flake8/linter cleanup

examples/chem_utils/canonicalize_smiles.py

@@ -1,4 +1,5 @@
 """Example for computing Canonicalize SMILES strings"""
+
 import pandas as pd
 from workbench.utils.chem_utils import canonicalize
 

examples/chem_utils/tautomerize_smiles.py

@@ -1,6 +1,8 @@
 """Example for Tautomerizing SMILES strings"""
+
 import pandas as pd
 from workbench.utils.chem_utils import tautomerize_smiles
+
 pd.options.display.max_columns = None
 pd.options.display.width = 1200
 
@@ -24,7 +26,7 @@
     # Urea undergoes a proton shift between nitrogen atoms.
     {"id": "Urea", "smiles": "C(=O)(N)N", "expected": "NC(N)=O"},
     # Phenol standardizes hydroxyl group placement in the aromatic system.
-    {"id": "Phenol", "smiles": "c1ccc(cc1)O", "expected": "Oc1ccccc1"}
+    {"id": "Phenol", "smiles": "c1ccc(cc1)O", "expected": "Oc1ccccc1"},
 ]
 
 # Convert test data to a DataFrame

src/workbench/core/transforms/features_to_model/features_to_model.py

@@ -287,15 +287,14 @@ def create_and_register_model(self):
 if __name__ == "__main__":
     """Exercise the FeaturesToModel Class"""
 
-    """
-
     # Regression Model
     input_uuid = "abalone_features"
     output_uuid = "abalone-regression"
     to_model = FeaturesToModel(input_uuid, output_uuid, model_type=ModelType.REGRESSOR)
     to_model.set_output_tags(["abalone", "public"])
     to_model.transform(target_column="class_number_of_rings", description="Abalone Regression")
 
+    """
     # Classification Model
     input_uuid = "wine_features"
     output_uuid = "wine-classification"
@@ -357,7 +356,6 @@ def create_and_register_model(self):
     to_model = FeaturesToModel(input_uuid, output_uuid, model_type=ModelType.CLASSIFIER, custom_script=my_custom_script)
     to_model.set_output_tags(["wine", "custom"])
     to_model.transform(target_column="wine_class", description="Wine Custom Classification")
-    """
 
     # Molecular Descriptors Model
     scripts_root = Path(__file__).resolve().parents[3] / "model_scripts"
@@ -368,7 +366,6 @@ def create_and_register_model(self):
     to_model.set_output_tags(["smiles", "molecular descriptors"])
     to_model.transform(target_column=None, feature_list=["smiles"], description="Smiles to Molecular Descriptors")
 
-    """
     # Molecular Fingerprints Model
     scripts_root = Path(__file__).resolve().parents[3] / "model_scripts"
     my_script = scripts_root / "custom_models" / "chem_info" / "morgan_fingerprints.py"

src/workbench/model_scripts/custom_models/chem_info/local_utils.py

@@ -473,7 +473,9 @@ def canonicalize(df: pd.DataFrame, remove_mol_col: bool = True) -> pd.DataFrame:
     df["molecule"] = df["molecule"].apply(lambda mol: remove_disconnected_fragments(mol) if mol else None)
 
     # Convert molecules to canonical SMILES (preserving isomeric information)
-    df["smiles_canonical"] = df["molecule"].apply(lambda mol: Chem.MolToSmiles(mol, isomericSmiles=True) if mol else None)
+    df["smiles_canonical"] = df["molecule"].apply(
+        lambda mol: Chem.MolToSmiles(mol, isomericSmiles=True) if mol else None
+    )
 
     # Drop intermediate RDKit molecule column if requested
     if remove_mol_col:

src/workbench/utils/chem_utils.py

@@ -713,7 +713,9 @@ def canonicalize(df: pd.DataFrame, remove_mol_col: bool = True) -> pd.DataFrame:
     df["molecule"] = df["molecule"].apply(lambda mol: remove_disconnected_fragments(mol) if mol else None)
 
     # Convert molecules to canonical SMILES (preserving isomeric information)
-    df["smiles_canonical"] = df["molecule"].apply(lambda mol: Chem.MolToSmiles(mol, isomericSmiles=True) if mol else None)
+    df["smiles_canonical"] = df["molecule"].apply(
+        lambda mol: Chem.MolToSmiles(mol, isomericSmiles=True) if mol else None
+    )
 
     # Drop intermediate RDKit molecule column if requested
     if remove_mol_col: