# Source code for pymatgen.io.vasp.sets

    # coding: utf-8
    # Copyright (c) Pymatgen Development Team.
    # Distributed under the terms of the MIT License.

    """
    This module defines the VaspInputSet abstract base class and a concrete
    implementation for the parameters developed and tested by the core team
    of pymatgen, including the Materials Virtual Lab, Materials Project and the MIT
    high throughput project.  The basic concept behind an input set is to specify
    a scheme to generate a consistent set of VASP inputs from a structure
    without further user intervention. This ensures comparability across
    runs.

    Read the following carefully before implementing new input sets:

    1. 99% of what needs to be done can be done by specifying user_incar_settings
       to override some of the defaults of various input sets. Unless there is an
       extremely good reason to add a new set, DO NOT add one. E.g., if you want
       to turn the hubbard U off, just set "LDAU": False as a user_incar_setting.
    2. All derivative input sets should inherit from one of the usual MPRelaxSet or
       MITRelaxSet, and proper superclass delegation should be used where possible.
       In particular, you are not supposed to implement your own as_dict or
       from_dict for derivative sets unless you know what you are doing.
       Improper overriding the as_dict and from_dict protocols is the major
       cause of implementation headaches. If you need an example, look at how the
       MPStaticSet or MPNonSCFSets are constructed.

    The above are recommendations. The following are UNBREAKABLE rules:

    1. All input sets must take in a structure or list of structures as the first
       argument.
    2. user_incar_settings, user_kpoints_settings and user_<whatever>_settings are
       ABSOLUTE. Any new sets you implement must obey this. If a user wants to
       override your settings, you assume he knows what he is doing. Do not
       magically override user supplied settings. You can issue a warning if you
       think the user is wrong.
    3. All input sets must save all supplied args and kwargs as instance variables.
       E.g., self.my_arg = my_arg and self.kwargs = kwargs in the __init__. This
       ensures the as_dict and from_dict work correctly.
    """

    import abc
    import glob
    import os
    import re
    import shutil
    import warnings
    from copy import deepcopy
    from itertools import chain
    from pathlib import Path
    from typing import List, Union, Optional
    from zipfile import ZipFile

    import numpy as np
    from monty.dev import deprecated
    from monty.io import zopen
    from monty.json import MSONable
    from monty.serialization import loadfn

    from pymatgen.analysis.structure_matcher import StructureMatcher
    from pymatgen.core.periodic_table import Specie, Element
    from pymatgen.core.sites import PeriodicSite
    from pymatgen.core.structure import Structure
    from pymatgen.io.lobster import Lobsterin
    from pymatgen.io.vasp.inputs import Incar, Poscar, Potcar, Kpoints, VaspInput
    from pymatgen.io.vasp.outputs import Vasprun, Outcar
    from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
    from pymatgen.symmetry.bandstructure import HighSymmKpath

    __author__ = (
        "Shyue Ping Ong, Wei Chen, Will Richards, Geoffroy Hautier, " "Anubhav Jain"
    )
    __copyright__ = "Copyright 2011, The Materials Project"
    __version__ = "1.0"
    __maintainer__ = "Shyue Ping Ong"
    __email__ = "shyuep@gmail.com"
    __date__ = "May 28 2016"

    MODULE_DIR = Path(__file__).resolve().parent

    class VaspInputSet(MSONable, metaclass=abc.ABCMeta):
        """
        Base class representing a set of Vasp input parameters with a structure
        supplied as init parameters. Typically, you should not inherit from this
        class. Start from DictSet or MPRelaxSet or MITRelaxSet.
        """

        @property
        @abc.abstractmethod
        def incar(self):
            """Incar object"""
            pass

        @property
        @abc.abstractmethod
        def kpoints(self):
            """Kpoints object"""
            pass

        @property
        @abc.abstractmethod
        def poscar(self):
            """Poscar object"""
            pass

        @property
        def potcar_symbols(self):
            """
            List of POTCAR symbols.
            """
            elements = self.poscar.site_symbols
            potcar_symbols = []
            settings = self._config_dict["POTCAR"]

            if isinstance(settings[elements[-1]], dict):
                for el in elements:
                    potcar_symbols.append(settings[el]["symbol"] if el in settings else el)
            else:
                for el in elements:
                    potcar_symbols.append(settings.get(el, el))

            return potcar_symbols

        @property
        def potcar(self):
            """
            Potcar object.
            """
            potcar = Potcar(self.potcar_symbols, functional=self.potcar_functional)

            # warn if the selected POTCARs do not correspond to the chosen
            # potcar_functional
            for psingle in potcar:
                if self.potcar_functional not in psingle.identify_potcar()[0]:
                    warnings.warn(
                        "POTCAR data with symbol {} is not known by pymatgen to\
                        correspond with the selected potcar_functional {}. This POTCAR\
                        is known to correspond with functionals {}. Please verify that\
                        you are using the right POTCARs!"
                            .format(psingle.symbol,
                                    self.potcar_functional,
                                    psingle.identify_potcar(mode='data')[0]),
                        BadInputSetWarning,
                    )

            return potcar

        @property  # type: ignore
        @deprecated(message="Use the get_vasp_input() method instead.")
        def all_input(self):
            """
            Returns all input files as a dict of {filename: vasp object}

            Returns:
                dict of {filename: object}, e.g., {'INCAR': Incar object, ...}
            """
            return {
                "INCAR": self.incar,
                "KPOINTS": self.kpoints,
                "POSCAR": self.poscar,
                "POTCAR": self.potcar,
            }

        def get_vasp_input(self) -> VaspInput:
            """

            Returns:
                VaspInput
            """
            return VaspInput(
                incar=self.incar,
                kpoints=self.kpoints,
                poscar=self.poscar,
                potcar=self.potcar,
            )

        def write_input(self, output_dir, make_dir_if_not_present=True, include_cif=False,
                        potcar_spec=False, zip_output=False):
            """
            Writes a set of VASP input to a directory.

            Args:
                output_dir (str): Directory to output the VASP input files
                make_dir_if_not_present (bool): Set to True if you want the
                    directory (and the whole path) to be created if it is not
                    present.
                include_cif (bool): Whether to write a CIF file in the output
                    directory for easier opening by VESTA.
                potcar_spec (bool): Instead of writing the POTCAR, write a "POTCAR.spec".
                    This is intended to help sharing an input set with people who might
                    not have a license to specific Potcar files. Given a "POTCAR.spec",
                    the specific POTCAR file can be re-generated using pymatgen with the
                    "generate_potcar" function in the pymatgen CLI.
                zip_output (bool): If True, output will be zipped into a file with the
                    same name as the InputSet (e.g., MPStaticSet.zip)
            """
            if potcar_spec:
                if make_dir_if_not_present and not os.path.exists(output_dir):
                    os.makedirs(output_dir)

                with zopen(os.path.join(output_dir, "POTCAR.spec"), "wt") as f:
                    f.write("\n".join(self.potcar_symbols))

                for k, v in {"INCAR": self.incar,
                             "POSCAR": self.poscar,
                             "KPOINTS": self.kpoints
                             }.items():
                    if v is not None:
                        with zopen(os.path.join(output_dir, k), "wt") as f:
                            f.write(v.__str__())
            else:
                vinput = self.get_vasp_input()
                vinput.write_input(output_dir, make_dir_if_not_present=make_dir_if_not_present)

            cifname = ""
            if include_cif:
                s = vinput["POSCAR"].structure
                cifname = Path(output_dir) / ("%s.cif" % re.sub(r"\s", "", s.formula))
                s.to(filename=cifname)

            if zip_output:
                filename = self.__class__.__name__ + ".zip"
                with ZipFile(filename, "w") as zip:
                    for file in ["INCAR", "POSCAR", "KPOINTS", "POTCAR", "POTCAR.spec", cifname]:
                        try:
                            zip.write(file)
                            os.remove(file)
                        except FileNotFoundError:
                            pass

        def as_dict(self, verbosity=2):
            """
            Args:
                verbosity: Verbosity for generated dict. If 1, structure is
                excluded.

            Returns:
                MSONable dict
            """
            d = MSONable.as_dict(self)
            if verbosity == 1:
                d.pop("structure", None)
            return d

    def _load_yaml_config(fname):
        config = loadfn(str(MODULE_DIR / ("%s.yaml" % fname)))
        if "PARENT" in config:
            parent_config = _load_yaml_config(config["PARENT"])
            for k, v in parent_config.items():
                if k not in config:
                    config[k] = v
                elif isinstance(v, dict):
                    v_new = config.get(k, {})
                    v_new.update(v)
                    config[k] = v_new
        return config

    class DictSet(VaspInputSet):
        """
        Concrete implementation of VaspInputSet that is initialized from a dict
        settings. This allows arbitrary settings to be input. In general,
        this is rarely used directly unless there is a source of settings in yaml
        format (e.g., from a REST interface). It is typically used by other
        VaspInputSets for initialization.

        Special consideration should be paid to the way the MAGMOM initialization
        for the INCAR is done. The initialization differs depending on the type of
        structure and the configuration settings. The order in which the magmom is
        determined is as follows:

        1. If the site itself has a magmom setting, that is used.
        2. If the species on the site has a spin setting, that is used.
        3. If the species itself has a particular setting in the config file, that
           is used, e.g., Mn3+ may have a different magmom than Mn4+.
        4. Lastly, the element symbol itself is checked in the config file. If
           there are no settings, VASP's default of 0.6 is used.
        """

        def __init__(
                self,
                structure,
                config_dict,
                files_to_transfer=None,
                user_incar_settings=None,
                user_kpoints_settings=None,
                user_potcar_settings=None,
                constrain_total_magmom=False,
                sort_structure=True,
                potcar_functional=None,
                user_potcar_functional=None,
                force_gamma=False,
                reduce_structure=None,
                vdw=None,
                use_structure_charge=False,
                standardize=False,
                sym_prec=0.1,
                international_monoclinic=True,
        ):
            """
            Args:
                structure (Structure): The Structure to create inputs for.
                config_dict (dict): The config dictionary to use.
                files_to_transfer (dict): A dictionary of {filename: filepath}. This
                    allows the transfer of files from a previous calculation.
                user_incar_settings (dict): User INCAR settings. This allows a user
                    to override INCAR settings, e.g., setting a different MAGMOM for
                    various elements or species. Note that in the new scheme,
                    ediff_per_atom and hubbard_u are no longer args. Instead, the
                    config_dict supports EDIFF_PER_ATOM and EDIFF keys. The former
                    scales with # of atoms, the latter does not. If both are
                    present, EDIFF is preferred. To force such settings, just supply
                    user_incar_settings={"EDIFF": 1e-5, "LDAU": False} for example.
                    The keys 'LDAUU', 'LDAUJ', 'LDAUL' are special cases since
                    pymatgen defines different values depending on what anions are
                    present in the structure, so these keys can be defined in one
                    of two ways, e.g. either {"LDAUU":{"O":{"Fe":5}}} to set LDAUU
                    for Fe to 5 in an oxide, or {"LDAUU":{"Fe":5}} to set LDAUU to
                    5 regardless of the input structure.

