text: # Copyright (c) Pymatgen Development Team.
  # Distributed under the terms of the MIT License.
  
  """
  Module containing classes to generate grain boundaries.
  """
  
  import itertools
  import logging
  import warnings
  from fractions import Fraction
  from functools import reduce
  from math import cos, floor, gcd
  
  import numpy as np
  from monty.fractions import lcm
  
  from pymatgen.core.lattice import Lattice
  from pymatgen.core.sites import PeriodicSite
  from pymatgen.core.structure import Structure
  from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
  
  # This module implements representations of grain boundaries, as well as
  # algorithms for generating them.
  
  __author__ = "Xiang-Guo Li"
  __copyright__ = "Copyright 2018, The Materials Virtual Lab"
  __version__ = "0.1"
  __maintainer__ = "Xiang-Guo Li"
  __email__ = "xil110@ucsd.edu"
  __date__ = "7/30/18"
  
  logger = logging.getLogger(__name__)
  
  
  class GrainBoundary(Structure):
      """
      Subclass of Structure representing a GrainBoundary (gb) object.
      Implements additional attributes pertaining to gbs, but the
      init method does not actually implement any algorithm that
      creates a gb. This is a DUMMY class who's init method only holds
      information about the gb. Also has additional methods that returns
      other information about a gb such as sigma value.
  
      Note that all gbs have the gb surface normal oriented in the c-direction.
      This means the lattice vectors a and b are in the gb surface plane (at
       least for one grain) and the c vector is out of the surface plane
       (though not necessary perpendicular to the surface.)
      """
  
      def __init__(
          self,
          lattice,
          species,
          coords,
          rotation_axis,
          rotation_angle,
          gb_plane,
          join_plane,
          init_cell,
          vacuum_thickness,
          ab_shift,
          site_properties,
          oriented_unit_cell,
          validate_proximity=False,
          coords_are_cartesian=False,
      ):
          """
          Makes a gb structure, a structure object with additional information
          and methods pertaining to gbs.
  
          Args:
              lattice (Lattice/3x3 array): The lattice, either as a
                  :class:`pymatgen.core.lattice.Lattice` or
                  simply as any 2D array. Each row should correspond to a lattice
                  vector. E.g., [[10,0,0], [20,10,0], [0,0,30]] specifies a
                  lattice with lattice vectors [10,0,0], [20,10,0] and [0,0,30].
              species ([Species]): Sequence of species on each site. Can take in
                  flexible input, including:
  
                  i.  A sequence of element / species specified either as string
                      symbols, e.g. ["Li", "Fe2+", "P", ...] or atomic numbers,
                      e.g., (3, 56, ...) or actual Element or Species objects.
  
                  ii. List of dict of elements/species and occupancies, e.g.,
                      [{"Fe" : 0.5, "Mn":0.5}, ...]. This allows the setup of
                      disordered structures.
              coords (Nx3 array): list of fractional/cartesian coordinates of
                  each species.
              rotation_axis (list): Rotation axis of GB in the form of a list of
                  integers, e.g. [1, 1, 0].
              rotation_angle (float, in unit of degree): rotation angle of GB.
              gb_plane (list): Grain boundary plane in the form of a list of integers
                  e.g.: [1, 2, 3].
              join_plane (list): Joining plane of the second grain in the form of a list of
                  integers. e.g.: [1, 2, 3].
              init_cell (Structure): initial bulk structure to form the GB.
              site_properties (dict): Properties associated with the sites as a
                  dict of sequences, The sequences have to be the same length as
                  the atomic species and fractional_coords. For gb, you should
                  have the 'grain_label' properties to classify the sites as 'top',
                  'bottom', 'top_incident', or 'bottom_incident'.
              vacuum_thickness (float in angstrom): The thickness of vacuum inserted
                  between two grains of the GB.
              ab_shift (list of float, in unit of crystal vector a, b): The relative
                  shift along a, b vectors.
              oriented_unit_cell (Structure): oriented unit cell of the bulk init_cell.
                  Help to accurate calculate the bulk properties that are consistent
                  with gb calculations.
              validate_proximity (bool): Whether to check if there are sites
                  that are less than 0.01 Ang apart. Defaults to False.
              coords_are_cartesian (bool): Set to True if you are providing
                  coordinates in Cartesian coordinates. Defaults to False.
          """
          self.oriented_unit_cell = oriented_unit_cell
          self.rotation_axis = rotation_axis
          self.rotation_angle = rotation_angle
          self.gb_plane = gb_plane
          self.join_plane = join_plane
          self.init_cell = init_cell
          self.vacuum_thickness = vacuum_thickness
          self.ab_shift = ab_shift
          super().__init__(
              lattice,
              species,
              coords,
              validate_proximity=validate_proximity,
              coords_are_cartesian=coords_are_cartesian,
              site_properties=site_properties,
          )
  
      def copy(self):
          """
          Convenience method to get a copy of the structure, with options to add
          site properties.
  
          Returns:
              A copy of the Structure, with optionally new site_properties and
              optionally sanitized.
          """
          return GrainBoundary(
              self.lattice,
              self.species_and_occu,
              self.frac_coords,
              self.rotation_axis,
              self.rotation_angle,
              self.gb_plane,
              self.join_plane,
              self.init_cell,
              self.vacuum_thickness,
              self.ab_shift,
              self.site_properties,
              self.oriented_unit_cell,
          )
  
      def get_sorted_structure(self, key=None, reverse=False):
          """
          Get a sorted copy of the structure. The parameters have the same
          meaning as in list.sort. By default, sites are sorted by the
          electronegativity of the species. Note that Slab has to override this
          because of the different __init__ args.
          Args:
              key: Specifies a function of one argument that is used to extract
                  a comparison key from each list element: key=str.lower. The
                  default value is None (compare the elements directly).
              reverse (bool): If set to True, then the list elements are sorted
                  as if each comparison were reversed.
          """
          sites = sorted(self, key=key, reverse=reverse)
          s = Structure.from_sites(sites)
          return GrainBoundary(
              s.lattice,
              s.species_and_occu,
              s.frac_coords,
              self.rotation_axis,
              self.rotation_angle,
              self.gb_plane,
              self.join_plane,
              self.init_cell,
              self.vacuum_thickness,
              self.ab_shift,
              self.site_properties,
              self.oriented_unit_cell,
          )
  
      @property
      def sigma(self):
          """
          This method returns the sigma value of the gb.
          If using 'quick_gen' to generate GB, this value is not valid.
          """
          return int(round(self.oriented_unit_cell.volume / self.init_cell.volume))
  
      @property
      def sigma_from_site_prop(self):
          """
          This method returns the sigma value of the gb from site properties.
          If the GB structure merge some atoms due to the atoms too closer with
          each other, this property will not work.
          """
          num_coi = 0
          if None in self.site_properties["grain_label"]:
              raise RuntimeError("Site were merged, this property do not work")
          for tag in self.site_properties["grain_label"]:
              if "incident" in tag:
                  num_coi += 1
          return int(round(self.num_sites / num_coi))
  
      @property
      def top_grain(self):
          """
          return the top grain (Structure) of the GB.
          """
          top_sites = []
          for i, tag in enumerate(self.site_properties["grain_label"]):
              if "top" in tag:
                  top_sites.append(self.sites[i])
          return Structure.from_sites(top_sites)
  
      @property
      def bottom_grain(self):
          """
          return the bottom grain (Structure) of the GB.
          """
          bottom_sites = []
          for i, tag in enumerate(self.site_properties["grain_label"]):
              if "bottom" in tag:
                  bottom_sites.append(self.sites[i])
          return Structure.from_sites(bottom_sites)
  
      @property
      def coincidents(self):
          """
          return the a list of coincident sites.
          """
          coincident_sites = []
          for i, tag in enumerate(self.site_properties["grain_label"]):
              if "incident" in tag:
                  coincident_sites.append(self.sites[i])
          return coincident_sites
  
      def __str__(self):
          comp = self.composition
          outs = [
              f"Gb Summary ({comp.formula})",
              f"Reduced Formula: {comp.reduced_formula}",
              f"Rotation axis: {self.rotation_axis}",
              f"Rotation angle: {self.rotation_angle}",
              f"GB plane: {self.gb_plane}",
              f"Join plane: {self.join_plane}",
              f"vacuum thickness: {self.vacuum_thickness}",
              f"ab_shift: {self.ab_shift}",
          ]
  
          def to_s(x, rjust=10):
              return (f"{x:0.6f}").rjust(rjust)
  
          outs.append("abc   : " + " ".join([to_s(i) for i in self.lattice.abc]))
          outs.append("angles: " + " ".join([to_s(i) for i in self.lattice.angles]))
          outs.append(f"Sites ({len(self)})")
          for i, site in enumerate(self):
              outs.append(
                  " ".join(
                      [
                          str(i + 1),
                          site.species_string,
                          " ".join([to_s(j, 12) for j in site.frac_coords]),
                      ]
                  )
              )
          return "\n".join(outs)
  
      def as_dict(self):
          """
          Returns:
              Dictionary representation of GrainBoundary object
          """
          d = super().as_dict()
          d["@module"] = self.__class__.__module__
          d["@class"] = self.__class__.__name__
          d["init_cell"] = self.init_cell.as_dict()
          d["rotation_axis"] = self.rotation_axis
          d["rotation_angle"] = self.rotation_angle
          d["gb_plane"] = self.gb_plane
          d["join_plane"] = self.join_plane
          d["vacuum_thickness"] = self.vacuum_thickness
          d["ab_shift"] = self.ab_shift
          d["oriented_unit_cell"] = self.oriented_unit_cell.as_dict()
          return d
  
      @classmethod
      def from_dict(cls, d):
          """
          Generates a GrainBoundary object from a dictionary created by as_dict().
  
          Args:
              d: dict
  
          Returns:
              GrainBoundary object
          """
          lattice = Lattice.from_dict(d["lattice"])
          sites = [PeriodicSite.from_dict(sd, lattice) for sd in d["sites"]]
          s = Structure.from_sites(sites)
  
          return GrainBoundary(
              lattice=lattice,
              species=s.species_and_occu,
              coords=s.frac_coords,
              rotation_axis=d["rotation_axis"],
              rotation_angle=d["rotation_angle"],
              gb_plane=d["gb_plane"],
              join_plane=d["join_plane"],
              init_cell=Structure.from_dict(d["init_cell"]),
              vacuum_thickness=d["vacuum_thickness"],
              ab_shift=d["ab_shift"],
              oriented_unit_cell=Structure.from_dict(d["oriented_unit_cell"]),
              site_properties=s.site_properties,
          )
  
  
  class GrainBoundaryGenerator:
      """
      This class is to generate grain boundaries (GBs) from bulk
      conventional cell (fcc, bcc can from the primitive cell), and works for Cubic,
      Tetragonal, Orthorhombic, Rhombohedral, and Hexagonal systems.
      It generate GBs from given parameters, which includes
      GB plane, rotation axis, rotation angle.
  
      This class works for any general GB, including twist, tilt and mixed GBs.
      The three parameters, rotation axis, GB plane and rotation angle, are
      sufficient to identify one unique GB. While sometimes, users may not be able
      to tell what exactly rotation angle is but prefer to use sigma as an parameter,
      this class also provides the function that is able to return all possible
      rotation angles for a specific sigma value.
      The same sigma value (with rotation axis fixed) can correspond to
      multiple rotation angles.
      Users can use structure matcher in pymatgen to get rid of the redundant structures.
      """
  
      def __init__(self, initial_structure, symprec=0.1, angle_tolerance=1):
          """
          initial_structure (Structure): Initial input structure. It can
                 be conventional or primitive cell (primitive cell works for bcc and fcc).
                 For fcc and bcc, using conventional cell can lead to a non-primitive
                 grain boundary structure.
                 This code supplies Cubic, Tetragonal, Orthorhombic, Rhombohedral, and
                 Hexagonal systems.
          symprec (float): Tolerance for symmetry finding. Defaults to 0.1 (the value used
                  in Materials Project), which is for structures with slight deviations
                  from their proper atomic positions (e.g., structures relaxed with
                  electronic structure codes).
                  A smaller value of 0.01 is often used for properly refined
                  structures with atoms in the proper symmetry coordinates.
                  User should make sure the symmetry is what you want.
          angle_tolerance (float): Angle tolerance for symmetry finding.
          """
          analyzer = SpacegroupAnalyzer(initial_structure, symprec, angle_tolerance)
          self.lat_type = analyzer.get_lattice_type()[0]
          if self.lat_type == "t":
              # need to use the conventional cell for tetragonal
              initial_structure = analyzer.get_conventional_standard_structure()
              a, b, c = initial_structure.lattice.abc
              # c axis of tetragonal structure not in the third direction
              if abs(a - b) > symprec:
                  # a == c, rotate b to the third direction
                  if abs(a - c) < symprec:
                      initial_structure.make_supercell([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
                  # b == c, rotate a to the third direction
                  else:
                      initial_structure.make_supercell([[0, 1, 0], [0, 0, 1], [1, 0, 0]])
          elif self.lat_type == "h":
              alpha, beta, gamma = initial_structure.lattice.angles
              # c axis is not in the third direction
              if abs(gamma - 90) < angle_tolerance:
                  # alpha = 120 or 60, rotate b, c to a, b vectors
                  if abs(alpha - 90) > angle_tolerance:
                      initial_structure.make_supercell([[0, 1, 0], [0, 0, 1], [1, 0, 0]])
                  # beta = 120 or 60, rotate c, a to a, b vectors
                  elif abs(beta - 90) > angle_tolerance:
                      initial_structure.make_supercell([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
          elif self.lat_type == "r":
              # need to use primitive cell for rhombohedra
              initial_structure = analyzer.get_primitive_standard_structure()
          elif self.lat_type == "o":
              # need to use the conventional cell for orthorombic
              initial_structure = analyzer.get_conventional_standard_structure()
          self.initial_structure = initial_structure
  
