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[wannier90] First commit with working version

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Implements the Wannier90 converter, provides related docs and a test
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Gabriele Sclauzero 2016-02-03 16:49:40 +01:00 committed by gsclauzero
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doc/guide/conversion.rst
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For more options of this converter, have a look at the For more options of this converter, have a look at the
:ref:`refconverters` section of the reference manual. :ref:`refconverters` section of the reference manual.
Wannier90 Converter
-------------------
Using this converter it is possible to convert the output of
:program:`Wannier90` (http://wannier.org) calculations of
Maximally Localized Wannier Functions (MLWF) and create a HDF5 archive
suitable for one-shot DMFT calculations with the
:class:`SumkDFT <pytriqs.applications.dft.sumk_dft.SumkDFT>` class.
The user must supply two files in order to run the Wannier90 Converter:
#. The file :file:`seedname_hr.dat`, which contains the DFT Hamiltonian
in the MLWF basis calculated through :program:`wannier90` with ``hr_plot = true``
(please refer to the :program:`wannier90` documentation).
#. A file named :file:`seedname.inp`, which contains the required
information about the :math:`\mathbf{k}`-point mesh, the electron density,
the correlated shell structure, ... (see below).
Here and in the following, the keyword ``seedname`` should always be intended
as a placeholder for the actual prefix chosen by the user when creating the
input for :program:`wannier90`.
Once these two files are available, one can use the converter as follows::
from pytriqs.applications.dft.converters import Wannier90Converter
Converter = Wannier90Converter(seedname='seedname')
Converter.convert_dft_input()
The converter input :file:`seedname.inp` is a simple text file with
the following format:
.. literalinclude:: images_scripts/LaVO3_w90.inp
The example shows the input for the perovskite crystal of LaVO\ :sub:`3`
in the room-temperature `Pnma` symmetry. The unit cell contains four
symmetry-equivalent correlated sites (the V atoms) and the total number
of electrons per unit cell is 8 (see second line).
The first line specifies how to generate the :math:`\mathbf{k}`-point
mesh that will be used to obtain :math:`H(\mathbf{k})`
by Fourier transforming :math:`H(\mathbf{R})`.
Currently implemented options are:
* :math:`\Gamma`-centered uniform grid with dimensions
:math:`n_{k_x} \times n_{k_y} \times n_{k_z}`;
specify ``0`` followed by the three grid dimensions,
like in the example above
* :math:`\Gamma`-centered uniform grid with dimensions
automatically determined by the converter (from the number of
:math:`\mathbf{R}` vectors found in :file:`seedname_hr.dat`);
just specify ``-1``
Inside :file:`seedname.inp`, it is crucial to correctly specify the
correlated shell structure, which depends on the contents of the
:program:`wannier90` output :file:`seedname_hr.dat` and on the order
of the MLWFs contained in it.
The number of MLWFs must be equal to, or greater than the total number
of correlated orbitals (i.e., the sum of all ``dim`` in :file:`seedname.inp`).
If the converter finds fewer MLWFs inside :file:`seedname_hr.dat`, then it
stops with an error; if it finds more MLWFs, then it assumes that the
additional MLWFs correspond to uncorrelated orbitals (e.g., the O-\ `2p` shells).
When reading the hoppings :math:`\langle w_i | H(\mathbf{R}) | w_j \rangle`
(where :math:`w_i` is the :math:`i`-th MLWF), the converter also assumes that
the first indices correspond to the correlated shells (in our example,
the V-t\ :sub:`2g` shells). Therefore, the MLWFs corresponding to the
uncorrelated shells (if present) must be listed **after** those of the
correlated shells.
With the :program:`wannier90` code, this can be achieved this by listing the
projections for the uncorrelated shells after those for the correlated shells.
In our `Pnma`-LaVO\ :sub:`3` example, for instance, we could use::
Begin Projections
V:l=2,mr=2,3,5:z=0,0,1:x=-1,1,0
O:l=1:mr=1,2,3:z=0,0,1:x=-1,1,0
End Projections
where the ``x=-1,1,0`` option indicates that the V--O bonds in the octahedra are
rotated by (approximatively) 45 degrees with respect to the axes of the `Pbnm` cell.
The converter will analyse the matrix elements of the local hamiltonian
to find the symmetry matrices `rot_mat` needed for the global-to-local
transformation of the basis set for correlated orbitals
(see section :ref:`hdfstructure`).
The matrices are obtained by finding the unitary transformations that diagonalize
:math:`\langle w_i | H_I(\mathbf{R}=0,0,0) | w_j \rangle`, where :math:`I` runs
over the correlated shells and `i,j` belong to the same shell (more details elsewhere...).
If two correlated shells are defined as equivalent in :file:`seedname.inp`,
then the corresponding eigenvalues have to match within a threshold of 10\ :sup:`-5`,
otherwise the converter will produce an error/warning.
If this happens, please carefully check your data in :file:`seedname_hr.dat`.
This method might fail in non-trivial cases (i.e., more than one correlated
shell is present) when there are some degenerate eigenvalues:
so far tests have not shown any issue, but one must be careful in those cases
(the converter will print a warning message).
The current implementation of the Wannier90 Converter has some limitations:
* Since :program:`wannier90` does not make use of symmetries (symmetry-reduction
of the :math:`\mathbf{k}`-point grid is not possible), the converter always
sets ``symm_op=0`` (see the :ref:`hdfstructure` section).
* No charge self-consistency possible at the moment.
* Calculations with spin-orbit (``SO=1``) are not supported.
* The spin-polarized case (``SP=1``) is not yet tested.
* The post-processing routines in the module
:class:`SumkDFTTools <pytriqs.applications.dft.sumk_dft_tools.SumkDFTTools>`
were not tested with this converter.
* ``proj_mat_all`` are not used, so there are no projectors onto the
uncorrelated orbitals for now.
MPI issues MPI issues
---------- ----------

