# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
import os
import re
import itertools
import warnings
import logging
import math
import six
import numpy as np
from numpy.linalg import det
from collections import OrderedDict, namedtuple
from hashlib import md5
from monty.io import zopen
from monty.os.path import zpath
from monty.json import MontyDecoder
from enum import Enum
from tabulate import tabulate
import scipy.constants as const
from pymatgen import SETTINGS
from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Structure
from pymatgen.core.periodic_table import Element, get_el_sp
from pymatgen.electronic_structure.core import Magmom
from monty.design_patterns import cached_class
from pymatgen.util.string import str_delimited
from pymatgen.util.io_utils import clean_lines
from monty.json import MSONable
"""
Classes for reading/manipulating/writing VASP input files. All major VASP input
files.
"""
__author__ = "Shyue Ping Ong, Geoffroy Hautier, Rickard Armiento, " + \
"Vincent L Chevrier, Stephen Dacek"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "1.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__status__ = "Production"
__date__ = "Jul 16, 2012"
logger = logging.getLogger(__name__)
[docs]class Poscar(MSONable):
"""
Object for representing the data in a POSCAR or CONTCAR file.
Please note that this current implementation. Most attributes can be set
directly.
Args:
structure (Structure): Structure object.
comment (str): Optional comment line for POSCAR. Defaults to unit
cell formula of structure. Defaults to None.
selective_dynamics (Nx3 array): bool values for selective dynamics,
where N is number of sites. Defaults to None.
true_names (bool): Set to False is the names in the POSCAR are not
well-defined and ambiguous. This situation arises commonly in
vasp < 5 where the POSCAR sometimes does not contain element
symbols. Defaults to True.
velocities (Nx3 array): Velocities for the POSCAR. Typically parsed
in MD runs or can be used to initialize velocities.
predictor_corrector (Nx3 array): Predictor corrector for the POSCAR.
Typically parsed in MD runs.
.. attribute:: structure
Associated Structure.
.. attribute:: comment
Optional comment string.
.. attribute:: true_names
Boolean indication whether Poscar contains actual real names parsed
from either a POTCAR or the POSCAR itself.
.. attribute:: selective_dynamics
Selective dynamics attribute for each site if available. A Nx3 array of
booleans.
.. attribute:: velocities
Velocities for each site (typically read in from a CONTCAR). A Nx3
array of floats.
.. attribute:: predictor_corrector
Predictor corrector coordinates and derivatives for each site; i.e.
a list of three 1x3 arrays for each site (typically read in from a MD
CONTCAR).
.. attribute:: predictor_corrector_preamble
Predictor corrector preamble contains the predictor-corrector key,
POTIM, and thermostat parameters that precede the site-specic predictor
corrector data in MD CONTCAR
.. attribute:: temperature
Temperature of velocity Maxwell-Boltzmann initialization. Initialized
to -1 (MB hasn"t been performed).
"""
def __init__(self, structure, comment=None, selective_dynamics=None,
true_names=True, velocities=None, predictor_corrector=None,
predictor_corrector_preamble=None):
if structure.is_ordered:
site_properties = {}
if selective_dynamics:
site_properties["selective_dynamics"] = selective_dynamics
if velocities:
site_properties["velocities"] = velocities
if predictor_corrector:
site_properties["predictor_corrector"] = predictor_corrector
self.structure = structure.copy(site_properties=site_properties)
self.true_names = true_names
self.comment = structure.formula if comment is None else comment
self.predictor_corrector_preamble = predictor_corrector_preamble
else:
raise ValueError("Structure with partial occupancies cannot be "
"converted into POSCAR!")
self.temperature = -1
@property
def velocities(self):
return self.structure.site_properties.get("velocities")
@property
def selective_dynamics(self):
return self.structure.site_properties.get("selective_dynamics")
@property
def predictor_corrector(self):
return self.structure.site_properties.get("predictor_corrector")
@velocities.setter
def velocities(self, velocities):
self.structure.add_site_property("velocities", velocities)
@selective_dynamics.setter
def selective_dynamics(self, selective_dynamics):
self.structure.add_site_property("selective_dynamics",
selective_dynamics)
@predictor_corrector.setter
def predictor_corrector(self, predictor_corrector):
self.structure.add_site_property("predictor_corrector",
predictor_corrector)
@property
def site_symbols(self):
"""
Sequence of symbols associated with the Poscar. Similar to 6th line in
vasp 5+ POSCAR.
"""
syms = [site.specie.symbol for site in self.structure]
return [a[0] for a in itertools.groupby(syms)]
@property
def natoms(self):
"""
Sequence of number of sites of each type associated with the Poscar.
Similar to 7th line in vasp 5+ POSCAR or the 6th line in vasp 4 POSCAR.
"""
syms = [site.specie.symbol for site in self.structure]
return [len(tuple(a[1])) for a in itertools.groupby(syms)]
def __setattr__(self, name, value):
if name in ("selective_dynamics", "velocities"):
if value is not None and len(value) > 0:
value = np.array(value)
dim = value.shape
if dim[1] != 3 or dim[0] != len(self.structure):
raise ValueError(name + " array must be same length as" +
" the structure.")
value = value.tolist()
super(Poscar, self).__setattr__(name, value)
[docs] @staticmethod
def from_file(filename, check_for_POTCAR=True, read_velocities=True):
"""
Reads a Poscar from a file.
The code will try its best to determine the elements in the POSCAR in
the following order:
1. If check_for_POTCAR is True, the code will try to check if a POTCAR
is in the same directory as the POSCAR and use elements from that by
default. (This is the VASP default sequence of priority).
2. If the input file is Vasp5-like and contains element symbols in the
6th line, the code will use that if check_for_POTCAR is False or there
is no POTCAR found.
3. Failing (2), the code will check if a symbol is provided at the end
of each coordinate.
If all else fails, the code will just assign the first n elements in
increasing atomic number, where n is the number of species, to the
Poscar. For example, H, He, Li, .... This will ensure at least a
unique element is assigned to each site and any analysis that does not
require specific elemental properties should work fine.
Args:
filename (str): File name containing Poscar data.
check_for_POTCAR (bool): Whether to check if a POTCAR is present
in the same directory as the POSCAR. Defaults to True.
read_velocities (bool): Whether to read or not velocities if they
are present in the POSCAR. Default is True.
Returns:
Poscar object.
"""
dirname = os.path.dirname(os.path.abspath(filename))
names = None
if check_for_POTCAR:
for f in os.listdir(dirname):
if f == "POTCAR":
try:
potcar = Potcar.from_file(os.path.join(dirname, f))
names = [sym.split("_")[0] for sym in potcar.symbols]
[get_el_sp(n) for n in names] # ensure valid names
except:
names = None
with zopen(filename, "rt") as f:
return Poscar.from_string(f.read(), names,
read_velocities=read_velocities)
[docs] @staticmethod
def from_string(data, default_names=None, read_velocities=True):
"""
Reads a Poscar from a string.
The code will try its best to determine the elements in the POSCAR in
the following order:
1. If default_names are supplied and valid, it will use those. Usually,
default names comes from an external source, such as a POTCAR in the
same directory.
