Black formatted

Author: Alirezak2n

ideeplc/__init__.py

@@ -1,3 +1,3 @@
 """iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system"""
-__version__ = "1.4.0"
 
+__version__ = "1.4.0"

ideeplc/data_initialize.py

@@ -146,7 +146,9 @@ def data_initialize_chunked(
             raise
 
         if errors:
-            LOGGER.warning(f"Errors encountered during conversion in chunk {chunk_idx}: {errors}")
+            LOGGER.warning(
+                f"Errors encountered during conversion in chunk {chunk_idx}: {errors}"
+            )
 
         prediction_dataset = MyDataset(sequences, tr)
 
@@ -160,9 +162,7 @@ def data_initialize_chunked(
         yield df, prediction_dataset, x_shape
 
 
-
 def get_input_shape_from_first_chunk(csv_path: str, chunk_size: int = 10000, **kwargs):
-
     """
     Get the input shape from the first valid chunk of a CSV file.
 
@@ -171,12 +171,10 @@ def get_input_shape_from_first_chunk(csv_path: str, chunk_size: int = 10000, **k
     :return: x_shape for model initialization.
     """
     for _, dataset_chunk, x_shape in data_initialize_chunked(
-
         csv_path=csv_path, chunk_size=chunk_size, **kwargs
-
     ):
         LOGGER.info(f"Detected input shape from first valid chunk: {x_shape}")
         return x_shape
 
     LOGGER.error("No valid chunks found in the input file.")
-    raise ValueError("No valid chunks found in the input file.")
+    raise ValueError("No valid chunks found in the input file.")

ideeplc/fine_tuning.py

@@ -169,4 +169,4 @@ def fine_tune(self, layers_to_freeze=None):
                 break
 
         LOGGER.info("Fine-tuning complete.")
-        return best_model
+        return best_model

ideeplc/ideeplc_core.py

@@ -75,11 +75,9 @@ def main(args):
 
         # For model initialization, only inspect the first valid chunk
         x_shape = get_input_shape_from_first_chunk(
-
             csv_path=args.input,
             chunk_size=chunk_size,
             mod_features_csv=getattr(args, "mod_features", None),
-
         )
 
         # Initialize model
@@ -105,13 +103,11 @@ def main(args):
         if args.finetune:
             LOGGER.info("Fine-tuning the model")
 
-
             matrix_input, _ = data_initialize(
                 csv_path=args.input,
                 mod_features_csv=getattr(args, "mod_features", None),
             )
 
-
             fine_tuner = iDeepLCFineTuner(
                 model=model,
                 train_data=matrix_input,
@@ -139,7 +135,6 @@ def main(args):
             batch_size=batch_size,
             chunk_size=chunk_size,
             mod_features_csv=getattr(args, "mod_features", None),
-
         )
         LOGGER.info("Prediction completed.")
 
@@ -155,4 +150,4 @@ def main(args):
 
     except Exception as e:
         LOGGER.error(f"An error occurred during execution: {e}")
-        raise e
+        raise e

ideeplc/mod_features.py

@@ -39,8 +39,10 @@ def main(argv=None):
     args = parser.parse_args(argv)
 
     feature_table = build_user_mod_feature_table(args.input, args.output)
-    LOGGER.info("Wrote %d modification feature rows to %s", len(feature_table), args.output)
+    LOGGER.info(
+        "Wrote %d modification feature rows to %s", len(feature_table), args.output
+    )
 
 
 if __name__ == "__main__":
-    main()
+    main()

ideeplc/predict.py

@@ -68,7 +68,6 @@ def predict(
     chunk_size: int = 10000,
     dataloader_input: DataLoader = None,
     mod_features_csv: str = None,
-
 ):
     """
     Load a trained model and evaluate it on test datasets in chunks.
@@ -132,7 +131,9 @@ def predict(
             return loss, correlation, all_predictions, all_ground_truth
 
         if batch_size is None:
-            raise ValueError("batch_size must be provided when dataloader_input is not used.")
+            raise ValueError(
+                "batch_size must be provided when dataloader_input is not used."
+            )
 
         if save_results:
             output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -145,7 +146,6 @@ def predict(
                 chunk_size=chunk_size,
                 mod_features_csv=mod_features_csv,
             ),
-
         ):
             LOGGER.info(
                 f"Processing chunk {chunk_idx} with {len(dataset_chunk)} entries and shape {x_shape}."
@@ -234,4 +234,4 @@ def predict(
 
     except Exception as e:
         LOGGER.error(f"An error occurred during prediction: {e}")
-        raise e
+        raise e

ideeplc/utilities.py

@@ -527,9 +527,7 @@ def df_to_matrix(
     if errors:
         print("Encoding errors encountered:")
         for peptide, idx, step, err in errors[:10]:
-            print(
-                f" - index {idx}, step {step}, peptide '{peptide}': {err}"
-            )
+            print(f" - index {idx}, step {step}, peptide '{peptide}': {err}")
 
     if single_input:
         return seqs_stack[0]