                    If a None value is given, that key is unset. For example,
                    {"ENCUT": None} will remove ENCUT from the incar settings.
                user_kpoints_settings (dict or Kpoints): Allow user to override kpoints
                    setting by supplying a dict E.g., {"reciprocal_density": 1000}.
                    User can also supply Kpoints object. Default is None.
                user_potcar_settings (dict: Allow user to override POTCARs. E.g.,
                    {"Gd": "Gd_3"}. This is generally not recommended. Default is None.
                constrain_total_magmom (bool): Whether to constrain the total magmom
                    (NUPDOWN in INCAR) to be the sum of the expected MAGMOM for all
                    species. Defaults to False.
                sort_structure (bool): Whether to sort the structure (using the
                    default sort order of electronegativity) before generating input
                    files. Defaults to True, the behavior you would want most of the
                    time. This ensures that similar atomic species are grouped
                    together.
                user_potcar_functional (str): Functional to use. Default (None) is to use
                    the functional in the config dictionary. Valid values:
                    "PBE", "PBE_52", "PBE_54", "LDA", "LDA_52", "LDA_54", "PW91",
                    "LDA_US", "PW91_US".
                force_gamma (bool): Force gamma centered kpoint generation. Default
                    (False) is to use the Automatic Density kpoint scheme, which
                    will use the Gamma centered generation scheme for hexagonal
                    cells, and Monkhorst-Pack otherwise.
                reduce_structure (None/str): Before generating the input files,
                    generate the reduced structure. Default (None), does not
                    alter the structure. Valid values: None, "niggli", "LLL".
                vdw: Adds default parameters for van-der-Waals functionals supported
                    by VASP to INCAR. Supported functionals are: DFT-D2, undamped
                    DFT-D3, DFT-D3 with Becke-Jonson damping, Tkatchenko-Scheffler,
                    Tkatchenko-Scheffler with iterative Hirshfeld partitioning,
                    MBD@rSC, dDsC, Dion's vdW-DF, DF2, optPBE, optB88, optB86b and
                    rVV10.
                use_structure_charge (bool): If set to True, then the public
                    variable used for setting the overall charge of the
                    structure (structure.charge) is used to set the NELECT
                    variable in the INCAR
                    Default is False (structure's overall charge is not used)
                standardize (float): Whether to standardize to a primitive standard
                    cell. Defaults to False.
                sym_prec (float): Tolerance for symmetry finding.
                international_monoclinic (bool): Whether to use international convention
                    (vs Curtarolo) for monoclinic. Defaults True.
            """
            if reduce_structure:
                structure = structure.get_reduced_structure(reduce_structure)
            if sort_structure:
                structure = structure.get_sorted_structure()

            self._structure = structure
            self._config_dict = deepcopy(config_dict)
            self.files_to_transfer = files_to_transfer or {}
            self.constrain_total_magmom = constrain_total_magmom
            self.sort_structure = sort_structure
            self.force_gamma = force_gamma
            self.reduce_structure = reduce_structure
            self.user_incar_settings = user_incar_settings or {}
            self.user_kpoints_settings = user_kpoints_settings or {}
            self.user_potcar_settings = user_potcar_settings
            self.vdw = vdw.lower() if vdw is not None else None
            self.use_structure_charge = use_structure_charge
            self.standardize = standardize
            self.sym_prec = sym_prec
            self.international_monoclinic = international_monoclinic

            if (
                    self.user_incar_settings.get("KSPACING")
                    and user_kpoints_settings is not None
            ):
                warnings.warn(
                    "You have specified KSPACING and also supplied kpoints "
                    "settings. KSPACING only has effect when there is no "
                    "KPOINTS file. Since both settings were given, pymatgen"
                    "will generate a KPOINTS file and ignore KSPACING."
                    "Remove the `user_kpoints_settings` argument to enable KSPACING.",
                    BadInputSetWarning,
                )

            if self.vdw:
                vdw_par = loadfn(str(MODULE_DIR / "vdW_parameters.yaml"))
                try:
                    self._config_dict["INCAR"].update(vdw_par[self.vdw])
                except KeyError:
                    raise KeyError(
                        "Invalid or unsupported van-der-Waals "
                        "functional. Supported functionals are "
                        "%s." % vdw_par.keys()
                    )
            # read the POTCAR_FUNCTIONAL from the .yaml
            self.potcar_functional = self._config_dict.get("POTCAR_FUNCTIONAL", "PBE")

            if potcar_functional is not None and user_potcar_functional is not None:
                raise ValueError(
                    "Received both 'potcar_functional' and "
                    "'user_potcar_functional arguments. 'potcar_functional "
                    "is deprecated."
                )
            if potcar_functional:
                warnings.warn(
                    "'potcar_functional' argument is deprecated. Use "
                    "'user_potcar_functional' instead.",
                    DeprecationWarning,
                )
                self.potcar_functional = potcar_functional
            elif user_potcar_functional:
                self.potcar_functional = user_potcar_functional

            # warn if a user is overriding POTCAR_FUNCTIONAL
            if self.potcar_functional != self._config_dict.get("POTCAR_FUNCTIONAL"):
                warnings.warn(
                    "Overriding the POTCAR functional is generally not recommended "
                    " as it significantly affect the results of calculations and "
                    "compatibility with other calculations done with the same "
                    "input set. Note that some POTCAR symbols specified in "
                    "the configuration file may not be available in the selected "
                    "functional.",
                    BadInputSetWarning,
                )

            if self.user_potcar_settings:
                warnings.warn(
                    "Overriding POTCARs is generally not recommended as it "
                    "significantly affect the results of calculations and "
                    "compatibility with other calculations done with the same "
                    "input set. In many instances, it is better to write a "
                    "subclass of a desired input set and override the POTCAR in "
                    "the subclass to be explicit on the differences.",
                    BadInputSetWarning,
                )
                for k, v in self.user_potcar_settings.items():
                    self._config_dict["POTCAR"][k] = v

        @property
        def structure(self) -> Structure:
            """
            :return: Structure
            """
            if self.standardize and self.sym_prec:
                return standardize_structure(
                    self._structure,
                    sym_prec=self.sym_prec,
                    international_monoclinic=self.international_monoclinic,
                )
            else:
                return self._structure

        @property
        def incar(self) -> Incar:
            """
            :return: Incar
            """
            settings = dict(self._config_dict["INCAR"])
            for k, v in self.user_incar_settings.items():
                if v is None:
                    try:
                        del settings[k]
                    except KeyError:
                        settings[k] = v
                elif k == "KSPACING" and self.user_kpoints_settings != {}:
                    pass  # Ignore KSPACING if user_kpoints_settings are given
                else:
                    settings[k] = v
            structure = self.structure
            incar = Incar()
            comp = structure.composition
            elements = sorted(
                [el for el in comp.elements if comp[el] > 0], key=lambda e: e.X
            )
            most_electroneg = elements[-1].symbol
            poscar = Poscar(structure)
            hubbard_u = settings.get("LDAU", False)

            for k, v in settings.items():
                if k == "MAGMOM":
                    mag = []
                    for site in structure:
                        if hasattr(site, "magmom"):
                            mag.append(site.magmom)
                        elif hasattr(site.specie, "spin"):
                            mag.append(site.specie.spin)
                        elif str(site.specie) in v:
                            mag.append(v.get(str(site.specie)))
                        else:
                            mag.append(v.get(site.specie.symbol, 0.6))
                    incar[k] = mag
                elif k in ("LDAUU", "LDAUJ", "LDAUL"):
                    if hubbard_u:
                        if hasattr(structure[0], k.lower()):
                            m = dict(
                                [
                                    (site.specie.symbol, getattr(site, k.lower()))
                                    for site in structure
                                ]
                            )
                            incar[k] = [m[sym] for sym in poscar.site_symbols]
                        # lookup specific LDAU if specified for most_electroneg atom
                        elif most_electroneg in v.keys() and isinstance(
                                v[most_electroneg], dict
                        ):
                            incar[k] = [
                                v[most_electroneg].get(sym, 0)
                                for sym in poscar.site_symbols
                            ]
                        # else, use fallback LDAU value if it exists
                        else:
                            incar[k] = [
                                v.get(sym, 0)
                                if isinstance(v.get(sym, 0), (float, int))
                                else 0
                                for sym in poscar.site_symbols
                            ]
                elif k.startswith("EDIFF") and k != "EDIFFG":
                    if "EDIFF" not in settings and k == "EDIFF_PER_ATOM":
                        incar["EDIFF"] = float(v) * structure.num_sites
                    else:
                        incar["EDIFF"] = float(settings["EDIFF"])
                else:
                    incar[k] = v

            has_u = hubbard_u and sum(incar["LDAUU"]) > 0
            if has_u:
                # modify LMAXMIX if LSDA+U and you have d or f electrons
                # note that if the user explicitly sets LMAXMIX in settings it will
                # override this logic.
                if "LMAXMIX" not in settings.keys():
                    # contains f-electrons
                    if any([el.Z > 56 for el in structure.composition]):
                        incar["LMAXMIX"] = 6
                    # contains d-electrons
                    elif any([el.Z > 20 for el in structure.composition]):
                        incar["LMAXMIX"] = 4
            else:
                for key in list(incar.keys()):
                    if key.startswith("LDAU"):
                        del incar[key]

            if self.constrain_total_magmom:
                nupdown = sum([mag if abs(mag) > 0.6 else 0 for mag in incar["MAGMOM"]])
                incar["NUPDOWN"] = nupdown

            if self.use_structure_charge:
                incar["NELECT"] = self.nelect

            # Ensure adequate number of KPOINTS are present for the tetrahedron
            # method (ISMEAR=-5). If KSPACING is in the INCAR file the number
            # of kpoints is not known before calling VASP, but a warning is raised
            # when the KSPACING value is > 0.5 (2 reciprocal Angstrom).
            # An error handler in Custodian is available to
            # correct overly large KSPACING values (small number of kpoints)
            # if necessary.
            # if "KSPACING" not in self.user_incar_settings.keys():
            if self.kpoints is not None:
                if np.product(self.kpoints.kpts) < 4 and incar.get("ISMEAR", 0) == -5:
                    incar["ISMEAR"] = 0

            if self.user_incar_settings.get("KSPACING", 0) > 0.5 and incar.get(
                    "ISMEAR", 0 == -5
            ):
                warnings.warn(
                    "Large KSPACING value detected with ISMEAR = -5. Ensure that VASP "
                    "generates an adequate number of KPOINTS, lower KSPACING, or "
                    "set ISMEAR = 0",
                    BadInputSetWarning,
                )

            if all([k.is_metal for k in structure.composition.keys()]):
                if incar.get("NSW", 0) > 0 and incar.get("ISMEAR", 1) < 1:
                    warnings.warn(
                        "Relaxation of likely metal with ISMEAR < 1 "
                        "detected. Please see VASP recommendations on "
                        "ISMEAR for metals.",
                        BadInputSetWarning,
                    )

            return incar

        @property
        def poscar(self) -> Poscar:
            """
            :return: Poscar
            """
            return Poscar(self.structure)

        @property
        def nelect(self) -> float:
            """
            Gets the default number of electrons for a given structure.
            """
            # if structure is not sorted this can cause problems, so must take
            # care to remove redundant symbols when counting electrons
            site_symbols = list(set(self.poscar.site_symbols))
            structure = self.structure
            nelect = 0.0
            for ps in self.potcar:
                if ps.element in site_symbols:
                    site_symbols.remove(ps.element)
                    nelect += (
                            structure.composition.element_composition[ps.element] * ps.ZVAL
                    )

            if self.use_structure_charge:
                return nelect - structure.charge
            else:
                return nelect

        @property
        def kpoints(self) -> Union[Kpoints, None]:
            """
            Returns a KPOINTS file using the fully automated grid method. Uses
            Gamma centered meshes for hexagonal cells and Monk grids otherwise.

            If KSPACING is set in user_incar_settings (or the INCAR file), no
            file is created because VASP will automatically generate the kpoints.

            Algorithm:
                Uses a simple approach scaling the number of divisions along each
                reciprocal lattice vector proportional to its length.
            """
            # Return None if KSPACING is present in the INCAR, because this will
            # cause VASP to generate the kpoints automatically
            if self.user_incar_settings.get("KSPACING") or self._config_dict["INCAR"].get(
                    "KSPACING"
            ):
                if self.user_kpoints_settings == {}:
                    return None

            settings = self.user_kpoints_settings or self._config_dict.get("KPOINTS")

            if isinstance(settings, Kpoints):
                return settings

            # Return None if KSPACING is present in the INCAR, because this will
            # cause VASP to generate the kpoints automatically
            if (
                    self.user_incar_settings.get("KSPACING")
                    and self.user_kpoints_settings == {}
            ):
                return None

            # If grid_density is in the kpoints_settings use
            # Kpoints.automatic_density
            if settings.get("grid_density"):
                return Kpoints.automatic_density(
                    self.structure, int(settings["grid_density"]), self.force_gamma
                )

            # If reciprocal_density is in the kpoints_settings use
            # Kpoints.automatic_density_by_vol
            elif settings.get("reciprocal_density"):
                return Kpoints.automatic_density_by_vol(
                    self.structure, int(settings["reciprocal_density"]), self.force_gamma
                )

            # If length is in the kpoints_settings use Kpoints.automatic
            elif settings.get("length"):
                return Kpoints.automatic(settings["length"])

            # Raise error. Unsure of which kpoint generation to use
            else:
                raise ValueError(
                    "Invalid KPoint Generation algo : Supported Keys are "
                    "grid_density: for Kpoints.automatic_density generation, "
                    "reciprocal_density: for KPoints.automatic_density_by_vol "
                    "generation, and length  : for Kpoints.automatic generation"
                )

        def __str__(self):
            return self.__class__.__name__

        def __repr__(self):
            return self.__class__.__name__

        def write_input(
                self,
                output_dir: str,
                make_dir_if_not_present: bool = True,
                include_cif: bool = False,
                potcar_spec: bool = False,
                zip_output: bool = False,
        ):
            """
            Writes out all input to a directory.

