def test_ordered_primitive_to_disordered_supercell(self):
sm_atoms = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size = 'num_atoms',
comparator=OrderDisorderElementComparator())
sm_sites = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size = 'num_sites',
comparator=OrderDisorderElementComparator())
lp = Lattice.orthorhombic(10, 20, 30)
pcoords = [[0, 0, 0],
[0.5, 0.5, 0.5]]
ls = Lattice.orthorhombic(20,20,30)
scoords = [[0, 0, 0],
[0.5, 0, 0],
[0.25, 0.5, 0.5],
[0.75, 0.5, 0.5]]
s1 = Structure(lp, ['Na', 'Cl'], pcoords)
s2 = Structure(ls, [{'Na':0.5}, {'Na':0.5}, {'Cl':0.5}, {'Cl':0.5}], scoords)
self.assertTrue(sm_sites.fit(s1, s2))
self.assertFalse(sm_atoms.fit(s1, s2))
def test_disordered_primitive_to_ordered_supercell(self):
sm_atoms = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size = 'num_atoms',
comparator=OrderDisorderElementComparator())
sm_sites = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size = 'num_sites',
comparator=OrderDisorderElementComparator())
lp = Lattice.orthorhombic(10, 20, 30)
pcoords = [[0, 0, 0],
[0.5, 0.5, 0.5]]
ls = Lattice.orthorhombic(20,20,30)
scoords = [[0, 0, 0],
[0.75, 0.5, 0.5]]
prim = Structure(lp, [{'Na':0.5}, {'Cl':0.5}], pcoords)
supercell = Structure(ls, ['Na', 'Cl'], scoords)
supercell.make_supercell([[-1,1,0],[0,1,1],[1,0,0]])
self.assertFalse(sm_sites.fit(prim, supercell))
self.assertTrue(sm_atoms.fit(prim, supercell))
self.assertRaises(ValueError, sm_atoms.get_s2_like_s1, prim, supercell)
self.assertEqual(len(sm_atoms.get_s2_like_s1(supercell, prim)), 4)
def test_get_supercell_matrix(self):
sm = StructureMatcher(ltol=0.1, stol=0.3, angle_tol=2,
primitive_cell=False, scale=True,
attempt_supercell=True)
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0,0.1],[0,0,0.2],[.7,.4,.5]])
s1.make_supercell([2,1,1])
s2 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0.1,0],[0,0.1,-0.95],[-.7,.5,.375]])
result = sm.get_supercell_matrix(s1, s2)
self.assertTrue((result == [[-2,0,0],[0,1,0],[0,0,1]]).all())
s1 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0,0.1],[0,0,0.2],[.7,.4,.5]])
s1.make_supercell([[1, -1, 0],[0, 0, -1],[0, 1, 0]])
s2 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0.1,0],[0,0.1,-0.95],[-.7,.5,.375]])
result = sm.get_supercell_matrix(s1, s2)
self.assertTrue((result == [[-1,-1,0],[0,0,-1],[0,1,0]]).all())
#test when the supercell is a subset
sm = StructureMatcher(ltol=0.1, stol=0.3, angle_tol=2,
primitive_cell=False, scale=True,
attempt_supercell=True, allow_subset=True)
del s1[0]
result = sm.get_supercell_matrix(s1, s2)
self.assertTrue((result == [[-1,-1,0],[0,0,-1],[0,1,0]]).all())
def test_subset(self):
sm = StructureMatcher(
ltol=0.2,
stol=0.3,
angle_tol=5,
primitive_cell=False,
scale=True,
attempt_supercell=False,
allow_subset=True,
)
l = Lattice.orthorhombic(10, 20, 30)
s1 = Structure(l, ["Si", "Si", "Ag"], [[0, 0, 0.1], [0, 0, 0.2], [0.7, 0.4, 0.5]])
s2 = Structure(l, ["Si", "Ag"], [[0, 0.1, 0], [-0.7, 0.5, 0.4]])
result = sm.get_s2_like_s1(s1, s2)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords, [0, 0, 0.1])), 1)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords, [0.7, 0.4, 0.5])), 1)
