mirror of
https://github.com/triqs/dft_tools
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[transport] Delete obsolete SumK_Transport files
This commit is contained in:
Michel Ferrero
Priyanka Seth
parent
6f6c8d1c56
commit
287c44116b
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@ -1,902 +0,0 @@
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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, L. Pourovskii, V. Vildosola
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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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#=======================================================================================================================
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# #################################################################
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# Code for Transport/Optic calculations based on SumK_LDA... class
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# by Xiaoyu Deng <xiaoyu.deng@gmail.com>
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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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import pytriqs.utility.dichotomy as Dichotomy
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from pytriqs.gf.local import *
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import pytriqs.utility.mpi as myMPI
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from datetime import datetime
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from pytriqs.applications.dft.sumk_lda import *
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from pytriqs.applications.dft.sumk_lda_tools import *
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import string
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import copy
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import SumK_LDA_Transport_Wien2k_input as Wien
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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 exists" % filename
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for line in open(filename, 'r') :
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for x in line.replace('D', 'E').split() :
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yield string.atof(x)
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def Read_Fortran_File2(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 exists" % filename
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for line in open(filename, 'r') :
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for x in line.replace('D', 'E').split() :
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try:
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yield string.atof(x)
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except GeneratorExit:
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raise
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except:
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yield x.replace('E', 'D')
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def fermidis(x):
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return 1.0/(numpy.exp(x)+1)
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# OLD
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#class TransportEtOptic(SumK_LDA_tools):
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# NEW
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class TransportEtOptic(SumkLDATools):
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"""transport and optic related functions
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calculates distributions: Tr A(k,w) v(k) A(k, w) v(k) or Tr A(k,w) v(k) A(k, w+Omega) v(k)
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.based on thi for ik in xrange(selfs distribution other properties could be obtained.
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!!! worked for cases with spin orbital interaction.
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!!! for non-SOI, need check! Be careful!.
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"""
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def initbyWien(self, wiencase):
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""" read in necessary parameters from wien file.
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symmetries:
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volume:
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velocities:
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"""
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# k_dep_projection is the general case!.
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assert self.k_dep_projection == 1, "Not implemented!"
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self.Nspinblocs = self.SP + 1 - self.SO
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# suffix of wien2k output file
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self.blocksuffix=[[""],["up","dn"]]
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self.velocities=None
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self.bandwin=None
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self.Vol = None
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self.symm = None
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self.nsymm = None
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if myMPI.is_master_node():
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CASE = Wien.WienStruct(wiencase)
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CASE.readSGsymm(wiencase)
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if(self.Nspinblocs == 1): # paramagnetic , without SOI; or spin polarized with SOI
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self.velocities = [Wien.Velocities(wiencase,bln) for bln in self.blocksuffix[0]]
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else:
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self.velocities = [Wien.Velocities(wiencase, bln) for bln in self.blocksuffix[1] ]
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# read in band window for each k points. "CASE.oubwin".
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bandwin=self.bandwinfromwiencase(wiencase=wiencase)
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self.bandwin=bandwin
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self.Vol = CASE.VolumePC
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self.symm = CASE.symmcartesian
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self.nsymm = CASE.nsymm
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myMPI.barrier()
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self.velocities=myMPI.bcast(self.velocities)
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self.bandwin=myMPI.bcast(self.bandwin)
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self.Vol=myMPI.bcast(self.Vol)
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self.symm=myMPI.bcast(self.symm)
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self.nsymm=myMPI.bcast(self.nsymm)
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def Transportdistribution_Boltz(self, wiencase, mshape=None, broadening=0.01, energywindow=None, Nomega=1000, loadpw=False):
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"""This is for transport calculation of Boltzmann theory
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calculate \sum_k Tr vv delta(\omega-enk) which is transportdistribution in Boltzmann theory
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Just use the LDA hamiltonian and velocity, DMFT self energy is not needed.
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mshape defines the indices of directions. xx,yy,zz,xy,yz,zx.
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mshape is 3x3 matrix, mshape[0,0]=1 --> calculate xx, mshape[1,1]=1 --> calculate yy, mshape[1,2]=1 --> calculate xy,
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by default, xx is calculated.
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"""
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if mshape==None:
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mshape=numpy.zeros(9).reshape(3,3)
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mshape[0,0]=1
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assert mshape.shape == (3, 3), "mshape should be 3x3"
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velocities = self.velocities
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mu = 0
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assert self.k_dep_projection == 1, "k_dep_projection = 0 is NOT implemented!"
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if energywindow is None:
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omminplot = -1.0
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ommaxplot = 1.0
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else:
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omminplot = energywindow[0]
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ommaxplot = energywindow[1]
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deltaomega = (ommaxplot - omminplot) / Nomega
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# transport distribution :output P(\omega)_xy should has the same dimension as defined in mshape.
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self.Pw = numpy.zeros((mshape.sum(), Nomega), dtype=numpy.float_)
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mlist = []
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for ir in xrange(3):
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for ic in xrange(3):
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if(mshape[ir][ic] == 1):
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mlist.append((ir, ic))
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if loadpw:
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with open("TD_Boltz_mp.dat", "r") as pwin:
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for iw in xrange(Nomega):
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fstr = pwin.readline().split()
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aomega = iw * deltaomega + omminplot
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assert abs(float(fstr[0]) - aomega) <= 1e-8, "mesh not match when load transportdistribution"
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for ipw in range(mshape.sum()): self.Pw[ipw, iw] = float(fstr[ipw + 1])
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print "Blotz Pw loaded"
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return
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# for ik=0
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ik = 0
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# block_names for green function and self energy
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bln = self.block_names[self.SO]
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ntoi = self.names_to_ind[self.SO]
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ikarray = numpy.array(range(self.n_k))
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for isp in range(self.Nspinblocs):
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for ik in myMPI.slice_array(ikarray):
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n_orb = self.n_orbitals[ik][isp]
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mupat = numpy.ones(n_orb, numpy.float_) * mu
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Ms = copy.deepcopy(mupat)
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ind = ntoi[bln[isp]]
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Ms = numpy.diag(self.hopping[ik,ind,0:n_orb,0:n_orb].real) - mupat
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if(ik%100==0):
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print "ik,isp", ik, isp
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kvel = velocities[isp].vks[ik]
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# in general, bandwindows for Annkw and for velocities are not the same.
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# one should make sure the same window are using before go further. otherwise the matrix size are not match.
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Pwtem = numpy.zeros((mshape.sum(), Nomega), dtype=numpy.float_)
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#symmetry loop
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# this symmetrization could be done first to speed up... To be done.
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for Rmat in self.symm:
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# get new velocity.
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Rkvel = copy.deepcopy(kvel.vel)
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for vnb1 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
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Rkvel[vnb1][vnb1][:] = numpy.dot(Rmat, Rkvel[vnb1][vnb1][:])
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ipw = 0
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for (ir, ic) in mlist:
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for iw in xrange(Nomega):
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omega = deltaomega * iw + omminplot
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bmin = max(self.bandwin[isp][ik, 0], kvel.bandwin[0])
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bmax = min(self.bandwin[isp][ik, 1], kvel.bandwin[1])
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for ib in xrange(bmax - bmin + 1):
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ibb = ib + bmin - self.bandwin[isp][ik, 0]
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ibv = ib + bmin - kvel.bandwin[0]
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enk = Ms[ibb] - omega
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Ag = 1.0 / numpy.sqrt(2 * numpy.pi) / broadening * numpy.exp(-enk ** 2 / 2.0 / broadening ** 2)
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vkr = Rkvel[ibv][ibv][ir]
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vkc = Rkvel[ibv][ibv][ic]
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#Pwtem[ipw,iw]+=(vkr*vkc*Ag).real
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Pwtem[ipw, iw] += vkr * vkc * Ag
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ipw += 1
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self.Pw += Pwtem * self.bz_weights[ik] / self.nsymm
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myMPI.barrier()
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self.Pw = myMPI.all_reduce(myMPI.world, self.Pw, lambda x, y : x + y)
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# for non-magnetic case, the total weight is doubled because of spin degeneracy.
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self.Pw *= (2 - self.SP)
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# scattering is needed here.
