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


"""
This module contains the object used to describe the possible bonded atoms based on a Voronoi analysis.
"""

__author__ = "David Waroquiers"
__copyright__ = "Copyright 2012, The Materials Project"
__credits__ = "Geoffroy Hautier"
__version__ = "2.0"
__maintainer__ = "David Waroquiers"
__email__ = "david.waroquiers@gmail.com"
__date__ = "Feb 20, 2016"

import logging
import time

import numpy as np
from monty.json import MSONable
from scipy.spatial import Voronoi

from pymatgen.analysis.chemenv.utils.coordination_geometry_utils import (
    get_lower_and_upper_f,
    my_solid_angle,
    rectangle_surface_intersection,
)
from pymatgen.analysis.chemenv.utils.defs_utils import AdditionalConditions
from pymatgen.analysis.chemenv.utils.math_utils import normal_cdf_step
from pymatgen.core.sites import PeriodicSite
from pymatgen.core.structure import Structure


def from_bson_voronoi_list2(bson_nb_voro_list2, structure):
    """
    Returns the voronoi_list needed for the VoronoiContainer object from a bson-encoded voronoi_list.

    Args:
        bson_nb_voro_list2: List of periodic sites involved in the Voronoi.
        structure: Structure object.

    Returns:
        The voronoi_list needed for the VoronoiContainer (with PeriodicSites as keys of the dictionary - not
        allowed in the BSON format).
    """
    voronoi_list = [None] * len(bson_nb_voro_list2)
    for isite, voro in enumerate(bson_nb_voro_list2):
        if voro is None or voro == "None":
            continue
        voronoi_list[isite] = []
        for psd, dd in voro:
            struct_site = structure[dd["index"]]
            periodic_site = PeriodicSite(
                struct_site._species,
                struct_site.frac_coords + psd[1],
                struct_site._lattice,
                properties=struct_site.properties,
            )
            dd["site"] = periodic_site
            voronoi_list[isite].append(dd)
    return voronoi_list


class DetailedVoronoiContainer(MSONable):
    """
    Class used to store the full Voronoi of a given structure.
    """

    AC = AdditionalConditions()
    default_voronoi_cutoff = 10.0
    default_normalized_distance_tolerance = 1e-5
    default_normalized_angle_tolerance = 1e-3

    def __init__(
        self,
        structure=None,
        voronoi_list2=None,
        voronoi_cutoff=default_voronoi_cutoff,
        isites=None,
        normalized_distance_tolerance=default_normalized_distance_tolerance,
        normalized_angle_tolerance=default_normalized_angle_tolerance,
        additional_conditions=None,
        valences=None,
        maximum_distance_factor=None,
        minimum_angle_factor=None,
    ):
        """
        Constructor for the VoronoiContainer object. Either a structure is given, in which case the Voronoi is
        computed, or the different components of the VoronoiContainer are given (used in the from_dict method).

        Args:
            structure: Structure for which the Voronoi is computed.
            voronoi_list2: List of voronoi polyhedrons for each site.
            voronoi_cutoff: cutoff used for the voronoi.
            isites: indices of sites for which the Voronoi has to be computed.
            normalized_distance_tolerance: Tolerance for two normalized distances to be considered equal.
            normalized_angle_tolerance:Tolerance for two normalized angles to be considered equal.
            additional_conditions: Additional conditions to be used.
            valences: Valences of all the sites in the structure (used when additional conditions require it).
            maximum_distance_factor: The maximum distance factor to be considered.
            minimum_angle_factor: The minimum angle factor to be considered.

        Raises:
            RuntimeError if the Voronoi cannot be constructed.
        """
        self.normalized_distance_tolerance = normalized_distance_tolerance
        self.normalized_angle_tolerance = normalized_angle_tolerance
        if additional_conditions is None:
            self.additional_conditions = [self.AC.NONE, self.AC.ONLY_ACB]
        else:
            self.additional_conditions = additional_conditions
        self.valences = valences
        self.maximum_distance_factor = maximum_distance_factor
        self.minimum_angle_factor = minimum_angle_factor
        if isites is None:
            indices = list(range(len(structure)))
        else:
            indices = isites
        self.structure = structure
        logging.debug("Setting Voronoi list")
        if voronoi_list2 is not None:
            self.voronoi_list2 = voronoi_list2
        else:
            self.setup_voronoi_list(indices=indices, voronoi_cutoff=voronoi_cutoff)
        logging.debug("Setting neighbors distances and angles")
        t1 = time.process_time()
        self.setup_neighbors_distances_and_angles(indices=indices)
        t2 = time.process_time()
        logging.debug("Neighbors distances and angles set up in {:.2f} seconds".format(t2 - t1))

    def setup_voronoi_list(self, indices, voronoi_cutoff):
        """
        Set up of the voronoi list of neighbours by calling qhull.

        Args:
            indices: indices of the sites for which the Voronoi is needed.
            voronoi_cutoff: Voronoi cutoff for the search of neighbours.

