# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
from __future__ import annotations
from numbers import Integral
import numpy as np
from sisl import Grid
from sisl._internal import set_module
from .._help import grid_reduce_indices
from ..sile import add_sile, sile_fh_open
from .car import carSileVASP
from .sile import SileVASP
__all__ = ["chgSileVASP"]
@set_module("sisl.io.vasp")
class chgSileVASP(carSileVASP):
"""Charge density plus geometry
This file-object handles the charge-density from VASP
"""
[docs]
@sile_fh_open(True)
def read_grid(self, index=0, dtype=np.float64, **kwargs) -> Grid:
r"""Reads the charge density from the file and returns with a grid (plus geometry)
Parameters
----------
index : int or array_like, optional
the index of the grid to read.
For spin-polarized calculations, 0 and 1 refer to the charge (spin-up plus spin-down) and
magnetitization (spin-up minus spin-down), respectively.
For non-collinear calculations, 0 refers to the charge while 1, 2 and 3 to
the magnetization in the :math:`\sigma_1`, :math:`\sigma_2`, and :math:`\sigma_3` directions, respectively.
The directions are related via the VASP input option ``SAXIS``.
TOTAL, x, y, z charge density with the Cartesian directions equal to the charge
magnetization.
For array-like they refer to the fractional contributions for each corresponding index.
dtype : numpy.dtype, optional
grid stored dtype
spin : optional
same as `index` argument. `spin` argument has precedence.
Examples
--------
Read the spin polarization from a spin-polarized CHGCAR file
>>> fh = sisl.get_sile('CHGCAR')
>>> charge = fh.read_grid()
>>> spin = fh.read_grid(1)
>>> up_density = fh.read_grid([0.5, 0.5])
>>> assert np.allclose((charge + spin).grid / 2, up_density.grid)
>>> down_density = fh.read_grid([0.5, -0.5])
>>> assert np.allclose((charge - spin).grid / 2, down_density.grid)
Returns
-------
Grid
charge density grid with associated geometry
"""
index = kwargs.get("spin", index)
geom = self.read_geometry()
V = geom.lattice.volume
rl = self.readline
# Now we are past the cell and geometry
# We can now read the size of CHGCAR
rl()
nx, ny, nz = list(map(int, rl().split()))
n = nx * ny * nz
is_index = True
if isinstance(index, Integral):
max_index = index + 1
else:
is_index = False
max_index = len(index)
vals = []
vext = vals.extend
is_chgcar = True
i = 0
while i < n * max_index:
line = rl().split()
# CHG: 10 columns, CHGCAR: 5 columns
if is_chgcar and len(line) > 5:
# we have a data line with more than 5 columns, must be a CHG file
is_chgcar = False
vext(line)
i = len(vals)
if i % n == 0 and i < n * max_index:
if is_chgcar:
# Read over augmentation occupancies
line = rl()
while "augmentation" in line:
occ = int(line.split()[-1])
j = 0
while j < occ:
j += len(rl().split())
line = rl()
# read over an additional block with geom.na entries???
j = len(line.split())
while j < geom.na:
j += len(rl().split())
# one line of nx, ny, nz
assert np.allclose(list(map(int, rl().split())), [nx, ny, nz])
# Cut size before proceeding (otherwise it *may* fail)
vals = np.array(vals).astype(dtype, copy=False)
if is_index:
val = vals[n * index : n * (index + 1)].reshape(nz, ny, nx)
else:
vals = vals[: n * max_index].reshape(-1, nz, ny, nx)
val = grid_reduce_indices(vals, index, axis=0)
del vals
# Make it C-ordered with nx, ny, nz
val = np.swapaxes(val, 0, 2) / V
# Create the grid with data
# Since we populate the grid data afterwards there
# is no need to create a bigger grid than necessary.
grid = Grid([1, 1, 1], dtype=dtype, geometry=geom)
grid.grid = val
return grid
# CHG has low-precision, so the user should prefer CHGCAR
add_sile("CHG", chgSileVASP, gzip=True)
add_sile("CHGCAR", chgSileVASP, gzip=True)