# test with fewer species in s2
s1 = Structure(l, ["Si", "Ag", "Si"], [[0, 0, 0.1], [0, 0, 0.2], [0.7, 0.4, 0.5]])
s2 = Structure(l, ["Si", "Si"], [[0, 0.1, 0], [-0.7, 0.5, 0.4]])
result = sm.get_s2_like_s1(s1, s2)
mindists = np.min(s1.lattice.get_all_distances(s1.frac_coords, result.frac_coords), axis=0)
self.assertLess(np.max(mindists), 1e-6)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords, [0, 0, 0.1])), 1)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords, [0.7, 0.4, 0.5])), 1)
# test with not enough sites in s1
# test with fewer species in s2
s1 = Structure(l, ["Si", "Ag", "Cl"], [[0, 0, 0.1], [0, 0, 0.2], [0.7, 0.4, 0.5]])
s2 = Structure(l, ["Si", "Si"], [[0, 0.1, 0], [-0.7, 0.5, 0.4]])
self.assertEqual(sm.get_s2_like_s1(s1, s2), None)
def test_get_mapping(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=False,
allow_subset = True)
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Ag', 'Si', 'Si'],
[[.7,.4,.5],[0,0,0.1],[0,0,0.2]])
s1.make_supercell([2,1,1])
s2 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0.1,-0.95],[0,0.1,0],[-.7,.5,.375]])
shuffle = [2,0,1,3,5,4]
s1 = Structure.from_sites([s1[i] for i in shuffle])
#test the mapping
s2.make_supercell([2,1,1])
#equal sizes
for i, x in enumerate(sm.get_mapping(s1, s2)):
self.assertEqual(s1[x].species_and_occu,
s2[i].species_and_occu)
del s1[0]
#s1 is subset of s2
for i, x in enumerate(sm.get_mapping(s2, s1)):
self.assertEqual(s1[i].species_and_occu,
s2[x].species_and_occu)
#s2 is smaller than s1
del s2[0]
del s2[1]
self.assertRaises(ValueError, sm.get_mapping, s2, s1)
def test_get_s2_large_s2(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=False,
attempt_supercell=True, allow_subset=False,
supercell_size='volume')
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Ag', 'Si', 'Si'],
[[.7,.4,.5],[0,0,0.1],[0,0,0.2]])
l2 = Lattice.orthorhombic(1.01, 2.01, 3.01)
s2 = Structure(l2, ['Si', 'Si', 'Ag'],
[[0,0.1,-0.95],[0,0.1,0],[-.7,.5,.375]])
s2.make_supercell([[0,-1,0],[1,0,0],[0,0,1]])
result = sm.get_s2_like_s1(s1, s2)
for x,y in zip(s1, result):
self.assertLess(x.distance(y), 0.08)
def test_rms_vs_minimax(self):
# This tests that structures with adjusted RMS less than stol, but minimax
# greater than stol are treated properly
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5, primitive_cell=False)
l = Lattice.orthorhombic(1, 2, 12)
sp = ["Si", "Si", "Al"]
s1 = Structure(l, sp, [[0.5, 0, 0], [0, 0, 0], [0, 0, 0.5]])
s2 = Structure(l, sp, [[0.5, 0, 0], [0, 0, 0], [0, 0, 0.6]])
self.assertArrayAlmostEqual(sm.get_rms_dist(s1, s2), (0.32 ** 0.5 / 2, 0.4))
self.assertEqual(sm.fit(s1, s2), False)
self.assertEqual(sm.fit_anonymous(s1, s2), False)
self.assertEqual(sm.get_mapping(s1, s2), None)
def test_find_match2(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5, primitive_cell=True, scale=True, attempt_supercell=False)
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ["Si", "Si"], [[0, 0, 0.1], [0, 0, 0.2]])
s2 = Structure(l, ["Si", "Si"], [[0, 0.1, 0], [0, 0.1, -0.95]])
s1, s2, fu, s1_supercell = sm._preprocess(s1, s2, False)
match = sm._strict_match(s1, s2, fu, s1_supercell=False, use_rms=True, break_on_match=False)
scale_matrix = match[2]
s2.make_supercell(scale_matrix)
