# 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.
"""
An abstract Potential class to provide an easy access for the available potentials. Currently implemented for the
OpenKim https://openkim.org database.
"""
import os
from abc import ABC, abstractmethod
import pandas
from pyiron_base import state
from pyiron_snippets.resources import ResourceNotFound, ResourceResolver
__author__ = "Martin Boeckmann, Jan Janssen"
__copyright__ = (
"Copyright 2021, Max-Planck-Institut für Eisenforschung GmbH - "
"Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Jan Janssen"
__email__ = "janssen@mpie.de"
__status__ = "development"
__date__ = "Sep 1, 2017"
[docs]
class PotentialAbstract(ABC):
"""
The PotentialAbstract class loads a list of available potentials and sorts them. Afterwards the potentials can be
accessed through:
PotentialAbstract.<Element>.<Element> or PotentialAbstract.find_potentials_set({<Element>, <Element>}
Args:
potential_df:
default_df:
selected_atoms:
"""
@property
@abstractmethod
def resource_plugin_name(self) -> str:
"""Return the name of the folder of this plugin/code in the pyiron resources.
One of lammps/vasp/sphinx, to be overriden in the specific sub classes."""
pass
[docs]
def __init__(self, potential_df, default_df=None, selected_atoms=None):
self._potential_df = potential_df
self._default_df = default_df
if selected_atoms is not None:
self._selected_atoms = selected_atoms
else:
self._selected_atoms = []
[docs]
def find(self, element):
"""
Find the potentials
Args:
element (set, str): element or set of elements for which you want the possible LAMMPS potentials
Returns:
list: of possible potentials for the element or the combination of elements
"""
if isinstance(element, set):
element = element
elif isinstance(element, list):
element = set(element)
elif isinstance(element, str):
element = set([element])
else:
raise TypeError("Only, str, list and set supported!")
return self._potential_df[
[
True if set(element).issubset(species) else False
for species in self._potential_df["Species"].values
]
]
def find_by_name(self, potential_name):
mask = self._potential_df["Name"] == potential_name
if not mask.any():
raise ValueError(
"Potential '{}' not found in database.".format(potential_name)
)
return self._potential_df[mask]
[docs]
def list(self):
"""
List the available potentials
Returns:
list: of possible potentials for the element or the combination of elements
"""
return self._potential_df
def __getattr__(self, item):
return self[item]
def __getitem__(self, item):
potential_df = self.find(element=item)
selected_atoms = self._selected_atoms + [item]
return PotentialAbstract(
potential_df=potential_df,
default_df=self._default_df,
selected_atoms=selected_atoms,
)
def __str__(self):
return str(self.list())
@classmethod
def _get_resolver(cls):
"""Return a ResourceResolver that can be searched for potential files or potential dataframes.
This exists primarily so that the lammps and sphinx sub classes can overload it to add their conda package
specific resource paths.
Returns:
:class:`.ResourceResolver`
"""
return ResourceResolver(
state.settings.resource_paths,
cls.resource_plugin_name,
"potentials",
)
@classmethod
def _get_potential_df(cls, file_name_lst):
"""
Args:
file_name_lst (set):
Returns:
pandas.DataFrame:
"""
env = os.environ
def read_csv(path):
return pandas.read_csv(
path,
index_col=0,
converters={
"Species": lambda x: x.replace("'", "").strip("[]").split(", "),
"Config": lambda x: (
x.replace("'", "").replace("\\n", "\n").strip("[]").split(", ")
),
"Filename": lambda x: x.replace("'", "").strip("[]").split(", "),
},
)
files = cls._get_resolver().list(file_name_lst)
if len(files) > 0:
return pandas.concat(map(read_csv, files), ignore_index=True)
else:
raise ValueError(
f"Was not able to locate the potential files in {cls._get_resolver()}!"
)
@classmethod
def _get_potential_default_df(
cls,
file_name_lst={"potentials_vasp_pbe_default.csv"},
):
"""
Args:
file_name_lst (set):
Returns:
pandas.DataFrame:
"""
try:
return pandas.read_csv(
cls._get_resolver().first(file_name_lst), index_col=0
)
except ResourceNotFound:
raise ValueError("Was not able to locate the potential files.") from None
@classmethod
def find_potential_file(cls, path):
res = cls._get_resolver()
try:
return res.first(path)
except ResourceNotFound:
raise ValueError(f"Could not find file '{path}' in {res}!") from None
