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Revision of Wannier90Converter: 1) write the Hamiltonian in Bloch space

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for charge self-consistent calculations, and 2) spin-orbit coupling

if bloch_basis = True:
* if "seedname_u.dat" (and in case of disentanglement "seedname_u_dis.dat")
  present, write hopping in Bloch basis
* "proj_mat" transforming from Bloch to orbital space
* diagonal hoppings are directly read from "seedname.eig"
* fermi weights and band_window of Wannier Hamiltonian are read from DFT
  output and "seedname.nnkp", written into new subgroup "dft_misc_input"
* automatic calculation of "density_required"
* implemented for Quantum Espresso (read from "seedname.nscf.out" if
  verbosity = 'high') and VASP (read from "OUTCAR"/"LOCPROJ")

* spin-orbit coupling SO = 1 implemented
* substitute k_mesh and bz_weights with kpts and kpt_weights,
  respectively (previous names kept for compatibility)
* updated tests
This commit is contained in:
sobeck 2020-09-15 16:36:57 +02:00 committed by phibeck
parent 60b1a247ce
commit aeaebb04ae
3 changed files with 427 additions and 108 deletions
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535
python/triqs_dft_tools/converters/wannier90.py
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@ -24,14 +24,15 @@
# Wannier90 to HDF5 converter for the SumkDFT class of dfttools/TRIQS;
#
# written by Gabriele Sclauzero (Materials Theory, ETH Zurich), Dec 2015 -- Jan 2016,
# and updated by Maximilian Merkel (Materials Theory, ETH Zurich), Aug 2020,
# updated by Maximilian Merkel (Materials Theory, ETH Zurich), Aug 2020 -- Jan 2021,
# and by Sophie Beck (Materials Theory, ETH Zurich), Sep 2020 -- Jan 2021,
# 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
# - in bloch_basis mode, the number of wannier functions per shell has to be equal
# to the dim of the shell
#
# Things to be improved/checked:
# - the case with SP=1 might work, but was never tested (do we need to define
@ -46,11 +47,11 @@
import numpy
import math
from h5 import *
from .converter_tools import *
from itertools import product
import os.path
from itertools import product
from h5 import HDFArchive
from .converter_tools import ConverterTools
import triqs.utility.mpi as mpi
class Wannier90Converter(ConverterTools):
@ -59,8 +60,8 @@ class Wannier90Converter(ConverterTools):
"""
def __init__(self, seedname, hdf_filename=None, dft_subgrp='dft_input',
symmcorr_subgrp='dft_symmcorr_input', repacking=False,
rot_mat_type='hloc_diag'):
symmcorr_subgrp='dft_symmcorr_input', misc_subgrp='dft_misc_input',
repacking=False, rot_mat_type='hloc_diag', bloch_basis=False):
"""
Initialise the class.
@ -74,11 +75,15 @@ class Wannier90Converter(ConverterTools):
Name of subgroup storing necessary DFT data
symmcorr_subgrp : string, optional
Name of subgroup storing correlated-shell symmetry data
misc_subgrp : string, optional
Name of subgroup storing miscellaneous DFT data.
repacking : boolean, optional
Does the hdf5 archive need to be repacked to save space?
rot_mat_type : string, optional
Type of rot_mat used
Can be 'hloc_diag', 'wannier', 'none'
bloch_basis : boolean, optional
Should the Hamiltonian be written in Bloch rather than Wannier basis?
"""
self._name = "Wannier90Converter"
@ -93,11 +98,13 @@ class Wannier90Converter(ConverterTools):
self.w90_seed = seedname
self.dft_subgrp = dft_subgrp
self.symmcorr_subgrp = symmcorr_subgrp
self.misc_subgrp = misc_subgrp
self.fortran_to_replace = {'D': 'E'}
# threshold below which matrix elements from wannier90 should be
# considered equal
self._w90zero = 2.e-6
self.rot_mat_type = rot_mat_type
self.bloch_basis = bloch_basis
if self.rot_mat_type not in ('hloc_diag', 'wannier', 'none'):
raise ValueError('Parameter rot_mat_type invalid, should be one of'
+ '"hloc_diag", "wannier", "none"')
@ -113,14 +120,14 @@ class Wannier90Converter(ConverterTools):
- dft_subgrp
- symmcorr_subgrp
in the hdf5 archive.
