mirror of https://github.com/triqs/dft_tools
854 lines
39 KiB
Python
854 lines
39 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) 2019 by A. D. N. James, A. Hampel and M. Aichhorn
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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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Elk converter
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"""
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from types import *
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import numpy
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from .converter_tools import *
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import os.path
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from h5 import *
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from locale import atof
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from triqs_dft_tools.converters.elktools import readElkfiles as read_Elk
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from triqs_dft_tools.converters.elktools import ElkConverterTools as Elk_tools
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class ElkConverter(ConverterTools,Elk_tools,read_Elk):
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"""
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Conversion from Elk 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, filename, hdf_filename=None,
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dft_subgrp='dft_input', symmcorr_subgrp='dft_symmcorr_input',
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bc_subgrp='dft_bandchar_input', symmpar_subgrp='dft_symmpar_input',
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bands_subgrp='dft_bands_input', misc_subgrp='dft_misc_input',
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transp_subgrp='dft_transp_input',cont_subgrp='dft_contours_input',
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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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filename : string
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Base name of DFT 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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parproj_subgrp : string, optional
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Name of subgroup storing partial projector data.
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symmpar_subgrp : string, optional
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Name of subgroup storing partial-projector symmetry data.
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bands_subgrp : string, optional
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Name of subgroup storing band data.
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misc_subgrp : string, optional
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Name of subgroup storing miscellaneous DFT data.
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transp_subgrp : string, optional
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Name of subgroup storing transport 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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assert isinstance(filename, str), "ElkConverter: 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 = filename + '.h5'
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self.hdf_file = hdf_filename
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self.dft_file = 'PROJ.OUT'
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self.band_file = 'BAND.OUT'
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self.eval_file = 'EIGVAL.OUT'
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self.efermi_file = 'EFERMI.OUT'
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self.kp_file = 'KPOINTS.OUT'
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self.geom_file='GEOMETRY.OUT'
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self.dft_subgrp = dft_subgrp
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self.symmcorr_subgrp = symmcorr_subgrp
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self.bc_subgrp = bc_subgrp
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self.symmpar_subgrp = symmpar_subgrp
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self.bands_subgrp = bands_subgrp
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self.misc_subgrp = misc_subgrp
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self.transp_subgrp = transp_subgrp
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self.cont_subgrp = cont_subgrp
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self.fortran_to_replace = {'D': 'E'}
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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 check_dens(self,n_k,nstsv,occ,bz_weights,n_spin_blocs,band_window,SO):
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"""
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Check the charge density below the correlated energy window and up to the Fermi level
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"""
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density_required=0.0E-7
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charge_below=0.0E-7
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#calculate the valence charge and charge below lower energy window bound
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#Elk does not use the tetrahedron method when calculating these charges
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for ik in range(n_k):
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for ist in range(nstsv):
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#calculate the charge over all the bands
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density_required+=occ[ik][ist]*bz_weights[ik]
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for isp in range(n_spin_blocs):
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#Convert occ list from elk to two index format for spins
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jst=int((isp)*nstsv*0.5)
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#Take lowest index in band_window for SO system
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if(SO==0):
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nst=band_window[isp][ik, 0]-1
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else:
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band=[band_window[0][ik, 0],band_window[1][ik, 0]]
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nst=min(band)-1
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#calculate the charge below energy window
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for ist in range(jst,nst):
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charge_below+=occ[ik][ist]*bz_weights[ik]
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#return charges
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return(density_required,charge_below)
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def rotsym(self,n_shells,shells,n_symm,ind,basis,T,mat):
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"""
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Rotates the symmetry matrices into basis defined by the T unitary matrix
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the outputted projectors are rotated to the irreducible representation
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and then reduced in size to the orbitals used to construct the projectors.
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"""
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for ish in range(n_shells):
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#check that the T matrix is not the Identity (i.e. not using spherical
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#harmonics).
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if(basis[ish]!=0):
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#put mat into temporary matrix
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temp=mat
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#index range of lm values used to create the Wannier projectors
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min_ind=numpy.min(ind[ish][:])
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max_ind=numpy.max(ind[ish][:])+1
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#dimension of lm values used to construct the projectors
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dim=shells[ish]['dim']
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#loop over all symmetries
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for isym in range(n_symm):
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#rotate symmetry matrix into basis defined by T
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mat[isym][ish]=numpy.matmul(T[ish],mat[isym][ish])
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mat[isym][ish]=numpy.matmul(mat[isym][ish],T[ish].conjugate().transpose())
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#put desired subset of transformed symmetry matrix into temp matrix for symmetry isym
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for id in range(len(ind[ish])):
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i=ind[ish][id]
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for jd in range(len(ind[ish][:])):
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j=ind[ish][jd]
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temp[isym][ish][id,jd]=mat[isym][ish][i,j]
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#put temp matrix into mat
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mat=temp
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#reduce size of lm arrays in mat lm dim
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for isym in range(n_symm):
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dim=shells[ish]['dim']
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mat[isym][ish]=mat[isym][ish][:dim,:dim]
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return mat
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def update_so_quatities(self,n_shells,shells,n_corr_shells,corr_shells,n_inequiv_shells,dim_reps,n_k,n_symm,n_orbitals,proj_mat,T,su2,mat,sym=True):
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"""
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Changes the array sizes and elements for arrays used in spin-orbit coupled calculations.