      def gb_from_parameters(
          self,
          rotation_axis,
          rotation_angle,
          expand_times=4,
          vacuum_thickness=0.0,
          ab_shift=[0, 0],
          normal=False,
          ratio=None,
          plane=None,
          max_search=20,
          tol_coi=1.0e-8,
          rm_ratio=0.7,
          quick_gen=False,
      ):
          """
          Args:
              rotation_axis (list): Rotation axis of GB in the form of a list of integer
                  e.g.: [1, 1, 0]
              rotation_angle (float, in unit of degree): rotation angle used to generate GB.
                  Make sure the angle is accurate enough. You can use the enum* functions
                  in this class to extract the accurate angle.
                  e.g.: The rotation angle of sigma 3 twist GB with the rotation axis
                  [1, 1, 1] and GB plane (1, 1, 1) can be 60.000000000 degree.
                  If you do not know the rotation angle, but know the sigma value, we have
                  provide the function get_rotation_angle_from_sigma which is able to return
                  all the rotation angles of sigma value you provided.
              expand_times (int): The multiple times used to expand one unit grain to larger grain.
                  This is used to tune the grain length of GB to warrant that the two GBs in one
                  cell do not interact with each other. Default set to 4.
              vacuum_thickness (float, in angstrom): The thickness of vacuum that you want to insert
                  between two grains of the GB. Default to 0.
              ab_shift (list of float, in unit of a, b vectors of Gb): in plane shift of two grains
              normal (logic):
                  determine if need to require the c axis of top grain (first transformation matrix)
                  perperdicular to the surface or not.
                  default to false.
              ratio (list of integers):
                  lattice axial ratio.
                  For cubic system, ratio is not needed.
                  For tetragonal system, ratio = [mu, mv], list of two integers,
                  that is, mu/mv = c2/a2. If it is irrational, set it to none.
                  For orthorhombic system, ratio = [mu, lam, mv], list of three integers,
                  that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None.
                  e.g. mu:lam:mv = c2,None,a2, means b2 is irrational.
                  For rhombohedral system, ratio = [mu, mv], list of two integers,
                  that is, mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha).
                  If irrational, set it to None.
                  For hexagonal system, ratio = [mu, mv], list of two integers,
                  that is, mu/mv = c2/a2. If it is irrational, set it to none.
                  This code also supplies a class method to generate the ratio from the
                  structure (get_ratio). User can also make their own approximation and
                  input the ratio directly.
              plane (list): Grain boundary plane in the form of a list of integers
                  e.g.: [1, 2, 3]. If none, we set it as twist GB. The plane will be perpendicular
                  to the rotation axis.
              max_search (int): max search for the GB lattice vectors that give the smallest GB
                  lattice. If normal is true, also max search the GB c vector that perpendicular
                  to the plane. For complex GB, if you want to speed up, you can reduce this value.
                  But too small of this value may lead to error.
              tol_coi (float): tolerance to find the coincidence sites. When making approximations to
                  the ratio needed to generate the GB, you probably need to increase this tolerance to
                  obtain the correct number of coincidence sites. To check the number of coincidence
                  sites are correct or not, you can compare the generated Gb object's sigma_from_site_prop
                  with enum* sigma values (what user expected by input).
              rm_ratio (float): the criteria to remove the atoms which are too close with each other.
                  rm_ratio*bond_length of bulk system is the criteria of bond length, below which the atom
                  will be removed. Default to 0.7.
              quick_gen (bool): whether to quickly generate a supercell, if set to true, no need to
                  find the smallest cell.
  
          Returns:
             Grain boundary structure (gb object).
          """
          lat_type = self.lat_type
          # if the initial structure is primitive cell in cubic system,
          # calculate the transformation matrix from its conventional cell
          # to primitive cell, basically for bcc and fcc systems.
          trans_cry = np.eye(3)
          if lat_type == "c":
              analyzer = SpacegroupAnalyzer(self.initial_structure)
              convention_cell = analyzer.get_conventional_standard_structure()
              vol_ratio = self.initial_structure.volume / convention_cell.volume
              # bcc primitive cell, belong to cubic system
              if abs(vol_ratio - 0.5) < 1.0e-3:
                  trans_cry = np.array([[0.5, 0.5, -0.5], [-0.5, 0.5, 0.5], [0.5, -0.5, 0.5]])
                  logger.info("Make sure this is for cubic with bcc primitive cell")
              # fcc primitive cell, belong to cubic system
              elif abs(vol_ratio - 0.25) < 1.0e-3:
                  trans_cry = np.array([[0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]])
                  logger.info("Make sure this is for cubic with fcc primitive cell")
              else:
                  logger.info("Make sure this is for cubic with conventional cell")
          elif lat_type == "t":
              logger.info("Make sure this is for tetragonal system")
              if ratio is None:
                  logger.info("Make sure this is for irrational c2/a2")
              elif len(ratio) != 2:
                  raise RuntimeError("Tetragonal system needs correct c2/a2 ratio")
          elif lat_type == "o":
              logger.info("Make sure this is for orthorhombic system")
              if ratio is None:
                  raise RuntimeError("CSL does not exist if all axial ratios are irrational for an orthorhombic system")
              if len(ratio) != 3:
                  raise RuntimeError("Orthorhombic system needs correct c2:b2:a2 ratio")
          elif lat_type == "h":
              logger.info("Make sure this is for hexagonal system")
              if ratio is None:
                  logger.info("Make sure this is for irrational c2/a2")
              elif len(ratio) != 2:
                  raise RuntimeError("Hexagonal system needs correct c2/a2 ratio")
          elif lat_type == "r":
              logger.info("Make sure this is for rhombohedral system")
              if ratio is None:
                  logger.info("Make sure this is for irrational (1+2*cos(alpha)/cos(alpha) ratio")
              elif len(ratio) != 2:
                  raise RuntimeError("Rhombohedral system needs correct (1+2*cos(alpha)/cos(alpha) ratio")
          else:
              raise RuntimeError(
                  "Lattice type not implemented. This code works for cubic, "
                  "tetragonal, orthorhombic, rhombehedral, hexagonal systems"
              )
  
          # transform four index notation to three index notation for hexagonal and rhombohedral
          if len(rotation_axis) == 4:
              u1 = rotation_axis[0]
              v1 = rotation_axis[1]
              w1 = rotation_axis[3]
              if lat_type.lower() == "h":
                  u = 2 * u1 + v1
                  v = 2 * v1 + u1
                  w = w1
                  rotation_axis = [u, v, w]
              elif lat_type.lower() == "r":
                  u = 2 * u1 + v1 + w1
                  v = v1 + w1 - u1
                  w = w1 - 2 * v1 - u1
                  rotation_axis = [u, v, w]
  
          # make sure gcd(rotation_axis)==1
          if reduce(gcd, rotation_axis) != 1:
              rotation_axis = [int(round(x / reduce(gcd, rotation_axis))) for x in rotation_axis]
          # transform four index notation to three index notation for plane
          if plane is not None:
              if len(plane) == 4:
                  u1 = plane[0]
                  v1 = plane[1]
                  w1 = plane[3]
                  plane = [u1, v1, w1]
          # set the plane for grain boundary when plane is None.
          if plane is None:
              if lat_type.lower() == "c":
                  plane = rotation_axis
              else:
                  if lat_type.lower() == "h":
                      if ratio is None:
                          c2_a2_ratio = 1
                      else:
                          c2_a2_ratio = ratio[0] / ratio[1]
                      metric = np.array([[1, -0.5, 0], [-0.5, 1, 0], [0, 0, c2_a2_ratio]])
                  elif lat_type.lower() == "r":
                      if ratio is None:
                          cos_alpha = 0.5
                      else:
                          cos_alpha = 1.0 / (ratio[0] / ratio[1] - 2)
                      metric = np.array(
                          [
                              [1, cos_alpha, cos_alpha],
                              [cos_alpha, 1, cos_alpha],
                              [cos_alpha, cos_alpha, 1],
                          ]
                      )
                  elif lat_type.lower() == "t":
                      if ratio is None:
                          c2_a2_ratio = 1
                      else:
                          c2_a2_ratio = ratio[0] / ratio[1]
                      metric = np.array([[1, 0, 0], [0, 1, 0], [0, 0, c2_a2_ratio]])
                  elif lat_type.lower() == "o":
                      for i in range(3):
                          if ratio[i] is None:
                              ratio[i] = 1
                      metric = np.array(
                          [
                              [1, 0, 0],
                              [0, ratio[1] / ratio[2], 0],
                              [0, 0, ratio[0] / ratio[2]],
                          ]
                      )
                  else:
                      raise RuntimeError("Lattice type has not implemented.")
  
                  plane = np.matmul(rotation_axis, metric)
                  fractions = [Fraction(x).limit_denominator() for x in plane]
                  least_mul = reduce(lcm, [f.denominator for f in fractions])
                  plane = [int(round(x * least_mul)) for x in plane]
  
          if reduce(gcd, plane) != 1:
              index = reduce(gcd, plane)
              plane = [int(round(x / index)) for x in plane]
  
          t1, t2 = self.get_trans_mat(
              r_axis=rotation_axis,
              angle=rotation_angle,
              normal=normal,
              trans_cry=trans_cry,
              lat_type=lat_type,
              ratio=ratio,
              surface=plane,
              max_search=max_search,
              quick_gen=quick_gen,
          )
  
          # find the join_plane
          if lat_type.lower() != "c":
              if lat_type.lower() == "h":
                  if ratio is None:
                      mu, mv = [1, 1]
                  else:
                      mu, mv = ratio
                  trans_cry1 = np.array([[1, 0, 0], [-0.5, np.sqrt(3.0) / 2.0, 0], [0, 0, np.sqrt(mu / mv)]])
              elif lat_type.lower() == "r":
                  if ratio is None:
                      c2_a2_ratio = 1
                  else:
                      mu, mv = ratio
                      c2_a2_ratio = 3.0 / (2 - 6 * mv / mu)
                  trans_cry1 = np.array(
                      [
                          [0.5, np.sqrt(3.0) / 6.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)],
                          [-0.5, np.sqrt(3.0) / 6.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)],
                          [0, -1 * np.sqrt(3.0) / 3.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)],
                      ]
                  )
              else:
                  if lat_type.lower() == "t":
                      if ratio is None:
                          mu, mv = [1, 1]
                      else:
                          mu, mv = ratio
                      lam = mv
                  elif lat_type.lower() == "o":
                      new_ratio = [1 if v is None else v for v in ratio]
                      mu, lam, mv = new_ratio
                  trans_cry1 = np.array([[1, 0, 0], [0, np.sqrt(lam / mv), 0], [0, 0, np.sqrt(mu / mv)]])
          else:
              trans_cry1 = trans_cry
          grain_matrix = np.dot(t2, trans_cry1)
          plane_init = np.cross(grain_matrix[0], grain_matrix[1])
          if lat_type.lower() != "c":
              plane_init = np.dot(plane_init, trans_cry1.T)
          join_plane = self.vec_to_surface(plane_init)
  
          parent_structure = self.initial_structure.copy()
          # calculate the bond_length in bulk system.
          if len(parent_structure) == 1:
              temp_str = parent_structure.copy()
              temp_str.make_supercell([1, 1, 2])
              distance = temp_str.distance_matrix
          else:
              distance = parent_structure.distance_matrix
          bond_length = np.min(distance[np.nonzero(distance)])
  
          # top grain
          top_grain = fix_pbc(parent_structure * t1)
  
          # obtain the smallest oriended cell
          if normal and not quick_gen:
              t_temp = self.get_trans_mat(
                  r_axis=rotation_axis,
                  angle=rotation_angle,
                  normal=False,
                  trans_cry=trans_cry,
                  lat_type=lat_type,
                  ratio=ratio,
                  surface=plane,
                  max_search=max_search,
              )
              oriended_unit_cell = fix_pbc(parent_structure * t_temp[0])
              t_matrix = oriended_unit_cell.lattice.matrix
              normal_v_plane = np.cross(t_matrix[0], t_matrix[1])
              unit_normal_v = normal_v_plane / np.linalg.norm(normal_v_plane)
              unit_ab_adjust = (t_matrix[2] - np.dot(unit_normal_v, t_matrix[2]) * unit_normal_v) / np.dot(
                  unit_normal_v, t_matrix[2]
              )
          else:
              oriended_unit_cell = top_grain.copy()
              unit_ab_adjust = 0.0
  