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)
mpi.report("\nReading input from %s..." % self.inp_file)
# R is a generator : each R.Next() will return the next number in the
# file
@ -139,8 +146,10 @@ class Wannier90Converter(ConverterTools):
else:
# some default grid, if everything else fails...
nki = [8, 8, 8]
# read the total number of electrons per cell
# read the total number of electrons per cell if not in bloch basis
# in bloch basis, this is later calculated from the partial occupations
density_required = float(next(R))
# 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,
@ -149,6 +158,7 @@ class Wannier90Converter(ConverterTools):
# 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 = float(next(R))
except:
@ -169,17 +179,40 @@ class Wannier90Converter(ConverterTools):
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
# Determine if any shell requires SO
if any([corr_shell['SO']==1 for corr_shell in corr_shells]):
SO = 1
SP = 1
print('Spin-orbit interaction turned on')
else:
SO = 0
SP = 0
# Only one block supported - either non-spin-polarized or spin-orbit coupled
assert SP == SO, 'Spin-polarized calculations not implemented'
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
n_spin_blocs = SP + 1 - SO
assert n_spin_blocs > 0, 'Input error, if SO=1, SP must be 1.'
if SO == 1:
for shell_list in [shells, corr_shells]:
for entry in shell_list:
entry['dim'] *= 2
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)
mpi.report('Total number of WFs expected in the correlated shells: {0:d}'.format(dim_corr_shells))
# 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 output is in bloch_basis, we use k mesh from seedname_u.mat for consistency
if kmesh_mode >= 0 and not self.bloch_basis:
n_k, kpts, kpt_weights = self.kmesh_build(nki, kmesh_mode)
self.n_k = n_k
self.kpts = kpts
# determine the number of inequivalent correlated shells and maps,
# needed for further processing
@ -190,15 +223,6 @@ class Wannier90Converter(ConverterTools):
shells_map = [inequiv_to_corr[corr_to_inequiv[ish]]
for ish in range(n_corr_shells)]
mpi.report("Mapping: " + format(shells_map))
mpi.report("Subtracting %f eV from the Fermi level." % self.fermi_energy)
# 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)]
@ -207,33 +231,37 @@ class Wannier90Converter(ConverterTools):
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_))
T.append(numpy.zeros([lmax, lmax], dtype=complex))
spin_w90name = ['_up', '_down']
hamr_full = []
umat_full = []
udismat_full = []
bandmat_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):
for isp in range(n_spin_blocs):
# begin loop on isp
# build filename according to wannier90 conventions
if SP == 1:
if SP == 1 and SO == 0:
mpi.report(
"Reading information for spin component n. %d" % isp)
hr_file = self.w90_seed + spin_w90name[isp] + '_hr.dat'
file_seed = self.w90_seed + spin_w90name[isp]
else:
hr_file = self.w90_seed + '_hr.dat'
file_seed = self.w90_seed
# 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)
"\nThe Hamiltonian in MLWF basis is extracted from %s files..." % file_seed)
(nr, rvec, rdeg, nw, hamr, u_mat, udis_mat,
band_mat, k_mesh_from_umat) = self.read_wannier90data(file_seed, kmesh_mode)
# number of R vectors, their indices, their degeneracy, number of
# WFs, H(R)
mpi.report("... done: %d R vectors, %d WFs found" % (nr, nw))
mpi.report("\n... done: %d R vectors, %d WFs found" % (nr, nw))
if isp == 0:
# set or check some quantities that must be the same for both
@ -241,7 +269,14 @@ class Wannier90Converter(ConverterTools):
self.nrpt = nr
# k-point grid: (if not defined before)
if kmesh_mode == -1:
if self.bloch_basis:
mpi.report('Reading k mesh from seedname_u.mat file')
kpts = k_mesh_from_umat
n_k = len(kpts)
kpt_weights = numpy.full(n_k, 1/n_k)
self.n_k = n_k
self.kpts = kpts
elif 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)]