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self.Pw *= 1.0/numpy.pi/2.0/broadening
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if myMPI.is_master_node():
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with open("TD_Boltz.dat", "w") as pwout:
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for iw in xrange(Nomega):
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omega = deltaomega * iw + omminplot
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pwout.write(str(omega) + " ")
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for i in range(self.Pw.shape[0]):
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pwout.write(str(self.Pw[i, iw]) + " ")
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pwout.write("\n")
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def Transportdistribution(self, wiencase, mshape=None, broadening=0.00, energywindow=None, loadpw=False):
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"""calculate Tr A(k,w) v(k) A(k, w) v(k).
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mshape defines the indices of directions. xx,yy,zz,xy,yz,zx.
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mshape is 3x3 matrix, mshape[0,0]=1 --> calculate xx, mshape[1,1]=1 --> calculate yy, mshape[1,2]=1 --> calculate xy,
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by default, xx is calculated.
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"""
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if mshape==None:
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mshape=numpy.zeros(9).reshape(3,3)
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mshape[0,0]=1
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assert mshape.shape == (3, 3), "mshape should be 3x3"
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assert hasattr(self, "Sigma_imp"), "Set Sigma First!"
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velocities = self.velocities
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mu = self.chemical_potential
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if myMPI.is_master_node():
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print "Chemical_Potential", mu
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# use k-dependent-projections.
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assert self.k_dep_projection == 1, "Not implemented!"
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# form self energy from impurity self energy and double counting term.
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stmp = self.add_dc()
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#set mesh and energy range for spectrals functions
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# one could construct self energy within only a small energy range when calculating transports.
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M = [x for x in self.Sigma_imp[0].mesh]
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N_om = len(M)
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if energywindow is None:
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omminplot = M[0].real - 0.001
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ommaxplot = M[N_om - 1].real + 0.001
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else:
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omminplot = energywindow[0]
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ommaxplot = energywindow[1]
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# set mesh for Pw, only mesh in focused energyrange is needed. Mpw is just the index of mesh need in M
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Mpw = [i for i in xrange(len(M)) if (M[i].real > omminplot and M[i].real < ommaxplot)]
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# output P(\omega)_xy should has the same dimension as defined in mshape.
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self.Pw = numpy.zeros((mshape.sum(), N_om), dtype=numpy.float)
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mlist = []
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for ir in xrange(3):
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for ic in xrange(3):
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if(mshape[ir][ic] == 1):
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mlist.append((ir, ic))
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ik = 0
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# will construct G in the end; don't be mislead by
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# nomenclature; S is sometimes sigma, sometimes sigma^-1, etc.
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bln = self.block_names[self.SO]
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ntoi = self.names_to_ind[self.SO]
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S = BlockGf(name_block_generator=[(bln[isp], GfReFreq(indices=range(self.n_orbitals[ik][isp]), mesh=self.Sigma_imp[0].mesh)) for isp in range(self.Nspinblocs) ], make_copies=False)
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mupat = [numpy.identity(self.n_orbitals[ik][isp], numpy.complex_) * mu for isp in range(self.Nspinblocs)] # construct mupat
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Annkw = [numpy.zeros((self.n_orbitals[ik][isp], self.n_orbitals[ik][isp], N_om), dtype=numpy.complex_) for isp in range(self.Nspinblocs)]
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ikarray = numpy.array(range(self.n_k))
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for ik in myMPI.slice_array(ikarray):
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#for ik in xrange(self.n_k):
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unchangesize = all([ self.n_orbitals[ik][isp] == mupat[isp].shape[0] for isp in range(self.Nspinblocs)])
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if (not unchangesize):
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# recontruct green functions.
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S = BlockGf(name_block_generator=[(bln[isp], GfReFreq(indices=range(self.n_orbitals[ik][isp]),
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mesh=self.Sigma_imp[0].mesh))
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for isp in range(self.Nspinblocs) ],
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make_copies=False)
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# change size of mupat
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mupat = [numpy.identity(self.n_orbitals[ik][isp], numpy.complex_) * mu for isp in range(self.Nspinblocs)] # construct mupat
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#Annkw=numpy.zeros((self.n_orbitals[ik],self.n_orbitals[ik],N_om),dtype=numpy.complex_)
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Annkw = [numpy.zeros((self.n_orbitals[ik][isp], self.n_orbitals[ik][isp], N_om), dtype=numpy.complex_) for isp in range(self.Nspinblocs)]
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# get lattice green functions.
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# S <<= A_Omega_Plus_B(A=1, B=1j * broadening)
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S <<= 1*Omega+1j*broadening
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Ms = copy.deepcopy(mupat)
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for ibl in range(self.Nspinblocs):
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n_orb = self.n_orbitals[ik][ibl]
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ind = ntoi[bln[ibl]]
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Ms[ibl] = self.hopping[ik,ind,0:n_orb,0:n_orb].real - mupat[ibl]
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S -= Ms
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tmp = S.copy()
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for icrsh in xrange(self.n_corr_shells):
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for sig, gf in tmp: tmp[sig] <<= self.upfold(ik, icrsh, sig, stmp[icrsh][sig], gf)
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S -= tmp
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S.invert()
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#hence we have A(k,\omega)_nn' for a special k points.
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for isp in range(self.Nspinblocs):
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# Annkw[isp].real = -copy.deepcopy(S[self.block_names[self.SO][isp]]._data.array).imag / numpy.pi
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Annkw[isp].real = -copy.deepcopy(S[self.block_names[self.SO][isp]].data.swapaxes(0,1).swapaxes(1,2)).imag / numpy.pi
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# A=-1/pi*Im G
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# for different spin velocties might be different
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for isp in range(self.Nspinblocs):
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if(ik%100==0):
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print "ik,isp", ik, isp
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kvel = velocities[isp].vks[ik]
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# in general, bandwindows for Annkw and for velocities are not the same.
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# one should make sure the same window are using before go further. otherwise the matrix size are not match.
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Pwtem = numpy.zeros((mshape.sum(), N_om), dtype=numpy.float_)
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#symmetry loop
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# how to symmetrize this part???
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for Rmat in self.symm:
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# get new velocity.
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Rkvel = copy.deepcopy(kvel.vel)
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for vnb1 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
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for vnb2 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
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Rkvel[vnb1][vnb2][:] = numpy.dot(Rmat, Rkvel[vnb1][vnb2][:])
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ipw = 0
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bmin = max(self.bandwin[isp][ik, 0], kvel.bandwin[0])
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bmax = min(self.bandwin[isp][ik, 1], kvel.bandwin[1])
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Astart = bmin - self.bandwin[isp][ik, 0]
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Aend = bmax - self.bandwin[isp][ik, 0] + 1
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vstart = bmin - kvel.bandwin[0]
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vend = bmax - kvel.bandwin[0] + 1
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for (ir, ic) in mlist:
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#for iw in xrange(N_om):
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for iw in Mpw:
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#if (M[iw]>omminplot) and (M[iw]<ommaxplot):
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# here use bandwin to construct match matrix for A and velocity.
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Annkwt = Annkw[isp][Astart:Aend, Astart:Aend, iw]
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Rkveltr = Rkvel[vstart:vend, vstart:vend, ir]
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Rkveltc = Rkvel[vstart:vend, vstart:vend, ic]
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#print Annkwt.shape,Rkvel[...,ir].shape
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Pwtem[ipw, iw] += numpy.dot(numpy.dot(numpy.dot(Rkveltr, Annkwt), Rkveltc), Annkwt).trace().real
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ipw += 1
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# k sum and spin sum.
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self.Pw += Pwtem * self.bz_weights[ik] / self.nsymm
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self.Pw = myMPI.all_reduce(myMPI.world, self.Pw, lambda x, y : x + y)
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# for non-magnetic case, the total weight is doubled because of spin degeneracy.
