"""This module contains some utility functions and classes that are used in the chemenv package."""

from __future__ import annotations

import math
from typing import TYPE_CHECKING

import numpy as np
from numpy.linalg import norm
from scipy.integrate import quad
from scipy.interpolate import UnivariateSpline
from scipy.spatial import ConvexHull

from pymatgen.analysis.chemenv.utils.chemenv_errors import SolidAngleError

if TYPE_CHECKING:
    from collections.abc import Callable
    from typing import Self

    from numpy.typing import ArrayLike

__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"


def get_lower_and_upper_f(surface_calculation_options):
    """Get the lower and upper functions defining a surface in the distance-angle space of neighbors.

    Args:
        surface_calculation_options: Options for the surface.

    Returns:
        Dictionary containing the "lower" and "upper" functions for the surface.
    """
    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"]
    if surface_calculation_options["type"] == "standard_elliptic":
        lower_and_upper_functions = quarter_ellipsis_functions(xx=(mindist, maxang), yy=(maxdist, minang))
    elif surface_calculation_options["type"] == "standard_diamond":
        deltadist = surface_calculation_options["distance_bounds"]["delta"]
        deltaang = surface_calculation_options["angle_bounds"]["delta"]
        lower_and_upper_functions = diamond_functions(
            xx=(mindist, maxang), yy=(maxdist, minang), x_y0=deltadist, y_x0=deltaang
        )
    elif surface_calculation_options["type"] == "standard_spline":
        lower_points = surface_calculation_options["lower_points"]
        upper_points = surface_calculation_options["upper_points"]
        degree = surface_calculation_options["degree"]
        lower_and_upper_functions = spline_functions(
            lower_points=lower_points, upper_points=upper_points, degree=degree
        )
    else:
        raise ValueError(f'Surface calculation of type "{surface_calculation_options["type"]}" is not implemented')
    return lower_and_upper_functions


def function_comparison(f1, f2, x1, x2, numpoints_check=500):
    """
    Method that compares two functions.

    Args:
        f1: First function to compare
        f2: Second function to compare
        x1: Lower bound of the interval to compare
        x2: Upper bound of the interval to compare
        numpoints_check: Number of points used to compare the functions

    Returns:
        str: '=' if the functions are equal, '<' if f1 is always lower than f2, '>' if f1 is always larger than f2,
            f1 is always lower than or equal to f2 ("<"), f1 is always larger than or equal to f2 (">") on the
            interval [x1, x2]. If the two functions cross, a RuntimeError is thrown (i.e. we expect to compare
            functions that do not cross...)
    """
    xx = np.linspace(x1, x2, num=numpoints_check)
    y1 = f1(xx)
    y2 = f2(xx)
    if np.all(y1 < y2):
        return "<"
    if np.all(y1 > y2):
        return ">"
    if np.all(y1 == y2):
        return "="
    if np.all(y1 <= y2):
        return "<="
    if np.all(y1 >= y2):
        return ">="
    raise RuntimeError("Error in comparing functions f1 and f2 ...")


def quarter_ellipsis_functions(xx: ArrayLike, yy: ArrayLike) -> dict[str, Callable]:
    """
    Method that creates two quarter-ellipse functions based on points xx and yy. The ellipsis is supposed to
    be aligned with the axes. The two ellipsis pass through the two points xx and yy.

    Args:
        xx: First point
        yy: Second point

    Returns:
        A dictionary with the lower and upper quarter ellipsis functions.
    """
    xx = np.asarray(xx)
    yy = np.asarray(yy)
    if np.any(xx == yy):
        raise RuntimeError("Invalid points for quarter_ellipsis_functions")
    if np.all(xx < yy) or np.all(xx > yy):
        if xx[0] < yy[0]:
            p1 = xx
            p2 = yy
        else:
            p1 = yy
            p2 = xx
        c_lower = np.array([p1[0], p2[1]])
        c_upper = np.array([p2[0], p1[1]])
        b2 = (p2[1] - p1[1]) ** 2
    else:
        if xx[0] < yy[0]:
            p1 = xx
            p2 = yy
        else:
            p1 = yy
            p2 = xx
        c_lower = np.array([p2[0], p1[1]])
        c_upper = np.array([p1[0], p2[1]])
        b2 = (p1[1] - p2[1]) ** 2
    b2_over_a2 = b2 / (p2[0] - p1[0]) ** 2

    def lower(x):
        return c_lower[1] - np.sqrt(b2 - b2_over_a2 * (x - c_lower[0]) ** 2)

    def upper(x):
        return c_upper[1] + np.sqrt(b2 - b2_over_a2 * (x - c_upper[0]) ** 2)

    return {"lower": lower, "upper": upper}


def spline_functions(lower_points, upper_points, degree=3):
    """
    Method that creates two (upper and lower) spline functions based on points lower_points and upper_points.