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0 6 4 6
8.0
4
0 0 2 3 0 0
1 0 2 3 0 0
2 0 2 3 0 0
3 0 2 3 0 0

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@ -5,19 +5,25 @@ Converters
Wien2k Converter Wien2k Converter
---------------- ----------------
.. autoclass:: converters.wien2k_converter.Wien2kConverter .. autoclass:: dft.converters.wien2k_converter.Wien2kConverter
:members: :members:
:special-members: :special-members:
:show-inheritance: :show-inheritance:
H(k) Converter H(k) Converter
-------------- --------------
.. autoclass:: converters.hk_converter.HkConverter .. autoclass:: dft.converters.hk_converter.HkConverter
:members:
:special-members:
Wannier90 Converter
--------------
.. autoclass:: dft.converters.wannier90_converter.Wannier90Converter
:members: :members:
:special-members: :special-members:
Converter Tools Converter Tools
--------------- ---------------
.. autoclass:: converters.converter_tools.ConverterTools .. autoclass:: dft.converters.converter_tools.ConverterTools
:members: :members:
:special-members: :special-members:

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from wien2k_converter import Wien2kConverter from wien2k_converter import Wien2kConverter
from hk_converter import HkConverter from hk_converter import HkConverter
from wannier90_converter import Wannier90Converter
__all__ =['Wien2kConverter','HkConverter'] __all__ =['Wien2kConverter','HkConverter','Wannier90Converter']

