Source code for dft.converters.hk_converter
##########################################################################
#
# TRIQS: a Toolbox for Research in Interacting Quantum Systems
#
# Copyright (C) 2011 by M. Aichhorn
#
# 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 types import *
import numpy
from pytriqs.archive import *
import pytriqs.utility.mpi as mpi
from math import sqrt
from converter_tools import *
[docs]class HkConverter(ConverterTools):
"""
Conversion from general H(k) file to an hdf5 file that can be used as input for the SumKDFT class.
"""
[docs] def __init__(self, filename, hdf_filename=None, dft_subgrp='dft_input', symmcorr_subgrp='dft_symmcorr_input', repacking=False):
"""
Initialise the class.
Parameters
----------
filename : string
Name of file containing the H(k) and other relevant data.
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.
The group is actually empty; it is just included for compatibility.
repacking : boolean, optional
Does the hdf5 archive need to be repacked to save space?
"""
assert type(
filename) == StringType, "HkConverter: filename must be a filename."
if hdf_filename is None:
hdf_filename = filename + '.h5'
self.hdf_file = hdf_filename
self.dft_file = filename
self.dft_subgrp = dft_subgrp
self.symmcorr_subgrp = symmcorr_subgrp
self.fortran_to_replace = {'D': 'E', '(': ' ', ')': ' ', ',': ' '}
# Checks if h5 file is there and repacks it if wanted:
import os.path
if (os.path.exists(self.hdf_file) and repacking):
ConverterTools.repack(self)
[docs] def convert_dft_input(self, first_real_part_matrix=True, only_upper_triangle=False, weights_in_file=False):
"""
Reads the appropriate files and stores the data for the dft_subgrp in the hdf5 archive.
Parameters
----------
first_real_part_matrix : boolean, optional
Should all the real components for given k be read in first, followed by the imaginary parts?
only_upper_triangle : boolean, optional
Should only the upper triangular part of H(k) be read in?
weights_in_file : boolean, optional
Are the k-point weights to be read in?
"""
# Read and write only on the master node
if not (mpi.is_master_node()):
return
mpi.report("Reading input from %s..." % self.dft_file)
# R is a generator : each R.Next() will return the next number in the
# file
R = ConverterTools.read_fortran_file(
self, self.dft_file, self.fortran_to_replace)
try:
# the energy conversion factor is 1.0, we assume eV in files
energy_unit = 1.0
# read the number of k points
n_k = int(R.next())
k_dep_projection = 0
SP = 0 # no spin-polarision
SO = 0 # no spin-orbit
# total charge below energy window is set to 0
charge_below = 0.0
# density required, for setting the chemical potential
density_required = R.next()
symm_op = 0 # No symmetry groups for the k-sum
# the information on the non-correlated shells is needed for
# defining dimension of matrices:
# number of shells considered in the Wanniers
n_shells = int(R.next())
# corresponds to index R in formulas
# now read the information about the shells (atom, sort, l, dim):
shell_entries = ['atom', 'sort', 'l', 'dim']
shells = [{name: int(val) for name, val in zip(
shell_entries, R)} for ish in range(n_shells)]
# number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
n_corr_shells = int(R.next())
# corresponds to index R in formulas
# now read the information about the shells (atom, sort, l, dim, SO
# flag, irep):
corr_shell_entries = ['atom', 'sort', 'l', 'dim','SO','irep']
corr_shells = [{name: int(val) for name, val in zip(
corr_shell_entries, R)} for icrsh in range(n_corr_shells)]
# determine the number of inequivalent correlated shells and maps,
# needed for further reading
[n_inequiv_shells, corr_to_inequiv,
inequiv_to_corr] = ConverterTools.det_shell_equivalence(self, corr_shells)
use_rotations = 0
rot_mat = [numpy.identity(
corr_shells[icrsh]['dim'], numpy.complex_) for icrsh in range(n_corr_shells)]
rot_mat_time_inv = [0 for i in range(n_corr_shells)]
# Representative representations are read from file
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):
# number of representatives ("subsets"), e.g. t2g and eg
n_reps[ish] = int(R.next())
dim_reps[ish] = [int(R.next()) for i in range(
n_reps[ish])] # dimensions of the subsets
# The transformation matrix:
# is of dimension 2l+1, it is taken to be standard d (as in
# Wien2k)
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[ish] = numpy.array([[0.0, 0.0, 1.0, 0.0, 0.0],
[1.0 / sqrt(2.0), 0.0, 0.0,
0.0, 1.0 / sqrt(2.0)],
[-1.0 / sqrt(2.0), 0.0, 0.0,
0.0, 1.0 / sqrt(2.0)],
[0.0, 1.0 /
sqrt(2.0), 0.0, -1.0 / sqrt(2.0), 0.0],
[0.0, 1.0 / sqrt(2.0), 0.0, 1.0 / sqrt(2.0), 0.0]])