            Args:
                output_dir (str): Directory to output the VASP input files
                make_dir_if_not_present (bool): Set to True if you want the
                    directory (and the whole path) to be created if it is not
                    present.
                include_cif (bool): Whether to write a CIF file in the output
                    directory for easier opening by VESTA.
                potcar_spec (bool): Instead of writing the POTCAR, write a "POTCAR.spec".
                    This is intended to help sharing an input set with people who might
                    not have a license to specific Potcar files. Given a "POTCAR.spec",
                    the specific POTCAR file can be re-generated using pymatgen with the
                    "generate_potcar" function in the pymatgen CLI.
            """
            super().write_input(
                output_dir=output_dir,
                make_dir_if_not_present=make_dir_if_not_present,
                include_cif=include_cif,
                potcar_spec=potcar_spec,
                zip_output=zip_output
            )
            for k, v in self.files_to_transfer.items():
                with zopen(v, "rb") as fin, zopen(str(Path(output_dir) / k), "wb") as fout:
                    shutil.copyfileobj(fin, fout)

    class MITRelaxSet(DictSet):
        """
        Standard implementation of VaspInputSet utilizing parameters in the MIT
        High-throughput project.
        The parameters are chosen specifically for a high-throughput project,
        which means in general pseudopotentials with fewer electrons were chosen.

        Please refer::

            A Jain, G. Hautier, C. Moore, S. P. Ong, C. Fischer, T. Mueller,
            K. A. Persson, G. Ceder. A high-throughput infrastructure for density
            functional theory calculations. Computational Materials Science,
            2011, 50(8), 2295-2310. doi:10.1016/j.commatsci.2011.02.023
        """

        CONFIG = _load_yaml_config("MITRelaxSet")

        def __init__(self, structure, **kwargs):
            """
            :param structure: Structure
            :param kwargs: Same as those supported by DictSet.
            """
            super().__init__(structure, MITRelaxSet.CONFIG, **kwargs)
            self.kwargs = kwargs

    class MPRelaxSet(DictSet):
        """
        Implementation of VaspInputSet utilizing parameters in the public
        Materials Project. Typically, the pseudopotentials chosen contain more
        electrons than the MIT parameters, and the k-point grid is ~50% more dense.
        The LDAUU parameters are also different due to the different psps used,
        which result in different fitted values.
        """

        CONFIG = _load_yaml_config("MPRelaxSet")

        def __init__(self, structure, **kwargs):
            """
            :param structure: Structure
            :param kwargs: Same as those supported by DictSet.
            """
            super().__init__(structure, MPRelaxSet.CONFIG, **kwargs)
            self.kwargs = kwargs

    class MPScanRelaxSet(DictSet):
        """
        Class for writing a relax input set using Strongly Constrained and
        Appropriately Normed (SCAN) semilocal density functional.

        Notes:
            1. This functional is only available from VASP.5.4.3 upwards.

            2. Meta-GGA calculations require POTCAR files that include
            information on the kinetic energy density of the core-electrons,
            i.e. "PBE_52" or "PBE_54". Make sure the POTCARs include the
            following lines (see VASP wiki for more details):

                $ grep kinetic POTCAR
                kinetic energy-density
                mkinetic energy-density pseudized
                kinetic energy density (partial)
        """

        CONFIG = _load_yaml_config("MPSCANRelaxSet")

        def __init__(self, structure, bandgap=0, **kwargs):
            """
            Args:
                structure (Structure): Input structure.
                bandgap (int): Bandgap of the structure in eV. The bandgap is used to
                        compute the appropriate k-point density and determine the
                        smearing settings.

                        Metallic systems (default, bandgap = 0) use a KSPACING value of 0.22
                        and Methfessel-Paxton order 2 smearing (ISMEAR=2, SIGMA=0.2).

                        Non-metallic systems (bandgap > 0) use the tetrahedron smearing
                        method (ISMEAR=-5, SIGMA=0.05). The KSPACING value is
                        calculated from the bandgap via Eqs. 25 and 29 of Wisesa, McGill,
                        and Mueller [1] (see References). Note that if 'user_incar_settings'
                        or 'user_kpoints_settings' override KSPACING, the calculation from
                        bandgap is not performed.

                vdw (str): set "rVV10" to enable SCAN+rVV10, which is a versatile
                        van der Waals density functional by combing the SCAN functional
                        with the rVV10 non-local correlation functional. rvv10 is the only
                        dispersion correction available for SCAN at this time.
                **kwargs: Same as those supported by DictSet.

            References:
                [1] P. Wisesa, K.A. McGill, T. Mueller, Efficient generation of
                generalized Monkhorst-Pack grids through the use of informatics,
                Phys. Rev. B. 93 (2016) 1–10. doi:10.1103/PhysRevB.93.155109.
            """
            super().__init__(structure, MPScanRelaxSet.CONFIG, **kwargs)
            self.bandgap = bandgap
            self.kwargs = kwargs

            if self.potcar_functional not in ["PBE_52", "PBE_54"]:
                raise ValueError("SCAN calculations require PBE_52 or PBE_54!")

            # self.kwargs.get("user_incar_settings", {
            updates = {}
            # select the KSPACING and smearing parameters based on the bandgap
            if self.bandgap == 0:
                updates["KSPACING"] = 0.22
                updates["SIGMA"] = 0.2
                updates["ISMEAR"] = 2
            else:
                rmin = 25.22 - 1.87 * bandgap  # Eq. 25
                kspacing = 2 * np.pi * 1.0265 / (rmin - 1.0183)  # Eq. 29
                # cap the KSPACING at a max of 0.44, per internal benchmarking
                if kspacing > 0.44:
                    kspacing = 0.44
                updates["KSPACING"] = kspacing
                updates["ISMEAR"] = -5
                updates["SIGMA"] = 0.05

            # Don't overwrite things the user has supplied
            if kwargs.get("user_incar_settings", {}).get("KSPACING"):
                del updates["KSPACING"]

            if kwargs.get("user_incar_settings", {}).get("ISMEAR"):
                del updates["ISMEAR"]

            if kwargs.get("user_incar_settings", {}).get("SIGMA"):
                del updates["SIGMA"]

            if self.vdw:
                if self.vdw != "rvv10":
                    warnings.warn(
                        "Use of van der waals functionals other than rVV10 "
                        "with SCAN is not supported at this time. "
                    )
                    # delete any vdw parameters that may have been added to the INCAR
                    vdw_par = loadfn(str(MODULE_DIR / "vdW_parameters.yaml"))
                    for k, v in vdw_par[self.vdw].items():
                        try:
                            del self._config_dict["INCAR"][k]
                        except KeyError:
                            pass

            self._config_dict["INCAR"].update(updates)

    class MPMetalRelaxSet(MPRelaxSet):
        """
        Implementation of VaspInputSet utilizing parameters in the public
        Materials Project, but with tuning for metals. Key things are a denser
        k point density, and a
        """

        CONFIG = _load_yaml_config("MPRelaxSet")

        def __init__(self, structure, **kwargs):
            """
            :param structure: Structure
            :param kwargs: Same as those supported by DictSet.
            """
            super().__init__(structure, **kwargs)
            self._config_dict["INCAR"].update({"ISMEAR": 1, "SIGMA": 0.2})
            self._config_dict["KPOINTS"].update({"reciprocal_density": 200})
            self.kwargs = kwargs

    class MPHSERelaxSet(DictSet):
        """
        Same as the MPRelaxSet, but with HSE parameters.
        """

        CONFIG = _load_yaml_config("MPHSERelaxSet")

        def __init__(self, structure, **kwargs):
            """
            :param structure: Structure
            :param kwargs: Same as those supported by DictSet.
            """
            super().__init__(structure, MPHSERelaxSet.CONFIG, **kwargs)
            self.kwargs = kwargs

    class MPStaticSet(MPRelaxSet):
        """
        Creates input files for a static calculation.
        """

        def __init__(
                self,
                structure,
                prev_incar=None,
                prev_kpoints=None,
                lepsilon=False,
                lcalcpol=False,
                reciprocal_density=100,
                small_gap_multiply=None,
                **kwargs
        ):
            """
            Args:
                structure (Structure): Structure from previous run.
                prev_incar (Incar): Incar file from previous run.
                prev_kpoints (Kpoints): Kpoints from previous run.
                lepsilon (bool): Whether to add static dielectric calculation
                reciprocal_density (int): For static calculations, we usually set the
                    reciprocal density by volume. This is a convenience arg to change
                    that, rather than using user_kpoints_settings. Defaults to 100,
                    which is ~50% more than that of standard relaxation calculations.
                small_gap_multiply ([float, float]): If the gap is less than
                    1st index, multiply the default reciprocal_density by the 2nd
                    index.
                **kwargs: kwargs supported by MPRelaxSet.
            """
            super().__init__(structure, **kwargs)
            if isinstance(prev_incar, str):
                prev_incar = Incar.from_file(prev_incar)
            if isinstance(prev_kpoints, str):
                prev_kpoints = Kpoints.from_file(prev_kpoints)

            self.prev_incar = prev_incar
            self.prev_kpoints = prev_kpoints
            self.reciprocal_density = reciprocal_density
            self.kwargs = kwargs
            self.lepsilon = lepsilon
            self.lcalcpol = lcalcpol
            self.small_gap_multiply = small_gap_multiply

        @property
        def incar(self):
            """
            :return: Incar
            """
            parent_incar = super().incar
            incar = (
                Incar(self.prev_incar)
                if self.prev_incar is not None
                else Incar(parent_incar)
            )

            incar.update(
                {
                    "IBRION": -1,
                    "ISMEAR": -5,
                    "LAECHG": True,
                    "LCHARG": True,
                    "LORBIT": 11,
                    "LVHAR": True,
                    "LWAVE": False,
                    "NSW": 0,
                    "ICHARG": 0,
                    "ALGO": "Normal",
                }
            )

            if self.lepsilon:
                incar["IBRION"] = 8
                incar["LEPSILON"] = True

                # LPEAD=T: numerical evaluation of overlap integral prevents
                # LRF_COMMUTATOR errors and can lead to better expt. agreement
                # but produces slightly different results
                incar["LPEAD"] = True

                # Note that DFPT calculations MUST unset NSW. NSW = 0 will fail
                # to output ionic.
                incar.pop("NSW", None)
                incar.pop("NPAR", None)

            if self.lcalcpol:
                incar["LCALCPOL"] = True

            for k in ["MAGMOM", "NUPDOWN"] + list(
                    self.kwargs.get("user_incar_settings", {}).keys()
            ):
                # For these parameters as well as user specified settings, override
                # the incar settings.
                if parent_incar.get(k, None) is not None:
                    incar[k] = parent_incar[k]
                else:
                    incar.pop(k, None)

            # use new LDAUU when possible b/c the Poscar might have changed
            # representation
            if incar.get("LDAU"):
                u = incar.get("LDAUU", [])
                j = incar.get("LDAUJ", [])
                if sum([u[x] - j[x] for x, y in enumerate(u)]) > 0:
                    for tag in ("LDAUU", "LDAUL", "LDAUJ"):
                        incar.update({tag: parent_incar[tag]})
                # ensure to have LMAXMIX for GGA+U static run
                if "LMAXMIX" not in incar:
                    incar.update({"LMAXMIX": parent_incar["LMAXMIX"]})

            # Compare ediff between previous and staticinputset values,
            # choose the tighter ediff
            incar["EDIFF"] = min(incar.get("EDIFF", 1), parent_incar["EDIFF"])
            return incar

        @property
        def kpoints(self) -> Optional[Kpoints]:
            """
            :return: Kpoints
            """
            self._config_dict["KPOINTS"]["reciprocal_density"] = self.reciprocal_density
            kpoints = super().kpoints

            # Prefer to use k-point scheme from previous run
            # except for when lepsilon = True is specified
            if kpoints is not None:
                if self.prev_kpoints and self.prev_kpoints.style != kpoints.style:
                    if (self.prev_kpoints.style == Kpoints.supported_modes.Monkhorst) and (
                            not self.lepsilon
                    ):
                        k_div = [kp + 1 if kp % 2 == 1 else kp for kp in kpoints.kpts[0]]
                        kpoints = Kpoints.monkhorst_automatic(k_div)
                    else:
                        kpoints = Kpoints.gamma_automatic(kpoints.kpts[0])
            return kpoints

        def override_from_prev_calc(self, prev_calc_dir="."):
            """
            Update the input set to include settings from a previous calculation.

