def get_nn_info(self, structure: Structure,
n: int) -> List[Dict]:
"""
Get all near-neighbor sites as well as the associated image locations
and weights of the site with index n using the closest neighbor
distance-based method.
Args:
structure (Structure): input structure.
n (integer): index of site for which to determine near
neighbors.
Returns:
siw (list of tuples (Site, array, float)): tuples, each one
of which represents a neighbor site, its image location,
and its weight.
"""
site = structure[n]
neighs_dists = structure.get_neighbors(site, self.cutoff)
siw = []
for nn in neighs_dists:
siw.append({'site': nn,
'image': self._get_image(structure, nn),
'weight': nn.nn_distance,
'site_index': self._get_original_site(structure, nn)})
return siw
def is_equivalent(structure : Structure, atoms_1 : tuple, atoms_2 : tuple , eps=0.05):
"""
Find Vacancy Strucutres for diffusion into and out of the specified atom_i site.
:param structure: Structure
Structure to calculate diffusion pathways
:param atom_i: int
Atom to get diffion path from
:return: [ Structure ]
"""
# To Find Pathway, look for voronoi edges
structure = structure.copy() # type: Structure
coords = get_midpoint(structure, atoms_1[0], atoms_1[1])
structure.append('H', coords)
coords = get_midpoint(structure, atoms_2[0], atoms_2[1])
structure.append('H', coords)
dist_1 = structure.get_neighbors(structure[-2], 3)
dist_2 = structure.get_neighbors(structure[-1], 3)
dist_1.sort(key=lambda x: x[1])
dist_2.sort(key=lambda x: x[1])
for (site_a, site_b) in zip(dist_1, dist_2):
if abs(site_a[1] - site_b[1]) > eps:
return False
elif site_a[0].specie != site_b[0].specie:
return False
return True
def anion_inter(x, *args):
cations = args[0]
anions = args[1]
lattice = args[2]
Species_list = args[3]
num_atoms = args[4]
cutoff_distance = args[5]
BVpara = args[6]
Formal_Valence = args[7]
wycks = args[8]
space_group = args[9]
b0 = args[10]
center_atom_in_regular_poly = args[11]
max_angle = args[12]
nearest_neighbor_distance = args[13]
Shannon_anion_anion = args[14]
pymat_structure = Structure(lattice, Species_list, x.reshape(num_atoms, 3))
anions_indx = pymat_structure.indices_from_symbol(anions[0])
logic = 0
for anion in anions_indx:
NN = pymat_structure.get_neighbors(pymat_structure.sites[anion],
4,
include_index=True)
#print len(NN)
for neigh in NN:
if str(neigh[0].specie.symbol) in anions:
#print neigh[1]
logic += 0.25 / (1 + np.exp(50 *
(neigh[1] - Shannon_anion_anion)))
#print logic
return logic / 10
def angle_fun(x, *args):
cations = args[0]
anions = args[1]
lattice = args[2]
Species_list = args[3]
num_atoms = args[4]
cutoff_distance = args[5]
BVpara = args[6]
Formal_Valence = args[7]
wycks = args[8]
space_group = args[9]
b0 = args[10]
center_atom_in_regular_poly = args[11]
max_angle = args[12]
nearest_neighbor_distance = args[13]
E_angle = 0
pymat_structure = Structure(lattice, Species_list, x.reshape(num_atoms, 3))
for i, ion in enumerate(Species_list):
if str(ion) == center_atom_in_regular_poly:
NN = pymat_structure.get_neighbors(pymat_structure.sites[i],
nearest_neighbor_distance,
include_index=True)
for x in itertools.combinations(NN, 2):
'''I compute the angles manually because I found inconsitancies
in the pymatgen angle calculator. This may not be the case
in newer versions. Please test.'''
ba = x[0][0].coords - pymat_structure.cart_coords[i]
bc = x[1][0].coords - pymat_structure.cart_coords[i]
cosine_angle = np.dot(
ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
angle = np.arccos(cosine_angle)
degr = np.rad2deg(angle)
if np.isnan(angle) or 0 <= degr <= 20 or 150 <= degr <= 180:
#print "true"
pass
else:
#print "angle = {}".format(np.rad2deg(angle))
diff = np.array(
[abs(np.pi - angle),
abs(np.pi / 2 - angle)])
''' Be mindful that this sum E_angle grows proportionally with
the number of ions in the cell. Therefore, if the number of ions is large
E_angle may dominate the minimizer. Benchmarking and Scaling is needed... BY UNDERGRAD'''
E_angle += abs(np.deg2rad(max_angle) - diff.min())
#print "This is the angle sum {}".format(E_angle)
return (E_angle)
def check_hydrogens(s: Structure,
neighbor_threshold: float = 2.0,
strictness: str = 'CH') -> bool:
"""
checks if there are any hydrogens in a structure object.
Args:
s (pymatgen structure object): structure to be checked
neighbor_threshold (float): threshold for distance that is still considered to be bonded
strictness (str): available levels: 'tight': returns false if there is no H at all, 'medium'
returns false if there are carbons but no hydrogens, 'CH' (default) checks if there are
carbons with less or equal 2 non-hydrogen neighbors (e.g. the most common case for aromatic rings).
