
|
|
|
|
# variable currpos points to the current line in the file
|
|
currpos = 2
|
|
try:
|
|
ir = 0
|
|
# read the degeneracy of the R vectors (needed for the Fourier
|
|
# transform)
|
|
while ir < nrpt:
|
|
currpos += 1
|
|
for x in hr_data[currpos].split():
|
|
if ir >= nrpt:
|
|
raise IndexError("wrong number of R vectors??")
|
|
rvec_deg[ir] = int(x)
|
|
ir += 1
|
|
# for each direct lattice vector R read the block of the
|
|
# Hamiltonian H(R)
|
|
for ir, jj, ii in product(range(nrpt), range(num_wf), range(num_wf)):
|
|
# advance one line, split the line into tokens
|
|
currpos += 1
|
|
cline = hr_data[currpos].split()
|
|
# check if the orbital indexes in the file make sense
|
|
if int(cline[3]) != ii + 1 or int(cline[4]) != jj + 1:
|
|
mpi.report(
|
|
"Inconsistent indices at %s%s of R n. %s" % (ii, jj, ir))
|
|
rcurr = numpy.array([int(cline[0]), int(cline[1]), int(cline[2])])
|
|
if ii == 0 and jj == 0:
|
|
rvec_idx[ir] = rcurr
|
|
rprec = rcurr
|
|
else:
|
|
# check if the vector indices are consistent
|
|
if not numpy.array_equal(rcurr, rprec):
|
|
mpi.report(
|
|
"Inconsistent indices for R vector n. %s" % ir)
|
|
|
|
# fill h_of_r with the matrix elements of the Hamiltonian
|
|
h_of_r[ir][ii, jj] = complex(float(cline[5]), float(cline[6]))
|
|
|
|
except ValueError:
|
|
mpi.report("Wrong data or structure in file %s" % hr_filename)
|
|
|
|
# first, get the input for u_mat
|
|
if self.bloch_basis:
|
|
u_mat = u_mat[:, 0] + 1j * u_mat[:, 1]
|
|
u_mat = u_mat.reshape((n_k, num_wf, num_wf)).transpose((0, 2, 1))
|
|
else:
|
|
if kmesh_mode == -1:
|
|
n_k = numpy.prod([2 * rvec_idx[:, idir].max() + 1 for idir in range(3)])
|
|
# Wannier basis; fill u_mat with identity
|
|
u_mat = numpy.zeros([n_k, num_wf, num_wf], dtype=complex)
|
|
for ik in range(n_k):
|
|
u_mat[ik,:,:] = numpy.identity(num_wf,dtype=complex)
|
|
|
|
# now, check what is needed in the case of disentanglement:
|
|
# The file seedname_u_dis.mat contains only the bands inside the window
|
|
# and then fills the rest up with zeros. Therefore, we need to put the
|
|
# entries from udis_data in the correct position in udis_mat, i.e.
|
|
# shifting by the number of bands below dis_window_min
|
|
if self.bloch_basis:
|
|
# reshape band_data
|
|
band_mat = band_data.reshape(n_k, num_ks_bands)
|
|
else:
|
|
# Not used in wannier basis
|
|
band_mat = None
|
|
|
|
if self.bloch_basis and disentangle:
|
|
# Determine which bands are inside the band window
|
|
inside_window = numpy.logical_and(band_mat >= dis_window_min,
|
|
band_mat <= dis_window_max)
|
|
n_inside_per_k = numpy.sum(inside_window, axis=1)
|
|
|
|
# Reformats udis_data as complex, without header
|
|
udis_data = udis_data[:, 0] + 1j * udis_data[:, 1]
|
|
udis_data = udis_data.reshape((n_k, num_wf*num_ks_bands+1))[:, 1:]
|
|
udis_data = udis_data.reshape((n_k, num_wf, num_ks_bands))
|
|
|
|
#initiate U disentanglement matrices and fill from file "seedname_u_dis.mat"
|
|
udis_mat = numpy.zeros([n_k, num_ks_bands, num_wf], dtype=complex)
|
|
for ik in range(n_k):
|
|
udis_mat[ik, inside_window[ik]] = udis_data[ik, :, :n_inside_per_k[ik]].T
|
|
if not numpy.allclose(udis_data[ik, :, n_inside_per_k[ik]:], 0):
|
|
raise ValueError('This error could come from rounding of the band window in the seedname.wout. '
|
|
+ 'Never use default outer window but something wider and '
|
|
+ 'check that your outer window is not close to any band energy.')
