########################################################################## # # TRIQS: a Toolbox for Research in Interacting Quantum Systems # # Copyright (C) 2011 by M. Aichhorn, L. Pourovskii, V. Vildosola # # TRIQS is free software: you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation, either version 3 of the License, or (at your option) any later # version. # # TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more # details. # # You should have received a copy of the GNU General Public License along with # TRIQS. If not, see . # ########################################################################## ### # Wannier90 to HDF5 converter for the SumkDFT class of dfttools/TRIQS; # # written by Gabriele Sclauzero (Materials Theory, ETH Zurich), Dec 2015 -- Jan 2016, # under the supervision of Claude Ederer (Materials Theory). # Partially based on previous work by K. Dymkovski and the DFT_tools/TRIQS team. # # Limitations of the current implementation: # - the case with SO=1 is not considered at the moment # - the T rotation matrices are not used in this implementation # - projectors for uncorrelated shells (proj_mat_all) cannot be set # # Things to be improved/checked: # - the case with SP=1 might work, but was never tested (do we need to define # rot_mat_time_inv also if symm_op = 0?) # - the calculation of rot_mat in find_rot_mat() relies on the eigenvalues of H(0); # this might fail in presence of degenerate eigenvalues (now just prints warning) # - the FFT is always done in serial mode (because all converters run serially); # this can become very slow with a large number of R-vectors/k-points # - make the code more MPI safe (error handling): if we run with more than one process # and an error occurs on the masternode, the calculation does not abort ### from types import * import numpy import math from pytriqs.archive import * from converter_tools import * from itertools import product import os.path class Wannier90Converter(ConverterTools): """ Conversion from Wannier90 output to an hdf5 file that can be used as input for the SumkDFT class. """ def __init__(self, seedname, hdf_filename=None, dft_subgrp='dft_input', symmcorr_subgrp='dft_symmcorr_input', repacking=False): """ Initialise the class. Parameters ---------- seedname : string Base name of Wannier90 files hdf_filename : string, optional Name of hdf5 archive to be created dft_subgrp : string, optional Name of subgroup storing necessary DFT data symmcorr_subgrp : string, optional Name of subgroup storing correlated-shell symmetry data repacking : boolean, optional Does the hdf5 archive need to be repacked to save space? """ self._name = "Wannier90Converter" assert type(seedname) == StringType, self._name + \ ": Please provide the DFT files' base name as a string." if hdf_filename is None: hdf_filename = seedname + '.h5' self.hdf_file = hdf_filename # if the w90 output is seedname_hr.dat, the input file for the # converter must be called seedname.inp self.inp_file = seedname + '.inp' self.w90_seed = seedname self.dft_subgrp = dft_subgrp self.symmcorr_subgrp = symmcorr_subgrp self.fortran_to_replace = {'D': 'E'} # threshold below which matrix elements from wannier90 should be # considered equal self._w90zero = 2.e-6 # Checks if h5 file is there and repacks it if wanted: if (os.path.exists(self.hdf_file) and repacking): ConverterTools.repack(self) def convert_dft_input(self): """ Reads the appropriate files and stores the data for the - dft_subgrp - symmcorr_subgrp in the hdf5 archive. """ # Read and write only on the master node if not (mpi.is_master_node()): return mpi.report("Reading input from %s..." % self.inp_file) # R is a generator : each R.Next() will return the next number in the # file R = ConverterTools.read_fortran_file( self, self.inp_file, self.fortran_to_replace) shell_entries = ['atom', 'sort', 'l', 'dim'] corr_shell_entries = ['atom', 'sort', 'l', 'dim', 'SO', 'irep'] # First, let's read the input file with the parameters needed for the # conversion try: # read k - point mesh generation option kmesh_mode = int(R.next()) if kmesh_mode >= 0: # read k-point mesh size from input nki = [int(R.next()) for idir in range(3)] else: # some default grid, if everything else fails... nki = [8, 8, 8] # read the total number of electrons per cell density_required = float(R.next()) # we do not read