# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
import math
import os
import warnings
from typing import Optional, cast
import numpy as np
from ase.atoms import Atoms
from vaspparser.dft.volumetric import VolumetricData
from vaspparser.vasp.structure import (
atoms_from_string,
get_species_list_from_potcar,
)
__author__ = "Sudarsan Surendralal"
__copyright__ = (
"Copyright 2021, Max-Planck-Institut für Eisenforschung GmbH - "
"Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Sudarsan Surendralal"
__email__ = "surendralal@mpie.de"
__status__ = "production"
__date__ = "Sep 1, 2017"
class VaspVolumetricData(VolumetricData):
"""
General class for parsing and manipulating volumetric static within VASP. The basic idea of the Base class is
adapted from the pymatgen vasp VolumtricData class
http://pymatgen.org/_modules/pymatgen/io/vasp/outputs.html#VolumetricData
"""
[docs]
def __init__(self):
super().__init__()
self.atoms = None
self._diff_data = None
self._total_data = None
[docs]
def from_file(self, filename: str, normalize: bool = True):
"""
Parsing the contents of from a file
Args:
filename (str): Path of file to parse
normalize (boolean): Flag to normalize by the volume of the cell
"""
try:
self.atoms, vol_data_list = self._read_vol_data(
filename=filename, normalize=normalize
)
except (ValueError, IndexError, TypeError):
try:
self.atoms, vol_data_list = self._read_vol_data_old(
filename=filename, normalize=normalize
)
except (ValueError, IndexError, TypeError):
raise ValueError(f"Unable to parse file: {filename}")
if self.atoms is not None:
self._total_data = vol_data_list[0]
if len(vol_data_list) > 1:
self._diff_data = vol_data_list[1]
@staticmethod
def _read_vol_data_old(filename: str, normalize: bool = True):
"""
Convenience method to parse a generic volumetric static file in the vasp like format.
Used by subclasses for parsing the file. This routine is adapted from the pymatgen vasp VolumetricData
class with very minor modifications. The new parser is faster
http://pymatgen.org/_modules/pymatgen/io/vasp/outputs.html#VolumetricData.
Args:
filename (str): Path of file to parse
normalize (boolean): Flag to normalize by the volume of the cell
"""
if os.stat(filename).st_size == 0:
warnings.warn(
"File:" + filename + "seems to be corrupted/empty", stacklevel=2
)
return None, None
poscar_read = False
poscar_string: list[str] = []
dataset: Optional[np.ndarray] = None
all_dataset: list[np.ndarray] = []
dim: Optional[list[int]] = None
dimline: Optional[str] = None
read_dataset = False
ngrid_pts = 0
data_count = 0
atoms = None
volume = 1.0
with open(filename) as f:
for line in f:
line = line.strip()
if read_dataset:
if dataset is None or dim is None:
raise ValueError("Volumetric grid is not initialized")
toks = line.split()
for tok in toks:
if data_count < ngrid_pts:
# This complicated procedure is necessary because
# vasp outputs x as the fastest index, followed by y
# then z.
x = data_count % dim[0]
y = int(math.floor(data_count / dim[0])) % dim[1]
z = int(math.floor(data_count / dim[0] / dim[1]))
dataset[x, y, z] = float(tok)
data_count += 1
if data_count >= ngrid_pts:
read_dataset = False
data_count = 0
all_dataset.append(dataset)
elif not poscar_read:
if line != "" or len(poscar_string) == 0:
poscar_string.append(line)
elif line == "":
try:
atoms = cast(Atoms, atoms_from_string(poscar_string))
except ValueError:
pot_str = filename.split("/")
pot_str[-1] = "POTCAR"
potcar_file = "/".join(pot_str)
species = get_species_list_from_potcar(potcar_file)
atoms = cast(
Atoms,
atoms_from_string(poscar_string, species_list=species),
)
volume = atoms.get_volume()
poscar_read = True
elif not dim:
dim = [int(i) for i in line.split()]
ngrid_pts = dim[0] * dim[1] * dim[2]
dimline = line
read_dataset = True
dataset = np.zeros(dim)
elif line == dimline:
read_dataset = True
dataset = np.zeros(dim)
if not normalize:
volume = 1.0
if len(all_dataset) == 0:
warnings.warn(
"File:" + filename + "seems to be corrupted/empty", stacklevel=2
)
return None, None
if len(all_dataset) == 2:
data = {
"total": all_dataset[0] / volume,
"diff": all_dataset[1] / volume,
}
return atoms, [data["total"], data["diff"]]
else:
data = {"total": all_dataset[0] / volume}
return atoms, [data["total"]]
def _read_vol_data(self, filename: str, normalize: bool = True):
"""
Parses the VASP volumetric type files (CHGCAR, LOCPOT, PARCHG etc). Rather than looping over individual values,
this function utilizes numpy indexing resulting in a parsing efficiency of at least 10%.
