def test_get_points_in_sphere(self):
# This is a non-niggli representation of a cubic lattice
latt = Lattice([[1,5,0],[0,1,0],[5,0,1]])
# evenly spaced points array between 0 and 1
pts = np.array(list(itertools.product(range(5), repeat=3))) / 5
pts = latt.get_fractional_coords(pts)
self.assertEqual(len(latt.get_points_in_sphere(
pts, [0, 0, 0], 0.20001)), 7)
self.assertEqual(len(latt.get_points_in_sphere(
pts, [0.5, 0.5, 0.5], 1.0001)), 552)
def test_get_points_in_sphere(self):
# This is a non-niggli representation of a cubic lattice
latt = Lattice([[1, 5, 0], [0, 1, 0], [5, 0, 1]])
# evenly spaced points array between 0 and 1
pts = np.array(list(itertools.product(range(5), repeat=3))) / 5
pts = latt.get_fractional_coords(pts)
self.assertEqual(
len(latt.get_points_in_sphere(pts, [0, 0, 0], 0.20001)), 7)
self.assertEqual(
len(latt.get_points_in_sphere(pts, [0.5, 0.5, 0.5], 1.0001)), 552)
def test_get_points_in_sphere(self):
# This is a non-niggli representation of a cubic lattice
latt = Lattice([[1, 5, 0], [0, 1, 0], [5, 0, 1]])
# evenly spaced points array between 0 and 1
pts = np.array(list(itertools.product(range(5), repeat=3))) / 5
pts = latt.get_fractional_coords(pts)
# Test getting neighbors within 1 neighbor distance of the origin
fcoords, dists, inds, images = latt.get_points_in_sphere(pts, [0, 0, 0], 0.20001, zip_results=False)
self.assertEqual(len(fcoords), 7) # There are 7 neighbors
self.assertEqual(np.isclose(dists, 0.2).sum(), 6) # 6 are at 0.2
self.assertEqual(np.isclose(dists, 0).sum(), 1) # 1 is at 0
self.assertEqual(len(set(inds)), 7) # They have unique indices
self.assertArrayEqual(images[np.isclose(dists, 0)], [[0, 0, 0]])
# More complicated case, using the zip output
result = latt.get_points_in_sphere(pts, [0.5, 0.5, 0.5], 1.0001)
self.assertEqual(len(result), 552)
self.assertEqual(len(result[0]), 4) # coords, dists, ind, supercell
def test_get_points_in_sphere(self):
# This is a non-niggli representation of a cubic lattice
latt = Lattice([[1, 5, 0], [0, 1, 0], [5, 0, 1]])
# evenly spaced points array between 0 and 1
pts = np.array(list(itertools.product(range(5), repeat=3))) / 5
pts = latt.get_fractional_coords(pts)
# Test getting neighbors within 1 neighbor distance of the origin
fcoords, dists, inds, images = latt.get_points_in_sphere(pts, [0, 0, 0], 0.20001,
zip_results=False)
self.assertEqual(len(fcoords), 7) # There are 7 neighbors
self.assertEqual(np.isclose(dists, 0.2).sum(), 6) # 6 are at 0.2
self.assertEqual(np.isclose(dists, 0).sum(), 1) # 1 is at 0
self.assertEqual(len(set(inds)), 7) # They have unique indices
self.assertArrayEqual(images[np.isclose(dists, 0)], [[0, 0, 0]])
# More complicated case, using the zip output
result = latt.get_points_in_sphere(pts, [0.5, 0.5, 0.5], 1.0001)
self.assertEqual(len(result), 552)
self.assertEqual(len(result[0]), 4) # coords, dists, ind, supercell