2. If there are no valid default names but the input file is Vasp5-like
and contains element symbols in the 6th line, the code will use that.
3. Failing (2), the code will check if a symbol is provided at the end
of each coordinate.
If all else fails, the code will just assign the first n elements in
increasing atomic number, where n is the number of species, to the
Poscar. For example, H, He, Li, .... This will ensure at least a
unique element is assigned to each site and any analysis that does not
require specific elemental properties should work fine.
Args:
data (str): String containing Poscar data.
default_names ([str]): Default symbols for the POSCAR file,
usually coming from a POTCAR in the same directory.
read_velocities (bool): Whether to read or not velocities if they
are present in the POSCAR. Default is True.
Returns:
Poscar object.
"""
# "^\s*$" doesn't match lines with no whitespace
chunks = re.split(r"\n\s*\n", data.rstrip(), flags=re.MULTILINE)
try:
if chunks[0] == "":
chunks.pop(0)
chunks[0] = "\n" + chunks[0]
except IndexError:
raise ValueError("Empty POSCAR")
# Parse positions
lines = tuple(clean_lines(chunks[0].split("\n"), False))
comment = lines[0]
scale = float(lines[1])
lattice = np.array([[float(i) for i in line.split()]
for line in lines[2:5]])
if scale < 0:
# In vasp, a negative scale factor is treated as a volume. We need
# to translate this to a proper lattice vector scaling.
vol = abs(det(lattice))
lattice *= (-scale / vol) ** (1 / 3)
else:
lattice *= scale
vasp5_symbols = False
try:
natoms = [int(i) for i in lines[5].split()]
ipos = 6
except ValueError:
vasp5_symbols = True
symbols = lines[5].split()
"""
Atoms and number of atoms in POSCAR written with vasp appear on
multiple lines when atoms of the same type are not grouped together
and more than 20 groups are then defined ...
Example :
Cr16 Fe35 Ni2
1.00000000000000
8.5415010000000002 -0.0077670000000000 -0.0007960000000000
-0.0077730000000000 8.5224019999999996 0.0105580000000000
-0.0007970000000000 0.0105720000000000 8.5356889999999996
Fe Cr Fe Cr Fe Cr Fe Cr Fe Cr Fe Cr Fe Cr Fe Ni Fe Cr Fe Cr
Fe Ni Fe Cr Fe
1 1 2 4 2 1 1 1 2 1 1 1 4 1 1 1 5 3 6 1
2 1 3 2 5
Direct
...
"""
nlines_symbols = 1
for nlines_symbols in range(1, 11):
try:
int(lines[5+nlines_symbols].split()[0])
break
except ValueError:
pass
for iline_symbols in range(6, 5+nlines_symbols):
symbols.extend(lines[iline_symbols].split())
natoms = []
iline_natoms_start = 5+nlines_symbols
for iline_natoms in range(iline_natoms_start,
iline_natoms_start+nlines_symbols):
natoms.extend([int(i) for i in lines[iline_natoms].split()])
atomic_symbols = list()
for i in range(len(natoms)):
atomic_symbols.extend([symbols[i]] * natoms[i])
ipos = 5+2*nlines_symbols
postype = lines[ipos].split()[0]
sdynamics = False
# Selective dynamics
if postype[0] in "sS":
sdynamics = True
ipos += 1
postype = lines[ipos].split()[0]
cart = postype[0] in "cCkK"
nsites = sum(natoms)
# If default_names is specified (usually coming from a POTCAR), use
# them. This is in line with Vasp"s parsing order that the POTCAR
# specified is the default used.
if default_names:
try:
atomic_symbols = []
for i in range(len(natoms)):
atomic_symbols.extend([default_names[i]] * natoms[i])
vasp5_symbols = True
except IndexError:
pass
if not vasp5_symbols:
ind = 3 if not sdynamics else 6
try:
# Check if names are appended at the end of the coordinates.
atomic_symbols = [l.split()[ind]
for l in lines[ipos + 1:ipos + 1 + nsites]]
# Ensure symbols are valid elements
if not all([Element.is_valid_symbol(sym)
for sym in atomic_symbols]):
raise ValueError("Non-valid symbols detected.")
vasp5_symbols = True
except (ValueError, IndexError):
# Defaulting to false names.
atomic_symbols = []
for i in range(len(natoms)):
sym = Element.from_Z(i + 1).symbol
atomic_symbols.extend([sym] * natoms[i])
warnings.warn("Elements in POSCAR cannot be determined. "
"Defaulting to false names %s." %
" ".join(atomic_symbols))
# read the atomic coordinates
coords = []
selective_dynamics = list() if sdynamics else None
for i in range(nsites):
toks = lines[ipos + 1 + i].split()
crd_scale = scale if cart else 1
coords.append([float(j) * crd_scale for j in toks[:3]])
if sdynamics:
selective_dynamics.append([tok.upper()[0] == "T"
for tok in toks[3:6]])
struct = Structure(lattice, atomic_symbols, coords,
to_unit_cell=False, validate_proximity=False,
coords_are_cartesian=cart)
if read_velocities:
# Parse velocities if any
velocities = []
if len(chunks) > 1:
for line in chunks[1].strip().split("\n"):
velocities.append([float(tok) for tok in line.split()])
# Parse the predictor-corrector data
predictor_corrector = []
predictor_corrector_preamble = None
if len(chunks) > 2:
lines = chunks[2].strip().split("\n")
# There are 3 sets of 3xN Predictor corrector parameters
# So can't be stored as a single set of "site_property"
# First line in chunk is a key in CONTCAR
# Second line is POTIM
# Third line is the thermostat parameters
predictor_corrector_preamble = (lines[0] + "\n" + lines[1]
+ "\n" + lines[2])
# Rest is three sets of parameters, each set contains
# x, y, z predictor-corrector parameters for every atom in orde
lines = lines[3:]
for st in range(nsites):
d1 = [float(tok) for tok in lines[st].split()]
d2 = [float(tok) for tok in lines[st+nsites].split()]
d3 = [float(tok) for tok in lines[st+2*nsites].split()]
predictor_corrector.append([d1,d2,d3])
else:
velocities = None
predictor_corrector = None
predictor_corrector_preamble = None
return Poscar(struct, comment, selective_dynamics, vasp5_symbols,
velocities=velocities,
predictor_corrector=predictor_corrector,
predictor_corrector_preamble=predictor_corrector_preamble)
[docs] def get_string(self, direct=True, vasp4_compatible=False,
significant_figures=6):
"""
Returns a string to be written as a POSCAR file. By default, site
symbols are written, which means compatibility is for vasp >= 5.
Args:
direct (bool): Whether coordinates are output in direct or
cartesian. Defaults to True.
vasp4_compatible (bool): Set to True to omit site symbols on 6th
line to maintain backward vasp 4.x compatibility. Defaults
to False.
significant_figures (int): No. of significant figures to
output all quantities. Defaults to 6. Note that positions are
output in fixed point, while velocities are output in
scientific format.
Returns:
String representation of POSCAR.