@ -249,9 +284,10 @@ class Wannier90Converter(ConverterTools):
# 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
n_k, kpts, kpt_weights = self.kmesh_build(nki)
self.n_k = n_k
self.kpts = kpts
# set the R vectors and their degeneracy
self.rvec = rvec
@ -273,9 +309,10 @@ class Wannier90Converter(ConverterTools):
# 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
# get second dimension of udis_mat which corresponds to number of bands in window
# n_bands_max corresponds to numpy.max(n_orbitals)
n_bands_max = udis_mat.shape[1]
n_orbitals = numpy.full([self.n_k, n_spin_blocs], n_bands_max)
else:
# consistency check between the _up and _down file contents
if nr != self.nrpt:
@ -286,8 +323,9 @@ class Wannier90Converter(ConverterTools):
"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))
umat_full.append(u_mat)
udismat_full.append(udis_mat)
bandmat_full.append(band_mat)
for ir in range(nr):
# checks if the Hamiltonian is real (it should, if
@ -322,39 +360,92 @@ class Wannier90Converter(ConverterTools):
"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))
# Reads misc input needed for CSC calculations
if self.bloch_basis:
if os.path.isfile(self.w90_seed + '.nscf.out'):
fermi_weight_file = self.w90_seed + '.nscf.out'
print('Reading DFT band occupations from Quantum Espresso output {}'.format(fermi_weight_file))
elif os.path.isfile('LOCPROJ'):
fermi_weight_file = 'LOCPROJ'
print('Reading DFT band occupations from Vasp output {}'.format(fermi_weight_file))
else:
raise IOError('seedname.nscf.out or LOCPROJ required in bloch_basis mode')
f_weights, band_window, self.fermi_energy = self.convert_misc_input(fermi_weight_file,
self.w90_seed + '.nnkp', n_spin_blocs)
# Get density from k-point weighted average and sum over all spins and bands
density_required = numpy.sum(f_weights.T * kpt_weights) * (2 - SP)
print('Overwriting required density with DFT result {:.5f}'.format(density_required))
print('and using the DFT Fermi energy {:.5f} eV\n'.format(self.fermi_energy))
if not self.bloch_basis:
mpi.report("The k-point grid has dimensions: %d, %d, %d" % tuple(nki))
# if calculations are spin-polarized, then renormalize k-point weights
if SP == 1:
bz_weights = 0.5 * bz_weights
if SP == 1 and SO == 0:
kpt_weights *= 0.5
# 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_)
# Third, initialise the projectors
k_dep_projection = 0 # at the moment not really used, but might get important
proj_mat = numpy.zeros([self.n_k, n_spin_blocs, n_corr_shells, max(
[crsh['dim'] for crsh in corr_shells]), numpy.max(n_orbitals)], dtype=complex)
iorb = 0
# Projectors simply consist in identity matrix blocks selecting those MLWFs that
# correspond to the specific correlated shell indexed by icrsh.
# Projectors are either identity matrix blocks to use with Wannier basis
# OR correspond to the overlap between Kohn-Sham and Wannier orbitals as
# P_{nu,alpha](k) = <w_{alpha,k}|psi_{nu,k}>
# NOTE: we assume that the correlated orbitals appear at the beginning of the H(R)
# file and that the ordering of MLWFs matches the corr_shell info from
# the input.
for 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
for isp in range(n_spin_blocs):
# now combine udismat and umat
u_total = numpy.einsum('abc,acd->abd',udismat_full[isp],umat_full[isp])
# transpose and write into proj_mat
u_temp = numpy.transpose(u_total.conj(),(0,2,1))
for icrsh in range(n_corr_shells):
dim = corr_shells[icrsh]['dim']
proj_mat[:, isp, icrsh, 0:dim, :] = u_temp[:,iorb:iorb+dim,:]
iorb += dim
# Then, compute the hoppings in reciprocal space
hopping = numpy.zeros([self.n_k, n_spin_blocs, numpy.max(n_orbitals), numpy.max(n_orbitals)], dtype=complex)
for isp in range(n_spin_blocs):
# if bloch_basis is True, use Kohn-Sham eigenvalues as hamk
# this ensures that the calculation of the band-correlation energy
# is consistent with SumkDFT's calc_density_correction
if self.bloch_basis:
