def test_enhance_atoms():
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)
for i, idx in enumerate(atoms['atom_index'].values):
molid = atoms['molecule_name'][i]
mol = 0
for ml_fnd in mols:
if ml_fnd.molid == molid:
mol = ml_fnd
atid = atoms['atom_index'][i]
assert p_table.index(atoms['typestr'][i]) == mol.types[atid]
assert np.array_equal(atoms['conn'][i], mol.conn[atid])
assert np.array_equal(atoms['distance'][i], mol.dist[atid])
def get_BCAI_features(atoms, bonds, struc, targetflag='CCS', training=True): target = flag_to_target(targetflag) BCAI.enhance_structure_dict(structure_dict) BCAI.enhance_atoms(atoms, structure_dict) bonds = BCAI.enhance_bonds(bonds, structure_dict) triplets = BCAI.make_triplets(bonds["molecule_name"].unique(), structure_dict) atoms = pd.DataFrame(atoms) bonds = pd.DataFrame(bonds) triplets = pd.DataFrame(triplets) atoms.sort_values(['molecule_name','atom_index'],inplace=True) bonds.sort_values(['molecule_name','atom_index_0','atom_index_1'],inplace=True) triplets.sort_values(['molecule_name','atom_index_0','atom_index_1','atom_index_2'],inplace=True) embeddings, atoms, bonds, triplets = BCAI.add_embedding(atoms, bonds, triplets) bonds.dropna() atoms.dropna() means, stds = BCAI.get_scaling(bonds) bonds = BCAI.add_scaling(bonds, means, stds) Dset = BCAI.create_dataset(atoms, bonds, triplets, labeled = True, max_count = 10**10, mol_order=mol_order) if training: x, y, r, mol_order = save_split_dataset(Dset) else: x, y, r, mol_order = save_dataset(Dset) return Dset, atoms, bonds, struc, x, y, r
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_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_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_enhance_structure_dict():
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)
for mol in mols:
assert structure_dict[mol.molid]['typesstr'] == [
p_table[type] for type in mol.types
]
assert np.array_equal(structure_dict[mol.molid]['positions'], mol.xyz)
assert np.array_equal(structure_dict[mol.molid]['conn'], mol.conn)
assert np.array_equal(structure_dict[mol.molid]['distances'], mol.dist)
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)
