Source code for pymatgen.core.lattice

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

"""
Defines the classes relating to 3D lattices.
"""

import math
import itertools
import warnings
from functools import reduce
import collections

from fractions import Fraction
from typing import List, Union, Dict, Tuple, Iterator, Optional, Sequence

import numpy as np
from numpy.linalg import inv
from numpy import pi, dot, transpose

from monty.json import MSONable
from monty.dev import deprecated

from pymatgen.util.coord import pbc_shortest_vectors
from pymatgen.util.num import abs_cap
from pymatgen.util.typing import Vector3Like


__author__ = "Shyue Ping Ong, Michael Kocher"
__copyright__ = "Copyright 2011, The Materials Project"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"


[docs]class Lattice(MSONable): """ A lattice object. Essentially a matrix with conversion matrices. In general, it is assumed that length units are in Angstroms and angles are in degrees unless otherwise stated. """ # Properties lazily generated for efficiency. def __init__(self, matrix: Union[List[float], List[List[float]], np.ndarray]): """ Create a lattice from any sequence of 9 numbers. Note that the sequence is assumed to be read one row at a time. Each row represents one lattice vector. Args: matrix: Sequence of numbers in any form. Examples of acceptable input. i) An actual numpy array. ii) [[1, 0, 0], [0, 1, 0], [0, 0, 1]] iii) [1, 0, 0 , 0, 1, 0, 0, 0, 1] iv) (1, 0, 0, 0, 1, 0, 0, 0, 1) 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]. """ m = np.array(matrix, dtype=np.float64).reshape((3, 3)) m.setflags(write=False) self._matrix = m # type: np.ndarray self._inv_matrix = None # type: Optional[np.ndarray] self._diags = None self._lll_matrix_mappings = {} # type: Dict[float, np.ndarray] self._lll_inverse = None @property def lengths(self) -> Tuple[float, float, float]: """ :return: The lengths (a, b, c) of the lattice. """ return tuple(np.sqrt(np.sum(self._matrix ** 2, axis=1)).tolist()) # type: ignore @property def angles(self) -> Tuple[float, float, float]: """ Returns the angles (alpha, beta, gamma) of the lattice. """ m = self._matrix lengths = self.lengths angles = np.zeros(3) for i in range(3): j = (i + 1) % 3 k = (i + 2) % 3 angles[i] = abs_cap(dot(m[j], m[k]) / (lengths[j] * lengths[k])) angles = np.arccos(angles) * 180.0 / pi return tuple(angles.tolist()) # type: ignore @property def is_orthogonal(self) -> bool: """ :return: Whether all angles are 90 degrees. """ return all([abs(a - 90) < 1e-5 for a in self.angles]) def __format__(self, fmt_spec=""): """ Support format printing. Supported formats are: 1. "l" for a list format that can be easily copied and pasted, e.g., ".3fl" prints something like "[[10.000, 0.000, 0.000], [0.000, 10.000, 0.000], [0.000, 0.000, 10.000]]" 2. "p" for lattice parameters ".1fp" prints something like "{10.0, 10.0, 10.0, 90.0, 90.0, 90.0}" 3. Default will simply print a 3x3 matrix form. E.g., 10.000 0.000 0.000 0.000 10.000 0.000 0.000 0.000 10.000 """ m = self._matrix.tolist() if fmt_spec.endswith("l"): fmt = "[[{}, {}, {}], [{}, {}, {}], [{}, {}, {}]]" fmt_spec = fmt_spec[:-1] elif fmt_spec.endswith("p"): fmt = "{{{}, {}, {}, {}, {}, {}}}" fmt_spec = fmt_spec[:-1] m = (self.lengths, self.angles) else: fmt = "{} {} {}\n{} {} {}\n{} {} {}" return fmt.format(*[format(c, fmt_spec) for row in m for c in row])
[docs] def copy(self): """Deep copy of self.""" return self.__class__(self.matrix.copy())
@property def matrix(self) -> np.ndarray: """Copy of matrix representing the Lattice""" return self._matrix @property def inv_matrix(self) -> np.ndarray: """ Inverse of lattice matrix. """ if self._inv_matrix is None: self._inv_matrix = inv(self._matrix) self._inv_matrix.setflags(write=False) return self._inv_matrix @property def metric_tensor(self) -> np.ndarray: """ The metric tensor of the lattice. """ return dot(self._matrix, self._matrix.T)
[docs] def get_cartesian_coords(self, fractional_coords: Vector3Like) -> np.ndarray: """ Returns the cartesian coordinates given fractional coordinates. Args: fractional_coords (3x1 array): Fractional coords. Returns: Cartesian coordinates """ return dot(fractional_coords, self._matrix)
[docs] def get_fractional_coords(self, cart_coords: Vector3Like) -> np.ndarray: """ Returns the fractional coordinates given cartesian coordinates. Args: cart_coords (3x1 array): Cartesian coords. Returns: Fractional coordinates. """ return dot(cart_coords, self.inv_matrix)
[docs] def get_vector_along_lattice_directions(self, cart_coords: Vector3Like): """ Returns the coordinates along lattice directions given cartesian coordinates. Note, this is different than a projection of the cartesian vector along the lattice parameters. It is simply the fractional coordinates multiplied by the lattice vector magnitudes. For example, this method is helpful when analyzing the dipole moment (in units of electron Angstroms) of a ferroelectric crystal. See the `Polarization` class in `pymatgen.analysis.ferroelectricity.polarization`. Args: cart_coords (3x1 array): Cartesian coords. Returns: Lattice coordinates. """ return self.lengths * self.get_fractional_coords(cart_coords)
[docs] def d_hkl(self, miller_index: Vector3Like) -> float: """ Returns the distance between the hkl plane and the origin Args: miller_index ([h,k,l]): Miller index of plane Returns: d_hkl (float) """ gstar = self.reciprocal_lattice_crystallographic.metric_tensor hkl = np.array(miller_index) return 1 / ((dot(dot(hkl, gstar), hkl.T)) ** (1 / 2))
[docs] @staticmethod def cubic(a: float): """ Convenience constructor for a cubic lattice. Args: a (float): The *a* lattice parameter of the cubic cell. Returns: Cubic lattice of dimensions a x a x a. """ return Lattice([[a, 0.0, 0.0], [0.0, a, 0.0], [0.0, 0.0, a]])
[docs] @staticmethod def tetragonal(a: float, c: float): """ Convenience constructor for a tetragonal lattice. Args: a (float): *a* lattice parameter of the tetragonal cell. c (float): *c* lattice parameter of the tetragonal cell. Returns: Tetragonal lattice of dimensions a x a x c. """ return Lattice.from_parameters(a, a, c, 90, 90, 90)