# diagonal Kohn-Sham bands
# TODO: test for system with self.nwfs > dim_corr_shells
hamk = [numpy.diag(bandmat_full[isp][ik]) for ik in range(self.n_k)]
# Sanity check if the local Hamiltonian with the projector method
# corresponds to W90 result
wannier_ham = self.fourier_ham(hamr_full[isp])
for ik in range(self.n_k):
proj_mat_flattened = proj_mat[ik, isp].reshape(self.nwfs, numpy.max(n_orbitals))
downfolded_ham = proj_mat_flattened.dot(hamk[ik].dot(proj_mat_flattened.conj().T))
if not numpy.allclose(downfolded_ham, wannier_ham[ik], atol=self._w90zero, rtol=0):
mpi.report('WARNING: mismatch between downfolded Hamiltonian and '
+ f'Fourier transformed H(R). First occurred at kpt {ik}:')
with numpy.printoptions(formatter={'complexfloat': '{:+.3f}'.format}):
mpi.report('Downfolded Hamiltonian, P H_eig P')
mpi.report(downfolded_ham)
mpi.report('\nWannier Hamiltonian, Fourier(H(r))')
mpi.report(wannier_ham[ik])
break
# else for an isolated set of bands use fourier transform of H(R)
else:
# make Fourier transform H(R) -> H(k) : it can be done one spin at a time
hamk = self.fourier_ham(hamr_full[isp])
# finally write hamk into hoppings
for ik in range(self.n_k):
hopping[ik, isp] = hamk[ik] - numpy.identity(numpy.max(n_orbitals)) * self.fermi_energy
hopping *= energy_unit
mpi.report("Subtracting {:.5f} eV from the Fermi level.".format(self.fermi_energy))
# bz_weights required by triqs h5 standard but soon to be replaced by kpt_weights
bz_weights = kpt_weights
# Finally, save all required data into the HDF archive:
# use_rotations is supposed to be an int = 0, 1, no bool
@ -367,18 +458,25 @@ class Wannier90Converter(ConverterTools):
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']
'n_inequiv_shells', 'corr_to_inequiv', 'inequiv_to_corr', 'kpt_weights', 'kpts']
for it in things_to_save:
ar[self.dft_subgrp][it] = locals()[it]
def read_wannier90hr(self, hr_filename="wannier_hr.dat"):
if self.bloch_basis:
# Store Fermi weights to 'dft_misc_input'
if not (self.misc_subgrp in ar):
ar.create_group(self.misc_subgrp)
ar[self.misc_subgrp]['dft_fermi_weights'] = f_weights
ar[self.misc_subgrp]['band_window'] = band_window+1 # Change to 1-based index
def read_wannier90data(self, wannier_seed="wannier", kmesh_mode=0):
"""
Method for reading the seedname_hr.dat file produced by Wannier90 (http://wannier.org)
Parameters
----------
hr_filename : string
full name of the H(R) file produced by Wannier90 (usually seedname_hr.dat)
wannier_seed : string
seedname to read H(R) file produced by Wannier90 (usually seedname_hr.dat)
Returns
-------
@ -392,21 +490,30 @@ class Wannier90Converter(ConverterTools):
number of Wannier functions found
h_of_r : list of numpy.array
<w_i|H(R)|w_j> = Hamilonian matrix elements in the Wannier basis
u_mat : numpy.array
U_mn^k = unitary matrix elements which mix the Kohn-Sham states
udis_mat : numpy.array
U^dis(k) = rectangular matrix for entangled bands
band_mat : numpy.array
\epsilon_nk = Kohn-Sham eigenvalues (in eV) needed for entangled bands
If not self.bloch_basis unused and therefore None
k_mesh : numpy.array
The k mesh read from the seedname_u.mat file to ensure consistency
If not self.bloch_basis unused and therefore None
"""
# Read only from the master node
if not (mpi.is_master_node()):
return
hr_filename = wannier_seed + '_hr.dat'
try:
with open(hr_filename, "r") as hr_filedesc:
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])
mpi.report('reading {:20}...{}'.format(hr_filename,hr_data[0].strip('\n')))
try:
# reads number of Wannier functions per spin
@ -415,11 +522,79 @@ class Wannier90Converter(ConverterTools):
except ValueError:
mpi.report("Could not read number of WFs or R vectors")
k_mesh = None
if not self.bloch_basis:
# For kmesh_mode == -1, size of automatic k mesh known when rvec_idx
# have been read. For kmesh_mode >= 0, kpts have been determined already
if kmesh_mode >= 0:
n_k = self.n_k
else:
