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(

src/ptwt/matmul_transform_3.py

@@ -1,4 +1,5 @@
 """Implement 3D separable boundary transforms."""
+
 # Written by the Pytorch wavelet toolbox team in 2024
 
 import sys
@@ -122,7 +123,8 @@ def _construct_analysis_matrices(
                     f"depth, height, and width ({current_depth}, {current_height},"
                     f"{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
             # the conv matrices require even length inputs.
@@ -340,7 +342,8 @@ def _construct_synthesis_matrices(
                     f" depth, height and width ({current_depth}, {current_height}, "
                     f"{current_width}) is smaller than the filter length {filt_len}."
                     f" 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
             # the conv matrices require even length inputs.

src/ptwt/packets.py

@@ -1,4 +1,5 @@
 """Compute analysis wavelet packet representations."""
+
 # Created on Fri Apr 6 2021 by moritz (wolter@cs.uni-bonn.de)
 
 import collections
@@ -382,9 +383,7 @@ def get_natural_order(self, level: int) -> List[str]:
         """
         return ["".join(p) for p in product(["a", "h", "v", "d"], repeat=level)]
 
-    def _get_wavedec(
-        self, shape: Tuple[int, ...]
-    ) -> Callable[
+    def _get_wavedec(self, shape: Tuple[int, ...]) -> Callable[
         [torch.Tensor],
         List[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]],
     ]:
@@ -415,9 +414,7 @@ def _get_wavedec(
                 wavedec2, wavelet=self.wavelet, level=1, mode=self.mode, axes=self.axes
             )
 
-    def _get_waverec(
-        self, shape: Tuple[int, ...]
-    ) -> Callable[
+    def _get_waverec(self, shape: Tuple[int, ...]) -> Callable[
         [List[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]]],
         torch.Tensor,
     ]:
@@ -471,7 +468,7 @@ def _transform_tuple_to_fsdict_func(
         def _fsdict_func(
             coeffs: List[
                 Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]
-            ]
+            ],
         ) -> torch.Tensor:
             a, (h, v, d) = coeffs
             return fsdict_func([cast(torch.Tensor, a), {"ad": h, "da": v, "dd": d}])

src/ptwt/separable_conv_transform.py

@@ -6,6 +6,7 @@
 Under the hood, code in this module transforms all dimensions
 using torch.nn.functional.conv1d and it's transpose.
 """
+
 # Written by the Pytorch wavelet toolbox team in 2024
 from functools import partial
 from typing import Dict, List, Optional, Tuple, Union

src/ptwt/sparse_math.py

@@ -1,4 +1,5 @@
 """Efficiently construct fwt operations using sparse matrices."""
+
 # Written by moritz ( @ wolter.tech ) 17.09.21
 from itertools import product
 from typing import List

src/ptwt/wavelets_learnable.py

@@ -2,6 +2,7 @@
 
 See https://arxiv.org/pdf/2004.09569.pdf for more information.
 """
+
 # Created by moritz wolter@cs.uni-bonn.de, 14.05.20
 # Inspired by Ripples in Mathematics, Jensen and La Cour-Harbo, Chapter 7.7
 # import pywt

tests/_mackey_glass.py

@@ -1,4 +1,5 @@
 """Generate artificial time-series data for debugging purposes."""
+
 from typing import Optional, Union
 
 import torch

tests/test_convolution_fwt.py

@@ -1,4 +1,5 @@
 """Test the conv-fwt code."""
+
 # Written by moritz ( @ wolter.tech ) in 2021
 import numpy as np
 import pytest

tests/test_cwt.py

@@ -1,4 +1,5 @@
 """Test the continuous transformation code."""
+
 from typing import Union
 
 import numpy as np

tests/test_dtypes.py

@@ -1,20 +1,20 @@
 """Test dtype support for the fwt code."""
+
 # Written by moritz ( @ wolter.tech ) in 2025
 import numpy as np
 import pytest
 import pywt
 import torch
 from scipy import datasets
 
-from src.ptwt.conv_transform import (
-    _flatten_2d_coeff_lst,
-    wavedec,
-    waverec,
-)
+from src.ptwt.conv_transform import _flatten_2d_coeff_lst
 from src.ptwt.conv_transform_2 import wavedec2, waverec2
 
+
 @pytest.mark.slow
-@pytest.mark.parametrize("dtype", [torch.float64, torch.float32, torch.float16, torch.bfloat16])
+@pytest.mark.parametrize(
+    "dtype", [torch.float64, torch.float32, torch.float16, torch.bfloat16]
+)
 def test_2d_wavedec_rec(dtype):
     """Ensure pywt.wavedec2 and ptwt.wavedec2 produce the same coefficients.
 
@@ -45,4 +45,4 @@ def test_2d_wavedec_rec(dtype):
     assert np.allclose(flat_coeff_list_pywt, flat_coeff_list_ptwt.numpy(), atol=1e-3)
     rec = waverec2(coeff2d, wavelet)
     rec = rec.numpy().squeeze().astype(np.float32)
-    assert np.allclose(face, rec[:, : face.shape[1], : face.shape[2]], atol=1e-3)
+    assert np.allclose(face, rec[:, : face.shape[1], : face.shape[2]], atol=1e-3)

tests/test_jit.py

@@ -1,4 +1,5 @@
 """Ensure pytorch's torch.jit.trace feature works properly."""
+
 from typing import NamedTuple
 
 import numpy as np

tests/test_matrix_fwt.py

@@ -1,4 +1,5 @@
 """Test the fwt and ifwt matrices."""
+
 # Written by moritz ( @ wolter.tech ) in 2021
 from typing import List
 

tests/test_matrix_fwt_2.py

@@ -1,4 +1,5 @@
 """Test code for the 2d boundary wavelets."""
+
 # Created by moritz ( wolter@cs.uni-bonn.de ), 08.09.21
 import numpy as np
 import pytest

tests/test_matrix_fwt_3.py

@@ -1,4 +1,5 @@
 """Test the 3d matrix-fwt code."""
+
 from typing import List
 
 import numpy as np

tests/test_packets.py

@@ -1,4 +1,5 @@
 """Test the wavelet packet code."""
+
 # Created on Fri Apr 6 2021 by moritz (wolter@cs.uni-bonn.de)
 from itertools import product
 

tests/test_sparse_math.py

@@ -1,4 +1,5 @@
 """Test the sparse math code from ptwt.sparse_math."""
+
 # Written by moritz ( @ wolter.tech ) in 2021
 import numpy as np
 import pytest

tests/test_util.py

@@ -1,4 +1,5 @@
 """Test the util methods."""
+
 from typing import Tuple
 
 import numpy as np

tests/test_wavelet.py

@@ -1,4 +1,5 @@
 """Test the adaptive wavelet cost functions."""
+
 import pytest
 import pywt
 import torch