          # bottom grain, using top grain's lattice matrix
          bottom_grain = fix_pbc(parent_structure * t2, top_grain.lattice.matrix)
  
          # label both grains with 'top','bottom','top_incident','bottom_incident'
          n_sites = top_grain.num_sites
          t_and_b = Structure(
              top_grain.lattice,
              top_grain.species + bottom_grain.species,
              list(top_grain.frac_coords) + list(bottom_grain.frac_coords),
          )
          t_and_b_dis = t_and_b.lattice.get_all_distances(
              t_and_b.frac_coords[0:n_sites], t_and_b.frac_coords[n_sites : n_sites * 2]
          )
          index_incident = np.nonzero(t_and_b_dis < np.min(t_and_b_dis) + tol_coi)
  
          top_labels = []
          for i in range(n_sites):
              if i in index_incident[0]:
                  top_labels.append("top_incident")
              else:
                  top_labels.append("top")
          bottom_labels = []
          for i in range(n_sites):
              if i in index_incident[1]:
                  bottom_labels.append("bottom_incident")
              else:
                  bottom_labels.append("bottom")
          top_grain = Structure(
              Lattice(top_grain.lattice.matrix),
              top_grain.species,
              top_grain.frac_coords,
              site_properties={"grain_label": top_labels},
          )
          bottom_grain = Structure(
              Lattice(bottom_grain.lattice.matrix),
              bottom_grain.species,
              bottom_grain.frac_coords,
              site_properties={"grain_label": bottom_labels},
          )
  
          # expand both grains
          top_grain.make_supercell([1, 1, expand_times])
          bottom_grain.make_supercell([1, 1, expand_times])
          top_grain = fix_pbc(top_grain)
          bottom_grain = fix_pbc(bottom_grain)
  
          # determine the top-grain location.
          edge_b = 1.0 - max(bottom_grain.frac_coords[:, 2])
          edge_t = 1.0 - max(top_grain.frac_coords[:, 2])
          c_adjust = (edge_t - edge_b) / 2.0
  
          # construct all species
          all_species = []
          all_species.extend([site.specie for site in bottom_grain])
          all_species.extend([site.specie for site in top_grain])
  
          half_lattice = top_grain.lattice
          # calculate translation vector, perpendicular to the plane
          normal_v_plane = np.cross(half_lattice.matrix[0], half_lattice.matrix[1])
          unit_normal_v = normal_v_plane / np.linalg.norm(normal_v_plane)
          translation_v = unit_normal_v * vacuum_thickness
  
          # construct the final lattice
          whole_matrix_no_vac = np.array(half_lattice.matrix)
          whole_matrix_no_vac[2] = half_lattice.matrix[2] * 2
          whole_matrix_with_vac = whole_matrix_no_vac.copy()
          whole_matrix_with_vac[2] = whole_matrix_no_vac[2] + translation_v * 2
          whole_lat = Lattice(whole_matrix_with_vac)
  
          # construct the coords, move top grain with translation_v
          all_coords = []
          grain_labels = bottom_grain.site_properties["grain_label"] + top_grain.site_properties["grain_label"]
          for site in bottom_grain:
              all_coords.append(site.coords)
          for site in top_grain:
              all_coords.append(
                  site.coords
                  + half_lattice.matrix[2] * (1 + c_adjust)
                  + unit_ab_adjust * np.linalg.norm(half_lattice.matrix[2] * (1 + c_adjust))
                  + translation_v
                  + ab_shift[0] * whole_matrix_with_vac[0]
                  + ab_shift[1] * whole_matrix_with_vac[1]
              )
  
          gb_with_vac = Structure(
              whole_lat,
              all_species,
              all_coords,
              coords_are_cartesian=True,
              site_properties={"grain_label": grain_labels},
          )
          # merge closer atoms. extract near gb atoms.
          cos_c_norm_plane = np.dot(unit_normal_v, whole_matrix_with_vac[2]) / whole_lat.c
          range_c_len = abs(bond_length / cos_c_norm_plane / whole_lat.c)
          sites_near_gb = []
          sites_away_gb = []
          for site in gb_with_vac.sites:
              if (
                  site.frac_coords[2] < range_c_len
                  or site.frac_coords[2] > 1 - range_c_len
                  or (site.frac_coords[2] > 0.5 - range_c_len and site.frac_coords[2] < 0.5 + range_c_len)
              ):
                  sites_near_gb.append(site)
              else:
                  sites_away_gb.append(site)
          if len(sites_near_gb) >= 1:
              s_near_gb = Structure.from_sites(sites_near_gb)
              s_near_gb.merge_sites(tol=bond_length * rm_ratio, mode="d")
              all_sites = sites_away_gb + s_near_gb.sites
              gb_with_vac = Structure.from_sites(all_sites)
  
          # move coordinates into the periodic cell.
          gb_with_vac = fix_pbc(gb_with_vac, whole_lat.matrix)
          return GrainBoundary(
              whole_lat,
              gb_with_vac.species,
              gb_with_vac.cart_coords,
              rotation_axis,
              rotation_angle,
              plane,
              join_plane,
              self.initial_structure,
              vacuum_thickness,
              ab_shift,
              site_properties=gb_with_vac.site_properties,
              oriented_unit_cell=oriended_unit_cell,
              coords_are_cartesian=True,
          )
  
      def get_ratio(self, max_denominator=5, index_none=None):
          """
          find the axial ratio needed for GB generator input.
          Args:
              max_denominator (int): the maximum denominator for
                  the computed ratio, default to be 5.
              index_none (int): specify the irrational axis.
                  0-a, 1-b, 2-c. Only may be needed for orthorhombic system.
          Returns:
                 axial ratio needed for GB generator (list of integers).
  
          """
          structure = self.initial_structure
          lat_type = self.lat_type
          if lat_type in ("t", "h"):
              # For tetragonal and hexagonal system, ratio = c2 / a2.
              a, c = (structure.lattice.a, structure.lattice.c)
              if c > a:
                  frac = Fraction(c**2 / a**2).limit_denominator(max_denominator)
                  ratio = [frac.numerator, frac.denominator]
              else:
                  frac = Fraction(a**2 / c**2).limit_denominator(max_denominator)
                  ratio = [frac.denominator, frac.numerator]
          elif lat_type == "r":
              # For rhombohedral system, ratio = (1 + 2 * cos(alpha)) / cos(alpha).
              cos_alpha = cos(structure.lattice.alpha / 180 * np.pi)
              frac = Fraction((1 + 2 * cos_alpha) / cos_alpha).limit_denominator(max_denominator)
              ratio = [frac.numerator, frac.denominator]
          elif lat_type == "o":
              # For orthorhombic system, ratio = c2:b2:a2.If irrational for one axis, set it to None.
              ratio = [None] * 3
              lat = (structure.lattice.c, structure.lattice.b, structure.lattice.a)
              index = [0, 1, 2]
              if index_none is None:
                  min_index = np.argmin(lat)
                  index.pop(min_index)
                  frac1 = Fraction(lat[index[0]] ** 2 / lat[min_index] ** 2).limit_denominator(max_denominator)
                  frac2 = Fraction(lat[index[1]] ** 2 / lat[min_index] ** 2).limit_denominator(max_denominator)
                  com_lcm = lcm(frac1.denominator, frac2.denominator)
                  ratio[min_index] = com_lcm
                  ratio[index[0]] = frac1.numerator * int(round(com_lcm / frac1.denominator))
                  ratio[index[1]] = frac2.numerator * int(round(com_lcm / frac2.denominator))
              else:
                  index.pop(index_none)
                  if lat[index[0]] > lat[index[1]]:
                      frac = Fraction(lat[index[0]] ** 2 / lat[index[1]] ** 2).limit_denominator(max_denominator)
                      ratio[index[0]] = frac.numerator
                      ratio[index[1]] = frac.denominator
                  else:
                      frac = Fraction(lat[index[1]] ** 2 / lat[index[0]] ** 2).limit_denominator(max_denominator)
                      ratio[index[1]] = frac.numerator
                      ratio[index[0]] = frac.denominator
          elif lat_type == "c":
              # Cubic system does not need axial ratio.
              return None
          else:
              raise RuntimeError("Lattice type not implemented.")
          return ratio
  
      @staticmethod
      def get_trans_mat(
          r_axis,
          angle,
          normal=False,
          trans_cry=np.eye(3),
          lat_type="c",
          ratio=None,
          surface=None,
          max_search=20,
          quick_gen=False,
      ):
          """
          Find the two transformation matrix for each grain from given rotation axis,
          GB plane, rotation angle and corresponding ratio (see explanation for ratio
          below).
          The structure of each grain can be obtained by applying the corresponding
          transformation matrix to the conventional cell.
          The algorithm for this code is from reference, Acta Cryst, A32,783(1976).
  
          Args:
              r_axis (list of three integers, e.g. u, v, w
                      or four integers, e.g. u, v, t, w for hex/rho system only):
                      the rotation axis of the grain boundary.
              angle (float, in unit of degree) :
                      the rotation angle of the grain boundary
              normal (logic):
                      determine if need to require the c axis of one grain associated with
                      the first transformation matrix perperdicular to the surface or not.
                      default to false.
              trans_cry (3 by 3 array):
                      if the structure given are primitive cell in cubic system, e.g.
                      bcc or fcc system, trans_cry is the transformation matrix from its
                      conventional cell to the primitive cell.
              lat_type ( one character):
                      'c' or 'C': cubic system
                       't' or 'T': tetragonal system
                       'o' or 'O': orthorhombic system
                       'h' or 'H': hexagonal system
                       'r' or 'R': rhombohedral system
                       default to cubic system
              ratio (list of integers):
                      lattice axial ratio.
                      For cubic system, ratio is not needed.
                      For tetragonal system, ratio = [mu, mv], list of two integers,
                      that is, mu/mv = c2/a2. If it is irrational, set it to none.
                      For orthorhombic system, ratio = [mu, lam, mv], list of three integers,
                      that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None.
                      e.g. mu:lam:mv = c2,None,a2, means b2 is irrational.
                      For rhombohedral system, ratio = [mu, mv], list of two integers,
                      that is, mu/mv is the ratio of (1+2*cos(alpha)/cos(alpha).
                      If irrational, set it to None.
                      For hexagonal system, ratio = [mu, mv], list of two integers,
                      that is, mu/mv = c2/a2. If it is irrational, set it to none.
              surface (list of three integers, e.g. h, k, l
                       or four integers, e.g. h, k, i, l for hex/rho system only):
                      the miller index of grain boundary plane, with the format of [h,k,l]
                      if surface is not given, the default is perpendicular to r_axis, which is
                      a twist grain boundary.
              max_search (int): max search for the GB lattice vectors that give the smallest GB
                  lattice. If normal is true, also max search the GB c vector that perpendicular
                  to the plane.
              quick_gen (bool): whether to quickly generate a supercell, if set to true, no need to
                  find the smallest cell.
          Returns:
              t1 (3 by 3 integer array):
                      The transformation array for one grain.
              t2 (3 by 3 integer array):
                      The transformation array for the other grain
          """
          # transform four index notation to three index notation
          if len(r_axis) == 4:
              u1 = r_axis[0]
              v1 = r_axis[1]
              w1 = r_axis[3]
              if lat_type.lower() == "h":
                  u = 2 * u1 + v1
                  v = 2 * v1 + u1
                  w = w1
                  r_axis = [u, v, w]
              elif lat_type.lower() == "r":
                  u = 2 * u1 + v1 + w1
                  v = v1 + w1 - u1
                  w = w1 - 2 * v1 - u1
                  r_axis = [u, v, w]
  
          # make sure gcd(r_axis)==1
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
  
          if surface is not None:
              if len(surface) == 4:
                  u1 = surface[0]
                  v1 = surface[1]
                  w1 = surface[3]
                  surface = [u1, v1, w1]
          # set the surface for grain boundary.
          if surface is None:
              if lat_type.lower() == "c":
                  surface = r_axis
              else:
                  if lat_type.lower() == "h":
                      if ratio is None:
                          c2_a2_ratio = 1
                      else:
                          c2_a2_ratio = ratio[0] / ratio[1]
                      metric = np.array([[1, -0.5, 0], [-0.5, 1, 0], [0, 0, c2_a2_ratio]])
                  elif lat_type.lower() == "r":
                      if ratio is None:
                          cos_alpha = 0.5
                      else:
                          cos_alpha = 1.0 / (ratio[0] / ratio[1] - 2)
                      metric = np.array(
                          [
                              [1, cos_alpha, cos_alpha],
                              [cos_alpha, 1, cos_alpha],
                              [cos_alpha, cos_alpha, 1],
                          ]
                      )
                  elif lat_type.lower() == "t":
                      if ratio is None:
                          c2_a2_ratio = 1
                      else:
                          c2_a2_ratio = ratio[0] / ratio[1]
                      metric = np.array([[1, 0, 0], [0, 1, 0], [0, 0, c2_a2_ratio]])
                  elif lat_type.lower() == "o":
                      for i in range(3):
                          if ratio[i] is None:
                              ratio[i] = 1
                      metric = np.array(
                          [
                              [1, 0, 0],
                              [0, ratio[1] / ratio[2], 0],
                              [0, 0, ratio[0] / ratio[2]],
                          ]
                      )
                  else:
                      raise RuntimeError("Lattice type has not implemented.")
  
