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# coding: utf-8 

# Copyright (c) Pymatgen Development Team. 

# Distributed under the terms of the MIT License. 

 

from __future__ import division, unicode_literals 

 

""" 

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" 

 

import os 

import re 

import itertools 

import warnings 

import logging 

 

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.core.lattice import Lattice 

from pymatgen.core.structure import Structure 

from pymatgen.core.periodic_table import Element, get_el_sp 

from monty.design_patterns import cached_class 

from pymatgen.util.string_utils import str_delimited 

from pymatgen.util.io_utils import clean_lines 

from monty.json import MSONable 

 

 

logger = logging.getLogger(__name__) 

 

 

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) 

 

@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) 

 

@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("\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() 

natoms = [int(i) for i in lines[6].split()] 

atomic_symbols = list() 

for i in range(len(natoms)): 

atomic_symbols.extend([symbols[i]] * natoms[i]) 

ipos = 7 

 

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 {}." 

.format(" ".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) 

 

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() 

 

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)) 

 

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} 

 

@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)) 

 

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()) 

 

 

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 params.get("MAGMOM") 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) 

 

def as_dict(self): 

d = dict(self) 

d["@module"] = self.__class__.__module__ 

d["@class"] = self.__class__.__name__ 

return d 

 

@classmethod 

def from_dict(cls, d): 

return Incar({k: v for k, v in d.items() if k not in ("@module", 

"@class")}) 

 

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) 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: 

for m, g in itertools.groupby(self[k]): 

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) 

 

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__()) 

 

@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()) 

 

@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("(\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) 

 

@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") 

bool_keys = ("LDAU", "LWAVE", "LSCALU", "LCHARG", "LPLANE", 

"LHFCALC", "ADDGRID", "LSORBIT", "LNONCOLLINEAR") 

float_keys = ("EDIFF", "SIGMA", "TIME", "ENCUTFOCK", "HFSCREEN", 

"POTIM", "EDIFFG") 

int_keys = ("NSW", "NBANDS", "NELMIN", "ISIF", "IBRION", "ISPIN", 

"ICHARG", "NELM", "ISMEAR", "NPAR", "LDAUPRINT", "LMAXMIX", 

"ENCUT", "NSIM", "NKRED", "NUPDOWN", "ISPIND", "LDAUTYPE") 

 

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 hirerachy 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 

try: 

if key not in ("TITEL", "SYSTEM"): 

return re.search(r"^-?[0-9]+", val.capitalize()).group(0) 

else: 

return val.capitalize() 

except: 

return val.capitalize() 

 

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) 

 

 

class Kpoints_supported_modes(Enum): 

Automatic = 0 

Gamma = 1 

Monkhorst = 2 

Line_mode = 3 

Cartesian = 4 

Reciprocal = 5 

 

def __str__(self): 

return self.name 

 

@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) 

 

 

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 

 

@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]]) 

 

@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) 

 

@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) 

 

@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 generated KPOINTS with grid density = " + \ 

"{} / atom".format(kppa) 

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] 

if all([k <= 1 for k in num_div]): 

return Kpoints(comment, 0, Kpoints.supported_modes.Gamma, 

[[1, 1, 1]], [0, 0, 0]) 

 

#ensure that numDiv[i] > 0 

num_div = [i if i > 0 else 1 for i in num_div] 

 

is_hexagonal = latt.is_hexagonal() 

 

# 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] 

 

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]) 

 

@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 generated KPOINTS with grid density = " + \ 

"{} / atom".format(kppa) 

num_kpts = 0 

return Kpoints(comment, num_kpts, style, [num_div], [0, 0, 0]) 

 

@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 = int(round(kppvol * vol * structure.num_sites)) 

return Kpoints.automatic_density(structure, kppa, force_gamma=force_gamma) 

 

@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)) 

 

@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()) 

 

@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("^\s*([\d+\.\-Ee]+)\s+([\d+\.\-Ee]+)\s+" 

"([\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("([e0-9\.\-]+)\s+([e0-9\.\-]+)\s+([e0-9\.\-]+)" 

"\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) 

 

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 __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" 

 

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 

 

@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")) 

 

 

def get_potcar_dir(): 

if "VASP_PSP_DIR" in os.environ: 

return os.environ["VASP_PSP_DIR"] 

return None 

 

 

def parse_string(s): 

return "{}".format(s.strip()) 

 

 

def parse_bool(s): 

m = re.match(r"^\.?([T|F|t|f])[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!") 

 

 

def parse_float(s): 

return float(re.search(r"^-?\d*\.?\d*[e|E]?-?\d*", s).group(0)) 

 

 

def parse_int(s): 

return int(re.match(r"^-?[0-9]+", s).group(0)) 

 

 

def parse_list(s): 

return [float(y) for y in re.split("\s+", s.strip()) if not y.isalpha()] 

 

 

@cached_class 

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, 

"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, 

"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} 

 

Orbital = namedtuple('Orbital', ['n', 'l', 'j', 'E', 'occ']) 

Description = namedtuple('OrbitalDescription', ['l', 'E', 

'Type', "Rcut", 

"Type2", "Rcut2"]) 

 

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): 

self.keywords[key] = self.parse_functions[key](val) 

 

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) 

for line in description_string.group(1).splitlines(): 

description = array_search.findall(line) 

if description: 

descriptions.append(self.Description(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 " 

r"energy argument \(eV\)\s*\n" 

r"(.*?)END of PSCTR-controll" 

r" parameters", 

search_lines) 