s2.translate_sites(range(len(s2)), match[3])
self.assertAlmostEqual(np.sum(s2.frac_coords), 0.3)
self.assertAlmostEqual(np.sum(s2.frac_coords[:, :2]), 0)
def test_find_match1(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5, primitive_cell=True, scale=True, attempt_supercell=False)
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ["Si", "Si", "Ag"], [[0, 0, 0.1], [0, 0, 0.2], [0.7, 0.4, 0.5]])
s2 = Structure(l, ["Si", "Si", "Ag"], [[0, 0.1, 0], [0, 0.1, -0.95], [0.7, 0.5, 0.375]])
s1, s2, fu, s1_supercell = sm._preprocess(s1, s2, False)
match = sm._strict_match(s1, s2, fu, s1_supercell=True, use_rms=True, break_on_match=False)
scale_matrix = match[2]
s2.make_supercell(scale_matrix)
fc = s2.frac_coords + match[3]
fc -= np.round(fc)
self.assertAlmostEqual(np.sum(fc), 0.9)
self.assertAlmostEqual(np.sum(fc[:, :2]), 0.1)
cart_dist = np.sum(match[1] * (l.volume / 3) ** (1 / 3))
self.assertAlmostEqual(cart_dist, 0.15)
def test_find_match2(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=True, scale=True,
attempt_supercell=False)
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Si', 'Si'], [[0,0,0.1],[0,0,0.2]])
s2 = Structure(l, ['Si', 'Si'], [[0,0.1,0],[0,0.1,-0.95]])
match = sm._find_match(s1, s2, break_on_match = False,
use_rms = True, niggli = False)
scale_matrix = np.round(np.dot(match[2].matrix,
s2.lattice.inv_matrix)).astype('int')
s2.make_supercell(scale_matrix)
fc = s2.frac_coords + match[3]
fc -= np.round(fc)
self.assertAlmostEqual(np.sum(fc), 0.3)
self.assertAlmostEqual(np.sum(fc[:,:2]), 0)
def test_get_s2_like_s1(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True)
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0,0.1],[0,0,0.2],[.7,.4,.5]])
s1.make_supercell([2,1,1])
s2 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0.1,0],[0,0.1,-0.95],[-.7,.5,.375]])
result = sm.get_s2_like_s1(s1, s2)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0.35,0.4,0.5])), 1)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0,0,0.125])), 1)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0,0,0.175])), 1)
def test_cart_dists(self):
sm = StructureMatcher()
l = Lattice.orthorhombic(1, 2, 3)
s1 = np.array([[0.13, 0.25, 0.37], [0.1, 0.2, 0.3]])
s2 = np.array([[0.11, 0.22, 0.33]])
s3 = np.array([[0.1, 0.2, 0.3], [0.11, 0.2, 0.3]])
s4 = np.array([[0.1, 0.2, 0.3], [0.1, 0.6, 0.7]])
mask = np.array([[False, False]])
mask2 = np.array([[False, True]])
mask3 = np.array([[False, False], [False, False]])
mask4 = np.array([[False, True], [False, True]])
n1 = (len(s1) / l.volume) ** (1/3)
n2 = (len(s2) / l.volume) ** (1/3)
self.assertRaises(ValueError, sm._cart_dists, s2, s1, l, mask.T, n2)
self.assertRaises(ValueError, sm._cart_dists, s1, s2, l, mask.T, n1)
d, ft, s = sm._cart_dists(s1, s2, l, mask, n1)
self.assertTrue(np.allclose(d, [0]))
self.assertTrue(np.allclose(ft, [-0.01, -0.02, -0.03]))
self.assertTrue(np.allclose(s, [1]))
#check that masking best value works
d, ft, s = sm._cart_dists(s1, s2, l, mask2, n1)
self.assertTrue(np.allclose(d, [0]))
self.assertTrue(np.allclose(ft, [0.02, 0.03, 0.04]))
self.assertTrue(np.allclose(s, [0]))
#check that averaging of translation is done properly
