# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.

import os
import posixpath

import numpy as np
import pandas
import tables
import warnings
from pyiron_base import GenericParameters, Settings
from pyiron.atomistics.job.potentials import PotentialAbstract, find_potential_file_base

__author__ = "Jan Janssen"
__copyright__ = (
    "Copyright 2020, Max-Planck-Institut für Eisenforschung GmbH - "
    "Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Jan Janssen"
__email__ = "janssen@mpie.de"
__status__ = "development"
__date__ = "Sep 1, 2017"

s = Settings()


class VaspPotentialAbstract(PotentialAbstract):
    """

    Args:
        potential_df:
        default_df:
        selected_atoms:
    """

    def __init__(self, potential_df=None, default_df=None, selected_atoms=None):
        if potential_df is None:
            potential_df = self._get_potential_df(
                plugin_name="vasp",
                file_name_lst={"potentials_vasp.csv"},
                backward_compatibility_name="vasppotentials",
            )
        super(VaspPotentialAbstract, self).__init__(
            potential_df=potential_df,
            default_df=default_df,
            selected_atoms=selected_atoms,
        )

    def default(self):
        if self._default_df is not None:
            return pandas.concat(
                [
                    self._potential_df[
                        (
                            self._potential_df["Name"]
                            == self._default_df.loc[atom].values[0]
                        )
                    ]
                    for atom in self._selected_atoms
                ]
            )
        return None

    def find_default(self, element):
        if isinstance(element, set):
            element = element
        elif isinstance(element, list):
            element = set(element)
        elif isinstance(element, str):
            element = set([element])
        else:
            raise TypeError("Only, str, list and set supported!")
        element_lst = list(element)
        if self._default_df is not None:
            merged_lst = list(set(self._selected_atoms + element_lst))
            return pandas.concat(
                [
                    self._potential_df[
                        (
                            self._potential_df["Name"]
                            == self._default_df.loc[atom].values[0]
                        )
                    ]
                    for atom in merged_lst
                ]
            )
        return None

    def find(self, element):
        if isinstance(element, set):
            element = element
        elif isinstance(element, list):
            element = set(element)
        elif isinstance(element, str):
            element = set([element])
        else:
            raise TypeError("Only, str, list and set supported!")
        element_lst = list(element)
        merged_lst = list(set(self._selected_atoms + element_lst))
        return pandas.concat(
            [super(VaspPotentialAbstract, self).find({atom}) for atom in merged_lst]
        )

    def list(self):
        if len(self._selected_atoms) != 0:
            return pandas.concat(
                [
                    super(VaspPotentialAbstract, self).find({atom})
                    for atom in self._selected_atoms
                ]
            )
        else:
            return pandas.DataFrame({})

    def list_potential_names(self):
        df = self.list()
        if len(df) != 0:
            return list(self.list()["Name"])
        else:
            return []

    @staticmethod
    def _return_potential_file(file_name):
        for resource_path in s.resource_paths:
            resource_path_potcar = os.path.join(
                resource_path, "vasp", "potentials", file_name
            )
            if os.path.exists(resource_path_potcar):
                return resource_path_potcar
        return None

    def __dir__(self):
        return [val.replace("-", "_") for val in self.list_potential_names()]

    def __getitem__(self, item):
        item_replace = item.replace("_gga_pbe", "-gga-pbe").replace("_lda", "-lda")
        if item_replace in self.list_potential_names():
            df = self.list()
            return self._return_potential_file(
                file_name=list(df[df["Name"] == item_replace]["Filename"])[0][0]
            )
        selected_atoms = self._selected_atoms + [item]
        return VaspPotentialAbstract(
            potential_df=self._potential_df,
            default_df=self._default_df,
            selected_atoms=selected_atoms,
        )


class VaspPotentialFile(VaspPotentialAbstract):
    """
    The Potential class is derived from the PotentialAbstract class, but instead of loading the potentials from a list,
    the potentials are loaded from a file.

