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# fmt: off
"""Tests for NeighborList"""
import numpy as np
import pytest
from ase import Atoms
from ase.build import bulk
from ase.neighborlist import (
NeighborList,
NewPrimitiveNeighborList,
PrimitiveNeighborList,
)
@pytest.mark.parametrize(
'primitive',
[PrimitiveNeighborList, NewPrimitiveNeighborList],
)
@pytest.mark.parametrize('bothways', [False, True])
@pytest.mark.parametrize('self_interaction', [False, True])
@pytest.mark.parametrize('sorted', [False, True])
def test_unique(
sorted: bool,
self_interaction: bool,
bothways: bool,
primitive,
):
"""Test if there are no duplicates in the neighbor lists"""
atoms = Atoms('H2', positions=[(0, 0, 0), (0, 0, 1)])
nl = NeighborList(
[0.5, 0.5],
skin=0.1,
sorted=sorted,
self_interaction=self_interaction,
bothways=bothways,
primitive=primitive,
)
nl.update(atoms)
tmp = []
for i in range(len(atoms)):
neighbors, offsets = nl.get_neighbors(i)
tmp += [(i, n, *o) for n, o in zip(neighbors, offsets)]
assert len(set(tmp)) == len(tmp)
def count(nl: NeighborList, atoms: Atoms):
"""Count the numbers of neighboring atoms for all the atoms
Returns
-------
d : float
Sum of distances over all nearest-neighbor pairs
c : npt.NDArray[np.int_]
Numbers of neighboring atoms for all the atoms
"""
c = np.zeros(len(atoms), int)
R = atoms.get_positions()
cell = atoms.get_cell()
d = 0.0
for a in range(len(atoms)):
i, offsets = nl.get_neighbors(a)
for j in i:
c[j] += 1
c[a] += len(i)
d += (((R[i] + np.dot(offsets, cell) - R[a])**2).sum(1)**0.5).sum()
return d, c
# scipy sparse uses matrix subclass internally
@pytest.mark.filterwarnings('ignore:the matrix subclass')
@pytest.mark.slow()
@pytest.mark.parametrize('sorted', [False, True])
def test_supercell(sorted):
"""Test if NeighborList works for a supercell as expected"""
atoms = Atoms(numbers=range(10),
cell=[(0.2, 1.2, 1.4),
(1.4, 0.1, 1.6),
(1.3, 2.0, -0.1)])
rng = np.random.RandomState(42)
atoms.set_scaled_positions(3 * rng.random((10, 3)) - 1)
for p1 in range(2):
for p2 in range(2):
for p3 in range(2):
# print(p1, p2, p3)
atoms.set_pbc((p1, p2, p3))
cutoffs = atoms.numbers * 0.2 + 0.5
nl = NeighborList(cutoffs, skin=0.0, sorted=sorted)
nl.update(atoms)
d, c = count(nl, atoms)
atoms2 = atoms.repeat((p1 + 1, p2 + 1, p3 + 1))
cutoffs2 = atoms2.numbers * 0.2 + 0.5
nl2 = NeighborList(cutoffs2, skin=0.0, sorted=sorted)
nl2.update(atoms2)
d2, c2 = count(nl2, atoms2)
c2.shape = (-1, 10) # row: images, column: atoms
# if the sum of nearest-neighbor distances gets larger
# according to the supercell size
dd = d * (p1 + 1) * (p2 + 1) * (p3 + 1) - d2
assert abs(dd) < 1e-10
# if each repeated image has the same numbers of neighbors
assert not (c2 - c).any()
def test_H2():
h2 = Atoms('H2', positions=[(0, 0, 0), (0, 0, 1)])
nl = NeighborList([0.5, 0.5], skin=0.1, sorted=True, self_interaction=False)
nl2 = NeighborList([0.5, 0.5], skin=0.1, sorted=True,
self_interaction=False,
primitive=NewPrimitiveNeighborList)
assert nl2.update(h2)
assert nl.update(h2)
assert not nl.update(h2)
assert (nl.get_neighbors(0)[0] == [1]).all()
m = np.zeros((2, 2))
m[0, 1] = 1
assert np.array_equal(nl.get_connectivity_matrix(sparse=False), m)
assert np.array_equal(nl.get_connectivity_matrix(sparse=True).todense(), m)
assert np.array_equal(nl.get_connectivity_matrix().todense(),
nl2.get_connectivity_matrix().todense())
h2[1].z += 0.09
assert not nl.update(h2)
assert (nl.get_neighbors(0)[0] == [1]).all()
h2[1].z += 0.09
assert nl.update(h2)
assert (nl.get_neighbors(0)[0] == []).all()
assert nl.nupdates == 2
def test_H2_shape_and_type():
h2 = Atoms('H2', positions=[(0, 0, 0), (0, 0, 1)])