d, ft, s = sm._cart_dists(s1, s3, l, mask3, n1)
self.assertTrue(np.allclose(d, [0.08093341]*2))
self.assertTrue(np.allclose(ft, [0.01, 0.025, 0.035]))
self.assertTrue(np.allclose(s, [1, 0]))
#check distances are large when mask allows no 'real' mapping
d, ft, s = sm._cart_dists(s1, s4, l, mask4, n1)
self.assertTrue(np.min(d) > 1e8)
self.assertTrue(np.min(ft) > 1e8)
def test_subset(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=False,
allow_subset=True)
l = Lattice.orthorhombic(10, 20, 30)
s1 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0,0.1],[0,0,0.2],[.7,.4,.5]])
s2 = Structure(l, ['Si', 'Ag'],
[[0,0.1,0],[-.7,.5,.4]])
result = sm.get_s2_like_s1(s1, s2)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0,0,0.1])), 1)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0.7,0.4,0.5])), 1)
#test with fewer species in s2
s1 = Structure(l, ['Si', 'Ag', 'Si'],
[[0,0,0.1],[0,0,0.2],[.7,.4,.5]])
s2 = Structure(l, ['Si', 'Si'],
[[0,0.1,0],[-.7,.5,.4]])
result = sm.get_s2_like_s1(s1, s2)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0,0,0.1])), 1)
self.assertEqual(len(find_in_coord_list_pbc(result.frac_coords,
[0.7,0.4,0.5])), 1)
#test with not enough sites in s1
#test with fewer species in s2
s1 = Structure(l, ['Si', 'Ag', 'Cl'],
[[0,0,0.1],[0,0,0.2],[.7,.4,.5]])
s2 = Structure(l, ['Si', 'Si'],
[[0,0.1,0],[-.7,.5,.4]])
self.assertEqual(sm.get_s2_like_s1(s1, s2), None)
def test_supercell_subsets(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True, allow_subset=True,
supercell_size='volume')
sm_no_s = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True, allow_subset=False,
supercell_size='volume')
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Ag', 'Si', 'Si'],
[[.7,.4,.5],[0,0,0.1],[0,0,0.2]])
s1.make_supercell([2,1,1])
s2 = Structure(l, ['Si', 'Si', 'Ag'],
[[0,0.1,-0.95],[0,0.1,0],[-.7,.5,.375]])
shuffle = [0,2,1,3,4,5]
s1 = Structure.from_sites([s1[i] for i in shuffle])
#test when s1 is exact supercell of s2
result = sm.get_s2_like_s1(s1, s2)
for a, b in zip(s1, result):
self.assertTrue(a.distance(b) < 0.08)
self.assertEqual(a.species_and_occu, b.species_and_occu)
self.assertTrue(sm.fit(s1, s2))
self.assertTrue(sm.fit(s2, s1))
self.assertTrue(sm_no_s.fit(s1, s2))
self.assertTrue(sm_no_s.fit(s2, s1))
rms = (0.048604032430991401, 0.059527539448807391)
self.assertTrue(np.allclose(sm.get_rms_dist(s1, s2), rms))
self.assertTrue(np.allclose(sm.get_rms_dist(s2, s1), rms))
#test when the supercell is a subset of s2
subset_supercell = s1.copy()
del subset_supercell[0]
result = sm.get_s2_like_s1(subset_supercell, s2)
self.assertEqual(len(result), 6)
for a, b in zip(subset_supercell, result):
self.assertTrue(a.distance(b) < 0.08)
self.assertEqual(a.species_and_occu, b.species_and_occu)
self.assertTrue(sm.fit(subset_supercell, s2))
self.assertTrue(sm.fit(s2, subset_supercell))
self.assertFalse(sm_no_s.fit(subset_supercell, s2))
self.assertFalse(sm_no_s.fit(s2, subset_supercell))
rms = (0.053243049896333279, 0.059527539448807336)
self.assertTrue(np.allclose(sm.get_rms_dist(subset_supercell, s2), rms))
self.assertTrue(np.allclose(sm.get_rms_dist(s2, subset_supercell), rms))
#test when s2 (once made a supercell) is a subset of s1