"""
# This corrects for VASP really annoying bug of crashing on lattices
# which have triple product < 0. We will just invert the lattice
# vectors.
latt = self.structure.lattice
if np.linalg.det(latt.matrix) < 0:
latt = Lattice(-latt.matrix)
lines = [self.comment, "1.0", str(latt)]
if self.true_names and not vasp4_compatible:
lines.append(" ".join(self.site_symbols))
lines.append(" ".join([str(x) for x in self.natoms]))
if self.selective_dynamics:
lines.append("Selective dynamics")
lines.append("direct" if direct else "cartesian")
format_str = "{{:.{0}f}}".format(significant_figures)
for (i, site) in enumerate(self.structure):
coords = site.frac_coords if direct else site.coords
line = " ".join([format_str.format(c) for c in coords])
if self.selective_dynamics is not None:
sd = ["T" if j else "F" for j in self.selective_dynamics[i]]
line += " %s %s %s" % (sd[0], sd[1], sd[2])
line += " " + site.species_string
lines.append(line)
if self.velocities:
try:
lines.append("")
for v in self.velocities:
lines.append(" ".join([format_str.format(i) for i in v]))
except:
warnings.warn("Velocities are missing or corrupted.")
if self.predictor_corrector:
lines.append("")
if self.predictor_corrector_preamble:
lines.append(self.predictor_corrector_preamble)
pred = np.array(self.predictor_corrector)
for col in range(3):
for z in pred[:,col]:
lines.append(" ".join([format_str.format(i) for i in z]))
else:
warnings.warn(
"Preamble information missing or corrupt. "
"Writing Poscar with no predictor corrector data.")
return "\n".join(lines) + "\n"
def __repr__(self):
return self.get_string()
def __str__(self):
"""
String representation of Poscar file.
"""
return self.get_string()
[docs] def write_file(self, filename, **kwargs):
"""
Writes POSCAR to a file. The supported kwargs are the same as those for
the Poscar.get_string method and are passed through directly.
"""
with zopen(filename, "wt") as f:
f.write(self.get_string(**kwargs))
[docs] def as_dict(self):
return {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"structure": self.structure.as_dict(),
"true_names": self.true_names,
"selective_dynamics": np.array(
self.selective_dynamics).tolist(),
"velocities": self.velocities,
"predictor_corrector": self.predictor_corrector,
"comment": self.comment}
[docs] @classmethod
def from_dict(cls, d):
return Poscar(Structure.from_dict(d["structure"]),
comment=d["comment"],
selective_dynamics=d["selective_dynamics"],
true_names=d["true_names"],
velocities=d.get("velocities", None),
predictor_corrector=d.get("predictor_corrector", None))
[docs] def set_temperature(self, temperature):
"""
Initializes the velocities based on Maxwell-Boltzmann distribution.
Removes linear, but not angular drift (same as VASP)
Scales the energies to the exact temperature (microcanonical ensemble)
Velocities are given in A/fs. This is the vasp default when
direct/cartesian is not specified (even when positions are given in
direct coordinates)
Overwrites imported velocities, if any.
Args:
temperature (float): Temperature in Kelvin.
"""
# mean 0 variance 1
velocities = np.random.randn(len(self.structure), 3)
# in AMU, (N,1) array
atomic_masses = np.array([site.specie.atomic_mass.to("kg")
for site in self.structure])
dof = 3 * len(self.structure) - 3
# scale velocities due to atomic masses
# mean 0 std proportional to sqrt(1/m)
velocities /= atomic_masses[:, np.newaxis] ** (1 / 2)
# remove linear drift (net momentum)
velocities -= np.average(atomic_masses[:, np.newaxis] * velocities,
axis=0) / np.average(atomic_masses)
# scale velocities to get correct temperature
energy = np.sum(1 / 2 * atomic_masses *
np.sum(velocities ** 2, axis=1))
scale = (temperature * dof / (2 * energy / const.k)) ** (1 / 2)
velocities *= scale * 1e-5 # these are in A/fs
self.temperature = temperature
try:
del self.structure.site_properties["selective_dynamics"]
except KeyError:
pass
try:
del self.structure.site_properties["predictor_corrector"]
except KeyError:
pass
# returns as a list of lists to be consistent with the other
# initializations
self.structure.add_site_property("velocities", velocities.tolist())
[docs]class Incar(dict, MSONable):
"""
INCAR object for reading and writing INCAR files. Essentially consists of
a dictionary with some helper functions
"""
def __init__(self, params=None):
"""
Creates an Incar object.
Args:
params (dict): A set of input parameters as a dictionary.
"""
super(Incar, self).__init__()
if params:
# if Incar contains vector-like magmoms given as a list
# of floats, convert to a list of lists
if (params.get("MAGMOM") and isinstance(params["MAGMOM"][0], (int, float))) \
and (params.get("LSORBIT") or params.get("LNONCOLLINEAR")):
val = []
for i in range(len(params["MAGMOM"])//3):
val.append(params["MAGMOM"][i*3:(i+1)*3])
params["MAGMOM"] = val
self.update(params)
def __setitem__(self, key, val):
"""
Add parameter-val pair to Incar. Warns if parameter is not in list of
valid INCAR tags. Also cleans the parameter and val by stripping
leading and trailing white spaces.
"""
super(Incar, self).__setitem__(
key.strip(), Incar.proc_val(key.strip(), val.strip())
if isinstance(val, six.string_types) else val)
[docs] def as_dict(self):
d = dict(self)
d["@module"] = self.__class__.__module__
d["@class"] = self.__class__.__name__
return d
[docs] @classmethod
def from_dict(cls, d):
if d.get("MAGMOM") and isinstance(d["MAGMOM"][0], dict):
d["MAGMOM"] = [Magmom.from_dict(m) for m in d["MAGMOM"]]
return Incar({k: v for k, v in d.items() if k not in ("@module",
"@class")})
[docs] def get_string(self, sort_keys=False, pretty=False):
"""
Returns a string representation of the INCAR. The reason why this
method is different from the __str__ method is to provide options for
pretty printing.
Args:
sort_keys (bool): Set to True to sort the INCAR parameters
alphabetically. Defaults to False.
pretty (bool): Set to True for pretty aligned output. Defaults
to False.
"""
keys = self.keys()
if sort_keys:
keys = sorted(keys)
lines = []
for k in keys:
if k == "MAGMOM" and isinstance(self[k], list):
value = []
if (isinstance(self[k][0], list) or isinstance(self[k][0], Magmom)) and \
(self.get("LSORBIT") or self.get("LNONCOLLINEAR")):
value.append(" ".join(str(i) for j in self[k] for i in j))
elif self.get("LSORBIT") or self.get("LNONCOLLINEAR"):
for m, g in itertools.groupby(self[k]):
value.append("3*{}*{}".format(len(tuple(g)), m))
else:
# float() to ensure backwards compatibility between
# float magmoms and Magmom objects
for m, g in itertools.groupby(self[k], lambda x: float(x)):
value.append("{}*{}".format(len(tuple(g)), m))
lines.append([k, " ".join(value)])
elif isinstance(self[k], list):
lines.append([k, " ".join([str(i) for i in self[k]])])
else:
lines.append([k, self[k]])
if pretty:
return str(tabulate([[l[0], "=", l[1]] for l in lines],
tablefmt="plain"))
else:
return str_delimited(lines, None, " = ") + "\n"
def __str__(self):
return self.get_string(sort_keys=True, pretty=False)
[docs] def write_file(self, filename):
"""
Write Incar to a file.