    Args:
        xc (str): Exchange correlation functional ['PBE', 'LDA']
    """

    def __init__(self, xc=None, selected_atoms=None):
        potential_df = self._get_potential_df(
            plugin_name="vasp",
            file_name_lst={"potentials_vasp.csv"},
            backward_compatibility_name="vasppotentials",
        )
        if xc == "PBE":
            default_df = self._get_potential_default_df(
                plugin_name="vasp",
                file_name_lst={"potentials_vasp_pbe_default.csv"},
                backward_compatibility_name="defaultvasppbe",
            )
            potential_df = potential_df[(potential_df["Model"] == "gga-pbe")]
        elif xc == "GGA":
            default_df = self._get_potential_default_df(
                plugin_name="vasp",
                file_name_lst={"potentials_vasp_pbe_default.csv"},
                backward_compatibility_name="defaultvasppbe",
            )
            potential_df = potential_df[(potential_df["Model"] == "gga-pbe")]
        elif xc == "LDA":
            default_df = self._get_potential_default_df(
                plugin_name="vasp",
                file_name_lst={"potentials_vasp_lda_default.csv"},
                backward_compatibility_name="defaultvasplda",
            )
            potential_df = potential_df[(potential_df["Model"] == "lda")]
        else:
            raise ValueError(
                'The exchange correlation functional has to be set and it can either be "LDA" or "PBE"'
            )
        super(VaspPotentialFile, self).__init__(
            potential_df=potential_df,
            default_df=default_df,
            selected_atoms=selected_atoms,
        )

    def add_new_element(self, parent_element, new_element):
        """
        Adding a new user defined element with a different POTCAR file. It is assumed that the file exists

        Args:
            parent_element (str): Parent element
            new_element (str): Name of the new element (the name of the folder where the new POTCAR file exists

        """
        ds = self.find_default(element=parent_element)
        ds["Species"].values[0][0] = new_element
        path_list = ds["Filename"].values[0][0].split("/")
        path_list[-2] = new_element
        name_list = ds["Name"].values[0].split("-")
        name_list[0] = new_element
        ds["Name"].values[0] = "-".join(name_list)
        ds["Filename"].values[0][0] = "/".join(path_list)
        self._potential_df = self._potential_df.append(ds)
        if new_element not in self._default_df.index.values:
            ds = pandas.Series()
            ds.name = new_element
            ds["Name"] = "-".join(name_list)
            self._default_df = self._default_df.append(ds)
        else:
            self._default_df.loc[new_element] = "-".join(name_list)


class VaspPotential(object):
    """
    The Potential class is derived from the PotentialAbstract class, but instead of loading the potentials from a list,
    the potentials are loaded from a file.

    Args:
        path (str): path to the potential list
    """

    def __init__(self, selected_atoms=None):
        self.pbe = VaspPotentialFile(xc="PBE", selected_atoms=selected_atoms)
        self.lda = VaspPotentialFile(xc="LDA", selected_atoms=selected_atoms)


class VaspPotentialSetter(object):
    def __init__(self, element_lst):
        super(VaspPotentialSetter, self).__setattr__("_element_lst", element_lst)
        super(VaspPotentialSetter, self).__setattr__(
            "_potential_dict", {el: None for el in element_lst}
        )

    def __getattr__(self, item):
        if item in self._element_lst:
            return item
        else:
            raise AttributeError

    def __setitem__(self, key, value):
        self.__setattr__(key=key, value=value)

    def __setattr__(self, key, value):
        if key in self._element_lst:
            self._potential_dict[key] = value
        else:
            raise AttributeError

    def to_dict(self):
        return self._potential_dict

    def __repr__(self):
        return self._potential_dict.__repr__()


def find_potential_file(path):
    return find_potential_file_base(
        path=path,
        resource_path_lst=s.resource_paths,
        rel_path=os.path.join("vasp", "potentials")
    )


def get_enmax_among_species(symbol_lst, return_list=False, xc="PBE"):
    """
    DEPRECATED: Please use `get_enmax_among_potentials`.

    Given a list of species symbols, finds the largest applicable encut.