            Args:
                prev_calc_dir (str): The path to the previous calculation directory.

            Returns:
                The input set with the settings (structure, k-points, incar, etc)
                updated using the previous VASP run.
            """
            vasprun, outcar = get_vasprun_outcar(prev_calc_dir)

            self.prev_incar = vasprun.incar
            self.prev_kpoints = vasprun.kpoints

            if self.standardize:
                warnings.warn(
                    "Use of standardize=True with from_prev_run is not "
                    "recommended as there is no guarantee the copied "
                    "files will be appropriate for the standardized "
                    "structure."
                )

            self._structure = get_structure_from_prev_run(vasprun, outcar)

            # multiply the reciprocal density if needed
            if self.small_gap_multiply:
                gap = vasprun.eigenvalue_band_properties[0]
                if gap <= self.small_gap_multiply[0]:
                    self.reciprocal_density = (
                            self.reciprocal_density * self.small_gap_multiply[1]
                    )

            return self

        @classmethod
        def from_prev_calc(cls, prev_calc_dir, **kwargs):
            """
            Generate a set of Vasp input files for static calculations from a
            directory of previous Vasp run.

            Args:
                prev_calc_dir (str): Directory containing the outputs(
                    vasprun.xml and OUTCAR) of previous vasp run.
                **kwargs: All kwargs supported by MPStaticSet, other than prev_incar
                    and prev_structure and prev_kpoints which are determined from
                    the prev_calc_dir.
            """
            input_set = cls(_dummy_structure, **kwargs)
            return input_set.override_from_prev_calc(prev_calc_dir=prev_calc_dir)

    class MPHSEBSSet(MPHSERelaxSet):
        """
        Implementation of a VaspInputSet for HSE band structure computations.
        Remember that HSE band structures must be self-consistent in VASP. A
        band structure along symmetry lines for instance needs BOTH a uniform
        grid with appropriate weights AND a path along the lines with weight 0.

        Thus, the "Uniform" mode is just like regular static SCF but allows
        adding custom kpoints (e.g., corresponding to known VBM/CBM) to the
        uniform grid that have zero weight (e.g., for better gap estimate).

        The "Gap" mode behaves just like the "Uniform" mode, however, if starting
        from a previous calculation, the VBM and CBM k-points will automatically
        be added to ``added_kpoints``.

        The "Line" mode is just like Uniform mode, but additionally adds
        k-points along symmetry lines with zero weight.
        """

        def __init__(
                self,
                structure,
                user_incar_settings=None,
                added_kpoints=None,
                mode="Gap",
                reciprocal_density=None,
                copy_chgcar=True,
                kpoints_line_density=20,
                **kwargs
        ):
            """
            Args:
                structure (Structure): Structure to compute
                user_incar_settings (dict): A dict specifying additional incar
                    settings
                added_kpoints (list): a list of kpoints (list of 3 number list)
                    added to the run. The k-points are in fractional coordinates
                mode (str): "Line" - generate k-points along symmetry lines for
                    bandstructure. "Uniform" - generate uniform k-points grid.
                reciprocal_density (int): k-point density to use for uniform mesh.
                copy_chgcar (bool): Whether to copy the CHGCAR of a previous run.
                kpoints_line_density (int): k-point density for high symmetry lines
                **kwargs (dict): Any other parameters to pass into DictSet.
            """
            super().__init__(structure, **kwargs)
            self.user_incar_settings = user_incar_settings or {}
            self._config_dict["INCAR"].update(
                {
                    "NSW": 0,
                    "ISMEAR": 0,
                    "SIGMA": 0.05,
                    "ISYM": 3,
                    "LCHARG": False,
                    "NELMIN": 5,
                }
            )
            self.added_kpoints = added_kpoints if added_kpoints is not None else []
            self.mode = mode

            if (
                    not reciprocal_density
                    or "reciprocal_density" not in self.user_kpoints_settings
            ):
                self.reciprocal_density = 50
            else:
                self.reciprocal_density = (
                        reciprocal_density or self.user_kpoints_settings["reciprocal_density"]
                )

            self.kpoints_line_density = kpoints_line_density
            self.copy_chgcar = copy_chgcar

        @property
        def kpoints(self) -> Kpoints:
            """
            :return: Kpoints
            """
            kpts = []  # type: List[Union[int, float, None]]
            weights = []  # type: List[Union[float, None]]
            all_labels = []  # type: List[Union[str, None]]
            structure = self.structure

            # for both modes, include the Uniform mesh w/standard weights
            grid = Kpoints.automatic_density_by_vol(structure, self.reciprocal_density).kpts
            ir_kpts = SpacegroupAnalyzer(structure, symprec=0.1).get_ir_reciprocal_mesh(
                grid[0]
            )
            for k in ir_kpts:
                kpts.append(k[0])
                weights.append(int(k[1]))
                all_labels.append(None)

            # for both modes, include any user-added kpoints w/zero weight
            for k in self.added_kpoints:
                kpts.append(k)
                weights.append(0.0)
                all_labels.append("user-defined")

            # for line mode only, add the symmetry lines w/zero weight
            if self.mode.lower() == "line":
                kpath = HighSymmKpath(structure)
                frac_k_points, labels = kpath.get_kpoints(
                    line_density=self.kpoints_line_density, coords_are_cartesian=False
                )

                for k in range(len(frac_k_points)):
                    kpts.append(frac_k_points[k])
                    weights.append(0.0)
                    all_labels.append(labels[k])

            comment = (
                "HSE run along symmetry lines"
                if self.mode.lower() == "line"
                else "HSE run on uniform grid"
            )

            return Kpoints(
                comment=comment,
                style=Kpoints.supported_modes.Reciprocal,
                num_kpts=len(kpts),
                kpts=kpts,
                kpts_weights=weights,
                labels=all_labels,
            )

        def override_from_prev_calc(self, prev_calc_dir="."):
            """
            Update the input set to include settings from a previous calculation.

            Args:
                prev_calc_dir (str): The path to the previous calculation directory.

            Returns:
                The input set with the settings (structure, k-points, incar, etc)
                updated using the previous VASP run.
            """
            vasprun, outcar = get_vasprun_outcar(prev_calc_dir)

            self._structure = get_structure_from_prev_run(vasprun, outcar)

            # note: recommend not standardizing the cell because we want to retain
            # k-points
            if self.standardize:
                warnings.warn(
                    "Use of standardize=True with from_prev_calc is not "
                    "recommended as there is no guarantee the copied "
                    "files will be appropriate for the standardized "
                    "structure."
                )

            if self.mode.lower() == "gap":
                added_kpoints = []

                bs = vasprun.get_band_structure()
                vbm, cbm = bs.get_vbm()["kpoint"], bs.get_cbm()["kpoint"]
                if vbm:
                    added_kpoints.append(vbm.frac_coords)
                if cbm:
                    added_kpoints.append(cbm.frac_coords)

                self.added_kpoints.extend(added_kpoints)

            files_to_transfer = {}
            if self.copy_chgcar:
                chgcars = sorted(glob.glob(str(Path(prev_calc_dir) / "CHGCAR*")))
                if chgcars:
                    files_to_transfer["CHGCAR"] = str(chgcars[-1])

            self.files_to_transfer.update(files_to_transfer)

            return self

        @classmethod
        def from_prev_calc(cls, prev_calc_dir, **kwargs):
            """
            Generate a set of Vasp input files for HSE calculations from a
            directory of previous Vasp run.

            Args:
                prev_calc_dir (str): Directory containing the outputs
                    (vasprun.xml and OUTCAR) of previous vasp run.
                **kwargs: All kwargs supported by MPHSEBSStaticSet, other than
                    prev_structure which is determined from the previous calc dir.
            """
            input_set = cls(_dummy_structure, **kwargs)
            return input_set.override_from_prev_calc(prev_calc_dir=prev_calc_dir)

    class MPNonSCFSet(MPRelaxSet):
        """
        Init a MPNonSCFSet. Typically, you would use the classmethod
        from_prev_calc to initialize from a previous SCF run.
        """

        def __init__(
                self,
                structure,
                prev_incar=None,
                mode="line",
                nedos=2001,
                dedos=0.005,
                reciprocal_density=100,
                sym_prec=0.1,
                kpoints_line_density=20,
                optics=False,
                copy_chgcar=True,
                nbands_factor=1.2,
                small_gap_multiply=None,
                **kwargs
        ):
            """
            Args:
                structure (Structure): Structure to compute
                prev_incar (Incar/string): Incar file from previous run.
                mode (str): Line, Uniform or Boltztrap mode supported.
                nedos (int): nedos parameter. Default to 2001.
                dedos (float): setting nedos=0 and uniform mode in from_prev_calc,
                    an automatic nedos will be calculated using the total energy range
                    divided by the energy step dedos
                reciprocal_density (int): density of k-mesh by reciprocal
                    volume (defaults to 100)
                sym_prec (float): Symmetry precision (for Uniform mode).
                kpoints_line_density (int): Line density for Line mode.
                optics (bool): whether to add dielectric function
                copy_chgcar: Whether to copy the old CHGCAR when starting from a
                    previous calculation.
                nbands_factor (float): Multiplicative factor for NBANDS when starting
                    from a previous calculation. Choose a higher number if you are
                    doing an LOPTICS calculation.
                small_gap_multiply ([float, float]): When starting from a previous
                    calculation, if the gap is less than 1st index, multiply the default
                    reciprocal_density by the 2nd index.
                **kwargs: kwargs supported by MPRelaxSet.
            """
            super().__init__(structure, **kwargs)
            if isinstance(prev_incar, str):
                prev_incar = Incar.from_file(prev_incar)
            self.prev_incar = prev_incar
            self.kwargs = kwargs
            self.nedos = nedos
            self.dedos = dedos
            self.reciprocal_density = reciprocal_density
            self.sym_prec = sym_prec
            self.kpoints_line_density = kpoints_line_density
            self.optics = optics
            self.mode = mode.lower()
            self.copy_chgcar = copy_chgcar
            self.nbands_factor = nbands_factor
            self.small_gap_multiply = small_gap_multiply

            if self.mode.lower() not in ["line", "uniform", "boltztrap"]:
                raise ValueError(
                    "Supported modes for NonSCF runs are 'Line', "
                    "'Uniform' and 'Boltztrap!"
                )

            if (self.mode.lower() != "uniform" or nedos < 2000) and optics:
                warnings.warn(
                    "It is recommended to use Uniform mode with a high "
                    "NEDOS for optics calculations."
                )

        @property
        def incar(self) -> Incar:
            """
            :return: Incar
            """
            incar = super().incar
            if self.prev_incar is not None:
                incar.update({k: v for k, v in self.prev_incar.items()})

            # Overwrite necessary INCAR parameters from previous runs
            incar.update(
                {
                    "IBRION": -1,
                    "LCHARG": False,
                    "LORBIT": 11,
                    "LWAVE": False,
                    "NSW": 0,
                    "ISYM": 0,
                    "ICHARG": 11,
                }
            )

            if self.mode.lower() == "uniform":
                # use tetrahedron method for DOS and optics calculations
                incar.update({"ISMEAR": -5, "ISYM": 2})
            else:
                # if line mode, can't use ISMEAR=-5; also use small sigma to avoid
                # partial occupancies for small band gap materials.
                # finally, explicit k-point generation (needed for bolztrap mode)
                # is incompatible with ISMEAR = -5.
                incar.update({"ISMEAR": 0, "SIGMA": 0.01})

            incar.update(self.kwargs.get("user_incar_settings", {}))

            if self.mode.lower() in "uniform":
                # Set smaller steps for DOS and optics output
                incar["NEDOS"] = self.nedos

            if self.optics:
                incar["LOPTICS"] = True

            incar.pop("MAGMOM", None)

            return incar

        @property
        def kpoints(self) -> Optional[Kpoints]:
            """
            :return: Kpoints
            """
            # override pymatgen kpoints if provided
            user_kpoints = self.kwargs.get("user_kpoints_settings", None)
            if isinstance(user_kpoints, Kpoints):
                return user_kpoints

            if self.mode.lower() == "line":
                kpath = HighSymmKpath(self.structure)
                frac_k_points, k_points_labels = kpath.get_kpoints(
                    line_density=self.kpoints_line_density, coords_are_cartesian=False
                )
                kpoints = Kpoints(
                    comment="Non SCF run along symmetry lines",
                    style=Kpoints.supported_modes.Reciprocal,
                    num_kpts=len(frac_k_points),
                    kpts=frac_k_points,
                    labels=k_points_labels,
                    kpts_weights=[1] * len(frac_k_points),
                )
            elif self.mode.lower() == "boltztrap":
                kpoints = Kpoints.automatic_density_by_vol(
                    self.structure, self.reciprocal_density
                )
                mesh = kpoints.kpts[0]
                ir_kpts = SpacegroupAnalyzer(
                    self.structure, symprec=self.sym_prec
                ).get_ir_reciprocal_mesh(mesh)
                kpts = []
                weights = []
                for k in ir_kpts:
                    kpts.append(k[0])
                    weights.append(int(k[1]))
                kpoints = Kpoints(
                    comment="Non SCF run on uniform grid",
                    style=Kpoints.supported_modes.Reciprocal,
                    num_kpts=len(ir_kpts),
                    kpts=kpts,
                    kpts_weights=weights,
                )
            else:
                self._config_dict["KPOINTS"]["reciprocal_density"] = self.reciprocal_density
                return super().kpoints

            return kpoints

        def override_from_prev_calc(self, prev_calc_dir="."):
            """
            Update the input set to include settings from a previous calculation.