[docs] @staticmethod def orthorhombic(a: float, b: float, c: float): """ Convenience constructor for an orthorhombic lattice. Args: a (float): *a* lattice parameter of the orthorhombic cell. b (float): *b* lattice parameter of the orthorhombic cell. c (float): *c* lattice parameter of the orthorhombic cell. Returns: Orthorhombic lattice of dimensions a x b x c. """ return Lattice.from_parameters(a, b, c, 90, 90, 90)
[docs] @staticmethod def monoclinic(a: float, b: float, c: float, beta: float): """ Convenience constructor for a monoclinic lattice. Args: a (float): *a* lattice parameter of the monoclinc cell. b (float): *b* lattice parameter of the monoclinc cell. c (float): *c* lattice parameter of the monoclinc cell. beta (float): *beta* angle between lattice vectors b and c in degrees. Returns: Monoclinic lattice of dimensions a x b x c with non right-angle beta between lattice vectors a and c. """ return Lattice.from_parameters(a, b, c, 90, beta, 90)
[docs] @staticmethod def hexagonal(a: float, c: float): """ Convenience constructor for a hexagonal lattice. Args: a (float): *a* lattice parameter of the hexagonal cell. c (float): *c* lattice parameter of the hexagonal cell. Returns: Hexagonal lattice of dimensions a x a x c. """ return Lattice.from_parameters(a, a, c, 90, 90, 120)
[docs] @staticmethod def rhombohedral(a: float, alpha: float): """ Convenience constructor for a rhombohedral lattice. Args: a (float): *a* lattice parameter of the rhombohedral cell. alpha (float): Angle for the rhombohedral lattice in degrees. Returns: Rhombohedral lattice of dimensions a x a x a. """ return Lattice.from_parameters(a, a, a, alpha, alpha, alpha)
@staticmethod @deprecated(message="Use Lattice.from_parameters instead. This will be removed in v2020.*") def from_lengths_and_angles(abc: Sequence[float], ang: Sequence[float]): """ Create a Lattice using unit cell lengths and angles (in degrees). Args: abc (3x1 array): Lattice parameters, e.g. (4, 4, 5). ang (3x1 array): Lattice angles in degrees, e.g., (90,90,120). Returns: A Lattice with the specified lattice parameters. """ return Lattice.from_parameters(abc[0], abc[1], abc[2], ang[0], ang[1], ang[2])
[docs] @classmethod def from_parameters( cls, a: float, b: float, c: float, alpha: float, beta: float, gamma: float, vesta: bool = False, ): """ Create a Lattice using unit cell lengths and angles (in degrees). Args: a (float): *a* lattice parameter. b (float): *b* lattice parameter. c (float): *c* lattice parameter. alpha (float): *alpha* angle in degrees. beta (float): *beta* angle in degrees. gamma (float): *gamma* angle in degrees. vesta: True if you import Cartesian coordinates from VESTA. Returns: Lattice with the specified lattice parameters. """ angles_r = np.radians([alpha, beta, gamma]) cos_alpha, cos_beta, cos_gamma = np.cos(angles_r) sin_alpha, sin_beta, sin_gamma = np.sin(angles_r) if vesta: c1 = c * cos_beta c2 = (c * (cos_alpha - (cos_beta * cos_gamma))) / sin_gamma vector_a = [float(a), 0.0, 0.0] vector_b = [b * cos_gamma, b * sin_gamma, 0] vector_c = [c1, c2, math.sqrt(c ** 2 - c1 ** 2 - c2 ** 2)] else: val = (cos_alpha * cos_beta - cos_gamma) / (sin_alpha * sin_beta) # Sometimes rounding errors result in values slightly > 1. val = abs_cap(val) gamma_star = np.arccos(val) vector_a = [a * sin_beta, 0.0, a * cos_beta] vector_b = [ -b * sin_alpha * np.cos(gamma_star), b * sin_alpha * np.sin(gamma_star), b * cos_alpha, ] vector_c = [0.0, 0.0, float(c)] return Lattice([vector_a, vector_b, vector_c])
[docs] @classmethod def from_dict(cls, d: Dict, fmt: str = None, **kwargs): """ Create a Lattice from a dictionary containing the a, b, c, alpha, beta, and gamma parameters if fmt is None. If fmt == "abivars", the function build a `Lattice` object from a dictionary with the Abinit variables `acell` and `rprim` in Bohr. If acell is not given, the Abinit default is used i.e. [1,1,1] Bohr Example: Lattice.from_dict(fmt="abivars", acell=3*[10], rprim=np.eye(3)) """ if fmt == "abivars": from pymatgen.io.abinit.abiobjects import lattice_from_abivars kwargs.update(d) return lattice_from_abivars(cls=cls, **kwargs) if "matrix" in d: return cls(d["matrix"]) return cls.from_parameters(d["a"], d["b"], d["c"], d["alpha"], d["beta"], d["gamma"])
@property def a(self) -> float: """ *a* lattice parameter. """ return self.lengths[0] @property def b(self) -> float: """ *b* lattice parameter. """ return self.lengths[1] @property def c(self) -> float: """ *c* lattice parameter. """ return self.lengths[2] @property def abc(self) -> Tuple[float, float, float]: """ Lengths of the lattice vectors, i.e. (a, b, c) """ return self.lengths @property def alpha(self) -> float: """ Angle alpha of lattice in degrees. """ return self.angles[0] @property def beta(self) -> float: """ Angle beta of lattice in degrees. """ return self.angles[1] @property def gamma(self) -> float: """ Angle gamma of lattice in degrees. """ return self.angles[2] @property def volume(self) -> float: """ Volume of the unit cell. """ m = self._matrix return float(abs(dot(np.cross(m[0], m[1]), m[2]))) @property def parameters(self) -> Tuple[float, float, float, float, float, float]: """ Returns: (a, b, c, alpha, beta, gamma). """ return (*self.lengths, *self.angles) @property # type: ignore @deprecated(message="Use Lattice.parameters instead. This will be removed in v2020.