mirror of
https://github.com/triqs/dft_tools
synced 2024-11-09 07:33:47 +01:00
304 lines
13 KiB
Python
304 lines
13 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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import string
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from math import sqrt
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def read_fortran_file (filename):
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""" Returns a generator that yields all numbers in the Fortran file as float, one by one"""
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import os.path
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if not(os.path.exists(filename)) : raise IOError, "File %s does not exist."%filename
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for line in open(filename,'r') :
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for x in line.replace('D','E').replace('(',' ').replace(')',' ').replace(',',' ').split() :
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yield string.atof(x)
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class HkConverter:
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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 SumK_LDA class.
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"""
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def __init__(self, hk_file, hdf_file, lda_subgrp = 'SumK_LDA', symm_subgrp = 'SymmCorr', repacking = False):
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"""
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Init of the class.
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on.
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"""
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assert type(hk_file)==StringType,"hk_file must be a filename"
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self.hdf_file = hdf_file
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self.lda_file = hk_file
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self.lda_subgrp = lda_subgrp
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self.symm_subgrp = symm_subgrp
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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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self.__repack()
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def convert_dmft_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 input files, and stores the data in the HDFfile
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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()): return
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mpi.report("Reading input from %s..."%self.lda_file)
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# R is a generator : each R.Next() will return the next number in the file
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R = read_fortran_file(self.lda_file)
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try:
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energy_unit = 1.0 # the energy conversion factor is 1.0, we assume eV in files
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n_k = int(R.next()) # read the number of k points
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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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charge_below = 0.0 # total charge below energy window is set to 0
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density_required = R.next() # density required, for setting the chemical potential
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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 defining dimension of matrices:
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n_shells = int(R.next()) # number of shells considered in the Wanniers
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# corresponds to index R in formulas
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# now read the information about the shells:
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shells = [ [ int(R.next()) for i in range(4) ] for icrsh in range(n_shells) ] # reads iatom, sort, l, dim
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n_corr_shells = int(R.next()) # number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
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# corresponds to index R in formulas
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# now read the information about the shells:
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corr_shells = [ [ int(R.next()) for i in range(6) ] for icrsh in range(n_corr_shells) ] # reads iatom, sort, l, dim, SO flag, irep
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self.inequiv_shells(corr_shells) # determine the number of inequivalent correlated shells, has to be known for further reading...
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use_rotations = 0
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rot_mat = [numpy.identity(corr_shells[icrsh][3],numpy.complex_) for icrsh in xrange(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(self.n_inequiv_corr_shells)]
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dim_reps = [0 for i in range(self.n_inequiv_corr_shells)]
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T = []
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for icrsh in range(self.n_inequiv_corr_shells):
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n_reps[icrsh] = int(R.next()) # number of representatives ("subsets"), e.g. t2g and eg
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dim_reps[icrsh] = [int(R.next()) for i in range(n_reps[icrsh])] # 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 Wien2k)
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ll = 2*corr_shells[self.invshellmap[icrsh]][2]+1
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lmax = ll * (corr_shells[self.invshellmap[icrsh]][4] + 1)
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T.append(numpy.zeros([lmax,lmax],numpy.complex_))
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T[icrsh] = 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, 0.0, 1.0/sqrt(2.0)],
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[-1.0/sqrt(2.0), 0.0, 0.0, 0.0, 1.0/sqrt(2.0)],
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[0.0, 1.0/sqrt(2.0), 0.0, -1.0/sqrt(2.0), 0.0],
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[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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n_spin_blocs = SP + 1 - SO # number of spins to read for Norbs and Ham, NOT Projectors
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# define the number of n_orbitals for all k points: it is the number of total bands and independent of k!
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n_orb = sum([ shells[ish][3] for ish in range(n_shells) ])
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n_orbitals = numpy.ones([n_k,n_spin_blocs],numpy.int) * n_orb
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# Initialise the projectors:
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proj_mat = numpy.zeros([n_k,n_spin_blocs,n_corr_shells,max(numpy.array(corr_shells)[:,3]),max(n_orbitals)],numpy.complex_)
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# Read the projectors from the file:
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for ik in xrange(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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no = 0
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for i in range(n_shells):
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if (no==0):
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if ((shells[i][0]==corr_shells[icrsh][0]) and (shells[i][1]==corr_shells[icrsh][1])):
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no = corr_shells[icrsh][3]
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else:
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offset += shells[i][3]
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proj_mat[ik,isp,icrsh,0:no,offset:offset+no] = numpy.identity(no)
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# now define the arrays for weights and hopping ...