            Args:
                prev_calc_dir (str): The path to the previous calculation directory.

            Returns:
                The input set with the settings (structure, k-points, incar, etc)
                updated using the previous VASP run.
            """
            vasprun, outcar = get_vasprun_outcar(prev_calc_dir)

            self.prev_incar = vasprun.incar

            # Get a Magmom-decorated structure
            self._structure = get_structure_from_prev_run(vasprun, outcar)

            if self.standardize:
                warnings.warn(
                    "Use of standardize=True with from_prev_run is not "
                    "recommended as there is no guarantee the copied "
                    "files will be appropriate for the standardized"
                    " structure. copy_chgcar is enforced to be false."
                )
                self.copy_chgcar = False

            # Turn off spin when magmom for every site is smaller than 0.02.
            if outcar and outcar.magnetization:
                site_magmom = np.array([i["tot"] for i in outcar.magnetization])
                ispin = 2 if np.any(site_magmom[np.abs(site_magmom) > 0.02]) else 1

            elif vasprun.is_spin:
                ispin = 2

            else:
                ispin = 1

            nbands = int(np.ceil(vasprun.parameters["NBANDS"] * self.nbands_factor))
            self.prev_incar.update({"ISPIN": ispin, "NBANDS": nbands})

            files_to_transfer = {}

            if self.copy_chgcar:
                chgcars = sorted(glob.glob(str(Path(prev_calc_dir) / "CHGCAR*")))
                if chgcars:
                    files_to_transfer["CHGCAR"] = str(chgcars[-1])

            self.files_to_transfer.update(files_to_transfer)

            # multiply the reciprocal density if needed:
            if self.small_gap_multiply:
                gap = vasprun.eigenvalue_band_properties[0]
                if gap <= self.small_gap_multiply[0]:
                    self.reciprocal_density = (
                            self.reciprocal_density * self.small_gap_multiply[1]
                    )
                    self.kpoints_line_density = (
                            self.kpoints_line_density * self.small_gap_multiply[1]
                    )

            # automatic setting of nedos using the total energy range and the energy step dedos
            if self.nedos == 0:
                emax = max([eigs.max() for eigs in vasprun.eigenvalues.values()])
                emin = min([eigs.min() for eigs in vasprun.eigenvalues.values()])
                self.nedos = int((emax - emin) / self.dedos)

            return self

        @classmethod
        def from_prev_calc(cls, prev_calc_dir, **kwargs):
            """
            Generate a set of Vasp input files for NonSCF calculations from a
            directory of previous static Vasp run.

            Args:
                prev_calc_dir (str): The directory contains the outputs(
                    vasprun.xml and OUTCAR) of previous vasp run.
                **kwargs: All kwargs supported by MPNonSCFSet, other than structure,
                    prev_incar and prev_chgcar which are determined from the
                    prev_calc_dir.
            """
            input_set = cls(_dummy_structure, **kwargs)
            return input_set.override_from_prev_calc(prev_calc_dir=prev_calc_dir)

    class MPSOCSet(MPStaticSet):
        """
        An input set for running spin-orbit coupling (SOC) calculations.
        """

        def __init__(
                self,
                structure,
                saxis=(0, 0, 1),
                copy_chgcar=True,
                nbands_factor=1.2,
                reciprocal_density=100,
                small_gap_multiply=None,
                magmom=None,
                **kwargs
        ):
            """
            Args:
                structure (Structure): the structure must have the 'magmom' site
                    property and each magnetic moment value must have 3
                    components. eg: ``magmom = [[0,0,2], ...]``
                saxis (tuple): magnetic moment orientation
                copy_chgcar: Whether to copy the old CHGCAR. Defaults to True.
                nbands_factor (float): Multiplicative factor for NBANDS. Choose a
                    higher number if you are doing an LOPTICS calculation.
                reciprocal_density (int): density of k-mesh by reciprocal volume.
                small_gap_multiply ([float, float]): If the gap is less than
                    1st index, multiply the default reciprocal_density by the 2nd
                    index.
                magmom (list[list[float]]): Override for the structure magmoms.
                **kwargs: kwargs supported by MPStaticSet.
            """

            if not hasattr(structure[0], "magmom") and not isinstance(
                    structure[0].magmom, list
            ):
                raise ValueError(
                    "The structure must have the 'magmom' site "
                    "property and each magnetic moment value must have 3 "
                    "components. eg:- magmom = [0,0,2]"
                )

            super().__init__(structure, reciprocal_density=reciprocal_density, **kwargs)
            self.saxis = saxis
            self.copy_chgcar = copy_chgcar
            self.nbands_factor = nbands_factor
            self.small_gap_multiply = small_gap_multiply
            self.magmom = magmom

        @property
        def incar(self) -> Incar:
            """
            :return: Incar
            """
            incar = super().incar
            if self.prev_incar is not None:
                incar.update({k: v for k, v in self.prev_incar.items()})

            # Overwrite necessary INCAR parameters from previous runs
            incar.update(
                {"ISYM": -1, "LSORBIT": "T", "ICHARG": 11, "SAXIS": list(self.saxis)}
            )
            incar.update(self.kwargs.get("user_incar_settings", {}))

            return incar

        def override_from_prev_calc(self, prev_calc_dir="."):
            """
            Update the input set to include settings from a previous calculation.

            Args:
                prev_calc_dir (str): The path to the previous calculation directory.

            Returns:
                The input set with the settings (structure, k-points, incar, etc)
                updated using the previous VASP run.
            """
            vasprun, outcar = get_vasprun_outcar(prev_calc_dir)

            self.prev_incar = vasprun.incar

            # Remove magmoms from previous INCAR, since we will prefer
            # the final calculated magmoms
            # TODO: revisit in context of MPStaticSet incar logic
            if "MAGMOM" in self.prev_incar:
                del self.prev_incar["magmom"]

            # Get a magmom-decorated structure
            self._structure = get_structure_from_prev_run(vasprun, outcar)
            if self.standardize:
                warnings.warn(
                    "Use of standardize=True with from_prev_run is not "
                    "recommended as there is no guarantee the copied "
                    "files will be appropriate for the standardized"
                    " structure. copy_chgcar is enforced to be false."
                )
                self.copy_chgcar = False

            # override magmom if provided
            if self.magmom:
                self._structure = self._structure.copy(
                    site_properties={"magmom": self.magmom}
                )

            # magmom has to be 3D for SOC calculation.
            if hasattr(self._structure[0], "magmom"):
                if not isinstance(self._structure[0].magmom, list):
                    self._structure = self._structure.copy(
                        site_properties={
                            "magmom": [[0, 0, site.magmom] for site in self._structure]
                        }
                    )
            else:
                raise ValueError(
                    "Neither the previous structure has magmom "
                    "property nor magmom provided"
                )

            nbands = int(np.ceil(vasprun.parameters["NBANDS"] * self.nbands_factor))
            self.prev_incar.update({"NBANDS": nbands})

            files_to_transfer = {}
            if self.copy_chgcar:
                chgcars = sorted(glob.glob(str(Path(prev_calc_dir) / "CHGCAR*")))
                if chgcars:
                    files_to_transfer["CHGCAR"] = str(chgcars[-1])

            self.files_to_transfer.update(files_to_transfer)

            # multiply the reciprocal density if needed:
            if self.small_gap_multiply:
                gap = vasprun.eigenvalue_band_properties[0]
                if gap <= self.small_gap_multiply[0]:
                    self.reciprocal_density = (
                            self.reciprocal_density * self.small_gap_multiply[1]
                    )

            return self

        @classmethod
        def from_prev_calc(cls, prev_calc_dir, **kwargs):
            """
            Generate a set of Vasp input files for SOC calculations from a
            directory of previous static Vasp run. SOC calc requires all 3
            components for MAGMOM for each atom in the structure.

            Args:
                prev_calc_dir (str): The directory contains the outputs(
                    vasprun.xml and OUTCAR) of previous vasp run.
                **kwargs: All kwargs supported by MPSOCSet, other than structure,
                    prev_incar and prev_chgcar which are determined from the
                    prev_calc_dir.
            """
            input_set = cls(_dummy_structure, **kwargs)
            return input_set.override_from_prev_calc(prev_calc_dir=prev_calc_dir)

    class MPNMRSet(MPStaticSet):
        """
        Init a MPNMRSet.
        """

        def __init__(
                self,
                structure,
                mode="cs",
                isotopes=None,
                prev_incar=None,
                reciprocal_density=100,
                **kwargs
        ):
            """
            Args:
                structure (Structure): Structure to compute
                mode (str): The NMR calculation to run
                                "cs": for Chemical Shift
                                "efg" for Electric Field Gradient
                isotopes (list): list of Isotopes for quadrupole moments
                prev_incar (Incar): Incar file from previous run.
                reciprocal_density (int): density of k-mesh by reciprocal
                                        volume (defaults to 100)
                **kwargs: kwargs supported by MPStaticSet.
            """
            self.mode = mode
            self.isotopes = isotopes if isotopes else []
            super().__init__(
                structure,
                prev_incar=prev_incar,
                reciprocal_density=reciprocal_density,
                **kwargs
            )

        @property
        def incar(self):
            """
            :return: Incar
            """
            incar = super().incar

            if self.mode.lower() == "cs":
                incar.update(
                    {
                        "LCHIMAG": True,
                        "EDIFF": -1.0e-10,
                        "ISYM": 0,
                        "LCHARG": False,
                        "LNMR_SYM_RED": True,
                        "NELMIN": 10,
                        "NSLPLINE": True,
                        "PREC": "ACCURATE",
                        "SIGMA": 0.01,
                    }
                )
            elif self.mode.lower() == "efg":

                isotopes = {ist.split("-")[0]: ist for ist in self.isotopes}

                quad_efg = [
                    Specie(p).get_nmr_quadrupole_moment(isotopes.get(p, None))
                    for p in self.poscar.site_symbols
                ]

                incar.update(
                    {
                        "ALGO": "FAST",
                        "EDIFF": -1.0e-10,
                        "ISYM": 0,
                        "LCHARG": False,
                        "LEFG": True,
                        "QUAD_EFG": quad_efg,
                        "NELMIN": 10,
                        "PREC": "ACCURATE",
                        "SIGMA": 0.01,
                    }
                )
            incar.update(self.kwargs.get("user_incar_settings", {}))

            return incar

    class MVLElasticSet(MPRelaxSet):
        """
        MVL denotes VASP input sets that are implemented by the Materials Virtual
        Lab (http://www.materialsvirtuallab.org) for various research.