Args:
filename (str): filename to write to.
"""
with zopen(filename, "wt") as f:
f.write(self.__str__())
[docs] @staticmethod
def from_file(filename):
"""
Reads an Incar object from a file.
Args:
filename (str): Filename for file
Returns:
Incar object
"""
with zopen(filename, "rt") as f:
return Incar.from_string(f.read())
[docs] @staticmethod
def from_string(string):
"""
Reads an Incar object from a string.
Args:
string (str): Incar string
Returns:
Incar object
"""
lines = list(clean_lines(string.splitlines()))
params = {}
for line in lines:
m = re.match(r'(\w+)\s*=\s*(.*)', line)
if m:
key = m.group(1).strip()
val = m.group(2).strip()
val = Incar.proc_val(key, val)
params[key] = val
return Incar(params)
[docs] @staticmethod
def proc_val(key, val):
"""
Static helper method to convert INCAR parameters to proper types, e.g.,
integers, floats, lists, etc.
Args:
key: INCAR parameter key
val: Actual value of INCAR parameter.
"""
list_keys = ("LDAUU", "LDAUL", "LDAUJ", "MAGMOM", "DIPOL",
"LANGEVIN_GAMMA", "QUAD_EFG", "EINT")
bool_keys = ("LDAU", "LWAVE", "LSCALU", "LCHARG", "LPLANE", "LUSE_VDW",
"LHFCALC", "ADDGRID", "LSORBIT", "LNONCOLLINEAR")
float_keys = ("EDIFF", "SIGMA", "TIME", "ENCUTFOCK", "HFSCREEN",
"POTIM", "EDIFFG", "AGGAC", "PARAM1", "PARAM2")
int_keys = ("NSW", "NBANDS", "NELMIN", "ISIF", "IBRION", "ISPIN",
"ICHARG", "NELM", "ISMEAR", "NPAR", "LDAUPRINT", "LMAXMIX",
"ENCUT", "NSIM", "NKRED", "NUPDOWN", "ISPIND", "LDAUTYPE",
"IVDW")
def smart_int_or_float(numstr):
if numstr.find(".") != -1 or numstr.lower().find("e") != -1:
return float(numstr)
else:
return int(numstr)
try:
if key in list_keys:
output = []
toks = re.findall(
r"(-?\d+\.?\d*)\*?(-?\d+\.?\d*)?\*?(-?\d+\.?\d*)?", val)
for tok in toks:
if tok[2] and "3" in tok[0]:
output.extend(
[smart_int_or_float(tok[2])] * int(tok[0])
* int(tok[1]))
elif tok[1]:
output.extend([smart_int_or_float(tok[1])] *
int(tok[0]))
else:
output.append(smart_int_or_float(tok[0]))
return output
if key in bool_keys:
m = re.match(r"^\.?([T|F|t|f])[A-Za-z]*\.?", val)
if m:
if m.group(1) == "T" or m.group(1) == "t":
return True
else:
return False
raise ValueError(key + " should be a boolean type!")
if key in float_keys:
return float(re.search(r"^-?\d*\.?\d*[e|E]?-?\d*", val).group(0))
if key in int_keys:
return int(re.match(r"^-?[0-9]+", val).group(0))
except ValueError:
pass
# Not in standard keys. We will try a hierarchy of conversions.
try:
val = int(val)
return val
except ValueError:
pass
try:
val = float(val)
return val
except ValueError:
pass
if "true" in val.lower():
return True
if "false" in val.lower():
return False
return val.strip().capitalize()
[docs] def diff(self, other):
"""
Diff function for Incar. Compares two Incars and indicates which
parameters are the same and which are not. Useful for checking whether
two runs were done using the same parameters.
Args:
other (Incar): The other Incar object to compare to.
Returns:
Dict of the following format:
{"Same" : parameters_that_are_the_same,
"Different": parameters_that_are_different}
Note that the parameters are return as full dictionaries of values.
E.g. {"ISIF":3}
"""
similar_param = {}
different_param = {}
for k1, v1 in self.items():
if k1 not in other:
different_param[k1] = {"INCAR1": v1, "INCAR2": None}
elif v1 != other[k1]:
different_param[k1] = {"INCAR1": v1, "INCAR2": other[k1]}
else:
similar_param[k1] = v1
for k2, v2 in other.items():
if k2 not in similar_param and k2 not in different_param:
if k2 not in self:
different_param[k2] = {"INCAR1": None, "INCAR2": v2}
return {"Same": similar_param, "Different": different_param}
def __add__(self, other):
"""
Add all the values of another INCAR object to this object.
Facilitates the use of "standard" INCARs.
"""
params = {k: v for k, v in self.items()}
for k, v in other.items():
if k in self and v != self[k]:
raise ValueError("Incars have conflicting values!")
else:
params[k] = v
return Incar(params)
[docs]class Kpoints_supported_modes(Enum):
Automatic = 0
Gamma = 1
Monkhorst = 2
Line_mode = 3
Cartesian = 4
Reciprocal = 5
def __str__(self):
return self.name
[docs] @staticmethod
def from_string(s):
c = s.lower()[0]
for m in Kpoints_supported_modes:
if m.name.lower()[0] == c:
return m
raise ValueError("Can't interprete Kpoint mode %s" % s)
[docs]class Kpoints(MSONable):
"""
KPOINT reader/writer.
"""
supported_modes = Kpoints_supported_modes
def __init__(self, comment="Default gamma", num_kpts=0,
style=supported_modes.Gamma,
kpts=((1, 1, 1),), kpts_shift=(0, 0, 0),
kpts_weights=None, coord_type=None, labels=None,
tet_number=0, tet_weight=0, tet_connections=None):
"""
Highly flexible constructor for Kpoints object. The flexibility comes
at the cost of usability and in general, it is recommended that you use
the default constructor only if you know exactly what you are doing and
requires the flexibility. For most usage cases, the three automatic
schemes can be constructed far more easily using the convenience static
constructors (automatic, gamma_automatic, monkhorst_automatic) and it
is recommended that you use those.
Args:
comment (str): String comment for Kpoints
num_kpts: Following VASP method of defining the KPOINTS file, this
parameter is the number of kpoints specified. If set to 0
(or negative), VASP automatically generates the KPOINTS.
style: Style for generating KPOINTS. Use one of the
Kpoints.supported_modes enum types.
kpts (2D array): 2D array of kpoints. Even when only a single
specification is required, e.g. in the automatic scheme,
the kpts should still be specified as a 2D array. e.g.,
[[20]] or [[2,2,2]].
kpts_shift (3x1 array): Shift for Kpoints.
kpts_weights: Optional weights for kpoints. Weights should be
integers. For explicit kpoints.
coord_type: In line-mode, this variable specifies whether the
Kpoints were given in Cartesian or Reciprocal coordinates.
labels: In line-mode, this should provide a list of labels for
each kpt. It is optional in explicit kpoint mode as comments for
k-points.
tet_number: For explicit kpoints, specifies the number of
tetrahedrons for the tetrahedron method.
tet_weight: For explicit kpoints, specifies the weight for each
tetrahedron for the tetrahedron method.
tet_connections: For explicit kpoints, specifies the connections
of the tetrahedrons for the tetrahedron method.
