Apply suggestions from code review

HypVINN/data_loader/data_utils.py

@@ -129,7 +129,7 @@ def transform_axial2sagittal(vol: np.ndarray, axial2sagittal: bool = True) -> np
     ----------
     vol : np.ndarray
         The image volume to transform.
-    axial2sagittal : bool, optional
+    axial2sagittal : bool, default=True
         A flag to determine the direction of the transformation. If True, transform from axial to sagittal. If False,
         transform from sagittal to axial. (Default: True).
 

HypVINN/data_loader/dataset.py

@@ -74,7 +74,7 @@ def __init__(
             The original zoom of the subject.
         cfg : CfgNode
             The configuration object.
-        mode : ModalityMode, optional
+        mode : ModalityMode, default="t1t2"
             The running mode of the HypVINN model. (Default: "t1t2").
         transforms : Callable, optional
             The transformations to apply to the images. (Default: None).

HypVINN/inference.py

@@ -370,7 +370,7 @@ def run(
             The tensor to update with the prediction probabilities.
         out_res : float, optional
             The resolution of the output. Default is None.
-        mode : ModalityMode, optional
+        mode : ModalityMode, default="t1t2"
             The mode of the modalities. Default is 't1t2'.
 
         Returns

HypVINN/run_prediction.py

@@ -59,14 +59,14 @@ def optional_path(a: Path | str) -> Optional[Path]:
 
     Parameters
     ----------
-    a : Path | str
+    a : Path, str
         The input to convert.
 
     Returns
     -------
-    Optional[Path]
+    Path, optional
         The converted Path object.
-    """
+    f"""
     if isinstance(a, Path):
         return a
     if a.lower() in ("none", ""):
@@ -190,9 +190,9 @@ def main(
     ----------
     out_dir : Path
         The output directory where the results will be stored.
-    t2 : Optional[Path]
+    t2 : Path, optional
         The path to the T2 image to process.
-    orig_name : Optional[Path]
+    orig_name : Path, optional
         The original name of the input image.
     sid : str
         The subject ID.
@@ -208,23 +208,23 @@ def main(
         The path to the coronal configuration file.
     cfg_sag : Path
         The path to the sagittal configuration file.
-    hypo_segfile : str, optional
+    hypo_segfile : str, default="{HYPVINN_SEG_NAME}"
         The name of the hypothalamus segmentation file. Default is HYPVINN_SEG_NAME.
-    allow_root : bool, optional
+    allow_root : bool, default=False
         Whether to allow running as root user. Default is False.
     qc_snapshots : bool, optional
         Whether to create QC snapshots. Default is False.
-    reg_mode : Literal["coreg", "robust", "none"], optional
+    reg_mode : "coreg", "robust", "none", default="coreg"
         The registration mode to use. Default is "coreg".
-    threads : int, optional
+    threads : int, default=-1
         The number of threads to use. Default is -1, which uses all available threads.
-    batch_size : int, optional
+    batch_size : int, default=1
         The batch size to use. Default is 1.
-    async_io : bool, optional
+    async_io : bool, default=False
         Whether to use asynchronous I/O. Default is False.
-    device : str, optional
+    device : str, default="auto"
         The device to use. Default is "auto", which automatically selects the device.
-    viewagg_device : str, optional
+    viewagg_device : str, default="auto"
         The view aggregation device to use. Default is "auto", which automatically selects the device.
 
     Returns
@@ -461,9 +461,9 @@ def load_volumes(
     ----------
     mode : ModalityMode
         The mode of operation. Can be 't1', 't2', or 't1t2'.
-    t1_path : Optional[Path], optional
+    t1_path : Path, optional
         The path to the T1 image. Default is None.
-    t2_path : Optional[Path], optional
+    t2_path : Path, optional
         The path to the T2 image. Default is None.
 
     Returns
@@ -583,7 +583,7 @@ def get_prediction(
         A dictionary containing the configurations for each plane.
     out_scale : optional
         The output scale. Default is None.
-    mode : ModalityMode, optional
+    mode : ModalityMode, default="t1t2"
         The mode of operation. Can be 't1', 't2', or 't1t2'. Default is 't1t2'.
 
     Returns
@@ -644,7 +644,7 @@ def set_up_cfgs(
         The configuration node to load.
     out_dir : Path
         The output directory where the results will be stored.
-    batch_size : int, optional
+    batch_size : int, default=1
         The batch size to use. Default is 1.
 
     Returns

HypVINN/utils/img_processing_utils.py

@@ -74,7 +74,7 @@ def save_segmentation(
         The directory where the subject's data is stored.
     seg_file : Path
         The file where the segmentation results will be saved.
-    save_mask : bool, optional
+    save_mask : bool, default=False
         Whether to save the mask or not. Default is False.
 
     Returns
@@ -181,7 +181,7 @@ def get_clean_mask(segmentation: np.ndarray, optic=False) \
     ----------
     segmentation : np.ndarray
         The input segmentation mask.
-    optic : bool, optional
+    optic : bool, default=False
         A flag indicating whether to consider optic labels. Default is False.
 
     Returns

HypVINN/utils/mode_config.py

@@ -33,9 +33,9 @@ def get_hypinn_mode(
 
     Parameters
     ----------
-    t1_path : Optional[Path], default=None
+    t1_path : Path, optional
         The path to the T1 file.
-    t2_path : Optional[Path], default=None
+    t2_path : Path, optional
         The path to the T2 file.
 
     Returns

HypVINN/utils/stats_utils.py

@@ -37,7 +37,7 @@ def compute_stats(
 
     Returns
     -------
-    int | str
+    int, str
         The return value of the main function from FastSurferCNN.segstats.
         Exit code. Returns 0 upon successful execution.
 

HypVINN/utils/visualization_utils.py

@@ -41,14 +41,14 @@ def remove_values_from_list(the_list, val):
 
 
 def get_lut(lookup_table_path: Path = HYPVINN_LUT):
-    """
+    f"""
     Retrieve a lookup table (LUT) from a file.
 
     This function reads a file and constructs a lookup table (LUT) from it.
 
     Parameters
     ----------
-    lookup_table_path: Path, default=HYPVINN_LUT
+    lookup_table_path: Path, default="{HYPVINN_LUT}"
         The path to the file from which the LUT will be constructed.
 
     Returns
@@ -70,14 +70,14 @@ def get_lut(lookup_table_path: Path = HYPVINN_LUT):
 
 
 def map_hyposeg2label(hyposeg: np.ndarray, lut_file: Path = HYPVINN_LUT):
-    """
+    f"""
     Map a HypVINN segmentation to a continuous label space using a lookup table.
 
     Parameters
     ----------
     hyposeg : np.ndarray
         The original segmentation map.
-    lut_file : Path, default=HYPVINN_LUT
+    lut_file : Path, default="{HYPVINN_LUT}"
         The path to the lookup table file.
 
     Returns
@@ -242,7 +242,7 @@ def plot_qc_images(
         padd: int = 45,
         lut_file: Path = HYPVINN_LUT,
         slice_step: int = 2):
-    """
+    f"""
     Plot the quality control images for the subject.
 
     Parameters
@@ -255,7 +255,7 @@ def plot_qc_images(
         The path to the predicted image.
     padd : int, default=45
         The padding value for cropping the images and segmentations.
-    lut_file : Path, default=HYPVINN_LUT
+    lut_file : Path, default="{HYPVINN_LUT}"
         The path to the lookup table file.
     slice_step : int, default=2
         The step size for selecting indices from the predicted segmentation.