        This input set is used to calculate elastic constants in VASP. It is used
        in the following work::

            Z. Deng, Z. Wang, I.-H. Chu, J. Luo, S. P. Ong.
            “Elastic Properties of Alkali Superionic Conductor Electrolytes
            from First Principles Calculations”, J. Electrochem. Soc.
            2016, 163(2), A67-A74. doi: 10.1149/2.0061602jes

        To read the elastic constants, you may use the Outcar class which parses the
        elastic constants.
        """

        def __init__(self, structure, potim=0.015, **kwargs):
            """
            Args:
                scale (float): POTIM parameter. The default of 0.015 is usually fine,
                    but some structures may require a smaller step.
                user_incar_settings (dict): A dict specifying additional incar
                    settings.
                kwargs:
                    Parameters supported by MPRelaxSet.
            """
            super().__init__(structure, **kwargs)
            self._config_dict["INCAR"].update({"IBRION": 6, "NFREE": 2, "POTIM": potim})
            self._config_dict["INCAR"].pop("NPAR", None)

    class MVLGWSet(DictSet):
        """
        MVL denotes VASP input sets that are implemented by the Materials Virtual
        Lab (http://www.materialsvirtuallab.org) for various research. This is a
        flexible input set for GW calculations.

        Note that unlike all other input sets in this module, the PBE_54 series of
        functional is set as the default. These have much improved performance for
        GW calculations.

        A typical sequence is mode="STATIC" -> mode="DIAG" -> mode="GW" ->
        mode="BSE". For all steps other than the first one (static), the
        recommendation is to use from_prev_calculation on the preceding run in
        the series.
        """

        CONFIG = _load_yaml_config("MVLGWSet")

        SUPPORTED_MODES = ("DIAG", "GW", "STATIC", "BSE")

        def __init__(
                self,
                structure,
                prev_incar=None,
                nbands=None,
                reciprocal_density=100,
                mode="STATIC",
                copy_wavecar=True,
                nbands_factor=5,
                ncores=16,
                **kwargs
        ):
            r"""
            Args:
                structure (Structure): Input structure.
                prev_incar (Incar/string): Incar file from previous run.
                mode (str): Supported modes are "STATIC" (default), "DIAG", "GW",
                    and "BSE".
                nbands (int): For subsequent calculations, it is generally
                    recommended to perform NBANDS convergence starting from the
                    NBANDS of the previous run for DIAG, and to use the exact same
                    NBANDS for GW and BSE. This parameter is used by
                    from_previous_calculation to set nband.
                copy_wavecar: Whether to copy the old WAVECAR, WAVEDER and associated
                    files when starting from a previous calculation.
                nbands_factor (int): Multiplicative factor for NBANDS when starting
                    from a previous calculation. Only applies if mode=="DIAG".
                    Need to be tested for convergence.
                ncores (int): Numbers of cores used for the calculation. VASP will alter
                    NBANDS if it was not dividable by ncores. Only applies if
                    mode=="DIAG".
                **kwargs: All kwargs supported by DictSet. Typically,
                    user_incar_settings is a commonly used option.
            """
            super().__init__(structure, MVLGWSet.CONFIG, **kwargs)
            self.prev_incar = prev_incar
            self.nbands = nbands
            self.reciprocal_density = reciprocal_density
            self.mode = mode.upper()
            if self.mode not in MVLGWSet.SUPPORTED_MODES:
                raise ValueError(
                    "%s not one of the support modes : %s"
                    % (self.mode, MVLGWSet.SUPPORTED_MODES)
                )
            self.kwargs = kwargs
            self.copy_wavecar = copy_wavecar
            self.nbands_factor = nbands_factor
            self.ncores = ncores

        @property
        def kpoints(self):
            """
            Generate gamma center k-points mesh grid for GW calc,
            which is requested by GW calculation.
            """
            return Kpoints.automatic_density_by_vol(
                self.structure, self.reciprocal_density, force_gamma=True
            )

        @property
        def incar(self):
            """
            :return: Incar
            """
            parent_incar = super().incar
            incar = (
                Incar(self.prev_incar)
                if self.prev_incar is not None
                else Incar(parent_incar)
            )

            if self.mode == "DIAG":
                # Default parameters for diagonalization calculation.
                incar.update({"ALGO": "Exact", "NELM": 1, "LOPTICS": True, "LPEAD": True})
            elif self.mode == "GW":
                # Default parameters for GW calculation.
                incar.update({"ALGO": "GW0", "NELM": 1, "NOMEGA": 80, "ENCUTGW": 250})
                incar.pop("EDIFF", None)
                incar.pop("LOPTICS", None)
                incar.pop("LPEAD", None)
            elif self.mode == "BSE":
                # Default parameters for BSE calculation.
                incar.update({"ALGO": "BSE", "ANTIRES": 0, "NBANDSO": 20, "NBANDSV": 20})

            if self.nbands:
                incar["NBANDS"] = self.nbands

            # Respect user set INCAR.
            incar.update(self.kwargs.get("user_incar_settings", {}))

            return incar

        def override_from_prev_calc(self, prev_calc_dir="."):
            """
            Update the input set to include settings from a previous calculation.

            Args:
                prev_calc_dir (str): The path to the previous calculation directory.

            Returns:
                The input set with the settings (structure, k-points, incar, etc)
                updated using the previous VASP run.
            """
            vasprun, outcar = get_vasprun_outcar(prev_calc_dir)
            self.prev_incar = vasprun.incar
            self._structure = vasprun.final_structure

            if self.standardize:
                warnings.warn(
                    "Use of standardize=True with from_prev_run is not "
                    "recommended as there is no guarantee the copied "
                    "files will be appropriate for the standardized "
                    "structure."
                )

            self.nbands = int(vasprun.parameters["NBANDS"])
            if self.mode.upper() == "DIAG":
                self.nbands = int(
                    np.ceil(self.nbands * self.nbands_factor / self.ncores) * self.ncores
                )

            # copy WAVECAR, WAVEDER (derivatives)
            files_to_transfer = {}
            if self.copy_wavecar:
                for fname in ("WAVECAR", "WAVEDER", "WFULL"):
                    w = sorted(glob.glob(str(Path(prev_calc_dir) / (fname + "*"))))
                    if w:
                        if fname == "WFULL":
                            for f in w:
                                fname = Path(f).name
                                fname = fname.split(".")[0]
                                files_to_transfer[fname] = f
                        else:
                            files_to_transfer[fname] = str(w[-1])

            self.files_to_transfer.update(files_to_transfer)

            return self

        @classmethod
        def from_prev_calc(cls, prev_calc_dir, mode="DIAG", **kwargs):
            """
            Generate a set of Vasp input files for GW or BSE calculations from a
            directory of previous Exact Diag Vasp run.

            Args:
                prev_calc_dir (str): The directory contains the outputs(
                    vasprun.xml of previous vasp run.
                mode (str): Supported modes are "STATIC", "DIAG" (default), "GW",
                    and "BSE".
                **kwargs: All kwargs supported by MVLGWSet, other than structure,
                    prev_incar and mode, which are determined from the
                    prev_calc_dir.
            """
            input_set = cls(_dummy_structure, mode=mode, **kwargs)
            return input_set.override_from_prev_calc(prev_calc_dir=prev_calc_dir)

    class MVLSlabSet(MPRelaxSet):
        """
        Class for writing a set of slab vasp runs,
        including both slabs (along the c direction) and orient unit cells (bulk),
        to ensure the same KPOINTS, POTCAR and INCAR criterion.
        """

        def __init__(
                self,
                structure,
                k_product=50,
                bulk=False,
                auto_dipole=False,
                set_mix=True,
                sort_structure=True,
                **kwargs
        ):
            """
            :param structure: Structure
            :param k_product: default to 50, kpoint number * length for a & b
                directions, also for c direction in bulk calculations
            :param bulk:
            :param auto_dipole:
            :param set_mix:
            :param sort_structure:
            :param kwargs: Other kwargs supported by :class:`DictSet`.
            """
            super().__init__(structure, **kwargs)

            if sort_structure:
                structure = structure.get_sorted_structure()

            self.k_product = k_product
            self.bulk = bulk
            self.auto_dipole = auto_dipole
            self.kwargs = kwargs
            self.set_mix = set_mix
            self.kpt_calc = None

            slab_incar = {
                "EDIFF": 1e-4,
                "EDIFFG": -0.02,
                "ENCUT": 400,
                "ISMEAR": 0,
                "SIGMA": 0.05,
                "ISIF": 3,
            }
            if not self.bulk:
                slab_incar["ISIF"] = 2
                slab_incar["LVTOT"] = True
                if self.set_mix:
                    slab_incar["AMIN"] = 0.01
                    slab_incar["AMIX"] = 0.2
                    slab_incar["BMIX"] = 0.001
                slab_incar["NELMIN"] = 8
                if self.auto_dipole:
                    weights = [s.species.weight for s in structure]
                    center_of_mass = np.average(
                        structure.frac_coords, weights=weights, axis=0
                    )

                    slab_incar["IDIPOL"] = 3
                    slab_incar["LDIPOL"] = True
                    slab_incar["DIPOL"] = center_of_mass

            self._config_dict["INCAR"].update(slab_incar)

        @property
        def kpoints(self):
            """
            k_product, default to 50, is kpoint number * length for a & b
                directions, also for c direction in bulk calculations
            Automatic mesh & Gamma is the default setting.
            """

            # To get input sets, the input structure has to has the same number
            # of required parameters as a Structure object (ie. 4). Slab
            # attributes aren't going to affect the VASP inputs anyways so
            # converting the slab into a structure should not matter

            kpt = super().kpoints
            kpt.comment = "Automatic mesh"
            kpt.style = "Gamma"

            # use k_product to calculate kpoints, k_product = kpts[0][0] * a
            lattice_abc = self.structure.lattice.abc
            kpt_calc = [
                int(self.k_product / lattice_abc[0] + 0.5),
                int(self.k_product / lattice_abc[1] + 0.5),
                1,
            ]

            self.kpt_calc = kpt_calc
            # calculate kpts (c direction) for bulk. (for slab, set to 1)
            if self.bulk:
                kpt_calc[2] = int(self.k_product / lattice_abc[2] + 0.5)

            kpt.kpts[0] = kpt_calc

            return kpt

        def as_dict(self, verbosity=2):
            """
            :param verbosity: Verbosity of dict. E.g., whether to include Structure.
            :return: MSONAble dict
            """
            d = MSONable.as_dict(self)
            if verbosity == 1:
                d.pop("structure", None)
            return d

    class MVLGBSet(MPRelaxSet):
        """
        Class for writing a vasp input files for grain boundary calculations, slab
        or bulk.
        """

        def __init__(
                self, structure, k_product=40, slab_mode=False, is_metal=True, **kwargs
        ):
            r"""

            Args:
                structure(Structure): provide the structure
                k_product: Kpoint number * length for a & b directions, also for c
                    direction in bulk calculations. Default to 40.
                slab_mode (bool): Defaults to False. Use default (False) for a
                    bulk supercell. Use True if you are performing calculations on a
                    slab-like (i.e., surface) of the GB, for example, when you are
                    calculating the work of separation.
                is_metal (bool): Defaults to True. This determines whether an ISMEAR of
                    1 is used (for metals) or not (for insulators and semiconductors)
                    by default. Note that it does *not* override user_incar_settings,
                    which can be set by the user to be anything desired.
                **kwargs:
                    Other kwargs supported by :class:`MPRelaxSet`.
            """
            super().__init__(structure, **kwargs)
            self.k_product = k_product
            self.slab_mode = slab_mode
            self.is_metal = is_metal

        @property
        def kpoints(self):
            """
            k_product, default to 40, is kpoint number * length for a & b
            directions, also for c direction in bulk calculations
            Automatic mesh & Gamma is the default setting.
            """

            # To get input sets, the input structure has to has the same number
            # of required parameters as a Structure object.

            kpt = super().kpoints
            kpt.comment = "Generated by pymatgen's MVLGBSet"
            kpt.style = "Gamma"

            # use k_product to calculate kpoints, k_product = kpts[0][0] * a
            lengths = self.structure.lattice.abc
            kpt_calc = [
                int(self.k_product / lengths[0] + 0.5),
                int(self.k_product / lengths[1] + 0.5),
                int(self.k_product / lengths[2] + 0.5),
            ]

            if self.slab_mode:
                kpt_calc[2] = 1

            kpt.kpts[0] = kpt_calc

            return kpt

        @property
        def incar(self):
            """
            :return: Incar
            """
            incar = super().incar

            # The default incar setting is used for metallic system, for
            # insulator or semiconductor, ISMEAR need to be changed.
            incar.update(
                {
                    "LCHARG": False,
                    "NELM": 60,
                    "PREC": "Normal",
                    "EDIFFG": -0.02,
                    "ICHARG": 0,
                    "NSW": 200,
                    "EDIFF": 0.0001,
                }
            )

            if self.is_metal:
                incar["ISMEAR"] = 1
                incar["LDAU"] = False

            if self.slab_mode:
                # for clean grain boundary and bulk relaxation, full optimization
                # relaxation (ISIF=3) is used. For slab relaxation (ISIF=2) is used.
                incar["ISIF"] = 2
                incar["NELMIN"] = 8

            incar.update(self.user_incar_settings)

            return incar

    class MVLRelax52Set(DictSet):
        """
        Implementation of VaspInputSet utilizing the public Materials Project
        parameters for INCAR & KPOINTS and VASP's recommended PAW potentials for
        POTCAR.