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"""
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#change dim for each shell
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for ish in range(n_shells):
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shells[ish]['dim'] *= 2
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for ish in range(n_corr_shells):
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corr_shells[ish]['dim'] *= 2
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for ish in range(n_inequiv_shells):
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dim_reps[ish]=[2*dim_reps[ish][i] for i in range(len(dim_reps[ish]))]
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#Make temporary array of original n_orbitals
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n_orbitals_orig=n_orbitals
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#Make SO n_orbitals array
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#loop over k-points
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for ik in range(n_k):
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#new orbital array
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n_orbitals[ik,0]=max(n_orbitals[ik,:])
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#reduce array size
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n_orbitals=n_orbitals[:,:1]
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#Resize proj_mat, mat, T
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#make temporary projector array
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proj_mat_tmp = numpy.zeros([n_k, 1, n_corr_shells, max([crsh['dim'] for crsh in corr_shells]), numpy.max(n_orbitals)], complex)
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for ish in range(n_corr_shells):
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#update proj_mat
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for ik in range(n_k):
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#extra array elements in "dim" dimension
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size=int(0.5*corr_shells[ish]['dim'])
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#put each spinor into tmp array and ensure elements are assigned correctly in case of change of max(n_orbitals)
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proj_mat_tmp[ik][0][ish][0:size][0:n_orbitals_orig[ik,0]]=proj_mat[ik][0][ish][0:size][0:n_orbitals_orig[ik,0]]
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#put other spinor projectors into extra "dim" elements
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proj_mat_tmp[ik][0][ish][size:2*size][0:n_orbitals_orig[ik,1]]=proj_mat[ik][1][ish][0:size][0:n_orbitals_orig[ik,1]]
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#update T
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#extra array elements in each dimension
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size=2*corr_shells[ish]['l']+1
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#extend the arrays
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T[ish]=numpy.lib.pad(T[ish],((0,size),(0,size)),'constant',constant_values=(0.0))
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#make block diagonal
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T[ish][size:2*size,size:2*size]=T[ish][0:size,0:size]
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#update the symmetries arrays if needed
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if(sym):
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#update mat - This includes the spin SU(2) matrix for spin-coupled calculations
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#size of each quadrant in the lm symmetry array.
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size=int(0.5*corr_shells[ish]['dim'])
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#temporary spin block array for SU(2) spin operations on mat
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spinmat = numpy.zeros([size,2,size,2],complex)
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for isym in range(n_symm):
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#expand size of array
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mat[isym][ish]=numpy.lib.pad(mat[isym][ish],((0,size),(0,size)),'constant',constant_values=(0.0))
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#make arraye block diagonal
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mat[isym][ish][size:2*size,size:2*size]=mat[isym][ish][0:size,0:size]
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#apply SU(2) spin matrices to lm symmetries
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#put mat into array of spin blocks
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for i1,i2 in numpy.ndindex(2,2):
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spinmat[0:size,i1,0:size,i2] = mat[isym][ish][i1*size:(i1+1)*size,i2*size:(i2+1)*size]
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#apply the SU(2) spin matrices
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for ilm,jlm in numpy.ndindex(size,size):
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spinmat[ilm,:,jlm,:] = numpy.dot(su2[isym][:,:],spinmat[ilm,:,jlm,:])
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#put spinmat into back in mat format
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for i1,i2 in numpy.ndindex(2,2):
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mat[isym][ish][i1*size:(i1+1)*size,i2*size:(i2+1)*size] = spinmat[0:size,i1,0:size,i2]
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#assign arrays and delete temporary arrays
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del proj_mat
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proj_mat = proj_mat_tmp
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del proj_mat_tmp, n_orbitals_orig
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return shells,corr_shells,dim_reps,n_orbitals,proj_mat,T,mat
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def sort_dft_eigvalues(self,n_spin_blocs,SO,n_k,n_orbitals,band_window,en,energy_unit):
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"""
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Rearranges the energy eigenvalue arrays into TRIQS format
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"""
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hopping = numpy.zeros([n_k, n_spin_blocs, numpy.max(n_orbitals), numpy.max(n_orbitals)], complex)
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#loop over spin
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for isp in range(n_spin_blocs):
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#loop over k-points
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for ik in range(n_k):
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#loop over bands
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for ist in range(0,n_orbitals[ik, isp]):
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#converter index for spin polarised Elk indices and take SO into consideration
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if(SO==0):
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jst=int(band_window[isp][ik, 0]-1+ist)
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else:
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band=[band_window[0][ik, 0],band_window[1][ik, 0]]