*") def lengths_and_angles(self) -> Tuple[Tuple[float, float, float], Tuple[float, float, float]]: """ Returns (lattice lengths, lattice angles). """ return self.lengths, self.angles @property def reciprocal_lattice(self) -> "Lattice": """ Return the reciprocal lattice. Note that this is the standard reciprocal lattice used for solid state physics with a factor of 2 * pi. If you are looking for the crystallographic reciprocal lattice, use the reciprocal_lattice_crystallographic property. The property is lazily generated for efficiency. """ v = np.linalg.inv(self._matrix).T return Lattice(v * 2 * np.pi) @property def reciprocal_lattice_crystallographic(self) -> "Lattice": """ Returns the *crystallographic* reciprocal lattice, i.e., no factor of 2 * pi. """ return Lattice(self.reciprocal_lattice.matrix / (2 * np.pi)) @property def lll_matrix(self) -> np.ndarray: """ :return: The matrix for LLL reduction """ if 0.75 not in self._lll_matrix_mappings: self._lll_matrix_mappings[0.75] = self._calculate_lll() return self._lll_matrix_mappings[0.75][0] @property def lll_mapping(self) -> np.ndarray: """ :return: The mapping between the LLL reduced lattice and the original lattice. """ if 0.75 not in self._lll_matrix_mappings: self._lll_matrix_mappings[0.75] = self._calculate_lll() return self._lll_matrix_mappings[0.75][1] @property def lll_inverse(self) -> np.ndarray: """ :return: Inverse of self.lll_mapping. """ return np.linalg.inv(self.lll_mapping) def __repr__(self): outs = [ "Lattice", " abc : " + " ".join(map(repr, self.lengths)), " angles : " + " ".join(map(repr, self.angles)), " volume : " + repr(self.volume), " A : " + " ".join(map(repr, self._matrix[0])), " B : " + " ".join(map(repr, self._matrix[1])), " C : " + " ".join(map(repr, self._matrix[2])), ] return "\n".join(outs) def __eq__(self, other): """ A lattice is considered to be equal to another if the internal matrix representation satisfies np.allclose(matrix1, matrix2) to be True. """ if other is None: return False # shortcut the np.allclose if the memory addresses are the same # (very common in Structure.from_sites) return self is other or np.allclose(self.matrix, other.matrix) def __ne__(self, other): return not self.__eq__(other) def __hash__(self): return 7 def __str__(self): return "\n".join([" ".join(["%.6f" % i for i in row]) for row in self._matrix])
[docs] def as_dict(self, verbosity: int = 0) -> Dict: """ Json-serialization dict representation of the Lattice. Args: verbosity (int): Verbosity level. Default of 0 only includes the matrix representation. Set to 1 for more details. """ d = { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "matrix": self._matrix.tolist(), } a, b, c, alpha, beta, gamma = self.parameters if verbosity > 0: d.update( { "a": a, "b": b, "c": c, "alpha": alpha, "beta": beta, "gamma": gamma, "volume": self.volume, } ) return d
[docs] def find_all_mappings( self, other_lattice: "Lattice", ltol: float = 1e-5, atol: float = 1, skip_rotation_matrix: bool = False, ) -> Iterator[Tuple["Lattice", Optional[np.ndarray], np.ndarray]]: """ Finds all mappings between current lattice and another lattice. Args: other_lattice (Lattice): Another lattice that is equivalent to this one. ltol (float): Tolerance for matching lengths. Defaults to 1e-5. atol (float): Tolerance for matching angles. Defaults to 1. skip_rotation_matrix (bool): Whether to skip calculation of the rotation matrix Yields: (aligned_lattice, rotation_matrix, scale_matrix) if a mapping is found. aligned_lattice is a rotated version of other_lattice that has the same lattice parameters, but which is aligned in the coordinate system of this lattice so that translational points match up in 3D. rotation_matrix is the rotation that has to be applied to other_lattice to obtain aligned_lattice, i.e., aligned_matrix = np.inner(other_lattice, rotation_matrix) and op = SymmOp.from_rotation_and_translation(rotation_matrix) aligned_matrix = op.operate_multi(latt.matrix) Finally, scale_matrix is the integer matrix that expresses aligned_matrix as a linear combination of this lattice, i.e., aligned_matrix = np.dot(scale_matrix, self.matrix) None is returned if no matches are found. """ lengths = other_lattice.lengths (alpha, beta, gamma) = other_lattice.angles frac, dist, _, _ = self.get_points_in_sphere( [[0, 0, 0]], [0, 0, 0], max(lengths) * (1 + ltol), zip_results=False ) cart = self.get_cartesian_coords(frac) # this can't be broadcast because they're different lengths inds = [ np.logical_and(dist / l < 1 + ltol, dist / l > 1 / (1 + ltol)) # type: ignore for l in lengths ] c_a, c_b, c_c = (cart[i] for i in inds) f_a, f_b, f_c = (frac[i] for i in inds) l_a, l_b, l_c = (np.sum(c ** 2, axis=-1) ** 0.5 for c in (c_a, c_b, c_c)) def get_angles(v1, v2, l1, l2): x = np.inner(v1, v2) / l1[:, None] / l2 x[x > 1] = 1 x[x < -1] = -1 angles = np.arccos(x) * 180.0 / pi return angles alphab = np.abs(get_angles(c_b, c_c, l_b, l_c) - alpha) < atol betab = np.abs(get_angles(c_a, c_c, l_a, l_c) - beta) < atol gammab = np.abs(get_angles(c_a, c_b, l_a, l_b) - gamma) < atol for i, all_j in enumerate(gammab): inds = np.logical_and( all_j[:, None], np.logical_and(alphab, betab[i][None, :]) ) for j, k in np.argwhere(inds): scale_m = np.array((f_a[i], f_b[j], f_c[k]), dtype=np.int) # type: ignore if abs(np.linalg.det(scale_m)) < 1e-8: continue aligned_m = np.array((c_a[i], c_b[j], c_c[k])) if skip_rotation_matrix: rotation_m = None else: rotation_m = np.linalg.solve(aligned_m, other_lattice.matrix) yield Lattice(aligned_m), rotation_m, scale_m
[docs] def find_mapping( self, other_lattice: "Lattice", ltol: float = 1e-5, atol: float = 1, skip_rotation_matrix: bool = False, ) -> Optional[Tuple["Lattice", Optional[np.ndarray], np.ndarray]]: """ Finds a mapping between current lattice and another lattice. There are an infinite number of choices of basis vectors for two entirely equivalent lattices. This method returns a mapping that maps other_lattice to this lattice. Args: other_lattice (Lattice): Another lattice that is equivalent to this one. ltol (float): Tolerance for matching lengths. Defaults to 1e-5. atol (float): Tolerance for matching angles. Defaults to 1. Returns: (aligned_lattice, rotation_matrix, scale_matrix) if a mapping is found. aligned_lattice is a rotated version of other_lattice that has the same lattice parameters, but which is aligned in the coordinate system of this lattice so that translational points match up in 3D. rotation_matrix is the rotation that has to be applied to other_lattice to obtain aligned_lattice, i.e., aligned_matrix = np.inner(other_lattice, rotation_matrix) and op = SymmOp.from_rotation_and_translation(rotation_matrix) aligned_matrix = op.operate_multi(latt.matrix) Finally, scale_matrix is the integer matrix that expresses aligned_matrix as a linear combination of this lattice, i.e., aligned_matrix = np.dot(scale_matrix, self.matrix) None is returned if no matches are found. """ for x in self.find_all_mappings( other_lattice, ltol, atol, skip_rotation_matrix=skip_rotation_matrix ): return x return None
