Lint

src/autoseg/gp_filters/random_noise.py

@@ -3,30 +3,31 @@
 import random
 from skimage.util import random_noise
 
+
 class RandomNoiseAugment(gp.BatchFilter):
     """
     Random Noise Augmentation for Gunpowder.
 
     This class applies random noise augmentation to the specified array in a Gunpowder batch.
 
     Args:
-        array (str): 
+        array (str):
             The name of the array in the batch to which noise should be applied.
-        seed (int, optional): 
+        seed (int, optional):
             Seed for the random number generator. Default is None.
-        clip (bool, optional): 
+        clip (bool, optional):
             Whether to clip the values after applying noise. Default is True.
-        **kwargs: 
+        **kwargs:
             Additional keyword arguments to be passed to the `random_noise` function.
 
     Attributes:
-        array (str): 
+        array (str):
             The name of the array in the batch to which noise is applied.
-        seed (int): 
+        seed (int):
             Seed for the random number generator.
-        clip (bool): 
+        clip (bool):
             Whether to clip the values after applying noise.
-        kwargs (dict): 
+        kwargs (dict):
             Additional keyword arguments passed to the `random_noise` function.
     """
 
@@ -48,11 +49,11 @@ def prepare(self, request):
         Prepare the dependencies for processing based on the requested batch.
 
         Args:
-            request (BatchRequest): 
+            request (BatchRequest):
                 The requested batch.
 
         Returns:
-            BatchRequest: 
+            BatchRequest:
                 The dependencies for processing.
         """
         deps = gp.BatchRequest()
@@ -64,9 +65,9 @@ def process(self, batch, request):
         Apply random noise augmentation to the specified array in the batch.
 
         Args:
-            batch (Batch): 
+            batch (Batch):
                 The input batch.
-            request (BatchRequest): 
+            request (BatchRequest):
                 The requested batch.
         """
         raw = batch.arrays[self.array]

src/autoseg/gp_filters/smooth_array.py

@@ -3,22 +3,23 @@
 import random
 from scipy.ndimage import gaussian_filter
 
+
 class SmoothArray(gp.BatchFilter):
     """
     Smooth Array in a Gunpowder Batch.
 
     This class applies Gaussian smoothing to a 3D array in a Gunpowder batch.
 
     Args:
-        array (str): 
+        array (str):
             The name of the array in the batch to be smoothed.
-        blur_range (tuple): 
+        blur_range (tuple):
             The range of sigma values for the Gaussian filter.
 
     Attributes:
-        array (str): 
+        array (str):
             The name of the array in the batch to be smoothed.
-        range (tuple): 
+        range (tuple):
             The range of sigma values for the Gaussian filter.
     """
 
@@ -31,9 +32,9 @@ def process(self, batch, request):
         Apply Gaussian smoothing to the specified array in the batch.
 
         Args:
-            batch (Batch): 
+            batch (Batch):
                 The input batch.
-            request (BatchRequest): 
+            request (BatchRequest):
                 The requested batch.
         """
         array = batch[self.array].data

src/autoseg/losses/ACLSDLoss.py

@@ -9,7 +9,7 @@ class WeightedACLSD_MSELoss(torch.nn.MSELoss):
     weighting term for Auto-Context LSD (ACLSD) segmentation.
 
     Parameters:
-        aff_lambda (float, optional): 
+        aff_lambda (float, optional):
             Weighting factor for the affinity loss. Default is 1.0.
     """
 
@@ -23,15 +23,15 @@ def _calc_loss(self, prediction, target, weights):
         Calculates the weighted mean squared error loss.
 
         Args:
-            prediction (torch.Tensor): 
+            prediction (torch.Tensor):
                 Predicted affinities.
-            target (torch.Tensor): 
+            target (torch.Tensor):
                 Ground truth affinities.
-            weights (torch.Tensor): 
+            weights (torch.Tensor):
                 Weighting factor for each affinity.
 