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jst=int(min(band)-1+ist)
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#correlated window energies
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hopping[ik,isp,ist,ist]=en[ik][jst]*energy_unit
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return hopping
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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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- misc_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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filext='.OUT'
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dft_file='PROJ'+filext
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mpi.report("Reading %s" % dft_file)
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#Energy conversion - Elk uses Hartrees
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energy_unit = 27.2113850560 # Elk uses hartrees
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#The projectors change size per k-point
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k_dep_projection = 1
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#Symmetries are used
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symm_op = 1 # Use symmetry groups for the k-sum
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shells=[]
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#read information about projectors calculated in the Elk calculation
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[gen_info,n_corr_shells,n_inequiv_shells,corr_to_inequiv,inequiv_to_corr,corr_shells,n_reps,dim_reps,ind,basis,T] = read_Elk.read_proj(self,dft_file)
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#get info for HDF5 file from gen_info
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n_k=gen_info['n_k']
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SP=gen_info['spinpol']-1
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#Elk uses spinor wavefunctions. Therefore these two spinor wavefunctions have spin-orbit coupling incorporated in them. Here we read in the spinors
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n_spin_blocs = SP + 1
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SO=gen_info['SO']
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n_atoms=gen_info['natm']
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#Elk only calculates Wannier projectors (no theta projectors generated):
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n_shells=n_corr_shells
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for ish in range(n_shells):
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shells.append(corr_shells[ish].copy())
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#remove last 2 entries from corr_shlls
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del shells[ish]['SO']
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del shells[ish]['irep']
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shells[ish]['dim'] = 2*shells[ish]['l']+1
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#read eigenvalues calculated in the Elk calculation
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mpi.report("Reading %s and EFERMI.OUT" % self.eval_file)
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[en,occ,nstsv]=read_Elk.read_eig(self)
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#read projectors calculated in the Elk calculation
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proj_mat = numpy.zeros([n_k, n_spin_blocs, n_corr_shells, max([crsh['dim'] for crsh in corr_shells]), nstsv], complex)
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mpi.report("Reading projector(s)")
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for ish in range(n_corr_shells):
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[n_orbitals,band_window,rep,proj_mat]=read_Elk.read_projector(self,corr_shells,n_spin_blocs,ish,proj_mat,ind,T,basis,filext)
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#read kpoints calculated in the Elk calculation
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mpi.report("Reading %s" % self.kp_file)
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[bz_weights,vkl]=read_Elk.read_kpoints(self)
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#symmetry matrix
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mpi.report("Reading GEOMETRY.OUT")
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#read in atom positions, the symmetry operators (in lattice coordinates) and lattice vectors
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[ns, na, atpos]=read_Elk.read_geometry(self)
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#Read symmetry files
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mpi.report("Reading SYMCRYS.OUT")
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[n_symm,spinmat,symmat,tr] = read_Elk.readsym(self)
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mpi.report("Reading LATTICE.OUT")
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[amat,amatinv,bmat,bmatinv,cell_vol] = read_Elk.readlat(self)
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#calculating atom permutations
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perm = Elk_tools.gen_perm(self,n_symm,ns,na,n_atoms,symmat,tr,atpos)
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#determine the cartesian lattice symmetries and the spin axis rotations
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#required for the spinors (for SO for now)
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su2 = []
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symmatc=[]
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for isym in range(n_symm):
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#convert the lattice symmetry matrices into cartesian coordinates
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tmp = numpy.matmul(amat,symmat[isym])
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symmatc.append(numpy.matmul(tmp,amatinv))
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#convert the spin symmetry matrices into cartesian coordinates
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spinmatc = numpy.matmul(amat,spinmat[isym])
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spinmatc = numpy.matmul(spinmatc,amatinv)
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#calculate the rotation angle and spin axis vector in cartesian coordinates
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[v,th] = self.rotaxang(spinmatc[:,:])
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#calculate the SU(2) matrix from the angle and spin axis vector
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su2.append(self.axangsu2(v,th))
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del tmp
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#calculating the symmetries in complex harmonics
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mat = Elk_tools.symlat_to_complex_harmonics(self,n_symm,n_corr_shells,symmatc,corr_shells)
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mat = self.rotsym(n_corr_shells,corr_shells,n_symm,ind,basis,T,mat)
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#The reading is done. Some variables may need to change for TRIQS compatibility.
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#Alter size of some of the arrays if spin orbit coupling is enabled.
