reformatted with black

pyaging/predict/_pred.py

@@ -134,7 +134,9 @@ def predict_age(
         )
 
         # Add predicted ages and clock metadata to adata
-        add_pred_ages_and_clock_metadata_adata(adata, predicted_ages, clock_name, dir, logger, indent_level=2)
+        add_pred_ages_and_clock_metadata_adata(
+            adata, predicted_ages, clock_name, dir, logger, indent_level=2
+        )
 
         # Return adata to original size and number of features
         adata = filter_missing_features(adata, logger, indent_level=2)

pyaging/predict/_pred_utils.py

@@ -695,7 +695,7 @@ def add_pred_ages_and_clock_metadata_adata(
 
     This function appends the predicted ages, obtained from a biological aging clock or similar model, to
     the AnnData object's `obs` attribute. The predicted ages are added as a new column, named after the
-    clock used to generate these predictions. 
+    clock used to generate these predictions.
 
     Parameters
     ----------
@@ -816,6 +816,7 @@ def filter_missing_features(
 
     return adata
 
+
 @progress("Set PyTorch device")
 def set_torch_device(logger, indent_level: int = 1) -> None:
     """

pyaging/preprocess/_preprocess_utils.py

@@ -203,8 +203,11 @@ def create_anndata_object(
     # Log the number and names of columns being dropped
     num_columns_dropped = len(columns_with_nas)
     if num_columns_dropped > 0:
-        logger.warning(f"Dropping {num_columns_dropped} columns with only NAs: {list(columns_with_nas)[:np.min([3, num_columns_dropped])]}, etc.", indent_level=indent_level+1)
-
+        logger.warning(
+            f"Dropping {num_columns_dropped} columns with only NAs: {list(columns_with_nas)[:np.min([3, num_columns_dropped])]}, etc.",
+            indent_level=indent_level + 1,
+        )
+
     # Drop columns with only NAs
     df = df.drop(columns=columns_with_nas, axis=1)