s2_missing_site = s2.copy()
del s2_missing_site[1]
result = sm.get_s2_like_s1(s1, s2_missing_site)
for a, b in zip((s1[i] for i in (0, 2, 4, 5)), result):
self.assertTrue(a.distance(b) < 0.08)
self.assertEqual(a.species_and_occu, b.species_and_occu)
self.assertTrue(sm.fit(s1, s2_missing_site))
self.assertTrue(sm.fit(s2_missing_site, s1))
self.assertFalse(sm_no_s.fit(s1, s2_missing_site))
self.assertFalse(sm_no_s.fit(s2_missing_site, s1))
rms = (0.029763769724403633, 0.029763769724403987)
self.assertTrue(np.allclose(sm.get_rms_dist(s1, s2_missing_site), rms))
self.assertTrue(np.allclose(sm.get_rms_dist(s2_missing_site, s1), rms))
def test_supercell_subsets(self):
sm = StructureMatcher(ltol=0.2,
stol=0.3,
angle_tol=5,
primitive_cell=False,
scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size='volume')
sm_no_s = StructureMatcher(ltol=0.2,
stol=0.3,
angle_tol=5,
primitive_cell=False,
scale=True,
attempt_supercell=True,
allow_subset=False,
supercell_size='volume')
l = Lattice.orthorhombic(1, 2, 3)
s1 = Structure(l, ['Ag', 'Si', 'Si'],
[[.7, .4, .5], [0, 0, 0.1], [0, 0, 0.2]])
s1.make_supercell([2, 1, 1])
s2 = Structure(l, ['Si', 'Si', 'Ag'],
[[0, 0.1, -0.95], [0, 0.1, 0], [-.7, .5, .375]])
shuffle = [0, 2, 1, 3, 4, 5]
s1 = Structure.from_sites([s1[i] for i in shuffle])
#test when s1 is exact supercell of s2
result = sm.get_s2_like_s1(s1, s2)
for a, b in zip(s1, result):
self.assertTrue(a.distance(b) < 0.08)
self.assertEqual(a.species_and_occu, b.species_and_occu)
self.assertTrue(sm.fit(s1, s2))
self.assertTrue(sm.fit(s2, s1))
self.assertTrue(sm_no_s.fit(s1, s2))
self.assertTrue(sm_no_s.fit(s2, s1))
rms = (0.048604032430991401, 0.059527539448807391)
self.assertTrue(np.allclose(sm.get_rms_dist(s1, s2), rms))
self.assertTrue(np.allclose(sm.get_rms_dist(s2, s1), rms))
#test when the supercell is a subset of s2
subset_supercell = s1.copy()
del subset_supercell[0]
result = sm.get_s2_like_s1(subset_supercell, s2)
self.assertEqual(len(result), 6)
for a, b in zip(subset_supercell, result):
self.assertTrue(a.distance(b) < 0.08)
self.assertEqual(a.species_and_occu, b.species_and_occu)
self.assertTrue(sm.fit(subset_supercell, s2))
self.assertTrue(sm.fit(s2, subset_supercell))
self.assertFalse(sm_no_s.fit(subset_supercell, s2))
self.assertFalse(sm_no_s.fit(s2, subset_supercell))
rms = (0.053243049896333279, 0.059527539448807336)
self.assertTrue(np.allclose(sm.get_rms_dist(subset_supercell, s2),
rms))
self.assertTrue(np.allclose(sm.get_rms_dist(s2, subset_supercell),
rms))
#test when s2 (once made a supercell) is a subset of s1
s2_missing_site = s2.copy()
del s2_missing_site[1]
result = sm.get_s2_like_s1(s1, s2_missing_site)
for a, b in zip((s1[i] for i in (0, 2, 4, 5)), result):
self.assertTrue(a.distance(b) < 0.08)
self.assertEqual(a.species_and_occu, b.species_and_occu)
self.assertTrue(sm.fit(s1, s2_missing_site))
self.assertTrue(sm.fit(s2_missing_site, s1))
self.assertFalse(sm_no_s.fit(s1, s2_missing_site))
self.assertFalse(sm_no_s.fit(s2_missing_site, s1))
rms = (0.029763769724403633, 0.029763769724403987)
self.assertTrue(np.allclose(sm.get_rms_dist(s1, s2_missing_site), rms))
self.assertTrue(np.allclose(sm.get_rms_dist(s2_missing_site, s1), rms))