Format is a list of tuples, [ (sym_weight, [tet_vertices]),
...]
The default behavior of the constructor is for a Gamma centered,
1x1x1 KPOINTS with no shift.
"""
if num_kpts > 0 and (not labels) and (not kpts_weights):
raise ValueError("For explicit or line-mode kpoints, either the "
"labels or kpts_weights must be specified.")
self.comment = comment
self.num_kpts = num_kpts
self.kpts = kpts
self.style = style
self.coord_type = coord_type
self.kpts_weights = kpts_weights
self.kpts_shift = kpts_shift
self.labels = labels
self.tet_number = tet_number
self.tet_weight = tet_weight
self.tet_connections = tet_connections
@property
def style(self):
return self._style
@style.setter
def style(self, style):
if isinstance(style, six.string_types):
style = Kpoints.supported_modes.from_string(style)
if style in (Kpoints.supported_modes.Automatic,
Kpoints.supported_modes.Gamma,
Kpoints.supported_modes.Monkhorst) and len(self.kpts) > 1:
raise ValueError("For fully automatic or automatic gamma or monk "
"kpoints, only a single line for the number of "
"divisions is allowed.")
self._style = style
[docs] @staticmethod
def automatic(subdivisions):
"""
Convenient static constructor for a fully automatic Kpoint grid, with
gamma centered Monkhorst-Pack grids and the number of subdivisions
along each reciprocal lattice vector determined by the scheme in the
VASP manual.
Args:
subdivisions: Parameter determining number of subdivisions along
each reciprocal lattice vector.
Returns:
Kpoints object
"""
return Kpoints("Fully automatic kpoint scheme", 0,
style=Kpoints.supported_modes.Automatic,
kpts=[[subdivisions]])
[docs] @staticmethod
def gamma_automatic(kpts=(1, 1, 1), shift=(0, 0, 0)):
"""
Convenient static constructor for an automatic Gamma centered Kpoint
grid.
Args:
kpts: Subdivisions N_1, N_2 and N_3 along reciprocal lattice
vectors. Defaults to (1,1,1)
shift: Shift to be applied to the kpoints. Defaults to (0,0,0).
Returns:
Kpoints object
"""
return Kpoints("Automatic kpoint scheme", 0,
Kpoints.supported_modes.Gamma, kpts=[kpts],
kpts_shift=shift)
[docs] @staticmethod
def monkhorst_automatic(kpts=(2, 2, 2), shift=(0, 0, 0)):
"""
Convenient static constructor for an automatic Monkhorst pack Kpoint
grid.
Args:
kpts: Subdivisions N_1, N_2 and N_3 along reciprocal lattice
vectors. Defaults to (2,2,2)
shift: Shift to be applied to the kpoints. Defaults to (0,0,0).
Returns:
Kpoints object
"""
return Kpoints("Automatic kpoint scheme", 0,
Kpoints.supported_modes.Monkhorst, kpts=[kpts],
kpts_shift=shift)
[docs] @staticmethod
def automatic_density(structure, kppa, force_gamma=False):
"""
Returns an automatic Kpoint object based on a structure and a kpoint
density. Uses Gamma centered meshes for hexagonal cells and
Monkhorst-Pack grids otherwise.
Algorithm:
Uses a simple approach scaling the number of divisions along each
reciprocal lattice vector proportional to its length.
Args:
structure (Structure): Input structure
kppa (int): Grid density
force_gamma (bool): Force a gamma centered mesh (default is to
use gamma only for hexagonal cells or odd meshes)
Returns:
Kpoints
"""
comment = "pymatgen 4.7.6+ generated KPOINTS with grid density = " + \
"%.0f / atom" % kppa
if math.fabs((math.floor(kppa ** (1 / 3) + 0.5)) ** 3 - kppa) < 1:
kppa += kppa * 0.01
latt = structure.lattice
lengths = latt.abc
ngrid = kppa / structure.num_sites
mult = (ngrid * lengths[0] * lengths[1] * lengths[2]) ** (1 / 3)
num_div = [int(math.floor(max(mult / l, 1))) for l in lengths]
is_hexagonal = latt.is_hexagonal()
has_odd = any([i % 2 == 1 for i in num_div])
if has_odd or is_hexagonal or force_gamma:
style = Kpoints.supported_modes.Gamma
else:
style = Kpoints.supported_modes.Monkhorst
return Kpoints(comment, 0, style, [num_div], [0, 0, 0])
[docs] @staticmethod
def automatic_gamma_density(structure, kppa):
"""
Returns an automatic Kpoint object based on a structure and a kpoint
density. Uses Gamma centered meshes always. For GW.
Algorithm:
Uses a simple approach scaling the number of divisions along each
reciprocal lattice vector proportional to its length.
Args:
structure:
Input structure
kppa:
Grid density
"""
latt = structure.lattice
lengths = latt.abc
ngrid = kppa / structure.num_sites
mult = (ngrid * lengths[0] * lengths[1] * lengths[2]) ** (1 / 3)
num_div = [int(round(mult / l)) for l in lengths]
# ensure that numDiv[i] > 0
num_div = [i if i > 0 else 1 for i in num_div]
# VASP documentation recommends to use even grids for n <= 8 and odd
# grids for n > 8.
num_div = [i + i % 2 if i <= 8 else i - i % 2 + 1 for i in num_div]
style = Kpoints.supported_modes.Gamma
comment = "pymatgen 4.7.6+ generated KPOINTS with grid density = " + \
"{} / atom".format(kppa)
num_kpts = 0
return Kpoints(comment, num_kpts, style, [num_div], [0, 0, 0])
[docs] @staticmethod
def automatic_density_by_vol(structure, kppvol, force_gamma=False):
"""
Returns an automatic Kpoint object based on a structure and a kpoint
density per inverse Angstrom of reciprocal cell.
Algorithm:
Same as automatic_density()
Args:
structure (Structure): Input structure
kppvol (int): Grid density per Angstrom^(-3) of reciprocal cell
force_gamma (bool): Force a gamma centered mesh
Returns:
Kpoints
"""
vol = structure.lattice.reciprocal_lattice.volume
kppa = kppvol * vol * structure.num_sites
return Kpoints.automatic_density(structure, kppa,
force_gamma=force_gamma)
[docs] @staticmethod
def automatic_linemode(divisions, ibz):
"""
Convenient static constructor for a KPOINTS in mode line_mode.
gamma centered Monkhorst-Pack grids and the number of subdivisions
along each reciprocal lattice vector determined by the scheme in the
VASP manual.
Args:
divisions: Parameter determining the number of k-points along each
hight symetry lines.
ibz: HighSymmKpath object (pymatgen.symmetry.bandstructure)
Returns:
Kpoints object
"""
kpoints = list()
labels = list()
for path in ibz.kpath["path"]:
kpoints.append(ibz.kpath["kpoints"][path[0]])
labels.append(path[0])
for i in range(1, len(path) - 1):
kpoints.append(ibz.kpath["kpoints"][path[i]])
labels.append(path[i])
kpoints.append(ibz.kpath["kpoints"][path[i]])
labels.append(path[i])
kpoints.append(ibz.kpath["kpoints"][path[-1]])
labels.append(path[-1])
return Kpoints("Line_mode KPOINTS file",
style=Kpoints.supported_modes.Line_mode,
coord_type="Reciprocal",
kpts=kpoints,
labels=labels,
num_kpts=int(divisions))
[docs] @staticmethod
def from_file(filename):
"""
Reads a Kpoints object from a KPOINTS file.