# first, read u matrices from 'seedname_u.mat'
u_filename = wannier_seed + '_u.mat'
with open(u_filename,'r') as u_file:
u_data = u_file.readlines()
# reads number of kpoints and number of wannier functions
n_k, num_wf_u, _ = map(int, u_data[1].split())
assert num_wf_u == num_wf, '#WFs must be identical for *_u.mat and *_hr.dat'
mpi.report('reading {:20}...{}'.format(u_filename,u_data[0].strip('\n')))
# Reads k mesh from all lines with 3 floats
k_mesh = numpy.loadtxt((line for line in u_data if line.count('.') == 3))
assert k_mesh.shape == (n_k, 3)
# Reads u matrices from all lines with 2 floats
u_mat = numpy.loadtxt((line for line in u_data if line.count('.') == 2))
assert u_mat.shape == (n_k*num_wf*num_wf, 2)
mpi.report('Writing h5 archive in projector formalism: H(k) defined in KS Bloch basis')
try:
# read 'seedname_u_dis.mat'
udis_filename = wannier_seed + '_u_dis.mat'
# if it exists the Kohn-Sham eigenvalues and the window are needed
band_filename = wannier_seed + '.eig'
wout_filename = wannier_seed + '.wout'
with open(udis_filename,'r') as udis_file:
udis_data = udis_file.readlines()
disentangle = True
except IOError:
disentangle = False
mpi.report('WARNING: File {} missing.'.format(udis_filename))
mpi.report('Assuming an isolated set of bands. Check if this is what you want!')
# read Kohn-Sham eigenvalues from 'seedname.eig'
mpi.report('Reading {}'.format(band_filename))
band_data = numpy.loadtxt(band_filename, usecols=2)
if disentangle:
# reads number of kpoints, number of wannier functions and bands
num_k_udis, num_wf_udis, num_ks_bands = map(int, udis_data[1].split())
assert num_k_udis == n_k, '#k points must be identical for *.inp and *_u_dis.mat'
assert num_wf_udis == num_wf, '#WFs must be identical for *_u.mat and *_hr.dat'
mpi.report('Found {:22}...{}, '.format(udis_filename,udis_data[0].strip('\n')))
udis_data = numpy.loadtxt(udis_data, usecols=(0, 1), skiprows=2)
# read disentanglement window from 'seedname.wout'
with open(wout_filename) as wout_file:
for line in wout_file:
if 'Outer:' in line:
content = line.split()
index = content.index('Outer:') + 1
dis_window_min = float(content[index])
dis_window_max = float(content[index+2])
break
mpi.report('and {} for disentanglement energy window.'.format(wout_filename))
else:
num_ks_bands = num_wf
# allocate arrays to save the R vector indexes and degeneracies and the
# Hamiltonian
rvec_idx = numpy.zeros((nrpt, 3), dtype=int)
rvec_deg = numpy.zeros(nrpt, dtype=int)
h_of_r = [numpy.zeros((num_wf, num_wf), dtype=numpy.complex_)
h_of_r = [numpy.zeros((num_wf, num_wf), dtype=complex)
for n in range(nrpt)]
# variable currpos points to the current line in the file
@ -437,7 +612,7 @@ class Wannier90Converter(ConverterTools):
ir += 1
# for each direct lattice vector R read the block of the
# Hamiltonian H(R)
for ir, jj, ii in product(list(range(nrpt)), list(range(num_wf)), list(range(num_wf))):
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()
@ -445,8 +620,7 @@ class Wannier90Converter(ConverterTools):
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])])
rcurr = numpy.array([int(cline[0]), int(cline[1]), int(cline[2])])
if ii == 0 and jj == 0:
rvec_idx[ir] = rcurr
rprec = rcurr
@ -457,16 +631,57 @@ class Wannier90Converter(ConverterTools):
"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]))
h_of_r[ir][ii, jj] = complex(float(cline[5]), float(cline[6]))
except ValueError:
mpi.report("Wrong data or structure in file %s" % hr_filename)
# first, get the input for u_mat
if self.bloch_basis:
u_mat = u_mat[:, 0] + 1j * u_mat[:, 1]
u_mat = u_mat.reshape((n_k, num_wf, num_wf)).transpose((0, 2, 1))
else:
if kmesh_mode == -1:
n_k = numpy.prod([2 * rvec_idx[:, idir].max() + 1 for idir in range(3)])
# Wannier basis; fill u_mat with identity
u_mat = numpy.zeros([n_k, num_wf, num_wf], dtype=complex)
for ik in range(n_k):
u_mat[ik,:,:] = numpy.identity(num_wf,dtype=complex)