nl = NeighborList([0.1, 0.1], skin=0.1, bothways=True,
self_interaction=False)
assert nl.update(h2)
assert nl.get_neighbors(0)[1].shape == (0, 3)
assert nl.get_neighbors(0)[1].dtype == int
def test_fcc():
x = bulk('X', 'fcc', a=2**0.5)
nl = NeighborList([0.5], skin=0.01, bothways=True, self_interaction=False)
nl.update(x)
assert len(nl.get_neighbors(0)[0]) == 12
nl = NeighborList([0.5] * 27, skin=0.01, bothways=True,
self_interaction=False)
nl.update(x * (3, 3, 3))
for a in range(27):
assert len(nl.get_neighbors(a)[0]) == 12
assert not np.any(nl.get_neighbors(13)[1])
def test_use_scaled_positions():
c = 0.0058
for NeighborListClass in [PrimitiveNeighborList, NewPrimitiveNeighborList]:
nl = NeighborListClass([c, c],
skin=0.0,
sorted=True,
self_interaction=False,
use_scaled_positions=True)
nl.update([True, True, True],
np.eye(3) * 7.56,
np.array([[0, 0, 0],
[0, 0, 0.99875]]))
n0, d0 = nl.get_neighbors(0)
n1, d1 = nl.get_neighbors(1)
# != is xor
assert (np.all(n0 == [0]) and np.all(d0 == [0, 0, 1])) != \
(np.all(n1 == [1]) and np.all(d1 == [0, 0, -1]))
def test_empty_neighbor_list():
# Test empty neighbor list
nl = PrimitiveNeighborList([])
nl.update([True, True, True],
np.eye(3) * 7.56,
np.zeros((0, 3)))
@pytest.mark.parametrize('bothways', [False, True])
@pytest.mark.parametrize('self_interaction', [False, True])
@pytest.mark.parametrize('sort', [False, True])
def test_equivalence_of_primitive_classes(sort, self_interaction, bothways):
"""Test if two primitive neighbor-list classes make the same naighbors"""
# diamond structure in the primitive cell
pbc_c = np.array([True, True, True])
cell_cv = np.array([[0., 3.37316113, 3.37316113],
[3.37316113, 0., 3.37316113],
[3.37316113, 3.37316113, 0.]])
spos_ac = np.array([[0., 0., 0.],
[0.25, 0.25, 0.25]])
natoms = len(spos_ac)
cutoff_a = np.array([8.0, 8.0])
info = [[] for _ in range(2)] # neighbor info collector for each primitive
primitives = [PrimitiveNeighborList, NewPrimitiveNeighborList]
for ip, primitive in enumerate(primitives):
nl = primitive(
cutoff_a,
skin=0.0,
sorted=sort,
self_interaction=self_interaction,
bothways=bothways,
use_scaled_positions=True,
)
nl.update(pbc_c, cell_cv, spos_ac)
# collect neighbor info into a list of tuples
# each tuple has the form (i1, i2, o1, o2, o3)
# i1: 1st atom
# i2: 2nd atom
# o1: offset along 1st cell vector
# o2: offset along 2nd cell vector
# o3: offset along 3rd cell vector
for i in range(natoms):
info[ip].extend([(i, n, *o) for n, o in zip(*nl.get_neighbors(i))])
def reverse(t: tuple):
return t[1], t[0], -t[2], -t[3], -t[4]
for ip in range(2):
# (i1, i2, +o1, +o2, +o3) and (i2, i1, -o1, -o2, -o3) is the same pair
# the following guarantees i0 <= i1
info[ip] = [t if t[0] <= t[1] else reverse(t) for t in info[ip]]
info[ip] = sorted(info[ip]) # sort by i1, i2, o1, o2, o3
# check if the both primitive classes provide the same neighbors
assert np.all(info[0] == info[1])
def test_small_cell_and_large_cutoff():
# See: https://gitlab.com/ase/ase/-/issues/441
cutoff = 50
atoms = bulk('Cu', 'fcc', a=3.6)
atoms *= (2, 2, 2)
atoms.set_pbc(False)
radii = cutoff * np.ones(len(atoms.get_atomic_numbers()))
neighborhood_new = NeighborList(
radii, skin=0.0, self_interaction=False, bothways=True,
primitive=NewPrimitiveNeighborList
)
neighborhood_old = NeighborList(
radii, skin=0.0, self_interaction=False, bothways=True,
primitive=PrimitiveNeighborList
)
neighborhood_new.update(atoms)
neighborhood_old.update(atoms)
n0, d0 = neighborhood_new.get_neighbors(0)
n1, d1 = neighborhood_old.get_neighbors(0)
assert np.all(n0 == n1)
assert np.all(d0 == d1)
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