[docs] def get_lll_reduced_lattice(self, delta: float = 0.75) -> "Lattice": """ :param delta: Delta parameter. :return: LLL reduced Lattice. """ if delta not in self._lll_matrix_mappings: self._lll_matrix_mappings[delta] = self._calculate_lll() return Lattice(self._lll_matrix_mappings[delta][0])
def _calculate_lll(self, delta: float = 0.75) -> Tuple[np.ndarray, np.ndarray]: """ Performs a Lenstra-Lenstra-Lovasz lattice basis reduction to obtain a c-reduced basis. This method returns a basis which is as "good" as possible, with "good" defined by orthongonality of the lattice vectors. This basis is used for all the periodic boundary condition calculations. Args: delta (float): Reduction parameter. Default of 0.75 is usually fine. Returns: Reduced lattice matrix, mapping to get to that lattice. """ # Transpose the lattice matrix first so that basis vectors are columns. # Makes life easier. a = self._matrix.copy().T b = np.zeros((3, 3)) # Vectors after the Gram-Schmidt process u = np.zeros((3, 3)) # Gram-Schmidt coeffieicnts m = np.zeros(3) # These are the norm squared of each vec. b[:, 0] = a[:, 0] m[0] = dot(b[:, 0], b[:, 0]) for i in range(1, 3): u[i, 0:i] = dot(a[:, i].T, b[:, 0:i]) / m[0:i] b[:, i] = a[:, i] - dot(b[:, 0:i], u[i, 0:i].T) m[i] = dot(b[:, i], b[:, i]) k = 2 mapping = np.identity(3, dtype=np.double) while k <= 3: # Size reduction. for i in range(k - 1, 0, -1): q = round(u[k - 1, i - 1]) if q != 0: # Reduce the k-th basis vector. a[:, k - 1] = a[:, k - 1] - q * a[:, i - 1] mapping[:, k - 1] = mapping[:, k - 1] - q * mapping[:, i - 1] uu = list(u[i - 1, 0: (i - 1)]) uu.append(1) # Update the GS coefficients. u[k - 1, 0:i] = u[k - 1, 0:i] - q * np.array(uu) # Check the Lovasz condition. if dot(b[:, k - 1], b[:, k - 1]) >= ( delta - abs(u[k - 1, k - 2]) ** 2 ) * dot(b[:, (k - 2)], b[:, (k - 2)]): # Increment k if the Lovasz condition holds. k += 1 else: # If the Lovasz condition fails, # swap the k-th and (k-1)-th basis vector v = a[:, k - 1].copy() a[:, k - 1] = a[:, k - 2].copy() a[:, k - 2] = v v_m = mapping[:, k - 1].copy() mapping[:, k - 1] = mapping[:, k - 2].copy() mapping[:, k - 2] = v_m # Update the Gram-Schmidt coefficients for s in range(k - 1, k + 1): u[s - 1, 0: (s - 1)] = ( dot(a[:, s - 1].T, b[:, 0: (s - 1)]) / m[0: (s - 1)] ) b[:, s - 1] = a[:, s - 1] - dot( b[:, 0: (s - 1)], u[s - 1, 0: (s - 1)].T ) m[s - 1] = dot(b[:, s - 1], b[:, s - 1]) if k > 2: k -= 1 else: # We have to do p/q, so do lstsq(q.T, p.T).T instead. p = dot(a[:, k:3].T, b[:, (k - 2): k]) q = np.diag(m[(k - 2): k]) result = np.linalg.lstsq(q.T, p.T, rcond=None)[0].T # type: ignore u[k:3, (k - 2): k] = result return a.T, mapping.T
[docs] def get_lll_frac_coords(self, frac_coords: Vector3Like) -> np.ndarray: """ Given fractional coordinates in the lattice basis, returns corresponding fractional coordinates in the lll basis. """ return dot(frac_coords, self.lll_inverse)
[docs] def get_frac_coords_from_lll(self, lll_frac_coords: Vector3Like) -> np.ndarray: """ Given fractional coordinates in the lll basis, returns corresponding fractional coordinates in the lattice basis. """ return dot(lll_frac_coords, self.lll_mapping)
[docs] def get_niggli_reduced_lattice(self, tol: float = 1e-5) -> "Lattice": """ Get the Niggli reduced lattice using the numerically stable algo proposed by R. W. Grosse-Kunstleve, N. K. Sauter, & P. D. Adams, Acta Crystallographica Section A Foundations of Crystallography, 2003, 60(1), 1-6. doi:10.1107/S010876730302186X Args: tol (float): The numerical tolerance. The default of 1e-5 should result in stable behavior for most cases. Returns: Niggli-reduced lattice. """ # lll reduction is more stable for skewed cells matrix = self.lll_matrix e = tol * self.volume ** (1 / 3) # Define metric tensor G = np.dot(matrix, matrix.T) # This sets an upper limit on the number of iterations. for count in range(100): # The steps are labelled as Ax as per the labelling scheme in the # paper. (A, B, C, E, N, Y) = ( G[0, 0], G[1, 1], G[2, 2], 2 * G[1, 2], 2 * G[0, 2], 2 * G[0, 1], ) if A > B + e or (abs(A - B) < e and abs(E) > abs(N) + e): # A1 M = [[0, -1, 0], [-1, 0, 0], [0, 0, -1]] G = dot(transpose(M), dot(G, M)) if (B > C + e) or (abs(B - C) < e and abs(N) > abs(Y) + e): # A2 M = [[-1, 0, 0], [0, 0, -1], [0, -1, 0]] G = dot(transpose(M), dot(G, M)) continue l = 0 if abs(E) < e else E / abs(E) m = 0 if abs(N) < e else N / abs(N) n = 0 if abs(Y) < e else Y / abs(Y) if l * m * n == 1: # A3 i = -1 if l == -1 else 1 j = -1 if m == -1 else 1 k = -1 if n == -1 else 1 M = [[i, 0, 0], [0, j, 0], [0, 0, k]] G = dot(transpose(M), dot(G, M)) elif l * m * n == 0 or l * m * n == -1: # A4 i = -1 if l == 1 else 1 j = -1 if m == 1 else 1 k = -1 if n == 1 else 1 if i * j * k == -1: if n == 0: k = -1 elif m == 0: j = -1 elif l == 0: i = -1 M = [[i, 0, 0], [0, j, 0], [0, 0, k]] G = dot(transpose(M), dot(G, M)) (A, B, C, E, N, Y) = ( G[0, 0], G[1, 1], G[2, 2], 2 * G[1, 2], 2 * G[0, 2], 2 * G[0, 1], ) # A5 if ( abs(E) > B + e or (abs(E - B) < e and 2 * N < Y - e) or (abs(E + B) < e and Y < -e) ): M = [[1, 0, 0], [0, 1, -E / abs(E)], [0, 0, 1]] G = dot(transpose(M), dot(G, M)) continue # A6 if ( abs(N) > A + e or (abs(A - N) < e and 2 * E < Y - e) or (abs(A + N) < e and Y < -e) ): M = [[1, 0, -N / abs(N)], [0, 1, 0], [0, 0, 1]] G = dot(transpose(M), dot(G, M)) continue # A7 if ( abs(Y) > A + e or (abs(A - Y) < e and 2 * E < N - e) or (abs(A + Y) < e and N < -e) ): M = [[1, -Y / abs(Y), 0], [0, 1, 0], [0, 0, 1]] G = dot(transpose(M), dot(G, M)) continue # A8 if E + N + Y + A + B < -e or (abs(E + N + Y + A + B) < e < Y + (A + N) * 2): M = [[1, 0, 1], [0, 1, 1], [0, 0, 1]] G = dot(transpose(M), dot(G, M)) continue break A = G[0, 0] B = G[1, 1] C = G[2, 2] E = 2 * G[1, 2] N = 2 * G[0, 2] Y = 2 * G[0, 1] a = math.sqrt(A) b = math.sqrt(B) c = math.sqrt(C) alpha = math.acos(E / 2 / b / c) / math.pi * 180 beta = math.acos(N / 2 / a / c) / math.pi * 180 gamma = math.acos(Y / 2 / a / b) / math.pi * 180 latt = Lattice.from_parameters(a, b, c, alpha, beta, gamma) mapped = self.find_mapping(latt, e, skip_rotation_matrix=True) if mapped is not None: if np.linalg.det(mapped[0].matrix) > 0: return mapped[0] return Lattice(-mapped[0].matrix) raise ValueError("can't find niggli")