# now, check what is needed in the case of disentanglement:
# The file seedname_u_dis.mat contains only the bands inside the window
# and then fills the rest up with zeros. Therefore, we need to put the
# entries from udis_data in the correct position in udis_mat, i.e.
# shifting by the number of bands below dis_window_min
if self.bloch_basis:
# reshape band_data
band_mat = band_data.reshape(n_k, num_ks_bands)
else:
# Not used in wannier basis
band_mat = None
if self.bloch_basis and disentangle:
# Determine which bands are inside the band window
inside_window = numpy.logical_and(band_mat >= dis_window_min,
band_mat <= dis_window_max)
n_inside_per_k = numpy.sum(inside_window, axis=1)
# Reformats udis_data as complex, without header
udis_data = udis_data[:, 0] + 1j * udis_data[:, 1]
udis_data = udis_data.reshape((n_k, num_wf*num_ks_bands+1))[:, 1:]
udis_data = udis_data.reshape((n_k, num_wf, num_ks_bands))
#initiate U disentanglement matrices and fill from file "seedname_u_dis.mat"
udis_mat = numpy.zeros([n_k, num_ks_bands, num_wf], dtype=complex)
for ik in range(n_k):
udis_mat[ik, inside_window[ik]] = udis_data[ik, :, :n_inside_per_k[ik]].T
assert numpy.allclose(udis_data[ik, :, n_inside_per_k[ik]:], 0)
else:
# no disentanglement; fill udis_mat with identity
udis_mat = numpy.array([numpy.identity(num_wf,dtype=complex)] * n_k)
# return the data into variables
return nrpt, rvec_idx, rvec_deg, num_wf, h_of_r
return nrpt, rvec_idx, rvec_deg, num_wf, h_of_r, u_mat, udis_mat, band_mat, k_mesh
def find_rot_mat(self, n_sh, sh_lst, sh_map, ham0):
"""
@ -588,7 +803,7 @@ class Wannier90Converter(ConverterTools):
return succeeded, rot_mat
def kmesh_build(self, msize=None, mmode=0):
def kmesh_build(self, msize, mmode=0):
"""
Method for the generation of the k-point mesh.
Right now it only supports the option for generating a full grid containing k=0,0,0.
@ -604,7 +819,7 @@ class Wannier90Converter(ConverterTools):
-------
nkpt : integer
total number of k-points in the mesh
k_mesh : numpy.array[nkpt,3] of floats
kpts : numpy.array[nkpt,3] of floats
the coordinates of all k-points
wk : numpy.array[nkpt] of floats
the weight of each k-point
@ -614,30 +829,26 @@ class Wannier90Converter(ConverterTools):
if mmode != 0:
raise ValueError("Mesh generation mode not supported: %s" % mmode)
assert len(msize) == 3
# a regular mesh including Gamma point
# total number of k-points
nkpt = msize[0] * msize[1] * msize[2]
kmesh = numpy.zeros((nkpt, 3), dtype=float)
ii = 0
for ix, iy, iz in product(list(range(msize[0])), list(range(msize[1])), list(range(msize[2]))):
kmesh[ii, :] = [float(ix) / msize[0], float(iy) /
msize[1], float(iz) / msize[2]]
ii += 1
nkpt = numpy.prod(msize)
kpts = numpy.array(list(product(range(msize[0]), range(msize[1]), range(msize[2]))))
kpts = kpts / numpy.array(msize)
# weight is equal for all k-points because wannier90 uses uniform grid on whole BZ
# (normalization is always 1 and takes into account spin degeneracy)
wk = numpy.ones([nkpt], dtype=float) / float(nkpt)
wk = numpy.full(nkpt, 1/nkpt)
return nkpt, kmesh, wk
return nkpt, kpts, wk
def fourier_ham(self, norb, h_of_r):
def fourier_ham(self, h_of_r):
"""
Method for obtaining H(k) from H(R) via Fourier transform
The R vectors and k-point mesh are read from global module variables
Parameters
----------
norb : integer
number of orbitals
h_of_r : list of numpy.array[norb,norb]
Hamiltonian H(R) in Wannier basis
@ -648,14 +859,122 @@ class Wannier90Converter(ConverterTools):
"""
twopi = 2 * numpy.pi
h_of_k = [numpy.zeros((norb, norb), dtype=numpy.complex_)
h_of_k = [numpy.zeros((self.nwfs, self.nwfs), dtype=complex)
for ik in range(self.n_k)]
ridx = numpy.array(list(range(self.nrpt)))