    Args:
        lower_points:
            Points defining the lower function.
        upper_points:
            Points defining the upper function.
        degree:
            Degree for the spline function

    Returns:
        A dictionary with the lower and upper spline functions.
    """
    lower_xx = np.array([pp[0] for pp in lower_points])
    lower_yy = np.array([pp[1] for pp in lower_points])
    upper_xx = np.array([pp[0] for pp in upper_points])
    upper_yy = np.array([pp[1] for pp in upper_points])

    lower_spline = UnivariateSpline(lower_xx, lower_yy, k=degree, s=0)
    upper_spline = UnivariateSpline(upper_xx, upper_yy, k=degree, s=0)

    def lower(x):
        return lower_spline(x)

    def upper(x):
        return upper_spline(x)

    return {"lower": lower, "upper": upper}


def diamond_functions(xx, yy, y_x0, x_y0):
    r"""
    Method that creates two upper and lower functions based on points xx and yy
    as well as intercepts defined by y_x0 and x_y0. The resulting functions
    form kind of a distorted diamond-like structure aligned from
    point xx to point yy.

    Schematically :

    xx is symbolized by x, yy is symbolized by y, y_x0 is equal to the distance
    from x to a, x_y0 is equal to the distance from x to b, the lines a-p and
    b-q are parallel to the line x-y such that points p and q are
    obtained automatically.
    In case of an increasing diamond the lower function is x-b-q and the upper
    function is a-p-y while in case of a
    decreasing diamond, the lower function is a-p-y and the upper function is
    x-b-q.

           Increasing diamond      |     Decreasing diamond
                     p--y                    x----b
                    /  /|                    |\    \
                   /  / |                    | \    q
                  /  /  |                    a  \   |
                 a  /   |                     \  \  |
                 | /    q                      \  \ |
                 |/    /                        \  \|
                 x----b                          p--y

    Args:
        xx:
            First point
        yy:
            Second point

    Returns:
        A dictionary with the lower and upper diamond functions.
    """
    np_xx = np.array(xx)
    np_yy = np.array(yy)
    if np.any(np_xx == np_yy):
        raise RuntimeError("Invalid points for diamond_functions")
    if np.all(np_xx < np_yy) or np.all(np_xx > np_yy):
        if np_xx[0] < np_yy[0]:
            p1 = np_xx
            p2 = np_yy
        else:
            p1 = np_yy
            p2 = np_xx
    elif np_xx[0] < np_yy[0]:
        p1 = np_xx
        p2 = np_yy
    else:
        p1 = np_yy
        p2 = np_xx
    slope = (p2[1] - p1[1]) / (p2[0] - p1[0])
    if slope > 0.0:
        x_bpoint = p1[0] + x_y0
        myy = p1[1]
        bq_intercept = myy - slope * x_bpoint
        myx = p1[0]
        myy = p1[1] + y_x0
        ap_intercept = myy - slope * myx
        x_ppoint = (p2[1] - ap_intercept) / slope

        def lower(x):
            return np.where(x <= x_bpoint, p1[1] * np.ones_like(x), slope * x + bq_intercept)

        def upper(x):
            return np.where(x >= x_ppoint, p2[1] * np.ones_like(x), slope * x + ap_intercept)

    else:
        x_bpoint = p1[0] + x_y0
        myy = p1[1]
        bq_intercept = myy - slope * x_bpoint
        myx = p1[0]
        myy = p1[1] - y_x0
        ap_intercept = myy - slope * myx
        x_ppoint = (p2[1] - ap_intercept) / slope

        def lower(x):
            return np.where(x >= x_ppoint, p2[1] * np.ones_like(x), slope * x + ap_intercept)

        def upper(x):
            return np.where(x <= x_bpoint, p1[1] * np.ones_like(x), slope * x + bq_intercept)

    return {"lower": lower, "upper": upper}


def rectangle_surface_intersection(
    rectangle,
    f_lower,
    f_upper,
    bounds_lower=None,
    bounds_upper=None,
    check=True,
    numpoints_check=500,
):
    """
    Method to calculate the surface of the intersection of a rectangle (aligned with axes) and another surface
    defined by two functions f_lower and f_upper.