                  surface = np.matmul(r_axis, metric)
                  fractions = [Fraction(x).limit_denominator() for x in surface]
                  least_mul = reduce(lcm, [f.denominator for f in fractions])
                  surface = [int(round(x * least_mul)) for x in surface]
  
          if reduce(gcd, surface) != 1:
              index = reduce(gcd, surface)
              surface = [int(round(x / index)) for x in surface]
  
          if lat_type.lower() == "h":
              # set the value for u,v,w,mu,mv,m,n,d,x
              # check the reference for the meaning of these parameters
              u, v, w = r_axis
              # make sure mu, mv are coprime integers.
              if ratio is None:
                  mu, mv = [1, 1]
                  if w != 0:
                      if u != 0 or (v != 0):
                          raise RuntimeError("For irrational c2/a2, CSL only exist for [0,0,1] or [u,v,0] and m = 0")
              else:
                  mu, mv = ratio
              if gcd(mu, mv) != 1:
                  temp = gcd(mu, mv)
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
              d = (u**2 + v**2 - u * v) * mv + w**2 * mu
              if abs(angle - 180.0) < 1.0e0:
                  m = 0
                  n = 1
              else:
                  fraction = Fraction(
                      np.tan(angle / 2 / 180.0 * np.pi) / np.sqrt(float(d) / 3.0 / mu)
                  ).limit_denominator()
                  m = fraction.denominator
                  n = fraction.numerator
  
              # construct the rotation matrix, check reference for details
              r_list = [
                  (u**2 * mv - v**2 * mv - w**2 * mu) * n**2 + 2 * w * mu * m * n + 3 * mu * m**2,
                  (2 * v - u) * u * mv * n**2 - 4 * w * mu * m * n,
                  2 * u * w * mu * n**2 + 2 * (2 * v - u) * mu * m * n,
                  (2 * u - v) * v * mv * n**2 + 4 * w * mu * m * n,
                  (v**2 * mv - u**2 * mv - w**2 * mu) * n**2 - 2 * w * mu * m * n + 3 * mu * m**2,
                  2 * v * w * mu * n**2 - 2 * (2 * u - v) * mu * m * n,
                  (2 * u - v) * w * mv * n**2 - 3 * v * mv * m * n,
                  (2 * v - u) * w * mv * n**2 + 3 * u * mv * m * n,
                  (w**2 * mu - u**2 * mv - v**2 * mv + u * v * mv) * n**2 + 3 * mu * m**2,
              ]
              m = -1 * m
              r_list_inv = [
                  (u**2 * mv - v**2 * mv - w**2 * mu) * n**2 + 2 * w * mu * m * n + 3 * mu * m**2,
                  (2 * v - u) * u * mv * n**2 - 4 * w * mu * m * n,
                  2 * u * w * mu * n**2 + 2 * (2 * v - u) * mu * m * n,
                  (2 * u - v) * v * mv * n**2 + 4 * w * mu * m * n,
                  (v**2 * mv - u**2 * mv - w**2 * mu) * n**2 - 2 * w * mu * m * n + 3 * mu * m**2,
                  2 * v * w * mu * n**2 - 2 * (2 * u - v) * mu * m * n,
                  (2 * u - v) * w * mv * n**2 - 3 * v * mv * m * n,
                  (2 * v - u) * w * mv * n**2 + 3 * u * mv * m * n,
                  (w**2 * mu - u**2 * mv - v**2 * mv + u * v * mv) * n**2 + 3 * mu * m**2,
              ]
              m = -1 * m
              F = 3 * mu * m**2 + d * n**2
              all_list = r_list + r_list_inv + [F]
              com_fac = reduce(gcd, all_list)
              sigma = F / com_fac
              r_matrix = (np.array(r_list) / com_fac / sigma).reshape(3, 3)
          elif lat_type.lower() == "r":
              # set the value for u,v,w,mu,mv,m,n,d
              # check the reference for the meaning of these parameters
              u, v, w = r_axis
              # make sure mu, mv are coprime integers.
              if ratio is None:
                  mu, mv = [1, 1]
                  if u + v + w != 0:
                      if u != v or u != w:
                          raise RuntimeError(
                              "For irrational ratio_alpha, CSL only exist for [1,1,1] or [u, v, -(u+v)] and m =0"
                          )
              else:
                  mu, mv = ratio
              if gcd(mu, mv) != 1:
                  temp = gcd(mu, mv)
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
              d = (u**2 + v**2 + w**2) * (mu - 2 * mv) + 2 * mv * (v * w + w * u + u * v)
              if abs(angle - 180.0) < 1.0e0:
                  m = 0
                  n = 1
              else:
                  fraction = Fraction(np.tan(angle / 2 / 180.0 * np.pi) / np.sqrt(float(d) / mu)).limit_denominator()
                  m = fraction.denominator
                  n = fraction.numerator
  
              # construct the rotation matrix, check reference for details
              r_list = [
                  (mu - 2 * mv) * (u**2 - v**2 - w**2) * n**2
                  + 2 * mv * (v - w) * m * n
                  - 2 * mv * v * w * n**2
                  + mu * m**2,
                  2 * (mv * u * n * (w * n + u * n - m) - (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                  2 * (mv * u * n * (v * n + u * n + m) + (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                  2 * (mv * v * n * (w * n + v * n + m) + (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                  (mu - 2 * mv) * (v**2 - w**2 - u**2) * n**2
                  + 2 * mv * (w - u) * m * n
                  - 2 * mv * u * w * n**2
                  + mu * m**2,
                  2 * (mv * v * n * (v * n + u * n - m) - (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                  2 * (mv * w * n * (w * n + v * n - m) - (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                  2 * (mv * w * n * (w * n + u * n + m) + (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                  (mu - 2 * mv) * (w**2 - u**2 - v**2) * n**2
                  + 2 * mv * (u - v) * m * n
                  - 2 * mv * u * v * n**2
                  + mu * m**2,
              ]
              m = -1 * m
              r_list_inv = [
                  (mu - 2 * mv) * (u**2 - v**2 - w**2) * n**2
                  + 2 * mv * (v - w) * m * n
                  - 2 * mv * v * w * n**2
                  + mu * m**2,
                  2 * (mv * u * n * (w * n + u * n - m) - (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                  2 * (mv * u * n * (v * n + u * n + m) + (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                  2 * (mv * v * n * (w * n + v * n + m) + (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                  (mu - 2 * mv) * (v**2 - w**2 - u**2) * n**2
                  + 2 * mv * (w - u) * m * n
                  - 2 * mv * u * w * n**2
                  + mu * m**2,
                  2 * (mv * v * n * (v * n + u * n - m) - (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                  2 * (mv * w * n * (w * n + v * n - m) - (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                  2 * (mv * w * n * (w * n + u * n + m) + (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                  (mu - 2 * mv) * (w**2 - u**2 - v**2) * n**2
                  + 2 * mv * (u - v) * m * n
                  - 2 * mv * u * v * n**2
                  + mu * m**2,
              ]
              m = -1 * m
              F = mu * m**2 + d * n**2
              all_list = r_list_inv + r_list + [F]
              com_fac = reduce(gcd, all_list)
              sigma = F / com_fac
              r_matrix = (np.array(r_list) / com_fac / sigma).reshape(3, 3)
          else:
              u, v, w = r_axis
              if lat_type.lower() == "c":
                  mu = 1
                  lam = 1
                  mv = 1
              elif lat_type.lower() == "t":
                  if ratio is None:
                      mu, mv = [1, 1]
                      if w != 0:
                          if u != 0 or (v != 0):
                              raise RuntimeError("For irrational c2/a2, CSL only exist for [0,0,1] or [u,v,0] and m = 0")
                  else:
                      mu, mv = ratio
                  lam = mv
              elif lat_type.lower() == "o":
                  if None in ratio:
                      mu, lam, mv = ratio
                      non_none = [i for i in ratio if i is not None]
                      if len(non_none) < 2:
                          raise RuntimeError("No CSL exist for two irrational numbers")
                      non1, non2 = non_none
                      if mu is None:
                          lam = non1
                          mv = non2
                          mu = 1
                          if w != 0:
                              if u != 0 or (v != 0):
                                  raise RuntimeError("For irrational c2, CSL only exist for [0,0,1] or [u,v,0] and m = 0")
                      elif lam is None:
                          mu = non1
                          mv = non2
                          lam = 1
                          if v != 0:
                              if u != 0 or (w != 0):
                                  raise RuntimeError("For irrational b2, CSL only exist for [0,1,0] or [u,0,w] and m = 0")
                      elif mv is None:
                          mu = non1
                          lam = non2
                          mv = 1
                          if u != 0:
                              if w != 0 or (v != 0):
                                  raise RuntimeError("For irrational a2, CSL only exist for [1,0,0] or [0,v,w] and m = 0")
                  else:
                      mu, lam, mv = ratio
                      if u == 0 and v == 0:
                          mu = 1
                      if u == 0 and w == 0:
                          lam = 1
                      if v == 0 and w == 0:
                          mv = 1
  
              # make sure mu, lambda, mv are coprime integers.
              if reduce(gcd, [mu, lam, mv]) != 1:
                  temp = reduce(gcd, [mu, lam, mv])
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
                  lam = int(round(lam / temp))
              d = (mv * u**2 + lam * v**2) * mv + w**2 * mu * mv
              if abs(angle - 180.0) < 1.0e0:
                  m = 0
                  n = 1
              else:
                  fraction = Fraction(np.tan(angle / 2 / 180.0 * np.pi) / np.sqrt(d / mu / lam)).limit_denominator()
                  m = fraction.denominator
                  n = fraction.numerator
              r_list = [
                  (u**2 * mv * mv - lam * v**2 * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                  2 * lam * (v * u * mv * n**2 - w * mu * m * n),
                  2 * mu * (u * w * mv * n**2 + v * lam * m * n),
                  2 * mv * (u * v * mv * n**2 + w * mu * m * n),
                  (v**2 * mv * lam - u**2 * mv * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                  2 * mv * mu * (v * w * n**2 - u * m * n),
                  2 * mv * (u * w * mv * n**2 - v * lam * m * n),
                  2 * lam * mv * (v * w * n**2 + u * m * n),
                  (w**2 * mu * mv - u**2 * mv * mv - v**2 * mv * lam) * n**2 + lam * mu * m**2,
              ]
              m = -1 * m
              r_list_inv = [
                  (u**2 * mv * mv - lam * v**2 * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                  2 * lam * (v * u * mv * n**2 - w * mu * m * n),
                  2 * mu * (u * w * mv * n**2 + v * lam * m * n),
                  2 * mv * (u * v * mv * n**2 + w * mu * m * n),
                  (v**2 * mv * lam - u**2 * mv * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                  2 * mv * mu * (v * w * n**2 - u * m * n),
                  2 * mv * (u * w * mv * n**2 - v * lam * m * n),
                  2 * lam * mv * (v * w * n**2 + u * m * n),
                  (w**2 * mu * mv - u**2 * mv * mv - v**2 * mv * lam) * n**2 + lam * mu * m**2,
              ]
              m = -1 * m
              F = mu * lam * m**2 + d * n**2
              all_list = r_list + r_list_inv + [F]
              com_fac = reduce(gcd, all_list)
              sigma = F / com_fac
              r_matrix = (np.array(r_list) / com_fac / sigma).reshape(3, 3)
  
          if sigma > 1000:
              raise RuntimeError("Sigma >1000 too large. Are you sure what you are doing, Please check the GB if exist")
          # transform surface, r_axis, r_matrix in terms of primitive lattice
          surface = np.matmul(surface, np.transpose(trans_cry))
          fractions = [Fraction(x).limit_denominator() for x in surface]
          least_mul = reduce(lcm, [f.denominator for f in fractions])
          surface = [int(round(x * least_mul)) for x in surface]
          if reduce(gcd, surface) != 1:
              index = reduce(gcd, surface)
              surface = [int(round(x / index)) for x in surface]
          r_axis = np.rint(np.matmul(r_axis, np.linalg.inv(trans_cry))).astype(int)
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
          r_matrix = np.dot(np.dot(np.linalg.inv(trans_cry.T), r_matrix), trans_cry.T)
          # set one vector of the basis to the rotation axis direction, and
          # obtain the corresponding transform matrix
          eye = np.eye(3, dtype=int)
          for h in range(3):
              if abs(r_axis[h]) != 0:
                  eye[h] = np.array(r_axis)
                  k = h + 1 if h + 1 < 3 else abs(2 - h)
                  l = h + 2 if h + 2 < 3 else abs(1 - h)
                  break
          trans = eye.T
          new_rot = np.array(r_matrix)
  
