Fix remaining typing issues

Author: RalfG

im2deep/__main__.py

@@ -215,13 +215,13 @@ def _run_predict(*args, **kwargs):
 
     # Parse input files
     LOGGER.info("Parsing input files...")
-    psm_list = parse_input(kwargs.get("precursors"))
+    psm_list = parse_input(Path(kwargs.get("precursors")))  # type: ignore[invalid-arg]
 
     # Run prediction
     LOGGER.info("Running CCS prediction...")
     if kwargs.get("calibration_precursors"):
         LOGGER.info("Calibration file provided, performing calibration and prediction...")
-        psm_list_cal = parse_input(kwargs.get("calibration_precursors"))
+        psm_list_cal = parse_input(Path(kwargs.get("calibration_precursors")))  # type: ignore[invalid-arg]
         predictions = core.predict_and_calibrate(psm_list, psm_list_cal, *args, **kwargs)
     else:
         LOGGER.info(

im2deep/core.py

@@ -58,7 +58,7 @@ def predict_and_calibrate(
     psm_list_cal: PSMList,
     psm_list_reference: PSMList | None = None,
     model: torch.nn.Module | PathLike | str | None = None,
-    calibration: Calibration | None = None,
+    calibration: LinearCCSCalibration | None = None,
     multi: bool = False,
     predict_kwargs: dict | None = None,
     **kwargs,
@@ -128,6 +128,11 @@ def predict_and_calibrate(
         )
 
     if not calibration.is_fitted:
+        if psm_df_reference is None:
+            raise ValueError(
+                "Reference PSM list must be provided for calibration fitting when using a custom" \
+                "calibration object."
+            )
         LOGGER.info("Fitting calibration...")
         if any(psm_list_cal["is_decoy"]):
             LOGGER.warning(

tests/test_cli.py

@@ -196,7 +196,7 @@ def test_setup_logging_default(self):
         """Test setup_logging with info level."""
         import logging
 
-        from im2deep.utils import setup_logging
+        from im2deep._io_helpers import setup_logging
 
         setup_logging("info")
 
@@ -207,7 +207,7 @@ def test_setup_logging_debug(self):
         """Test setup_logging with debug level."""
         import logging
 
-        from im2deep.utils import setup_logging
+        from im2deep._io_helpers import setup_logging
 
         setup_logging("debug")
 
@@ -218,7 +218,7 @@ def test_setup_logging_warning(self):
         """Test setup_logging with warning level."""
         import logging
 
-        from im2deep.utils import setup_logging
+        from im2deep._io_helpers import setup_logging
 
         setup_logging("warning")
 
@@ -229,7 +229,7 @@ def test_setup_logging_affects_submodules(self):
         """Test that setup_logging affects all im2deep submodules."""
         import logging
 
-        from im2deep.utils import setup_logging
+        from im2deep._io_helpers import setup_logging
 
         setup_logging("debug")
 
@@ -245,6 +245,6 @@ def test_default_command_group_import(self):
         """Test that DefaultCommandGroup can be imported."""
         import click
 
-        from im2deep.utils import DefaultCommandGroup
+        from im2deep._io_helpers import DefaultCommandGroup
 
         assert issubclass(DefaultCommandGroup, click.Group)

tests/test_core.py

@@ -74,7 +74,7 @@ def test_predict_with_kwargs(self, mock_dataset, mock_predict, sample_psm_list):
     def test_predict_invalid_psm_list(self):
         """Test prediction with invalid PSMList."""
         with pytest.raises(IM2DeepError):
-            core.predict([1, 2, 3])
+            core.predict([1, 2, 3])  # type: ignore[invalid-arg]
 
 
 class TestPredictAndCalibrate:
@@ -171,7 +171,7 @@ def transform(self, *args, **kwargs):
         predictions = core.predict_and_calibrate(
             psm_list=sample_psm_list,
             psm_list_cal=sample_psm_list_with_ccs,
-            calibration=custom_calibration,
+            calibration=custom_calibration,  # type: ignore[invalid-arg]
         )
 
         assert isinstance(predictions, np.ndarray)

tests/test_model_ops.py

@@ -171,8 +171,8 @@ def test_predict_loop_single_output(self):
         with patch("im2deep._model_ops.track", return_value=mock_data):
             predictions = _model_ops._predict_loop(
                 model=model,
-                data_loader=mock_data,
-                device="cpu",  # type: ignore
+                data_loader=mock_data,  # type: ignore[unknown-arg]
+                device="cpu",
             )
 
             assert isinstance(predictions, torch.Tensor)
@@ -194,8 +194,8 @@ def test_predict_loop_multi_output(self):
         with patch("im2deep._model_ops.track", return_value=mock_data):
             predictions = _model_ops._predict_loop(
                 model=model,
-                data_loader=mock_data,
-                device="cpu",  # type: ignore
+                data_loader=mock_data,  # type: ignore[unknown-arg]
+                device="cpu",
             )
 
             assert isinstance(predictions, torch.Tensor)
@@ -217,8 +217,8 @@ def test_predict_loop_no_grad(self):
         with patch("im2deep._model_ops.track", return_value=mock_data):
             predictions = _model_ops._predict_loop(
                 model=model,
-                data_loader=mock_data,
-                device="cpu",  # type: ignore
+                data_loader=mock_data,  # type: ignore[unknown-arg]
+                device="cpu",
             )
 
             assert not predictions.requires_grad
@@ -262,7 +262,7 @@ class TestGetLossFunction:
     def test_get_loss_function_single(self):
         """Test getting single-output loss function."""
         loss = _model_ops._get_loss_function(multi=False)
-        assert isinstance(loss, torch.nn.modules.loss._Loss)
+        assert isinstance(loss, torch.nn.modules.loss._Loss)  # type: ignore[unresolved-attr]
 
     @patch("im2deep._model_ops.FlexibleLossSorted")
     def test_get_loss_function_multi(self, mock_loss):