RRKJ_array = [] 

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 

 

def write_file(self, filename): 

with zopen(filename, "wt") as f: 

f.write(self.__str__()) 

 

@staticmethod 

def from_file(filename): 

with zopen(filename, "rt") as f: 

return PotcarSingle(f.read()) 

 

@staticmethod 

def from_symbol_and_functional(symbol, functional="PBE"): 

funcdir = PotcarSingle.functional_dir[functional] 

d = get_potcar_dir() 

if d is None: 

raise ValueError("No POTCAR directory found. Please set " 

"the VASP_PSP_DIR environment variable") 

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') 

 

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, (self.Orbital, self.Description)): 

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) 

 

 

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. 

sym_potcar_map (dict): Allows a user to specify a specific element 

symbol to raw POTCAR mapping. 

""" 

 

DEFAULT_FUNCTIONAL = "PBE" 

 

FUNCTIONAL_CHOICES = list(PotcarSingle.functional_dir.keys()) 

 

def __init__(self, symbols=None, functional=DEFAULT_FUNCTIONAL, 

sym_potcar_map=None): 

super(Potcar, self).__init__() 

self.functional = functional 

if symbols is not None: 

self.set_symbols(symbols, functional, sym_potcar_map) 

 

def as_dict(self): 

return {"functional": self.functional, "symbols": self.symbols, 

"@module": self.__class__.__module__, 

"@class": self.__class__.__name__} 

 

@classmethod 

def from_dict(cls, d): 

return Potcar(symbols=d["symbols"], functional=d["functional"]) 

 

@staticmethod 

def from_file(filename): 

with zopen(filename, "rt") as reader: 

fdata = reader.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" 

 

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] 

 

def set_symbols(self, symbols, functional=DEFAULT_FUNCTIONAL, 

sym_potcar_map=None): 

""" 

Initialize the POTCAR from a set of symbols. Currently, the POTCARs can 

be fetched from a location specified in the environment variable 

VASP_PSP_DIR or in a pymatgen.cfg or specified explicitly in a map. 

 

Args: 

symbols ([str]): A list of element symbols 

functional (str): The functional to use from the config file 

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) 

 

 

class VaspInput(dict, MSONable): 

""" 

Class to contain a set of vasp input objects corresponding to a run. 

 

Args: 

incar: Incar object. 

kpoints: Kpoints object. 

poscar: Poscar object. 

potcar: Potcar object. 

optional_files: Other input files supplied as a dict of { 

filename: object}. The object should follow standard pymatgen 

conventions in implementing a as_dict() and from_dict method. 

""" 

 

def __init__(self, incar, kpoints, poscar, potcar, optional_files=None, 

**kwargs): 

super(VaspInput, self).__init__(**kwargs) 

self.update({'INCAR': incar, 

'KPOINTS': kpoints, 

'POSCAR': poscar, 

'POTCAR': potcar}) 

if optional_files is not None: 

self.update(optional_files) 

 

def __str__(self): 

output = [] 

for k, v in self.items(): 

output.append(k) 

output.append(str(v)) 

output.append("") 

return "\n".join(output) 

 

def as_dict(self): 

d = {k: v.as_dict() for k, v in self.items()} 

d["@module"] = self.__class__.__module__ 

d["@class"] = self.__class__.__name__ 

return d 

 

@classmethod 

def from_dict(cls, d): 

dec = MontyDecoder() 

sub_d = {"optional_files": {}} 

for k, v in d.items(): 

if k in ["INCAR", "POSCAR", "POTCAR", "KPOINTS"]: 

sub_d[k.lower()] = dec.process_decoded(v) 

elif k not in ["@module", "@class"]: 

sub_d["optional_files"][k] = dec.process_decoded(v) 

return cls(**sub_d) 

 

def write_input(self, output_dir=".", make_dir_if_not_present=True): 

""" 

Write VASP input to a directory. 

 

Args: 

output_dir (str): Directory to write to. Defaults to current 

directory ("."). 

make_dir_if_not_present (bool): Create the directory if not 

present. Defaults to True. 

""" 

if make_dir_if_not_present and not os.path.exists(output_dir): 

os.makedirs(output_dir) 

for k, v in self.items(): 

with zopen(os.path.join(output_dir, k), "wt") as f: 

f.write(v.__str__()) 

 

@staticmethod 

def from_directory(input_dir, optional_files=None): 

""" 

Read in a set of VASP input from a directory. Note that only the 

standard INCAR, POSCAR, POTCAR and KPOINTS files are read unless 

optional_filenames is specified. 

 

Args: 

input_dir (str): Directory to read VASP input from. 

optional_files (dict): Optional files to read in as well as a 

dict of {filename: Object type}. Object type must have a 

static method from_file. 

""" 

sub_d = {} 

for fname, ftype in [("INCAR", Incar), ("KPOINTS", Kpoints), 

("POSCAR", Poscar), ("POTCAR", Potcar)]: 

fullzpath = zpath(os.path.join(input_dir, fname)) 

sub_d[fname.lower()] = ftype.from_file(fullzpath) 

sub_d["optional_files"] = {} 

if optional_files is not None: 

for fname, ftype in optional_files.items(): 

sub_d["optional_files"][fname] = \ 

ftype.from_file(os.path.join(input_dir, fname)) 

return VaspInput(**sub_d)