Source code for ase.geometry.geometry

# Copyright (C) 2010, Jesper Friis
# (see accompanying license files for details).

"""Utility tools for atoms/geometry manipulations.
   - convenient creation of slabs and interfaces of
different orientations.
   - detection of duplicate atoms / atoms within cutoff radius

import itertools
import numpy as np
from ase.geometry import complete_cell
from ase.geometry.minkowski_reduction import minkowski_reduce
from ase.utils import pbc2pbc

def translate_pretty(fractional, pbc):
    """Translates atoms such that fractional positions are minimized."""

    for i in range(3):
        if not pbc[i]:

        indices = np.argsort(fractional[:, i])
        sp = fractional[indices, i]

        widths = (np.roll(sp, 1) - sp) % 1.0
        fractional[:, i] -= sp[np.argmin(widths)]
        fractional[:, i] %= 1.0
    return fractional

[docs]def wrap_positions(positions, cell, pbc=True, center=(0.5, 0.5, 0.5), pretty_translation=False, eps=1e-7): """Wrap positions to unit cell. Returns positions changed by a multiple of the unit cell vectors to fit inside the space spanned by these vectors. See also the :meth:`ase.Atoms.wrap` method. Parameters: positions: float ndarray of shape (n, 3) Positions of the atoms cell: float ndarray of shape (3, 3) Unit cell vectors. pbc: one or 3 bool For each axis in the unit cell decides whether the positions will be moved along this axis. center: three float The positons in fractional coordinates that the new positions will be nearest possible to. pretty_translation: bool Translates atoms such that fractional coordinates are minimized. eps: float Small number to prevent slightly negative coordinates from being wrapped. Example: >>> from ase.geometry import wrap_positions >>> wrap_positions([[-0.1, 1.01, -0.5]], ... [[1, 0, 0], [0, 1, 0], [0, 0, 4]], ... pbc=[1, 1, 0]) array([[ 0.9 , 0.01, -0.5 ]]) """ if not hasattr(center, '__len__'): center = (center,) * 3 pbc = pbc2pbc(pbc) shift = np.asarray(center) - 0.5 - eps # Don't change coordinates when pbc is False shift[np.logical_not(pbc)] = 0.0 assert np.asarray(cell)[np.asarray(pbc)].any(axis=1).all(), (cell, pbc) cell = complete_cell(cell) fractional = np.linalg.solve(cell.T, np.asarray(positions).T).T - shift if pretty_translation: fractional = translate_pretty(fractional, pbc) shift = np.asarray(center) - 0.5 shift[np.logical_not(pbc)] = 0.0 fractional += shift else: for i, periodic in enumerate(pbc): if periodic: fractional[:, i] %= 1.0 fractional[:, i] += shift[i] return, cell)
[docs]def get_layers(atoms, miller, tolerance=0.001): """Returns two arrays describing which layer each atom belongs to and the distance between the layers and origo. Parameters: miller: 3 integers The Miller indices of the planes. Actually, any direction in reciprocal space works, so if a and b are two float vectors spanning an atomic plane, you can get all layers parallel to this with miller=np.cross(a,b). tolerance: float The maximum distance in Angstrom along the plane normal for counting two atoms as belonging to the same plane. Returns: tags: array of integres Array of layer indices for each atom. levels: array of floats Array of distances in Angstrom from each layer to origo. Example: >>> import numpy as np >>> from ase.spacegroup import crystal >>> atoms = crystal('Al', [(0,0,0)], spacegroup=225, cellpar=4.05) >>> np.round(atoms.positions, decimals=5) array([[ 0. , 0. , 0. ], [ 0. , 2.025, 2.025], [ 2.025, 0. , 2.025], [ 2.025, 2.025, 0. ]]) >>> get_layers(atoms, (0,0,1)) # doctest: +ELLIPSIS (array([0, 1, 1, 0]...), array([ 0. , 2.025])) """ miller = np.asarray(miller) metric =, atoms.cell.T) c = np.linalg.solve(metric.T, miller.T).T miller_norm = np.sqrt(, miller)) d =, miller) / miller_norm keys = np.argsort(d) ikeys = np.argsort(keys) mask = np.concatenate(([True], np.diff(d[keys]) > tolerance)) tags = np.cumsum(mask)[ikeys] if tags.min() == 1: tags -= 1 levels = d[keys][mask] return tags, levels