If those have also no hydrogen bonded to them, it will return False
Returns:
"""
symbols = s.symbol_set
return_val = True
if strictness == 'tight':
logger.debug('running H check with tight strictness')
if not 'H' in symbols:
return False
elif strictness == 'medium':
logger.debug('running H check with medium strictness')
if 'C' in symbols:
if not 'H' in symbols:
return False
elif strictness == 'CH':
logger.debug('running H check with CH strictness')
if 'C' in symbols:
c_sites = s.indices_from_symbol('C')
for c_site in c_sites:
neighbors = s.get_neighbors(s[c_site], neighbor_threshold)
neighbors_symbol_list = [
neighbor_site[0].species_string
for neighbor_site in neighbors
]
neighbors_no_h = [
neighbor_site for neighbor_site in neighbors
if neighbor_site[0].species_string != 'H'
]
if len(neighbors_symbol_list) == 0:
return False
if len(neighbors_no_h) <= 2:
if len(neighbors) - len(neighbors_no_h) == 0:
return False
return return_val
class StructureFeaturesTest(PymatgenTest):
def setUp(self):
self.diamond = Structure(
Lattice([[2.189, 0, 1.264], [0.73, 2.064, 1.264], [0, 0, 2.528]]),
["C0+", "C0+"], [[2.554, 1.806, 4.423], [0.365, 0.258, 0.632]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=True,
site_properties=None)
self.diamond_no_oxi = Structure(
Lattice([[2.189, 0, 1.264], [0.73, 2.064, 1.264], [0, 0, 2.528]]),
["C", "C"], [[2.554, 1.806, 4.423], [0.365, 0.258, 0.632]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=True,
site_properties=None)
self.nacl = Structure(Lattice([[3.485, 0,
2.012], [1.162, 3.286, 2.012],
[0, 0, 4.025]]), ["Na1+", "Cl1-"],
[[0, 0, 0], [2.324, 1.643, 4.025]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=True,
site_properties=None)
self.cscl = Structure(Lattice([[4.209, 0, 0], [0, 4.209, 0],
[0, 0, 4.209]]), ["Cl1-", "Cs1+"],
[[2.105, 2.1045, 2.1045], [0, 0, 0]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=True,
site_properties=None)
self.ni3al = Structure(
Lattice([[3.52, 0, 0], [0, 3.52, 0],
[0, 0, 3.52]]), [
"Al",
] + ["Ni"] * 3,
[[0, 0, 0], [0.5, 0.5, 0], [0.5, 0, 0.5], [0, 0.5, 0.5]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=False,
site_properties=None)
self.sc = Structure(Lattice([[3.52, 0, 0], [0, 3.52, 0],
[0, 0, 3.52]]), ["Al"], [[0, 0, 0]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=False)
self.bond_angles = range(5, 180, 5)
def test_density_features(self):
df = DensityFeatures()
f = df.featurize(self.diamond)
self.assertAlmostEqual(f[0], 3.49, 2)
self.assertAlmostEqual(f[1], 5.71, 2)
self.assertAlmostEqual(f[2], 0.25, 2)
f = df.featurize(self.nacl)
self.assertAlmostEqual(f[0], 2.105, 2)
self.assertAlmostEqual(f[1], 23.046, 2)
self.assertAlmostEqual(f[2], 0.620, 2)
nacl_disordered = copy.deepcopy(self.nacl)
nacl_disordered.replace_species({"Cl1-": "Cl0.99H0.01"})
self.assertFalse(df.precheck(nacl_disordered))
structures = [self.diamond, self.nacl, nacl_disordered]
df2 = pd.DataFrame({"structure": structures})
self.assertAlmostEqual(df.precheck_dataframe(df2, "structure"), 2 / 3)
def test_global_symmetry(self):
gsf = GlobalSymmetryFeatures()
self.assertEqual(gsf.featurize(self.diamond), [227, "cubic", 1, True])
def test_dimensionality(self):
cscl = PymatgenTest.get_structure("CsCl")
df = Dimensionality(bonds={("Cs", "Cl"): 3.5})
self.assertEqual(df.featurize(cscl)[0], 1)
df = Dimensionality(bonds={("Cs", "Cl"): 3.7})
self.assertEqual(df.featurize(cscl)[0], 3)
def test_rdf_and_peaks(self):
## Test diamond
rdforig = RadialDistributionFunction().featurize(self.diamond)
rdf = rdforig[0]
# Make sure it the last bin is cutoff-bin_max
self.assertAlmostEqual(max(rdf['distances']), 19.9)
# Verify bin sizes
self.assertEqual(len(rdf['distribution']), 200)
# Make sure it gets all of the peaks
self.assertEqual(np.count_nonzero(rdf['distribution']), 116)
# Check the values for a few individual peaks
self.assertAlmostEqual(rdf['distribution'][int(round(1.5 / 0.1))],
15.12755155)
self.assertAlmostEqual(rdf['distribution'][int(round(2.9 / 0.1))],
12.53193948)
self.assertAlmostEqual(rdf['distribution'][int(round(19.9 / 0.1))],
0.822126129)
# Repeat test with NaCl (omitting comments). Altering cutoff distance
rdforig = RadialDistributionFunction(cutoff=10).featurize(self.nacl)
rdf = rdforig[0]
self.assertAlmostEqual(max(rdf['distances']), 9.9)
self.assertEqual(len(rdf['distribution']), 100)
self.assertEqual(np.count_nonzero(rdf['distribution']), 11)
self.assertAlmostEqual(rdf['distribution'][int(round(2.8 / 0.1))],
27.09214168)
self.assertAlmostEqual(rdf['distribution'][int(round(4.0 / 0.1))],
26.83338723)
self.assertAlmostEqual(rdf['distribution'][int(round(9.8 / 0.1))],
3.024406467)
# Repeat test with CsCl. Altering cutoff distance and bin_size
rdforig = RadialDistributionFunction(cutoff=8,