[docs] def scale(self, new_volume: float) -> "Lattice": """ Return a new Lattice with volume new_volume by performing a scaling of the lattice vectors so that length proportions and angles are preserved. Args: new_volume: New volume to scale to. Returns: New lattice with desired volume. """ versors = self.matrix / self.abc geo_factor = abs(dot(np.cross(versors[0], versors[1]), versors[2])) ratios = np.array(self.abc) / self.c new_c = (new_volume / (geo_factor * np.prod(ratios))) ** (1 / 3.0) return Lattice(versors * (new_c * ratios))
[docs] def get_wigner_seitz_cell(self) -> List[List[np.ndarray]]: """ Returns the Wigner-Seitz cell for the given lattice. Returns: A list of list of coordinates. Each element in the list is a "facet" of the boundary of the Wigner Seitz cell. For instance, a list of four coordinates will represent a square facet. """ vec1 = self._matrix[0] vec2 = self._matrix[1] vec3 = self._matrix[2] list_k_points = [] for i, j, k in itertools.product([-1, 0, 1], [-1, 0, 1], [-1, 0, 1]): list_k_points.append(i * vec1 + j * vec2 + k * vec3) from scipy.spatial import Voronoi tess = Voronoi(list_k_points) to_return = [] for r in tess.ridge_dict: if r[0] == 13 or r[1] == 13: to_return.append([tess.vertices[i] for i in tess.ridge_dict[r]]) return to_return
[docs] def get_brillouin_zone(self) -> List[List[np.ndarray]]: """ Returns the Wigner-Seitz cell for the reciprocal lattice, aka the Brillouin Zone. Returns: A list of list of coordinates. Each element in the list is a "facet" of the boundary of the Brillouin Zone. For instance, a list of four coordinates will represent a square facet. """ return self.reciprocal_lattice.get_wigner_seitz_cell()
[docs] def dot( self, coords_a: Vector3Like, coords_b: Vector3Like, frac_coords: bool = False ) -> np.ndarray: """ Compute the scalar product of vector(s). Args: coords_a, coords_b: Array-like objects with the coordinates. frac_coords (bool): Boolean stating whether the vector corresponds to fractional or cartesian coordinates. Returns: one-dimensional `numpy` array. """ coords_a, coords_b = ( np.reshape(coords_a, (-1, 3)), np.reshape(coords_b, (-1, 3)), ) if len(coords_a) != len(coords_b): raise ValueError("") if np.iscomplexobj(coords_a) or np.iscomplexobj(coords_b): raise TypeError("Complex array!") if not frac_coords: cart_a, cart_b = coords_a, coords_b else: cart_a = np.reshape( [self.get_cartesian_coords(vec) for vec in coords_a], (-1, 3) ) cart_b = np.reshape( [self.get_cartesian_coords(vec) for vec in coords_b], (-1, 3) ) return np.array([dot(a, b) for a, b in zip(cart_a, cart_b)])
[docs] def norm(self, coords: Vector3Like, frac_coords: bool = True) -> float: """ Compute the norm of vector(s). Args: coords: Array-like object with the coordinates. frac_coords: Boolean stating whether the vector corresponds to fractional or cartesian coordinates. Returns: one-dimensional `numpy` array. """ return np.sqrt(self.dot(coords, coords, frac_coords=frac_coords))
[docs] def get_points_in_sphere( self, frac_points: List[Vector3Like], center: Vector3Like, r: float, zip_results=True, ) -> Union[ List[Tuple[np.ndarray, float, int, np.ndarray]], List[np.ndarray], ]: """ Find all points within a sphere from the point taking into account periodic boundary conditions. This includes sites in other periodic images. Algorithm: 1. place sphere of radius r in crystal and determine minimum supercell (parallelpiped) which would contain a sphere of radius r. for this we need the projection of a_1 on a unit vector perpendicular to a_2 & a_3 (i.e. the unit vector in the direction b_1) to determine how many a_1"s it will take to contain the sphere. Nxmax = r * length_of_b_1 / (2 Pi) 2. keep points falling within r. Args: frac_points: All points in the lattice in fractional coordinates. center: Cartesian coordinates of center of sphere. r: radius of sphere. zip_results (bool): Whether to zip the results together to group by point, or return the raw fcoord, dist, index arrays Returns: if zip_results: [(fcoord, dist, index, supercell_image) ...] since most of the time, subsequent processing requires the distance, index number of the atom, or index of the image else: fcoords, dists, inds, image """ try: from pymatgen.optimization.neighbors import find_points_in_spheres # type: ignore except ImportError: return self.get_points_in_sphere_py(frac_points=frac_points, center=center, r=r, zip_results=zip_results) else: frac_points = np.ascontiguousarray(frac_points, dtype=float) r = float(r) lattice_matrix = np.array(self.matrix) lattice_matrix = np.ascontiguousarray(lattice_matrix) cart_coords = self.get_cartesian_coords(frac_points) _, indices, images, distances = \ find_points_in_spheres(all_coords=cart_coords, center_coords=np.ascontiguousarray([center], dtype=float), r=r, pbc=np.array([1, 1, 1]), lattice=lattice_matrix, tol=1e-8) if len(indices) < 1: return [] if zip_results else [()] * 4 fcoords = frac_points[indices] + images if zip_results: return list( zip( fcoords, distances, indices, images, ) ) return [ fcoords, distances, indices, images, ]