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self.Pw *= (2 - self.SP)
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if myMPI.is_master_node():
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with open("TD_DMFT.dat", "w") as pwout:
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for iw in xrange(N_om):
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if (M[iw].real > omminplot) and (M[iw].real < ommaxplot):
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pwout.write(str(M[iw].real) + " ")
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for i in range(self.Pw.shape[0]):
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pwout.write(str(self.Pw[i, iw]) + " ")
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pwout.write("\n")
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|
||||
def OpticDistribution(self, wiencase, mshape=None, broadening=0.01, energywindow=None, Qmesh=[0.5], Beta=50, loadpw=False):
|
||||
"""calculate Tr A(k,w) v(k) A(k, w+q) v(k) and optics.
|
||||
energywindow is the regime for omega integral
|
||||
Qmesh contains the frequencies of the optic conductivitity. I repin the Qmesh to the self-energy mesh,
|
||||
so the exact value might not exactly the same as given in the list.
|
||||
|
||||
mshape defines the indices of directions. xx,yy,zz,xy,yz,zx.
|
||||
mshape is 3x3 matrix, mshape[0,0]=1 --> calculate xx, mshape[1,1]=1 --> calculate yy, mshape[1,2]=1 --> calculate xy,
|
||||
by default, xx is calculated.
|
||||
"""
|
||||
assert mshape.shape == (3, 3), "mshape should be 3x3"
|
||||
|
||||
assert hasattr(self, "Sigma_imp"), "Set Sigma First!"
|
||||
assert ((self.SP == 0) and (self.SO == 0)), "For SP and SO implementation of spaghettis has to be changed!"
|
||||
velocities = self.velocities
|
||||
# calculate A(k,w):
|
||||
mu = self.chemical_potential
|
||||
|
||||
#we need this somehow for k_dep_projections. So we have to face the problem that the size of A(k,\omega) will
|
||||
#change, and more, the band index for the A(k,\omega) matrix is not known yet.
|
||||
|
||||
# use k-dependent-projections.
|
||||
assert self.k_dep_projection == 1, "Not implemented!"
|
||||
|
||||
# form self energy from impurity self energy and double counting term.
|
||||
stmp = self.add_dc()
|
||||
|
||||
#set mesh and energyrange.
|
||||
M = [x for x in self.Sigma_imp[0].mesh]
|
||||
deltaM = numpy.abs(M[0] - M[1])
|
||||
N_om = len(M)
|
||||
if energywindow is None:
|
||||
omminplot = M[0].real - 0.001
|
||||
ommaxplot = M[N_om - 1].real + 0.001
|
||||
else:
|
||||
omminplot = energywindow[0]
|
||||
ommaxplot = energywindow[1]
|
||||
|
||||
# define exact mesh for optic conductivity
|
||||
Qmesh_ex = [int(x / deltaM) for x in Qmesh]
|
||||
if myMPI.is_master_node():
|
||||
print "Qmesh ", Qmesh
|
||||
print "mesh interval in self energy ", deltaM
|
||||
print "Qmesh / mesh interval ", Qmesh_ex
|
||||
|
||||
# output P(\omega)_xy should has the same dimension as defined in mshape.
|
||||
self.Pw_optic = numpy.zeros((mshape.sum(), len(Qmesh), N_om), dtype=numpy.float_)
|
||||
|
||||
mlist = []
|
||||
for ir in xrange(3):
|
||||
for ic in xrange(3):
|
||||
if(mshape[ir][ic] == 1):
|
||||
mlist.append((ir, ic))
|
||||
ik = 0
|
||||
|
||||
bln = self.block_names[self.SO]
|
||||
ntoi = self.names_to_ind[self.SO]
|
||||
|
||||
S = BlockGf(name_block_generator=[(bln[isp], GfReFreq(indices=range(self.n_orbitals[ik][isp]), mesh=self.Sigma_imp[0].mesh)) for isp in range(self.Nspinblocs) ], make_copies=False)
|
||||
mupat = [numpy.identity(self.n_orbitals[ik][isp], numpy.complex_) * mu for isp in range(self.Nspinblocs)] # construct mupat
|
||||
Annkw = [numpy.zeros((self.n_orbitals[ik][isp], self.n_orbitals[ik][isp], N_om), dtype=numpy.complex_) for isp in range(self.Nspinblocs)]
|
||||
|
||||
ikarray = numpy.array(range(self.n_k))
|
||||
for ik in myMPI.slice_array(ikarray):
|
||||
unchangesize = all([ self.n_orbitals[ik][isp] == mupat[isp].shape[0] for isp in range(self.Nspinblocs)])
|
||||
if (not unchangesize):
|
||||
# recontruct green functions.
|
||||
S = BlockGf(name_block_generator=[(bln[isp], GfReFreq(indices=range(self.n_orbitals[ik][isp]), mesh=self.Sigma_imp[0].mesh)) for isp in range(self.Nspinblocs) ], make_copies=False)
|
||||
|
||||
# S = GF(name_block_generator=[ (s, GFBloc_ReFreq(Indices=BS, Mesh=self.Sigma_imp[0].mesh)) for s in ['up', 'down'] ], Copy=False)
|
||||
# mupat = numpy.identity(self.n_orbitals[ik], numpy.complex_) # change size of mupat
|
||||
mupat = [numpy.identity(self.n_orbitals[ik][isp], numpy.complex_) * mu for isp in range(self.Nspinblocs)] # construct mupat
|
||||
# mupat *= mu
|
||||
|
||||
#set a temporary array storing spectral functions with band index. Note, usually we should have spin index
|
||||
#Annkw=numpy.zeros((self.n_orbitals[ik],self.n_orbitals[ik],N_om),dtype=numpy.complex_)
|
||||
Annkw = [numpy.zeros((self.n_orbitals[ik][isp], self.n_orbitals[ik][isp], N_om), dtype=numpy.complex_) for isp in range(self.Nspinblocs)]
|
||||
|
||||
# get lattice green functions.
|
||||
# S <<= A_Omega_Plus_B(A=1, B=1j * broadening)
|
||||
S <<= 1*Omega + 1j*broadening
|
||||
|
||||
Ms = copy.deepcopy(mupat)
|
||||
for ibl in range(self.Nspinblocs):
|
||||
ind = ntoi[bln[ibl]]
|
||||
n_orb = self.n_orbitals[ik][ibl]
|
||||
Ms[ibl] = self.hopping[ik,ind,0:n_orb,0:n_orb].real - mupat[ibl]
|
||||
S -= Ms
|
||||
|
||||
# print S[self.block_names[self.SO][0]].data
|
||||
tmp = S.copy() # init temporary storage
|
||||
## substract self energy
|
||||
for icrsh in xrange(self.n_corr_shells):
|
||||
for sig, gf in tmp: tmp[sig] <<= self.upfold(ik, icrsh, sig, stmp[icrsh][sig], gf)
|
||||
S -= tmp
|
||||
|
||||
S.invert()
|
||||
|
||||
for isp in range(self.Nspinblocs):
|
||||
Annkw[isp].real = -copy.deepcopy(S[self.block_names[self.SO][isp]].data.swapaxes(0,1).swapaxes(1,2)).imag / numpy.pi
|
||||
|
||||
for isp in range(self.Nspinblocs):
|
||||
if(ik%100==0):
|
||||
print "ik,isp", ik, isp
|
||||
kvel = velocities[isp].vks[ik]
|
||||
|
||||
Pwtem = numpy.zeros((mshape.sum(), len(Qmesh_ex), N_om), dtype=numpy.float_)
|
||||
|
||||
#symmetry loop
|
||||
for Rmat in self.symm:
|
||||
# get new velocity.
|
||||
Rkvel = copy.deepcopy(kvel.vel)
|
||||
for vnb1 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
|
||||
for vnb2 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
|
||||
Rkvel[vnb1][vnb2][:] = numpy.dot(Rmat, Rkvel[vnb1][vnb2][:])
|
||||
ipw = 0
|
||||
for (ir, ic) in mlist:
|
||||
for iw in xrange(N_om):
|
||||
|
||||
if(M[iw].real > 5.0 / Beta):
|
||||
continue
|
||||
for iq in range(len(Qmesh_ex)):
|
||||
#if(Qmesh_ex[iq]==0 or iw+Qmesh_ex[iq]>=N_om ):
|
||||
# here use fermi distribution to truncate self energy mesh.
|
||||
if(Qmesh_ex[iq] == 0 or iw + Qmesh_ex[iq] >= N_om or M[iw].real + Qmesh[iq] < -10.0 / Beta or M[iw].real >10.0 / Beta):
|
||||
continue
|
||||
|
||||
if (M[iw].real > omminplot) and (M[iw].real < ommaxplot):