bin_size=0.5).featurize(self.cscl)
rdf = rdforig[0]
self.assertAlmostEqual(max(rdf['distances']), 7.5)
self.assertEqual(len(rdf['distribution']), 16)
self.assertEqual(np.count_nonzero(rdf['distribution']), 5)
self.assertAlmostEqual(rdf['distribution'][int(round(3.5 / 0.5))],
6.741265585)
self.assertAlmostEqual(rdf['distribution'][int(round(4.0 / 0.5))],
3.937582548)
self.assertAlmostEqual(rdf['distribution'][int(round(7.0 / 0.5))],
1.805505363)
def test_prdf(self):
# Test a few peaks in diamond
# These expected numbers were derived by performing
# the calculation in another code
distances, prdf = PartialRadialDistributionFunction().compute_prdf(
self.diamond)
self.assertEqual(len(prdf.values()), 1)
self.assertAlmostEqual(prdf[('C', 'C')][int(round(1.4 / 0.1))], 0)
self.assertAlmostEqual(prdf[('C', 'C')][int(round(1.5 / 0.1))],
1.32445167622)
self.assertAlmostEqual(max(distances), 19.9)
self.assertAlmostEqual(prdf[('C', 'C')][int(round(19.9 / 0.1))],
0.07197902)
# Test a few peaks in CsCl, make sure it gets all types correctly
distances, prdf = PartialRadialDistributionFunction(
cutoff=10).compute_prdf(self.cscl)
self.assertEqual(len(prdf.values()), 4)
self.assertAlmostEqual(max(distances), 9.9)
self.assertAlmostEqual(prdf[('Cs', 'Cl')][int(round(3.6 / 0.1))],
0.477823197)
self.assertAlmostEqual(prdf[('Cl', 'Cs')][int(round(3.6 / 0.1))],
0.477823197)
self.assertAlmostEqual(prdf[('Cs', 'Cs')][int(round(3.6 / 0.1))], 0)
# Do Ni3Al, make sure it captures the antisymmetry of Ni/Al sites
distances, prdf = PartialRadialDistributionFunction(cutoff=10, bin_size=0.5)\
.compute_prdf(self.ni3al)
self.assertEqual(len(prdf.values()), 4)
self.assertAlmostEqual(prdf[('Ni', 'Al')][int(round(2 / 0.5))],
0.125236677)
self.assertAlmostEqual(prdf[('Al', 'Ni')][int(round(2 / 0.5))],
0.37571003)
self.assertAlmostEqual(prdf[('Al', 'Al')][int(round(2 / 0.5))], 0)
# Check the fit operation
featurizer = PartialRadialDistributionFunction()
featurizer.fit([self.diamond, self.cscl, self.ni3al])
self.assertEqual({'Cs', 'Cl', 'C', 'Ni', 'Al'},
set(featurizer.elements_))
featurizer.exclude_elems = ['Cs', 'Al']
featurizer.fit([self.diamond, self.cscl, self.ni3al])
self.assertEqual({'Cl', 'C', 'Ni'}, set(featurizer.elements_))
featurizer.include_elems = ['H']
featurizer.fit([self.diamond, self.cscl, self.ni3al])
self.assertEqual({'H', 'Cl', 'C', 'Ni'}, set(featurizer.elements_))
# Check the feature labels
featurizer.exclude_elems = ()
featurizer.include_elems = ()
featurizer.elements_ = ['Al', 'Ni']
labels = featurizer.feature_labels()
n_bins = len(featurizer._make_bins()) - 1
self.assertEqual(3 * n_bins, len(labels))
self.assertIn('Al-Ni PRDF r=0.00-0.10', labels)
# Check the featurize method
featurizer.elements_ = ['C']
features = featurizer.featurize(self.diamond)
prdf = featurizer.compute_prdf(self.diamond)[1]
self.assertArrayAlmostEqual(features, prdf[('C', 'C')])
# Check the featurize_dataframe
df = pd.DataFrame.from_dict({"structure": [self.diamond, self.cscl]})
featurizer.fit(df["structure"])
df = featurizer.featurize_dataframe(df, col_id="structure")
self.assertEqual(df["Cs-Cl PRDF r=0.00-0.10"][0], 0.0)
self.assertAlmostEqual(df["Cl-Cl PRDF r=19.70-19.80"][1], 0.049, 3)
self.assertEqual(df["Cl-Cl PRDF r=19.90-20.00"][0], 0.0)
# Make sure labels and features are in the same order
featurizer.elements_ = ['Al', 'Ni']
features = featurizer.featurize(self.ni3al)
labels = featurizer.feature_labels()
prdf = featurizer.compute_prdf(self.ni3al)[1]
self.assertEqual((n_bins * 3, ), features.shape)
self.assertTrue(labels[0].startswith('Al-Al'))
self.assertTrue(labels[n_bins].startswith('Al-Ni'))
self.assertTrue(labels[2 * n_bins].startswith('Ni-Ni'))
self.assertArrayAlmostEqual(
features,
np.hstack(
[prdf[('Al', 'Al')], prdf[('Al', 'Ni')], prdf[('Ni', 'Ni')]]))
def test_redf(self):
d = ElectronicRadialDistributionFunction().featurize(self.diamond)[0]
self.assertAlmostEqual(int(1000 * d["distances"][0]), 25)
self.assertAlmostEqual(int(1000 * d["distribution"][0]), 0)
self.assertAlmostEqual(
int(1000 * d["distances"][len(d["distances"]) - 1]), 6175)
self.assertAlmostEqual(
int(1000 * d["distribution"][len(d["distances"]) - 1]), 0)
d = ElectronicRadialDistributionFunction().featurize(self.nacl)[0]
self.assertAlmostEqual(int(1000 * d["distances"][0]), 25)
self.assertAlmostEqual(int(1000 * d["distribution"][0]), 0)
self.assertAlmostEqual(int(1000 * d["distances"][56]), 2825)
self.assertAlmostEqual(int(1000 * d["distribution"][56]), -2108)
self.assertAlmostEqual(
int(1000 * d["distances"][len(d["distances"]) - 1]), 9875)
d = ElectronicRadialDistributionFunction().featurize(self.cscl)[0]
self.assertAlmostEqual(int(1000 * d["distances"][0]), 25)
self.assertAlmostEqual(int(1000 * d["distribution"][0]), 0)