Args:
filename (str): filename to read from.
Returns:
Kpoints object
"""
with zopen(filename, "rt") as f:
return Kpoints.from_string(f.read())
[docs] @staticmethod
def from_string(string):
"""
Reads a Kpoints object from a KPOINTS string.
Args:
string (str): KPOINTS string.
Returns:
Kpoints object
"""
lines = [line.strip() for line in string.splitlines()]
comment = lines[0]
num_kpts = int(lines[1].split()[0].strip())
style = lines[2].lower()[0]
# Fully automatic KPOINTS
if style == "a":
return Kpoints.automatic(int(lines[3]))
coord_pattern = re.compile(r'^\s*([\d+.\-Ee]+)\s+([\d+.\-Ee]+)\s+'
r'([\d+.\-Ee]+)')
# Automatic gamma and Monk KPOINTS, with optional shift
if style == "g" or style == "m":
kpts = [int(i) for i in lines[3].split()]
kpts_shift = (0, 0, 0)
if len(lines) > 4 and coord_pattern.match(lines[4]):
try:
kpts_shift = [int(i) for i in lines[4].split()]
except ValueError:
pass
return Kpoints.gamma_automatic(kpts, kpts_shift) if style == "g" \
else Kpoints.monkhorst_automatic(kpts, kpts_shift)
# Automatic kpoints with basis
if num_kpts <= 0:
style = Kpoints.supported_modes.Cartesian if style in "ck" \
else Kpoints.supported_modes.Reciprocal
kpts = [[float(j) for j in lines[i].split()] for i in range(3, 6)]
kpts_shift = [float(i) for i in lines[6].split()]
return Kpoints(comment=comment, num_kpts=num_kpts, style=style,
kpts=kpts, kpts_shift=kpts_shift)
# Line-mode KPOINTS, usually used with band structures
if style == "l":
coord_type = "Cartesian" if lines[3].lower()[0] in "ck" \
else "Reciprocal"
style = Kpoints.supported_modes.Line_mode
kpts = []
labels = []
patt = re.compile(r'([e0-9.\-]+)\s+([e0-9.\-]+)\s+([e0-9.\-]+)'
r'\s*!*\s*(.*)')
for i in range(4, len(lines)):
line = lines[i]
m = patt.match(line)
if m:
kpts.append([float(m.group(1)), float(m.group(2)),
float(m.group(3))])
labels.append(m.group(4).strip())
return Kpoints(comment=comment, num_kpts=num_kpts, style=style,
kpts=kpts, coord_type=coord_type, labels=labels)
# Assume explicit KPOINTS if all else fails.
style = Kpoints.supported_modes.Cartesian if style in "ck" \
else Kpoints.supported_modes.Reciprocal
kpts = []
kpts_weights = []
labels = []
tet_number = 0
tet_weight = 0
tet_connections = None
for i in range(3, 3 + num_kpts):
toks = lines[i].split()
kpts.append([float(j) for j in toks[0:3]])
kpts_weights.append(float(toks[3]))
if len(toks) > 4:
labels.append(toks[4])
else:
labels.append(None)
try:
# Deal with tetrahedron method
if lines[3 + num_kpts].strip().lower()[0] == "t":
toks = lines[4 + num_kpts].split()
tet_number = int(toks[0])
tet_weight = float(toks[1])
tet_connections = []
for i in range(5 + num_kpts, 5 + num_kpts + tet_number):
toks = lines[i].split()
tet_connections.append((int(toks[0]),
[int(toks[j])
for j in range(1, 5)]))
except IndexError:
pass
return Kpoints(comment=comment, num_kpts=num_kpts,
style=Kpoints.supported_modes[str(style)],
kpts=kpts, kpts_weights=kpts_weights,
tet_number=tet_number, tet_weight=tet_weight,
tet_connections=tet_connections, labels=labels)
[docs] def write_file(self, filename):
"""
Write Kpoints to a file.
Args:
filename (str): Filename to write to.
"""
with zopen(filename, "wt") as f:
f.write(self.__str__())
def __repr__(self):
return self.__str__()
def __str__(self):
lines = [self.comment, str(self.num_kpts), self.style.name]
style = self.style.name.lower()[0]
if style == "l":
lines.append(self.coord_type)
for i in range(len(self.kpts)):
lines.append(" ".join([str(x) for x in self.kpts[i]]))
if style == "l":
lines[-1] += " ! " + self.labels[i]
if i % 2 == 1:
lines[-1] += "\n"
elif self.num_kpts > 0:
if self.labels is not None:
lines[-1] += " %i %s" % (self.kpts_weights[i],
self.labels[i])
else:
lines[-1] += " %i" % (self.kpts_weights[i])
# Print tetrahedron parameters if the number of tetrahedrons > 0
if style not in "lagm" and self.tet_number > 0:
lines.append("Tetrahedron")
lines.append("%d %f" % (self.tet_number, self.tet_weight))
for sym_weight, vertices in self.tet_connections:
lines.append("%d %d %d %d %d" % (sym_weight, vertices[0],
vertices[1], vertices[2],
vertices[3]))
# Print shifts for automatic kpoints types if not zero.
if self.num_kpts <= 0 and tuple(self.kpts_shift) != (0, 0, 0):
lines.append(" ".join([str(x) for x in self.kpts_shift]))
return "\n".join(lines) + "\n"
[docs] def as_dict(self):
"""json friendly dict representation of Kpoints"""
d = {"comment": self.comment, "nkpoints": self.num_kpts,
"generation_style": self.style.name, "kpoints": self.kpts,
"usershift": self.kpts_shift,
"kpts_weights": self.kpts_weights, "coord_type": self.coord_type,
"labels": self.labels, "tet_number": self.tet_number,
"tet_weight": self.tet_weight,
"tet_connections": self.tet_connections}
optional_paras = ["genvec1", "genvec2", "genvec3", "shift"]
for para in optional_paras:
if para in self.__dict__:
d[para] = self.__dict__[para]
d["@module"] = self.__class__.__module__
d["@class"] = self.__class__.__name__
return d
[docs] @classmethod
def from_dict(cls, d):
comment = d.get("comment", "")
generation_style = d.get("generation_style")
kpts = d.get("kpoints", [[1, 1, 1]])
kpts_shift = d.get("usershift", [0, 0, 0])
num_kpts = d.get("nkpoints", 0)
return cls(comment=comment, kpts=kpts, style=generation_style,
kpts_shift=kpts_shift, num_kpts=num_kpts,
kpts_weights=d.get("kpts_weights"),
coord_type=d.get("coord_type"),
labels=d.get("labels"), tet_number=d.get("tet_number", 0),
tet_weight=d.get("tet_weight", 0),
tet_connections=d.get("tet_connections"))
[docs]def parse_string(s):
return "{}".format(s.strip())
[docs]def parse_bool(s):
m = re.match(r"^\.?([TFtf])[A-Za-z]*\.?", s)
if m:
if m.group(1) == "T" or m.group(1) == "t":
return True
else:
return False
raise ValueError(s + " should be a boolean type!")