|
||||
# here use bandwin to construct match matrix for A and velocity.
|
||||
bmin = max(self.bandwin[isp][ik, 0], kvel.bandwin[0])
|
||||
bmax = min(self.bandwin[isp][ik, 1], kvel.bandwin[1])
|
||||
Astart = bmin - self.bandwin[isp][ik, 0]
|
||||
Aend = bmax - self.bandwin[isp][ik, 0] + 1
|
||||
vstart = bmin - kvel.bandwin[0]
|
||||
vend = bmax - kvel.bandwin[0] + 1
|
||||
Annkwl = Annkw[isp][Astart:Aend, Astart:Aend, iw]
|
||||
Annkwr = Annkw[isp][Astart:Aend, Astart:Aend, iw + Qmesh_ex[iq]]
|
||||
Rkveltr = Rkvel[vstart:vend, vstart:vend, ir]
|
||||
Rkveltc = Rkvel[vstart:vend, vstart:vend, ic]
|
||||
#print Annkwt.shape,Rkvel[...,ir].shape
|
||||
Pwtem[ipw, iq, iw] += numpy.dot(numpy.dot(numpy.dot(Rkveltr, Annkwl), Rkveltc), Annkwr).trace().real
|
||||
ipw += 1
|
||||
|
||||
# k sum and spin sum.
|
||||
self.Pw_optic += Pwtem * self.bz_weights[ik] / self.nsymm
|
||||
|
||||
|
||||
|
||||
self.Pw_optic = myMPI.all_reduce(myMPI.world, self.Pw_optic, lambda x, y : x + y)
|
||||
self.Pw_optic *= (2 - self.SP)
|
||||
|
||||
|
||||
|
||||
|
||||
# just back up TD_optic data
|
||||
if myMPI.is_master_node():
|
||||
with open("TD_Optic_DMFT.dat", "w") as pwout:
|
||||
#shape
|
||||
L1,L2,L3=self.Pw_optic.shape
|
||||
pwout.write("%s %s %s\n"%(L1,L2,L3))
|
||||
#dump Qmesh
|
||||
Qmeshr=[i*deltaM for i in Qmesh_ex]
|
||||
#dump self energy mesh
|
||||
#dump Pw_optic
|
||||
for iq in xrange(L2):
|
||||
pwout.write(str(Qmeshr[iq])+" ")
|
||||
pwout.write("\n")
|
||||
for iw in xrange(L3):
|
||||
pwout.write(str(M[iw].real)+" ")
|
||||
pwout.write("\n")
|
||||
for i in xrange(L1):
|
||||
for iq in xrange(L2):
|
||||
for iw in xrange(L3):
|
||||
pwout.write(str(self.Pw_optic[i, iq, iw]) + " ")
|
||||
pwout.write("\n")
|
||||
|
||||
# sum over omega to get optic conductivity for ik in xrange(self
|
||||
if myMPI.is_master_node():
|
||||
OpticConductivity = numpy.zeros((mshape.sum(), len(Qmesh)), dtype=numpy.float_)
|
||||
for im in range(mshape.sum()):
|
||||
for iq in range(len(Qmesh)):
|
||||
for iw in xrange(N_om):
|
||||
omegaT = M[iw].real * Beta
|
||||
omega_aug = Qmesh_ex[iq] * deltaM
|
||||
OpticConductivity[im, iq] += self.Pw_optic[im, iq, iw] * (fermidis(omegaT) - fermidis(omegaT + omega_aug * Beta)) / omega_aug
|
||||
OpticConductivity *= deltaM
|
||||
OpticConductivity *= 10700 / self.Vol
|
||||
with open("Optic_con.dat", "wOptic_con") as opt:
|
||||
for iq in range(len(Qmesh_ex)):
|
||||
opt.write(str(Qmesh_ex[iq] * deltaM) + " ")
|
||||
for im in range(mshape.sum()):
|
||||
opt.write(str(OpticConductivity[im, iq]) + " ")
|
||||
opt.write("\n")
|
||||
|
||||
def loadOpticTD(self,OpticTDFile="TD_Optic_DMFT.dat",Beta=40):
|
||||
""" load optic conductivity distribution and calculate Optical Conductivty
|
||||
"""
|
||||
if myMPI.is_master_node():
|
||||
with open(OpticTDFile,"r") as pw:
|
||||
L1,L2,L3=(int(i) for i in pw.readline().split())
|
||||
#QMeshr=numpy.zeros(L2,dtype=numpy.float)
|
||||
#M=numpy.zeros(L3,dtype=numpy.float)
|
||||
#Pw_optic=numpy.zeros((L1,L2,L3), dtype=numpy.float)
|
||||
|
||||
QMeshr=numpy.array([float(i) for i in pw.readline().split()])
|
||||
M=numpy.array([float(i) for i in pw.readline().split()])
|
||||
Pw_optic=numpy.array([float(i) for i in pw.readline().split()]).reshape(L1,L2,L3)
|
||||
|
||||
OpticConductivity = numpy.zeros((L1, L2), dtype=numpy.float)
|
||||
deltaM=M[1]-M[0]
|
||||
for im in xrange(L1):
|
||||
for iq in xrange(L2):
|
||||
for iw in xrange(L3):
|
||||
omegaT = M[iw] * Beta
|
||||
omega_aug = QMeshr[iq]
|
||||
OpticConductivity[im, iq] += Pw_optic[im, iq, iw] * (fermidis(omegaT) - fermidis(omegaT + omega_aug * Beta)) / omega_aug
|
||||
OpticConductivity *= deltaM
|
||||
## transform to standard unit as in resistivity
|
||||
OpticConductivity *= 10700 / self.Vol
|
||||
##
|
||||
with open("Optic_con.dat", "w") as opt:
|
||||
for iq in xrange(L2):
|
||||
opt.write(str(QMeshr[iq]) + " ")
|
||||
for im in xrange(L1):
|
||||
opt.write(str(OpticConductivity[im, iq]) + " ")
|
||||
opt.write("\n")
|
||||
|
||||
|
||||
|
||||
def OpticDistribution_LDA(self, wiencase, mshape=None, broadening=0.01, energywindow=None, Qmesh=[0.5], Beta=50, loadpw=False):
|
||||
"""calculate Tr A(k,w) v(k) A(k, w+q) v(k) and optics. A constant self-energy is used to mimick noninteracting case.
|
||||
It is not the best way to calculate optic for LDA. Just to compare.
|
||||
energywindow is the regime for omega integral
|
||||
Qmesh contains the frequencies of the optic conductivitity. I repin the Qmesh to the self-energy mesh,
|
||||
so the exact value might not exactly the same as given in the list.
|
||||
|
||||
mshape defines the indices of directions. xx,yy,zz,xy,yz,zx.
|
||||
mshape is 3x3 matrix, mshape[0,0]=1 --> calculate xx, mshape[1,1]=1 --> calculate yy, mshape[1,2]=1 --> calculate xy,
|
||||
by default, xx is calculated.
|
||||
"""
|
||||
assert mshape.shape == (3, 3), "mshape should be 3x3"
|
||||
|
||||
assert hasattr(self, "Sigma_imp"), "Set Sigma First!"
|
||||
assert ((self.SP == 0) and (self.SO == 0)), "For SP and SO implementation of spaghettis has to be changed!"
|
||||
velocities = self.velocities
|
||||
# calculate A(k,w):
|
||||
mu = self.chemical_potential
|
||||
|
||||
#we need this somehow for k_dep_projections. So we have to face the problem that the size of A(k,\omega) will
|
||||
#change, and more, the band index for the A(k,\omega) matrix is not known yet.
|
||||
|
||||
# use k-dependent-projections.
|
||||
assert self.k_dep_projection == 1, "Not implemented!"
|
||||
|
||||
# form self energy from impurity self energy and double counting term.
|
||||
stmp = self.add_dc()
|
||||
|
||||
#set mesh and energyrange.
|
||||
M = [x for x in self.Sigma_imp[0].mesh]
|
||||
deltaM = numpy.abs(M[0] - M[1])
|
||||
N_om = len(M)
|
||||
if energywindow is None:
|
||||
omminplot = M[0] - 0.001
|
||||
ommaxplot = M[N_om - 1] + 0.001
|
||||
else:
|
||||
omminplot = energywindow[0]
|
||||
ommaxplot = energywindow[1]
|
||||
|
||||
# define exact mesh for optic conductivity
|
||||
Qmesh_ex = [int(x / deltaM) for x in Qmesh]
|
||||
if myMPI.is_master_node():
|
||||
print "Qmesh ", Qmesh
|
||||
print "mesh interval in self energy ", deltaM
|
||||
print "Qmesh / mesh interval ", Qmesh_ex
|
||||
|
||||
# output P(\omega)_xy should has the same dimension as defined in mshape.
|
||||
self.Pw_optic = numpy.zeros((mshape.sum(), len(Qmesh), N_om), dtype=numpy.float_)
|
||||
|
||||
mlist = []
|
||||
for ir in xrange(3):
|
||||
for ic in xrange(3):
|
||||
if(mshape[ir][ic] == 1):
|
||||
mlist.append((ir, ic))
|
||||
ik = 0
|
||||
|
||||
bln = self.block_names[self.SO]
|
||||
ntoi = self.names_to_ind[self.SO]
|
||||
|
||||
S = BlockGf(name_block_generator=[(bln[isp], GfReFreq(indices=range(self.n_orbitals[ik][isp]),
|
||||
mesh=self.Sigma_imp[0].mesh))
|
||||
for isp in range(self.Nspinblocs) ],
|
||||
make_copies=False)
|
||||
mupat = [numpy.identity(self.n_orbitals[ik][isp], numpy.complex_) * mu for isp in range(self.Nspinblocs)] # construct mupat
|
||||
Annkw = [numpy.zeros((self.n_orbitals[ik][isp], self.n_orbitals[ik][isp], N_om), dtype=numpy.complex_) for isp in range(self.Nspinblocs)]
|
||||
|
||||
ikarray = numpy.array(range(self.n_k))
|
||||
for ik in myMPI.slice_array(ikarray):
|
||||
unchangesize = all([ self.n_orbitals[ik][isp] == mupat[isp].shape[0] for isp in range(self.Nspinblocs)])
|
||||
if (not unchangesize):
|
||||
# recontruct green functions.
|
||||
S = BlockGf(name_block_generator=[(bln[isp], GfReFreq(indices=range(self.n_orbitals[ik][isp]),
|
||||
mesh=self.Sigma_imp[0].mesh))
|
||||
for isp in range(self.Nspinblocs) ],
|
||||
make_copies=False)
|
||||
|
||||
# S = GF(name_block_generator=[ (s, GFBloc_ReFreq(Indices=BS, Mesh=self.Sigma_imp[0].mesh)) for s in ['up', 'down'] ], Copy=False)
|
||||
# mupat = numpy.identity(self.n_orbitals[ik], numpy.complex_) # change size of mupat
|
||||
mupat = [numpy.identity(self.n_orbitals[ik][isp], numpy.complex_) * mu for isp in range(self.Nspinblocs)] # construct mupat
|
||||
# mupat *= mu
|
||||
|
||||
#set a temporary array storing spectral functions with band index. Note, usually we should have spin index
|
||||
#Annkw=numpy.zeros((self.n_orbitals[ik],self.n_orbitals[ik],N_om),dtype=numpy.complex_)
|
||||
Annkw = [numpy.zeros((self.n_orbitals[ik][isp], self.n_orbitals[ik][isp], N_om), dtype=numpy.complex_) for isp in range(self.Nspinblocs)]
|
||||
|
||||
# get lattice green functions.
|
||||
# S <<= A_Omega_Plus_B(A=1, B=1j * broadening)
|
||||
|
||||
S <<= 1*Omega + 1j*broadening
|
||||
|
||||
Ms = copy.deepcopy(mupat)
|
||||
for ibl in range(self.Nspinblocs):
|
||||
ind = ntoi[bln[ibl]]
|
||||
n_orb = self.n_orbitals[ik][ibl]
|
||||
Ms[ibl] = self.hopping[ik,ind,0:n_orb,0:n_orb].real - mupat[ibl]
|
||||
|
||||
S -= Ms
|
||||
# tmp = S.copy() # init temporary storage
|
||||
# ## substract self energy
|
||||
# for icrsh in xrange(self.n_corr_shells):
|
||||
# for sig, gf in tmp: tmp[sig] <<= self.upfold(ik, icrsh, sig, stmp[icrsh][sig], gf)
|
||||
# S -= tmp
|
||||
|
||||
S.invert()
|
||||
|
||||
for isp in range(self.Nspinblocs):
|
||||
Annkw[isp].real = -copy.deepcopy(S[self.block_names[self.SO][isp]].data.swapaxes(0,1).swapaxes(1,2)).imag / numpy.pi
|
||||
|
||||
for isp in range(self.Nspinblocs):
|
||||
if(ik%100==0):
|
||||
print "ik,isp", ik, isp
|
||||
kvel = velocities[isp].vks[ik]
|
||||
|
||||
Pwtem = numpy.zeros((mshape.sum(), len(Qmesh_ex), N_om), dtype=numpy.float_)
|
||||
|
||||
#symmetry loop
|
||||
for Rmat in self.symm:
|
||||
# get new velocity.
|
||||
Rkvel = copy.deepcopy(kvel.vel)
|
||||
for vnb1 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
|
||||
for vnb2 in xrange(kvel.bandwin[1] - kvel.bandwin[0] + 1):
|
||||
Rkvel[vnb1][vnb2][:] = numpy.dot(Rmat, Rkvel[vnb1][vnb2][:])
|
||||
ipw = 0
|
||||
for (ir, ic) in mlist:
|
||||
for iw in xrange(N_om):
|
||||
if(M[iw].real > 5.0 / Beta):
|
||||
continue
|
||||
for iq in range(len(Qmesh_ex)):
|
||||
#if(Qmesh_ex[iq]==0 or iw+Qmesh_ex[iq]>=N_om ):
|
||||
# here use fermi distribution to truncate self energy mesh.
|
||||
if(Qmesh_ex[iq] == 0 or iw + Qmesh_ex[iq] >= N_om
|
||||
or M[iw].real + Qmesh[iq] < -10.0 / Beta or M[iw].real >10.0 / Beta):
|
||||
continue
|
||||
|
||||
if (M[iw].real > omminplot) and (M[iw].real < ommaxplot):
|
||||
# here use bandwin to construct match matrix for A and velocity.
|
||||
bmin = max(self.bandwin[isp][ik, 0], kvel.bandwin[0])
|
||||
bmax = min(self.bandwin[isp][ik, 1], kvel.bandwin[1])
|
||||
Astart = bmin - self.bandwin[isp][ik, 0]
|
||||
Aend = bmax - self.bandwin[isp][ik, 0] + 1
|
||||
vstart = bmin - kvel.bandwin[0]
|
||||
vend = bmax - kvel.bandwin[0] + 1
|
||||
Annkwl = Annkw[isp][Astart:Aend, Astart:Aend, iw]
|
||||
Annkwr = Annkw[isp][Astart:Aend, Astart:Aend, iw + Qmesh_ex[iq]]
|
||||
Rkveltr = Rkvel[vstart:vend, vstart:vend, ir]
|
||||
Rkveltc = Rkvel[vstart:vend, vstart:vend, ic]
|
||||
#print Annkwt.shape,Rkvel[...,ir].shape
|
||||
Pwtem[ipw, iq, iw] += numpy.dot(numpy.dot(numpy.dot(Rkveltr, Annkwl), Rkveltc), Annkwr).trace().real
|
||||
ipw += 1
|
||||
|
||||
# k sum and spin sum.
|
||||
self.Pw_optic += Pwtem * self.bz_weights[ik] / self.nsymm
|
||||
|
||||
|
||||
|
||||
self.Pw_optic = myMPI.all_reduce(myMPI.world, self.Pw_optic, lambda x, y : x + y)
|
||||
self.Pw_optic *= (2 - self.SP)
|
||||
|
||||
|
||||
# just back up TD_optic data # just back up TD_optic data
|
||||
if myMPI.is_master_node():
|
||||
with open("TD_Optic_LDA.dat", "w") as pwout:
|
||||
L1,L2,L3=self.Pw_optic.shape
|
||||
pwout.write("%s %s %s\n"%(L1,L2,L3))
|
||||
Qmeshr=[i*deltaM for i in Qmesh_ex]
|
||||
for iq in xrange(L2):
|
||||
pwout.write(str(Qmeshr[iq])+" ")
|
||||
pwout.write("\n")
|
||||
for iw in xrange(L3):
|
||||
pwout.write(str(M[iw].real)+" ")
|
||||
pwout.write("\n")
|
||||
for i in xrange(L1):
|
||||
for iq in xrange(L2):
|
||||
for iw in xrange(L3):
|
||||
pwout.write(str(self.Pw_optic[i, iq, iw]) + " ")
|
||||
pwout.write("\n")
|
||||
|
||||
# sum over omega to get optic conductivity
|
||||
if myMPI.is_master_node():
|
||||
OpticConductivity = numpy.zeros((mshape.sum(), len(Qmesh)), dtype=numpy.float_)
|
||||
for im in range(mshape.sum()):
|
||||
for iq in range(len(Qmesh)):
|
||||
for iw in xrange(N_om):
|
||||
omegaT = M[iw].real * Beta
|
||||
omega_aug = Qmesh_ex[iq] * deltaM
|
||||
OpticConductivity[im, iq] += self.Pw_optic[im, iq, iw] * (fermidis(omegaT) - fermidis(omegaT + omega_aug * Beta)) / omega_aug
|
||||
OpticConductivity *= deltaM
|
||||
OpticConductivity *= 10700 / self.Vol
|
||||
with open("Optic_con_LDA.dat", "w") as opt:
|
||||
for iq in range(len(Qmesh_ex)):
|
||||
opt.write(str(Qmesh_ex[iq] * deltaM) + " ")
|
||||
for im in range(mshape.sum()):
|
||||
opt.write(str(OpticConductivity[im, iq]) + " ")
|
||||
opt.write("\n")
|
||||
|
||||
|
||||
# load transportdistribution Pw from file.
|
||||
def loadTD(self, filename, fermishift=0.0):
|
||||
""" load transport distribution from file. Assume energy mesh is uniform
|
||||
the first column is energy mesh. The others are TD values.