        Raises:
            RuntimeError: If an infinite vertex is found in the voronoi construction.
        """
        self.voronoi_list2 = [None] * len(self.structure)
        self.voronoi_list_coords = [None] * len(self.structure)
        logging.debug("Getting all neighbors in structure")
        struct_neighbors = self.structure.get_all_neighbors(voronoi_cutoff, include_index=True)
        t1 = time.process_time()
        logging.debug("Setting up Voronoi list :")

        for jj, isite in enumerate(indices):
            logging.debug("  - Voronoi analysis for site #{:d} ({:d}/{:d})".format(isite, jj + 1, len(indices)))
            site = self.structure[isite]
            neighbors1 = [(site, 0.0, isite)]
            neighbors1.extend(struct_neighbors[isite])
            distances = [i[1] for i in sorted(neighbors1, key=lambda s: s[1])]
            neighbors = [i[0] for i in sorted(neighbors1, key=lambda s: s[1])]
            qvoronoi_input = [s.coords for s in neighbors]
            voro = Voronoi(points=qvoronoi_input, qhull_options="o Fv")
            all_vertices = voro.vertices

            results2 = []
            maxangle = 0.0
            mindist = 10000.0
            for iridge, ridge_points in enumerate(voro.ridge_points):
                if 0 in ridge_points:
                    ridge_vertices_indices = voro.ridge_vertices[iridge]
                    if -1 in ridge_vertices_indices:
                        raise RuntimeError(
                            "This structure is pathological," " infinite vertex in the voronoi " "construction"
                        )

                    ridge_point2 = max(ridge_points)
                    facets = [all_vertices[i] for i in ridge_vertices_indices]
                    sa = my_solid_angle(site.coords, facets)
                    maxangle = max([sa, maxangle])

                    mindist = min([mindist, distances[ridge_point2]])
                    for iii, sss in enumerate(self.structure):
                        if neighbors[ridge_point2].is_periodic_image(sss, tolerance=1.0e-6):
                            myindex = iii
                            break
                    results2.append(
                        {
                            "site": neighbors[ridge_point2],
                            "angle": sa,
                            "distance": distances[ridge_point2],
                            "index": myindex,
                        }
                    )
            for dd in results2:
                dd["normalized_angle"] = dd["angle"] / maxangle
                dd["normalized_distance"] = dd["distance"] / mindist
            self.voronoi_list2[isite] = results2
            self.voronoi_list_coords[isite] = np.array([dd["site"].coords for dd in results2])
        t2 = time.process_time()
        logging.debug("Voronoi list set up in {:.2f} seconds".format(t2 - t1))

    def setup_neighbors_distances_and_angles(self, indices):
        """
        Initializes the angle and distance separations.