    Args:
        rectangle:
            Rectangle defined as : ((x1, x2), (y1, y2)).
        f_lower:
            Function defining the lower bound of the surface.
        f_upper:
            Function defining the upper bound of the surface.
        bounds_lower:
            Interval in which the f_lower function is defined.
        bounds_upper:
            Interval in which the f_upper function is defined.
        check:
            Whether to check if f_lower is always lower than f_upper.
        numpoints_check:
            Number of points used to check whether f_lower is always lower than f_upper

    Returns:
        The surface of the intersection of the rectangle and the surface defined by f_lower and f_upper.
    """
    x1 = np.min(rectangle[0])
    x2 = np.max(rectangle[0])
    y1 = np.min(rectangle[1])
    y2 = np.max(rectangle[1])
    # Check that f_lower is always lower than f_upper between x1 and x2 if no bounds are given or between the bounds
    #  of the f_lower and f_upper functions if they are given.
    if check:
        if bounds_lower is not None:
            if bounds_upper is not None:
                if not all(np.array(bounds_lower) == np.array(bounds_upper)):
                    raise ValueError("Bounds should be identical for both f_lower and f_upper")
                if "<" not in function_comparison(
                    f1=f_lower,
                    f2=f_upper,
                    x1=bounds_lower[0],
                    x2=bounds_lower[1],
                    numpoints_check=numpoints_check,
                ):
                    raise RuntimeError(
                        "Function f_lower is not always lower or equal to function f_upper within "
                        "the domain defined by the functions bounds."
                    )
            else:
                raise ValueError("Bounds are given for f_lower but not for f_upper")
        elif bounds_upper is not None:
            if bounds_lower is None:
                raise ValueError("Bounds are given for f_upper but not for f_lower")
            if "<" not in function_comparison(
                f1=f_lower,
                f2=f_upper,
                x1=bounds_lower[0],
                x2=bounds_lower[1],
                numpoints_check=numpoints_check,
            ):
                raise RuntimeError(
                    "Function f_lower is not always lower or equal to function f_upper within "
                    "the domain defined by the functions bounds."
                )
        elif "<" not in function_comparison(f1=f_lower, f2=f_upper, x1=x1, x2=x2, numpoints_check=numpoints_check):
            raise RuntimeError(
                "Function f_lower is not always lower or equal to function f_upper within "
                "the domain defined by x1 and x2."
            )
    if bounds_lower is None:
        raise NotImplementedError("Bounds should be given right now ...")
    if x2 < bounds_lower[0] or x1 > bounds_lower[1]:
        return 0.0, 0.0
    xmin = max(x1, bounds_lower[0])
    xmax = min(x2, bounds_lower[1])

    def diff(x):
        f_low_x = f_lower(x)
        f_up_x = f_upper(x)
        min_up = np.min([f_up_x, y2 * np.ones_like(f_up_x)], axis=0)
        max_lw = np.max([f_low_x, y1 * np.ones_like(f_low_x)], axis=0)
        zeros = np.zeros_like(f_up_x)
        upper = np.where(y2 >= f_low_x, np.where(y1 <= f_up_x, min_up, zeros), zeros)
        lower = np.where(y1 <= f_up_x, np.where(y2 >= f_low_x, max_lw, zeros), zeros)
        return upper - lower

    return quad(diff, xmin, xmax)


def solid_angle(center, coords):
    """
    Helper method to calculate the solid angle of a set of coords from the center.

    Args:
        center: Center to measure solid angle from.
        coords: List of coords to determine solid angle.

    Returns:
        The solid angle.
    """
    origin = np.array(center)
    r = [np.array(c) - origin for c in coords]
    r.append(r[0])
    cross_products = [np.cross(r[i + 1], r[i]) for i in range(len(r) - 1)]
    cross_products.append(np.cross(r[1], r[0]))
    phi = 0.0
    for idx in range(len(cross_products) - 1):
        try:
            value = math.acos(
                -np.dot(cross_products[idx], cross_products[idx + 1])
                / (np.linalg.norm(cross_products[idx]) * np.linalg.norm(cross_products[idx + 1]))
            )
        except ValueError:
            cos = -np.dot(cross_products[idx], cross_products[idx + 1]) / (
                np.linalg.norm(cross_products[idx]) * np.linalg.norm(cross_products[idx + 1])
            )
            if 0.999999999999 < cos < 1.000000000001:
                value = math.acos(1.0)
            elif -0.999999999999 > cos > -1.000000000001:
                value = math.acos(-1.0)
            else:
                raise SolidAngleError(cos)
        phi += value
    return phi + (3 - len(r)) * math.pi


def vectorsToMatrix(aa, bb):
    """
    Performs the vector multiplication of the elements of two vectors, constructing the 3x3 matrix.

    Args:
        aa: One vector of size 3
        bb: Another vector of size 3

    Returns:
        A 3x3 matrix M composed of the products of the elements of aa and bb : M_ij = aa_i * bb_j.
    """
    MM = np.zeros([3, 3], float)
    for ii in range(3):
        for jj in range(3):
            MM[ii, jj] = aa[ii] * bb[jj]
    return MM


def matrixTimesVector(MM, aa):
    """
    Args:
        MM: A matrix of size 3x3
        aa: A vector of size 3.