          # with the rotation matrix to construct the CSL lattice, check reference for details
          fractions = [Fraction(x).limit_denominator() for x in new_rot[:, k]]
          least_mul = reduce(lcm, [f.denominator for f in fractions])
          scale = np.zeros((3, 3))
          scale[h, h] = 1
          scale[k, k] = least_mul
          scale[l, l] = sigma / least_mul
          for i in range(least_mul):
              check_int = i * new_rot[:, k] + (sigma / least_mul) * new_rot[:, l]
              if all(np.round(x, 5).is_integer() for x in list(check_int)):
                  n_final = i
                  break
          try:
              n_final
          except NameError:
              raise RuntimeError("Something is wrong. Check if this GB exists or not")
          scale[k, l] = n_final
          # each row of mat_csl is the CSL lattice vector
          csl_init = np.rint(np.dot(np.dot(r_matrix, trans), scale)).astype(int).T
          if abs(r_axis[h]) > 1:
              csl_init = GrainBoundaryGenerator.reduce_mat(np.array(csl_init), r_axis[h], r_matrix)
          csl = np.rint(Lattice(csl_init).get_niggli_reduced_lattice().matrix).astype(int)
  
          # find the best slab supercell in terms of the conventional cell from the csl lattice,
          # which is the transformation matrix
  
          # now trans_cry is the transformation matrix from crystal to Cartesian coordinates.
          # for cubic, do not need to change.
          if lat_type.lower() != "c":
              if lat_type.lower() == "h":
                  trans_cry = np.array([[1, 0, 0], [-0.5, np.sqrt(3.0) / 2.0, 0], [0, 0, np.sqrt(mu / mv)]])
              elif lat_type.lower() == "r":
                  if ratio is None:
                      c2_a2_ratio = 1
                  else:
                      c2_a2_ratio = 3.0 / (2 - 6 * mv / mu)
                  trans_cry = np.array(
                      [
                          [0.5, np.sqrt(3.0) / 6.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)],
                          [-0.5, np.sqrt(3.0) / 6.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)],
                          [0, -1 * np.sqrt(3.0) / 3.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)],
                      ]
                  )
              else:
                  trans_cry = np.array([[1, 0, 0], [0, np.sqrt(lam / mv), 0], [0, 0, np.sqrt(mu / mv)]])
          t1_final = GrainBoundaryGenerator.slab_from_csl(
              csl, surface, normal, trans_cry, max_search=max_search, quick_gen=quick_gen
          )
          t2_final = np.array(np.rint(np.dot(t1_final, np.linalg.inv(r_matrix.T)))).astype(int)
          return t1_final, t2_final
  
      @staticmethod
      def enum_sigma_cubic(cutoff, r_axis):
          """
          Find all possible sigma values and corresponding rotation angles
          within a sigma value cutoff with known rotation axis in cubic system.
          The algorithm for this code is from reference, Acta Cryst, A40,108(1984)
          Args:
              cutoff (integer): the cutoff of sigma values.
              r_axis (list of three integers, e.g. u, v, w):
                      the rotation axis of the grain boundary, with the format of [u,v,w].
          Returns:
              sigmas (dict):
                      dictionary with keys as the possible integer sigma values
                      and values as list of the possible rotation angles to the
                      corresponding sigma values.
                      e.g. the format as
                      {sigma1: [angle11,angle12,...], sigma2: [angle21, angle22,...],...}
                      Note: the angles are the rotation angles of one grain respect to
                      the other grain.
                      When generate the microstructures of the grain boundary using these angles,
                      you need to analyze the symmetry of the structure. Different angles may
                      result in equivalent microstructures.
  
          """
          sigmas = {}
          # make sure gcd(r_axis)==1
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
  
          # count the number of odds in r_axis
          odd_r = len(list(filter(lambda x: x % 2 == 1, r_axis)))
          # Compute the max n we need to enumerate.
          if odd_r == 3:
              a_max = 4
          elif odd_r == 0:
              a_max = 1
          else:
              a_max = 2
          n_max = int(np.sqrt(cutoff * a_max / sum(np.array(r_axis) ** 2)))
          # enumerate all possible n, m to give possible sigmas within the cutoff.
          for n_loop in range(1, n_max + 1):
              n = n_loop
              m_max = int(np.sqrt(cutoff * a_max - n**2 * sum(np.array(r_axis) ** 2)))
              for m in range(0, m_max + 1):
                  if gcd(m, n) == 1 or m == 0:
                      if m == 0:
                          n = 1
                      else:
                          n = n_loop
                      # construct the quadruple [m, U,V,W], count the number of odds in
                      # quadruple to determine the parameter a, refer to the reference
                      quadruple = [m] + [x * n for x in r_axis]
                      odd_qua = len(list(filter(lambda x: x % 2 == 1, quadruple)))
                      if odd_qua == 4:
                          a = 4
                      elif odd_qua == 2:
                          a = 2
                      else:
                          a = 1
                      sigma = int(round((m**2 + n**2 * sum(np.array(r_axis) ** 2)) / a))
                      if 1 < sigma <= cutoff:
                          if sigma not in list(sigmas.keys()):
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n * np.sqrt(sum(np.array(r_axis) ** 2)) / m) / np.pi * 180
                              sigmas[sigma] = [angle]
                          else:
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n * np.sqrt(sum(np.array(r_axis) ** 2)) / m) / np.pi * 180
                              if angle not in sigmas[sigma]:
                                  sigmas[sigma].append(angle)
          return sigmas
  
      @staticmethod
      def enum_sigma_hex(cutoff, r_axis, c2_a2_ratio):
          """
          Find all possible sigma values and corresponding rotation angles
          within a sigma value cutoff with known rotation axis in hexagonal system.
          The algorithm for this code is from reference, Acta Cryst, A38,550(1982)
  
          Args:
              cutoff (integer): the cutoff of sigma values.
              r_axis (list of three integers, e.g. u, v, w
                      or four integers, e.g. u, v, t, w):
                      the rotation axis of the grain boundary.
              c2_a2_ratio (list of two integers, e.g. mu, mv):
                      mu/mv is the square of the hexagonal axial ratio, which is rational
                      number. If irrational, set c2_a2_ratio = None
          Returns:
              sigmas (dict):
                      dictionary with keys as the possible integer sigma values
                      and values as list of the possible rotation angles to the
                      corresponding sigma values.
                      e.g. the format as
                      {sigma1: [angle11,angle12,...], sigma2: [angle21, angle22,...],...}
                      Note: the angles are the rotation angle of one grain respect to the
                      other grain.
                      When generate the microstructure of the grain boundary using these
                      angles, you need to analyze the symmetry of the structure. Different
                      angles may result in equivalent microstructures.
  
          """
          sigmas = {}
          # make sure gcd(r_axis)==1
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
          # transform four index notation to three index notation
          if len(r_axis) == 4:
              u1 = r_axis[0]
              v1 = r_axis[1]
              w1 = r_axis[3]
              u = 2 * u1 + v1
              v = 2 * v1 + u1
              w = w1
          else:
              u, v, w = r_axis
  
          # make sure mu, mv are coprime integers.
          if c2_a2_ratio is None:
              mu, mv = [1, 1]
              if w != 0:
                  if u != 0 or (v != 0):
                      raise RuntimeError("For irrational c2/a2, CSL only exist for [0,0,1] or [u,v,0] and m = 0")
          else:
              mu, mv = c2_a2_ratio
              if gcd(mu, mv) != 1:
                  temp = gcd(mu, mv)
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
  
          # refer to the meaning of d in reference
          d = (u**2 + v**2 - u * v) * mv + w**2 * mu
  
          # Compute the max n we need to enumerate.
          n_max = int(np.sqrt((cutoff * 12 * mu * mv) / abs(d)))
  
          # Enumerate all possible n, m to give possible sigmas within the cutoff.
          for n in range(1, n_max + 1):
              if (c2_a2_ratio is None) and w == 0:
                  m_max = 0
              else:
                  m_max = int(np.sqrt((cutoff * 12 * mu * mv - n**2 * d) / (3 * mu)))
              for m in range(0, m_max + 1):
                  if gcd(m, n) == 1 or m == 0:
                      # construct the rotation matrix, refer to the reference
                      R_list = [
                          (u**2 * mv - v**2 * mv - w**2 * mu) * n**2 + 2 * w * mu * m * n + 3 * mu * m**2,
                          (2 * v - u) * u * mv * n**2 - 4 * w * mu * m * n,
                          2 * u * w * mu * n**2 + 2 * (2 * v - u) * mu * m * n,
                          (2 * u - v) * v * mv * n**2 + 4 * w * mu * m * n,
                          (v**2 * mv - u**2 * mv - w**2 * mu) * n**2 - 2 * w * mu * m * n + 3 * mu * m**2,
                          2 * v * w * mu * n**2 - 2 * (2 * u - v) * mu * m * n,
                          (2 * u - v) * w * mv * n**2 - 3 * v * mv * m * n,
                          (2 * v - u) * w * mv * n**2 + 3 * u * mv * m * n,
                          (w**2 * mu - u**2 * mv - v**2 * mv + u * v * mv) * n**2 + 3 * mu * m**2,
                      ]
                      m = -1 * m
                      # inverse of the rotation matrix
                      R_list_inv = [
                          (u**2 * mv - v**2 * mv - w**2 * mu) * n**2 + 2 * w * mu * m * n + 3 * mu * m**2,
                          (2 * v - u) * u * mv * n**2 - 4 * w * mu * m * n,
                          2 * u * w * mu * n**2 + 2 * (2 * v - u) * mu * m * n,
                          (2 * u - v) * v * mv * n**2 + 4 * w * mu * m * n,
                          (v**2 * mv - u**2 * mv - w**2 * mu) * n**2 - 2 * w * mu * m * n + 3 * mu * m**2,
                          2 * v * w * mu * n**2 - 2 * (2 * u - v) * mu * m * n,
                          (2 * u - v) * w * mv * n**2 - 3 * v * mv * m * n,
                          (2 * v - u) * w * mv * n**2 + 3 * u * mv * m * n,
                          (w**2 * mu - u**2 * mv - v**2 * mv + u * v * mv) * n**2 + 3 * mu * m**2,
                      ]
                      m = -1 * m
                      F = 3 * mu * m**2 + d * n**2
                      all_list = R_list_inv + R_list + [F]
                      # Compute the max common factors for the elements of the rotation matrix
                      # and its inverse.
                      com_fac = reduce(gcd, all_list)
                      sigma = int(round((3 * mu * m**2 + d * n**2) / com_fac))
                      if 1 < sigma <= cutoff:
                          if sigma not in list(sigmas.keys()):
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / 3.0 / mu)) / np.pi * 180
                              sigmas[sigma] = [angle]
                          else:
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / 3.0 / mu)) / np.pi * 180
                              if angle not in sigmas[sigma]:
                                  sigmas[sigma].append(angle)
              if m_max == 0:
                  break
          return sigmas
  
      @staticmethod
      def enum_sigma_rho(cutoff, r_axis, ratio_alpha):
          """
          Find all possible sigma values and corresponding rotation angles
          within a sigma value cutoff with known rotation axis in rhombohedral system.
          The algorithm for this code is from reference, Acta Cryst, A45,505(1989).
  
          Args:
              cutoff (integer): the cutoff of sigma values.
              r_axis (list of three integers, e.g. u, v, w
                      or four integers, e.g. u, v, t, w):
                      the rotation axis of the grain boundary, with the format of [u,v,w]
                      or Weber indices [u, v, t, w].
              ratio_alpha (list of two integers, e.g. mu, mv):
                      mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha) with rational number.
                      If irrational, set ratio_alpha = None.
          Returns:
              sigmas (dict):
                      dictionary with keys as the possible integer sigma values
                      and values as list of the possible rotation angles to the
                      corresponding sigma values.
                      e.g. the format as
                      {sigma1: [angle11,angle12,...], sigma2: [angle21, angle22,...],...}
                      Note: the angles are the rotation angle of one grain respect to the
                      other grain.
                      When generate the microstructure of the grain boundary using these
                      angles, you need to analyze the symmetry of the structure. Different
                      angles may result in equivalent microstructures.
  