self.assertAlmostEqual(int(1000 * d["distances"][72]), 3625)
self.assertAlmostEqual(int(1000 * d["distribution"][72]), -2194)
self.assertAlmostEqual(
int(1000 * d["distances"][len(d["distances"]) - 1]), 7275)
def test_coulomb_matrix(self):
# flat
cm = CoulombMatrix(flatten=True)
df = pd.DataFrame({"s": [self.diamond, self.nacl]})
with self.assertRaises(NotFittedError):
df = cm.featurize_dataframe(df, "s")
df = cm.fit_featurize_dataframe(df, "s")
labels = cm.feature_labels()
self.assertListEqual(labels,
["coulomb matrix eig 0", "coulomb matrix eig 1"])
self.assertArrayAlmostEqual(df[labels].iloc[0], [49.169453, 24.546758],
decimal=5)
self.assertArrayAlmostEqual(df[labels].iloc[1],
[153.774731, 452.894322],
decimal=5)
# matrix
species = ["C", "C", "H", "H"]
coords = [[0, 0, 0], [0, 0, 1.203], [0, 0, -1.06], [0, 0, 2.263]]
acetylene = Molecule(species, coords)
morig = CoulombMatrix(flatten=False).featurize(acetylene)
mtarget = [[36.858, 15.835391290, 2.995098235, 1.402827813], \
[15.835391290, 36.858, 1.4028278132103624, 2.9950982], \
[2.9368896127, 1.402827813, 0.5, 0.159279959], \
[1.4028278132, 2.995098235, 0.159279959, 0.5]]
self.assertAlmostEqual(int(np.linalg.norm(morig - np.array(mtarget))),
0)
m = CoulombMatrix(diag_elems=False,
flatten=False).featurize(acetylene)[0]
self.assertAlmostEqual(m[0][0], 0.0)
self.assertAlmostEqual(m[1][1], 0.0)
self.assertAlmostEqual(m[2][2], 0.0)
self.assertAlmostEqual(m[3][3], 0.0)
def test_sine_coulomb_matrix(self):
# flat
scm = SineCoulombMatrix(flatten=True)
df = pd.DataFrame({"s": [self.sc, self.ni3al]})
with self.assertRaises(NotFittedError):
df = scm.featurize_dataframe(df, "s")
df = scm.fit_featurize_dataframe(df, "s")
labels = scm.feature_labels()
self.assertEqual(labels[0], "sine coulomb matrix eig 0")
self.assertArrayAlmostEqual(df[labels].iloc[0],
[235.740418, 0.0, 0.0, 0.0],
decimal=5)
self.assertArrayAlmostEqual(
df[labels].iloc[1],
[232.578562, 1656.288171, 1403.106576, 1403.106576],
decimal=5)
# matrix
scm = SineCoulombMatrix(flatten=False)
sin_mat = scm.featurize(self.diamond)
mtarget = [[36.8581, 6.147068], [6.147068, 36.8581]]
self.assertAlmostEqual(np.linalg.norm(sin_mat - np.array(mtarget)),
0.0,
places=4)
scm = SineCoulombMatrix(diag_elems=False, flatten=False)
sin_mat = scm.featurize(self.diamond)[0]
self.assertEqual(sin_mat[0][0], 0)
self.assertEqual(sin_mat[1][1], 0)
def test_orbital_field_matrix(self):
ofm_maker = OrbitalFieldMatrix(flatten=False)
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((32, 32))
mtarget[1][1] = 1.4789015 # 1.3675444
mtarget[1][3] = 1.4789015 # 1.3675444
mtarget[3][1] = 1.4789015 # 1.3675444
mtarget[3][3] = 1.4789015 # 1.3675444 if for a coord# of exactly 4
for i in range(32):
for j in range(32):
if not i in [1, 3] and not j in [1, 3]:
self.assertEqual(ofm[i, j], 0.0)
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(np.linalg.norm(ofm - mtarget), 0.0, places=4)
ofm_maker = OrbitalFieldMatrix(True, flatten=False)
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((39, 39))
mtarget[1][1] = 1.4789015
mtarget[1][3] = 1.4789015
mtarget[3][1] = 1.4789015
mtarget[3][3] = 1.4789015
mtarget[1][33] = 1.4789015
mtarget[3][33] = 1.4789015
mtarget[33][1] = 1.4789015
mtarget[33][3] = 1.4789015
mtarget[33][33] = 1.4789015
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(np.linalg.norm(ofm - mtarget), 0.0, places=4)
ofm_flat = OrbitalFieldMatrix(period_tag=False, flatten=True)
self.assertEqual(len(ofm_flat.feature_labels()), 1024)
ofm_flat = OrbitalFieldMatrix(period_tag=True, flatten=True)
self.assertEqual(len(ofm_flat.feature_labels()), 1521)
ofm_vector = ofm_flat.featurize(self.diamond)
for ix in [40, 42, 72, 118, 120, 150, 1288, 1320]:
self.assertAlmostEqual(ofm_vector[ix], 1.4789015345821415)
def test_min_relative_distances(self):
self.assertAlmostEqual(
MinimumRelativeDistances().featurize(self.diamond_no_oxi)[0][0],
1.1052576)
self.assertAlmostEqual(
MinimumRelativeDistances().featurize(self.nacl)[0][0], 0.8891443)
self.assertAlmostEqual(
MinimumRelativeDistances().featurize(self.cscl)[0][0], 0.9877540)
def test_sitestatsfingerprint(self):
# Test matrix.
op_struct_fp = SiteStatsFingerprint.from_preset("OPSiteFingerprint",
stats=None)
opvals = op_struct_fp.featurize(self.diamond)
oplabels = op_struct_fp.feature_labels()
self.assertAlmostEqual(opvals[10][0], 0.9995, places=7)
self.assertAlmostEqual(opvals[10][1], 0.9995, places=7)
opvals = op_struct_fp.featurize(self.nacl)
self.assertAlmostEqual(opvals[18][0], 0.9995, places=7)
self.assertAlmostEqual(opvals[18][1], 0.9995, places=7)
opvals = op_struct_fp.featurize(self.cscl)
self.assertAlmostEqual(opvals[22][0], 0.9995, places=7)
self.assertAlmostEqual(opvals[22][1], 0.9995, places=7)