    Args:
        symbol_lst (list): The list of species symbols.
        return_list (bool): Whether to return the list of all ENMAX values (in the same order as `species_lst` along with
            the largest value). (Default is False.)
        xc ("GGA"/"PBE"/"LDA"): The exchange correlation functional for which the POTCARs were generated. (Default is "PBE".)

    Returns:
        (float): The largest ENMAX among the POTCAR files for all the species.
        [optional](list): The ENMAX value corresponding to each species.
    """
    warnings.warn(("get_enmax_among_species is deprecated as of v0.3.0. Please use get_enmax_among_potentials and note "
                   + "the adjustment to the signature (*args instead of list)"), DeprecationWarning)
    return get_enmax_among_potentials(*symbol_lst, return_list=return_list, xc=xc)


def get_enmax_among_potentials(*names, return_list=False, xc="PBE"):
    """
    Given potential names without XC information or elemental symbols, look over all the corresponding POTCAR files and
    find the largest ENMAX value.

    e.g. `get_enmax_among_potentials('Mg', 'Al_GW', 'Ca_pv', 'Ca_sv', xc='LDA')`

    Args:
        *names (str): Names of potentials or elemental symbols
        return_list (bool): Whether to return the list of all ENMAX values (in the same order as `names` as a second
            return value after providing the largest value). (Default is False.)
        xc ("GGA"/"PBE"/"LDA"): The exchange correlation functional for which the POTCARs were generated.
            (Default is "PBE".)

    Returns:
        (float): The largest ENMAX among the POTCAR files for all the requested names.
        [optional](list): The ENMAX value corresponding to each species.
    """
    def _get_just_element_from_name(name):
        return name.split('_')[0]

    def _get_index_of_exact_match(name, potential_names):
        try:
            return np.argwhere([name == strip_xc_from_potential_name(pn) for pn in potential_names])[0, 0]
        except IndexError:
            raise ValueError("Couldn't find {} among potential names for {}".format(name,
                                                                                    _get_just_element_from_name(name)))

    def _get_potcar_filename(name, exch_corr):
        potcar_table = VaspPotentialFile(xc=exch_corr).find(_get_just_element_from_name(name))
        return potcar_table['Filename'].values[
            _get_index_of_exact_match(name, potcar_table['Name'].values)
        ][0]

    enmax_lst = []
    for n in names:
        with open(find_potential_file(path=_get_potcar_filename(n, xc))) as pf:
            for i, line in enumerate(pf):
                if i == 14:
                    encut_str = line.split()[2][:-1]
                    enmax_lst.append(float(encut_str))
                    break

    if return_list:
        return max(enmax_lst), enmax_lst
    else:
        return max(enmax_lst)


def strip_xc_from_potential_name(name):
    return name.split('-')[0]


class Potcar(GenericParameters):
    pot_path_dict = {"GGA": "paw-gga-pbe", "PBE": "paw-gga-pbe", "LDA": "paw-lda"}

    def __init__(self, input_file_name=None, table_name="potcar"):
        GenericParameters.__init__(
            self,
            input_file_name=input_file_name,
            table_name=table_name,
            val_only=False,
            comment_char="#",
        )
        self._structure = None
        self.electrons_per_atom_lst = list()
        self.max_cutoff_lst = list()
        self.el_path_lst = list()
        self.el_path_dict = dict()
        self.modified_elements = dict()

    def potcar_set_structure(self, structure, modified_elements):
        self._structure = structure
        self._set_default_path_dict()
        self._set_potential_paths()
        self.modified_elements = modified_elements

    def modify(self, **modify):
        if "xc" in modify:
            xc_type = modify["xc"]
            self._set_default_path_dict()
            if xc_type not in self.pot_path_dict:
                raise ValueError("xc type not implemented: " + xc_type)
        GenericParameters.modify(self, **modify)
        if self._structure is not None:
            self._set_potential_paths()

