mirror of https://github.com/triqs/dft_tools
272 lines
12 KiB
Python
272 lines
12 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) 2011 by 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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from types import *
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import numpy
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from pytriqs.archive import *
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import pytriqs.utility.mpi as mpi
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from math import sqrt
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from converter_tools import *
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class HkConverter(ConverterTools):
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"""
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Conversion from general H(k) file 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, dft_subgrp='dft_input', symmcorr_subgrp='dft_symmcorr_input', 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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Name of file containing the H(k) and other relevant data.
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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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The group is actually empty; it is just included for compatibility.
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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 type(
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filename) == StringType, "HkConverter: filename must be a filename."
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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 = filename
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self.dft_subgrp = dft_subgrp
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self.symmcorr_subgrp = symmcorr_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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import os.path
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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 convert_dft_input(self, first_real_part_matrix=True, only_upper_triangle=False, weights_in_file=False):
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"""
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Reads the appropriate files and stores the data for the dft_subgrp in the hdf5 archive.
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Parameters
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----------
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first_real_part_matrix : boolean, optional
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Should all the real components for given k be read in first, followed by the imaginary parts?
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only_upper_triangle : boolean, optional
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Should only the upper triangular part of H(k) be read in?
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weights_in_file : boolean, optional
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Are the k-point weights to be read in?
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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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mpi.report("Reading input from %s..." % self.dft_file)
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# R is a generator : each R.Next() will return the next number in the
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# file
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R = ConverterTools.read_fortran_file(
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self, self.dft_file, self.fortran_to_replace)
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try:
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# the energy conversion factor is 1.0, we assume eV in files
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energy_unit = 1.0
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# read the number of k points
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n_k = int(R.next())
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k_dep_projection = 0
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SP = 0 # no spin-polarision
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SO = 0 # no spin-orbit
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# total charge below energy window is set to 0
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charge_below = 0.0
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# density required, for setting the chemical potential
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density_required = R.next()
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symm_op = 0 # No symmetry groups for the k-sum
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# the information on the non-correlated shells is needed for
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# defining dimension of matrices:
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# number of shells considered in the Wanniers
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n_shells = int(R.next())
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# corresponds to index R in formulas
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# now read the information about the shells (atom, sort, l, dim):
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shell_entries = ['atom', 'sort', 'l', 'dim']
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shells = [{name: int(val) for name, val in zip(
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shell_entries, R)} for ish in range(n_shells)]
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# number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
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n_corr_shells = int(R.next())
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# corresponds to index R in formulas
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# now read the information about the shells (atom, sort, l, dim, SO
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# flag, irep):
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corr_shell_entries = ['atom', 'sort', 'l', 'dim','SO','irep']
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corr_shells = [{name: int(val) for name, val in zip(
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corr_shell_entries, R)} for icrsh in range(n_corr_shells)]
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# determine the number of inequivalent correlated shells and maps,
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# needed for further reading
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[n_inequiv_shells, corr_to_inequiv,
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inequiv_to_corr] = ConverterTools.det_shell_equivalence(self, corr_shells)
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use_rotations = 0
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rot_mat = [numpy.identity(
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corr_shells[icrsh]['dim'], numpy.complex_) for icrsh in range(n_corr_shells)]
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rot_mat_time_inv = [0 for i in range(n_corr_shells)]
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# Representative representations are read from file
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n_reps = [1 for i in range(n_inequiv_shells)]
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dim_reps = [0 for i in range(n_inequiv_shells)]
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T = []
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for ish in range(n_inequiv_shells):
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# number of representatives ("subsets"), e.g. t2g and eg
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n_reps[ish] = int(R.next())
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dim_reps[ish] = [int(R.next()) for i in range(
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n_reps[ish])] # dimensions of the subsets
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# The transformation matrix:
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# is of dimension 2l+1, it is taken to be standard d (as in
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# Wien2k)
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ll = 2 * corr_shells[inequiv_to_corr[ish]]['l'] + 1
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lmax = ll * (corr_shells[inequiv_to_corr[ish]]['SO'] + 1)
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T.append(numpy.zeros([lmax, lmax], numpy.complex_))
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T[ish] = numpy.array([[0.0, 0.0, 1.0, 0.0, 0.0],
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[1.0 / sqrt(2.0), 0.0, 0.0,
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0.0, 1.0 / sqrt(2.0)],
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[-1.0 / sqrt(2.0), 0.0, 0.0,
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0.0, 1.0 / sqrt(2.0)],
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[0.0, 1.0 /
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sqrt(2.0), 0.0, -1.0 / sqrt(2.0), 0.0],
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[0.0, 1.0 / sqrt(2.0), 0.0, 1.0 / sqrt(2.0), 0.0]])
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# Spin blocks to be read:
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# number of spins to read for Norbs and Ham, NOT Projectors
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n_spin_blocs = SP + 1 - SO
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# define the number of n_orbitals for all k points: it is the
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# number of total bands and independent of k!