shells, because we have no additional shells beyond correlated ones, # and the data will be copied from corr_shells into shells (see below) # number of corr. shells (e.g. Fe d, Ce f) in the unit cell, n_corr_shells = int(R.next()) # now read the information about the correlated shells (atom, sort, # l, dim, SO flag, irep): corr_shells = [{name: int(val) for name, val in zip( corr_shell_entries, R)} for icrsh in range(n_corr_shells)] try: self.fermi_energy = R.next() except: self.fermi_energy = 0. except StopIteration: # a more explicit error if the file is corrupted. mpi.report(self._name + ": reading input file %s failed!" % self.inp_file) # close the input file R.close() # Set or derive some quantities # Wannier90 does not use symmetries to reduce the k-points # the following might change in future versions symm_op = 0 # copy corr_shells into shells (see above) n_shells = n_corr_shells shells = [] for ish in range(n_shells): shells.append({key: corr_shells[ish].get( key, None) for key in shell_entries}) ### SP = 0 # NO spin-polarised calculations for now SO = 0 # NO spin-orbit calculation for now charge_below = 0 # total charge below energy window NOT used for now energy_unit = 1.0 # should be understood as eV units ### # this is more general n_spin = SP + 1 - SO dim_corr_shells = sum([sh['dim'] for sh in corr_shells]) mpi.report( "Total number of WFs expected in the correlated shells: %d" % dim_corr_shells) # determine the number of inequivalent correlated shells and maps, # needed for further processing n_inequiv_shells, corr_to_inequiv, inequiv_to_corr = ConverterTools.det_shell_equivalence( self, corr_shells) mpi.report("Number of inequivalent shells: %d" % n_inequiv_shells) mpi.report("Shell representatives: " + format(inequiv_to_corr)) shells_map = [inequiv_to_corr[corr_to_inequiv[ish]] for ish in range(n_corr_shells)] mpi.report("Mapping: " + format(shells_map)) # build the k-point mesh, if its size was given on input (kmesh_mode >= 0), # otherwise it is built according to the data in the hr file (see # below) if kmesh_mode >= 0: n_k, k_mesh, bz_weights = self.kmesh_build(nki, kmesh_mode) self.n_k = n_k self.k_mesh = k_mesh # not used in this version: reset to dummy values? n_reps = [1 for i in range(n_inequiv_shells)] dim_reps = [0 for i in range(n_inequiv_shells)] T = [] for ish in range(n_inequiv_shells): ll = 2 * corr_shells[inequiv_to_corr[ish]]['l'] + 1 lmax = ll * (corr_shells[inequiv_to_corr[ish]]['SO'] + 1) T.append(numpy.zeros([lmax, lmax], numpy.complex_)) spin_w90name = ['_up', '_down'] hamr_full = [] # TODO: generalise to SP=1 (only partially done) rot_mat_time_inv = [0 for i in range(n_corr_shells)] # Second, let's read the file containing the Hamiltonian in WF basis # produced by Wannier90 for isp in range(n_spin): # begin loop on isp # build filename according to wannier90 conventions if SP == 1: mpi.report( "Reading information for spin component n. %d" % isp) hr_file = self.w90_seed + spin_w90name[isp] + '_hr.dat' else: hr_file = self.w90_seed + '_hr.dat' # now grab the data from the H(R) file mpi.report( "The Hamiltonian in MLWF basis is extracted from %s ..." % hr_file) nr, rvec, rdeg, nw, hamr = self.read_wannier90hr(hr_file) # number of R vectors, their indices, their degeneracy, number of # WFs, H(R) mpi.report("... done: %d R vectors, %d WFs found" % (nr, nw)) if isp == 0: # set or check some quantities that must be the same for both # spins self.nrpt = nr # k-point grid: (if not defined before) if kmesh_mode == -1: # the size of the k-point mesh is determined from the # largest R vector nki = [2 * rvec[:, idir].max() + 1 for idir in range(3)] # it will be the same as in the win only when nki is odd, because of the # wannier90 convention: if we have nki k-points along the i-th direction, # then we should get 2*(nki/2)+nki%2 R points along that # direction n_k, k_mesh, bz_weights = self.kmesh_build(nki) self.n_k = n_k self.k_mesh = k_mesh # set the R vectors and their degeneracy self.rvec = rvec self.rdeg = rdeg self.nwfs = nw # check that the total number of WFs makes sense if self.nwfs < dim_corr_shells: mpi.report( "ERROR: number of WFs in the file smaller than number of correlated orbitals!") elif self.nwfs > dim_corr_shells: # NOTE: correlated shells must appear before uncorrelated # ones inside the file mpi.report("Number of WFs larger