        Args:
            indices: Indices of the sites for which the Voronoi is needed.
        """
        self.neighbors_distances = [None] * len(self.structure)
        self.neighbors_normalized_distances = [None] * len(self.structure)
        self.neighbors_angles = [None] * len(self.structure)
        self.neighbors_normalized_angles = [None] * len(self.structure)
        for isite in indices:
            results = self.voronoi_list2[isite]
            if results is None:
                continue
            # Initializes neighbors distances and normalized distances groups
            self.neighbors_distances[isite] = []
            self.neighbors_normalized_distances[isite] = []
            normalized_distances = [nb_dict["normalized_distance"] for nb_dict in results]
            isorted_distances = np.argsort(normalized_distances)
            self.neighbors_normalized_distances[isite].append(
                {
                    "min": normalized_distances[isorted_distances[0]],
                    "max": normalized_distances[isorted_distances[0]],
                }
            )
            self.neighbors_distances[isite].append(
                {
                    "min": results[isorted_distances[0]]["distance"],
                    "max": results[isorted_distances[0]]["distance"],
                }
            )
            icurrent = 0
            nb_indices = {int(isorted_distances[0])}
            dnb_indices = {int(isorted_distances[0])}
            for idist in iter(isorted_distances):
                wd = normalized_distances[idist]
                if self.maximum_distance_factor is not None:
                    if wd > self.maximum_distance_factor:
                        self.neighbors_normalized_distances[isite][icurrent]["nb_indices"] = list(nb_indices)
                        self.neighbors_distances[isite][icurrent]["nb_indices"] = list(nb_indices)
                        self.neighbors_normalized_distances[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                        self.neighbors_distances[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                        break
                if np.isclose(
                    wd,
                    self.neighbors_normalized_distances[isite][icurrent]["max"],
                    rtol=0.0,
                    atol=self.normalized_distance_tolerance,
                ):
                    self.neighbors_normalized_distances[isite][icurrent]["max"] = wd
                    self.neighbors_distances[isite][icurrent]["max"] = results[idist]["distance"]
                    dnb_indices.add(int(idist))
                else:
                    self.neighbors_normalized_distances[isite][icurrent]["nb_indices"] = list(nb_indices)
                    self.neighbors_distances[isite][icurrent]["nb_indices"] = list(nb_indices)
                    self.neighbors_normalized_distances[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                    self.neighbors_distances[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                    dnb_indices = {int(idist)}
                    self.neighbors_normalized_distances[isite].append({"min": wd, "max": wd})
                    self.neighbors_distances[isite].append(
                        {
                            "min": results[idist]["distance"],
                            "max": results[idist]["distance"],
                        }
                    )
                    icurrent += 1
                nb_indices.add(int(idist))
            else:
                self.neighbors_normalized_distances[isite][icurrent]["nb_indices"] = list(nb_indices)
                self.neighbors_distances[isite][icurrent]["nb_indices"] = list(nb_indices)
                self.neighbors_normalized_distances[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                self.neighbors_distances[isite][icurrent]["dnb_indices"] = list(dnb_indices)
            for idist in range(len(self.neighbors_distances[isite]) - 1):
                dist_dict = self.neighbors_distances[isite][idist]
                dist_dict_next = self.neighbors_distances[isite][idist + 1]
                dist_dict["next"] = dist_dict_next["min"]
                ndist_dict = self.neighbors_normalized_distances[isite][idist]
                ndist_dict_next = self.neighbors_normalized_distances[isite][idist + 1]
                ndist_dict["next"] = ndist_dict_next["min"]
            if self.maximum_distance_factor is not None:
                dfact = self.maximum_distance_factor
            else:
                dfact = self.default_voronoi_cutoff / self.neighbors_distances[isite][0]["min"]
            self.neighbors_normalized_distances[isite][-1]["next"] = dfact
            self.neighbors_distances[isite][-1]["next"] = dfact * self.neighbors_distances[isite][0]["min"]
            # Initializes neighbors angles and normalized angles groups
            self.neighbors_angles[isite] = []
            self.neighbors_normalized_angles[isite] = []
            normalized_angles = [nb_dict["normalized_angle"] for nb_dict in results]
            isorted_angles = np.argsort(normalized_angles)[::-1]
            self.neighbors_normalized_angles[isite].append(
                {
                    "max": normalized_angles[isorted_angles[0]],
                    "min": normalized_angles[isorted_angles[0]],
                }
            )
            self.neighbors_angles[isite].append(
                {
                    "max": results[isorted_angles[0]]["angle"],
                    "min": results[isorted_angles[0]]["angle"],
                }
            )
            icurrent = 0
            nb_indices = {int(isorted_angles[0])}
            dnb_indices = {int(isorted_angles[0])}
            for iang in iter(isorted_angles):
                wa = normalized_angles[iang]
                if self.minimum_angle_factor is not None:
                    if wa < self.minimum_angle_factor:
                        self.neighbors_normalized_angles[isite][icurrent]["nb_indices"] = list(nb_indices)
                        self.neighbors_angles[isite][icurrent]["nb_indices"] = list(nb_indices)
                        self.neighbors_normalized_angles[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                        self.neighbors_angles[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                        break
                if np.isclose(
                    wa,
                    self.neighbors_normalized_angles[isite][icurrent]["min"],
                    rtol=0.0,
                    atol=self.normalized_angle_tolerance,
                ):
                    self.neighbors_normalized_angles[isite][icurrent]["min"] = wa
                    self.neighbors_angles[isite][icurrent]["min"] = results[iang]["angle"]
                    dnb_indices.add(int(iang))
                else:
                    self.neighbors_normalized_angles[isite][icurrent]["nb_indices"] = list(nb_indices)
                    self.neighbors_angles[isite][icurrent]["nb_indices"] = list(nb_indices)
                    self.neighbors_normalized_angles[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                    self.neighbors_angles[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                    dnb_indices = {int(iang)}
                    self.neighbors_normalized_angles[isite].append({"max": wa, "min": wa})
                    self.neighbors_angles[isite].append({"max": results[iang]["angle"], "min": results[iang]["angle"]})
                    icurrent += 1
                nb_indices.add(int(iang))
            else:
                self.neighbors_normalized_angles[isite][icurrent]["nb_indices"] = list(nb_indices)
                self.neighbors_angles[isite][icurrent]["nb_indices"] = list(nb_indices)
                self.neighbors_normalized_angles[isite][icurrent]["dnb_indices"] = list(dnb_indices)
                self.neighbors_angles[isite][icurrent]["dnb_indices"] = list(dnb_indices)
            for iang in range(len(self.neighbors_angles[isite]) - 1):
                ang_dict = self.neighbors_angles[isite][iang]
                ang_dict_next = self.neighbors_angles[isite][iang + 1]
                ang_dict["next"] = ang_dict_next["max"]
                nang_dict = self.neighbors_normalized_angles[isite][iang]
                nang_dict_next = self.neighbors_normalized_angles[isite][iang + 1]
                nang_dict["next"] = nang_dict_next["max"]
            if self.minimum_angle_factor is not None:
                afact = self.minimum_angle_factor
            else:
                afact = 0.0
            self.neighbors_normalized_angles[isite][-1]["next"] = afact
            self.neighbors_angles[isite][-1]["next"] = afact * self.neighbors_angles[isite][0]["max"]