# Test stats.
op_struct_fp = SiteStatsFingerprint.from_preset("OPSiteFingerprint")
opvals = op_struct_fp.featurize(self.diamond)
print(opvals, '**')
self.assertAlmostEqual(opvals[0], 0.0005, places=7)
self.assertAlmostEqual(opvals[1], 0, places=7)
self.assertAlmostEqual(opvals[2], 0.0005, places=7)
self.assertAlmostEqual(opvals[3], 0.0, places=7)
self.assertAlmostEqual(opvals[4], 0.0005, places=7)
self.assertAlmostEqual(opvals[18], 0.0805, places=7)
self.assertAlmostEqual(opvals[20], 0.9995, places=7)
self.assertAlmostEqual(opvals[21], 0, places=7)
self.assertAlmostEqual(opvals[22], 0.0075, places=7)
self.assertAlmostEqual(opvals[24], 0.2355, places=7)
self.assertAlmostEqual(opvals[-1], 0.0, places=7)
# Test coordination number
cn_fp = SiteStatsFingerprint.from_preset("JmolNN", stats=("mean", ))
cn_vals = cn_fp.featurize(self.diamond)
self.assertEqual(cn_vals[0], 4.0)
# Test the covariance
prop_fp = SiteStatsFingerprint(
SiteElementalProperty(properties=["Number", "AtomicWeight"]),
stats=["mean"],
covariance=True)
# Test the feature labels
labels = prop_fp.feature_labels()
self.assertEqual(3, len(labels))
# Test a structure with all the same type (cov should be zero)
features = prop_fp.featurize(self.diamond)
self.assertArrayAlmostEqual(features, [6, 12.0107, 0])
# Test a structure with only one atom (cov should be zero too)
features = prop_fp.featurize(self.sc)
self.assertArrayAlmostEqual([13, 26.9815386, 0], features)
# Test a structure with nonzero covariance
features = prop_fp.featurize(self.nacl)
self.assertArrayAlmostEqual([14, 29.22138464, 37.38969216], features)
def test_ewald(self):
# Add oxidation states to all of the structures
for s in [self.nacl, self.cscl, self.diamond]:
s.add_oxidation_state_by_guess()
# Test basic
ewald = EwaldEnergy(accuracy=2)
self.assertArrayAlmostEqual(ewald.featurize(self.diamond), [0])
self.assertAlmostEqual(ewald.featurize(self.nacl)[0], -8.84173626, 2)
self.assertLess(ewald.featurize(self.nacl),
ewald.featurize(self.cscl)) # Atoms are closer in NaCl
# Perform Ewald summation by "hand",
# Using the result from GULP
self.assertArrayAlmostEqual([-8.84173626], ewald.featurize(self.nacl),
2)
def test_bondfractions(self):
# Test individual structures with featurize
bf_md = BondFractions.from_preset("MinimumDistanceNN")
bf_md.no_oxi = True
bf_md.fit([self.diamond_no_oxi])
self.assertArrayEqual(bf_md.featurize(self.diamond), [1.0])
self.assertArrayEqual(bf_md.featurize(self.diamond_no_oxi), [1.0])
bf_voronoi = BondFractions.from_preset("VoronoiNN")
bf_voronoi.bbv = float("nan")
bf_voronoi.fit([self.nacl])
bond_fracs = bf_voronoi.featurize(self.nacl)
bond_names = bf_voronoi.feature_labels()
ref = {
'Na+ - Na+ bond frac.': 0.25,
'Cl- - Na+ bond frac.': 0.5,
'Cl- - Cl- bond frac.': 0.25
}
self.assertDictEqual(dict(zip(bond_names, bond_fracs)), ref)
# Test to make sure dataframe behavior is as intended
s_list = [self.diamond_no_oxi, self.ni3al]
df = pd.DataFrame.from_dict({'s': s_list})
bf_voronoi.fit(df['s'])
df = bf_voronoi.featurize_dataframe(df, 's')
# Ensure all data is properly labelled and organized
self.assertArrayEqual(df['C - C bond frac.'].as_matrix(),
[1.0, np.nan])
self.assertArrayEqual(df['Al - Ni bond frac.'].as_matrix(),
[np.nan, 0.5])
self.assertArrayEqual(df['Al - Al bond frac.'].as_matrix(),
[np.nan, 0.0])
self.assertArrayEqual(df['Ni - Ni bond frac.'].as_matrix(),
[np.nan, 0.5])
# Test to make sure bad_bond_values (bbv) are still changed correctly
# and check inplace behavior of featurize dataframe.
bf_voronoi.bbv = 0.0
df = pd.DataFrame.from_dict({'s': s_list})
df = bf_voronoi.featurize_dataframe(df, 's')
self.assertArrayEqual(df['C - C bond frac.'].as_matrix(), [1.0, 0.0])
self.assertArrayEqual(df['Al - Ni bond frac.'].as_matrix(), [0.0, 0.5])
self.assertArrayEqual(df['Al - Al bond frac.'].as_matrix(), [0.0, 0.0])
self.assertArrayEqual(df['Ni - Ni bond frac.'].as_matrix(), [0.0, 0.5])
def test_bob(self):
# Test a single fit and featurization
scm = SineCoulombMatrix(flatten=False)
bob = BagofBonds(coulomb_matrix=scm, token=' - ')
bob.fit([self.ni3al])
truth1 = [
235.74041833262768, 1486.4464890775491, 1486.4464890775491,
1486.4464890775491, 38.69353092306119, 38.69353092306119,
38.69353092306119, 38.69353092306119, 38.69353092306119,
38.69353092306119, 83.33991275736257, 83.33991275736257,
83.33991275736257, 83.33991275736257, 83.33991275736257,
83.33991275736257
]
truth1_labels = [
'Al site #0', 'Ni site #0', 'Ni site #1', 'Ni site #2',
'Al - Ni bond #0', 'Al - Ni bond #1', 'Al - Ni bond #2',
'Al - Ni bond #3', 'Al - Ni bond #4', 'Al - Ni bond #5',
'Ni - Ni bond #0', 'Ni - Ni bond #1', 'Ni - Ni bond #2',
'Ni - Ni bond #3', 'Ni - Ni bond #4', 'Ni - Ni bond #5'
]
self.assertAlmostEqual(bob.featurize(self.ni3al), truth1)
self.assertEqual(bob.feature_labels(), truth1_labels)