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#For SO in Elk, the eigenvalues and eigenvector band indices are in asscending order w.r.t energy
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if(SO==1):
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[shells,corr_shells,dim_reps,n_orbitals,proj_mat,T,mat]=self.update_so_quatities(n_shells,shells,n_corr_shells,corr_shells,n_inequiv_shells,dim_reps,n_k,n_symm,n_orbitals,proj_mat,T,su2,mat)
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#reduce n_spin_blocs
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n_spin_blocs = SP + 1 - SO
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#put the energy eigenvalues arrays in TRIQS format
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hopping = self.sort_dft_eigvalues(n_spin_blocs,SO,n_k,n_orbitals,band_window,en,energy_unit)
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#Elk does not use global to local matrix rotation (Rotloc) as is done in Wien2k. However, the projectors
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#require a symmetry matrix to rotate from jatom to iatom. Below finds the non inversion
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#symmetric matrices which were used in calculating the projectors
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use_rotations = 1
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rot_mat = [numpy.identity(corr_shells[icrsh]['dim'], complex) for icrsh in range(n_corr_shells)]
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for icrsh in range(n_corr_shells):
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#return inequivalent index
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incrsh = corr_to_inequiv[icrsh]
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#return first inequivalent corr_shell index
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jcrsh = inequiv_to_corr[incrsh]
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#want to rotate atom to first inequivalent atom in list
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iatom = corr_shells[jcrsh]['atom']
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for isym in range(n_symm):
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jatom=perm[isym][corr_shells[icrsh]['atom']-1]
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#determinant determines if crystal symmetry matrix has inversion symmetry (=-1)
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det = numpy.linalg.det(symmat[isym][:,:])
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if((jatom==iatom)&(det>0.0)):
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#local rotation which rotates equivalent atom into its local coordinate system
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#(inverse of the symmetry operator applied to the projectors in Elk)
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rot_mat[icrsh][:,:]=mat[isym][icrsh][:,:].conjugate().transpose()
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#used first desired symmetry in crystal symmetry list
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break
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# Elk does not currently use time inversion symmetry
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rot_mat_time_inv = [0 for i in range(n_corr_shells)]
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#Check that the charge of all the bands and below the correlated window have been calculated correctly
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[density_required, charge_below] = self.check_dens(n_k,nstsv,occ,bz_weights,n_spin_blocs,band_window,SO)
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#calculate the required charge (density_required) to remain charge neutral
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mpi.report("The total charge of the system = %f" %density_required)
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mpi.report("The charge below the correlated window = %f" %charge_below)
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mpi.report("The charge within the correlated window = %f" %(density_required - charge_below))
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#Elk interface does not calculate theta projectors, hence orbits are the same as Wannier projectors
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orbits=[]
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#remove the spatom index to avoid errors in the symmetry routines
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for ish in range(n_corr_shells):
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#remove "spatom"
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del corr_shells[ish]['spatom']
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orbits.append(corr_shells[ish].copy())
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for ish in range(n_shells):
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#remove "spatom"
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del shells[ish]['spatom']
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n_orbits=len(orbits)
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#Note that the T numpy array is defined for all shells.
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#new variable: dft_code - this determines which DFT code the inputs come from.
|
|
#used for certain routines within dft_tools if treating the inputs differently is required.
|
|
dft_code = 'elk'
|
|
|
|
# Save it to the HDF:
|
|
ar = HDFArchive(self.hdf_file, 'a')
|
|
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', 'dft_code']
|
|
for it in things_to_save:
|
|
ar[self.dft_subgrp][it] = locals()[it]
|
|
del ar
|
|
# Save it to the HDF:
|
|
ar = HDFArchive(self.hdf_file, 'a')
|
|
symm_subgrp=self.symmcorr_subgrp
|
|
#Elk does not use time inversion symmetry
|
|
time_inv = [0 for j in range(n_symm)]
|
|
mat_tinv = [numpy.identity(orbits[orb]['dim'], complex)
|
|
for orb in range(n_orbits)]
|
|
#Save all the symmetry data
|
|
if not (symm_subgrp in ar):
|
|
ar.create_group(symm_subgrp)
|
|
things_to_save_sym = ['n_symm', 'n_atoms', 'perm',
|
|
'orbits', 'SO', 'SP', 'time_inv', 'mat', 'mat_tinv']
|
|
for it in things_to_save_sym:
|
|
ar[symm_subgrp][it] = locals()[it]
|
|
del ar
|
|
#Save misc info
|
|
things_to_save_misc = ['band_window','vkl','nstsv']
|
|
# Save it to the HDF:
|
|
ar = HDFArchive(self.hdf_file, 'a')
|
|
if not (self.misc_subgrp in ar):
|
|
ar.create_group(self.misc_subgrp)
|
|
for it in things_to_save_misc:
|
|
ar[self.misc_subgrp][it] = locals()[it]
|
|
del ar
|
|
mpi.report('Converted the Elk ground state data')
|
|
|
|
|
|
def convert_bands_input(self):
|
|
"""
|
|
Reads the appropriate files and stores the data for the bands_subgrp in the hdf5 archive.
|
|
|
|
"""
|
|
# Read and write only on the master node
|
|
if not (mpi.is_master_node()):
|
|
return
|
|
filext='_WANBAND.OUT'
|
|
dft_file='PROJ'+filext
|
|
mpi.report("Reading %s" % dft_file)
|
|
#Energy conversion - Elk uses Hartrees
|
|
energy_unit = 27.2113850560 # Elk uses hartrees
|
|
shells=[]
|
|
#read information about projectors calculated in the Elk calculation
|
|
[gen_info,n_corr_shells,n_inequiv_shells,corr_to_inequiv,inequiv_to_corr,corr_shells,n_reps,dim_reps,ind,basis,T] = read_Elk.read_proj(self,dft_file)
|
|
#get info for HDF5 file from gen_info
|
|
n_k=gen_info['n_k']
|
|
SP=gen_info['spinpol']-1
|
|
#Elk uses spinor wavefunctions. Therefore these two spinor wavefunctions have spin-orbit coupling incorporated in them. Here we read in the spinors
|
|
n_spin_blocs = SP + 1
|
|
SO=gen_info['SO']
|
|
#Elk only calculates Wannier projectors (no theta projectors generated):
|
|
n_shells=n_corr_shells
|
|
for ish in range(n_shells):
|
|
shells.append(corr_shells[ish].copy())
|
|
#remove last 2 entries from corr_shlls
|
|
del shells[ish]['SO']
|
|
del shells[ish]['irep']
|
|
shells[ish]['dim'] = 2*shells[ish]['l']+1
|
|
|
|
#read in the band eigenvalues
|
|
mpi.report("Reading BAND.OUT")
|
|
en=numpy.loadtxt('BAND.OUT')
|
|
nstsv=int(len(en[:,1])/n_k)
|
|
#convert the en array into a workable format
|
|
entmp = numpy.zeros([n_k,nstsv], complex)
|
|
enj=0
|
|
for ist in range(nstsv):
|
|
for ik in range(n_k):
|
|
entmp[ik,ist]=en[enj,1]
|
|
enj+=1
|
|
del en
|
|
#read projectors
|
|
proj_mat = numpy.zeros([n_k, n_spin_blocs, n_corr_shells, max([crsh['dim'] for crsh in corr_shells]), nstsv], complex)
|
|
mpi.report("Reading projector(s)")
|
|
for ish in range(n_corr_shells):
|
|
[n_orbitals,band_window,rep,proj_mat]=read_Elk.read_projector(self,corr_shells,n_spin_blocs,ish,proj_mat,ind,T,basis,filext)
|
|
|
|
#alter arrays for spin-orbit coupling
|
|
if(SO==1):
|
|
mat=[]
|
|
su2=[]
|
|
n_symm=1
|
|
[shells,corr_shells,dim_reps,n_orbitals,proj_mat,T,mat]=self.update_so_quatities(n_shells,shells,n_corr_shells,corr_shells,n_inequiv_shells,dim_reps,n_k,n_symm,n_orbitals,proj_mat,T,su2,mat,sym=False)
|
|
#reduce n_spin_blocs
|
|
n_spin_blocs = SP + 1 - SO
|
|
|
|
#put the energy eigenvalues arrays in TRIQS format
|
|
hopping = self.sort_dft_eigvalues(n_spin_blocs,SO,n_k,n_orbitals,band_window,entmp,energy_unit)
|
|
|
|
# No partial projectors generated here, so set to 0:
|
|
n_parproj = numpy.array([0])
|
|
proj_mat_all = numpy.array([0])
|
|
|
|
# Save it to the HDF:
|
|
ar = HDFArchive(self.hdf_file, 'a')
|
|
if not (self.bands_subgrp in ar):
|
|
ar.create_group(self.bands_subgrp)
|
|
# The subgroup containing the data. If it does not exist, it is
|
|
# created. If it exists, the data is overwritten!
|
|
things_to_save = ['n_k', 'n_orbitals', 'proj_mat',
|
|
'hopping', 'n_parproj', 'proj_mat_all']
|
|
for it in things_to_save:
|
|
ar[self.bands_subgrp][it] = locals()[it]
|
|
del ar
|
|
mpi.report('Converted the band data')
|
|
|
|
def convert_contours_input(self,kgrid=None,ngrid=None):
|
|
r"""
|
|
Reads the appropriate files and stores the data for the cont_subgrp in the hdf5 archive.
|
|
|
|
Parameters
|
|
----------
|
|
kgrid : size (4,3) double numpy array, optional
|
|
Numpy array defining the reciprocal lattice vertices used in the Elk Fermi
|
|
surface calculation. Each row has the following meaning:
|
|
grid3d[0,:] - origin lattice vertex
|
|
grid3d[1,:] - b1 lattice vertex
|
|
grid3d[2,:] - b2 lattice vertex
|
|
grid3d[3,:] - b3 lattice vertex
|
|
ngrid : size (3) integer numpy array, optional
|
|
Numpy array for the number of points along each (b1,b2,b3) lattice vertices
|
|
|
|
Note that these inputs relate to the plot3d input of Elk.
|
|
"""
|
|
|
|
if not (mpi.is_master_node()):
|
|
return
|
|
filext='_FS.OUT'
|
|
dft_file='PROJ'+filext
|
|
mpi.report("Reading %s" % dft_file)
|
|
#read the symmetries and k-points first
|
|
#read kpoints calculated in the Elk FS calculation
|
|
mpi.report("Reading KPOINT_FS.OUT")
|
|
[bz_weights,vkl]=read_Elk.read_kpoints(self,filext=filext)
|
|
n_k=vkl[:,0].size
|
|
#Need lattice symmetries to unfold the irreducible BZ
|
|
#Read symmetry files
|
|
mpi.report("Reading SYMCRYS.OUT")
|
|
[n_symm,spinmat,symlat,tr] = read_Elk.readsym(self)
|
|
#generate full vectors for Fermi surface plotting along with index mapping
|
|
#to irreducible vector set.
|
|
if (ngrid is not None and kgrid is not None):
|
|
mpi.report('Using User defined k-mesh')
|
|
#check variables are in correct format
|
|
if ngrid.size != 3:
|
|
assert 0, "The input numpy ngrid is not the required size of 3!"
|
|
elif ngrid.dtype != int:
|
|
assert 0, "The input numpy ngrid is not an array of integers."
|
|
elif kgrid.shape != (4,3):
|
|
assert 0, "The input numpy kgrid is not the required size of (4x3)!"
|
|
#generate full set of k-points with mapping to reduced set
|
|
[BZ_vkl, BZ_iknr, BZ_n_k] = Elk_tools.plotpt3d(self,n_k,vkl,n_symm,symlat,kgrid,ngrid)
|
|
elif (ngrid is None and kgrid is None):
|
|
mpi.report('No grid dimension input for Fermi surface.')
|
|
mpi.report('Calculating k-points by folding out irreducible vectors instead if using symmetries.')
|
|
mpi.report('Warning! This may not equate to the same set of vectors used to generate the Fermi surface data.')
|
|
[BZ_vkl, BZ_iknr, BZ_n_k] = Elk_tools.bzfoldout(self,n_k,vkl,n_symm,symlat)
|
|
else:
|
|
assert 0, "Either input both ngrid and kgrid numpy arrays or neither."
|
|
#return all threads apart from master
|
|
if not (mpi.is_master_node()):
|
|
return
|
|
|
|
# Read and write the following only on the master thread
|
|
#Energy conversion - Elk uses Hartrees
|
|
energy_unit = 27.2113850560 # Elk uses hartrees
|
|
shells=[]
|
|
#read information about projectors calculated in the Elk calculation
|
|
[gen_info,n_corr_shells,n_inequiv_shells,corr_to_inequiv,inequiv_to_corr,corr_shells,n_reps,dim_reps,ind,basis,T] = read_Elk.read_proj(self,dft_file)
|
|
#get info for HDF5 file from gen_info
|
|
n_k=gen_info['n_k']
|
|
SP=gen_info['spinpol']-1
|
|
#Elk uses spinor wavefunctions. Therefore these two spinor wavefunctions have spin-orbit coupling incorporated in them. Here we read in the spinors
|
|
n_spin_blocs = SP + 1
|
|
SO=gen_info['SO']
|
|
#Elk only calculates Wannier projectors (no theta projectors generated):
|
|
n_shells=n_corr_shells
|
|
for ish in range(n_shells):
|
|
shells.append(corr_shells[ish].copy())
|
|
#remove last 2 entries from corr_shlls
|
|
del shells[ish]['SO']
|
|
del shells[ish]['irep']
|
|
shells[ish]['dim'] = 2*shells[ish]['l']+1
|
|
|
|
#read in the eigenvalues used for the FS calculation
|
|
mpi.report("Reading EIGVAL_FS.OUT and EFERMI.OUT")
|
|
[en,occ,nstsv]=read_Elk.read_eig(self,filext=filext)
|
|
|
|
#read projectors
|
|
proj_mat = numpy.zeros([n_k, n_spin_blocs, n_corr_shells, max([crsh['dim'] for crsh in corr_shells]), nstsv], complex)
|
|
mpi.report("Reading projector(s)")
|
|
for ish in range(n_corr_shells):
|
|
[n_orbitals,band_window,rep,proj_mat]=read_Elk.read_projector(self,corr_shells,n_spin_blocs,ish,proj_mat,ind,T,basis,filext)
|
|
|
|
mpi.report("Reading LATTICE.OUT")
|
|
[amat,amatinv,bmat,bmatinv,cell_vol] = read_Elk.readlat(self)
|
|
#Put eigenvalues into array of eigenvalues for the correlated window
|
|
#alter arrays for spin-orbit coupling
|
|
if(SO==1):
|
|
mat=[]
|
|
su2=[]
|
|
[shells,corr_shells,dim_reps,n_orbitals,proj_mat,T,mat]=self.update_so_quatities(n_shells,shells,n_corr_shells,corr_shells,n_inequiv_shells,dim_reps,n_k,n_symm,n_orbitals,proj_mat,T,su2,mat,sym=False)
|
|
#reduce n_spin_blocs
|
|
n_spin_blocs = SP + 1 - SO
|
|
|
|
#put the energy eigenvalues arrays in TRIQS format
|
|
hopping = self.sort_dft_eigvalues(n_spin_blocs,SO,n_k,n_orbitals,band_window,en,energy_unit)
|
|
|
|
# Save it to the HDF:
|
|
ar = HDFArchive(self.hdf_file, 'a')
|
|
if not (self.cont_subgrp in ar):
|
|
ar.create_group(self.cont_subgrp)
|
|
# The subgroup containing the data. If it does not exist, it is
|
|
# created. If it exists, the data is overwritten!
|
|
things_to_save = ['n_k','n_orbitals', 'proj_mat','bmat',
|
|
'BZ_n_k','BZ_iknr','BZ_vkl','hopping']
|
|
for it in things_to_save:
|
|
ar[self.cont_subgrp][it] = locals()[it]
|
|
del ar
|
|
mpi.report('Converted the Contours data')
|
|
|
|
# commented out for now - unsure using this produces DFT+DMFT PDOS.
|
|
# The data from BC.OUT are the band-resolved diagonal muffin-tin DFT density matrix elements used in Elk to calculate PDOS
|
|
# (the PDOS is calculated from the Trace over the bands indices). Although this is equivalent to using using projectors in DFT and is likely valid for DFT+DMFT,
|
|
# the equivalence needs to be thoroughly checked for DFT+DMFT, but would require theta (or similar) projectors from Elk to do so.
