mirror of https://github.com/triqs/dft_tools
612 lines
26 KiB
Python
612 lines
26 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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# 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 case with SO=1 is not considered at the moment
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# - the T rotation matrices are not used in this implementation
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# - projectors for uncorrelated shells (proj_mat_all) cannot be set
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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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# - the FFT is always done in serial mode (because all converters run serially);
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# this can become very slow with a large number of R-vectors/k-points
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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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###
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from types import *
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import numpy
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import math
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from pytriqs.archive import *
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from converter_tools import *
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from itertools import product
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import os.path
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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', repacking=False):
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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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repacking : boolean, optional
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Does the hdf5 archive need to be repacked to save space?
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"""
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self._name = "Wannier90Converter"
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assert type(seedname) == StringType, 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.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 = 2.e-6
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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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# Read and write only on the master node
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if not (mpi.is_master_node()):
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return
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mpi.report("Reading 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(R.next())
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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(R.next()) 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
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density_required = float(R.next())
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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(R.next())
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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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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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###
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SP = 0 # NO spin-polarised calculations for now
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SO = 0 # NO spin-orbit calculation for now
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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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###
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# this is more general
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n_spin = SP + 1 - SO
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dim_corr_shells = sum([sh['dim'] for sh in corr_shells])
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mpi.report(
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"Total number of WFs expected in the correlated shells: %d" % dim_corr_shells)
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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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# 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
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# below)
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if kmesh_mode >= 0:
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n_k, k_mesh, bz_weights = self.kmesh_build(nki, kmesh_mode)
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self.n_k = n_k
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self.k_mesh = k_mesh
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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], numpy.complex_))
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spin_w90name = ['_up', '_down']
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hamr_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):
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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:
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mpi.report(
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"Reading information for spin component n. %d" % isp)
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hr_file = self.w90_seed + spin_w90name[isp] + '_hr.dat'
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else:
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hr_file = self.w90_seed + '_hr.dat'
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# now grab the data from the H(R) file
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mpi.report(
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"The Hamiltonian in MLWF basis is extracted from %s ..." % hr_file)
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nr, rvec, rdeg, nw, hamr = self.read_wannier90hr(hr_file)
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# number of R vectors, their indices, their degeneracy, number of
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# WFs, H(R)
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mpi.report("... done: %d R vectors, %d WFs found" % (nr, nw))
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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 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, k_mesh, bz_weights = self.kmesh_build(nki)
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self.n_k = n_k
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self.k_mesh = k_mesh
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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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n_orbitals = numpy.ones(
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[self.n_k, n_spin], numpy.int) * self.nwfs
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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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# FIXME: when do we actually need deepcopy()?
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# hamr_full.append(deepcopy(hamr))
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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=1.e-9):
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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=1.e-15):
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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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mpi.report("The k-point grid has dimensions: %d, %d, %d" % tuple(nki))
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# if calculations are spin-polarized, then renormalize k-point weights
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if SP == 1:
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bz_weights = 0.5 * bz_weights
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# Third, compute the hoppings in reciprocal space
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hopping = numpy.zeros([self.n_k, n_spin, numpy.max(
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n_orbitals), numpy.max(n_orbitals)], numpy.complex_)
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for isp in range(n_spin):
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# make Fourier transform H(R) -> H(k) : it can be done one spin at
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# a time
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hamk = self.fourier_ham(self.nwfs, hamr_full[isp])
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# copy the H(k) in the right place of hoppings... is there a better
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# way to do this??
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for ik in range(self.n_k):
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#hopping[ik,isp,:,:] = deepcopy(hamk[ik][:,:])*energy_unit
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hopping[ik, isp, :, :] = hamk[ik][:, :] * energy_unit
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# Then, initialise the projectors
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k_dep_projection = 0 # we always have the same number of WFs at each k-point
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proj_mat = numpy.zeros([self.n_k, n_spin, n_corr_shells, max(
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[crsh['dim'] for crsh in corr_shells]), numpy.max(n_orbitals)], numpy.complex_)
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iorb = 0
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# Projectors simply consist in identity matrix blocks selecting those MLWFs that
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# correspond to the specific correlated shell indexed by icrsh.