    Returns:
        A vector of size 3 which is the product of the matrix by the vector
    """
    bb = np.zeros(3, float)
    for ii in range(3):
        bb[ii] = np.sum(MM[ii, :] * aa)
    return bb


def rotateCoords(coords, R):
    """
    Rotate the list of points using rotation matrix R.

    Args:
        coords: List of points to be rotated
        R: Rotation matrix

    Returns:
        List of rotated points.
    """
    new_coords = []
    for pp in coords:
        rpp = matrixTimesVector(R, pp)
        new_coords.append(rpp)
    return new_coords


def rotateCoordsOpt(coords, R):
    """
    Rotate the list of points using rotation matrix R.

    Args:
        coords: List of points to be rotated
        R: Rotation matrix

    Returns:
        List of rotated points.
    """
    return [np.dot(R, pp) for pp in coords]


def changebasis(uu, vv, nn, pps):
    """For a list of points given in standard coordinates (in terms of e1, e2 and e3), returns the same list
    expressed in the basis (uu, vv, nn), which is supposed to be orthonormal.

    Args:
        uu: First vector of the basis
        vv: Second vector of the basis
        nn: Third vector of the basis
        pps: List of points in basis (e1, e2, e3)

    Returns:
        List of points in basis (uu, vv, nn).
    """
    MM = np.zeros([3, 3], float)
    for ii in range(3):
        MM[ii, 0] = uu[ii]
        MM[ii, 1] = vv[ii]
        MM[ii, 2] = nn[ii]
    PP = np.linalg.inv(MM)

    return [matrixTimesVector(PP, pp) for pp in pps]


def collinear(p1, p2, p3=None, tolerance=0.25):
    """
    Checks if the three points p1, p2 and p3 are collinear or not within a given tolerance. The collinearity is
    checked by computing the area of the triangle defined by the three points p1, p2 and p3. If the area of this
    triangle is less than (tolerance x largest_triangle), then the three points are considered collinear. The
    largest_triangle is defined as the right triangle whose legs are the two smallest distances between the three
     points ie, its area is : 0.5 x (min(|p2-p1|,|p3-p1|,|p3-p2|) x second_min(|p2-p1|,|p3-p1|,|p3-p2|)).

    Args:
        p1: First point
        p2: Second point
        p3: Third point (origin [0.0, 0.0, 0.0 if not given])
        tolerance: Area tolerance for the collinearity test (0.25 gives about 0.125 deviation from the line)

    Returns:
        bool: True if the three points are considered as collinear within the given tolerance.
    """
    if p3 is None:
        triangle_area = 0.5 * np.linalg.norm(np.cross(p1, p2))
        dist = np.sort([np.linalg.norm(p2 - p1), np.linalg.norm(p1), np.linalg.norm(p2)])
    else:
        triangle_area = 0.5 * np.linalg.norm(np.cross(p1 - p3, p2 - p3))
        dist = np.sort([np.linalg.norm(p2 - p1), np.linalg.norm(p3 - p1), np.linalg.norm(p3 - p2)])
    largest_triangle_area = 0.5 * dist[0] * dist[1]
    return triangle_area < tolerance * largest_triangle_area


def anticlockwise_sort(pps):
    """
    Sort a list of 2D points in anticlockwise order.

    Args:
        pps: List of points to be sorted

    Returns:
        Sorted list of points.
    """
    new_pps = []
    angles = np.zeros(len(pps), float)
    for ipp, pp in enumerate(pps):
        angles[ipp] = np.arctan2(pp[1], pp[0])
    idx_sorted = np.argsort(angles)

    new_pps.extend(pps[idx_sorted[ii]] for ii in range(len(pps)))
    return new_pps


def anticlockwise_sort_indices(pps):
    """Get the indices that would sort a list of 2D points in anticlockwise order.

    Args:
        pps: List of points to be sorted

    Returns:
        Indices of the sorted list of points.
    """
    angles = np.zeros(len(pps), float)
    for ipp, pp in enumerate(pps):
        angles[ipp] = np.arctan2(pp[1], pp[0])
    return np.argsort(angles)


def sort_separation(separation):
    """Sort a separation.

    Args:
        separation: Initial separation.

    Returns:
        Sorted list of separation.
    """
    if len(separation[0]) > len(separation[2]):
        return [sorted(separation[2]), sorted(separation[1]), sorted(separation[0])]
    return [sorted(separation[0]), sorted(separation[1]), sorted(separation[2])]


def sort_separation_tuple(separation):
    """Sort a separation.

    Args:
        separation: Initial separation

    Returns:
        Sorted tuple of separation
    """
    if len(separation[0]) > len(separation[2]):
        return (
            tuple(sorted(separation[2])),
            tuple(sorted(separation[1])),
            tuple(sorted(separation[0])),
        )
    return (
        tuple(sorted(separation[0])),
        tuple(sorted(separation[1])),
        tuple(sorted(separation[2])),
    )


def separation_in_list(separation_indices, separation_indices_list):
    """
    Checks if the separation indices of a plane are already in the list.