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bz_weights = numpy.ones([n_k],numpy.float_)/ float(n_k) # w(k_index), default normalisation
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hopping = numpy.zeros([n_k,n_spin_blocs,max(n_orbitals),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 xrange(n_k) : 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 xrange(n_k) :
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no = n_orbitals[ik,isp]
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if (first_real_part_matrix):
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for i in xrange(no):
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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 xrange(istart,no):
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hopping[ik,isp,i,j] = R.next()
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for i in xrange(no):
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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 xrange(istart,no):
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hopping[ik,isp,i,j] += R.next() * 1j
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if ((only_upper_triangle)and(i!=j)): hopping[ik,isp,j,i] = hopping[ik,isp,i,j].conjugate()
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else:
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for i in xrange(no):
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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 xrange(istart,no):
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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)): hopping[ik,isp,j,i] = hopping[ik,isp,i,j].conjugate()
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# keep some things that we need for reading parproj:
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self.n_shells = n_shells
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self.shells = shells
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self.n_corr_shells = n_corr_shells
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self.corr_shells = corr_shells
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self.n_spin_blocs = n_spin_blocs
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self.n_orbitals = n_orbitals
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self.n_k = n_k
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self.SO = SO
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self.SP = SP
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self.energy_unit = energy_unit
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except StopIteration : # a more explicit error if the file is corrupted.
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raise "HK Converter : reading file lda_file failed!"
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R.close()
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#-----------------------------------------
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# Store the input into HDF5:
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ar = HDFArchive(self.hdf_file,'a')
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if not (self.lda_subgrp in ar): ar.create_group(self.lda_subgrp)
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# The subgroup containing the data. If it does not exist, it is created.
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# If it exists, the data is overwritten!!!
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ar[self.lda_subgrp]['energy_unit'] = energy_unit
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ar[self.lda_subgrp]['n_k'] = n_k
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ar[self.lda_subgrp]['k_dep_projection'] = k_dep_projection
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ar[self.lda_subgrp]['SP'] = SP
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ar[self.lda_subgrp]['SO'] = SO
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ar[self.lda_subgrp]['charge_below'] = charge_below
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ar[self.lda_subgrp]['density_required'] = density_required
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ar[self.lda_subgrp]['symm_op'] = symm_op
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ar[self.lda_subgrp]['n_shells'] = n_shells
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ar[self.lda_subgrp]['shells'] = shells
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ar[self.lda_subgrp]['n_corr_shells'] = n_corr_shells
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ar[self.lda_subgrp]['corr_shells'] = corr_shells
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ar[self.lda_subgrp]['use_rotations'] = use_rotations
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ar[self.lda_subgrp]['rot_mat'] = rot_mat
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ar[self.lda_subgrp]['rot_mat_time_inv'] = rot_mat_time_inv
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ar[self.lda_subgrp]['n_reps'] = n_reps
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ar[self.lda_subgrp]['dim_reps'] = dim_reps
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ar[self.lda_subgrp]['T'] = T
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ar[self.lda_subgrp]['n_orbitals'] = n_orbitals
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ar[self.lda_subgrp]['proj_mat'] = proj_mat
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ar[self.lda_subgrp]['bz_weights'] = bz_weights
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ar[self.lda_subgrp]['hopping'] = hopping
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del ar
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def __repack(self):
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"""Calls the h5repack routine, in order to reduce the file size of the hdf5 archive.
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Should only be used BEFORE the first invokation of HDFArchive in the program, otherwise
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the hdf5 linking is broken!!!"""
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import subprocess
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if not (mpi.is_master_node()): return
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mpi.report("Repacking the file %s"%self.hdf_file)
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retcode = subprocess.call(["h5repack","-i%s"%self.hdf_file, "-otemphgfrt.h5"])
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if (retcode!=0):
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mpi.report("h5repack failed!")
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else:
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subprocess.call(["mv","-f","temphgfrt.h5","%s"%self.hdf_file])
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def inequiv_shells(self,lst):
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"""
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The number of inequivalent shells is calculated from lst, and a mapping is given as
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map(i_corr_shells) = i_inequiv_corr_shells
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invmap(i_inequiv_corr_shells) = i_corr_shells
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in order to put the Self energies to all equivalent shells, and for extracting Gloc
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"""
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tmp = []
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self.shellmap = [0 for i in range(len(lst))]
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self.invshellmap = [0]
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self.n_inequiv_corr_shells = 1
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tmp.append( lst[0][1:3] )
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if (len(lst)>1):
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for i in range(len(lst)-1):
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fnd = False
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for j in range(self.n_inequiv_corr_shells):
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if (tmp[j]==lst[i+1][1:3]):
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fnd = True
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self.shellmap[i+1] = j
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if (fnd==False):
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self.shellmap[i+1] = self.n_inequiv_corr_shells
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self.n_inequiv_corr_shells += 1
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tmp.append( lst[i+1][1:3] )
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self.invshellmap.append(i+1)
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