[docs]def find_mic(v, cell, pbc=True): """Finds the minimum-image representation of vector(s) v""" pbc = cell.any(1) & pbc2pbc(pbc) v = np.array(v) single = len(v.shape) == 1 v = np.atleast_2d(v) if np.sum(pbc) > 0: cell = complete_cell(cell) rcell, _ = minkowski_reduce(cell, pbc=pbc) # in a Minkowski-reduced cell we only need to test nearest neighbors cs = [np.arange(-1 * p, p + 1) for p in pbc] neighbor_cells = list(itertools.product(*cs)) positions = wrap_positions(v, rcell, pbc=pbc, eps=0) vmin = positions.copy() vlen = np.linalg.norm(positions, axis=1) for nbr in neighbor_cells: trial = positions +, nbr) trial_len = np.linalg.norm(trial, axis=1) indices = np.where(trial_len < vlen) vmin[indices] = trial[indices] vlen[indices] = trial_len[indices] else: vmin = v.copy() vlen = np.linalg.norm(vmin, axis=1) if single: return vmin[0], vlen[0] else: return vmin, vlen
[docs]def get_angles(v1, v2, cell=None, pbc=None): """Get angles formed by two lists of vectors. calculate angle in degrees between vectors v1 and v2 Set a cell and pbc to enable minimum image convention, otherwise angles are taken as-is. """ # Check if using mic if cell is not None or pbc is not None: if cell is None or pbc is None: raise ValueError("cell or pbc must be both set or both be None") v1 = find_mic(v1, cell, pbc)[0] v2 = find_mic(v2, cell, pbc)[0] nv1 = np.linalg.norm(v1, axis=1)[:, np.newaxis] nv2 = np.linalg.norm(v2, axis=1)[:, np.newaxis] if (nv1 <= 0).any() or (nv2 <= 0).any(): raise ZeroDivisionError('Undefined angle') v1 /= nv1 v2 /= nv2 # We just normalized the vectors, but in some cases we can get # bad things like 1+2e-16. These we clip away: angles = np.arccos(np.einsum('ij,ij->i', v1, v2).clip(-1.0, 1.0)) return np.degrees(angles)
[docs]def get_distances(p1, p2=None, cell=None, pbc=None): """Return distance matrix of every position in p1 with every position in p2 if p2 is not set, it is assumed that distances between all positions in p1 are desired. p2 will be set to p1 in this case. Use set cell and pbc to use the minimum image convention. """ p1 = np.atleast_2d(p1) if p2 is None: np1 = len(p1) ind1, ind2 = np.triu_indices(np1, k=1) D = p1[ind2] - p1[ind1] else: p2 = np.atleast_2d(p2) D = (p2[np.newaxis, :, :] - p1[:, np.newaxis, :]).reshape((-1, 3)) # Check if using mic if cell is not None or pbc is not None: if cell is None or pbc is None: raise ValueError("cell or pbc must be both set or both be None") D, D_len = find_mic(D, cell, pbc) else: D_len = np.sqrt((D**2).sum(1)) if p2 is None: Dout = np.zeros((np1, np1, 3)) Dout[(ind1, ind2)] = D Dout -= np.transpose(Dout, axes=(1, 0, 2)) Dout_len = np.zeros((np1, np1)) Dout_len[(ind1, ind2)] = D_len Dout_len += Dout_len.T return Dout, Dout_len # Expand back to matrix indexing D.shape = (-1, len(p2), 3) D_len.shape = (-1, len(p2)) return D, D_len
[docs]def get_duplicate_atoms(atoms, cutoff=0.1, delete=False): """Get list of duplicate atoms and delete them if requested. Identify all atoms which lie within the cutoff radius of each other. Delete one set of them if delete == True. """ from scipy.spatial.distance import pdist dists = pdist(atoms.get_positions(), 'sqeuclidean') dup = np.nonzero(dists < cutoff**2) rem = np.array(_row_col_from_pdist(len(atoms), dup[0])) if delete: if rem.size != 0: del atoms[rem[:, 0]] else: return rem
def _row_col_from_pdist(dim, i): """Calculate the i,j index in the square matrix for an index in a condensed (triangular) matrix. """ i = np.array(i) b = 1 - 2 * dim x = (np.floor((-b - np.sqrt(b**2 - 8 * i)) / 2)).astype(int) y = (i + x * (b + x + 2) / 2 + 1).astype(int) if i.shape: return list(zip(x, y)) else: return [(x, y)]
[docs]def permute_axes(atoms, permutation): """Permute axes of unit cell and atom positions. Considers only cell and atomic positions. Other vector quantities such as momenta are not modified.""" assert (np.sort(permutation) == np.arange(3)).all() permuted = atoms.copy() scaled = permuted.get_scaled_positions() permuted.set_cell(permuted.cell.permute_axes(permutation), scale_atoms=False) permuted.set_scaled_positions(scaled[:, permutation]) permuted.set_pbc(permuted.pbc[permutation]) return permuted