# Test padding from fitting and dataframe featurization
bob.coulomb_matrix = CoulombMatrix(flatten=False)
bob.fit([self.ni3al, self.cscl, self.diamond_no_oxi])
df = pd.DataFrame({'structures': [self.cscl]})
df = bob.featurize_dataframe(df, 'structures')
self.assertEqual(len(df.columns.values), 25)
self.assertAlmostEqual(df['Cs site #0'][0], 7513.468312122532)
self.assertAlmostEqual(df['Al site #0'][0], 0.0)
self.assertAlmostEqual(df['Cs - Cl bond #1'][0], 135.74726437398044)
self.assertAlmostEqual(df['Al - Ni bond #0'][0], 0.0)
# Test error handling for bad fits or null fits
bob = BagofBonds(CoulombMatrix(flatten=False))
self.assertRaises(NotFittedError, bob.featurize, self.nacl)
bob.fit([self.ni3al, self.diamond])
self.assertRaises(ValueError, bob.featurize, self.nacl)\
def test_ward_prb_2017_lpd(self):
"""Test the local property difference attributes from Ward 2017"""
f = SiteStatsFingerprint.from_preset(
"LocalPropertyDifference_ward-prb-2017")
# Test diamond
features = f.featurize(self.diamond)
self.assertArrayAlmostEqual(features, [0] * (22 * 5))
features = f.featurize(self.diamond_no_oxi)
self.assertArrayAlmostEqual(features, [0] * (22 * 5))
# Test CsCl
big_face_area = np.sqrt(3) * 3 / 2 * (2 / 4 / 4)
small_face_area = 0.125
big_face_diff = 55 - 17
features = f.featurize(self.cscl)
labels = f.feature_labels()
my_label = 'mean local difference in Number'
self.assertAlmostEqual((8 * big_face_area * big_face_diff) /
(8 * big_face_area + 6 * small_face_area),
features[labels.index(my_label)],
places=3)
my_label = 'range local difference in Electronegativity'
self.assertAlmostEqual(0, features[labels.index(my_label)], places=3)
def test_ward_prb_2017_efftcn(self):
"""Test the effective coordination number attributes of Ward 2017"""
f = SiteStatsFingerprint.from_preset(
"CoordinationNumber_ward-prb-2017")
# Test Ni3Al
features = f.featurize(self.ni3al)
labels = f.feature_labels()
my_label = 'mean CN_VoronoiNN'
self.assertAlmostEqual(12, features[labels.index(my_label)])
self.assertArrayAlmostEqual([12, 12, 0, 12, 0], features)
def test_ward_prb_2017_strhet(self):
f = StructuralHeterogeneity()
# Test Ni3Al, which is uniform
features = f.featurize(self.ni3al)
self.assertArrayAlmostEqual([0, 1, 1, 0, 0, 0, 0, 0, 0], features)
# Do CsCl, which has variation in the neighbors
big_face_area = np.sqrt(3) * 3 / 2 * (2 / 4 / 4)
small_face_area = 0.125
average_dist = (8 * np.sqrt(
3) / 2 * big_face_area + 6 * small_face_area) \
/ (8 * big_face_area + 6 * small_face_area)
rel_var = (8 * abs(np.sqrt(3) / 2 - average_dist) * big_face_area +
6 * abs(1 - average_dist) * small_face_area) \
/ (8 * big_face_area + 6 * small_face_area) / average_dist
cscl = Structure(Lattice([[4.209, 0, 0], [0, 4.209, 0],
[0, 0, 4.209]]), ["Cl1-", "Cs1+"],
[[0.5, 0.5, 0.5], [0, 0, 0]],
validate_proximity=False,
to_unit_cell=False,
coords_are_cartesian=False,
site_properties=None)
features = f.featurize(cscl)
self.assertArrayAlmostEqual(
[0, 1, 1, rel_var, rel_var, 0, rel_var, 0, 0], features)
def test_packing_efficiency(self):
f = MaximumPackingEfficiency()
# Test L1_2
self.assertArrayAlmostEqual([np.pi / 3 / np.sqrt(2)],
f.featurize(self.ni3al))
# Test B1
self.assertArrayAlmostEqual([np.pi / 6],
f.featurize(self.nacl),
decimal=3)
def test_ordering_param(self):
f = ChemicalOrdering()
# Check that elemental structures return zero
features = f.featurize(self.diamond)
self.assertArrayAlmostEqual([0, 0, 0], features)
# Check result for CsCl
# These were calculated by hand by Logan Ward
features = f.featurize(self.cscl)
self.assertAlmostEqual(0.551982, features[0], places=5)
self.assertAlmostEqual(0.241225, features[1], places=5)
# Check for L1_2
features = f.featurize(self.ni3al)
self.assertAlmostEqual(1. / 3., features[0], places=5)
self.assertAlmostEqual(0.0303, features[1], places=5)
def test_composition_features(self):
comp = ElementProperty.from_preset("magpie")
f = StructureComposition(featurizer=comp)
# Test the fitting (should not crash)
f.fit([self.nacl, self.diamond])
# Test the features
features = f.featurize(self.nacl)
self.assertArrayAlmostEqual(comp.featurize(self.nacl.composition),
features)
# Test the citations/implementors
self.assertEqual(comp.citations(), f.citations())
self.assertEqual(comp.implementors(), f.implementors())
def test_xrd_powderPattern(self):
# default settings test
xpp = XRDPowderPattern()
pattern = xpp.featurize(self.diamond)
self.assertAlmostEqual(pattern[44], 0.19378, places=2)
self.assertEqual(len(pattern), 128)
# reduced range
xpp = XRDPowderPattern(two_theta_range=(0, 90))
pattern = xpp.featurize(self.diamond)
self.assertAlmostEqual(pattern[44], 0.4083, places=2)
self.assertEqual(len(pattern), 91)
self.assertEqual(len(xpp.feature_labels()), 91)
@unittest.skipIf(not (torch and cgcnn), "pytorch or cgcnn not installed.")