    Args:
        separation_indices: list of separation indices (three arrays of integers)
        separation_indices_list: list of the list of separation indices to be compared to

    Returns:
        bool: True if the separation indices are already in the list.
    """
    sorted_separation = sort_separation(separation_indices)
    for sep in separation_indices_list:
        if len(sep[1]) == len(sorted_separation[1]) and np.allclose(sorted_separation[1], sep[1]):
            return True
    return False


def is_anion_cation_bond(valences, ii, jj) -> bool:
    """
    Checks if two given sites are an anion and a cation.

    Args:
        valences: list of site valences
        ii: index of a site
        jj: index of another site

    Returns:
        bool: True if one site is an anion and the other is a cation (based on valences).
    """
    if valences == "undefined":
        return True
    if valences[ii] == 0 or valences[jj] == 0:
        return True
    return (valences[ii] > 0 > valences[jj]) or (valences[jj] > 0 > valences[ii])


class Plane:
    """Describe a plane."""

    TEST_2D_POINTS = (
        np.array([0, 0], float),
        np.array([1, 0], float),
        np.array([0, 1], float),
        np.array([-1, 0], float),
        np.array([0, -1], float),
        np.array([0, 2], float),
        np.array([2, 0], float),
        np.array([0, -2], float),
        np.array([-2, 0], float),
        np.array([1, 1], float),
        np.array([2, 2], float),
        np.array([-1, -1], float),
        np.array([-2, -2], float),
        np.array([1, 2], float),
        np.array([1, -2], float),
        np.array([-1, 2], float),
        np.array([-1, -2], float),
        np.array([2, 1], float),
        np.array([2, -1], float),
        np.array([-2, 1], float),
        np.array([-2, -1], float),
    )

    def __init__(self, coefficients, p1=None, p2=None, p3=None):
        """Initialize a plane from the 4 coefficients a, b, c and d of ax + by + cz + d = 0.

        Args:
            coefficients: abcd coefficients of the plane.
        """
        # Initializes the normal vector
        self.normal_vector = np.array([coefficients[0], coefficients[1], coefficients[2]], float)
        norm_v = np.linalg.norm(self.normal_vector)
        self.normal_vector /= norm_v
        non_zeros = np.argwhere(~np.isclose(self.normal_vector, 0.0)).flatten()
        zeros = list(set(range(3)) - set(non_zeros))
        if len(non_zeros) == 0:
            raise ValueError("Normal vector is equal to 0.0")
        if self.normal_vector[non_zeros[0]] < 0.0:
            self.normal_vector = -self.normal_vector
            dd = -np.float64(coefficients[3]) / norm_v
        else:
            dd = np.float64(coefficients[3]) / norm_v
        self._coefficients = np.array(
            [self.normal_vector[0], self.normal_vector[1], self.normal_vector[2], dd],
            np.float64,
        )
        self._crosses_origin = np.isclose(dd, 0.0, atol=1e-7, rtol=0.0)
        self.p1 = p1
        self.p2 = p2
        self.p3 = p3
        # Initializes 3 points belonging to the plane (useful for some methods)
        if self.p1 is None:
            self.init_3points(non_zeros, zeros)
        self.vector_to_origin = dd * self.normal_vector
        self.e1 = self.e2 = None
        self.e3 = self.normal_vector

    def init_3points(self, non_zeros, zeros):
        """Initialize three random points on this plane.

        Args:
            non_zeros: Indices of plane coefficients ([a, b, c]) that are not zero.
            zeros: Indices of plane coefficients ([a, b, c]) that are equal to zero.
        """
        if len(non_zeros) == 3:
            self.p1 = np.array([-self.d / self.a, 0.0, 0.0], float)
            self.p2 = np.array([0.0, -self.d / self.b, 0.0], float)
            self.p3 = np.array([0.0, 0.0, -self.d / self.c], float)
        elif len(non_zeros) == 2:
            self.p1 = np.zeros(3, float)
            self.p1[non_zeros[1]] = -self.d / self.coefficients[non_zeros[1]]
            self.p2 = np.array(self.p1)
            self.p2[zeros[0]] = 1.0
            self.p3 = np.zeros(3, float)
            self.p3[non_zeros[0]] = -self.d / self.coefficients[non_zeros[0]]
        elif len(non_zeros) == 1:
            self.p1 = np.zeros(3, float)
            self.p1[non_zeros[0]] = -self.d / self.coefficients[non_zeros[0]]
            self.p2 = np.array(self.p1)
            self.p2[zeros[0]] = 1.0
            self.p3 = np.array(self.p1)
            self.p3[zeros[1]] = 1.0

    def __str__(self):
        """String representation of the Plane."""
        return (
            f"Plane object\n  => Normal vector : {self.normal_vector}\n  => Equation of the plane"
            f" ax + by + cz + d = 0\n     with a = {self._coefficients[0]}\n          "
            f"b = {self._coefficients[1]}\n          c = {self._coefficients[2]}\n          d = {self._coefficients[3]}"
        )

    def is_in_plane(self, pp, dist_tolerance) -> bool:
        """
        Determines if point pp is in the plane within the tolerance dist_tolerance.