          """
          sigmas = {}
          # transform four index notation to three index notation
          if len(r_axis) == 4:
              u1 = r_axis[0]
              v1 = r_axis[1]
              w1 = r_axis[3]
              u = 2 * u1 + v1 + w1
              v = v1 + w1 - u1
              w = w1 - 2 * v1 - u1
              r_axis = [u, v, w]
          # make sure gcd(r_axis)==1
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
          u, v, w = r_axis
          # make sure mu, mv are coprime integers.
          if ratio_alpha is None:
              mu, mv = [1, 1]
              if u + v + w != 0:
                  if u != v or u != w:
                      raise RuntimeError(
                          "For irrational ratio_alpha, CSL only exist for [1,1,1] or [u, v, -(u+v)] and m =0"
                      )
          else:
              mu, mv = ratio_alpha
              if gcd(mu, mv) != 1:
                  temp = gcd(mu, mv)
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
  
          # refer to the meaning of d in reference
          d = (u**2 + v**2 + w**2) * (mu - 2 * mv) + 2 * mv * (v * w + w * u + u * v)
          # Compute the max n we need to enumerate.
          n_max = int(np.sqrt((cutoff * abs(4 * mu * (mu - 3 * mv))) / abs(d)))
  
          # Enumerate all possible n, m to give possible sigmas within the cutoff.
          for n in range(1, n_max + 1):
              if ratio_alpha is None and u + v + w == 0:
                  m_max = 0
              else:
                  m_max = int(np.sqrt((cutoff * abs(4 * mu * (mu - 3 * mv)) - n**2 * d) / (mu)))
              for m in range(0, m_max + 1):
                  if gcd(m, n) == 1 or m == 0:
                      # construct the rotation matrix, refer to the reference
                      R_list = [
                          (mu - 2 * mv) * (u**2 - v**2 - w**2) * n**2
                          + 2 * mv * (v - w) * m * n
                          - 2 * mv * v * w * n**2
                          + mu * m**2,
                          2 * (mv * u * n * (w * n + u * n - m) - (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                          2 * (mv * u * n * (v * n + u * n + m) + (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                          2 * (mv * v * n * (w * n + v * n + m) + (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                          (mu - 2 * mv) * (v**2 - w**2 - u**2) * n**2
                          + 2 * mv * (w - u) * m * n
                          - 2 * mv * u * w * n**2
                          + mu * m**2,
                          2 * (mv * v * n * (v * n + u * n - m) - (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                          2 * (mv * w * n * (w * n + v * n - m) - (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                          2 * (mv * w * n * (w * n + u * n + m) + (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                          (mu - 2 * mv) * (w**2 - u**2 - v**2) * n**2
                          + 2 * mv * (u - v) * m * n
                          - 2 * mv * u * v * n**2
                          + mu * m**2,
                      ]
                      m = -1 * m
                      # inverse of the rotation matrix
                      R_list_inv = [
                          (mu - 2 * mv) * (u**2 - v**2 - w**2) * n**2
                          + 2 * mv * (v - w) * m * n
                          - 2 * mv * v * w * n**2
                          + mu * m**2,
                          2 * (mv * u * n * (w * n + u * n - m) - (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                          2 * (mv * u * n * (v * n + u * n + m) + (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                          2 * (mv * v * n * (w * n + v * n + m) + (mu - mv) * m * w * n + (mu - 2 * mv) * u * v * n**2),
                          (mu - 2 * mv) * (v**2 - w**2 - u**2) * n**2
                          + 2 * mv * (w - u) * m * n
                          - 2 * mv * u * w * n**2
                          + mu * m**2,
                          2 * (mv * v * n * (v * n + u * n - m) - (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                          2 * (mv * w * n * (w * n + v * n - m) - (mu - mv) * m * v * n + (mu - 2 * mv) * w * u * n**2),
                          2 * (mv * w * n * (w * n + u * n + m) + (mu - mv) * m * u * n + (mu - 2 * mv) * w * v * n**2),
                          (mu - 2 * mv) * (w**2 - u**2 - v**2) * n**2
                          + 2 * mv * (u - v) * m * n
                          - 2 * mv * u * v * n**2
                          + mu * m**2,
                      ]
                      m = -1 * m
                      F = mu * m**2 + d * n**2
                      all_list = R_list_inv + R_list + [F]
                      # Compute the max common factors for the elements of the rotation matrix
                      #  and its inverse.
                      com_fac = reduce(gcd, all_list)
                      sigma = int(round(abs(F / com_fac)))
                      if 1 < sigma <= cutoff:
                          if sigma not in list(sigmas.keys()):
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / mu)) / np.pi * 180
                              sigmas[sigma] = [angle]
                          else:
                              if m == 0:
                                  angle = 180
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / mu)) / np.pi * 180.0
                              if angle not in sigmas[sigma]:
                                  sigmas[sigma].append(angle)
              if m_max == 0:
                  break
          return sigmas
  
      @staticmethod
      def enum_sigma_tet(cutoff, r_axis, c2_a2_ratio):
          """
          Find all possible sigma values and corresponding rotation angles
          within a sigma value cutoff with known rotation axis in tetragonal system.
          The algorithm for this code is from reference, Acta Cryst, B46,117(1990)
  
          Args:
              cutoff (integer): the cutoff of sigma values.
              r_axis (list of three integers, e.g. u, v, w):
                      the rotation axis of the grain boundary, with the format of [u,v,w].
              c2_a2_ratio (list of two integers, e.g. mu, mv):
                      mu/mv is the square of the tetragonal axial ratio with rational number.
                      if irrational, set c2_a2_ratio = None
          Returns:
              sigmas (dict):
                      dictionary with keys as the possible integer sigma values
                      and values as list of the possible rotation angles to the
                      corresponding sigma values.
                      e.g. the format as
                      {sigma1: [angle11,angle12,...], sigma2: [angle21, angle22,...],...}
                      Note: the angles are the rotation angle of one grain respect to the
                      other grain.
                      When generate the microstructure of the grain boundary using these
                      angles, you need to analyze the symmetry of the structure. Different
                      angles may result in equivalent microstructures.
  
          """
          sigmas = {}
          # make sure gcd(r_axis)==1
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
  
          u, v, w = r_axis
  
          # make sure mu, mv are coprime integers.
          if c2_a2_ratio is None:
              mu, mv = [1, 1]
              if w != 0:
                  if u != 0 or (v != 0):
                      raise RuntimeError("For irrational c2/a2, CSL only exist for [0,0,1] or [u,v,0] and m = 0")
          else:
              mu, mv = c2_a2_ratio
              if gcd(mu, mv) != 1:
                  temp = gcd(mu, mv)
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
  
          # refer to the meaning of d in reference
          d = (u**2 + v**2) * mv + w**2 * mu
  
          # Compute the max n we need to enumerate.
          n_max = int(np.sqrt((cutoff * 4 * mu * mv) / d))
  
          # Enumerate all possible n, m to give possible sigmas within the cutoff.
          for n in range(1, n_max + 1):
              if c2_a2_ratio is None and w == 0:
                  m_max = 0
              else:
                  m_max = int(np.sqrt((cutoff * 4 * mu * mv - n**2 * d) / mu))
              for m in range(0, m_max + 1):
                  if gcd(m, n) == 1 or m == 0:
                      # construct the rotation matrix, refer to the reference
                      R_list = [
                          (u**2 * mv - v**2 * mv - w**2 * mu) * n**2 + mu * m**2,
                          2 * v * u * mv * n**2 - 2 * w * mu * m * n,
                          2 * u * w * mu * n**2 + 2 * v * mu * m * n,
                          2 * u * v * mv * n**2 + 2 * w * mu * m * n,
                          (v**2 * mv - u**2 * mv - w**2 * mu) * n**2 + mu * m**2,
                          2 * v * w * mu * n**2 - 2 * u * mu * m * n,
                          2 * u * w * mv * n**2 - 2 * v * mv * m * n,
                          2 * v * w * mv * n**2 + 2 * u * mv * m * n,
                          (w**2 * mu - u**2 * mv - v**2 * mv) * n**2 + mu * m**2,
                      ]
                      m = -1 * m
                      # inverse of rotation matrix
                      R_list_inv = [
                          (u**2 * mv - v**2 * mv - w**2 * mu) * n**2 + mu * m**2,
                          2 * v * u * mv * n**2 - 2 * w * mu * m * n,
                          2 * u * w * mu * n**2 + 2 * v * mu * m * n,
                          2 * u * v * mv * n**2 + 2 * w * mu * m * n,
                          (v**2 * mv - u**2 * mv - w**2 * mu) * n**2 + mu * m**2,
                          2 * v * w * mu * n**2 - 2 * u * mu * m * n,
                          2 * u * w * mv * n**2 - 2 * v * mv * m * n,
                          2 * v * w * mv * n**2 + 2 * u * mv * m * n,
                          (w**2 * mu - u**2 * mv - v**2 * mv) * n**2 + mu * m**2,
                      ]
                      m = -1 * m
                      F = mu * m**2 + d * n**2
                      all_list = R_list + R_list_inv + [F]
                      # Compute the max common factors for the elements of the rotation matrix
                      #  and its inverse.
                      com_fac = reduce(gcd, all_list)
                      sigma = int(round((mu * m**2 + d * n**2) / com_fac))
                      if 1 < sigma <= cutoff:
                          if sigma not in list(sigmas.keys()):
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / mu)) / np.pi * 180
                              sigmas[sigma] = [angle]
                          else:
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / mu)) / np.pi * 180
                              if angle not in sigmas[sigma]:
                                  sigmas[sigma].append(angle)
              if m_max == 0:
                  break
  
          return sigmas
  
      @staticmethod
      def enum_sigma_ort(cutoff, r_axis, c2_b2_a2_ratio):
          """
          Find all possible sigma values and corresponding rotation angles
          within a sigma value cutoff with known rotation axis in orthorhombic system.
          The algorithm for this code is from reference, Scipta Metallurgica 27, 291(1992)
  
          Args:
              cutoff (integer): the cutoff of sigma values.
              r_axis (list of three integers, e.g. u, v, w):
                      the rotation axis of the grain boundary, with the format of [u,v,w].
              c2_b2_a2_ratio (list of three integers, e.g. mu,lamda, mv):
                      mu:lam:mv is the square of the orthorhombic axial ratio with rational
                      numbers. If irrational for one axis, set it to None.
                      e.g. mu:lam:mv = c2,None,a2, means b2 is irrational.
          Returns:
              sigmas (dict):
                      dictionary with keys as the possible integer sigma values
                      and values as list of the possible rotation angles to the
                      corresponding sigma values.
                      e.g. the format as
                      {sigma1: [angle11,angle12,...], sigma2: [angle21, angle22,...],...}
                      Note: the angles are the rotation angle of one grain respect to the
                      other grain.
                      When generate the microstructure of the grain boundary using these
                      angles, you need to analyze the symmetry of the structure. Different
                      angles may result in equivalent microstructures.
  
          """
          sigmas = {}
          # make sure gcd(r_axis)==1
          if reduce(gcd, r_axis) != 1:
              r_axis = [int(round(x / reduce(gcd, r_axis))) for x in r_axis]
  
          u, v, w = r_axis
          # make sure mu, lambda, mv are coprime integers.
          if None in c2_b2_a2_ratio:
              mu, lam, mv = c2_b2_a2_ratio
              non_none = [i for i in c2_b2_a2_ratio if i is not None]
              if len(non_none) < 2:
                  raise RuntimeError("No CSL exist for two irrational numbers")
              non1, non2 = non_none
              if reduce(gcd, non_none) != 1:
                  temp = reduce(gcd, non_none)
                  non1 = int(round(non1 / temp))
                  non2 = int(round(non2 / temp))
              if mu is None:
                  lam = non1
                  mv = non2
                  mu = 1
                  if w != 0:
                      if u != 0 or (v != 0):
                          raise RuntimeError("For irrational c2, CSL only exist for [0,0,1] or [u,v,0] and m = 0")
              elif lam is None:
                  mu = non1
                  mv = non2
                  lam = 1
                  if v != 0:
                      if u != 0 or (w != 0):
                          raise RuntimeError("For irrational b2, CSL only exist for [0,1,0] or [u,0,w] and m = 0")
              elif mv is None:
                  mu = non1
                  lam = non2
                  mv = 1
                  if u != 0:
                      if w != 0 or (v != 0):
                          raise RuntimeError("For irrational a2, CSL only exist for [1,0,0] or [0,v,w] and m = 0")
          else:
              mu, lam, mv = c2_b2_a2_ratio
              if reduce(gcd, c2_b2_a2_ratio) != 1:
                  temp = reduce(gcd, c2_b2_a2_ratio)
                  mu = int(round(mu / temp))
                  mv = int(round(mv / temp))
                  lam = int(round(lam / temp))
              if u == 0 and v == 0:
                  mu = 1
              if u == 0 and w == 0:
                  lam = 1
              if v == 0 and w == 0:
                  mv = 1
          # refer to the meaning of d in reference
          d = (mv * u**2 + lam * v**2) * mv + w**2 * mu * mv
  