def test_cgcnn_featurizer(self):
# Test regular classification.
cla_props, cla_atom_features, cla_structs = self._get_cgcnn_data()
atom_fea_len = 64
cgcnn_featurizer = \
CGCNNFeaturizer(atom_init_fea=cla_atom_features,
train_size=5, val_size=2, test_size=3,
atom_fea_len=atom_fea_len)
cgcnn_featurizer.fit(X=cla_structs, y=cla_props)
self.assertEqual(len(cgcnn_featurizer.feature_labels()), atom_fea_len)
state_dict = cgcnn_featurizer.model.state_dict()
self.assertEqual(state_dict['embedding.weight'].size(),
torch.Size([64, 92]))
self.assertEqual(state_dict['embedding.bias'].size(), torch.Size([64]))
self.assertEqual(state_dict['convs.0.fc_full.weight'].size(),
torch.Size([128, 169]))
self.assertEqual(state_dict['convs.1.bn1.weight'].size(),
torch.Size([128]))
self.assertEqual(state_dict['convs.2.bn2.bias'].size(),
torch.Size([64]))
self.assertEqual(state_dict['conv_to_fc.weight'].size(),
torch.Size([128, 64]))
self.assertEqual(state_dict['fc_out.weight'].size(),
torch.Size([2, 128]))
for struct in cla_structs:
result = cgcnn_featurizer.featurize(struct)
self.assertEqual(len(result), atom_fea_len)
# Test regular regression and default atom_init_fea and featurize_many.
reg_props, reg_atom_features, reg_structs = \
self._get_cgcnn_data("regression")
cgcnn_featurizer = \
CGCNNFeaturizer(task="regression", atom_fea_len=atom_fea_len,
train_size=6, val_size=2, test_size=2)
cgcnn_featurizer.fit(X=reg_structs, y=reg_props)
cgcnn_featurizer.set_n_jobs(1)
result = cgcnn_featurizer.featurize_many(entries=reg_structs)
self.assertEqual(
np.array(result).shape, (len(reg_structs), atom_fea_len))
# Test classification from pre-trained model.
cgcnn_featurizer = \
CGCNNFeaturizer(h_fea_len=32, n_conv=4,
pretrained_name='semi-metal-classification',
atom_init_fea=cla_atom_features, train_size=5,
val_size=2, test_size=3, atom_fea_len=atom_fea_len)
cgcnn_featurizer.fit(X=cla_structs, y=cla_props)
self.assertEqual(len(cgcnn_featurizer.feature_labels()), atom_fea_len)
validate_features = [
2.1295, 2.1288, 1.8504, 1.9175, 2.1094, 1.7770, 2.0471, 1.7426,
1.7288, 1.7770
]
for struct, validate_feature in zip(cla_structs, validate_features):
result = cgcnn_featurizer.featurize(struct)
self.assertEqual(len(result), atom_fea_len)
self.assertAlmostEqual(result[0], validate_feature, 4)
# Test regression from pre-trained model.
cgcnn_featurizer = \
CGCNNFeaturizer(task="regression", h_fea_len=32, n_conv=4,
pretrained_name='formation-energy-per-atom',
atom_init_fea=reg_atom_features,
train_size=5, val_size=2, test_size=3,
atom_fea_len=atom_fea_len)
cgcnn_featurizer.fit(X=reg_structs, y=reg_props)
self.assertEqual(len(cgcnn_featurizer.feature_labels()), atom_fea_len)
validate_features = [
1.6871, 1.5679, 1.5316, 1.6419, 1.6031, 1.4333, 1.5709, 1.5070,
1.5038, 1.4333
]
for struct, validate_feature in zip(reg_structs, validate_features):
result = cgcnn_featurizer.featurize(struct)
self.assertEqual(len(result), atom_fea_len)
self.assertAlmostEqual(result[-1], validate_feature, 4)
# Test warm start regression.
warm_start_file = os.path.join(test_dir,
'cgcnn_test_regression_model.pth.tar')
warm_start_model = torch.load(warm_start_file)
self.assertEqual(warm_start_model['epoch'], 31)
self.assertEqual(warm_start_model['best_epoch'], 9)
self.assertAlmostEqual(warm_start_model['best_mae_error'].numpy(),
2.3700, 4)
cgcnn_featurizer = \
CGCNNFeaturizer(task="regression", warm_start_file=warm_start_file,
epochs=100, atom_fea_len=atom_fea_len,
atom_init_fea=reg_atom_features,
train_size=6, val_size=2, test_size=2)
cgcnn_featurizer.fit(X=reg_structs, y=reg_props)
# If use CGCNN featurize_many(), you should change the multiprocessing
# start_method to 'spawn', because Gloo (that uses Infiniband) and
# NCCL2 are not fork safe, pytorch don't support them or just
# set n_jobs = 1 to avoid multiprocessing as follows.
set_start_method('spawn', force=True)
result = cgcnn_featurizer.featurize_many(entries=reg_structs)
self.assertEqual(
np.array(result).shape, (len(reg_structs), atom_fea_len))
# Test featurize_dataframe.
df = pd.DataFrame.from_dict({"structure": cla_structs})
cgcnn_featurizer = \
CGCNNFeaturizer(atom_init_fea=cla_atom_features,
train_size=5, val_size=2, test_size=3,
atom_fea_len=atom_fea_len)
cgcnn_featurizer.fit(X=df["structure"], y=cla_props)
self.assertEqual(len(cgcnn_featurizer.feature_labels()), atom_fea_len)
cgcnn_featurizer.set_n_jobs(1)
result = cgcnn_featurizer.featurize_dataframe(df, "structure")
self.assertTrue("CGCNN_feature_{}".format(atom_fea_len -
1) in result.columns)
self.assertEqual(
np.array(result).shape, (len(reg_structs), atom_fea_len + 1))
# Test fit_featurize_dataframe.
df = pd.DataFrame.from_dict({"structure": cla_structs})
cgcnn_featurizer = \
CGCNNFeaturizer(atom_init_fea=cla_atom_features,
train_size=5, val_size=2, test_size=3,
atom_fea_len=atom_fea_len)
result = cgcnn_featurizer.fit_featurize_dataframe(df,
"structure",
fit_args=[cla_props])
self.assertEqual(len(cgcnn_featurizer.feature_labels()), atom_fea_len)
self.assertTrue("CGCNN_feature_{}".format(atom_fea_len -
1) in result.columns)
self.assertEqual(
np.array(result).shape, (len(reg_structs), atom_fea_len + 1))
@staticmethod
def _get_cgcnn_data(task="classification"):
"""
Get cgcnn sample data.