[docs] def get_points_in_sphere_py( self, frac_points: List[Vector3Like], center: Vector3Like, r: float, zip_results=True, ) -> Union[ List[Tuple[np.ndarray, float, int, np.ndarray]], List[np.ndarray], ]: """ Find all points within a sphere from the point taking into account periodic boundary conditions. This includes sites in other periodic images. Algorithm: 1. place sphere of radius r in crystal and determine minimum supercell (parallelpiped) which would contain a sphere of radius r. for this we need the projection of a_1 on a unit vector perpendicular to a_2 & a_3 (i.e. the unit vector in the direction b_1) to determine how many a_1"s it will take to contain the sphere. Nxmax = r * length_of_b_1 / (2 Pi) 2. keep points falling within r. Args: frac_points: All points in the lattice in fractional coordinates. center: Cartesian coordinates of center of sphere. r: radius of sphere. zip_results (bool): Whether to zip the results together to group by point, or return the raw fcoord, dist, index arrays Returns: if zip_results: [(fcoord, dist, index, supercell_image) ...] since most of the time, subsequent processing requires the distance, index number of the atom, or index of the image else: fcoords, dists, inds, image """ cart_coords = self.get_cartesian_coords(frac_points) neighbors = get_points_in_spheres(all_coords=cart_coords, center_coords=np.array([center]), r=r, pbc=True, numerical_tol=1e-8, lattice=self, return_fcoords=True)[0] if len(neighbors) < 1: return [] if zip_results else [()] * 4 if zip_results: return neighbors return [np.array(i) for i in list(zip(*neighbors))]
@deprecated(get_points_in_sphere, "This is retained purely for checking purposes.") def get_points_in_sphere_old( self, frac_points: List[Vector3Like], center: Vector3Like, r: float, zip_results=True, ) -> Union[ List[Tuple[np.ndarray, float, int, np.ndarray]], Tuple[List[np.ndarray], List[float], List[int], List[np.ndarray]], ]: """ Find all points within a sphere from the point taking into account periodic boundary conditions. This includes sites in other periodic images. Algorithm: 1. place sphere of radius r in crystal and determine minimum supercell (parallelpiped) which would contain a sphere of radius r. for this we need the projection of a_1 on a unit vector perpendicular to a_2 & a_3 (i.e. the unit vector in the direction b_1) to determine how many a_1"s it will take to contain the sphere. Nxmax = r * length_of_b_1 / (2 Pi) 2. keep points falling within r. Args: frac_points: All points in the lattice in fractional coordinates. center: Cartesian coordinates of center of sphere. r: radius of sphere. zip_results (bool): Whether to zip the results together to group by point, or return the raw fcoord, dist, index arrays Returns: if zip_results: [(fcoord, dist, index, supercell_image) ...] since most of the time, subsequent processing requires the distance, index number of the atom, or index of the image else: fcoords, dists, inds, image """ # TODO: refactor to use lll matrix (nmax will be smaller) # Determine the maximum number of supercells in each direction # required to contain a sphere of radius n recp_len = np.array(self.reciprocal_lattice.abc) / (2 * pi) nmax = float(r) * recp_len + 0.01 # Get the fractional coordinates of the center of the sphere pcoords = self.get_fractional_coords(center) center = np.array(center) # Prepare the list of output atoms n = len(frac_points) fcoords = np.array(frac_points) % 1 indices = np.arange(n) # Generate all possible images that could be within `r` of `center` mins = np.floor(pcoords - nmax) maxes = np.ceil(pcoords + nmax) arange = np.arange(start=mins[0], stop=maxes[0], dtype=np.int) brange = np.arange(start=mins[1], stop=maxes[1], dtype=np.int) crange = np.arange(start=mins[2], stop=maxes[2], dtype=np.int) arange = arange[:, None] * np.array([1, 0, 0], dtype=np.int)[None, :] brange = brange[:, None] * np.array([0, 1, 0], dtype=np.int)[None, :] crange = crange[:, None] * np.array([0, 0, 1], dtype=np.int)[None, :] images = arange[:, None, None] + brange[None, :, None] + crange[None, None, :] # Generate the coordinates of all atoms within these images shifted_coords = fcoords[:, None, None, None, :] + images[None, :, :, :, :] # Determine distance from `center` cart_coords = self.get_cartesian_coords(fcoords) cart_images = self.get_cartesian_coords(images) coords = cart_coords[:, None, None, None, :] + cart_images[None, :, :, :, :] coords -= center[None, None, None, None, :] coords **= 2 d_2 = np.sum(coords, axis=4) # Determine which points are within `r` of `center` within_r = np.where(d_2 <= r ** 2) # `within_r` now contains the coordinates of each image that is # inside of the cutoff distance. It has 4 coordinates: # 0 - index of the image within `frac_points` # 1,2,3 - index of the supercell which holds the images in the x, y, z directions if zip_results: return list( zip( shifted_coords[within_r], np.sqrt(d_2[within_r]), indices[within_r[0]], images[within_r[1:]], ) ) return ( shifted_coords[within_r], np.sqrt(d_2[within_r]), indices[within_r[0]], images[within_r[1:]], )
[docs] def get_all_distances( self, fcoords1: Union[Vector3Like, List[Vector3Like]], fcoords2: Union[Vector3Like, List[Vector3Like]], ) -> np.ndarray: """ Returns the distances between two lists of coordinates taking into account periodic boundary conditions and the lattice. Note that this computes an MxN array of distances (i.e. the distance between each point in fcoords1 and every coordinate in fcoords2). This is different functionality from pbc_diff. Args: fcoords1: First set of fractional coordinates. e.g., [0.5, 0.6, 0.7] or [[1.1, 1.2, 4.3], [0.5, 0.6, 0.7]]. It can be a single coord or any array of coords. fcoords2: Second set of fractional coordinates. Returns: 2d array of cartesian distances. E.g the distance between fcoords1[i] and fcoords2[j] is distances[i,j] """ v, d2 = pbc_shortest_vectors(self, fcoords1, fcoords2, return_d2=True) return np.sqrt(d2)
[docs] def is_hexagonal( self, hex_angle_tol: float = 5, hex_length_tol: float = 0.01 ) -> bool: """ :param hex_angle_tol: Angle tolerance :param hex_length_tol: Length tolerance :return: Whether lattice corresponds to hexagonal lattice. """ lengths = self.lengths angles = self.angles right_angles = [i for i in range(3) if abs(angles[i] - 90) < hex_angle_tol] hex_angles = [i for i in range(3) if abs(angles[i] - 60) < hex_angle_tol or abs(angles[i] - 120) < hex_angle_tol] return ( len(right_angles) == 2 and len(hex_angles) == 1 and abs(lengths[right_angles[0]] - lengths[right_angles[1]]) < hex_length_tol )