          # Compute the max n we need to enumerate.
          n_max = int(np.sqrt((cutoff * 4 * mu * mv * mv * lam) / d))
          # Enumerate all possible n, m to give possible sigmas within the cutoff.
          for n in range(1, n_max + 1):
              mu_temp, lam_temp, mv_temp = c2_b2_a2_ratio
              if (mu_temp is None and w == 0) or (lam_temp is None and v == 0) or (mv_temp is None and u == 0):
                  m_max = 0
              else:
                  m_max = int(np.sqrt((cutoff * 4 * mu * mv * lam * mv - n**2 * d) / mu / lam))
              for m in range(0, m_max + 1):
  
                  if gcd(m, n) == 1 or m == 0:
                      # construct the rotation matrix, refer to the reference
                      R_list = [
                          (u**2 * mv * mv - lam * v**2 * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                          2 * lam * (v * u * mv * n**2 - w * mu * m * n),
                          2 * mu * (u * w * mv * n**2 + v * lam * m * n),
                          2 * mv * (u * v * mv * n**2 + w * mu * m * n),
                          (v**2 * mv * lam - u**2 * mv * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                          2 * mv * mu * (v * w * n**2 - u * m * n),
                          2 * mv * (u * w * mv * n**2 - v * lam * m * n),
                          2 * lam * mv * (v * w * n**2 + u * m * n),
                          (w**2 * mu * mv - u**2 * mv * mv - v**2 * mv * lam) * n**2 + lam * mu * m**2,
                      ]
                      m = -1 * m
                      # inverse of rotation matrix
                      R_list_inv = [
                          (u**2 * mv * mv - lam * v**2 * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                          2 * lam * (v * u * mv * n**2 - w * mu * m * n),
                          2 * mu * (u * w * mv * n**2 + v * lam * m * n),
                          2 * mv * (u * v * mv * n**2 + w * mu * m * n),
                          (v**2 * mv * lam - u**2 * mv * mv - w**2 * mu * mv) * n**2 + lam * mu * m**2,
                          2 * mv * mu * (v * w * n**2 - u * m * n),
                          2 * mv * (u * w * mv * n**2 - v * lam * m * n),
                          2 * lam * mv * (v * w * n**2 + u * m * n),
                          (w**2 * mu * mv - u**2 * mv * mv - v**2 * mv * lam) * n**2 + lam * mu * m**2,
                      ]
                      m = -1 * m
                      F = mu * lam * m**2 + d * n**2
                      all_list = R_list + R_list_inv + [F]
                      # Compute the max common factors for the elements of the rotation matrix
                      #  and its inverse.
                      com_fac = reduce(gcd, all_list)
                      sigma = int(round((mu * lam * m**2 + d * n**2) / com_fac))
                      if 1 < sigma <= cutoff:
                          if sigma not in list(sigmas.keys()):
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / mu / lam)) / np.pi * 180
                              sigmas[sigma] = [angle]
                          else:
                              if m == 0:
                                  angle = 180.0
                              else:
                                  angle = 2 * np.arctan(n / m * np.sqrt(d / mu / lam)) / np.pi * 180
                              if angle not in sigmas[sigma]:
                                  sigmas[sigma].append(angle)
              if m_max == 0:
                  break
  
          return sigmas
  
      @staticmethod
      def enum_possible_plane_cubic(plane_cutoff, r_axis, r_angle):
          """
          Find all possible plane combinations for GBs given a rotation axis and angle for
          cubic system, and classify them to different categories, including 'Twist',
          'Symmetric tilt', 'Normal tilt', 'Mixed' GBs.
  
          Args:
              plane_cutoff (integer): the cutoff of plane miller index.
              r_axis (list of three integers, e.g. u, v, w):
                      the rotation axis of the grain boundary, with the format of [u,v,w].
              r_angle (float): rotation angle of the GBs.
          Returns:
              all_combinations (dict):
                      dictionary with keys as GB type, e.g. 'Twist','Symmetric tilt',etc.
                      and values as the combination of the two plane miller index
                       (GB plane and joining plane).
          """
          all_combinations = {}
          all_combinations["Symmetric tilt"] = []
          all_combinations["Twist"] = []
          all_combinations["Normal tilt"] = []
          all_combinations["Mixed"] = []
          sym_plane = symm_group_cubic([[1, 0, 0], [1, 1, 0]])
          j = np.arange(0, plane_cutoff + 1)
          combination = []
          for i in itertools.product(j, repeat=3):
              if sum(abs(np.array(i))) != 0:
                  combination.append(list(i))
              if len(np.nonzero(i)[0]) == 3:
                  for i1 in range(3):
                      new_i = list(i).copy()
                      new_i[i1] = -1 * new_i[i1]
                      combination.append(new_i)
              elif len(np.nonzero(i)[0]) == 2:
                  new_i = list(i).copy()
                  new_i[np.nonzero(i)[0][0]] = -1 * new_i[np.nonzero(i)[0][0]]
                  combination.append(new_i)
          miller = np.array(combination)
          miller = miller[np.argsort(np.linalg.norm(miller, axis=1))]
          for i, val in enumerate(miller):
              if reduce(gcd, val) == 1:
                  matrix = GrainBoundaryGenerator.get_trans_mat(r_axis, r_angle, surface=val, quick_gen=True)
                  vec = np.cross(matrix[1][0], matrix[1][1])
                  miller2 = GrainBoundaryGenerator.vec_to_surface(vec)
                  if np.all(np.abs(np.array(miller2)) <= plane_cutoff):
                      cos_1 = abs(np.dot(val, r_axis) / np.linalg.norm(val) / np.linalg.norm(r_axis))
                      if 1 - cos_1 < 1.0e-5:
                          all_combinations["Twist"].append([list(val), miller2])
                      elif cos_1 < 1.0e-8:
                          sym_tilt = False
                          if np.sum(np.abs(val)) == np.sum(np.abs(miller2)):
                              ave = (np.array(val) + np.array(miller2)) / 2
                              ave1 = (np.array(val) - np.array(miller2)) / 2
                              for plane in sym_plane:
                                  cos_2 = abs(np.dot(ave, plane) / np.linalg.norm(ave) / np.linalg.norm(plane))
                                  cos_3 = abs(np.dot(ave1, plane) / np.linalg.norm(ave1) / np.linalg.norm(plane))
                                  if 1 - cos_2 < 1.0e-5 or 1 - cos_3 < 1.0e-5:
                                      all_combinations["Symmetric tilt"].append([list(val), miller2])
                                      sym_tilt = True
                                      break
                          if not sym_tilt:
                              all_combinations["Normal tilt"].append([list(val), miller2])
                      else:
                          all_combinations["Mixed"].append([list(val), miller2])
          return all_combinations
  
      @staticmethod
      def get_rotation_angle_from_sigma(sigma, r_axis, lat_type="C", ratio=None):
          """
          Find all possible rotation angle for the given sigma value.
  
          Args:
              sigma (integer):
                      sigma value provided
              r_axis (list of three integers, e.g. u, v, w
                      or four integers, e.g. u, v, t, w for hex/rho system only):
                      the rotation axis of the grain boundary.
              lat_type ( one character):
                      'c' or 'C': cubic system
                       't' or 'T': tetragonal system
                       'o' or 'O': orthorhombic system
                       'h' or 'H': hexagonal system
                       'r' or 'R': rhombohedral system
                       default to cubic system
              ratio (list of integers):
                      lattice axial ratio.
                      For cubic system, ratio is not needed.
                      For tetragonal system, ratio = [mu, mv], list of two integers,
                      that is, mu/mv = c2/a2. If it is irrational, set it to none.
                      For orthorhombic system, ratio = [mu, lam, mv], list of three integers,
                      that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None.
                      e.g. mu:lam:mv = c2,None,a2, means b2 is irrational.
                      For rhombohedral system, ratio = [mu, mv], list of two integers,
                      that is, mu/mv is the ratio of (1+2*cos(alpha)/cos(alpha).
                      If irrational, set it to None.
                      For hexagonal system, ratio = [mu, mv], list of two integers,
                      that is, mu/mv = c2/a2. If it is irrational, set it to none.
  
          Returns:
              rotation_angles corresponding to the provided sigma value.
              If the sigma value is not correct, return the rotation angle corresponding
              to the correct possible sigma value right smaller than the wrong sigma value provided.
          """
          if lat_type.lower() == "c":
              logger.info("Make sure this is for cubic system")
              sigma_dict = GrainBoundaryGenerator.enum_sigma_cubic(cutoff=sigma, r_axis=r_axis)
          elif lat_type.lower() == "t":
              logger.info("Make sure this is for tetragonal system")
              if ratio is None:
                  logger.info("Make sure this is for irrational c2/a2 ratio")
              elif len(ratio) != 2:
                  raise RuntimeError("Tetragonal system needs correct c2/a2 ratio")
              sigma_dict = GrainBoundaryGenerator.enum_sigma_tet(cutoff=sigma, r_axis=r_axis, c2_a2_ratio=ratio)
          elif lat_type.lower() == "o":
              logger.info("Make sure this is for orthorhombic system")
              if len(ratio) != 3:
                  raise RuntimeError("Orthorhombic system needs correct c2:b2:a2 ratio")
              sigma_dict = GrainBoundaryGenerator.enum_sigma_ort(cutoff=sigma, r_axis=r_axis, c2_b2_a2_ratio=ratio)
          elif lat_type.lower() == "h":
              logger.info("Make sure this is for hexagonal system")
              if ratio is None:
                  logger.info("Make sure this is for irrational c2/a2 ratio")
              elif len(ratio) != 2:
                  raise RuntimeError("Hexagonal system needs correct c2/a2 ratio")
              sigma_dict = GrainBoundaryGenerator.enum_sigma_hex(cutoff=sigma, r_axis=r_axis, c2_a2_ratio=ratio)
          elif lat_type.lower() == "r":
              logger.info("Make sure this is for rhombohedral system")
              if ratio is None:
                  logger.info("Make sure this is for irrational (1+2*cos(alpha)/cos(alpha) ratio")
              elif len(ratio) != 2:
                  raise RuntimeError("Rhombohedral system needs correct (1+2*cos(alpha)/cos(alpha) ratio")
              sigma_dict = GrainBoundaryGenerator.enum_sigma_rho(cutoff=sigma, r_axis=r_axis, ratio_alpha=ratio)
          else:
              raise RuntimeError("Lattice type not implemented")
  
          sigmas = list(sigma_dict.keys())
          if not sigmas:
              raise RuntimeError("This is a wriong sigma value, and no sigma exists smaller than this value.")
          if sigma in sigmas:
              rotation_angles = sigma_dict[sigma]
          else:
              sigmas.sort()
              warnings.warn(
                  "This is not the possible sigma value according to the rotation axis!"
                  "The nearest neighbor sigma and its corresponding angle are returned"
              )
              rotation_angles = sigma_dict[sigmas[-1]]
          rotation_angles.sort()
          return rotation_angles
  
      @staticmethod
      def slab_from_csl(csl, surface, normal, trans_cry, max_search=20, quick_gen=False):
          """
          By linear operation of csl lattice vectors to get the best corresponding
          slab lattice. That is the area of a,b vectors (within the surface plane)
          is the smallest, the c vector first, has shortest length perpendicular
          to surface [h,k,l], second, has shortest length itself.
  
          Args:
              csl (3 by 3 integer array):
                      input csl lattice.
              surface (list of three integers, e.g. h, k, l):
                      the miller index of the surface, with the format of [h,k,l]
              normal (logic):
                      determine if the c vector needs to perpendicular to surface
              trans_cry (3 by 3 array):
                      transform matrix from crystal system to orthogonal system
              max_search (int): max search for the GB lattice vectors that give the smallest GB
                  lattice. If normal is true, also max search the GB c vector that perpendicular
                  to the plane.
              quick_gen (bool): whether to quickly generate a supercell, no need to find the smallest
                  cell if set to true.
  
          Returns:
              t_matrix: a slab lattice ( 3 by 3 integer array):
          """
  
          # set the transform matrix in real space
          trans = trans_cry
          # transform matrix in reciprocal space
          ctrans = np.linalg.inv(trans.T)
  
          t_matrix = csl.copy()
          # vectors constructed from csl that perpendicular to surface
          ab_vector = []
          # obtain the miller index of surface in terms of csl.
          miller = np.matmul(surface, csl.T)
          if reduce(gcd, miller) != 1:
              miller = [int(round(x / reduce(gcd, miller))) for x in miller]
          miller_nonzero = []
          # quickly generate a supercell, normal is not work in this way
          if quick_gen:
              scale_factor = []
              eye = np.eye(3, dtype=int)
              for i, j in enumerate(miller):
                  if j == 0:
                      scale_factor.append(eye[i])
                  else:
                      miller_nonzero.append(i)
              if len(scale_factor) < 2:
                  index_len = len(miller_nonzero)
                  for i in range(index_len):
                      for j in range(i + 1, index_len):
                          lcm_miller = lcm(miller[miller_nonzero[i]], miller[miller_nonzero[j]])
                          l = [0, 0, 0]
                          l[miller_nonzero[i]] = -int(round(lcm_miller / miller[miller_nonzero[i]]))
                          l[miller_nonzero[j]] = int(round(lcm_miller / miller[miller_nonzero[j]]))
                          scale_factor.append(l)
                          if len(scale_factor) == 2:
                              break
              t_matrix[0] = np.array(np.dot(scale_factor[0], csl))
              t_matrix[1] = np.array(np.dot(scale_factor[1], csl))
              t_matrix[2] = csl[miller_nonzero[0]]
              if abs(np.linalg.det(t_matrix)) > 1000:
                  warnings.warn("Too large matrix. Suggest to use quick_gen=False")
              return t_matrix
  
          for i, j in enumerate(miller):
              if j == 0:
                  ab_vector.append(csl[i])
              else:
                  c_index = i
                  miller_nonzero.append(j)
  