        Args:
            pp: point to be tested
            dist_tolerance: tolerance on the distance to the plane within which point pp is considered in the plane

        Returns:
            bool: True if pp is in the plane.
        """
        return bool(np.abs(np.dot(self.normal_vector, pp) + self._coefficients[3]) <= dist_tolerance)

    def is_same_plane_as(self, plane) -> bool:
        """
        Checks whether the plane is identical to another Plane "plane".

        Args:
            plane: Plane to be compared to

        Returns:
            bool: True if the two facets are identical.
        """
        return np.allclose(self._coefficients, plane.coefficients)

    def is_in_list(self, plane_list) -> bool:
        """
        Checks whether the plane is identical to one of the Planes in the plane_list list of Planes.

        Args:
            plane_list: List of Planes to be compared to

        Returns:
            bool: True if the plane is in the list.
        """
        return any(self.is_same_plane_as(plane) for plane in plane_list)

    def indices_separate(self, points, dist_tolerance):
        """Get three lists containing the indices of the points lying on one side of the plane, on the plane
        and on the other side of the plane. The dist_tolerance parameter controls the tolerance to which a point
        is considered to lie on the plane or not (distance to the plane).

        Args:
            points: list of points
            dist_tolerance: tolerance to which a point is considered to lie on the plane
                or not (distance to the plane)

        Returns:
            The lists of indices of the points on one side of the plane, on the plane and
            on the other side of the plane.
        """
        side1 = []
        inplane = []
        side2 = []
        for ip, pp in enumerate(points):
            if self.is_in_plane(pp, dist_tolerance):
                inplane.append(ip)
            elif np.dot(pp + self.vector_to_origin, self.normal_vector) < 0.0:
                side1.append(ip)
            else:
                side2.append(ip)
        return [side1, inplane, side2]

    def distance_to_point(self, point):
        """Compute the absolute distance from the plane to the point.

        Args:
            point: Point for which distance is computed

        Returns:
            Distance between the plane and the point.
        """
        return np.abs(np.dot(self.normal_vector, point) + self.d)

    def distances(self, points):
        """Compute the distances from the plane to each of the points. Positive distances are on
        the side of the normal of the plane while negative distances are on the other side.

        Args:
            points: Points for which distances are computed

        Returns:
            Distances from the plane to the points (positive values on the side of the normal to the plane,
            negative values on the other side).
        """
        return [np.dot(self.normal_vector, pp) + self.d for pp in points]

    def distances_indices_sorted(self, points, sign=False):
        """Compute the distances from the plane to each of the points. Positive distances are
        on the side of the normal of the plane while negative distances are on the other
        side. Indices sorting the points from closest to furthest is also computed.

        Args:
            points: Points for which distances are computed
            sign: Whether to add sign information in the indices sorting the points distances

        Returns:
            Distances from the plane to the points (positive values on the side of the normal to the plane,
            negative values on the other side), as well as indices of the points from closest to furthest. For the
            latter, when the sign parameter is True, items of the sorting list are given as tuples of (index, sign).
        """
        distances = [np.dot(self.normal_vector, pp) + self.d for pp in points]
        indices = sorted(range(len(distances)), key=lambda k: np.abs(distances[k]))
        if sign:
            indices = [(ii, int(np.sign(distances[ii]))) for ii in indices]
        return distances, indices

    def distances_indices_groups(self, points, delta=None, delta_factor=0.05, sign=False):
        """Compute the distances from the plane to each of the points. Positive distances are on the side of the
        normal of the plane while negative distances are on the other side. Indices sorting the points from closest
        to furthest is also computed. Grouped indices are also given, for which indices of the distances that are
        separated by less than delta are grouped together. The delta parameter is either set explicitly or taken as
        a fraction (using the delta_factor parameter) of the maximal point distance.