    def _precompute_additional_conditions(self, ivoronoi, voronoi, valences):
        additional_conditions = {ac: [] for ac in self.additional_conditions}
        for ips, (ps, vals) in enumerate(voronoi):
            for ac in self.additional_conditions:
                additional_conditions[ac].append(
                    self.AC.check_condition(
                        condition=ac,
                        structure=self.structure,
                        parameters={
                            "valences": valences,
                            "neighbor_index": vals["index"],
                            "site_index": ivoronoi,
                        },
                    )
                )
        return additional_conditions

    def _precompute_distance_conditions(self, ivoronoi, voronoi):
        distance_conditions = []
        for idp, dp_dict in enumerate(self.neighbors_normalized_distances[ivoronoi]):
            distance_conditions.append([])
            dp = dp_dict["max"]
            for ips, (ps, vals) in enumerate(voronoi):
                distance_conditions[idp].append(
                    vals["normalized_distance"] <= dp
                    or np.isclose(
                        vals["normalized_distance"],
                        dp,
                        rtol=0.0,
                        atol=self.normalized_distance_tolerance / 2.0,
                    )
                )
        return distance_conditions

    def _precompute_angle_conditions(self, ivoronoi, voronoi):
        angle_conditions = []
        for iap, ap_dict in enumerate(self.neighbors_normalized_angles[ivoronoi]):
            angle_conditions.append([])
            ap = ap_dict["max"]
            for ips, (ps, vals) in enumerate(voronoi):
                angle_conditions[iap].append(
                    vals["normalized_angle"] >= ap
                    or np.isclose(
                        vals["normalized_angle"],
                        ap,
                        rtol=0.0,
                        atol=self.normalized_angle_tolerance / 2.0,
                    )
                )
        return angle_conditions

    # def neighbors_map(self, isite, distfactor, angfactor, additional_condition):
    #     if self.neighbors_normalized_distances[isite] is None:
    #         return None
    #     dist_where = np.argwhere(
    #         np.array([wd['min'] for wd in self.neighbors_normalized_distances[isite]]) <= distfactor)
    #     if len(dist_where) == 0:
    #         return None
    #     idist = dist_where[-1][0]
    #     ang_where = np.argwhere(np.array([wa['max'] for wa in self.neighbors_normalized_angles[isite]]) >= angfactor)
    #     if len(ang_where) == 0:
    #         return None
    #     iang = ang_where[0][0]
    #     if self.additional_conditions.count(additional_condition) != 1:
    #         return None
    #     i_additional_condition = self.additional_conditions.index(additional_condition)
    #     return {'i_distfactor': idist, 'i_angfactor': iang, 'i_additional_condition': i_additional_condition}

    def neighbors_surfaces(self, isite, surface_calculation_type=None, max_dist=2.0):
        """
        Get the different surfaces corresponding to the different distance-angle cutoffs for a given site.

        Args:
            isite: Index of the site
            surface_calculation_type: How to compute the surface.
            max_dist: The maximum distance factor to be considered.

        Returns:
            Surfaces for each distance-angle cutoff.
        """
        if self.voronoi_list2[isite] is None:
            return None
        bounds_and_limits = self.voronoi_parameters_bounds_and_limits(isite, surface_calculation_type, max_dist)
        distance_bounds = bounds_and_limits["distance_bounds"]
        angle_bounds = bounds_and_limits["angle_bounds"]
        surfaces = np.zeros((len(distance_bounds), len(angle_bounds)), np.float_)
        for idp in range(len(distance_bounds) - 1):
            this_dist_plateau = distance_bounds[idp + 1] - distance_bounds[idp]
            for iap in range(len(angle_bounds) - 1):
                this_ang_plateau = angle_bounds[iap + 1] - angle_bounds[iap]
                surfaces[idp][iap] = np.absolute(this_dist_plateau * this_ang_plateau)
        return surfaces

    def neighbors_surfaces_bounded(self, isite, surface_calculation_options=None):
        """
        Get the different surfaces (using boundaries) corresponding to the different distance-angle cutoffs
        for a given site.

        Args:
            isite: Index of the site.
            surface_calculation_options: Options for the boundaries.