|
|
else:
|
|
# no disentanglement; fill udis_mat with identity
|
|
udis_mat = numpy.array([numpy.identity(num_wf,dtype=complex)] * n_k)
|
|
|
|
# return the data into variables
|
|
return nrpt, rvec_idx, rvec_deg, num_wf, h_of_r, u_mat, udis_mat, band_mat, k_mesh
|
|
|
|
def find_rot_mat(self, n_sh, sh_lst, sh_map, ham0):
|
|
"""
|
|
Method for finding the matrices that bring from local to global coordinate systems
|
|
(and viceversa), based on the eigenvalues of H(R=0)
|
|
|
|
Parameters
|
|
----------
|
|
n_sh : integer
|
|
number of shells
|
|
sh_lst : list of shells-type dictionaries
|
|
contains the shells (could be correlated or not)
|
|
sh_map : list of integers
|
|
mapping between shells
|
|
ham0 : numpy.array of floats
|
|
local Hamiltonian matrix elements
|
|
|
|
Returns
|
|
-------
|
|
succeeded : integer
|
|
if 0, something failed in the construction of the matrices
|
|
rot_mat : list of numpy.array
|
|
rotation matrix for each of the shell
|
|
|
|
"""
|
|
|
|
# initialize the rotation matrices to identities
|
|
rot_mat = [numpy.identity(sh_lst[ish]['dim'], dtype=complex)
|
|
for ish in range(n_sh)]
|
|
succeeded = True
|
|
|
|
hs = ham0.shape
|
|
if hs[0] != hs[1] or hs[0] != sum([sh['dim'] for sh in sh_lst]):
|
|
mpi.report(
|
|
"find_rot_mat: wrong block structure of input Hamiltonian!")
|
|
# this error will lead into troubles later... early return
|
|
succeeded = False
|
|
return succeeded, rot_mat
|
|
|
|
# Method none as physically unsound option for testing
|
|
# Returns identity matrices as rotation matrices
|
|
if self.rot_mat_type == 'none':
|
|
mpi.report('WARNING: using the method "none" leads to physically wrong results. '
|
|
+ 'Only use for testing if other methods fail.')
|
|
succeeded = True
|
|
return succeeded, rot_mat
|
|
|
|
# TODO: better handling of degenerate eigenvalue case
|
|
eigval_lst = [None] * n_sh
|
|
eigvec_lst = [None] * n_sh
|
|
ham0_lst = [None] * n_sh
|
|
iwf = 0
|
|
# loop over shells
|
|
for ish in range(n_sh):
|
|
# nw = number of orbitals in this shell
|
|
nw = sh_lst[ish]["dim"]
|
|
# save the sub-block of H(0) corresponding to this shell
|
|
ham0_lst[ish] = ham0[iwf:iwf+nw, iwf:iwf+nw]
|
|
# diagonalize the sub-block for this shell
|
|
eigval, eigvec = numpy.linalg.eigh(ham0_lst[ish])
|
|
eigval_lst[ish] = eigval
|
|
eigvec_lst[ish] = eigvec
|
|
iwf += nw
|
|
# TODO: better handling of degenerate eigenvalue case
|
|
if sh_map[ish] != ish: # issue warning only when there are equivalent shells
|
|
for i in range(nw):
|
|
for j in range(i + 1, nw):
|
|
if abs(eigval[j] - eigval[i]) < self._w90zero:
|
|
mpi.report("WARNING: degenerate eigenvalue of H(0) detected for shell %d: " % (ish) +
|
|
"global-to-local transformation might not work!")
|
|
|
|
for ish in range(n_sh):
|
|
try:
|
|
# build rotation matrices either...
|
|
if self.rot_mat_type == 'hloc_diag':
|
|
# using the unitary transformations that diagonalize H(0)
|
|
rot_mat[ish] = eigvec_lst[ish]
|
|
elif self.rot_mat_type == 'wannier':
|
|
# or by combining those transformations (i.e. for each group,
|
|
# the representative site is chosen as the global frame of reference)
|
|
rot_mat[ish] = numpy.dot(eigvec_lst[ish],
|
|
eigvec_lst[sh_map[ish]].conjugate().transpose())
|
|
except ValueError:
|
|
mpi.report(
|
|
"Global-to-local rotation matrices cannot be constructed!")