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# NOTE: we assume that the correlated orbitals appear at the beginning of the H(R)
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# file and that the ordering of MLWFs matches the corr_shell info from
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# the input.
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for icrsh in range(n_corr_shells):
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norb = corr_shells[icrsh]['dim']
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proj_mat[:, :, icrsh, 0:norb, iorb:iorb +
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norb] = numpy.identity(norb, numpy.complex_)
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iorb += norb
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# Finally, save all required data into the HDF archive:
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with HDFArchive(self.hdf_file, 'a') as ar:
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if not (self.dft_subgrp in ar):
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ar.create_group(self.dft_subgrp)
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# The subgroup containing the data. If it does not exist, it is
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# created. If it exists, the data is overwritten!
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things_to_save = ['energy_unit', 'n_k', 'k_dep_projection', 'SP', 'SO', 'charge_below', 'density_required',
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'symm_op', 'n_shells', 'shells', 'n_corr_shells', 'corr_shells', 'use_rotations', 'rot_mat',
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'rot_mat_time_inv', 'n_reps', 'dim_reps', 'T', 'n_orbitals', 'proj_mat', 'bz_weights', 'hopping',
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'n_inequiv_shells', 'corr_to_inequiv', 'inequiv_to_corr']
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for it in things_to_save:
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ar[self.dft_subgrp][it] = locals()[it]
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def read_wannier90hr(self, hr_filename="wannier_hr.dat"):
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"""
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Method for reading the seedname_hr.dat file produced by Wannier90 (http://wannier.org)
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Parameters
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----------
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hr_filename : string
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full name of the H(R) file produced by Wannier90 (usually seedname_hr.dat)
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Returns
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-------
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nrpt : integer
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number of R vectors found in the file
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rvec_idx : numpy.array of integers
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Miller indices of the R vectors
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rvec_deg : numpy.array of floats
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weight of the R vectors
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num_wf : integer
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number of Wannier functions found
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h_of_r : list of numpy.array
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<w_i|H(R)|w_j> = Hamilonian matrix elements in the Wannier basis
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"""
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# Read only from the master node
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if not (mpi.is_master_node()):
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return
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try:
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with open(hr_filename, "r") as hr_filedesc:
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hr_data = hr_filedesc.readlines()
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hr_filedesc.close()
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except IOError:
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mpi.report("The file %s could not be read!" % hr_filename)
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mpi.report("Reading %s..." % hr_filename + hr_data[0])
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try:
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# reads number of Wannier functions per spin
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num_wf = int(hr_data[1])
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nrpt = int(hr_data[2])
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except ValueError:
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mpi.report("Could not read number of WFs or R vectors")
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# allocate arrays to save the R vector indexes and degeneracies and the
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# Hamiltonian
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rvec_idx = numpy.zeros((nrpt, 3), dtype=int)
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rvec_deg = numpy.zeros(nrpt, dtype=int)
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h_of_r = [numpy.zeros((num_wf, num_wf), dtype=numpy.complex_)
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for n in range(nrpt)]
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# variable currpos points to the current line in the file
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currpos = 2
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try:
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ir = 0
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# read the degeneracy of the R vectors (needed for the Fourier
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# transform)
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while ir < nrpt:
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currpos += 1
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for x in hr_data[currpos].split():
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if ir >= nrpt:
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raise IndexError("wrong number of R vectors??")