          if len(miller_nonzero) > 1:
              t_matrix[2] = csl[c_index]
              index_len = len(miller_nonzero)
              lcm_miller = []
              for i in range(index_len):
                  for j in range(i + 1, index_len):
                      com_gcd = gcd(miller_nonzero[i], miller_nonzero[j])
                      mil1 = int(round(miller_nonzero[i] / com_gcd))
                      mil2 = int(round(miller_nonzero[j] / com_gcd))
                      lcm_miller.append(max(abs(mil1), abs(mil2)))
              lcm_sorted = sorted(lcm_miller)
              if index_len == 2:
                  max_j = lcm_sorted[0]
              else:
                  max_j = lcm_sorted[1]
          else:
              if not normal:
                  t_matrix[0] = ab_vector[0]
                  t_matrix[1] = ab_vector[1]
                  t_matrix[2] = csl[c_index]
                  return t_matrix
              max_j = abs(miller_nonzero[0])
          max_j = min(max_j, max_search)
          # area of a, b vectors
          area = None
          # length of c vector
          c_norm = np.linalg.norm(np.matmul(t_matrix[2], trans))
          # c vector length along the direction perpendicular to surface
          c_length = np.abs(np.dot(t_matrix[2], surface))
          # check if the init c vector perpendicular to the surface
          if normal:
              c_cross = np.cross(np.matmul(t_matrix[2], trans), np.matmul(surface, ctrans))
              normal_init = np.linalg.norm(c_cross) < 1e-8
  
          j = np.arange(0, max_j + 1)
          combination = []
          for i in itertools.product(j, repeat=3):
              if sum(abs(np.array(i))) != 0:
                  combination.append(list(i))
              if len(np.nonzero(i)[0]) == 3:
                  for i1 in range(3):
                      new_i = list(i).copy()
                      new_i[i1] = -1 * new_i[i1]
                      combination.append(new_i)
              elif len(np.nonzero(i)[0]) == 2:
                  new_i = list(i).copy()
                  new_i[np.nonzero(i)[0][0]] = -1 * new_i[np.nonzero(i)[0][0]]
                  combination.append(new_i)
          for i in combination:
              if reduce(gcd, i) == 1:
                  temp = np.dot(np.array(i), csl)
                  if abs(np.dot(temp, surface) - 0) < 1.0e-8:
                      ab_vector.append(temp)
                  else:
                      # c vector length along the direction perpendicular to surface
                      c_len_temp = np.abs(np.dot(temp, surface))
                      # c vector length itself
                      c_norm_temp = np.linalg.norm(np.matmul(temp, trans))
                      if normal:
                          c_cross = np.cross(np.matmul(temp, trans), np.matmul(surface, ctrans))
                          if np.linalg.norm(c_cross) < 1.0e-8:
                              if normal_init:
                                  if c_norm_temp < c_norm:
                                      t_matrix[2] = temp
                                      c_norm = c_norm_temp
                              else:
                                  c_norm = c_norm_temp
                                  normal_init = True
                                  t_matrix[2] = temp
                      else:
                          if c_len_temp < c_length or (abs(c_len_temp - c_length) < 1.0e-8 and c_norm_temp < c_norm):
                              t_matrix[2] = temp
                              c_norm = c_norm_temp
                              c_length = c_len_temp
  
          if normal and (not normal_init):
              logger.info("Did not find the perpendicular c vector, increase max_j")
              while not normal_init:
                  if max_j == max_search:
                      warnings.warn("Cannot find the perpendicular c vector, please increase max_search")
                      break
                  max_j = 3 * max_j
                  max_j = min(max_j, max_search)
                  j = np.arange(0, max_j + 1)
                  combination = []
                  for i in itertools.product(j, repeat=3):
                      if sum(abs(np.array(i))) != 0:
                          combination.append(list(i))
                      if len(np.nonzero(i)[0]) == 3:
                          for i1 in range(3):
                              new_i = list(i).copy()
                              new_i[i1] = -1 * new_i[i1]
                              combination.append(new_i)
                      elif len(np.nonzero(i)[0]) == 2:
                          new_i = list(i).copy()
                          new_i[np.nonzero(i)[0][0]] = -1 * new_i[np.nonzero(i)[0][0]]
                          combination.append(new_i)
                  for i in combination:
                      if reduce(gcd, i) == 1:
                          temp = np.dot(np.array(i), csl)
                          if abs(np.dot(temp, surface) - 0) > 1.0e-8:
                              c_cross = np.cross(np.matmul(temp, trans), np.matmul(surface, ctrans))
                              if np.linalg.norm(c_cross) < 1.0e-8:
                                  # c vetor length itself
                                  c_norm_temp = np.linalg.norm(np.matmul(temp, trans))
                                  if normal_init:
                                      if c_norm_temp < c_norm:
                                          t_matrix[2] = temp
                                          c_norm = c_norm_temp
                                  else:
                                      c_norm = c_norm_temp
                                      normal_init = True
                                      t_matrix[2] = temp
                  if normal_init:
                      logger.info("Found perpendicular c vector")
  
          # find the best a, b vectors with their formed area smallest and average norm of a,b smallest.
          for i in itertools.combinations(ab_vector, 2):
              area_temp = np.linalg.norm(np.cross(np.matmul(i[0], trans), np.matmul(i[1], trans)))
              if abs(area_temp - 0) > 1.0e-8:
                  ab_norm_temp = np.linalg.norm(np.matmul(i[0], trans)) + np.linalg.norm(np.matmul(i[1], trans))
                  if area is None:
                      area = area_temp
                      ab_norm = ab_norm_temp
                      t_matrix[0] = i[0]
                      t_matrix[1] = i[1]
                  elif area_temp < area:
                      t_matrix[0] = i[0]
                      t_matrix[1] = i[1]
                      area = area_temp
                      ab_norm = ab_norm_temp
                  elif abs(area - area_temp) < 1.0e-8 and ab_norm_temp < ab_norm:
                      t_matrix[0] = i[0]
                      t_matrix[1] = i[1]
                      area = area_temp
                      ab_norm = ab_norm_temp
  
          # make sure we have a left-handed crystallographic system
          if np.linalg.det(np.matmul(t_matrix, trans)) < 0:
              t_matrix *= -1
  
          if normal and abs(np.linalg.det(t_matrix)) > 1000:
              warnings.warn("Too large matrix. Suggest to use Normal=False")
          return t_matrix
  
      @staticmethod
      def reduce_mat(mat, mag, r_matrix):
          """
          Reduce integer array mat's determinant mag times by linear combination
          of its row vectors, so that the new array after rotation (r_matrix) is
          still an integer array
  
          Args:
              mat (3 by 3 array): input matrix
              mag (integer): reduce times for the determinant
              r_matrix (3 by 3 array): rotation matrix
          Return:
              the reduced integer array
          """
          max_j = abs(int(round(np.linalg.det(mat) / mag)))
          reduced = False
          for h in range(3):
              k = h + 1 if h + 1 < 3 else abs(2 - h)
              l = h + 2 if h + 2 < 3 else abs(1 - h)
              j = np.arange(-max_j, max_j + 1)
              for j1, j2 in itertools.product(j, repeat=2):
                  temp = mat[h] + j1 * mat[k] + j2 * mat[l]
                  if all(np.round(x, 5).is_integer() for x in list(temp / mag)):
                      mat_copy = mat.copy()
                      mat_copy[h] = np.array([int(round(ele / mag)) for ele in temp])
                      new_mat = np.dot(mat_copy, np.linalg.inv(r_matrix.T))
                      if all(np.round(x, 5).is_integer() for x in list(np.ravel(new_mat))):
                          reduced = True
                          mat[h] = np.array([int(round(ele / mag)) for ele in temp])
                          break
              if reduced:
                  break
  
          if not reduced:
              warnings.warn("Matrix reduction not performed, may lead to non-primitive gb cell.")
          return mat
  
      @staticmethod
      def vec_to_surface(vec):
          """
          Transform a float vector to a surface miller index with integers.
  
          Args:
              vec (1 by 3 array float vector): input float vector
          Return:
              the surface miller index of the input vector.
          """
          miller = [None] * 3
          index = []
          for i, value in enumerate(vec):
              if abs(value) < 1.0e-8:
                  miller[i] = 0
              else:
                  index.append(i)
          if len(index) == 1:
              miller[index[0]] = 1
          else:
              min_index = np.argmin([i for i in vec if i != 0])
              true_index = index[min_index]
              index.pop(min_index)
              frac = []
              for i, value in enumerate(index):
                  frac.append(Fraction(vec[value] / vec[true_index]).limit_denominator(100))
              if len(index) == 1:
                  miller[true_index] = frac[0].denominator
                  miller[index[0]] = frac[0].numerator
              else:
                  com_lcm = lcm(frac[0].denominator, frac[1].denominator)
                  miller[true_index] = com_lcm
                  miller[index[0]] = frac[0].numerator * int(round(com_lcm / frac[0].denominator))
                  miller[index[1]] = frac[1].numerator * int(round(com_lcm / frac[1].denominator))
          return miller
  
  
  def factors(n):
      """
      Compute the factors of a integer.
      Args:
          n: the input integer
  
      Returns:
          a set of integers that are the factors of the input integer.
      """
      return set(
          reduce(
              list.__add__,
              ([i, n // i] for i in range(1, int(np.sqrt(n)) + 1) if n % i == 0),
          )
      )
  
  
  def fix_pbc(structure, matrix=None):
      """
      Set all frac_coords of the input structure within [0,1].
  
      Args:
          structure (pymatgen structure object):
              input structure
          matrix (lattice matrix, 3 by 3 array/matrix)
              new structure's lattice matrix, if none, use
              input structure's matrix
  
      Return:
          new structure with fixed frac_coords and lattice matrix
      """
  
      spec = []
      coords = []
      if matrix is None:
          latte = Lattice(structure.lattice.matrix)
      else:
          latte = Lattice(matrix)
  
      for site in structure:
          spec.append(site.specie)
          coord = np.array(site.frac_coords)
          for i in range(3):
              coord[i] -= floor(coord[i])
              if np.allclose(coord[i], 1):
                  coord[i] = 0
              elif np.allclose(coord[i], 0):
                  coord[i] = 0
              else:
                  coord[i] = round(coord[i], 7)
          coords.append(coord)
  
      return Structure(latte, spec, coords, site_properties=structure.site_properties)
  
  
  def symm_group_cubic(mat):
      """
       obtain cubic symmetric equivalents of the list of vectors.
  
      Args:
          matrix (lattice matrix, n by 3 array/matrix)
  
      Return:
          cubic symmetric equivalents of the list of vectors.
      """
      sym_group = np.zeros([24, 3, 3])
      sym_group[0, :] = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
      sym_group[1, :] = [[1, 0, 0], [0, -1, 0], [0, 0, -1]]
      sym_group[2, :] = [[-1, 0, 0], [0, 1, 0], [0, 0, -1]]
      sym_group[3, :] = [[-1, 0, 0], [0, -1, 0], [0, 0, 1]]
      sym_group[4, :] = [[0, -1, 0], [-1, 0, 0], [0, 0, -1]]
      sym_group[5, :] = [[0, -1, 0], [1, 0, 0], [0, 0, 1]]
      sym_group[6, :] = [[0, 1, 0], [-1, 0, 0], [0, 0, 1]]
      sym_group[7, :] = [[0, 1, 0], [1, 0, 0], [0, 0, -1]]
      sym_group[8, :] = [[-1, 0, 0], [0, 0, -1], [0, -1, 0]]
      sym_group[9, :] = [[-1, 0, 0], [0, 0, 1], [0, 1, 0]]
      sym_group[10, :] = [[1, 0, 0], [0, 0, -1], [0, 1, 0]]
      sym_group[11, :] = [[1, 0, 0], [0, 0, 1], [0, -1, 0]]
      sym_group[12, :] = [[0, 1, 0], [0, 0, 1], [1, 0, 0]]
      sym_group[13, :] = [[0, 1, 0], [0, 0, -1], [-1, 0, 0]]
      sym_group[14, :] = [[0, -1, 0], [0, 0, 1], [-1, 0, 0]]
      sym_group[15, :] = [[0, -1, 0], [0, 0, -1], [1, 0, 0]]
      sym_group[16, :] = [[0, 0, 1], [1, 0, 0], [0, 1, 0]]
      sym_group[17, :] = [[0, 0, 1], [-1, 0, 0], [0, -1, 0]]
      sym_group[18, :] = [[0, 0, -1], [1, 0, 0], [0, -1, 0]]
      sym_group[19, :] = [[0, 0, -1], [-1, 0, 0], [0, 1, 0]]
      sym_group[20, :] = [[0, 0, -1], [0, -1, 0], [-1, 0, 0]]
      sym_group[21, :] = [[0, 0, -1], [0, 1, 0], [1, 0, 0]]
      sym_group[22, :] = [[0, 0, 1], [0, -1, 0], [1, 0, 0]]
      sym_group[23, :] = [[0, 0, 1], [0, 1, 0], [-1, 0, 0]]
  
      mat = np.atleast_2d(mat)
      all_vectors = []
      for sym in sym_group:
          for vec in mat:
              all_vectors.append(np.dot(sym, vec))
      return np.unique(np.array(all_vectors), axis=0)
  