|
|
|
|
# check that eigenvalues are the same (within accuracy) for
|
|
# equivalent shells
|
|
if not numpy.allclose(eigval_lst[ish], eigval_lst[sh_map[ish]],
|
|
atol=self._w90zero, rtol=0):
|
|
mpi.report(f'ERROR: eigenvalue mismatch between equivalent shells! {ish:d}')
|
|
eigval_diff = eigval_lst[ish] - eigval_lst[sh_map[ish]]
|
|
mpi.report(f'Eigenvalue difference {eigval_diff}, but threshold set to {self._w90zero:.1e}.')
|
|
mpi.report('Consider lowering threshold if you are certain the mapping is correct.')
|
|
succeeded = False
|
|
|
|
# check that rotation matrices are unitary
|
|
# nw = number of orbitals in this shell
|
|
nw = sh_lst[ish]["dim"]
|
|
tmp_mat = numpy.dot(rot_mat[ish],rot_mat[ish].conjugate().transpose())
|
|
if not numpy.allclose(tmp_mat, numpy.identity(nw),
|
|
atol=self._w90zero, rtol=0):
|
|
mpi.report(f'ERROR: rot_mat for shell {ish:d} is not unitary!')
|
|
succeeded = False
|
|
|
|
# check that rotation matrices map equivalent H(0) blocks as they should
|
|
# (assuming representative shell as global frame of reference)
|
|
if self.rot_mat_type == 'hloc_diag':
|
|
tmp_mat = numpy.dot( rot_mat[ish],
|
|
rot_mat[sh_map[ish]].conjugate().transpose() )
|
|
elif self.rot_mat_type == 'wannier':
|
|
tmp_mat = rot_mat[ish]
|
|
tmp_mat = numpy.dot(tmp_mat.conjugate().transpose(),
|
|
numpy.dot(ham0_lst[ish],tmp_mat))
|
|
if not numpy.allclose(tmp_mat, ham0_lst[sh_map[ish]],
|
|
atol=self._w90zero, rtol=0):
|
|
mpi.report(f'ERROR: rot_mat does not map H(0) correctly! {ish:d}')
|
|
succeeded = False
|
|
|
|
# abort in case the rot_mat was not found correctly to avoid the user to ignore ERRORS
|
|
if not succeeded: mpi.MPI.COMM_WORLD.Abort(1)
|
|
|
|
return succeeded, rot_mat
|
|
|
|
def kmesh_build(self, msize, mmode=0):
|
|
"""
|
|
Method for the generation of the k-point mesh.
|
|
Right now it only supports the option for generating a full grid containing k=0,0,0.
|
|
|
|
Parameters
|
|
----------
|
|
msize : list of 3 integers
|
|
the dimensions of the mesh
|
|
mmode : integer
|
|
mesh generation mode (right now, only full grid available)
|
|
|
|
Returns
|
|
-------
|
|
nkpt : integer
|
|
total number of k-points in the mesh
|
|
kpts : numpy.array[nkpt,3] of floats
|
|
the coordinates of all k-points
|
|
wk : numpy.array[nkpt] of floats
|
|
the weight of each k-point
|
|
|
|
"""
|
|
|
|
if mmode != 0:
|
|
raise ValueError("Mesh generation mode not supported: %s" % mmode)
|
|
|
|
assert len(msize) == 3
|
|
|
|
# a regular mesh including Gamma point
|
|
# total number of k-points
|
|
nkpt = numpy.prod(msize)
|
|
kpts = numpy.array(list(product(range(msize[0]), range(msize[1]), range(msize[2]))))
|
|
kpts = kpts / numpy.array(msize)
|
|
# weight is equal for all k-points because wannier90 uses uniform grid on whole BZ
|
|
# (normalization is always 1 and takes into account spin degeneracy)
|
|
wk = numpy.full(nkpt, 1/nkpt)
|
|
|
|
return nkpt, kpts, wk
|
|
|
|
def fourier_ham(self, h_of_r):
|
|
"""
|
|
Method for obtaining H(k) from H(R) via Fourier transform
|
|
The R vectors and k-point mesh are read from global module variables
|
|
|
|
Parameters
|
|
----------
|
|
h_of_r : list of numpy.array[norb,norb]
|
|
Hamiltonian H(R) in Wannier basis
|
|
|
|
Returns
|
|
-------
|
|
h_of_k : list of numpy.array[norb,norb]
|
|
transformed Hamiltonian H(k) in Wannier basis
|
|
|
|
"""
|
|
|
|
h_of_k = [numpy.zeros((self.nwfs, self.nwfs), dtype=complex)
|
|
for ik in range(self.n_k)]
|
|
h_of_k_array = numpy.array(h_of_k, dtype=complex)
|
|
ridx = numpy.array(list(range(self.nrpt)))
|
|
|
|
for ir in mpi.slice_array(ridx):
|
|
for ik in list(range(self.n_k)):
|
|
rdotk = numpy.dot(self.kpts[ik], self.rvec[ir])
|
|
factor = numpy.exp(2j * numpy.pi * rdotk) / self.rdeg[ir]
|
|
h_of_k_array[ik, :, :] += factor * h_of_r[ir][:, :]
|
|
h_of_k_array = mpi.all_reduce(mpi.world, h_of_k_array, lambda x, y: x + y)
|
|
mpi.barrier()
|
|
|
|
h_of_k = list(h_of_k_array[:])
|
|
|
|
return h_of_k
|
|
|
|
def convert_misc_input(self, out_filename, nnkp_filename, n_spin_blocs):
|
|
"""
|
|
Reads input from DFT code calculations to get occupations
|
|
|
|
Parameters
|
|
----------
|
|
out_filename : string
|
|
filename of DFT output file containing occupation data
|
|
nnkp_file : string
|
|
filename of Wannier postproc_setup run
|
|
n_spin_blocs : int
|
|
SP + 1 - SO
|
|
|
|
Returns
|
|
-------
|
|
fermi_weights : numpy.array[self.n_k, n_spin_blocs ,n_orbitals]
|
|
occupations from DFT calculation
|
|
band_window : numpy.array[self.n_k, n_spin_blocs ,n_orbitals]
|
|
band indices of correlated subspace
|
|
fermi_energy : float
|
|
the DFT Kohn-Sham Fermi energy
|
|
kpt_basis: numpy.array[3, 3]
|
|
the basis vectors in reciprocal space
|
|
"""
|
|
|
|
assert n_spin_blocs == 1, 'spin-polarized not implemented'
|
|
assert 'nscf.out' in out_filename or out_filename == 'LOCPROJ'
|
|
|
|
occupations = []
|
|
reading_kpt_basis = False
|
|
lines_read_kpt_basis = 0
|
|
kpt_basis = numpy.zeros((3, 3))
|
|
|
|
if 'nscf.out' in out_filename:
|
|
occupations = []
|
|
with open(out_filename,'r') as out_file:
|
|
out_data = out_file.readlines()
|
|
# Reads number of Kohn-Sham states and Fermi energy
|
|
for line in out_data:
|
|
if 'number of Kohn-Sham states' in line:
|
|
n_ks = int(line.split()[-1])
|
|
elif 'Fermi energy' in line:
|
|
fermi_energy = float(line.split()[-2])
|
|
elif 'reciprocal axes' in line:
|
|
reading_kpt_basis = True
|
|
continue
|
|
elif reading_kpt_basis and lines_read_kpt_basis < 3:
|
|
kpt_basis[lines_read_kpt_basis, :] = line.split()[3:6]
|
|
lines_read_kpt_basis +=1
|
|
|
|
# get occupations
|
|
for ct, line in enumerate(out_data):
|
|
if line.strip() == 'End of band structure calculation':
|
|
break
|
|
|
|
assert 'k =' in out_data[ct + 2], 'Cannot read occupations. Set verbosity = "high" in {}'.format(out_filename)
|
|
out_data = out_data[ct+2:]
|
|
|
|
# block size of eigenvalues + occupations per k-point
|
|
n_block = int(2*numpy.ceil(n_ks/8)+5)
|
|
|
|
for ik in range(self.n_k):
|
|
# get data
|
|
k_block = [line.split() for line in out_data[ik*n_block+2:ik*n_block+n_block-1]]
|
|
# second half corresponds to occupations
|
|
occs = k_block[int(len(k_block)/2)+1:]
|
|
flattened_occs = [float(item) for sublist in occs for item in sublist]
|
|
occupations.append(flattened_occs)
|
|
else:
|
|
# Reads LOCPROJ
|
|
with open(out_filename, 'r') as file:
|
|
header = file.readline()
|
|
n_ks = int(header.split()[2])
|
|
fermi_energy = float(header.split()[4])
|
|
|
|
occupations = numpy.loadtxt((line for line in file if 'orbital' in line), usecols=5)
|
|
occupations = occupations.reshape((self.n_k, n_ks))
|
|
|
|
# Read reciprocal vectors from OUTCAR
|
|
with open('OUTCAR', 'r') as file:
|
|
for line in file:
|
|
if 'reciprocal lattice vectors' in line:
|
|
reading_kpt_basis = True
|
|
elif reading_kpt_basis:
|
|
kpt_basis[lines_read_kpt_basis, :] = line.split()[3:6]
|
|
lines_read_kpt_basis += 1
|
|
if lines_read_kpt_basis == 3:
|
|
break
|
|
|
|
# assume that all bands contribute, then remove from exclude_bands; python indexing
|
|
corr_bands = list(range(n_ks))
|
|
# read exclude_bands from "seedname.nnkp" file
|
|
with open(nnkp_filename, 'r') as nnkp_file:
|
|
read = False
|
|
skip = False
|
|
for line in nnkp_file:
|
|
if line.strip() == 'begin exclude_bands':
|
|
read = True
|
|
# skip one more line that contains total number of excluded bands
|
|
skip = True
|
|
continue
|
|
elif line.strip() == 'end exclude_bands':
|
|
read = False
|
|
continue
|
|
elif skip:
|
|
skip = False
|
|
continue
|
|
elif read:
|
|
# wannier index -1
|
|
corr_bands.remove(int(line)-1)
|
|
|
|
# For now, it is only supported to exclude the lowest and highest bands
|
|
# If bands in the middle are supposed to be excluded, this doesn't work with the band_window
|
|
# We'd need to manually add rows of zeros to the projectors
|
|
if numpy.any(numpy.diff(corr_bands) != 1):
|
|
raise NotImplementedError('Can only exclude the lowest or highest bands')
|
|
band_window = numpy.array([[(min(corr_bands), max(corr_bands))]*self.n_k]*n_spin_blocs)
|
|
|
|
included_occupations = numpy.array(occupations)[:, corr_bands]
|
|
# Adds spin dimension without changing the array
|
|
f_weights = included_occupations.reshape(included_occupations.shape[0], 1,
|
|
included_occupations.shape[1])
|
|
|
|
return f_weights, band_window, fermi_energy, kpt_basis
|
|
|
|
def lambda_matrix_w90_t2g(self):
|
|
"""
|
|
Adds local spin-orbit interaction term to the t2g subspace. Orbital order is assumed to be
|
|
xz_up, yz_up, xy_up, xz_dn, yz_dn, xy_dn as given by Wannier90 per default.
|
|
Parameters are defined as self.add_lambda = [lambda_x, lambda_y, lambda_z], representative of
|
|
the orbital coupling terms perpendicular to [x, y, z] i.e. [d_yz, d_xz, d_xy], respectively.
|
|
|
|
Returns
|
|
-------
|
|
lambda_matrix : numpy.array[6, 6]
|
|
local spin-orbit term to be added to H(0)
|
|
|
|
"""
|
|
|
|
lambda_x, lambda_y, lambda_z = self.add_lambda
|
|
|
|
lambda_matrix = numpy.zeros((6,6), dtype=complex)
|
|
lambda_matrix[0,1] = -1j*lambda_z/2.0
|
|
lambda_matrix[0,5] = 1j*lambda_x/2.0
|
|
lambda_matrix[1,5] = -lambda_y/2.0
|
|
lambda_matrix[2,3] = -1j*lambda_x/2.0
|
|
lambda_matrix[2,4] = lambda_y/2.0
|
|
lambda_matrix[3,4] = 1j*lambda_z/2.0
|
|
lambda_matrix += numpy.transpose(numpy.conjugate(lambda_matrix))
|
|
|
|
return lambda_matrix
|