lint

Author: kleinhenz

src/lobster/cmdline/_intervene.py

@@ -260,13 +260,13 @@ def intervene(cfg: DictConfig) -> bool:
                     all_data = np.concatenate((all_data, data), axis=1) if all_data is not None else np.array(data)
 
                 if all_data.shape[1] > 1000:
-                    print(f"{concept} {i+1} of {len(predict_dataloader)} Saving")
+                    print(f"{concept} {i + 1} of {len(predict_dataloader)} Saving")
                     df = pd.DataFrame(all_data.transpose(), columns=columns)
                     df.to_csv(f"{data_folder_dir}/{index}.csv", index=False)
                     index += 1
                     all_data = None
 
             if all_data is not None:
-                print(f"{concept} {i+1} of {len(predict_dataloader)} Saving")
+                print(f"{concept} {i + 1} of {len(predict_dataloader)} Saving")
                 df = pd.DataFrame(all_data.transpose(), columns=columns)
                 df.to_csv(f"{data_folder_dir}/{index}.csv", index=False)

src/lobster/cmdline/_intervene_multiproperty.py

@@ -386,13 +386,13 @@ def intervene_multiproperty(cfg: DictConfig) -> bool:
                 all_data = np.concatenate((all_data, data), axis=1) if all_data is not None else np.array(data)
 
             if all_data.shape[1] > 1000:
-                print(f"{i+1} of {len(predict_dataloader)} Saving")
+                print(f"{i + 1} of {len(predict_dataloader)} Saving")
                 df = pd.DataFrame(all_data.transpose(), columns=columns)
-                df.to_csv(f"{data_folder_dir}/{index__+1}_indexed.csv", index=False)
+                df.to_csv(f"{data_folder_dir}/{index__ + 1}_indexed.csv", index=False)
                 index__ += 1
                 all_data = None
 
         if all_data is not None:
-            print(f"{i+1} of {len(predict_dataloader)} Saving")
+            print(f"{i + 1} of {len(predict_dataloader)} Saving")
             df = pd.DataFrame(all_data.transpose(), columns=columns)
-            df.to_csv(f"{data_folder_dir}/{index__+1}_indexed.csv", index=False)
+            df.to_csv(f"{data_folder_dir}/{index__ + 1}_indexed.csv", index=False)

src/lobster/data/__init__.py

@@ -7,12 +7,12 @@
     DataFrameDatasetInMemory,
     DataFrameLightningDataModule,
 )
-from ._farthest_first_traversal import FarthestFirstTraversal, ranked_fft
-from ._fasta_datamodule import FastaLightningDataModule  # nopycln: import
 from ._dyab_data import (  # nopycln: import
     DyAbDataFrameDatasetInMemory,
     DyAbDataFrameLightningDataModule,
 )
+from ._farthest_first_traversal import FarthestFirstTraversal, ranked_fft
+from ._fasta_datamodule import FastaLightningDataModule  # nopycln: import
 from ._minhasher import LobsterMinHasher
 from ._mmseqs import MMSeqsRunner
 from ._structure_datamodule import PDBDataModule

src/lobster/transforms/_convert_seqs.py

@@ -6,10 +6,12 @@
 
 if find_spec("selfies"):
     import selfies as sf
+
     _SELFIES_AVAILABLE = True
 
 if find_spec("rdkit"):
     from rdkit import Chem
+
     _RDKIT_AVAILABLE = True
 
 

tests/lobster/transforms/test__convert_seqs.py

@@ -115,7 +115,7 @@ def test_convert_aa_to_smiles(self):
         # print(smi_seq)
         assert len(smi_seq) == 100, f"Failed for AA seq {aa_seq}, smiles seq does not have the expected length"
         assert smi_seq == (
-            "CC(C)C[C@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O)O)" "C(C)C)C(=O)N[C@H](C(=O)O)C(C)C"
+            "CC(C)C[C@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O)O)C(C)C)C(=O)N[C@H](C(=O)O)C(C)C"
         )
 
     def test_convert_smiles_to_aa(self):