This is a python class for dealing with VASP pseudo-wavefunction file WAVECAR.
It can be used to extract the planewave coefficients of any single Kohn-Sham (KS)
orbital from the file. In addition, by padding the planewave coefficients to a
3D grid and performing 3D Fourier Transform, the pseudo-wavefunction in real
space can also be obtained and saved to file that can be viewed with VESTA.

Transition dipole moment

With the knowledge of the planewave coefficients of the
transition dipole moment between
any two KS states can also be calculated.

Inverse Participation Ratio

IPR is a measure of the localization of Kohn-Sham states. For a particular KS
state \phi_j, it is defined as

                \sum_n |\phi_j(n)|^4 
IPR(\phi_j) = -------------------------
              |\sum_n |\phi_j(n)|^2||^2

where n iters over the number of grid points.

Electron Localization Function

(Still need to be tested!)

In quantum chemistry, the electron localization function (ELF) is a measure of
the likelihood of finding an electron in the neighborhood space of a reference
electron located at a given point and with the same spin. Physically, this
measures the extent of spatial localization of the reference electron and
provides a method for the mapping of electron pair probability in
multielectronic systems. (from wiki)

NOTE that if you are using VESTA to view the resulting ELF file, please rename
the output file as “ELFCAR”, otherwise there will be some error in the
isosurface plot! When VESTA read in CHG*/PARCHG/*.vasp to visualize isosurfaces
and sections, data values are divided by volume in the unit of bohr^3. The unit
of charge densities input by VESTA is, therefore, bohr^−3. For LOCPOT/ELFCAR
files, volume data are kept intact.

Band unfolding

Using the pseudo-wavefunction from supercell calculation, it is possible to
perform electronic band structure unfolding to obtain the effective band
structure. For more information, please refer to the following article and the
GPAW website.

V. Popescu and A. Zunger Extracting E versus k effective band structure
from supercell calculations on alloys and impurities Phys. Rev. B 85, 085201


  • Manual Installation

    Put and in any directory you like and add the
    path of the directory to PYTHONPATH

    export PYTHONPATH=/the/path/of/your/dir:${PYTHONPATH}


    • numpy
    • scipy
    • matplotlib
  • Using Pip

    pip install git+


Pseudowavefunction in real space

  1. Write a simple script and choose whichever state you like.

from vaspwfc import vaspwfc

wav = vaspwfc('./examples/wfc_r/WAVECAR')
# KS orbital in real space, double the size of the FT grid
phi = wav.wfc_r(ikpt=2, iband=27, ngrid=wav._ngrid * 2)
# Save the orbital into files. Since the wavefunction consist of complex
# numbers, the real and imaginary part are saved separately.
wav.save2vesta(phi, poscar='./examples/wfc_r/POSCAR')

# for WAVECAR from a noncollinear run, the wavefunction at each k-piont/band is
# a two component spinor. Turn on the lsorbit flag when reading WAVECAr.
xx = vaspwfc('examples/wfc_r/wavecar_mose2-wse2', lsorbit=True)
phi_spinor = xx.wfc_r(1, 1, 36, ngrid=xx._ngrid*2)
for ii in range(2):
    phi = phi_spinor[ii]
    prefix = 'spinor_{:02d}'.format(ii)
    xx.save2vesta(phi, prefix=prefix,

Below are the real (left) and imaginary (right) part of the selected KS orbital:

real part |
imaginary part

  1. Or you can also use the script wfcplot in the scripts folder

$ wfcplot -w WAVECAR -p POSCAR -s spin_index -k kpoint_index -n band_index      # for normal WAVECAR
$ wfcplot -w WAVECAR -p POSCAR -s spin_index -k kpoint_index -n band_index  -lgamma    # for gamma-only WAVECAR
$ wfcplot -w WAVECAR -p POSCAR -s spin_index -k kpoint_index -n band_index  -lgamma    # for noncollinear WAVECAR

Please refer to wfcplot -h for more information of the usage.

Electron Localization Function

import numpy as np
from vaspwfc import vaspwfc, save2vesta

kptw = [1, 6, 6, 6, 6, 6, 6, 12, 12, 12, 6, 6, 12, 12, 6, 6]

wfc = vaspwfc('./WAVECAR')
# chi = wfc.elf(kptw=kptw, ngrid=wfc._ngrid * 2)
chi = wfc.elf(kptw=kptw, ngrid=[20, 20, 150])
save2vesta(chi[0], lreal=True, poscar='POSCAR', prefix='elf')

Remember to rename the output file “elf_r.vasp” as “ELFCAR”!

Band unfolding

Here, we use MoS2 as an example to illustrate the procedures of band unfolding.
Below is the band structure of MoS2 using a primitive cell. The calculation was
performed with VASP and the input files can be found in the


  1. Create the supercell from the primitive cell, in my case, the supercell is of
    the size 3x3x1, which means that the transformation matrix between supercell
    and primitive cell is

     # The tranformation matrix between supercell and primitive cell.
     M = [[3.0, 0.0, 0.0],
          [0.0, 3.0, 0.0],
          [0.0, 0.0, 1.0]]
  2. In the second step, generate band path in the primitive Brillouin Zone (PBZ)
    and find the correspondig K points of the supercell BZ (SBZ) onto which they

    from unfold import make_kpath, removeDuplicateKpoints, find_K_from_k
    # high-symmetry point of a Hexagonal BZ in fractional coordinate
    kpts = [[0.0, 0.5, 0.0],            # M
            [0.0, 0.0, 0.0],            # G
            [1./3, 1./3, 0.0],          # K
            [0.0, 0.5, 0.0]]            # M
    # create band path from the high-symmetry points, 30 points inbetween each pair
    # of high-symmetry points
    kpath = make_kpath(kpts, nseg=30)
    K_in_sup = []
    for kk in kpath:
        kg, g = find_K_from_k(kk, M)
    # remove the duplicate K-points
    reducedK, kid = removeDuplicateKpoints(K_in_sup, return_map=True)
    # save to VASP KPOINTS
  3. Do one non-SCF calculation of the supercell using the folded K-points and
    obtain the corresponding pseudo-wavefunction. The input files are in
    examples/unfold/sup_3x3x1/. The effective band structure (EBS) and
    then be obtained by processing the WAVECAR file.

    from unfold import unfold
    # basis vector of the primitive cell
    cell = [[ 3.1850, 0.0000000000000000,  0.0],
            [-1.5925, 2.7582909110534373,  0.0],
            [ 0.0000, 0.0000000000000000, 35.0]]
    WaveSuper = unfold(M=M, wavecar='WAVECAR')
    from unfold import EBS_scatter
    sw = WaveSuper.spectral_weight(kpath)
    # show the effective band structure with scatter
    EBS_scatter(kpath, cell, sw, nseg=30, eref=-4.01,
            ylim=(-3, 4), 
    from unfold import EBS_cmaps
    e0, sf = WaveSuper.spectral_function(nedos=4000)
    # or show the effective band structure with colormap
    EBS_cmaps(kpath, cell, e0, sf, nseg=30, eref=-4.01,
            ylim=(-3, 4))

    The EBS from a 3x3x1 supercell calculation are shown below:

    real part |
    imaginary part

    Another example of EBS from a 3x3x1 supercell calculation, where we introduce a
    S vacancy in the structure.

    real part |
    imaginary part

    Yet another band unfolding example from a tetragonal 3x3x1 supercell
    calculation, where the transformation matrix is

     M = [[3.0, 0.0, 0.0],
          [3.0, 6.0, 0.0],
          [0.0, 0.0, 1.0]]

    real part |
    imaginary part

    Compared to the band structure of the primitive cell, there are some empty
    states at the top of figure. This is due to a too small value of NBANDS in
    supercell non-scf calculation, and thus those states are not included.

Band unfolding wth atomic contributions

After band unfolding, we can also superimpose the atomic contribution of each KS
states on the spectral weight. Below is the resulting unfolded band structure of
Ce-doped bilayer-MoS2. Refer to
./examples/unfold/[email protected]_3x3x1/ for the entire code.

imaginary part

Band re-ordering

Band re-ordering is possible by maximizing the overlap between nerghbouring
k-points. The overlap is defined as the inner product of the periodic part of
the Bloch wavefunctions.

                    `< u(n, k) | u(m, k-1) >`

Note, however, the WAVECAR only contains the pseudo-wavefunction, and thus the
pseudo u(n,k) are used in this function. Moreover, since the number of
planewaves for each k-points are different, the inner product is performed in
real space.

The overlap maximalization procedure is as follows:

  1. Pick out those bands with large overlap (> olap_cut).
  2. Assign those un-picked bands by maximizing the overlap.

An example band structure re-ordering is performed in MoS2. The result is shown
in the following image, where the left/right panel shows the
un-ordered/re-ordered band structure.

band_reorder |


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