def test_prdf(self):
# Test a few peaks in diamond
# These expected numbers were derived by performing
# the calculation in another code
p, r = PartialRadialDistributionFunction().featurize(self.diamond)
self.assertEquals(len(p), 1)
self.assertEquals(p[('C', 'C')][int(round(1.4 / 0.1))], 0)
self.assertAlmostEqual(p[('C', 'C')][int(round(1.5 / 0.1))],
1.324451676)
self.assertAlmostEqual(r.max(), 19.9)
self.assertAlmostEqual(p[('C', 'C')][int(round(19.9 / 0.1))],
0.07197902)
# Test a few peaks in CsCl, make sure it gets all types correctly
p, r = PartialRadialDistributionFunction().featurize(self.cscl,
cutoff=10)
self.assertEquals(len(p), 4)
self.assertAlmostEqual(r.max(), 9.9)
self.assertAlmostEquals(p[('Cs', 'Cl')][int(round(3.6 / 0.1))],
0.477823197)
self.assertAlmostEquals(p[('Cl', 'Cs')][int(round(3.6 / 0.1))],
0.477823197)
self.assertAlmostEquals(p[('Cs', 'Cs')][int(round(3.6 / 0.1))], 0)
# Do Ni3Al, make sure it captures the antisymmetry of Ni/Al sites
p, r = PartialRadialDistributionFunction().featurize(self.ni3al,
cutoff=10,
bin_size=0.5)
self.assertEquals(len(p), 4)
self.assertAlmostEquals(p[('Ni', 'Al')][int(round(2 / 0.5))],
0.125236677)
self.assertAlmostEquals(p[('Al', 'Ni')][int(round(2 / 0.5))],
0.37571003)
self.assertAlmostEquals(p[('Al', 'Al')][int(round(2 / 0.5))], 0)
def featurize_structure(df: pd.DataFrame) -> pd.DataFrame:
""" Decorate input `pandas.DataFrame` of structures with structural
features from matminer.
Currently applies the set of all matminer structure features.
Args:
df (pandas.DataFrame): the input dataframe with `"structure"`
column containing `pymatgen.Structure` objects.
Returns:
pandas.DataFrame: the decorated DataFrame.
"""
logging.info("Applying structure featurizers...")
df = df.copy()
structure_features = [
DensityFeatures(),
GlobalSymmetryFeatures(),
RadialDistributionFunction(),
CoulombMatrix(),
PartialRadialDistributionFunction(),
SineCoulombMatrix(),
EwaldEnergy(),
BondFractions(),
StructuralHeterogeneity(),
MaximumPackingEfficiency(),
ChemicalOrdering(),
XRDPowderPattern(),
BagofBonds()
]
featurizer = MultipleFeaturizer([feature.fit(df["structure"]) for feature in structure_features])
df = featurizer.featurize_dataframe(df, "structure", multiindex=True, ignore_errors=True)
df.columns = df.columns.map('|'.join).str.strip('|')
dist = df["RadialDistributionFunction|radial distribution function"][0]['distances'][:50]
for i, d in enumerate(dist):
_rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format(d)
df[_rdf_key] = df["RadialDistributionFunction|radial distribution function"].apply(lambda x: x['distribution'][i])
df = df.drop("RadialDistributionFunction|radial distribution function", axis=1)
_crystal_system = {
"cubic": 1, "tetragonal": 2, "orthorombic": 3,
"hexagonal": 4, "trigonal": 5, "monoclinic": 6, "triclinic": 7
}
df["GlobalSymmetryFeatures|crystal_system"] = df["GlobalSymmetryFeatures|crystal_system"].map(_crystal_system)
df["GlobalSymmetryFeatures|is_centrosymmetric"] = df["GlobalSymmetryFeatures|is_centrosymmetric"].map(int)
return clean_df(df)
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')]]))
CIFfiles.append(i) #List of CIF files
#Creates a list of pymatgen.structure objects and a name of each structure
structlist = []
namelist = []
structs = []
namecolumns = ['structure']
for i in CIFfiles:
structlist.append([Structure.from_file(directoryname + i)
]) #Converts CIF to pymatgen structure object
namelist.append(os.path.splitext(i)[0]) #Collects all the structure names
structs.append(Structure.from_file(directoryname + i))
#Creates Pandas dataframe with data being a list of structures and the row name being the structure name
dftest = pd.DataFrame(data=structlist, index=namelist, columns=namecolumns)
p = PartialRadialDistributionFunction()
p.fit(np.asarray(structs))
c = CoulombMatrix()
c.fit(np.asarray(structs))
erdf = ElectronicRadialDistributionFunction()
erdf.cutoff = 10 #longest diagonal of lattice...I picked a number semi-arbitrarily
#Featurizes the structures
featurizer = MultipleFeaturizer([
ElementProperty.from_preset('magpie'),
OxidationStates(),
AtomicOrbitals(),
BandCenter(),
ElectronegativityDiff(),