def test_make_bonds_df():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols()
#####
bonds = GNR.make_bonds_df(mols)
assert len(bonds["molecule_name"].unique()) == len(mols)
for idx, bond in enumerate(bonds):
molid = bonds['molecule_name'][idx]
at1 = bonds['atom_index_0'][idx]
at2 = bonds['atom_index_1'][idx]
mol = 0
for ml_fnd in mols:
if ml_fnd.molid == molid:
mol = ml_fnd
assert mol.coupling_len[at1][at2] == int(bonds['type'][idx][0])
assert mol.coupling[at1][at2] == bonds['scalar_coupling_constant'][idx]
def test_make_triplets():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
############
triplets = BCAI.make_triplets(bonds["molecule_name"].unique(),
structure_dict)
assert len(triplets["molecule_name"].unique()) == len(mols)
count = 0
for mol in mols:
for atom1, type1 in enumerate(mol.types):
for atom2, type2 in enumerate(mol.types):
if atom1 == atom2:
continue
for atom3, type3 in enumerate(mol.types):
if atom3 in [atom1, atom2] or atom3 < atom2:
continue
if mol.conn[atom1][atom2] != 1 or mol.conn[atom1][
atom3] != 1:
continue
row = triplets.loc[(triplets.molecule_name == mol.molid)
& (triplets.atom_index_0 == atom1)
& (triplets.atom_index_1 == atom2)
& (triplets.atom_index_2 == atom3)]
assert len(row.index) == 1
ba = mol.xyz[atom2] - mol.xyz[atom1]
bc = mol.xyz[atom3] - mol.xyz[atom1]
angle = np.sum(
ba * bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
angle = np.arccos(np.clip(angle, -1.0, 1.0))
assert angle == row.angle.values
count += 1
assert count == len(triplets.index)
def test_get_atomic_ACSF():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='ACSF')
def get_features_frommols(self,
args,
params={},
molcheck_run=False,
training=True,
max=200):
self.params = params
target = flag_to_target(args['targetflag'])
self.remove_mols(target)
if molcheck_run:
return
for mol in self.mols:
if len(mol.types) > max:
max = len(mol.types) + 1
print('WARNING, setting max atoms to ', max)
self.params['max'] = max
self.atoms = GNR.make_atom_df(self.mols)
self.struc = GNR.make_struc_dict(self.atoms)
if len(args['targetflag']) == 4:
self.bonds = GNR.make_bonds_df(self.mols)
if args['featureflag'] in ['aSLATM', 'CMAT', 'FCHL', 'ACSF']:
from autoenrich.ml.features import QML_features
self.atoms = QML_features.get_atomic_qml_features(
self.atoms,
self.bonds,
self.struc,
featureflag=args['featureflag'],
cutoff=params['cutoff'],
max=max)
elif args['featureflag'] == 'BCAI':
from autoenrich.ml.features import TFM_features
self.BCAI, self.atoms, self.bonds, self.struc, xfiles, rfiles, yfiles = TFM_features.get_BCAI_features(
self.atoms,
self.bonds,
self.struc,
targetflag=args['targetflag'],
training=training)
elif args['featureflag'] != 'dummy':
return
else:
print('Feature flag not recognised, no feature flag: ',
args['featureflag'])
return 0
def test_get_atomic_FCHL():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='FCHL')
assert atoms['atomic_rep'].values[0].shape == (5, 50)
def test_get_atomic_aSLATM():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='aSLATM')
assert len(atoms['atomic_rep'].values[0]) == 20105
def test_enhance_bonds():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
############
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
for idx, bond in enumerate(bonds):
molid = bonds['molecule_name'][idx]
at1 = bonds['atom_index_0'][idx]
at2 = bonds['atom_index_1'][idx]
mol = 0
for ml_fnd in mols:
if ml_fnd.molid == molid:
mol = ml_fnd
assert mol.coupling_len[at1][at2] == int(bonds['type'][idx][0])
assert mol.coupling[at1][at2] == bonds['scalar_coupling_constant'][idx]
if bonds['labeled_type'][idx] == '3JHH':
assert bonds['predict'][idx] == 1
else:
assert bonds['predict'][idx] == 0
for mol in mols:
for atom1, type1 in enumerate(mol.types):
for atom2, type2 in enumerate(mol.types):
if atom1 == atom2:
continue
row = bonds.loc[(bonds['molecule_name'] == mol.molid)
& (bonds['atom_index_0'] == atom1)
& (bonds['atom_index_1'] == atom2)]
cpl = row['scalar_coupling_constant'].values
if type1 == 1 and type2 == 1 and mol.coupling_len[atom1][
atom2] == 3:
assert row.predict.values == 1
assert mol.coupling[atom1][atom2] == cpl[0]
def test_get_atomic_cmat():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='CMAT')
assert len(atoms['atomic_rep'].values[0]) == 50 * (50 + 1) / 2
assert len(atoms['atomic_rep'].values[0].nonzero()[0]) == len(
mols[0].types) * (len(mols[0].types) + 1) / 2
def test_get_scaling():
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
#####
means, stds = BCAI.get_scaling(bonds)
def test_add_embedding():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
triplets = BCAI.make_triplets(bonds["molecule_name"].unique(),
structure_dict)
#####
embeddings, atoms, bonds, triplets = BCAI.add_embedding(
atoms, bonds, triplets)