[docs]def parse_float(s):
return float(re.search(r"^-?\d*\.?\d*[eE]?-?\d*", s).group(0))
[docs]def parse_int(s):
return int(re.match(r"^-?[0-9]+", s).group(0))
[docs]def parse_list(s):
return [float(y) for y in re.split(r"\s+", s.strip()) if not y.isalpha()]
Orbital = namedtuple('Orbital', ['n', 'l', 'j', 'E', 'occ'])
OrbitalDescription = namedtuple('OrbitalDescription',
['l', 'E', 'Type', "Rcut", "Type2", "Rcut2"])
[docs]class PotcarSingle(object):
"""
Object for a **single** POTCAR. The builder assumes the complete string is
the POTCAR contains the complete untouched data in "data" as a string and
a dict of keywords.
Args:
data:
Complete and single potcar file as a string.
.. attribute:: data
POTCAR data as a string.
.. attribute:: keywords
Keywords parsed from the POTCAR as a dict. All keywords are also
accessible as attributes in themselves. E.g., potcar.enmax,
potcar.encut, etc.
"""
functional_dir = {"PBE": "POT_GGA_PAW_PBE",
"PBE_52": "POT_GGA_PAW_PBE_52",
"PBE_54": "POT_GGA_PAW_PBE_54",
"LDA": "POT_LDA_PAW",
"LDA_52": "POT_LDA_PAW_52",
"LDA_54": "POT_LDA_PAW_54",
"PW91": "POT_GGA_PAW_PW91",
"LDA_US": "POT_LDA_US",
"PW91_US": "POT_GGA_US_PW91"}
functional_tags = {"pe": {"name": "PBE", "class": "GGA"},
"91": {"name": "PW91", "class": "GGA"},
"rp": {"name": "revPBE", "class": "GGA"},
"am": {"name": "AM05", "class": "GGA"},
"ps": {"name": "PBEsol", "class": "GGA"},
"pw": {"name": "PW86", "class": "GGA"},
"lm": {"name": "Langreth-Mehl-Hu", "class": "GGA"},
"pb": {"name": "Perdew-Becke", "class": "GGA"},
"ca": {"name": "Perdew-Zunger81", "class": "LDA"},
"hl": {"name": "Hedin-Lundquist", "class": "LDA"},
"wi": {"name": "Wigner Interpoloation", "class": "LDA"}}
parse_functions = {"LULTRA": parse_bool,
"LUNSCR": parse_bool,
"LCOR": parse_bool,
"LPAW": parse_bool,
"EATOM": parse_float,
"RPACOR": parse_float,
"POMASS": parse_float,
"ZVAL": parse_float,
"RCORE": parse_float,
"RWIGS": parse_float,
"ENMAX": parse_float,
"ENMIN": parse_float,
"EMMIN": parse_float,
"EAUG": parse_float,
"DEXC": parse_float,
"RMAX": parse_float,
"RAUG": parse_float,
"RDEP": parse_float,
"RDEPT": parse_float,
"QCUT": parse_float,
"QGAM": parse_float,
"RCLOC": parse_float,
"IUNSCR": parse_int,
"ICORE": parse_int,
"NDATA": parse_int,
"VRHFIN": parse_string,
"LEXCH": parse_string,
"TITEL": parse_string,
"STEP": parse_list,
"RRKJ": parse_list,
"GGA": parse_list}
def __init__(self, data):
self.data = data # raw POTCAR as a string
# Vasp parses header in vasprun.xml and this differs from the titel
self.header = data.split("\n")[0].strip()
search_lines = re.search(r"(?s)(parameters from PSCTR are:"
r".*?END of PSCTR-controll parameters)",
data).group(1)
self.keywords = {}
for key, val in re.findall(r"(\S+)\s*=\s*(.*?)(?=;|$)",
search_lines, flags=re.MULTILINE):
try:
self.keywords[key] = self.parse_functions[key](val)
except KeyError:
warnings.warn("Ignoring unknown variable type %s" % key)
PSCTR = OrderedDict()
array_search = re.compile(r"(-*[0-9.]+)")
orbitals = []
descriptions = []
atomic_configuration = re.search(r"Atomic configuration\s*\n?"
r"(.*?)Description", search_lines)
if atomic_configuration:
lines = atomic_configuration.group(1).splitlines()
num_entries = re.search(r"([0-9]+)", lines[0]).group(1)
num_entries = int(num_entries)
PSCTR['nentries'] = num_entries
for line in lines[1:]:
orbit = array_search.findall(line)
if orbit:
orbitals.append(self.Orbital(int(orbit[0]),
int(orbit[1]),
float(orbit[2]),
float(orbit[3]),
float(orbit[4])))
PSCTR['Orbitals'] = tuple(orbitals)
description_string = re.search(r"(?s)Description\s*\n"
r"(.*?)Error from kinetic"
r" energy argument \(eV\)",
search_lines)
if description_string:
for line in description_string.group(1).splitlines():
description = array_search.findall(line)
if description:
descriptions.append(
OrbitalDescription(
int(description[0]), float(description[1]),
int(description[2]), float(description[3]),
int(description[4]) if len(description) > 4 else None,
float(description[5]) if len(description) > 4 else None))
if descriptions:
PSCTR['OrbitalDescriptions'] = tuple(descriptions)
rrkj_kinetic_energy_string = re.search(
r"(?s)Error from kinetic energy argument \(eV\)\s*\n"
r"(.*?)END of PSCTR-controll parameters",
search_lines)
rrkj_array = []
if rrkj_kinetic_energy_string:
for line in rrkj_kinetic_energy_string.group(1).splitlines():
if "=" not in line:
rrkj_array += parse_list(line.strip('\n'))
if rrkj_array:
PSCTR['RRKJ'] = tuple(rrkj_array)
PSCTR.update(self.keywords)
self.PSCTR = OrderedDict(sorted(PSCTR.items(), key=lambda x: x[0]))
self.hash = self.get_potcar_hash()
def __str__(self):
return self.data + "\n"
@property
def electron_configuration(self):
el = Element.from_Z(self.atomic_no)
full_config = el.full_electronic_structure
nelect = self.nelectrons
config = []
while nelect > 0:
e = full_config.pop(-1)
config.append(e)
nelect -= e[-1]
return config
[docs] def write_file(self, filename):
with zopen(filename, "wt") as f:
f.write(self.__str__())
[docs] @staticmethod
def from_file(filename):
try:
with zopen(filename, "rt") as f:
return PotcarSingle(f.read())
except UnicodeDecodeError:
warnings.warn("POTCAR contains invalid unicode errors. "
"We will attempt to read it by ignoring errors.")