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rvec_deg[ir] = int(x)
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ir += 1
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# for each direct lattice vector R read the block of the
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# Hamiltonian H(R)
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for ir, jj, ii in product(range(nrpt), range(num_wf), range(num_wf)):
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# advance one line, split the line into tokens
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currpos += 1
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cline = hr_data[currpos].split()
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# check if the orbital indexes in the file make sense
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if int(cline[3]) != ii + 1 or int(cline[4]) != jj + 1:
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mpi.report(
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"Inconsistent indices at %s%s of R n. %s" % (ii, jj, ir))
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rcurr = numpy.array(
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[int(cline[0]), int(cline[1]), int(cline[2])])
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if ii == 0 and jj == 0:
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rvec_idx[ir] = rcurr
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rprec = rcurr
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else:
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# 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)
|
|
|
|
# return the data into variables
|
|
return nrpt, rvec_idx, rvec_deg, num_wf, h_of_r
|
|
|
|
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
|
|
-------
|
|
istatus : 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)]
|
|
istatus = 0
|
|
|
|
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!")
|
|
istatus = 0
|
|
# this error will lead into troubles later... early return
|
|
return istatus, rot_mat
|
|
|
|
# TODO: better handling of degenerate eigenvalue case
|
|
eigval_lst = []
|
|
eigvec_lst = []
|
|
iwf = 0
|
|
# loop over shells
|
|
for ish in range(n_sh):
|
|
# nw = number of orbitals in this shell
|
|
nw = sh_lst[ish]["dim"]
|
|
# diagonalize the sub-block of H(0) corresponding to this shell
|
|
eigval, eigvec = numpy.linalg.eigh(
|
|
ham0[iwf:iwf + nw, iwf:iwf + nw])
|
|
# find the indices sorting the eigenvalues in ascending order
|
|
eigsrt = eigval[0:nw].argsort()
|
|
# order eigenvalues and eigenvectors and save in a list
|
|
eigval_lst.append(eigval[eigsrt])
|
|
eigvec_lst.append(eigvec[eigsrt])
|
|
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 by combining the unitary
|
|
# transformations that diagonalize H(0)
|
|
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!")
|
|
|
|
istatus = 1
|
|
# 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=1.e-15):
|
|
mpi.report(
|
|
"ERROR: eigenvalue mismatch between equivalent shells! %d" % ish)
|
|
eigval_diff = eigval_lst[ish] - eigval_lst[sh_map[ish]]
|
|
mpi.report("Eigenvalue difference: " + format(eigval_diff))
|
|
istatus = 0
|
|
|
|
# TODO: add additional consistency check on rot_mat matrices?
|
|
|
|
return istatus, rot_mat
|
|
|
|
def kmesh_build(self, msize=None, 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
|
|
k_mesh : 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)
|
|
|
|
# a regular mesh including Gamma point
|
|
# total number of k-points
|
|
nkpt = msize[0] * msize[1] * msize[2]
|
|
kmesh = numpy.zeros((nkpt, 3), dtype=float)
|
|
ii = 0
|
|
for ix, iy, iz in product(range(msize[0]), range(msize[1]), range(msize[2])):
|
|
kmesh[ii, :] = [float(ix) / msize[0], float(iy) /
|
|
msize[1], float(iz) / msize[2]]
|
|
ii += 1
|
|
# 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.ones([nkpt], dtype=float) / float(nkpt)
|
|
|
|
return nkpt, kmesh, wk
|
|
|
|
def fourier_ham(self, norb, 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
|
|
----------
|
|
norb : integer
|
|
number of orbitals
|
|
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
|
|
|
|
"""
|
|
|
|
twopi = 2 * numpy.pi
|
|
h_of_k = [numpy.zeros((norb, norb), dtype=numpy.complex_)
|
|
for ik in range(self.n_k)]
|
|
ridx = numpy.array(range(self.nrpt))
|
|
for ik, ir in product(range(self.n_k), ridx):
|
|
rdotk = twopi * numpy.dot(self.k_mesh[ik], self.rvec[ir])
|
|
factor = (math.cos(rdotk) + 1j * math.sin(rdotk)) / \
|
|
float(self.rdeg[ir])
|
|
h_of_k[ik][:, :] += factor * h_of_r[ir][:, :]
|
|
|
|
return h_of_k
|