|
||||
fermishift is used to shift the TD mesh to mimick a rigid band shift.
|
||||
"""
|
||||
myMPI.barrier()
|
||||
self.TD = numpy.loadtxt(filename)
|
||||
self.TD[:, 0] -= fermishift
|
||||
|
||||
|
||||
# seebeck is just intetral of omega \int Pw f(\omega)f(-\omega)(\beta w).
|
||||
def Seebeck(self, Beta, index=0):
|
||||
""" get -A1/A0, that is Seebeck in unit k_B/e. index is used to select the column in self.tdintegral.
|
||||
Note: for nodiagonal element in Sxy this might not be right. so take care.
|
||||
"""
|
||||
if myMPI.is_master_node():
|
||||
print "A0, A1 %.5e %.5e " % (self.tdintegral(Beta, 0)[index], self.tdintegral(Beta, 1)[index])
|
||||
seb = -self.tdintegral(Beta, 1)[index] / self.tdintegral(Beta, 0)[index]
|
||||
print "Seebeck%d %.4f k_B/e %.4f x 10^(-6)V/K" % (index, seb, seb * 86.17)
|
||||
return seb
|
||||
|
||||
def Conductivity(self, Beta, index=0):
|
||||
""" #return 1/T*A0, that is Conductivity in unit 1/V
|
||||
return Conductivity
|
||||
"""
|
||||
if myMPI.is_master_node():
|
||||
Cond = Beta * self.tdintegral(Beta, 0)[index]
|
||||
#print "Beta*A0 ", Cond
|
||||
print "V in bohr^3 ", self.Vol
|
||||
Cond *= 10700.0 / self.Vol
|
||||
print "Conductivity%d %.4f x 10^4 Ohm^-1 cm^-1" % (index, Cond)
|
||||
print "Resistivity%d %.4f x 10^-4 Ohm cm" % (index, 1.0 / Cond)
|
||||
return Cond
|
||||
|
||||
def tdintegral(self, Beta, pn=0):
|
||||
"""calculate { \pi *\int Pw f(omega)f(-omega)(\beta\omega)^(pn)d\omega }
|
||||
"""
|
||||
M = self.TD[:, 0]
|
||||
domega = abs(M[1] - M[0])
|
||||
pwint = numpy.zeros(self.TD.shape[1] - 1)
|
||||
for ipw in range(self.TD.shape[1] - 1):
|
||||
for iw in xrange(len(M)):
|
||||
x = M[iw] * Beta
|
||||
pwint[ipw] += fermidis(x) * fermidis(-x) * self.TD[iw, ipw + 1] * numpy.float(x) ** pn * domega
|
||||
|
||||
return pwint
|
||||
|
||||
|
||||
def tdintcore(self, Beta):
|
||||
"""calculate { Pw f(omega)f(-omega) for check data)
|
||||
"""
|
||||
M = self.TD[:, 0]
|
||||
domega = abs(M[1] - M[0])
|
||||
pwint = numpy.zeros((self.TD.shape[1] - 1, M.size))
|
||||
for ipw in range(self.TD.shape[1] - 1):
|
||||
for iw in xrange(M.size):
|
||||
x = M[iw] * Beta
|
||||
pwint[ipw, iw] = self.TD[iw, ipw + 1] * fermidis(x) * fermidis(-x)
|
||||
#if(pwint[ipw,iw]>=0.3):
|
||||
# self.Pw[ipw,iw]=0.0
|
||||
|
||||
if myMPI.is_master_node():
|
||||
with open("tdintcore.dat", "w") as pwout:
|
||||
for iw in xrange(M.size):
|
||||
pwout.write(str(M[iw]) + " ")
|
||||
for i in range(pwint.shape[0]):
|
||||
pwout.write(str(pwint[i, iw]) + " ")
|
||||
pwout.write("\n")
|
||||
return pwint
|
||||
|
||||
|
||||
def bandwinfromwiencase(self, wiencase):
|
||||
""" read in the band window from wiencase.outbwin file.
|
||||
"""
|
||||
bandwin = [numpy.zeros((self.n_k, 2), dtype=int) for isp in range(self.SP + 1 - self.SO)]
|
||||
for isp in range(self.SP + 1 - self.SO):
|
||||
if(self.SP == 0 or self.SO == 1):
|
||||
winfile = Read_Fortran_File2(wiencase + ".oubwin")
|
||||
elif self.SP == 1 and isp == 0:
|
||||
winfile = Read_Fortran_File2(wiencase + ".oubwinup")
|
||||
elif self.SP == 1 and isp == 1:
|
||||
winfile = Read_Fortran_File2(wiencase + ".oubwindn")
|
||||
else:
|
||||
assert 0, "Reading bandwin error! Check self.SP and self.SO!"
|
||||
Nk = int(winfile.next())
|
||||
assert Nk == self.n_k, "Number of K points is unconsistent in case.oubwin"
|
||||
SO = int(winfile.next())
|
||||
assert SO == self.SO, "SO is unconsistent in case.oubwin"
|
||||
|
||||
for i in xrange(self.n_k):
|
||||
winfile.next()
|
||||
bandwin[isp][i, 0] = winfile.next()
|
||||
bandwin[isp][i, 1] = winfile.next()
|
||||
winfile.next()
|
||||
return bandwin
|
||||
|
||||
|
|
@ -1,431 +0,0 @@
|
|||
|
||||
################################################################################
|
||||
#
|
||||
# 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/>.
|
||||
#
|
||||
################################################################################
|
||||
|
||||
#=======================================================================================================================
|
||||
# #################################################################
|
||||
# Code for Transport/Optic calculations based on SumK_LDA... class
|
||||
# by Xiaoyu Deng <xiaoyu.deng@gmail.com>
|
||||
# The code read in files needed for transport/Optic calculations from Wien outputs.
|
||||
# including: symmetry, velocity, lattice constants.
|
||||
# The HDF convention is not adopted here since momentum file from Wien output is usually quite large
|
||||
# and it is not necessary to keep in in the HDF file.
|
||||
# #################################################################
|
||||
#=======================================================================================================================
|
||||
|
||||
|
||||
import numpy, sys, os.path
|
||||
|
||||
def Read_Fortran_File (filename):
|
||||
""" Returns a generator that yields all numbers in the Fortran file as float, one by one"""
|
||||
import os.path
|
||||
if not(os.path.exists(filename)) : raise IOError, "File %s does not exists" % filename
|
||||
for line in open(filename, 'r') :
|
||||
for x in line.replace('D', 'E').split() :
|
||||
yield string.atof(x)
|
||||
|
||||
class Velocity_k:
|
||||
"""momentum matrix for a single k points.
|
||||
"""
|
||||
def __init__(self):
|
||||
self.kp = [0.0, 0.0, 0.0]
|
||||
self.bandwin = [999, 0]
|
||||
# here a matrix for 3 vels. use list since the size of vel is to be determined.
|
||||
self.vel = []
|
||||
|
||||
|
||||
class Velocities:
|
||||
"""Class containig the velocities.