    def _set_default_path_dict(self):
        if self._structure is None:
            return
        vasp_potentials = VaspPotentialFile(xc=self.get("xc"))
        for i, el_obj in enumerate(self._structure.get_species_objects()):
            if isinstance(el_obj.Parent, str):
                el = el_obj.Parent
            else:
                el = el_obj.Abbreviation
            if isinstance(el_obj.tags, dict):
                if "pseudo_potcar_file" in el_obj.tags.keys():
                    new_element = el_obj.tags["pseudo_potcar_file"]
                    vasp_potentials.add_new_element(
                        parent_element=el, new_element=new_element
                    )
            key = vasp_potentials.find_default(el).Species.values[0][0]
            val = vasp_potentials.find_default(el).Name.values[0]
            self[key] = val

    def _set_potential_paths(self):
        element_list = (
            self._structure.get_species_symbols()
        )  # .ElementList.getSpecies()
        object_list = self._structure.get_species_objects()
        s.logger.debug("element list: {0}".format(element_list))
        self.el_path_lst = list()
        try:
            xc = self.get("xc")
        except tables.exceptions.NoSuchNodeError:
            xc = self.get("xc")
        s.logger.debug("XC: {0}".format(xc))
        vasp_potentials = VaspPotentialFile(xc=xc)
        for i, el_obj in enumerate(object_list):
            if isinstance(el_obj.Parent, str):
                el = el_obj.Parent
            else:
                el = el_obj.Abbreviation
            if (
                isinstance(el_obj.tags, dict)
                and "pseudo_potcar_file" in el_obj.tags.keys()
            ):
                new_element = el_obj.tags["pseudo_potcar_file"]
                vasp_potentials.add_new_element(
                    parent_element=el, new_element=new_element
                )
                el_path = find_potential_file(
                    path=vasp_potentials.find_default(new_element)["Filename"].values[
                        0
                    ][0]
                )
                if not (os.path.isfile(el_path)):
                    raise ValueError("such a file does not exist in the pp directory")
            elif el in self.modified_elements.keys():
                new_element = self.modified_elements[el]
                if os.path.isabs(new_element):
                    el_path = new_element
                else:
                    vasp_potentials.add_new_element(
                        parent_element=el, new_element=new_element
                    )
                    el_path = find_potential_file(
                        path=vasp_potentials.find_default(new_element)["Filename"].values[
                            0
                        ][0]
                    )
            else:
                el_path = find_potential_file(
                    path=vasp_potentials.find_default(el)["Filename"].values[0][0]
                )

            if not (os.path.isfile(el_path)):
                raise AssertionError()
            pot_name = "pot_" + str(i)

            if pot_name in self._dataset["Parameter"]:
                try:
                    ind = self._dataset["Parameter"].index(pot_name)
                except (ValueError, IndexError):
                    indices = np.core.defchararray.find(
                        self._dataset["Parameter"], pot_name
                    )
                    ind = np.where(indices == 0)[0][0]
                self._dataset["Value"][ind] = el_path
                self._dataset["Comment"][ind] = ""
            else:
                self._dataset["Parameter"].append("pot_" + str(i))
                self._dataset["Value"].append(el_path)
                self._dataset["Comment"].append("")
            self.el_path_lst.append(el_path)

    def write_file(self, file_name, cwd=None):
        """
        Args:
            file_name:
            cwd:
        Returns:
        """
        self.electrons_per_atom_lst = list()
        self.max_cutoff_lst = list()
        self._set_potential_paths()
        if cwd is not None:
            file_name = posixpath.join(cwd, file_name)
        f = open(file_name, "w")
        for el_file in self.el_path_lst:
            with open(el_file) as pot_file:
                for i, line in enumerate(pot_file):
                    f.write(line)
                    if i == 1:
                        self.electrons_per_atom_lst.append(int(float(line)))
                    elif i == 14:
                        mystr = line.split()[2][:-1]
                        self.max_cutoff_lst.append(float(mystr))
        f.close()

    def load_default(self):
        file_content = """\
xc  GGA  # LDA, GGA
"""
        self.load_string(file_content)
pandas.concat pandas.DataFrame pandas.Series