[docs] def get_distance_and_image( self, frac_coords1: Vector3Like, frac_coords2: Vector3Like, jimage: Optional[Union[List[int], np.ndarray]] = None, ) -> Tuple[float, np.ndarray]: """ Gets distance between two frac_coords assuming periodic boundary conditions. If the index jimage is not specified it selects the j image nearest to the i atom and returns the distance and jimage indices in terms of lattice vector translations. If the index jimage is specified it returns the distance between the frac_coords1 and the specified jimage of frac_coords2, and the given jimage is also returned. Args: frac_coords1 (3x1 array): Reference fcoords to get distance from. frac)coords2 (3x1 array): fcoords to get distance from. jimage (3x1 array): Specific periodic image in terms of lattice translations, e.g., [1,0,0] implies to take periodic image that is one a-lattice vector away. If jimage is None, the image that is nearest to the site is found. Returns: (distance, jimage): distance and periodic lattice translations of the other site for which the distance applies. This means that the distance between frac_coords1 and (jimage + frac_coords2) is equal to distance. """ if jimage is None: v, d2 = pbc_shortest_vectors( self, frac_coords1, frac_coords2, return_d2=True ) fc = self.get_fractional_coords(v[0][0]) + frac_coords1 - frac_coords2 fc = np.array(np.round(fc), dtype=np.int) return np.sqrt(d2[0, 0]), fc jimage = np.array(jimage) mapped_vec = self.get_cartesian_coords(jimage + frac_coords2 - frac_coords1) return np.linalg.norm(mapped_vec), jimage
[docs] def get_miller_index_from_coords( self, coords: Vector3Like, coords_are_cartesian: bool = True, round_dp: int = 4, verbose: bool = True, ) -> Tuple[int, int, int]: """ Get the Miller index of a plane from a list of site coordinates. A minimum of 3 sets of coordinates are required. If more than 3 sets of coordinates are given, the best plane that minimises the distance to all points will be calculated. Args: coords (iterable): A list or numpy array of coordinates. Can be cartesian or fractional coordinates. If more than three sets of coordinates are provided, the best plane that minimises the distance to all sites will be calculated. coords_are_cartesian (bool, optional): Whether the coordinates are in cartesian space. If using fractional coordinates set to False. round_dp (int, optional): The number of decimal places to round the miller index to. verbose (bool, optional): Whether to print warnings. Returns: (tuple): The Miller index. """ if coords_are_cartesian: coords = [self.get_fractional_coords(c) for c in coords] coords = np.asarray(coords) g = coords.sum(axis=0) / coords.shape[0] # run singular value decomposition _, _, vh = np.linalg.svd(coords - g) # get unitary normal vector u_norm = vh[2, :] return get_integer_index(u_norm, round_dp=round_dp, verbose=verbose)
[docs] def get_recp_symmetry_operation( self, symprec: float = 0.01) -> List: """ Find the symmetric operations of the reciprocal lattice, to be used for hkl transformations Args: symprec: default is 0.001 """ recp_lattice = self.reciprocal_lattice_crystallographic # get symmetry operations from input conventional unit cell # Need to make sure recp lattice is big enough, otherwise symmetry # determination will fail. We set the overall volume to 1. recp_lattice = recp_lattice.scale(1) # need a localized import of structure to build a # pseudo empty lattice for SpacegroupAnalyzer from pymatgen import Structure from pymatgen.symmetry.analyzer import SpacegroupAnalyzer recp = Structure(recp_lattice, ["H"], [[0, 0, 0]]) # Creates a function that uses the symmetry operations in the # structure to find Miller indices that might give repetitive slabs analyzer = SpacegroupAnalyzer(recp, symprec=symprec) recp_symmops = analyzer.get_symmetry_operations() return recp_symmops
[docs]def get_integer_index(miller_index: Sequence[float], round_dp: int = 4, verbose: bool = True) -> Tuple[int, int, int]: """ Attempt to convert a vector of floats to whole numbers. Args: miller_index (list of float): A list miller indexes. round_dp (int, optional): The number of decimal places to round the miller index to. verbose (bool, optional): Whether to print warnings. Returns: (tuple): The Miller index. """ mi = np.asarray(miller_index) # deal with the case we have small irregular floats # that are all equal or factors of each other mi /= min([m for m in mi if m != 0]) mi /= np.max(np.abs(mi)) # deal with the case we have nice fractions md = [Fraction(n).limit_denominator(12).denominator for n in mi] mi *= reduce(lambda x, y: x * y, md) int_miller_index = np.int_(np.round(mi, 1)) mi /= np.abs(reduce(math.gcd, int_miller_index)) # round to a reasonable precision mi = np.array([round(h, round_dp) for h in mi]) # need to recalculate this after rounding as values may have changed int_miller_index = np.int_(np.round(mi, 1)) if np.any(np.abs(mi - int_miller_index) > 1e-6) and verbose: warnings.warn("Non-integer encountered in Miller index") else: mi = int_miller_index # minimise the number of negative indexes mi += 0 # converts -0 to 0 def n_minus(index): return len([h for h in index if h < 0]) if n_minus(mi) > n_minus(mi * -1): mi *= -1 # if only one index is negative, make sure it is the smallest # e.g. (-2 1 0) -> (2 -1 0) if ( sum(mi != 0) == 2 and n_minus(mi) == 1 and abs(min(mi)) > max(mi) ): mi *= -1 return tuple(mi) # type: ignore
[docs]def get_points_in_spheres(all_coords: np.ndarray, center_coords: np.ndarray, r: float, pbc: Union[bool, List[bool]] = True, numerical_tol: float = 1e-8, lattice: Lattice = None, return_fcoords: bool = False, ) -> List[List[Tuple[np.ndarray, float, int, np.ndarray]]]: """ For each point in `center_coords`, get all the neighboring points in `all_coords` that are within the cutoff radius `r`. Args: all_coords: (list of cartesian coordinates) all available points center_coords: (list of cartesian coordinates) all centering points r: (float) cutoff radius pbc: (bool or a list of bool) whether to set periodic boundaries numerical_tol: (float) numerical tolerance lattice: (Lattice) lattice to consider when PBC is enabled return_fcoords: (bool) whether to return fractional coords when pbc is set. Returns: List[List[Tuple[coords, distance, index, image]]] """ if isinstance(pbc, bool): pbc = [pbc] * 3 pbc = np.array(pbc, dtype=bool) if return_fcoords and lattice is None: raise ValueError("Lattice needs to be supplied