         Returns:
-            torch.Tensor: 
+            torch.Tensor:
                 Weighted mean squared error loss.
         """
         scaled = weights * (prediction - target) ** 2
@@ -54,15 +54,15 @@ def forward(
         Calculates the weighted ACLSD MSE loss.
 
         Args:
-            pred_affs (torch.Tensor): 
+            pred_affs (torch.Tensor):
                 Predicted affinities.
-            gt_affs (torch.Tensor): 
+            gt_affs (torch.Tensor):
                 Ground truth affinities.
-            affs_weights (torch.Tensor): 
+            affs_weights (torch.Tensor):
                 Weighting factor for each affinity.
 
         Returns:
-            torch.Tensor: 
+            torch.Tensor:
                 Weighted ACLSD MSE loss.
         """
         aff_loss = self.aff_lambda * self._calc_loss(pred_affs, gt_affs, affs_weights)

src/autoseg/losses/GMSELoss.py

@@ -9,11 +9,11 @@ class Weighted_GMSELoss(torch.nn.Module):
     GAN (Generative Adversarial Network) loss term for enhanced data.
 
     Parameters:
-        aff_lambda (float, optional): 
+        aff_lambda (float, optional):
             Weighting factor for the affinity loss. Default is 1.0.
-        gan_lambda (float, optional): 
+        gan_lambda (float, optional):
             Weighting factor for the GAN loss. Default is 1.0.
-        discrim (torch.nn.Module, optional): 
+        discrim (torch.nn.Module, optional):
             Discriminator network for GAN loss.
     """
 
@@ -22,11 +22,11 @@ def __init__(self, aff_lambda=1.0, gan_lambda=1.0, discrim=None):
         Initializes the Weighted_MSELoss.
 
         Args:
-            aff_lambda (float, optional): 
+            aff_lambda (float, optional):
                 Weighting factor for the affinity loss. Default is 1.0.
-            gan_lambda (float, optional): 
+            gan_lambda (float, optional):
                 Weighting factor for the GAN loss. Default is 1.0.
-            discrim (torch.nn.Module, optional): 
+            discrim (torch.nn.Module, optional):
                 Discriminator network for GAN loss.
         """
         super(Weighted_GMSELoss, self).__init__()
@@ -40,15 +40,15 @@ def _calc_loss(self, prediction, target, weights=None):
         Calculates the weighted mean squared error loss.
 
         Args:
-            prediction (torch.Tensor): 
+            prediction (torch.Tensor):
                 Predicted values.
-            target (torch.Tensor): 
+            target (torch.Tensor):
                 Ground truth values.
-            weights (torch.Tensor, optional): 
+            weights (torch.Tensor, optional):
                 Weighting factor for each value.
 
         Returns:
-            torch.Tensor: 
+            torch.Tensor:
                 Weighted mean squared error loss.
         """
         if type(weights) != torch.Tensor:
@@ -79,25 +79,25 @@ def forward(
         Calculates the weighted MSE loss with GAN loss.
 
         Args:
-            pred_lsds (torch.Tensor): 
+            pred_lsds (torch.Tensor):
                 Predicted LSD values.
-            gt_lsds (torch.Tensor): 
+            gt_lsds (torch.Tensor):
                 Ground truth LSD values.
-            lsds_weights (torch.Tensor, optional): 
+            lsds_weights (torch.Tensor, optional):
                 Weighting factor for each LSD value.
-            pred_affs (torch.Tensor): 
+            pred_affs (torch.Tensor):
                 Predicted affinity values.
-            gt_affs (torch.Tensor): 
+            gt_affs (torch.Tensor):
                 Ground truth affinity values.
-            affs_weights (torch.Tensor, optional): 
+            affs_weights (torch.Tensor, optional):
                 Weighting factor for each affinity value.
-            pred_enhanced (torch.Tensor): 
+            pred_enhanced (torch.Tensor):
                 Predicted enhanced data.
-            gt_enhanced (torch.Tensor): 
+            gt_enhanced (torch.Tensor):
                 Ground truth enhanced data.
 