|
|
# code left here just in case.
|
|
|
|
# def dft_band_characters(self):
|
|
# """
|
|
# Reads in the band-resolved muffin-tin density matrix (band characters) generated in Elk
|
|
# to be used for PDOS plots.
|
|
# """
|
|
|
|
# #determine file extension
|
|
# fileext='.OUT'
|
|
# #read number of k-points and eigenstates
|
|
# things_to_read = ['n_k','n_orbitals']
|
|
# ar = HDFArchive(self.hdf_file, 'r')
|
|
# for it in things_to_read:
|
|
# setattr(self, it, ar[self.dft_subgrp][it])
|
|
# del ar
|
|
|
|
# if not (mpi.is_master_node()):
|
|
# return
|
|
# mpi.report("Reading BC%s"%(fileext))
|
|
|
|
# # get needed data from hdf file
|
|
# # from general info
|
|
# ar = HDFArchive(self.hdf_file, 'a')
|
|
# things_to_read = ['SP', 'SO']
|
|
# for it in things_to_read:
|
|
# if not hasattr(self, it):
|
|
# setattr(self, it, ar[self.dft_subgrp][it])
|
|
# #from misc info
|
|
# things_to_read = ['nstsv','band_window']
|
|
# for it in things_to_read:
|
|
# if not hasattr(self, it):
|
|
# setattr(self, it, ar[self.misc_subgrp][it])
|
|
# #from sym info
|
|
# things_to_read = ['n_atoms','perm']
|
|
# symm_subgrp=self.symmcorr_subgrp
|
|
# for it in things_to_read:
|
|
# if not hasattr(self, it):
|
|
# setattr(self, it, ar[symm_subgrp][it])
|
|
# del ar
|
|
|
|
# #read in band characters
|
|
# [bc,maxlm] = read_Elk.read_bc(self,fileext)
|
|
#note that bc is the band resolved inner product of the Elk muffin-tin wave functions in
|
|
#a diagonal lm basis (by default). These are used in Elk to calculate the DOS in a diagonal
|
|
#irreducible lm basis. This band resolved density matrix in the lm basis will be used
|
|
#to project to spectral funtion to get the muffin-tin contributions. There will be an
|
|
#interstitial contribution (as Elk uses an APW+lo basis) which is the difference between
|
|
#the total and summed muffin-tin contributions. Also note that the bc array should be
|
|
#symmetrised within Elk.
|
|
#general variables
|
|
# lmax = int(numpy.sqrt(maxlm)-1)
|
|
# n_spin_blocs = self.SP + 1 - self.SO
|
|
# so = self.SO + 1
|
|
# #get the sort entry which is just the species index for Elk
|
|
# [ns, na, atpos]=read_Elk.read_geometry(self)
|
|
# isrt=0
|
|
# sort=numpy.zeros([self.n_atoms],int)
|
|
# #arrange sort(species) order
|
|
# for i in range(ns):
|
|
# for ia in range(na[i]):
|
|
# sort[isrt]=i
|
|
# isrt+=1
|
|
# #updating n_shells to include all the atoms and l used in the Elk calculation.
|
|
# n_shells = self.n_atoms * (lmax+1)
|
|
# shells = []
|
|
# shell_entries = ['atom', 'sort', 'l', 'dim']
|
|
# for iat in range(self.n_atoms):
|
|
# for l in range(lmax+1):
|
|
# #sort is not known from Elk outputs
|
|
# tmp = [iat+1, sort[iat]+1, l, so*(2*l+1)]
|
|
# shells.append({name: int(val) for name, val in zip(shell_entries, tmp)})
|
|
# del tmp, ns, na, atpos, isrt, shell_entries
|
|
# #overwrite n_shells and shells
|
|
# things_to_save = ['n_shells', 'shells']
|
|
# ar = HDFArchive(self.hdf_file, 'a')
|
|
# for it in things_to_save:
|
|
# ar[self.dft_subgrp][it] = locals()[it]
|
|
|
|
|
|
# # Initialise P, here a double list of matrices:
|
|
# band_dens_muffin = numpy.zeros([self.n_k, n_spin_blocs, n_shells, so*(2*lmax+1), numpy.max(self.n_orbitals)], float)
|
|
# for ik in range(self.n_k):
|
|
# for isp in range(n_spin_blocs):
|
|
# ish=0
|
|
# for iat in range(self.n_atoms):
|
|
# for l in range(lmax+1):
|
|
# #variables for putting subset of bc in proj_mat_all
|
|
# lm_min=l**2
|
|
# lm_max=(l+1)**2
|
|
# nst=self.n_orbitals[ik,isp]
|
|
# ibot=self.band_window[isp][ik, 0]-1
|
|
# itop=ibot+nst
|
|
# dim=l*2+1
|
|
#check use of abs (negative values should be close to 0)
|
|
# band_dens_muffin[ik,isp,ish,0:dim,0:nst] = \
|
|
# bc[lm_min:lm_max,isp,iat,ibot:itop,ik]
|
|
# if(self.SO==1):
|
|
# band_dens_muffin[ik,isp,ish,dim:2*dim,0:nst] = \
|
|
# bc[lm_min:lm_max,1,iat,ibot:itop,ik]
|
|
# ish+=1
|
|
|
|
# things_to_save = ['band_dens_muffin']
|
|
# # Save it all to the HDF:
|
|
# with HDFArchive(self.hdf_file, 'a') as ar:
|
|
# if not (self.bc_subgrp in ar):
|
|
# ar.create_group(self.bc_subgrp)
|
|
# # The subgroup containing the data. If it does not exist, it is
|
|
# # created. If it exists, the data is overwritten!
|
|
# things_to_save = ['band_dens_muffin']
|
|
# for it in things_to_save:
|
|
# ar[self.bc_subgrp][it] = locals()[it]
|
|
|
|
|
|
def convert_transport_input(self):
|
|
"""
|
|
Reads the necessary information for transport calculations on:
|
|
|
|
- the optical band window and the velocity matrix elements from :file:`case.pmat`
|
|
|
|
and stores the data in the hdf5 archive.
|
|
|
|
"""
|
|
|
|
if not (mpi.is_master_node()):
|
|
return
|
|
|
|
# get needed data from hdf file
|
|
with HDFArchive(self.hdf_file, 'r') as ar:
|
|
if not (self.dft_subgrp in ar):
|
|
raise IOError("convert_transport_input: No %s subgroup in hdf file found! Call convert_dft_input first." % self.dft_subgrp)
|
|
things_to_read = ['SP', 'SO','n_k','n_orbitals']
|
|
for it in things_to_read:
|
|
if not hasattr(self, it):
|
|
setattr(self, it, ar[self.dft_subgrp][it])
|
|
#from misc info
|
|
things_to_read = ['band_window','vkl','nstsv']
|
|
for it in things_to_read:
|
|
if not hasattr(self, it):
|
|
setattr(self, it, ar[self.misc_subgrp][it])
|
|
|
|
#unlike in WIEN2k, Elk writes the velocities (momentum) matrix elements for all bands.
|
|
#Therefore, we can use the indices in the n_orbitals array to extract the desired elements.
|
|
#However, the PMAT.OUT file is in Fortran-binary, so the file is read in by python wrappers
|
|
#around the reading fortran code.
|
|
|
|
# Read relevant data from PMAT.OUT binary file
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###########################################
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# band_window_optics: same as Elk converter's band_window, but rearranged to be compatible
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# for the transport calculations.
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# velocities_k: velocity (momentum) matrix elements between all bands in band_window_optics
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# and each k-point.
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#load fortran wrapper module
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import triqs_dft_tools.converters.elktools.elkwrappers.getpmatelk as et
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#elk velocities for all bands
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pmat=numpy.zeros([self.nstsv,self.nstsv,3],dtype=complex)
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n_spin_blocks = self.SP + 1 - self.SO
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#TRIQS' velocities array used in its transport routines
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velocities_k = [[] for isp in range(n_spin_blocks)]
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#TRIQS' band_window array used in its transport routines
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band_window_optics = []
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mpi.report("Reading PMAT.OUT")
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#read velocities for each k-point
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for ik in range(self.n_k):
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#need to use a fortran array for wrapper
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f_vkl = numpy.asfortranarray(self.vkl[ik,:])
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#read the ik velocity using the wrapper
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pmat[:,:,:]=et.getpmatelk(ik+1,self.nstsv,f_vkl)
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#loop over spin
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for isp in range(n_spin_blocks):
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#no. correlated bands at ik
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nu1=self.band_window[isp][ik,0]-1
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nu2=self.band_window[isp][ik,1]-1
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n_bands=nu2-nu1+1
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#put into velocity array (code similar to that in wien.py.
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if n_bands <= 0:
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velocity_xyz = numpy.zeros((1, 1, 3), dtype=complex)
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else:
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velocity_xyz = numpy.zeros(
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(n_bands, n_bands, 3), dtype=complex)
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#CHECK these lines
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velocity_xyz[:,:,:]=pmat[nu1:nu2+1,nu1:nu2+1,:]
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velocities_k[isp].append(velocity_xyz)
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#rearrange Elk's band_window array into band_window_optics array format
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for isp in range(n_spin_blocks):
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band_window_optics_isp = []
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for ik in range(self.n_k):
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nu1=self.band_window[isp][ik,0]
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nu2=self.band_window[isp][ik,1]
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band_window_optics_isp.append((nu1, nu2))
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n_bands=nu2-nu1+1
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band_window_optics.append(numpy.array(band_window_optics_isp))
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#read in the cell volume from LATTICE.OUT
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mpi.report("Reading LATTICE.OUT")
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[amat,amatinv,bmat,bmatinv,cell_vol] = read_Elk.readlat(self)
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#read in the crystal symmetries
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mpi.report("Reading SYMCRYS.OUT")
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[n_symmetries,spinmat,rot_symmetries,tr] = read_Elk.readsym(self)
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# Put data to HDF5 file
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with HDFArchive(self.hdf_file, 'a') as ar:
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if not (self.transp_subgrp in ar):
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ar.create_group(self.transp_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 = ['band_window_optics', 'velocities_k']
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for it in things_to_save:
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ar[self.transp_subgrp][it] = locals()[it]
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things_to_save_misc = ['n_symmetries', 'rot_symmetries','cell_vol']
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for it in things_to_save_misc:
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ar[self.misc_subgrp][it] = locals()[it]
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mpi.report("Reading complete!")
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