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################################################################################
#
# 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 <http://www.gnu.org/licenses/>.
#
################################################################################
###
# 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 *
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:
kmesh_mode = int(R.next()) # read k-point mesh generation option
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]
density_required = float(R.next()) # read the total number of electrons per cell
# 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)
n_corr_shells = int(R.next()) # number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
# 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) ]
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
symm_op = 0 # Wannier90 does not use symmetries to reduce the k-points
# the following might change in future versions
### 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.fourierham(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:
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']
for it in things_to_save: ar[self.dft_subgrp][it] = locals()[it]
del ar
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
<w_i|H(R)|w_j> = 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:
num_wf = int(hr_data[1]) # reads number of Wannier functions per spin
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
for ir in range(nrpt):
# read the block of the Hamiltonian H(R)
for jj in range(num_wf):
for ii in range(num_wf):
# advance one line, split the line into tokens
currpos += 1
cline = hr_data[currpos].split()
# check if the orbital indexes in the file make sense
if int(cline[3]) != ii+1 or int(cline[4]) != jj+1:
mpi.report("Inconsistent indices at %s%s of R n. %s"%(ii,jj,ir))
rcurr = numpy.array([int(cline[0]), int(cline[1]), int(cline[2])])
if ii == 0 and jj == 0:
rvec_idx[ir] = rcurr
rprec = rcurr
else:
# check if the vector indices are consistent
if not numpy.array_equal(rcurr, rprec):
mpi.report("Inconsistent indices for R vector n. %s"%ir)
# fill h_of_r with the matrix elements of the Hamiltonian
h_of_r[ir][ii, jj] = complex(float(cline[5]), float(cline[6]))
except ValueError:
mpi.report("Wrong data or structure in file %s"%hr_filename)
# 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:
# a regular mesh including Gamma point
nkpt = msize[0] * msize[1] * msize[2] # total number of k-points
kmesh = numpy.zeros((nkpt, 3), dtype=float)
ii = 0
for ix in range(msize[0]):
for iy in range(msize[1]):
for iz in 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)
else:
raise ValueError("Mesh generation mode not supported: %s"%mmode)
return nkpt, kmesh, wk
def fourierham(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
"""
imag = 1j
twopi = 2 * numpy.pi
h_of_k = [numpy.zeros((norb, norb), dtype=numpy.complex_) for ik in range(self.n_k)]
for ik in range(self.n_k):
ridx = numpy.array(range(self.nrpt))
for ir in ridx:
rdotk = twopi * numpy.dot(self.k_mesh[ik], self.rvec[ir])
factor = (math.cos(rdotk) + imag * math.sin(rdotk)) / float(self.rdeg[ir])
h_of_k[ik][:, :] += factor * h_of_r[ir][:,:]
return h_of_k

3
test/CMakeLists.txt
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@ -5,10 +5,11 @@ find_package(TriqsTest)
FILE(GLOB all_h5_files RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.h5) FILE(GLOB all_h5_files RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.h5)
file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/${all_h5_files} DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/${all_h5_files} DESTINATION ${CMAKE_CURRENT_BINARY_DIR})
# Copy other files # Copy other files
FILE(COPY SrVO3.pmat SrVO3.struct SrVO3.outputs SrVO3.oubwin SrVO3.ctqmcout SrVO3.symqmc SrVO3.sympar SrVO3.parproj hk_convert_hamiltonian.hk DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) FILE(COPY SrVO3.pmat SrVO3.struct SrVO3.outputs SrVO3.oubwin SrVO3.ctqmcout SrVO3.symqmc SrVO3.sympar SrVO3.parproj hk_convert_hamiltonian.hk LaVO3-Pnma_hr.dat LaVO3-Pnma.inp DESTINATION ${CMAKE_CURRENT_BINARY_DIR})
triqs_add_python_test(wien2k_convert) triqs_add_python_test(wien2k_convert)
triqs_add_python_test(hk_convert) triqs_add_python_test(hk_convert)
triqs_add_python_test(w90_convert)
triqs_add_python_test(sumkdft_basic) triqs_add_python_test(sumkdft_basic)
triqs_add_python_test(srvo3_Gloc) triqs_add_python_test(srvo3_Gloc)
triqs_add_python_test(srvo3_transp) triqs_add_python_test(srvo3_transp)

7
test/LaVO3-Pnma.inp Normal file
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@ -0,0 +1,7 @@
0 3 2 3
8.0
4
0 0 2 3 0 0
1 0 2 3 0 0
2 0 2 3 0 0
3 0 2 3 0 0

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34
test/w90_convert.py Normal file
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@ -0,0 +1,34 @@
################################################################################
#
# 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 <http://www.gnu.org/licenses/>.
#
################################################################################
from pytriqs.applications.dft.converters import *
from pytriqs.archive import *
from pytriqs.utility.h5diff import h5diff
import pytriqs.utility.mpi as mpi
Converter = Wannier90Converter(seedname='LaVO3-Pnma',hdf_filename='w90_convert.out.h5')
Converter.convert_dft_input()
if mpi.is_master_node():
h5diff("w90_convert.out.h5","w90_convert.ref.h5")

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