|
||||
Provides container for velocities
|
||||
as a function of k and method to read them from case.pmat Wien2k file
|
||||
use as ClassInstance.vks[ik].vel[iband][jband][ix]
|
||||
"""
|
||||
|
||||
def __init__(self, wiencase, spinbl=""):
|
||||
if not(os.path.exists(wiencase + ".pmat" + spinbl)) : raise IOError, "File %s does not exists" % wiencase + ".pmat"
|
||||
|
||||
# expected format:
|
||||
# k nu nu2 (k denotes k-point, nu1 starting band index, nu2 ending band index
|
||||
# kpos ( read in if needed.
|
||||
# (real, im) (of velocity for all nu <=nu_prime within nu1 and nu2)
|
||||
# ... ...
|
||||
# k nu nu2
|
||||
# ...
|
||||
f = open(wiencase + ".pmat" + spinbl)
|
||||
self.vks = []
|
||||
|
||||
while 1:
|
||||
try:
|
||||
s = f.readline()
|
||||
if (s == ""):
|
||||
break
|
||||
except:
|
||||
break
|
||||
try:
|
||||
vec = Velocity_k()
|
||||
[k, nu1, nu2] = [int (x) for x in s.strip().split()]
|
||||
vec.bandwin[0] = nu1
|
||||
vec.bandwin[1] = nu2
|
||||
|
||||
vec.kp = f.readline().strip().split()
|
||||
dim = vec.bandwin[1] - vec.bandwin[0] + 1
|
||||
shape = (dim, dim, 3)
|
||||
vxyz = numpy.zeros(shape, dtype=complex)
|
||||
for nu in xrange(dim):
|
||||
for nu_prime in xrange(nu, dim):
|
||||
for i in xrange(3):
|
||||
s = f.readline().strip("\n ()").split(',')
|
||||
vxyz[nu][nu_prime][i] = float(s[0]) + float(s[1]) * 1j
|
||||
if(nu_prime != nu):
|
||||
vxyz[nu_prime][nu][i] = vxyz[nu][nu_prime][i].conjugate()
|
||||
|
||||
vec.vel = vxyz
|
||||
self.vks.append(vec)
|
||||
|
||||
except IOError:
|
||||
print("Reading case.pmat error. Wrong format?\n ")
|
||||
raise
|
||||
f.close()
|
||||
|
||||
|
||||
def getvel(self, k):
|
||||
# should return array at a given k. use as [iband][jband][i]
|
||||
return self.vks[k].vel
|
||||
|
||||
def plot(self):
|
||||
f = open("velband.dat", "w")
|
||||
bandid = numpy.array([vk.bandwin for vk in self.vks]).flatten()
|
||||
minb = bandid.min()
|
||||
maxb = bandid.max()
|
||||
for ib in range(minb, maxb + 1):
|
||||
ik = 0
|
||||
for vk in self.vks:
|
||||
if(vk.bandwin[0] <= ib and vk.bandwin[1] >= ib):
|
||||
f.write(str(ik) + " ")
|
||||
for i in range(3):
|
||||
f.write(str(vk.vel[ib - vk.bandwin[0]][ib - vk.bandwin[0]][i].real) + " ")
|
||||
f.write("\n")
|
||||
ik = ik + 1
|
||||
f.write("&\n")
|
||||
f.close()
|
||||
|
||||
|
||||
class SGsymmetry():
|
||||
""" read in symmetry of space group from wiencase.outputs other than wiencase.struct since
|
||||
in this file there are also symmetry operations in xyz coordinates..
|
||||
"""
|
||||
def __init__(self, wiencase):
|
||||
structfile = wiencase + ".outputs"
|
||||
self.nsymm = 1
|
||||
self.symm = []
|
||||
self.tau = []
|
||||
self.bravaismatrix = numpy.zeros((3, 3), dtype=numpy.float_)
|
||||
self.symmcartesian = []
|
||||
self.taucartesian = []
|
||||
with open(structfile, "r") as f:
|
||||
f.readline()
|
||||
f.readline()# bravais matrix
|
||||
for i in range(3):
|
||||
line = f.readline().strip().split()
|
||||
self.bravaismatrix[i, :] = numpy.array([numpy.float(item) for item in line])[:]
|
||||
print "bravais matrix", self.bravaismatrix
|
||||
|
||||
while 1:
|
||||
s = f.readline().strip(" ").split()
|
||||
try:
|
||||
if(s[0] == "PGBSYM:"):
|
||||
self.nsymm = int(s[-1])
|
||||
break
|
||||
except:
|
||||
continue
|
||||
|
||||
f.readline()
|
||||
f.readline()
|
||||
for i in range(self.nsymm):
|
||||
f.readline()
|
||||
## read symmcartesian
|
||||
symmt = numpy.zeros((3, 3), dtype=float)
|
||||
taut = numpy.zeros((3), dtype=float)
|
||||
for ir in range(3):
|
||||
s = f.readline().strip().split()
|
||||
for ic in range(3):
|
||||
symmt[ir, ic] = float(s[ic])
|
||||
s = f.readline().strip().split()
|
||||
for ir in range(3):
|
||||
taut[ir] = float(s[ir])
|
||||
|
||||
self.symmcartesian.append(symmt)
|
||||
self.taucartesian.append(taut)
|
||||
|
||||
##read symm
|
||||
symmt = numpy.zeros((3, 3), dtype=float)
|
||||
taut = numpy.zeros((3), dtype=float)
|
||||
for ir in range(3):
|
||||
s = f.readline().strip().split()
|
||||
for ic in range(3):
|
||||
symmt[ir, ic] = float(s[ic])
|
||||
taut[ir] = float(s[3])
|
||||
|
||||
self.symm.append(symmt)
|
||||
self.tau.append(taut)
|
||||
|
||||
f.readline()
|
||||
# end
|
||||
f.close()
|
||||
print "Read wiencase.outputs done!"
|
||||
|
||||
def checksymmxyz(self):
|
||||
''' This is to check symm in cartesian coordinates and in primitive cell lattice
|
||||
For details of symm, one should check wien/SRC_symmetry/, latsym.f, pglsym.f,pgbsym.f
|
||||
In general, if a lattice is orthorhombic, then symmcartesian is the same as symmprimitive
|
||||
(at most different by a transpose (or inversion)). If a lattice is not orthorhombic, these two symms could
|
||||
be related by bravais matrix.
|
||||
One special case is CXZ lattice. In Wien CXZ lattice contains two cases: orthorhombic, and
|
||||
monoclinic (with only gamma not equal to 90). For CXZ lattice monoclinic, symmcartesian is also the same as
|
||||
symmprimitive (or with transpose (or inversion)).
|
||||
'''
|
||||
for i in range(self.nsymm):
|
||||
mat = self.symmcartesian[i].transpose() # according to wien2k, why?
|
||||
bm = self.bravaismatrix
|
||||
bminv = numpy.linalg.inv(bm)#.transpose()
|
||||
res = numpy.dot(bminv, numpy.dot(mat, bm)) - self.symm[i]
|
||||
print i, res
|
||||
|
||||
|
||||
def size(self):
|
||||
return self.nsymm
|
||||
|
||||
def cellvolume(latticetype, latticeconstants, latticeangle):
|
||||
""" calculate cell volume: volumecc conventional cell, volumepc, primitive cell.
