def write_read_cycle(self):
smiles = write_smiles(self.mol)
note(self.mol.nodes(data=True))
note(self.mol.edges(data=True))
note(smiles)
# self.mol can exist in a mixed implicit/explicit H style. The reference
# must be one or the other, since we can't read in mixed mode. We want
# to be sure we produce the correct answer in both cases though.
for expl_H in (False, True):
ref_mol = self.mol.copy()
defaults = {'charge': 0, 'hcount': 0}
for node in ref_mol:
for key, val in defaults.items():
if key not in ref_mol.nodes[node]:
ref_mol.nodes[node][key] = val
if expl_H:
add_explicit_hydrogens(ref_mol)
else:
remove_explicit_hydrogens(ref_mol)
found = read_smiles(smiles,
explicit_hydrogen=expl_H,
reinterpret_aromatic=False)
note(found.nodes(data=True))
note(found.edges(data=True))
assertEqualGraphs(ref_mol, found)
def test_write_smiles(node_data, edge_data, expl_h):
mol = make_mol(node_data, edge_data)
smiles = write_smiles(mol)
found = read_smiles(smiles,
explicit_hydrogen=expl_h,
reinterpret_aromatic=False)
assertEqualGraphs(mol, found)
def LAZY_maker(infile,outfile,DOI,SOFTWARE,METHOD,NAME,EMAIL):
op = open(infile,'r')
text = op.read().splitlines()
op.close()
DataDictionary = OutputParser(text)
DataDictionary['DOI'] = DOI
DataDictionary['METHOD'] = METHOD
DataDictionary['NAME'] = NAME
DataDictionary['EMAIL'] = EMAIL
DataDictionary['fAfC'] = (DataDictionary['ARCH'][0][3]/np.sum(DataDictionary['ARCH'][0][3]))[0]
if len(DataDictionary['ARCH'])==1:
DataDictionary['NSC'] = 0
else:
DataDictionary['NSC'] = DataDictionary['ARCH'][1][3][0]
DataDictionary['PHASE'] =infile.split('/')[-2][:-5]
chainlist = list_convert_object(DataDictionary['ARCH'])
G = molecule(chainlist).create_network()
smiles = write_smiles(G)
DataDictionary['SMILES'] = smiles
DataDictionary['CLASSIFICATION'] = ['Polymers','Bottlebrushes','Computational','SCFT']
pif_dict = dict_to_pif(DataDictionary)
print(f'Writing...{outfile}')
op = open(f'{outfile}.json','w+')
op.write(pif_dict)
op.close()
def graph_to_canonical_smiles(G, allHsExplicit=True):
#yields a canonical string from a graph representing a molecule, with
#node attributes 'element'
sm = write_smiles(G)
indigo = Indigo()
mol = indigo.loadMolecule(sm)
mol.aromatize()
return mol.canonicalSmiles() #Does not include with H...
def find_elements():
# list all the elements appeared
special_case = ['b', 'c', 'o', 'p', 's']
element_list = []
longest_len =0
for path_name in paths:
path = paths[path_name]
df_smiles = pd.read_csv(os.path.join(path, 'names_smiles.txt'))
smiles_list = np.array(df_smiles.iloc[:, 1])
for smiles in smiles_list:
mol = read_smiles(smiles)
for node in mol.nodes:
if 'stereo' in mol.nodes[node]:
mol.nodes[node].pop('stereo') # discard stereo infomation by hand
new_smiles = write_smiles(mol)
length = 0
for i, ele in enumerate(new_smiles):
ele = str(ele)
#assert ele != 'n', 'SIMPLIFICATION FAILS'
if ele.islower() and (not ele in special_case) and i > 0 and\
str(new_smiles[i-1]).isupper(): # is the suffix of an element
continue
if ele.isupper() and i < len(new_smiles) - 1 and str(new_smiles[i+1]).islower() \
and (not str(new_smiles[i+1]) in special_case): # an element with 2 chars
ele = ele + str(new_smiles[i+1])
length += 1
if not ele in element_list:
element_list.append(ele)
if length > longest_len:
longest_len = length
print(element_list)
with open('element_list.txt', 'w') as f:
for item in element_list:
f.write("%s " % item)
f.write(f'{longest_len}')
#for idx, ele in enumerate(string):
# mol.nodes[idx]['element'] = ele
# mol.add_edges_from([(idx,idx+1)])
# count+=1
print(write_smiles(G))
# [O-]C(=O)C([C])([C])[C]
# fill_valence(mol, respect_hcount=True)
#print(write_smiles(mol))
# [O-]C(=O)C(C)(C)C
#plt.subplot(121)
#nx.draw(G, with_labels=True, font_weight='bold')
#plt.subplot(122)
# nx.draw(G)
#plt.show()
def augment_smiles(rdata, data_choice, make_1d_pading=True):
if os.path.exists(f'data/{data_choice}/data.json'):
with open(f'data/{data_choice}/data.json', 'r') as f:
new_data = json.load(f)
return new_data, 320
if not os.path.exists('./element_list.txt'):
print('no element list found!\n')
find_elements() # find all appeared elements if the list has not been abtained
new_data = []
with open('element_list.txt') as f:
lines = f.readlines()
element_list = lines[0].split(sep=' ')[:-1]
#longest_len = int(lines[0].split(sep=' ')[-1])
longest_len = 320
for item in rdata:
name = item['name']
smiles = item['SMILES']
if data_choice != 'test':
label = item['label']
weight = item['weight']
mol = read_smiles(smiles)
for node in mol.nodes:
if 'stereo' in mol.nodes[node]:
mol.nodes[node].pop('stereo') # discard stereo infomation by hand
degrees = np.array([mol.degree(idx) for idx in mol.nodes])
# find leaf nodes to generate different smiles for one mol
leaf_nodes = np.array(list(mol.nodes), dtype=int)[degrees == 1]
if leaf_nodes.shape[0] == 0:
leaf_nodes = [0]
try:
if len(leaf_nodes) > 5 and data_choice != 'test':
length = len(leaf_nodes)
idx_list = [idx for idx in range(0, length, length//4)]
leaf_nodes = leaf_nodes[idx_list] # at most 5 examples
new_smiles_list = [write_smiles(mol, start=list(mol.nodes)[leaf_node]) for leaf_node in leaf_nodes]
onehots_list = generate_onehots(new_smiles_list, element_list)
if data_choice != 'test':
tmp = [write_dict(name, onehots, smiles, label, weight, len(leaf_nodes)) \
for onehots, smiles in zip(onehots_list, new_smiles_list)]
else:
tmp = [write_dict(name, onehots, smiles) for onehots, smiles in zip(onehots_list, new_smiles_list)]
except Exception as inst:
print(inst)
continue
new_data += tmp
for item in new_data:
item['onehots'] = item['onehots'].tolist()
with open(f'data/{data_choice}/data.json', 'w') as f:
json.dump(new_data, f)
return new_data, longest_len