Args:
task (str): Classification or regression,
decided which sample data to return.
Returns:
id_prop_data (list): List of property data.
elem_embedding (list): List of element features.
struct_list (list): List of structure object.
"""
if task == "classification":
cgcnn_data_path = os.path.join(os.path.dirname(cgcnn.__file__),
"..", "data",
"sample-classification")
else:
cgcnn_data_path = os.path.join(os.path.dirname(cgcnn.__file__),
"..", "data", "sample-regression")
struct_list = list()
cif_list = list()
with open(os.path.join(cgcnn_data_path, "id_prop.csv")) as f:
reader = csv.reader(f)
id_prop_data = [row[1] for row in reader]
with open(os.path.join(cgcnn_data_path, "atom_init.json")) as f:
elem_embedding = json.load(f)
for file in os.listdir(cgcnn_data_path):
if file.endswith('.cif'):
cif_list.append(int(file[:-4]))
cif_list = sorted(cif_list)
for cif_name in cif_list:
crystal = Structure.from_file(
os.path.join(cgcnn_data_path, '{}.cif'.format(cif_name)))
struct_list.append(crystal)
return id_prop_data, elem_embedding, struct_list
def test_jarvisCFID(self):
# default (all descriptors)
jcf = JarvisCFID()
self.assertEqual(len(jcf.feature_labels()), 1557)
fvec = jcf.featurize(self.cscl)
self.assertEqual(len(fvec), 1557)
self.assertAlmostEqual(fvec[-1], 0.0, places=3)
self.assertAlmostEqual(fvec[1], 591.5814, places=3)
self.assertAlmostEqual(fvec[0], 1346.755, places=3)
# a combination of descriptors
jcf = JarvisCFID(use_chem=False, use_chg=False, use_cell=False)
self.assertEqual(len(jcf.feature_labels()), 737)
fvec = jcf.featurize(self.diamond)
self.assertAlmostEqual(fvec[-1], 24, places=3)
self.assertAlmostEqual(fvec[0], 0, places=3)
def test_SOAP(self):
# Test individual samples
soap = SOAP(n_max=4, l_max=2, r_cut=3.0)
soap.fit([self.diamond])
v = soap.featurize(self.diamond)
self.assertEqual(len(v), 30)
self.assertAlmostEqual(v[0], 5.299793243408203, places=6)
soap.fit([self.ni3al])
v = soap.featurize(self.ni3al)
self.assertEqual(len(v), 90)
self.assertAlmostEqual(v[0], 0.10329483449459076, places=6)
# Test dataframe fitting
df = pd.DataFrame({"s": [self.diamond, self.ni3al, self.nacl]})
soap.fit(df["s"])
df = soap.featurize_dataframe(df, "s", inplace=False)
self.assertTupleEqual(df.shape, (3, 451))
self.assertAlmostEqual(df["SOAP_449"].iloc[1],
3.029167413711548,
places=5)
def test_GlobalInstabilityIndex(self):
# Test diamond and ni3al fail precheck
gii = GlobalInstabilityIndex(r_cut=4.0, disordered_pymatgen=False)
self.assertFalse(gii.precheck(self.diamond))
self.assertFalse(gii.precheck(self.ni3al))
# Test they raise errors when featurizing
with self.assertRaises(AttributeError):
gii.featurize(self.ni3al)
with self.assertRaises(ValueError):
gii.featurize(self.diamond)
# Ordinary case of nacl
self.assertTrue(gii.precheck(self.nacl))
self.assertAlmostEqual(gii.featurize(self.nacl)[0], 0.08491655709)
# Test bond valence sums are accurate for NaCl.
# Values are closer to 0.915 than 1.0 due to structure specified here.
# Using CollCode181148 from the ICSD, I see bond valence sums of 0.979
site1, site2 = (self.nacl[0], self.nacl[1])
neighs1 = self.nacl.get_neighbors(site1, 4)
neighs2 = self.nacl.get_neighbors(site2, 4)
site_val1 = site1.species.elements[0].oxi_state
site_el1 = str(site1.species.element_composition.elements[0])
site_val2 = site2.species.elements[0].oxi_state
site_el2 = str(site2.species.element_composition.elements[0])
self.assertAlmostEqual(gii.calc_bv_sum(site_val1, site_el1, neighs1),
0.9150834429025214)
self.assertAlmostEqual(gii.calc_bv_sum(site_val2, site_el2, neighs2),
-0.915083442902522)
# Behavior when disorder is present
gii_pymat = GlobalInstabilityIndex(r_cut=4.0, disordered_pymatgen=True)
nacl_disordered = copy.deepcopy(self.nacl)
nacl_disordered.replace_species({"Cl1-": "Cl0.5Br0.5"})
nacl_disordered.add_oxidation_state_by_element({
'Na': 1,
'Cl': -1,
'Br': -1
})
self.assertTrue(gii.precheck(nacl_disordered))
with self.assertRaises(ValueError):
gii.featurize(nacl_disordered)
self.assertAlmostEqual(
gii_pymat.featurize(nacl_disordered)[0], 0.39766464)
def test_structural_complexity(self):
s = Structure.from_file("matminer/featurizers/tests/"
"Dy2HfS5_mp-1198001_computed.cif")
featurizer = StructuralComplexity()
ig, igbits = featurizer.featurize(s)
self.assertAlmostEqual(2.5, ig, places=3)
self.assertAlmostEqual(80, igbits, places=3)
s = Structure.from_file("matminer/featurizers/tests/"
"Cs2CeN5O17_mp-1198000_computed.cif")
featurizer = StructuralComplexity()
ig, igbits = featurizer.featurize(s)
self.assertAlmostEqual(3.764, ig, places=3)