Args:
filename (str): File to be parsed
normalize (bool): Normalize the data with respect to the volume (Recommended for CHGCAR files)
Returns:
pyiron.atomistics.structure.atoms.Atoms: The structure of the volumetric snapshot
list: A list of the volumetric data (length >1 for CHGCAR files with spin)
"""
if not os.path.getsize(filename) > 0:
warnings.warn("File:" + filename + "seems to be empty! ", stacklevel=2)
return None, None
with open(filename) as f:
struct_lines: list[str] = []
get_grid = False
n_x = 0
n_y = 0
n_z = 0
n_grid = 0
n_grid_str = None
total_data_list: list[np.ndarray] = []
atoms = None
for line in f:
strip_line = line.strip()
if not get_grid:
if strip_line == "":
get_grid = True
struct_lines.append(strip_line)
elif n_grid_str is None:
n_x, n_y, n_z = [int(val) for val in strip_line.split()]
n_grid = n_x * n_y * n_z
n_grid_str = " ".join([str(val) for val in [n_x, n_y, n_z]])
load_txt = np.asarray(
np.genfromtxt(f, max_rows=int(n_grid / 5))
).ravel()
if n_grid % 5 != 0:
add_line = np.genfromtxt(f, max_rows=1)
load_txt = np.append(load_txt, np.asarray(add_line).ravel())
total_data = self._fastest_index_reshape(load_txt, [n_x, n_y, n_z])
try:
atoms = cast(Atoms, atoms_from_string(struct_lines))
except ValueError:
pot_str = filename.split("/")
pot_str[-1] = "POTCAR"
potcar_file = "/".join(pot_str)
species = get_species_list_from_potcar(potcar_file)
atoms = cast(
Atoms, atoms_from_string(struct_lines, species_list=species)
)
if normalize:
total_data /= atoms.get_volume()
total_data_list.append(total_data)
elif atoms is not None:
grid_str = n_grid_str.replace(" ", "")
if grid_str == strip_line.replace(" ", ""):
load_txt = np.asarray(
np.genfromtxt(f, max_rows=int(n_grid / 5))
).ravel()
if n_grid % 5 != 0:
add_line = np.genfromtxt(f, max_rows=1)
load_txt = np.append(load_txt, np.asarray(add_line).ravel())
total_data = self._fastest_index_reshape(
load_txt, [n_x, n_y, n_z]
)
if normalize:
total_data /= atoms.get_volume()
total_data_list.append(total_data)
if len(total_data_list) == 0:
warnings.warn(
"File:"
+ filename
+ "seems to be corrupted/empty even after parsing!",
stacklevel=2,
)
return None, None
return atoms, total_data_list
@staticmethod
def _fastest_index_reshape(raw_data: np.ndarray, grid: list):
"""
Helper function to parse volumetric data with x-axis as the fastest index into a 3D numpy array
Args:
raw_data (numpy.ndarray): Raw unprocessed volumetric data which is flattened
grid (list/turple/numpy.ndarray): Sequence of the integer grid points [Nx, Ny, Nz]
Returns:
numpy.ndarray: A Nx $\times$ Ny $\times$ Nz numpy array
"""
n_x, n_y, n_z = grid
total_data = np.zeros((n_x, n_y, n_z))
all_data = raw_data[0 : np.prod(grid)]
all_indices = np.arange(len(all_data), dtype=int)
x_indices = all_indices % n_x
y_indices = all_indices / n_x % n_y
y_indices = np.array(y_indices, dtype=int)
z_indices = all_indices / (n_x * n_y)
z_indices = np.array(z_indices, dtype=int)
total_data[x_indices, y_indices, z_indices] = all_data
return total_data
@property
def total_data(self):
"""
numpy.ndarray: Total volumtric data (3D)
"""
return self._total_data
@total_data.setter
def total_data(self, val):
self._total_data = val
@property
def diff_data(self):
"""
numpy.ndarray: Volumtric difference data (3D)
"""
return self._diff_data
@diff_data.setter
def diff_data(self, val):
self._diff_data = val
def to_dict(self) -> dict:
volumetric_data_dict = {
"TYPE": str(type(self)),
"total": self.total_data,
}
if self.diff_data is not None:
volumetric_data_dict["diff"] = self.diff_data
return volumetric_data_dict