import codecs
with codecs.open(filename, "r", encoding="utf-8",
errors="ignore") as f:
return PotcarSingle(f.read())
[docs] @staticmethod
def from_symbol_and_functional(symbol, functional=None):
if functional is None:
functional = SETTINGS.get("PMG_DEFAULT_FUNCTIONAL", "PBE")
funcdir = PotcarSingle.functional_dir[functional]
d = SETTINGS.get("PMG_VASP_PSP_DIR")
if d is None:
raise ValueError(
"No POTCAR for %s with functional %s found. "
"Please set the PMG_VASP_PSP_DIR environment in "
".pmgrc.yaml, or you may need to set "
"PMG_DEFAULT_FUNCTIONAL to PBE_52 or PBE_54 if you "
"are using newer psps from VASP." % (symbol, functional))
paths_to_try = [os.path.join(d, funcdir, "POTCAR.{}".format(symbol)),
os.path.join(d, funcdir, symbol, "POTCAR")]
for p in paths_to_try:
p = os.path.expanduser(p)
p = zpath(p)
if os.path.exists(p):
return PotcarSingle.from_file(p)
raise IOError("You do not have the right POTCAR with functional " +
"{} and label {} in your VASP_PSP_DIR".format(functional,
symbol))
@property
def symbol(self):
"""
Symbol of POTCAR, e.g., Fe_pv
"""
return self.keywords["TITEL"].split(" ")[1].strip()
@property
def element(self):
"""
Attempt to return the atomic symbol based on the VRHFIN keyword.
"""
element = self.keywords["VRHFIN"].split(":")[0].strip()
#VASP incorrectly gives the element symbol for Xe as "X"
return "Xe" if element == "X" else element
@property
def atomic_no(self):
"""
Attempt to return the atomic number based on the VRHFIN keyword.
"""
return Element(self.element).Z
@property
def nelectrons(self):
return self.zval
@property
def potential_type(self):
if self.lultra:
return "US"
elif self.lpaw:
return "PAW"
else:
return "NC"
@property
def functional(self):
return self.functional_tags.get(self.LEXCH.lower(), {}).get('name')
@property
def functional_class(self):
return self.functional_tags.get(self.LEXCH.lower(), {}).get('class')
[docs] def get_potcar_hash(self):
hash_str = ""
for k, v in self.PSCTR.items():
hash_str += "{}".format(k)
if isinstance(v, int):
hash_str += "{}".format(v)
elif isinstance(v, float):
hash_str += "{:.3f}".format(v)
elif isinstance(v, bool):
hash_str += "{}".format(bool)
elif isinstance(v, (tuple, list)):
for item in v:
if isinstance(item, float):
hash_str += "{:.3f}".format(item)
elif isinstance(item, (Orbital, OrbitalDescription)):
for item_v in item:
if isinstance(item_v, (int, str)):
hash_str += "{}".format(item_v)
elif isinstance(item_v, float):
hash_str += "{:.3f}".format(item_v)
else:
hash_str += "{}".format(item_v) if item_v else ""
else:
hash_str += v.replace(" ", "")
self.hash_str = hash_str
return md5(hash_str.lower().encode('utf-8')).hexdigest()
def __getattr__(self, a):
"""
Delegates attributes to keywords. For example, you can use
potcarsingle.enmax to get the ENMAX of the POTCAR.
For float type properties, they are converted to the correct float. By
default, all energies in eV and all length scales are in Angstroms.
"""
try:
return self.keywords[a.upper()]
except:
raise AttributeError(a)
[docs]class Potcar(list, MSONable):
"""
Object for reading and writing POTCAR files for calculations. Consists of a
list of PotcarSingle.
Args:
symbols ([str]): Element symbols for POTCAR. This should correspond
to the symbols used by VASP. E.g., "Mg", "Fe_pv", etc.
functional (str): Functional used. To know what functional options
there are, use Potcar.FUNCTIONAL_CHOICES. Note that VASP has
different versions of the same functional. By default, the old
PBE functional is used. If you want the newer ones, use PBE_52 or
PBE_54. Note that if you intend to compare your results with the
Materials Project, you should use the default setting. You can also
override the default by setting PMG_DEFAULT_FUNCTIONAL in your
.pmgrc.yaml.
sym_potcar_map (dict): Allows a user to specify a specific element
symbol to raw POTCAR mapping.
"""
FUNCTIONAL_CHOICES = list(PotcarSingle.functional_dir.keys())
def __init__(self, symbols=None, functional=None, sym_potcar_map=None):
if functional is None:
functional = SETTINGS.get("PMG_DEFAULT_FUNCTIONAL", "PBE")
super(Potcar, self).__init__()
self.functional = functional
if symbols is not None:
self.set_symbols(symbols, functional, sym_potcar_map)
[docs] def as_dict(self):
return {"functional": self.functional, "symbols": self.symbols,
"@module": self.__class__.__module__,
"@class": self.__class__.__name__}
[docs] @classmethod
def from_dict(cls, d):
return Potcar(symbols=d["symbols"], functional=d["functional"])
[docs] @staticmethod
def from_file(filename):
try:
with zopen(filename, "rt") as f:
fdata = f.read()
except UnicodeDecodeError:
warnings.warn("POTCAR contains invalid unicode errors. "
"We will attempt to read it by ignoring errors.")
import codecs
with codecs.open(filename, "r", encoding="utf-8",
errors="ignore") as f:
fdata = f.read()
potcar = Potcar()
potcar_strings = re.compile(r"\n?(\s*.*?End of Dataset)",
re.S).findall(fdata)
functionals = []
for p in potcar_strings:
single = PotcarSingle(p)
potcar.append(single)
functionals.append(single.functional)
if len(set(functionals)) != 1:
raise ValueError("File contains incompatible functionals!")
else:
potcar.functional = functionals[0]
return potcar
def __str__(self):
return "\n".join([str(potcar).strip("\n") for potcar in self]) + "\n"
[docs] def write_file(self, filename):
"""
Write Potcar to a file.
Args:
filename (str): filename to write to.
"""
with zopen(filename, "wt") as f:
f.write(self.__str__())
@property
def symbols(self):
"""
Get the atomic symbols of all the atoms in the POTCAR file.
"""
return [p.symbol for p in self]
@symbols.setter
def symbols(self, symbols):
self.set_symbols(symbols, functional=self.functional)
@property
def spec(self):
"""
Get the atomic symbols and hash of all the atoms in the POTCAR file.
"""
return [{"symbol": p.symbol, "hash": p.get_potcar_hash()} for p in self]
[docs] def set_symbols(self, symbols, functional=None,
sym_potcar_map=None):
"""
Initialize the POTCAR from a set of symbols. Currently, the POTCARs can
be fetched from a location specified in .pmgrc.yaml. Use pmg config
to add this setting.
Args:
symbols ([str]): A list of element symbols
functional (str): The functional to use. If None, the setting
PMG_DEFAULT_FUNCTIONAL in .pmgrc.yaml is used, or if this is
not set, it will default to PBE.
sym_potcar_map (dict): A map of symbol:raw POTCAR string. If
sym_potcar_map is specified, POTCARs will be generated from
the given map data rather than the config file location.
"""
del self[:]
if sym_potcar_map:
for el in symbols:
self.append(PotcarSingle(sym_potcar_map[el]))
else:
for el in symbols:
p = PotcarSingle.from_symbol_and_functional(el, functional)
self.append(p)