        Returns:
            Surfaces for each distance-angle cutoff.
        """
        if self.voronoi_list2[isite] is None:
            return None
        if surface_calculation_options is None:
            surface_calculation_options = {
                "type": "standard_elliptic",
                "distance_bounds": {"lower": 1.2, "upper": 1.8},
                "angle_bounds": {"lower": 0.1, "upper": 0.8},
            }
        if surface_calculation_options["type"] in [
            "standard_elliptic",
            "standard_diamond",
            "standard_spline",
        ]:
            plot_type = {
                "distance_parameter": ("initial_normalized", None),
                "angle_parameter": ("initial_normalized", None),
            }
        else:
            raise ValueError(
                'Type "{}" for the surface calculation in DetailedVoronoiContainer '
                "is invalid".format(surface_calculation_options["type"])
            )
        max_dist = surface_calculation_options["distance_bounds"]["upper"] + 0.1
        bounds_and_limits = self.voronoi_parameters_bounds_and_limits(
            isite=isite, plot_type=plot_type, max_dist=max_dist
        )

        distance_bounds = bounds_and_limits["distance_bounds"]
        angle_bounds = bounds_and_limits["angle_bounds"]
        lower_and_upper_functions = get_lower_and_upper_f(surface_calculation_options=surface_calculation_options)
        mindist = surface_calculation_options["distance_bounds"]["lower"]
        maxdist = surface_calculation_options["distance_bounds"]["upper"]
        minang = surface_calculation_options["angle_bounds"]["lower"]
        maxang = surface_calculation_options["angle_bounds"]["upper"]

        f_lower = lower_and_upper_functions["lower"]
        f_upper = lower_and_upper_functions["upper"]
        surfaces = np.zeros((len(distance_bounds), len(angle_bounds)), np.float_)
        for idp in range(len(distance_bounds) - 1):
            dp1 = distance_bounds[idp]
            dp2 = distance_bounds[idp + 1]
            if dp2 < mindist or dp1 > maxdist:
                continue
            if dp1 < mindist:
                d1 = mindist
            else:
                d1 = dp1
            if dp2 > maxdist:
                d2 = maxdist
            else:
                d2 = dp2
            for iap in range(len(angle_bounds) - 1):
                ap1 = angle_bounds[iap]
                ap2 = angle_bounds[iap + 1]
                if ap1 > ap2:
                    ap1 = angle_bounds[iap + 1]
                    ap2 = angle_bounds[iap]
                if ap2 < minang or ap1 > maxang:
                    continue
                intersection, interror = rectangle_surface_intersection(
                    rectangle=((d1, d2), (ap1, ap2)),
                    f_lower=f_lower,
                    f_upper=f_upper,
                    bounds_lower=[mindist, maxdist],
                    bounds_upper=[mindist, maxdist],
                    check=False,
                )
                surfaces[idp][iap] = intersection
        return surfaces

    @staticmethod
    def _get_vertices_dist_ang_indices(parameter_indices_list):
        pp0 = [pp[0] for pp in parameter_indices_list]
        pp1 = [pp[1] for pp in parameter_indices_list]
        min_idist = min(pp0)
        min_iang = min(pp1)
        max_idist = max(pp0)
        max_iang = max(pp1)
        i_min_angs = np.argwhere(np.array(pp1) == min_iang)
        i_max_dists = np.argwhere(np.array(pp0) == max_idist)
        pp0_at_min_iang = [pp0[ii[0]] for ii in i_min_angs]
        pp1_at_max_idist = [pp1[ii[0]] for ii in i_max_dists]
        max_idist_at_min_iang = max(pp0_at_min_iang)
        min_iang_at_max_idist = min(pp1_at_max_idist)

        p1 = (min_idist, min_iang)
        p2 = (max_idist_at_min_iang, min_iang)
        p3 = (max_idist_at_min_iang, min_iang_at_max_idist)
        p4 = (max_idist, min_iang_at_max_idist)
        p5 = (max_idist, max_iang)
        p6 = (min_idist, max_iang)

        return [p1, p2, p3, p4, p5, p6]

    def maps_and_surfaces(
        self,
        isite,
        surface_calculation_type=None,
        max_dist=2.0,
        additional_conditions=None,
    ):
        """
        Get the different surfaces and their cn_map corresponding to the different distance-angle cutoffs
        for a given site.

        Args:
            isite: Index of the site
            surface_calculation_type: How to compute the surface.
            max_dist: The maximum distance factor to be considered.
            additional_conditions: If additional conditions have to be considered.

        Returns:
            Surfaces and cn_map's for each distance-angle cutoff.
        """
        if self.voronoi_list2[isite] is None:
            return None
        if additional_conditions is None:
            additional_conditions = [self.AC.ONLY_ACB]
        surfaces = self.neighbors_surfaces(
            isite=isite,
            surface_calculation_type=surface_calculation_type,
            max_dist=max_dist,
        )
        maps_and_surfaces = []
        for cn, value in self._unique_coordinated_neighbors_parameters_indices[isite].items():  # pylint: disable=E1101
            for imap, list_parameters_indices in enumerate(value):
                thissurf = 0.0
                for (idp, iap, iacb) in list_parameters_indices:
                    if iacb in additional_conditions:
                        thissurf += surfaces[idp, iap]
                maps_and_surfaces.append(
                    {
                        "map": (cn, imap),
                        "surface": thissurf,
                        "parameters_indices": list_parameters_indices,
                    }
                )
        return maps_and_surfaces

    def maps_and_surfaces_bounded(self, isite, surface_calculation_options=None, additional_conditions=None):
        """
        Get the different surfaces (using boundaries) and their cn_map corresponding to the different
        distance-angle cutoffs for a given site.

