/
potentials.py
198 lines (173 loc) · 7.24 KB
/
potentials.py
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# 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 pandas
import os
from pyiron_base import Settings
__author__ = "Martin Boeckmann, Jan Janssen"
__copyright__ = (
"Copyright 2020, 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"
s = Settings()
class PotentialAbstract(object):
"""
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:
"""
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 = []
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):
return self._potential_df[(self._potential_df["Name"] == potential_name)]
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())
@staticmethod
def _get_potential_df(plugin_name, file_name_lst, backward_compatibility_name):
"""
Args:
plugin_name (str):
file_name_lst (set):
backward_compatibility_name (str):
Returns:
pandas.DataFrame:
"""
for resource_path in s.resource_paths:
if os.path.exists(os.path.join(resource_path, plugin_name, "potentials")):
resource_path = os.path.join(resource_path, plugin_name, "potentials")
if "potentials" in resource_path:
for path, folder_lst, file_lst in os.walk(resource_path):
for periodic_table_file_name in file_name_lst:
if (
periodic_table_file_name in file_lst
and periodic_table_file_name.endswith(".csv")
):
return pandas.read_csv(
os.path.join(path, periodic_table_file_name),
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(", "),
},
)
elif (
periodic_table_file_name in file_lst
and periodic_table_file_name.endswith(".h5")
):
return pandas.read_hdf(
os.path.join(path, periodic_table_file_name), mode="r"
)
raise ValueError("Was not able to locate the potential files.")
@staticmethod
def _get_potential_default_df(
plugin_name,
file_name_lst={"potentials_vasp_pbe_default.csv"},
backward_compatibility_name="defaultvasppbe",
):
"""
Args:
plugin_name (str):
file_name_lst (set):
backward_compatibility_name (str):
Returns:
pandas.DataFrame:
"""
for resource_path in s.resource_paths:
pot_path = os.path.join(resource_path, plugin_name, "potentials")
if os.path.exists(pot_path):
resource_path = pot_path
if "potentials" in resource_path:
for path, folder_lst, file_lst in os.walk(resource_path):
for periodic_table_file_name in file_name_lst:
if (
periodic_table_file_name in file_lst
and periodic_table_file_name.endswith(".csv")
):
return pandas.read_csv(
os.path.join(path, periodic_table_file_name),
index_col=0,
)
elif (
periodic_table_file_name in file_lst
and periodic_table_file_name.endswith(".h5")
):
return pandas.read_hdf(
os.path.join(path, periodic_table_file_name), mode="r"
)
raise ValueError("Was not able to locate the potential files.")
def find_potential_file_base(path, resource_path_lst, rel_path):
if path is not None:
for resource_path in resource_path_lst:
path_direct = os.path.join(resource_path, path)
path_indirect = os.path.join(resource_path, rel_path, path)
if os.path.exists(path_direct):
return path_direct
elif os.path.exists(path_indirect):
return path_indirect
raise ValueError("Either the filename or the functional has to be defined.",
path, resource_path_lst)