        Keynotes from VASP manual:
            1. Recommended potentials for calculations using vasp.5.2+
            2. If dimers with short bonds are present in the compound (O2, CO,
                N2, F2, P2, S2, Cl2), it is recommended to use the h potentials.
                Specifically, C_h, O_h, N_h, F_h, P_h, S_h, Cl_h
            3. Released on Oct 28, 2018 by VASP. Please refer to VASP
                Manual 1.2, 1.3 & 10.2.1 for more details.
        """

        CONFIG = _load_yaml_config("MVLRelax52Set")

        def __init__(self, structure, **kwargs):
            """
            Args:
                structure (Structure): input structure.
                potcar_functional (str): choose from "PBE_52" and "PBE_54".
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """
            if kwargs.get("potcar_functional") or kwargs.get("user_potcar_functional"):
                super().__init__(structure, MVLRelax52Set.CONFIG, **kwargs)
            else:
                super().__init__(
                    structure,
                    MVLRelax52Set.CONFIG,
                    user_potcar_functional="PBE_52",
                    **kwargs
                )
            if self.potcar_functional not in ["PBE_52", "PBE_54"]:
                raise ValueError("Please select from PBE_52 and PBE_54!")

            self.kwargs = kwargs

    class MITNEBSet(MITRelaxSet):
        """
        Class for writing NEB inputs. Note that EDIFF is not on a per atom
        basis for this input set.
        """

        def __init__(self, structures, unset_encut=False, **kwargs):
            """
            Args:
                structures: List of Structure objects.
                unset_encut (bool): Whether to unset ENCUT.
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """
            if len(structures) < 3:
                raise ValueError("You need at least 3 structures for an NEB.")
            kwargs["sort_structure"] = False
            super().__init__(structures[0], **kwargs)
            self.structures = self._process_structures(structures)

            self.unset_encut = False
            if unset_encut:
                self._config_dict["INCAR"].pop("ENCUT", None)

            if "EDIFF" not in self._config_dict["INCAR"]:
                self._config_dict["INCAR"]["EDIFF"] = self._config_dict["INCAR"].pop(
                    "EDIFF_PER_ATOM"
                )

            # NEB specific defaults
            defaults = {
                "IMAGES": len(structures) - 2,
                "IBRION": 1,
                "ISYM": 0,
                "LCHARG": False,
                "LDAU": False,
            }
            self._config_dict["INCAR"].update(defaults)

        @property
        def poscar(self):
            """
            :return: Poscar for structure of first end point.
            """
            return Poscar(self.structures[0])

        @property
        def poscars(self):
            """
            :return: List of Poscars.
            """
            return [Poscar(s) for s in self.structures]

        @staticmethod
        def _process_structures(structures):
            """
            Remove any atom jumps across the cell
            """
            input_structures = structures
            structures = [input_structures[0]]
            for s in input_structures[1:]:
                prev = structures[-1]
                for i in range(len(s)):
                    t = np.round(prev[i].frac_coords - s[i].frac_coords)
                    if np.any(np.abs(t) > 0.5):
                        s.translate_sites([i], t, to_unit_cell=False)
                structures.append(s)
            return structures

        def write_input(
                self,
                output_dir,
                make_dir_if_not_present=True,
                write_cif=False,
                write_path_cif=False,
                write_endpoint_inputs=False,
        ):
            """
            NEB inputs has a special directory structure where inputs are in 00,
            01, 02, ....

            Args:
                output_dir (str): Directory to output the VASP input files
                make_dir_if_not_present (bool): Set to True if you want the
                    directory (and the whole path) to be created if it is not
                    present.
                write_cif (bool): If true, writes a cif along with each POSCAR.
                write_path_cif (bool): If true, writes a cif for each image.
                write_endpoint_inputs (bool): If true, writes input files for
                    running endpoint calculations.
            """
            output_dir = Path(output_dir)
            if make_dir_if_not_present and not output_dir.exists():
                output_dir.mkdir(parents=True)
            self.incar.write_file(str(output_dir / "INCAR"))
            self.kpoints.write_file(str(output_dir / "KPOINTS"))
            self.potcar.write_file(str(output_dir / "POTCAR"))

            for i, p in enumerate(self.poscars):
                d = output_dir / str(i).zfill(2)
                if not d.exists():
                    d.mkdir(parents=True)
                p.write_file(str(d / "POSCAR"))
                if write_cif:
                    p.structure.to(filename=str(d / "{}.cif".format(i)))
            if write_endpoint_inputs:
                end_point_param = MITRelaxSet(
                    self.structures[0], user_incar_settings=self.user_incar_settings
                )

                for image in ["00", str(len(self.structures) - 1).zfill(2)]:
                    end_point_param.incar.write_file(str(output_dir / image / "INCAR"))
                    end_point_param.kpoints.write_file(str(output_dir / image / "KPOINTS"))
                    end_point_param.potcar.write_file(str(output_dir / image / "POTCAR"))
            if write_path_cif:
                sites = set()
                lat = self.structures[0].lattice
                for site in chain(*(s.sites for s in self.structures)):
                    sites.add(PeriodicSite(site.species, site.frac_coords, lat))
                nebpath = Structure.from_sites(sorted(sites))
                nebpath.to(filename=str(output_dir / "path.cif"))

    class MITMDSet(MITRelaxSet):
        """
        Class for writing a vasp md run. This DOES NOT do multiple stage
        runs.
        """

        def __init__(
                self,
                structure,
                start_temp,
                end_temp,
                nsteps,
                time_step=2,
                spin_polarized=False,
                **kwargs
        ):
            r"""

            Args:
                structure (Structure): Input structure.
                start_temp (int): Starting temperature.
                end_temp (int): Final temperature.
                nsteps (int): Number of time steps for simulations. NSW parameter.
                time_step (int): The time step for the simulation. The POTIM
                    parameter. Defaults to 2fs.
                spin_polarized (bool): Whether to do spin polarized calculations.
                    The ISPIN parameter. Defaults to False.
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """
            # MD default settings
            defaults = {
                "TEBEG": start_temp,
                "TEEND": end_temp,
                "NSW": nsteps,
                "EDIFF_PER_ATOM": 0.000001,
                "LSCALU": False,
                "LCHARG": False,
                "LPLANE": False,
                "LWAVE": True,
                "ISMEAR": 0,
                "NELMIN": 4,
                "LREAL": True,
                "BMIX": 1,
                "MAXMIX": 20,
                "NELM": 500,
                "NSIM": 4,
                "ISYM": 0,
                "ISIF": 0,
                "IBRION": 0,
                "NBLOCK": 1,
                "KBLOCK": 100,
                "SMASS": 0,
                "POTIM": time_step,
                "PREC": "Low",
                "ISPIN": 2 if spin_polarized else 1,
                "LDAU": False,
            }

            super().__init__(structure, **kwargs)

            self.start_temp = start_temp
            self.end_temp = end_temp
            self.nsteps = nsteps
            self.time_step = time_step
            self.spin_polarized = spin_polarized
            self.kwargs = kwargs

            # use VASP default ENCUT
            self._config_dict["INCAR"].pop("ENCUT", None)

            if defaults["ISPIN"] == 1:
                self._config_dict["INCAR"].pop("MAGMOM", None)
            self._config_dict["INCAR"].update(defaults)

        @property
        def kpoints(self):
            """
            :return: Kpoints
            """
            return Kpoints.gamma_automatic()

    class MPMDSet(MPRelaxSet):
        """
        This a modified version of the old MITMDSet pre 2018/03/12.

        This set serves as the basis for the amorphous skyline paper.

        (1) Aykol, M.; Dwaraknath, S. S.; Sun, W.; Persson, K. A. Thermodynamic
            Limit for Synthesis of Metastable Inorganic Materials. Sci. Adv. 2018,
            4 (4).

        Class for writing a vasp md run. This DOES NOT do multiple stage runs.
        Precision remains normal, to increase accuracy of stress tensor.
        """

        def __init__(
                self, structure, start_temp, end_temp, nsteps, spin_polarized=False, **kwargs
        ):
            r"""
            Args:
                structure (Structure): Input structure.
                start_temp (int): Starting temperature.
                end_temp (int): Final temperature.
                nsteps (int): Number of time steps for simulations. NSW parameter.
                time_step (int): The time step for the simulation. The POTIM
                    parameter. Defaults to 2fs.
                spin_polarized (bool): Whether to do spin polarized calculations.
                    The ISPIN parameter. Defaults to False.
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """

            # MD default settings
            defaults = {
                "TEBEG": start_temp,
                "TEEND": end_temp,
                "NSW": nsteps,
                "EDIFF_PER_ATOM": 0.00001,
                "LSCALU": False,
                "LCHARG": False,
                "LPLANE": False,
                "LWAVE": True,
                "ISMEAR": 0,
                "NELMIN": 4,
                "LREAL": True,
                "BMIX": 1,
                "MAXMIX": 20,
                "NELM": 500,
                "NSIM": 4,
                "ISYM": 0,
                "ISIF": 0,
                "IBRION": 0,
                "NBLOCK": 1,
                "KBLOCK": 100,
                "SMASS": 0,
                "POTIM": 2,
                "PREC": "Normal",
                "ISPIN": 2 if spin_polarized else 1,
                "LDAU": False,
                "ADDGRID": True,
            }

            if Element("H") in structure.species:
                defaults["POTIM"] = 0.5
                defaults["NSW"] = defaults["NSW"] * 4

            super().__init__(structure, **kwargs)

            self.start_temp = start_temp
            self.end_temp = end_temp
            self.nsteps = nsteps
            self.spin_polarized = spin_polarized
            self.kwargs = kwargs

            # use VASP default ENCUT
            self._config_dict["INCAR"].pop("ENCUT", None)

            if defaults["ISPIN"] == 1:
                self._config_dict["INCAR"].pop("MAGMOM", None)
            self._config_dict["INCAR"].update(defaults)

        @property
        def kpoints(self):
            """
            :return: Kpoints
            """
            return Kpoints.gamma_automatic()

    class MVLNPTMDSet(MITMDSet):
        """
        Class for writing a vasp md run in NPT ensemble.

        Notes:
            To eliminate Pulay stress, the default ENCUT is set to a rather large
            value of ENCUT, which is 1.5 * ENMAX.
        """

        def __init__(
                self,
                structure,
                start_temp,
                end_temp,
                nsteps,
                time_step=2,
                spin_polarized=False,
                **kwargs
        ):
            r"""
            Args:
                structure (Structure): input structure.
                start_temp (int): Starting temperature.
                end_temp (int): Final temperature.
                nsteps(int): Number of time steps for simulations. NSW parameter.
                time_step (int): The time step for the simulation. The POTIM
                    parameter. Defaults to 2fs.
                spin_polarized (bool): Whether to do spin polarized calculations.
                    The ISPIN parameter. Defaults to False.
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """
            user_incar_settings = kwargs.get("user_incar_settings", {})

            # NPT-AIMD default settings
            defaults = {
                "IALGO": 48,
                "ISIF": 3,
                "LANGEVIN_GAMMA": [10] * structure.ntypesp,
                "LANGEVIN_GAMMA_L": 1,
                "MDALGO": 3,
                "PMASS": 10,
                "PSTRESS": 0,
                "SMASS": 0,
            }

            defaults.update(user_incar_settings)
            kwargs["user_incar_settings"] = defaults

            super().__init__(
                structure, start_temp, end_temp, nsteps, time_step, spin_polarized, **kwargs
            )

            # Set NPT-AIMD ENCUT = 1.5 * VASP_default
            enmax = [self.potcar[i].keywords["ENMAX"] for i in range(structure.ntypesp)]
            encut = max(enmax) * 1.5
            self._config_dict["INCAR"]["ENCUT"] = encut

    class MVLScanRelaxSet(MPRelaxSet):
        """
        Class for writing a relax input set using Strongly Constrained and
        Appropriately Normed (SCAN) semilocal density functional.

