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))