        Args:
            points: Points for which distances are computed
            delta: Distance interval for which two points are considered in the same group.
            delta_factor: If delta is None, the distance interval is taken as delta_factor times the maximal
            point distance.
            sign: Whether to add sign information in the indices sorting the points distances

        Returns:
            Distances from the plane to the points (positive values on the side of the normal to the plane,
            negative values on the other side), as well as indices of the points from closest to furthest and
            grouped indices of distances separated by less than delta. For the sorting list and the grouped
            indices, when the sign parameter is True, items are given as tuples of (index, sign).
        """
        distances, indices = self.distances_indices_sorted(points=points)
        if delta is None:
            delta = delta_factor * np.abs(distances[indices[-1]])
        iends = [
            ii
            for ii, idist in enumerate(indices, start=1)
            if ii == len(distances) or (np.abs(distances[indices[ii]]) - np.abs(distances[idist]) > delta)
        ]
        if sign:
            indices = [(ii, int(np.sign(distances[ii]))) for ii in indices]
        grouped_indices = [indices[iends[ii - 1] : iend] if ii > 0 else indices[:iend] for ii, iend in enumerate(iends)]
        return distances, indices, grouped_indices

    def projectionpoints(self, pps):
        """
        Projects each points in the point list pps on plane and returns the list of projected points.

        Args:
            pps: List of points to project on plane

        Returns:
            List of projected point on plane.
        """
        return [pp - np.dot(pp - self.p1, self.normal_vector) * self.normal_vector for pp in pps]

    def orthonormal_vectors(self):
        """Get a list of three orthogonal vectors, the two first being parallel to the plane and the
        third one is the normal vector of the plane.

        Returns:
            List of orthogonal vectors
        :raise: ValueError if all the coefficients are zero or if there is some other strange error.
        """
        if self.e1 is None:
            diff = self.p2 - self.p1
            self.e1 = diff / norm(diff)
            self.e2 = np.cross(self.e3, self.e1)
        return [self.e1, self.e2, self.e3]

    def project_and_to2dim_ordered_indices(self, pps, plane_center="mean"):
        """
        Projects each points in the point list pps on plane and returns the indices that would sort the
        list of projected points in anticlockwise order.

        Args:
            pps: List of points to project on plane

        Returns:
            List of indices that would sort the list of projected points.
        """
        pp2d = self.project_and_to2dim(pps, plane_center)
        return anticlockwise_sort_indices(pp2d)

    def project_and_to2dim(self, pps, plane_center):
        """
        Projects the list of points pps to the plane and changes the basis from 3D to the 2D basis of the plane.

        Args:
            pps: List of points to be projected

        Returns:
            :raise:
        """
        proj = self.projectionpoints(pps)
        [u1, u2, u3] = self.orthonormal_vectors()
        PP = np.array([[u1[0], u2[0], u3[0]], [u1[1], u2[1], u3[1]], [u1[2], u2[2], u3[2]]])
        xypps = []
        for pp in proj:
            xyzpp = np.dot(pp, PP)
            xypps.append(xyzpp[:2])
        if str(plane_center) == "mean":
            mean = np.zeros(2, float)
            for pp in xypps:
                mean += pp
            mean /= len(xypps)
            xypps = [pp - mean for pp in xypps]
        elif plane_center is not None:
            projected_plane_center = self.projectionpoints([plane_center])[0]
            xy_projected_plane_center = np.dot(projected_plane_center, PP)[:2]
            xypps = [pp - xy_projected_plane_center for pp in xypps]
        return xypps

    def fit_error(self, points, fit="least_square_distance"):
        """Evaluate the error for a list of points with respect to this plane.

        Args:
            points: List of points.
            fit: Type of fit error.

        Returns:
            Error for a list of points with respect to this plane.
        """
        if fit == "least_square_distance":
            return self.fit_least_square_distance_error(points)
        if fit == "maximum_distance":
            return self.fit_maximum_distance_error(points)
        return None

    def fit_least_square_distance_error(self, points):
        """Evaluate the sum of squared distances error for a list of points with respect to this plane.

        Args:
            points: List of points.

        Returns:
            Sum of squared distances error for a list of points with respect to this plane.
        """
        return np.sum([self.distance_to_point(pp) ** 2.0 for pp in points])

    def fit_maximum_distance_error(self, points):
        """Evaluate the max distance error for a list of points with respect to this plane.

        Args:
            points: List of points.

        Returns:
            Max distance error for a list of points with respect to this plane.
        """
        return np.max([self.distance_to_point(pp) for pp in points])

    @property
    def coefficients(self):
        """A copy of the plane coefficients as a numpy array."""
        return np.copy(self._coefficients)

    @property
    def abcd(self):
        """A tuple with the plane coefficients."""
        return tuple(self._coefficients[:4])

    @property
    def a(self):
        """Coefficient a of the plane."""
        return self._coefficients[0]

    @property
    def b(self):
        """Coefficient b of the plane."""
        return self._coefficients[1]

    @property
    def c(self):
        """Coefficient c of the plane."""
        return self._coefficients[2]

    @property
    def d(self):
        """Coefficient d of the plane."""
        return self._coefficients[3]

    @property
    def distance_to_origin(self):
        """Distance of the plane to the origin."""
        return self._coefficients[3]

    @property
    def crosses_origin(self):
        """Whether this plane crosses the origin (i.e. coefficient d is 0.0)."""
        return self._crosses_origin

    @classmethod
    def from_2points_and_origin(cls, p1, p2) -> Self:
        """Initialize plane from two points and the origin.