        Args:
            isite: Index of the site
            surface_calculation_options: Options for the boundaries.
            additional_conditions: If additional conditions have to be considered.

        Returns:
            Surfaces and cn_map's for each distance-angle cutoff.
        """
        if self.voronoi_list2[isite] is None:
            return None
        if additional_conditions is None:
            additional_conditions = [self.AC.ONLY_ACB]
        surfaces = self.neighbors_surfaces_bounded(isite=isite, surface_calculation_options=surface_calculation_options)
        maps_and_surfaces = []
        for cn, value in self._unique_coordinated_neighbors_parameters_indices[isite].items():  # pylint: disable=E1101
            for imap, list_parameters_indices in enumerate(value):
                thissurf = 0.0
                for (idp, iap, iacb) in list_parameters_indices:
                    if iacb in additional_conditions:
                        thissurf += surfaces[idp, iap]
                maps_and_surfaces.append(
                    {
                        "map": (cn, imap),
                        "surface": thissurf,
                        "parameters_indices": list_parameters_indices,
                    }
                )
        return maps_and_surfaces

    def neighbors(self, isite, distfactor, angfactor, additional_condition=None):
        """
        Get the neighbors of a given site corresponding to a given distance and angle factor.

        Args:
            isite: Index of the site.
            distfactor: Distance factor.
            angfactor: Angle factor.
            additional_condition: Additional condition to be used (currently not implemented).

        Returns:
            List of neighbors of the given site for the given distance and angle factors.
        """
        idist = None
        dfact = None
        for iwd, wd in enumerate(self.neighbors_normalized_distances[isite]):
            if distfactor >= wd["min"]:
                idist = iwd
                dfact = wd["max"]
            else:
                break
        iang = None
        afact = None
        for iwa, wa in enumerate(self.neighbors_normalized_angles[isite]):
            if angfactor <= wa["max"]:
                iang = iwa
                afact = wa["min"]
            else:
                break
        if idist is None or iang is None:
            raise ValueError("Distance or angle parameter not found ...")

        return [
            nb
            for nb in self.voronoi_list2[isite]
            if nb["normalized_distance"] <= dfact and nb["normalized_angle"] >= afact
        ]

    def voronoi_parameters_bounds_and_limits(self, isite, plot_type, max_dist):
        """
        Get the different boundaries and limits of the distance and angle factors for the given site.

        Args:
            isite: Index of the site.
            plot_type: Types of distance/angle parameters to get.
            max_dist: Maximum distance factor.

        Returns:
            Distance and angle bounds and limits.
        """
        # Initializes the distance and angle parameters
        if self.voronoi_list2[isite] is None:
            return None
        if plot_type is None:
            plot_type = {
                "distance_parameter": ("initial_inverse_opposite", None),
                "angle_parameter": ("initial_opposite", None),
            }
        dd = [dist["min"] for dist in self.neighbors_normalized_distances[isite]]
        dd[0] = 1.0
        if plot_type["distance_parameter"][0] == "initial_normalized":
            dd.append(max_dist)
            distance_bounds = np.array(dd)
            dist_limits = [1.0, max_dist]
        elif plot_type["distance_parameter"][0] == "initial_inverse_opposite":
            ddinv = [1.0 / dist for dist in dd]
            ddinv.append(0.0)
            distance_bounds = np.array([1.0 - invdist for invdist in ddinv])
            dist_limits = [0.0, 1.0]
        elif plot_type["distance_parameter"][0] == "initial_inverse3_opposite":
            ddinv = [1.0 / dist ** 3.0 for dist in dd]
            ddinv.append(0.0)
            distance_bounds = np.array([1.0 - invdist for invdist in ddinv])
            dist_limits = [0.0, 1.0]
        else:
            raise NotImplementedError(
                'Plotting type "{}" ' "for the distance is not implemented".format(plot_type["distance_parameter"])
            )
        if plot_type["angle_parameter"][0] == "initial_normalized":
            aa = [0.0]
            aa.extend([ang["max"] for ang in self.neighbors_normalized_angles[isite]])
            angle_bounds = np.array(aa)
        elif plot_type["angle_parameter"][0] == "initial_opposite":
            aa = [0.0]
            aa.extend([ang["max"] for ang in self.neighbors_normalized_angles[isite]])
            aa = [1.0 - ang for ang in aa]
            angle_bounds = np.array(aa)
        else:
            raise NotImplementedError(
                'Plotting type "{}" ' "for the angle is not implemented".format(plot_type["angle_parameter"])
            )
        ang_limits = [0.0, 1.0]
        return {
            "distance_bounds": distance_bounds,
            "distance_limits": dist_limits,
            "angle_bounds": angle_bounds,
            "angle_limits": ang_limits,
        }

    def is_close_to(self, other, rtol=0.0, atol=1e-8):
        """
        Whether two DetailedVoronoiContainer objects are close to each other.