|
||||
"""
|
||||
for i in range(3):
|
||||
latticeangle[i] *= 1.0 / 180 * numpy.pi
|
||||
a = latticeconstants[0]
|
||||
b = latticeconstants[1]
|
||||
c = latticeconstants[2]
|
||||
c_al = numpy.cos(latticeangle[0])
|
||||
c_be = numpy.cos(latticeangle[1])
|
||||
c_ga = numpy.cos(latticeangle[2])
|
||||
volumecc = a * b * c * numpy.sqrt(1 + 2 * c_al * c_be * c_ga - c_al ** 2 - c_be * 82 - c_ga ** 2)
|
||||
|
||||
det = {"P":1,
|
||||
"F":4,
|
||||
"B":2,
|
||||
"R":3,
|
||||
"H":1,
|
||||
"CXY":2,
|
||||
"CYZ":2,
|
||||
"CXZ":2
|
||||
}
|
||||
volumepc = volumecc / det[latticetype]
|
||||
|
||||
return volumecc, volumepc
|
||||
|
||||
|
||||
class WienStruct():
|
||||
""" parsing Wien Struct file
|
||||
"""
|
||||
def __init__(self, wiencase):
|
||||
structfile = wiencase + ".struct"
|
||||
|
||||
with open(structfile, "r") as infile:
|
||||
print "read in Wien case file %s" % structfile
|
||||
infile.readline()#title
|
||||
|
||||
tem = infile.readline() #lattice
|
||||
self.latticetype = tem[0:10].split()[0]
|
||||
self.ineqvsite = int(tem[27:30])
|
||||
try:
|
||||
self.sgrnumber = int(tem[30:33])
|
||||
except:
|
||||
self.sgrnumber = None
|
||||
try:
|
||||
self.sgrlabel = tem[34:38]
|
||||
except:
|
||||
self.sgrlabel = None
|
||||
|
||||
print self.latticetype, self.ineqvsite, self.sgrnumber, self.sgrlabel
|
||||
infile.readline()
|
||||
|
||||
tem = infile.readline() # lattice constants
|
||||
self.latticeconstants = [float(tem[0:10]), float(tem[10:20]), float(tem[20:30])]
|
||||
self.latticeangle = [float(tem[30:40]), float(tem[40:50]), float(tem[50:60])]
|
||||
print "Cell"
|
||||
print self.latticeconstants[:]
|
||||
print self.latticeangle[:]
|
||||
|
||||
self.positions = []
|
||||
self.atomsymbols = []
|
||||
self.multi = []
|
||||
self.atomnumbers = []
|
||||
self.locrotmatrix = []
|
||||
for isite in range(self.ineqvsite):
|
||||
tem = infile.readline()
|
||||
positions = []
|
||||
positions.append([float(tem[12:22]), float(tem[25:35]), float(tem[38:48])])
|
||||
|
||||
tem = infile.readline()
|
||||
multi = int(tem[15:17])
|
||||
self.multi.append(multi)
|
||||
|
||||
for im in range(multi - 1):
|
||||
tem = infile.readline()
|
||||
positions.append([float(tem[12:22]), float(tem[25:35]), float(tem[38:48])])
|
||||
#print positions
|
||||
self.positions.append(positions)
|
||||
#print self.positions
|
||||
tem = infile.readline().strip(" ").split()
|
||||
self.atomsymbols.append(tem[0])
|
||||
self.atomnumbers.append(float(tem[-1]))
|
||||
|
||||
mat = numpy.zeros((3, 3), dtype=numpy.float_)
|
||||
tem = infile.readline().strip(" ").split()
|
||||
#print tem[-3:],mat[0,:]
|
||||
mat[0, :] = tem[-3:]
|
||||
tem = infile.readline().strip(" ").split()
|
||||
mat[1, :] = tem[-3:]
|
||||
tem = infile.readline().strip(" ").split()
|
||||
mat[2, :] = tem[-3:]
|
||||
self.locrotmatrix.append(mat)
|
||||
|
||||
for ia in range(len(self.atomsymbols)):
|
||||
print "atom symbol : %s atom number: %d atom multi: %d" % (self.atomsymbols[ia], self.atomnumbers[ia], self.multi[ia])
|
||||
print "positions:"
|
||||
for im in self.positions[ia]:
|
||||
print im[:]
|
||||
|
||||
|
||||
# symmetry with lattice vector
|
||||
tem = infile.readline().strip(" ").split()
|
||||
self.symm = []
|
||||
self.tau = []
|
||||
self.Nsymm = int(tem[0])
|
||||
for isymm in range(self.Nsymm):
|
||||
symmt = numpy.zeros((3, 3), dtype=float)
|
||||
taut = numpy.zeros((3), dtype=float)
|
||||
for ir in range(3):
|
||||
s = infile.readline()
|
||||
for ic in range(3):
|
||||
symmt[ir][ic] = float(s[ic * 2:ic * 2 + 2])
|
||||
taut[ir] = float(s[7:17])
|
||||
self.symm.append(symmt)
|
||||
self.tau.append(taut)
|
||||
infile.readline()
|
||||
|
||||
#############
|
||||
print "Read in %s.struct done!" % wiencase
|
||||
|
||||
|
||||
|
||||
## convential Cell Volume and primitive Cell. In bohr^3 unit
|
||||
self.VolumeCC, self.VolumePC = cellvolume(self.latticetype, self.latticeconstants, self.latticeangle)
|
||||
|
||||
|
||||
|
||||
def readSGsymm(self, wiencase):
|
||||
""" read in symmetry of space group from wiencase.outputs other than wiencase.struct since
|
||||
in this file there are also symmetry operations in xyz coordinates..
|
||||
"""
|
||||
structfile = wiencase + ".outputs"
|
||||
self.nsymm = 1
|
||||
self.symm = []
|
||||
self.tau = []
|
||||
# note bravaismatrix is not accurate enough in wiencase.outputs file. Just use it for test.
|
||||
self.bravaismatrix = numpy.zeros((3, 3), dtype=numpy.float_)
|
||||
self.symmcartesian = []
|
||||
self.taucartesian = []
|
||||
with open(structfile, "r") as f:
|
||||
f.readline()
|
||||
f.readline()# bravais matrix
|
||||
for i in range(3):
|
||||
line = f.readline().strip().split()
|
||||
self.bravaismatrix[i, :] = numpy.array([numpy.float(item) for item in line])[:]
|
||||
print "bravais matrix", self.bravaismatrix
|
||||
|
||||
while 1:
|
||||
try:
|
||||
s = f.readline().strip(" ").split()
|
||||
if(s[0] == "PGBSYM:"):
|
||||
self.nsymm = int(s[-1])
|
||||
break
|
||||
except:
|
||||
assert "Error in read case.outputs"
|
||||
|
||||
#f.readline()
|
||||
#f.readline()
|
||||
for i in range(self.nsymm):
|
||||
while 1:
|
||||
s = f.readline().strip().split()
|
||||
if s[0] == "Symmetry":
|
||||
break
|
||||
|
||||
## read symmcartesian
|
||||
symmt = numpy.zeros((3, 3), dtype=float)
|
||||
taut = numpy.zeros((3), dtype=float)
|
||||
for ir in range(3):
|
||||
s = f.readline().strip().split()
|
||||
for ic in range(3):
|
||||
symmt[ir, ic] = float(s[ic])
|
||||
s = f.readline().strip().split()
|
||||
for ir in range(3):
|
||||
taut[ir] = float(s[ir])
|
||||
|
||||
self.symmcartesian.append(symmt)
|
||||
self.taucartesian.append(taut)
|
||||
|
||||
##read symm
|
||||
symmt = numpy.zeros((3, 3), dtype=float)
|
||||
taut = numpy.zeros((3), dtype=float)
|
||||
for ir in range(3):
|
||||
s = f.readline().strip().split()
|
||||
for ic in range(3):
|
||||
symmt[ir, ic] = numpy.float(s[ic])
|
||||
taut[ir] = numpy.float(s[3])
|
||||
|
||||
self.symm.append(symmt)
|
||||
self.tau.append(taut)
|
||||
|
||||
# end
|
||||
f.close()
|
||||
|
||||
def checksymmxyz(self):
|
||||
''' This is to check symm in cartesian coordinates and in primitive cell lattice
|
||||
For details of symm, should check wien/SRC_symmetry/, latsym.f, pglsym.f,pgbsym.f
|
||||
In general, if a lattice is orthorhombic, then symmcartesian is the same as symmprimitive
|
||||
(at most different by a transpose (or inversion)). If a lattice is not orthorhombic, these two symms could
|
||||
be related by bravais matrix.
|
||||
One special case is CXZ lattice. In Wien CXZ lattice contains two cases: orthorhombic, and
|
||||
monoclinic (with only gamma not equal to 90). For CXZ lattice monoclinic, symmcartesian is also the same as
|
||||
symmprimitive (or with transpose (or inversion)).
|
||||
'''
|
||||
ortho = numpy.abs(numpy.array(self.latticeangle) - 90.0).sum() <= 1e-6
|
||||
for i in range(self.nsymm):
|
||||
mat = self.symmcartesian[i].transpose() # according to wien2k, why?
|
||||
res = mat
|
||||
if (not ortho) and (self.latticetype != "CXZ") :
|
||||
bm = self.bravaismatrix
|
||||
bminv = numpy.linalg.inv(bm)#.transpose()
|
||||
res = numpy.dot(bminv, numpy.dot(mat, bm))
|
||||
res -= self.symm[i]
|
||||
print i, numpy.abs(res).sum()
|
||||
|
||||
## primitive cell vectors.+
|
||||
|
||||
Loading…
Reference in New Issue
Block a user