        Notes:
            1. This functional is only available from VASP.5.4.3 upwards.

            2. Meta-GGA calculations require POTCAR files that include
            information on the kinetic energy density of the core-electrons,
            i.e. "PBE_52" or "PBE_54". Make sure the POTCAR including the
            following lines (see VASP wiki for more details):

                $ grep kinetic POTCAR
                kinetic energy-density
                mkinetic energy-density pseudized
                kinetic energy density (partial)
        """

        def __init__(self, structure, **kwargs):
            r"""
            Args:
                structure (Structure): input structure.
                vdw (str): set "rVV10" to enable SCAN+rVV10, which is a versatile
                    van der Waals density functional by combing the SCAN functional
                    with the rVV10 non-local correlation functional.
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """
            # choose PBE_52 unless the user specifies something else
            if kwargs.get("potcar_functional") or kwargs.get("user_potcar_functional"):
                super().__init__(structure, **kwargs)
            else:
                super().__init__(structure, user_potcar_functional="PBE_52", **kwargs)

            if self.potcar_functional not in ["PBE_52", "PBE_54"]:
                raise ValueError("SCAN calculations required PBE_52 or PBE_54!")

            updates = {
                "ADDGRID": True,
                "EDIFF": 1e-05,
                "EDIFFG": -0.05,
                "LASPH": True,
                "LDAU": False,
                "METAGGA": "SCAN",
                "NELM": 200,
            }

            if kwargs.get("vdw", "").lower() == "rvv10":
                updates["BPARAM"] = 15.7  # This is the correct BPARAM for SCAN+rVV10

            self._config_dict["INCAR"].update(updates)

    class LobsterSet(MPRelaxSet):
        """
        Input set to prepare VASP runs that can be digested by Lobster (See cohp.de)
        """

        CONFIG = _load_yaml_config("MPRelaxSet")

        def __init__(
                self,
                structure: Structure,
                isym: int = -1,
                ismear: int = -5,
                reciprocal_density: int = None,
                address_basis_file: str = None,
                user_supplied_basis: dict = None,
                **kwargs
        ):
            """
            Args:
                structure (Structure): input structure.
                isym (int): ISYM entry for INCAR, only isym=-1 and isym=0 are allowed
                ismear (int): ISMEAR entry for INCAR, only ismear=-5 and ismear=0 are allowed
                reciprocal_density (int): density of k-mesh by reciprocal volume
                user_supplied_basis (dict): dict including basis functions for all elements in structure,
                    e.g. {"Fe": "3d 3p 4s", "O": "2s 2p"}; if not supplied, a standard basis is used
                address_basis_file (str): address to a file similar to "BASIS_PBE_54_standaard.yaml"
                    in pymatgen.io.lobster.lobster_basis
                **kwargs: Other kwargs supported by :class:`DictSet`.
            """
            warnings.warn(
                "Make sure that all parameters are okay! This is a brand new implementation."
            )

            if not (isym == -1 or isym == 0):
                raise ValueError("Lobster cannot digest WAVEFUNCTIONS with symmetry")
            if not (ismear == -5 or ismear == 0):
                raise ValueError("Lobster usually works with ismear=-5 or ismear=0")

            # newest potcars are preferred
            # Choose PBE_54 unless the user specifies a different potcar_functional
            if kwargs.get("potcar_functional") or kwargs.get("user_potcar_functional"):
                super().__init__(structure, **kwargs)
            else:
                super().__init__(structure, user_potcar_functional="PBE_54", **kwargs)

            # reciprocal density
            if self.user_kpoints_settings is not None:
                if (
                        not reciprocal_density
                        or "reciprocal_density" not in self.user_kpoints_settings
                ):
                    # test, if this is okay
                    self.reciprocal_density = 310
                else:
                    self.reciprocal_density = (
                            reciprocal_density
                            or self.user_kpoints_settings["reciprocal_density"]
                    )
            else:
                if not reciprocal_density:
                    # test, if this is okay
                    self.reciprocal_density = 310
                else:
                    self.reciprocal_density = reciprocal_density

            # might need to be adapted in the future
            ediff_per_atom = 5e-05

            self.isym = isym
            self.ismear = ismear
            self.user_supplied_basis = user_supplied_basis
            self.address_basis_file = address_basis_file
            # predefined basis! Check if the basis is okay! (charge spilling and bandoverlaps!)
            if user_supplied_basis is None and address_basis_file is None:
                basis = Lobsterin.get_basis(
                    structure=structure, potcar_symbols=self.potcar_symbols
                )
            elif address_basis_file is not None:
                basis = Lobsterin.get_basis(
                    structure=structure,
                    potcar_symbols=self.potcar_symbols,
                    address_basis_file=address_basis_file,
                )
            elif user_supplied_basis is not None:
                # test if all elements from structure are in user_supplied_basis
                for atomtype in structure.symbol_set:
                    if atomtype not in user_supplied_basis:
                        raise ValueError(
                            "There are no basis functions for the atom type "
                            + str(atomtype)
                        )
                basis = [key + " " + value for key, value in user_supplied_basis.items()]

            lobsterin = Lobsterin(settingsdict={"basisfunctions": basis})
            nbands = lobsterin._get_nbands(structure=structure)

            update_dict = {
                "EDIFF_PER_ATOM": ediff_per_atom,
                "NSW": 0,
                "LWAVE": True,
                "ISYM": isym,
                "NBANDS": nbands,
                "IBRION": -1,
                "ISMEAR": ismear,
                "LORBIT": 11,
                "ICHARG": 0,
                "ALGO": "Normal",
            }

            self._config_dict["INCAR"].update(update_dict)
            self._config_dict["KPOINTS"].update(
                {"reciprocal_density": self.reciprocal_density}
            )

    def get_vasprun_outcar(path, parse_dos=True, parse_eigen=True):
        """
        :param path: Path to get the vasprun.xml and OUTCAR.
        :param parse_dos: Whether to parse dos. Defaults to True.
        :param parse_eigen: Whether to parse eigenvalue. Defaults to True.
        :return:
        """
        path = Path(path)
        vruns = list(glob.glob(str(path / "vasprun.xml*")))
        outcars = list(glob.glob(str(path / "OUTCAR*")))

        if len(vruns) == 0 or len(outcars) == 0:
            raise ValueError(
                "Unable to get vasprun.xml/OUTCAR from prev calculation in %s" % path
            )
        vsfile_fullpath = str(path / "vasprun.xml")
        outcarfile_fullpath = str(path / "OUTCAR")
        vsfile = vsfile_fullpath if vsfile_fullpath in vruns else sorted(vruns)[-1]
        outcarfile = (
            outcarfile_fullpath if outcarfile_fullpath in outcars else sorted(outcars)[-1]
        )
        return (
            Vasprun(vsfile, parse_dos=parse_dos, parse_eigen=parse_eigen),
            Outcar(outcarfile),
        )

    def get_structure_from_prev_run(vasprun, outcar=None):
        """
        Process structure from previous run.

        Args:
            vasprun (Vasprun): Vasprun that contains the final structure
                from previous run.
            outcar (Outcar): Outcar that contains the magnetization info from
                previous run.

        Returns:
            Returns the magmom-decorated structure that can be passed to get
            Vasp input files, e.g. get_kpoints.
        """
        structure = vasprun.final_structure

        site_properties = {}
        # magmom
        if vasprun.is_spin:
            if outcar and outcar.magnetization:
                site_properties.update({"magmom": [i["tot"] for i in outcar.magnetization]})
            else:
                site_properties.update({"magmom": vasprun.parameters["MAGMOM"]})
        # ldau
        if vasprun.parameters.get("LDAU", False):
            for k in ("LDAUU", "LDAUJ", "LDAUL"):
                vals = vasprun.incar[k]
                m = {}
                l_val = []
                s = 0
                for site in structure:
                    if site.specie.symbol not in m:
                        m[site.specie.symbol] = vals[s]
                        s += 1
                    l_val.append(m[site.specie.symbol])
                if len(l_val) == len(structure):
                    site_properties.update({k.lower(): l_val})
                else:
                    raise ValueError(
                        "length of list {} not the same as" "structure".format(l_val)
                    )

        return structure.copy(site_properties=site_properties)

    def standardize_structure(structure, sym_prec=0.1, international_monoclinic=True):
        """
        Get the symmetrically standardized structure.

        Args:
            structure (Structure): The structure.
            sym_prec (float): Tolerance for symmetry finding for standardization.
            international_monoclinic (bool): Whether to use international
                convention (vs Curtarolo) for monoclinic. Defaults True.

        Returns:
            The symmetrized structure.
        """
        sym_finder = SpacegroupAnalyzer(structure, symprec=sym_prec)
        new_structure = sym_finder.get_primitive_standard_structure(
            international_monoclinic=international_monoclinic
        )

        # the primitive structure finding has had several bugs in the past
        # defend through validation
        vpa_old = structure.volume / structure.num_sites
        vpa_new = new_structure.volume / new_structure.num_sites

        if abs(vpa_old - vpa_new) / vpa_old > 0.02:
            raise ValueError(
                "Standardizing cell failed! VPA old: {}, VPA new: {}".format(
                    vpa_old, vpa_new
                )
            )

        sm = StructureMatcher()
        if not sm.fit(structure, new_structure):
            raise ValueError("Standardizing cell failed! Old structure doesn't match new.")

        return new_structure

    class BadInputSetWarning(UserWarning):
        """
        Warning class for bad but legal inputs.
        """

        pass

    def batch_write_input(
            structures,
            vasp_input_set=MPRelaxSet,
            output_dir=".",
            make_dir_if_not_present=True,
            subfolder=None,
            sanitize=False,
            include_cif=False,
            potcar_spec=False,
            zip_output=False,
            **kwargs
    ):
        """
        Batch write vasp input for a sequence of structures to
        output_dir, following the format output_dir/{group}/{formula}_{number}.

        Args:
            structures ([Structure]): Sequence of Structures.
            vasp_input_set (VaspInputSet): VaspInputSet class that creates
                vasp input files from structures. Note that a class should be
                supplied. Defaults to MPRelaxSet.
            output_dir (str): Directory to output files. Defaults to current
                directory ".".
            make_dir_if_not_present (bool): Create the directory if not present.
                Defaults to True.
            subfolder (callable): Function to create subdirectory name from
                structure. Defaults to simply "formula_count".
            sanitize (bool): Boolean indicating whether to sanitize the
                structure before writing the VASP input files. Sanitized output
                are generally easier for viewing and certain forms of analysis.
                Defaults to False.
            include_cif (bool): Whether to output a CIF as well. CIF files are
                generally better supported in visualization programs.
            potcar_spec (bool): Instead of writing the POTCAR, write a "POTCAR.spec".
                    This is intended to help sharing an input set with people who might
                    not have a license to specific Potcar files. Given a "POTCAR.spec",
                    the specific POTCAR file can be re-generated using pymatgen with the
                    "generate_potcar" function in the pymatgen CLI.
            zip_output (bool): If True, output will be zipped into a file with the
                same name as the InputSet (e.g., MPStaticSet.zip)
            **kwargs: Additional kwargs are passed to the vasp_input_set class
                in addition to structure.
        """
        output_dir = Path(output_dir)
        for i, s in enumerate(structures):
            formula = re.sub(r"\s+", "", s.formula)
            if subfolder is not None:
                subdir = subfolder(s)
                d = output_dir / subdir
            else:
                d = output_dir / "{}_{}".format(formula, i)
            if sanitize:
                s = s.copy(sanitize=True)
            v = vasp_input_set(s, **kwargs)
            v.write_input(
                str(d),
                make_dir_if_not_present=make_dir_if_not_present,
                include_cif=include_cif,
                potcar_spec=potcar_spec,
                zip_output=zip_output,
            )

    _dummy_structure = Structure(
        [1, 0, 0, 0, 1, 0, 0, 0, 1],
        ["I"],
        [[0, 0, 0]],
        site_properties={"magmom": [[0, 0, 1]]},
    )