/
potential.py
211 lines (181 loc) · 7.32 KB
/
potential.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
# 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.
from __future__ import print_function
import pandas as pd
import shutil
import os
from pyiron.base.settings.generic import Settings
from pyiron.base.generic.parameters import GenericParameters
from pyiron.atomistics.job.potentials import PotentialAbstract
__author__ = "Joerg Neugebauer, Sudarsan Surendralal, Jan Janssen"
__copyright__ = (
"Copyright 2019, 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"
s = Settings()
class LammpsPotential(GenericParameters):
"""
This module helps write commands which help in the control of parameters related to the potential used in LAMMPS
simulations
"""
def __init__(self, input_file_name=None):
super(LammpsPotential, self).__init__(
input_file_name=input_file_name,
table_name="potential_inp",
comment_char="#",
)
self._potential = None
self._attributes = {}
self._df = None
@property
def df(self):
return self._df
@df.setter
def df(self, new_dataframe):
self._df = new_dataframe
# ToDo: In future lammps should also support more than one potential file - that is currently not implemented.
try:
self.load_string("".join(list(new_dataframe["Config"])[0]))
except IndexError:
raise ValueError(
"Potential not found! "
"Validate the potential name by self.potential in self.list_potentials()."
)
def remove_structure_block(self):
self.remove_keys(["units"])
self.remove_keys(["atom_style"])
self.remove_keys(["dimension"])
@property
def files(self):
if list(self._df["Filename"])[0]:
absolute_file_paths = [
files for files in list(self._df["Filename"])[0] if os.path.isabs(files)
]
relative_file_paths = [
files
for files in list(self._df["Filename"])[0]
if not os.path.isabs(files)
]
for path in relative_file_paths:
for resource_path in s.resource_paths:
if os.path.exists(
os.path.join(resource_path, "lammps", "potentials")
):
resource_path = os.path.join(
resource_path, "lammps", "potentials"
)
if os.path.exists(os.path.join(resource_path, path)):
absolute_file_paths.append(os.path.join(resource_path, path))
break
if len(absolute_file_paths) != len(list(self._df["Filename"])[0]):
raise ValueError("Was not able to locate the potentials.")
else:
return absolute_file_paths
def copy_pot_files(self, working_directory):
if self.files is not None:
_ = [shutil.copy(path_pot, working_directory) for path_pot in self.files]
def get_element_lst(self):
return list(self._df["Species"])[0]
def to_hdf(self, hdf, group_name=None):
if self._df is not None:
with hdf.open("potential") as hdf_pot:
hdf_pot["Config"] = self._df["Config"].values[0]
hdf_pot["Filename"] = self._df["Filename"].values[0]
hdf_pot["Name"] = self._df["Name"].values[0]
hdf_pot["Model"] = self._df["Model"].values[0]
hdf_pot["Species"] = self._df["Species"].values[0]
super(LammpsPotential, self).to_hdf(hdf, group_name=group_name)
def from_hdf(self, hdf, group_name=None):
with hdf.open("potential") as hdf_pot:
try:
self._df = pd.DataFrame(
{
"Config": [hdf_pot["Config"]],
"Filename": [hdf_pot["Filename"]],
"Name": [hdf_pot["Name"]],
"Model": [hdf_pot["Model"]],
"Species": [hdf_pot["Species"]],
}
)
except ValueError:
pass
super(LammpsPotential, self).from_hdf(hdf, group_name=group_name)
class LammpsPotentialFile(PotentialAbstract):
"""
The Potential class is derived from the PotentialAbstract class, but instead of loading the potentials from a list,
the potentials are loaded from a file.
Args:
potential_df:
default_df:
selected_atoms:
"""
def __init__(self, potential_df=None, default_df=None, selected_atoms=None):
if potential_df is None:
potential_df = self._get_potential_df(
plugin_name="lammps",
file_name_lst={"potentials_lammps.csv"},
backward_compatibility_name="lammpspotentials",
)
super(LammpsPotentialFile, self).__init__(
potential_df=potential_df,
default_df=default_df,
selected_atoms=selected_atoms,
)
def default(self):
if self._default_df is not None:
atoms_str = "_".join(sorted(self._selected_atoms))
return self._default_df[
(self._default_df["Name"] == self._default_df.loc[atoms_str].values[0])
]
return None
def find_default(self, element):
"""
Find the potentials
Args:
element (set, str): element or set of elements for which you want the possible LAMMPS potentials
path (bool): choose whether to return the full path to the potential or just the potential name
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!")
element_lst = list(element)
if self._default_df is not None:
merged_lst = list(set(self._selected_atoms + element_lst))
atoms_str = "_".join(sorted(merged_lst))
return self._default_df[
(self._default_df["Name"] == self._default_df.loc[atoms_str].values[0])
]
return None
def __getitem__(self, item):
potential_df = self.find(element=item)
selected_atoms = self._selected_atoms + [item]
return LammpsPotentialFile(
potential_df=potential_df,
default_df=self._default_df,
selected_atoms=selected_atoms,
)
class PotentialAvailable(object):
def __init__(self, list_of_potentials):
self._list_of_potentials = list_of_potentials
def __getattr__(self, name):
if name in self._list_of_potentials:
return name
else:
raise AttributeError
def __dir__(self):
return self._list_of_potentials
def __repr__(self):
return str(dir(self))