lint.

Author: v0lta

noxfile.py

@@ -1,4 +1,5 @@
 """This module implements our CI function calls."""
+
 import nox
 
 

src/ptwt/__init__.py

@@ -1,4 +1,5 @@
 """Differentiable and gpu enabled fast wavelet transforms in PyTorch."""
+
 from ._util import Wavelet
 from .continuous_transform import cwt
 from .conv_transform import wavedec, waverec

src/ptwt/_stationary_transform.py

@@ -1,4 +1,5 @@
 """This module implements stationary wavelet transforms."""
+
 # Created by moritz wolter, in 2024
 
 from typing import List, Optional, Union

src/ptwt/_util.py

@@ -1,4 +1,5 @@
 """Utility methods to compute wavelet decompositions from a dataset."""
+
 from typing import Any, Callable, List, Optional, Protocol, Sequence, Tuple, Union
 
 import numpy as np

src/ptwt/continuous_transform.py

@@ -2,6 +2,7 @@
 
 This module is based on pywt's cwt implementation.
 """
+
 # Written by the Pytorch wavelet toolbox team in 2024
 from typing import Any, Tuple, Union
 

src/ptwt/conv_transform.py

@@ -2,6 +2,7 @@
 
 This module treats boundaries with edge-padding.
 """
+
 # Created by moritz wolter, 14.04.20
 from typing import List, Optional, Sequence, Tuple, Union
 

src/ptwt/conv_transform_2.py

@@ -3,6 +3,7 @@
 The implementation relies on torch.nn.functional.conv2d and
 torch.nn.functional.conv_transpose2d under the hood.
 """
+
 # Written by the Pytorch wavelet toolbox team in 2024
 
 

src/ptwt/conv_transform_3.py

@@ -2,6 +2,7 @@
 
 The functions here are based on torch.nn.functional.conv3d and it's transpose.
 """
+
 # Written by the Pytorch wavelet toolbox team in 2024
 
 from functools import partial
@@ -212,7 +213,10 @@ def wavedec3(
 
 def _waverec3d_fold_channels_3d_list(
     coeffs: List[Union[torch.Tensor, Dict[str, torch.Tensor]]],
-) -> Tuple[List[Union[torch.Tensor, Dict[str, torch.Tensor]]], List[int],]:
+) -> Tuple[
+    List[Union[torch.Tensor, Dict[str, torch.Tensor]]],
+    List[int],
+]:
     # fold the input coefficients for processing conv2d_transpose.
     fold_coeffs: List[Union[torch.Tensor, Dict[str, torch.Tensor]]] = []
     ds = list(_check_if_tensor(coeffs[0]).shape)

src/ptwt/matmul_transform.py

@@ -6,6 +6,7 @@
 in Strang Nguyen (p. 32), as well as the description
 of boundary filters in "Ripples in Mathematics" section 10.3 .
 """
+
 # Created by moritz (wolter@cs.uni-bonn.de) at 14.04.20
 import sys
 from typing import List, Optional, Union
@@ -281,7 +282,7 @@ def _construct_analysis_matrices(
                     f"level {curr_level}, the current signal length {curr_length} is "
                     f"smaller than the filter length {filt_len}. Therefore, the "
                     "transformation is only computed up to the decomposition level "
-                    f"{curr_level-1}.\n"
+                    f"{curr_level - 1}.\n"
                 )
                 break
 
@@ -563,7 +564,7 @@ def _construct_synthesis_matrices(
                     f"level {curr_level}, the current signal length {curr_length} is "
                     f"smaller than the filter length {filt_len}. Therefore, the "
                     "transformation is only computed up to the decomposition level "
-                    f"{curr_level-1}.\n"
+                    f"{curr_level - 1}.\n"
                 )
                 break
 

src/ptwt/matmul_transform_2.py

@@ -2,6 +2,7 @@
 
 This module uses boundary filters to minimize padding.
 """
+
 # Written by moritz ( @ wolter.tech ) in 2021
 import sys
 from functools import partial
@@ -364,7 +365,8 @@ def _construct_analysis_matrices(
                     f". At level {curr_level}, at least one of the current signal "
                     f"height and width ({current_height}, {current_width}) is smaller "
                     f"then the filter length {filt_len}. Therefore, the transformation "
-                    f"is only computed up to the decomposition level {curr_level-1}.\n"
+                    f"is only computed up to the decomposition "
+                    f" level {curr_level - 1}.\n"
                 )
                 break
             # the conv matrices require even length inputs.
@@ -678,7 +680,8 @@ def _construct_synthesis_matrices(
                     f". At level {curr_level}, at least one of the current signal "
                     f"height and width ({current_height}, {current_width}) is smaller "
                     f"then the filter length {filt_len}. Therefore, the transformation "
-                    f"is only computed up to the decomposition level {curr_level-1}.\n"
+                    f"is only computed up to the "
+                    f" decomposition level {curr_level - 1}.\n"
                 )
                 break
             current_height, current_width, pad_tuple = _matrix_pad_2(