# Spin blocks to be read:
# number of spins to read for Norbs and Ham, NOT Projectors
n_spin_blocs = SP + 1 - SO
# define the number of n_orbitals for all k points: it is the
# number of total bands and independent of k!
n_orbitals = numpy.ones(
[n_k, n_spin_blocs], numpy.int) * sum([sh['dim'] for sh in shells])
# Initialise the projectors:
proj_mat = numpy.zeros([n_k, n_spin_blocs, n_corr_shells, max(
[crsh['dim'] for crsh in corr_shells]), max(n_orbitals)], numpy.complex_)
# Read the projectors from the file:
for ik in range(n_k):
for icrsh in range(n_corr_shells):
for isp in range(n_spin_blocs):
# calculate the offset:
offset = 0
n_orb = 0
for ish in range(n_shells):
if (n_orb == 0):
if (shells[ish]['atom'] == corr_shells[icrsh]['atom']) and (shells[ish]['sort'] == corr_shells[icrsh]['sort']):
n_orb = corr_shells[icrsh]['dim']
else:
offset += shells[ish]['dim']
proj_mat[ik, isp, icrsh, 0:n_orb,
offset:offset + n_orb] = numpy.identity(n_orb)
# now define the arrays for weights and hopping ...
# w(k_index), default normalisation
bz_weights = numpy.ones([n_k], numpy.float_) / float(n_k)
hopping = numpy.zeros([n_k, n_spin_blocs, max(
n_orbitals), max(n_orbitals)], numpy.complex_)
if (weights_in_file):
# weights in the file
for ik in range(n_k):
bz_weights[ik] = R.next()
# if the sum over spins is in the weights, take it out again!!
sm = sum(bz_weights)
bz_weights[:] /= sm
# Grab the H
for isp in range(n_spin_blocs):
for ik in range(n_k):
n_orb = n_orbitals[ik, isp]
# first read all real components for given k, then read
# imaginary parts
if (first_real_part_matrix):
for i in range(n_orb):
if (only_upper_triangle):
istart = i
else:
istart = 0
for j in range(istart, n_orb):
hopping[ik, isp, i, j] = R.next()
for i in range(n_orb):
if (only_upper_triangle):
istart = i
else:
istart = 0
for j in range(istart, n_orb):
hopping[ik, isp, i, j] += R.next() * 1j
if ((only_upper_triangle)and(i != j)):
hopping[ik, isp, j, i] = hopping[
ik, isp, i, j].conjugate()
else: # read (real,im) tuple
for i in range(n_orb):
if (only_upper_triangle):
istart = i
else:
istart = 0
for j in range(istart, n_orb):
hopping[ik, isp, i, j] = R.next()
hopping[ik, isp, i, j] += R.next() * 1j
if ((only_upper_triangle)and(i != j)):
hopping[ik, isp, j, i] = hopping[
ik, isp, i, j].conjugate()
# keep some things that we need for reading parproj:
things_to_set = ['n_shells', 'shells', 'n_corr_shells', 'corr_shells',
'n_spin_blocs', 'n_orbitals', 'n_k', 'SO', 'SP', 'energy_unit']
for it in things_to_set:
setattr(self, it, locals()[it])
except StopIteration: # a more explicit error if the file is corrupted.
raise "HK Converter : reading file dft_file failed!"
R.close()
# Save to the HDF5:
ar = HDFArchive(self.hdf_file, 'a')
if not (self.dft_subgrp in ar):
ar.create_group(self.dft_subgrp)
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