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n_orbitals = numpy.ones(
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[n_k, n_spin_blocs], numpy.int) * sum([sh['dim'] for sh in shells])
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# Initialise the projectors:
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proj_mat = numpy.zeros([n_k, n_spin_blocs, n_corr_shells, max(
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[crsh['dim'] for crsh in corr_shells]), numpy.max(n_orbitals)], numpy.complex_)
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# Read the projectors from the file:
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for ik in range(n_k):
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for icrsh in range(n_corr_shells):
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for isp in range(n_spin_blocs):
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# calculate the offset:
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offset = 0
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n_orb = 0
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for ish in range(n_shells):
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if (n_orb == 0):
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if (shells[ish]['atom'] == corr_shells[icrsh]['atom']) and (shells[ish]['sort'] == corr_shells[icrsh]['sort']):
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n_orb = corr_shells[icrsh]['dim']
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else:
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offset += shells[ish]['dim']
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proj_mat[ik, isp, icrsh, 0:n_orb,
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offset:offset + n_orb] = numpy.identity(n_orb)
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# now define the arrays for weights and hopping ...
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# w(k_index), default normalisation
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bz_weights = numpy.ones([n_k], numpy.float_) / float(n_k)
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hopping = numpy.zeros([n_k, n_spin_blocs, numpy.max(
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n_orbitals), numpy.max(n_orbitals)], numpy.complex_)
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if (weights_in_file):
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# weights in the file
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for ik in range(n_k):
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bz_weights[ik] = R.next()
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# if the sum over spins is in the weights, take it out again!!
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sm = sum(bz_weights)
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bz_weights[:] /= sm
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# Grab the H
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for isp in range(n_spin_blocs):
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for ik in range(n_k):
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n_orb = n_orbitals[ik, isp]
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# first read all real components for given k, then read
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# imaginary parts
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if (first_real_part_matrix):
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for i in range(n_orb):
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if (only_upper_triangle):
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istart = i
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else:
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istart = 0
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for j in range(istart, n_orb):
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hopping[ik, isp, i, j] = R.next()
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for i in range(n_orb):
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if (only_upper_triangle):
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istart = i
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else:
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istart = 0
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for j in range(istart, n_orb):
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hopping[ik, isp, i, j] += R.next() * 1j
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if ((only_upper_triangle)and(i != j)):
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hopping[ik, isp, j, i] = hopping[
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ik, isp, i, j].conjugate()
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else: # read (real,im) tuple
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for i in range(n_orb):
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if (only_upper_triangle):
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istart = i
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else:
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istart = 0
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for j in range(istart, n_orb):
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hopping[ik, isp, i, j] = R.next()
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hopping[ik, isp, i, j] += R.next() * 1j
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if ((only_upper_triangle)and(i != j)):
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hopping[ik, isp, j, i] = hopping[
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ik, isp, i, j].conjugate()
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# keep some things that we need for reading parproj:
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things_to_set = ['n_shells', 'shells', 'n_corr_shells', 'corr_shells',
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'n_spin_blocs', 'n_orbitals', 'n_k', 'SO', 'SP', 'energy_unit']
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for it in things_to_set:
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setattr(self, it, locals()[it])
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except StopIteration: # a more explicit error if the file is corrupted.
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raise "HK Converter : reading file dft_file failed!"
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R.close()
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# Save to the HDF5:
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with HDFArchive(self.hdf_file, 'a') as ar:
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if not (self.dft_subgrp in ar):
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ar.create_group(self.dft_subgrp)
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things_to_save = ['energy_unit', 'n_k', 'k_dep_projection', 'SP', 'SO', 'charge_below', 'density_required',
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'symm_op', 'n_shells', 'shells', 'n_corr_shells', 'corr_shells', 'use_rotations', 'rot_mat',
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'rot_mat_time_inv', 'n_reps', 'dim_reps', 'T', 'n_orbitals', 'proj_mat', 'bz_weights', 'hopping',
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'n_inequiv_shells', 'corr_to_inequiv', 'inequiv_to_corr']
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for it in things_to_save:
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ar[self.dft_subgrp][it] = locals()[it]
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