        Args:
            other: Another DetailedVoronoiContainer to be compared with.
            rtol: Relative tolerance to compare values.
            atol: Absolute tolerance to compare values.

        Returns:
            True if the two DetailedVoronoiContainer are close to each other.
        """
        isclose = (
            np.isclose(
                self.normalized_angle_tolerance,
                other.normalized_angle_tolerance,
                rtol=rtol,
                atol=atol,
            )
            and np.isclose(
                self.normalized_distance_tolerance,
                other.normalized_distance_tolerance,
                rtol=rtol,
                atol=atol,
            )
            and self.additional_conditions == other.additional_conditions
            and self.valences == other.valences
        )
        if not isclose:
            return isclose
        for isite, site_voronoi in enumerate(self.voronoi_list2):
            self_to_other_nbs = {}
            for inb, nb in enumerate(site_voronoi):
                if nb is None:
                    if other.voronoi_list2[isite] is None:
                        continue
                    return False
                if other.voronoi_list2[isite] is None:
                    return False
                nb_other = None
                for inb2, nb2 in enumerate(other.voronoi_list2[isite]):
                    if nb["site"] == nb2["site"]:
                        self_to_other_nbs[inb] = inb2
                        nb_other = nb2
                        break
                if nb_other is None:
                    return False
                if not np.isclose(nb["distance"], nb_other["distance"], rtol=rtol, atol=atol):
                    return False
                if not np.isclose(nb["angle"], nb_other["angle"], rtol=rtol, atol=atol):
                    return False
                if not np.isclose(
                    nb["normalized_distance"],
                    nb_other["normalized_distance"],
                    rtol=rtol,
                    atol=atol,
                ):
                    return False
                if not np.isclose(
                    nb["normalized_angle"],
                    nb_other["normalized_angle"],
                    rtol=rtol,
                    atol=atol,
                ):
                    return False
                if nb["index"] != nb_other["index"]:
                    return False
                if nb["site"] != nb_other["site"]:
                    return False
        return True

    def get_rdf_figure(self, isite, normalized=True, figsize=None, step_function=None):
        """
        Get the Radial Distribution Figure for a given site.

        Args:
            isite: Index of the site.
            normalized: Whether to normalize distances.
            figsize: Size of the figure.
            step_function: Type of step function to be used for the RDF.

        Returns:
            Matplotlib figure.
        """

        def dp_func(dp):
            return 1.0 - 1.0 / np.power(dp, 3.0)

        import matplotlib.pyplot as plt

        if step_function is None:
            step_function = {"type": "normal_cdf", "scale": 0.0001}

        # Initializes the figure
        if figsize is None:
            fig = plt.figure()
        else:
            fig = plt.figure(figsize=figsize)
        subplot = fig.add_subplot(111)
        if normalized:
            dists = self.neighbors_normalized_distances[isite]
        else:
            dists = self.neighbors_distances[isite]

        if step_function["type"] == "step_function":
            isorted = np.argsort([dd["min"] for dd in dists])
            sorted_dists = [dists[ii]["min"] for ii in isorted]
            dnb_dists = [len(dists[ii]["dnb_indices"]) for ii in isorted]
            xx = [0.0]
            yy = [0.0]
            for idist, dist in enumerate(sorted_dists):
                xx.append(dist)
                xx.append(dist)
                yy.append(yy[-1])
                yy.append(yy[-1] + dnb_dists[idist])
            xx.append(1.1 * xx[-1])
            yy.append(yy[-1])
        elif step_function["type"] == "normal_cdf":
            scale = step_function["scale"]
            mydists = [dp_func(dd["min"]) for dd in dists]
            mydcns = [len(dd["dnb_indices"]) for dd in dists]
            xx = np.linspace(0.0, 1.1 * max(mydists), num=500)
            yy = np.zeros_like(xx)
            for idist, dist in enumerate(mydists):
                yy += mydcns[idist] * normal_cdf_step(xx, mean=dist, scale=scale)
        else:
            raise ValueError('Step function of type "{}" is not allowed'.format(step_function["type"]))
        subplot.plot(xx, yy)

        return fig

    def get_sadf_figure(self, isite, normalized=True, figsize=None, step_function=None):
        """
        Get the Solid Angle Distribution Figure for a given site.
        Args:
            isite: Index of the site.
            normalized: Whether to normalize angles.
            figsize: Size of the figure.
            step_function: Type of step function to be used for the SADF.