to compute fractional coordinates") center_coords_min = np.min(center_coords, axis=0) center_coords_max = np.max(center_coords, axis=0) # The lower bound of all considered atom coords global_min = center_coords_min - r - numerical_tol global_max = center_coords_max + r + numerical_tol if np.any(pbc): if lattice is None: raise ValueError("Lattice needs to be supplied when considering periodic boundary") recp_len = np.array(lattice.reciprocal_lattice.abc) maxr = np.ceil((r + 0.15) * recp_len / (2 * math.pi)) frac_coords = lattice.get_fractional_coords(center_coords) nmin_temp = np.floor(np.min(frac_coords, axis=0)) - maxr nmax_temp = np.ceil(np.max(frac_coords, axis=0)) + maxr nmin = np.zeros_like(nmin_temp) nmin[pbc] = nmin_temp[pbc] nmax = np.ones_like(nmax_temp) nmax[pbc] = nmax_temp[pbc] all_ranges = [np.arange(x, y, dtype='int64') for x, y in zip(nmin, nmax)] matrix = lattice.matrix # temporarily hold the fractional coordinates image_offsets = lattice.get_fractional_coords(all_coords) all_fcoords = [] # only wrap periodic boundary for k in range(3): if pbc[k]: # type: ignore all_fcoords.append(np.mod(image_offsets[:, k:k+1], 1)) else: all_fcoords.append(image_offsets[:, k:k+1]) all_fcoords = np.concatenate(all_fcoords, axis=1) image_offsets = image_offsets - all_fcoords coords_in_cell = np.dot(all_fcoords, matrix) # Filter out those beyond max range valid_coords = [] valid_images = [] valid_indices = [] for image in itertools.product(*all_ranges): coords = np.dot(image, matrix) + coords_in_cell valid_index_bool = np.all(np.bitwise_and(coords > global_min[None, :], coords < global_max[None, :]), axis=1) ind = np.arange(len(all_coords)) if np.any(valid_index_bool): valid_coords.append(coords[valid_index_bool]) valid_images.append(np.tile(image, [np.sum(valid_index_bool), 1]) - image_offsets[valid_index_bool]) valid_indices.extend([k for k in ind if valid_index_bool[k]]) if len(valid_coords) < 1: return [[]] * len(center_coords) valid_coords = np.concatenate(valid_coords, axis=0) valid_images = np.concatenate(valid_images, axis=0) else: valid_coords = all_coords valid_images = [[0, 0, 0]] * len(valid_coords) valid_indices = np.arange(len(valid_coords)) # Divide the valid 3D space into cubes and compute the cube ids all_cube_index = _compute_cube_index(valid_coords, global_min, r) nx, ny, nz = _compute_cube_index(global_max, global_min, r) + 1 all_cube_index = _three_to_one(all_cube_index, ny, nz) site_cube_index = _three_to_one(_compute_cube_index(center_coords, global_min, r), ny, nz) # create cube index to coordinates, images, and indices map cube_to_coords = collections.defaultdict(list) # type: Dict[int, List] cube_to_images = collections.defaultdict(list) # type: Dict[int, List] cube_to_indices = collections.defaultdict(list) # type: Dict[int, List] for i, j, k, l in zip(all_cube_index.ravel(), valid_coords, valid_images, valid_indices): cube_to_coords[i].append(j) cube_to_images[i].append(k) cube_to_indices[i].append(l) # find all neighboring cubes for each atom in the lattice cell site_neighbors = find_neighbors(site_cube_index, nx, ny, nz) neighbors = [] # type: List[List[Tuple[np.ndarray, float, int, np.ndarray]]] for i, j in zip(center_coords, site_neighbors): l1 = np.array(_three_to_one(j, ny, nz), dtype=int).ravel() # use the cube index map to find the all the neighboring # coords, images, and indices ks = [k for k in l1 if k in cube_to_coords] if not ks: neighbors.append([]) continue nn_coords = np.concatenate([cube_to_coords[k] for k in ks], axis=0) nn_images = itertools.chain(*[cube_to_images[k] for k in ks]) nn_indices = itertools.chain(*[cube_to_indices[k] for k in ks]) dist = np.linalg.norm(nn_coords - i[None, :], axis=1) nns: List[Tuple[np.ndarray, float, int, np.ndarray]] = [] for coord, index, image, d in zip(nn_coords, nn_indices, nn_images, dist): # filtering out all sites that are beyond the cutoff # Here there is no filtering of overlapping sites if d < r + numerical_tol: if return_fcoords and (lattice is not None): coord = np.round(lattice.get_fractional_coords(coord), 10) nn = (coord, float(d), int(index), image) nns.append(nn) neighbors.append(nns) return neighbors
# The following internal methods are used in the get_points_in_sphere method. def _compute_cube_index(coords: np.ndarray, global_min: float, radius: float ) -> np.ndarray: """ Compute the cube index from coordinates Args: coords: (nx3 array) atom coordinates global_min: (float) lower boundary of coordinates radius: (float) cutoff radius Returns: (nx3 array) int indices """ return np.array(np.floor((coords - global_min) / radius), dtype=int) def _one_to_three(label1d: np.ndarray, ny: int, nz: int) -> np.ndarray: """ Convert a 1D index array to 3D index array Args: label1d: (array) 1D index array ny: (int) number of cells in y direction nz: (int) number of cells in z direction Returns: (nx3) int array of index """ last = np.mod(label1d, nz) second = np.mod((label1d - last) / nz, ny) first = (label1d - last - second * nz) / (ny * nz) return np.concatenate([first, second, last], axis=1) def _three_to_one(label3d: np.ndarray, ny: int, nz: int) -> np.ndarray: """ The reverse of _one_to_three """ return np.array(label3d[:, 0] * ny * nz + label3d[:, 1] * nz + label3d[:, 2]).reshape((-1, 1))
[docs]def find_neighbors(label: np.ndarray, nx: int, ny: int, nz: int ) -> List[np.ndarray]: """ Given a cube index, find the neighbor cube indices Args: label: (array) (n,) or (n x 3) indice array nx: (int) number of cells in y direction ny: (int) number of cells in y direction nz: (int) number of cells in z direction Returns: neighbor cell indices """ array = [[-1, 0, 1]] * 3 neighbor_vectors = np.array(list(itertools.product(*array)), dtype=int) if np.shape(label)[1] == 1: label3d = _one_to_three(label, ny, nz) else: label3d = label all_labels = label3d[:, None, :] - neighbor_vectors[None, :, :] filtered_labels = [] # filter out out-of-bound labels i.e., label < 0 for labels in all_labels: ind = (labels[:, 0] < nx) * (labels[:, 1] < ny) * (labels[:, 2] < nz) * np.all(labels > -1e-5, axis=1) filtered_labels.append(labels[ind]) return filtered_labels