for ik, ir in product(list(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])
rdotk = numpy.dot(self.kpts[ik], self.rvec[ir])
factor = numpy.exp(2j * numpy.pi * rdotk) / self.rdeg[ir]
h_of_k[ik][:, :] += factor * h_of_r[ir][:, :]
return h_of_k
def convert_misc_input(self, out_filename, nnkp_filename, n_spin_blocs):
"""
Reads input from DFT code calculations to get occupations
Parameters
----------
out_filename : string
filename of DFT output file containing occupation data
nnkp_file : string
filename of Wannier postproc_setup run
n_spin_blocs : int
SP + 1 - SO
Returns
-------
fermi_weights : numpy.array[self.n_k, n_spin_blocs ,n_orbitals]
occupations from DFT calculation
band_window : numpy.array[self.n_k, n_spin_blocs ,n_orbitals]
band indices of correlated subspace
fermi_energy : float
the DFT Kohn-Sham Fermi energy
"""
# Read only from the master node
if not (mpi.is_master_node()):
return
assert n_spin_blocs == 1, 'spin-polarized not implemented'
assert 'nscf.out' in out_filename or out_filename == 'LOCPROJ'
occupations = []
if 'nscf.out' in out_filename:
occupations = []
with open(out_filename,'r') as out_file:
out_data = out_file.readlines()
# Reads number of Kohn-Sham states and total number of electrons
for line in out_data:
if 'number of electrons' in line:
n_total_electrons = float(line.split()[-1])
if 'number of Kohn-Sham states' in line:
n_ks = int(line.split()[-1])
if 'Fermi energy' in line:
fermi_energy = float(line.split()[-2])
# get occupations
for ct, line in enumerate(out_data):
if line.strip() == 'End of band structure calculation':
break
assert 'k = ' in out_data[ct + 2], 'Cannot read occupations. Set verbosity = "high" in {}'.format(out_filename)
out_data = out_data[ct+2:]
# block size of eigenvalues + occupations per k-point
n_block = int(2*numpy.ceil(n_ks/8)+5)
for ik in range(self.n_k):
# get data
k_block = [line.split() for line in out_data[ik*n_block+2:ik*n_block+n_block-1]]
# second half corresponds to occupations
occs = k_block[int(len(k_block)/2)+1:]
flattened_occs = [float(item) for sublist in occs for item in sublist]
occupations.append(flattened_occs)
else:
# Reads LOCPROJ
with open(out_filename, 'r') as file:
header = file.readline()
n_ks = int(header.split()[2])
fermi_energy = float(header.split()[4])
occupations = numpy.loadtxt((line for line in file if 'orbital' in line), usecols=5)
occupations = occupations.reshape((self.n_k, n_ks))
# assume that all bands contribute, then remove from exclude_bands; python indexing
corr_bands = list(range(n_ks))
# read exclude_bands from "seedname.nnkp" file
with open(nnkp_filename, 'r') as nnkp_file:
read = False
skip = False
for line in nnkp_file:
if line.strip() == 'begin exclude_bands':
read = True
# skip one more line that contains total number of excluded bands
skip = True
continue
elif line.strip() == 'end exclude_bands':
read = False
continue
elif skip:
skip = False
continue
elif read:
# wannier index -1
corr_bands.remove(int(line)-1)
# For now, it is only supported to exclude the lowest and highest bands
# If bands in the middle are supposed to be excluded, this doesn't work with the band_window
# We'd need to manually add rows of zeros to the projectors
if numpy.any(numpy.diff(corr_bands) != 1):
raise NotImplementedError('Can only exclude the lowest or highest bands')
band_window = numpy.array([[(min(corr_bands), max(corr_bands))]*self.n_k]*n_spin_blocs)
included_occupations = numpy.array(occupations)[:, corr_bands]
# Adds spin dimension without changing the array
f_weights = included_occupations.reshape(included_occupations.shape[0], 1,
included_occupations.shape[1])
return f_weights, band_window, fermi_energy

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