        Returns:
            Matplotlib figure.
        """

        def ap_func(ap):
            return np.power(ap, -0.1)

        import matplotlib.pyplot as plt

        if step_function is None:
            step_function = {"type": "step_function", "scale": 0.0001}

        # Initializes the figure
        if figsize is None:
            fig = plt.figure()
        else:
            fig = plt.figure(figsize=figsize)
        subplot = fig.add_subplot(111)
        if normalized:
            angs = self.neighbors_normalized_angles[isite]
        else:
            angs = self.neighbors_angles[isite]

        if step_function["type"] == "step_function":
            isorted = np.argsort([ap_func(aa["min"]) for aa in angs])
            sorted_angs = [ap_func(angs[ii]["min"]) for ii in isorted]
            dnb_angs = [len(angs[ii]["dnb_indices"]) for ii in isorted]
            xx = [0.0]
            yy = [0.0]
            for iang, ang in enumerate(sorted_angs):
                xx.append(ang)
                xx.append(ang)
                yy.append(yy[-1])
                yy.append(yy[-1] + dnb_angs[iang])
            xx.append(1.1 * xx[-1])
            yy.append(yy[-1])
        elif step_function["type"] == "normal_cdf":
            scale = step_function["scale"]
            myangs = [ap_func(aa["min"]) for aa in angs]
            mydcns = [len(dd["dnb_indices"]) for dd in angs]
            xx = np.linspace(0.0, 1.1 * max(myangs), num=500)
            yy = np.zeros_like(xx)
            for iang, ang in enumerate(myangs):
                yy += mydcns[iang] * normal_cdf_step(xx, mean=ang, scale=scale)
        else:
            raise ValueError('Step function of type "{}" is not allowed'.format(step_function["type"]))
        subplot.plot(xx, yy)

        return fig

    def __eq__(self, other):
        return (
            self.normalized_angle_tolerance == other.normalized_angle_tolerance
            and self.normalized_distance_tolerance == other.normalized_distance_tolerance
            and self.additional_conditions == other.additional_conditions
            and self.valences == other.valences
            and self.voronoi_list2 == other.voronoi_list2
            and self.structure == other.structure
        )

    def __ne__(self, other):
        return not self == other

    def to_bson_voronoi_list2(self):
        """
        Transforms the voronoi_list into a vlist + bson_nb_voro_list, that are BSON-encodable.

        Returns:
            [vlist, bson_nb_voro_list], to be used in the as_dict method.
        """
        bson_nb_voro_list2 = [None] * len(self.voronoi_list2)
        for ivoro, voro in enumerate(self.voronoi_list2):
            if voro is None or voro == "None":
                continue
            site_voro = []
            # {'site': neighbors[nn[1]],
            #  'angle': sa,
            #  'distance': distances[nn[1]],
            #  'index': myindex}
            for nb_dict in voro:
                site = nb_dict["site"]
                site_dict = {key: val for key, val in nb_dict.items() if key not in ["site"]}
                # site_voro.append([ps.as_dict(), dd]) [float(c) for c in self.frac_coords]
                diff = site.frac_coords - self.structure[nb_dict["index"]].frac_coords
                site_voro.append([[nb_dict["index"], [float(c) for c in diff]], site_dict])
            bson_nb_voro_list2[ivoro] = site_voro
        return bson_nb_voro_list2

    def as_dict(self):
        """
        Bson-serializable dict representation of the VoronoiContainer.

        Returns:
            dictionary that is BSON-encodable.
        """
        bson_nb_voro_list2 = self.to_bson_voronoi_list2()
        return {
            "@module": self.__class__.__module__,
            "@class": self.__class__.__name__,
            "bson_nb_voro_list2": bson_nb_voro_list2,
            # "neighbors_lists": self.neighbors_lists,
            "structure": self.structure.as_dict(),
            "normalized_angle_tolerance": self.normalized_angle_tolerance,
            "normalized_distance_tolerance": self.normalized_distance_tolerance,
            "additional_conditions": self.additional_conditions,
            "valences": self.valences,
            "maximum_distance_factor": self.maximum_distance_factor,
            "minimum_angle_factor": self.minimum_angle_factor,
        }

    @classmethod
    def from_dict(cls, d):
        """
        Reconstructs the VoronoiContainer object from a dict representation of the VoronoiContainer created using
        the as_dict method.

        Args:
            d: dict representation of the VoronoiContainer object.

        Returns:
            VoronoiContainer object.
        """
        structure = Structure.from_dict(d["structure"])
        voronoi_list2 = from_bson_voronoi_list2(d["bson_nb_voro_list2"], structure)
        maximum_distance_factor = d["maximum_distance_factor"] if "maximum_distance_factor" in d else None
        minimum_angle_factor = d["minimum_angle_factor"] if "minimum_angle_factor" in d else None
        return cls(
            structure=structure,
            voronoi_list2=voronoi_list2,
            # neighbors_lists=neighbors_lists,
            normalized_angle_tolerance=d["normalized_angle_tolerance"],
            normalized_distance_tolerance=d["normalized_distance_tolerance"],
            additional_conditions=d["additional_conditions"],
            valences=d["valences"],
            maximum_distance_factor=maximum_distance_factor,
            minimum_angle_factor=minimum_angle_factor,
        )
