Source code for pyiron.vasp.volumetric_data

# coding: utf-8
# 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 numpy as np
import os
from pyiron.base.settings.generic import Settings
from pyiron.vasp.structure import atoms_from_string, get_species_list_from_potcar
from pyiron.atomistics.volumetric.generic import VolumetricData

__author__ = "Sudarsan Surendralal"
__copyright__ = (
    "Copyright 2020, 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"


[docs]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 """ def __init__(self): super(VaspVolumetricData, self).__init__() self.atoms = None self._diff_data = None self._total_data = None
[docs] def from_file(self, filename, normalize=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("Unable to parse file: {}".format(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, normalize=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: s = Settings() s.logger.warning("File:" + filename + "seems to be corrupted/empty") return None, None poscar_read = False poscar_string = list() dataset = list() all_dataset = list() dim = None dimline = None read_dataset = False ngrid_pts = 0 data_count = 0 atoms = None volume = None with open(filename, "r") as f: for line in f: line = line.strip() if read_dataset: 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 = 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 = 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: s = Settings() s.logger.warning("File:" + filename + "seems to be corrupted/empty") 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, normalize=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: s = Settings() s.logger.warning("File:" + filename + "seems to be empty! ") return None, None with open(filename, "r") as f: struct_lines = list() get_grid = False n_x = 0 n_y = 0 n_z = 0 n_grid = 0 n_grid_str = None total_data_list = list() 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.genfromtxt(f, max_rows=int(n_grid / 5)) load_txt = np.hstack(load_txt) if n_grid % 5 != 0: add_line = np.genfromtxt(f, max_rows=1) load_txt = np.append(load_txt, np.hstack(add_line)) total_data = self._fastest_index_reshape(load_txt, [n_x, n_y, n_z]) try: 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 = 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.genfromtxt(f, max_rows=int(n_grid / 5)) load_txt = np.hstack(load_txt) if n_grid % 5 != 0: add_line = np.genfromtxt(f, max_rows=1) load_txt = np.hstack( np.append(load_txt, np.hstack(add_line)) ) 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: s = Settings() s.logger.warning( "File:" + filename + "seems to be corrupted/empty even after parsing!" ) return None, None return atoms, total_data_list @staticmethod def _fastest_index_reshape(raw_data, grid): """ 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
[docs] def to_hdf(self, hdf5, group_name="volumetric_data"): """ Writes the data as a group to a HDF5 file Args: hdf5 (pyiron.base.generic.hdfio.ProjectHDFio): The HDF file/path to write the data to group_name (str): The name of the group under which the data must be stored as """ with hdf5.open(group_name) as hdf_vd: hdf_vd["TYPE"] = str(type(self)) hdf_vd["total"] = self.total_data if self.diff_data is not None: hdf_vd["diff"] = self.diff_data
[docs] def from_hdf(self, hdf5, group_name="volumetric_data"): """ Recreating the VolumetricData instance by reading data from the HDF5 files Args: hdf5 (pyiron.base.generic.hdfio.ProjectHDFio): The HDF file/path to write the data to group_name (str): The name of the group under which the data must be stored as Returns: pyiron.atomistics.volumetric.generic.VolumetricData: The VolumetricData instance """ with hdf5.open(group_name) as hdf_vd: self._total_data = hdf_vd["total"] if "diff" in hdf_vd.list_nodes(): self._diff_data = hdf_vd["diff"]