than correlated orbitals:\n" + "WFs from %d to %d treated as uncorrelated" % (dim_corr_shells + 1, self.nwfs)) else: mpi.report( "Number of WFs equal to number of correlated orbitals") # we assume spin up and spin down always have same total number # of WFs n_orbitals = numpy.ones( [self.n_k, n_spin], numpy.int) * self.nwfs else: # consistency check between the _up and _down file contents if nr != self.nrpt: mpi.report( "Different number of R vectors for spin-up/spin-down!") if nw != self.nwfs: mpi.report( "Different number of WFs for spin-up/spin-down!") hamr_full.append(hamr) # FIXME: when do we actually need deepcopy()? # hamr_full.append(deepcopy(hamr)) for ir in range(nr): # checks if the Hamiltonian is real (it should, if # wannierisation worked fine) if numpy.abs((hamr[ir].imag.max()).max()) > self._w90zero: mpi.report( "H(R) has large complex components at R %d" % ir) # copy the R=0 block corresponding to the correlated shells # into another variable (needed later for finding rot_mat) if rvec[ir, 0] == 0 and rvec[ir, 1] == 0 and rvec[ir, 2] == 0: ham_corr0 = hamr[ir][0:dim_corr_shells, 0:dim_corr_shells] # checks if ham0 is Hermitian if not numpy.allclose(ham_corr0.transpose().conjugate(), ham_corr0, atol=self._w90zero, rtol=1.e-9): raise ValueError("H(R=0) matrix is not Hermitian!") # find rot_mat symmetries by diagonalising the on-site Hamiltonian # of the first spin if isp == 0: use_rotations, rot_mat = self.find_rot_mat( n_corr_shells, corr_shells, shells_map, ham_corr0) else: # consistency check use_rotations_, rot_mat_ = self.find_rot_mat( n_corr_shells, corr_shells, shells_map, ham_corr0) if (use_rotations and not use_rotations_): mpi.report( "Rotations cannot be used for spin component n. %d" % isp) for icrsh in range(n_corr_shells): if not numpy.allclose(rot_mat_[icrsh], rot_mat[icrsh], atol=self._w90zero, rtol=1.e-15): mpi.report( "Rotations for spin component n. %d do not match!" % isp) # end loop on isp 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: bz_weights = 0.5 * bz_weights # Third, compute the hoppings in reciprocal space hopping = numpy.zeros([self.n_k, n_spin, numpy.max( n_orbitals), numpy.max(n_orbitals)], numpy.complex_) for isp in range(n_spin): # make Fourier transform H(R) -> H(k) : it can be done one spin at # a time hamk = self.fourier_ham(self.nwfs, hamr_full[isp]) # copy the H(k) in the right place of hoppings... is there a better # way to do this?? for ik in range(self.n_k): #hopping[ik,isp,:,:] = deepcopy(hamk[ik][:,:])*energy_unit hopping[ik, isp, :, :] = hamk[ik][:, :] * energy_unit # Then, initialise the projectors k_dep_projection = 0 # we always have the same number of WFs at each k-point proj_mat = numpy.zeros([self.n_k, n_spin, n_corr_shells, max( [crsh['dim'] for crsh in corr_shells]), numpy.max(n_orbitals)], numpy.complex_) iorb = 0 # Projectors simply consist in identity matrix blocks selecting those MLWFs that # correspond to the specific correlated shell indexed by icrsh. # 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 icrsh in range(n_corr_shells): norb = corr_shells[icrsh]['dim'] proj_mat[:, :, icrsh, 0:norb, iorb:iorb + norb] = numpy.identity(norb, numpy.complex_) iorb += norb # Finally, save all required data into the HDF archive: 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'] for it in things_to_save: ar[self.dft_subgrp][it] = locals()[it] def read_wannier90hr(self, hr_filename="wannier_hr.dat"): """ Method for reading the seedname_hr.dat file produced by Wannier90 (http://wannier.org) Parameters ---------- hr_filename : string full name of the 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 = Hamilonian matrix elements in the Wannier basis """ # Read only from the master node if not (mpi.is_master_node()): return try: with open(hr_filename, "r") as hr_filedesc: hr_data = hr_filedesc.readlines() hr_filedesc.close() except IOError: mpi.report("The file %s could not be read!" % hr_filename) mpi.report("Reading %s..." % hr_filename + hr_data[0]) 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") # 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=numpy.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 if not numpy.any(rcurr) and ii == jj: h_of_r[ir][ii, jj] = complex(float(cline[5]) - self.fermi_energy, float(cline[6])) else: 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