        Args:
            p1: First point.
            p2: Second point.

        Returns:
            Plane.
        """
        return cls.from_3points(p1, p2, np.zeros(3))

    @classmethod
    def from_3points(cls, p1, p2, p3) -> Self:
        """Initialize plane from three points.

        Args:
            p1: First point.
            p2: Second point.
            p3: Third point.

        Returns:
            Plane.
        """
        nn = np.cross(p1 - p3, p2 - p3)
        normal_vector = nn / norm(nn)
        non_zeros = np.argwhere(~np.isclose(normal_vector, 0.0))
        if normal_vector[non_zeros[0, 0]] < 0.0:
            normal_vector = -normal_vector
        dd = -np.dot(normal_vector, p1)
        coefficients = np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], float)
        return cls(coefficients, p1=p1, p2=p2, p3=p3)

    @classmethod
    def from_npoints(cls, points, best_fit="least_square_distance") -> Self:
        """Initialize plane from a list of points.

        If the number of points is larger than 3, will use a least square fitting or max distance fitting.

        Args:
            points: List of points.
            best_fit: Type of fitting procedure for more than 3 points.

        Returns:
            Plane
        """
        if len(points) == 2:
            return cls.from_2points_and_origin(points[0], points[1])
        if len(points) == 3:
            return cls.from_3points(points[0], points[1], points[2])
        if best_fit == "least_square_distance":
            return cls.from_npoints_least_square_distance(points)
        if best_fit == "maximum_distance":
            return cls.from_npoints_maximum_distance(points)

        raise ValueError("Cannot initialize Plane.")

    @classmethod
    def from_npoints_least_square_distance(cls, points) -> Self:
        """Initialize plane from a list of points using a least square fitting procedure.

        Args:
            points: List of points.

        Returns:
            Plane.
        """
        mean_point = np.array([sum(pp[ii] for pp in points) for ii in range(3)], float)
        mean_point /= len(points)
        AA = np.zeros((len(points), 3), float)
        for ii, pp in enumerate(points):
            for jj in range(3):
                AA[ii, jj] = pp[jj] - mean_point[jj]
        _UU, SS, Vt = np.linalg.svd(AA)
        imin = np.argmin(SS)
        normal_vector = Vt[imin]
        non_zeros = np.argwhere(~np.isclose(normal_vector, 0.0))
        if normal_vector[non_zeros[0, 0]] < 0.0:
            normal_vector = -normal_vector
        dd = -np.dot(normal_vector, mean_point)
        coefficients = np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], float)
        return cls(coefficients)

    @classmethod
    def perpendicular_bisector(cls, p1, p2) -> Self:
        """Initialize a plane from the perpendicular bisector of two points.

        The perpendicular bisector of two points is the plane perpendicular to the vector joining these two points
        and passing through the middle of the segment joining the two points.

        Args:
            p1: First point.
            p2: Second point.

        Returns:
            Plane.
        """
        middle_point = 0.5 * (p1 + p2)
        normal_vector = p2 - p1
        dd = -np.dot(normal_vector, middle_point)
        return cls(np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], float))

    @classmethod
    def from_npoints_maximum_distance(cls, points) -> Self:
        """Initialize plane from a list of points using a max distance fitting procedure.

        Args:
            points: List of points.

        Returns:
            Plane.
        """
        convex_hull = ConvexHull(points)
        heights = []
        ipoints_heights = []
        for idx, _simplex in enumerate(convex_hull.simplices):
            cc = convex_hull.equations[idx]
            plane = Plane.from_coefficients(cc[0], cc[1], cc[2], cc[3])
            distances = [plane.distance_to_point(pp) for pp in points]
            ipoint_height = np.argmax(distances)
            heights.append(distances[ipoint_height])
            ipoints_heights.append(ipoint_height)
        imin_height = np.argmin(heights)
        normal_vector = convex_hull.equations[imin_height, 0:3]
        cc = convex_hull.equations[imin_height]
        highest_point = points[ipoints_heights[imin_height]]
        middle_point = (
            Plane.from_coefficients(cc[0], cc[1], cc[2], cc[3]).projectionpoints([highest_point])[0] + highest_point
        ) / 2
        dd = -np.dot(normal_vector, middle_point)
        return cls(np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], float))

    @classmethod
    def from_coefficients(cls, a, b, c, d) -> Self:
        """Initialize plane from its coefficients.

        Args:
            a: a coefficient of the plane.
            b: b coefficient of the plane.
            c: c coefficient of the plane.
            d: d coefficient of the plane.

        Returns:
            Plane.
        """
        return cls(np.array([a, b, c, d], float))