         Returns:
-            torch.Tensor: 
+            torch.Tensor:
                 Combined weighted MSE loss with GAN loss.
         """
         # calculate MSE loss for LSD and Affs

src/autoseg/postprocess/segment_mws.py

@@ -19,21 +19,21 @@ def get_validation_segmentation(
     Get validation segmentation using the specified segmentation style.
 
     Parameters:
-        segmentation_style (str): 
+        segmentation_style (str):
             Style of segmentation ("mws" or "mergetree").
-        iteration (str): 
+        iteration (str):
             Iteration or checkpoint to use (default: "latest").
-        raw_file (str): 
+        raw_file (str):
             Path to the input Zarr dataset containing raw data.
-        raw_dataset (str): 
+        raw_dataset (str):
             Name of the raw dataset in the input Zarr file.
-        out_file (str): 
+        out_file (str):
             Path to the output Zarr file for storing predictions.
-        pred_affs (bool): 
+        pred_affs (bool):
             Flag to indicate whether to predict affinities.
 
     Returns:
-        bool: 
+        bool:
             True if segmentation is successful, False otherwise.
     """
     out_datasets = [

src/autoseg/postprocess/segment_skel_correct.py

@@ -24,25 +24,25 @@ def get_skel_correct_segmentation(
     Generate segmentation with skeleton-based correction using RUSTY_MWS.
 
     Parameters:
-        predict_affs (bool): 
+        predict_affs (bool):
             Flag to indicate whether to predict affinities.
-        raw_file (str): 
+        raw_file (str):
             Path to the input Zarr dataset containing raw data.
-        raw_dataset (str): 
+        raw_dataset (str):
             Name of the raw dataset in the input Zarr file.
-        out_file (str): 
+        out_file (str):
             Path to the output Zarr file for storing predictions.
-        out_datasets (list): 
+        out_datasets (list):
             List of tuples specifying output dataset names and channel counts.
-        iteration (str): 
+        iteration (str):
             Iteration or checkpoint to use (default: "latest").
-        model_path (str): 
+        model_path (str):
             Path to the directory containing the trained model checkpoints.
-        voxel_size (int):  
+        voxel_size (int):
             Voxel size in all three dimensions.
 
     Returns:
-        None: 
+        None:
             No return value. Segmentation with skeleton-based correction is stored in the specified Zarr file.
     """
     if predict_affs:

src/autoseg/predict/network_predictions.py

@@ -13,7 +13,7 @@
 
 
 def predict_task(
-    model:torch.nn.Module,
+    model: torch.nn.Module,
     model_type: str,
     iteration: int,
     raw_file: str,
@@ -33,31 +33,31 @@ def predict_task(
     Predict affinities using a trained deep learning model.
 
     Parameters:
-        model: 
+        model:
             Trained deep learning model.
-        model_type (str): 
+        model_type (str):
             Type of the model ("MTLSD", "ACLSD", "STELARR").
-        iteration (int): 
+        iteration (int):
             Iteration or checkpoint of the trained model to use.
-        raw_file (str): 
+        raw_file (str):
             Path to the input Zarr or N5 dataset or TIFF file containing raw data.
-        raw_dataset (str): 
+        raw_dataset (str):
             Name of the raw dataset in the input Zarr or N5 file.
-        out_file (str): 
+        out_file (str):
             Path to the output Zarr file for storing predictions.
-        out_datasets (list): 
+        out_datasets (list):
             List of tuples specifying output dataset names and channel counts.
-        num_workers (int): 
+        num_workers (int):
             Number of parallel workers for blockwise processing.
-        n_gpu (int): 
+        n_gpu (int):
             Number of GPUs available for prediction.
-        model_path (str): 
+        model_path (str):
             Path to the directory containing the trained model checkpoints.
-        voxel_size (int): 
+        voxel_size (int):
             Voxel size in all three dimensions.
 
     Returns:
-        None: 
+        None:
             No return value. Predictions are stored in the specified Zarr file.
     """
     if type(iteration) == str and "latest" in iteration: