Address linter remarks

src/ptwt/_stationary_transform.py

@@ -115,7 +115,8 @@ def _iswt(
     """Inverts a 1d stationary wavelet transform.
 
     Args:
-        coeffs (Sequence[torch.Tensor]): The coefficients as computed by the swt function.
+        coeffs (Sequence[torch.Tensor]): The coefficients as computed
+            by the swt function.
         wavelet (Union[pywt.Wavelet, str]): The wavelet used for the forward transform.
         axis (int, optional): The axis the forward trasform was computed over.
             Defaults to -1.

src/ptwt/_util.py

@@ -193,7 +193,6 @@ def _map_result(
     data: Union[WaveletCoeffDetailTuple2d, WaveletCoeffDetailDict],
     function: Callable[[torch.Tensor], torch.Tensor],
 ) -> Union[WaveletCoeffDetailTuple2d, WaveletCoeffDetailDict]:
-    return_tuple = isinstance(data, tuple)
     approx = function(data[0])
     result_lst: list[
         Union[

src/ptwt/conv_transform_2.py

@@ -4,7 +4,6 @@
 torch.nn.functional.conv_transpose2d under the hood.
 """
 
-from collections.abc import Sequence
 from functools import partial
 from typing import Optional, Union, cast
 
@@ -169,7 +168,7 @@ def wavedec2(
             last two. Defaults to (-2, -1).
 
     Returns:
-        WaveletTransformReturn2d: A tuple containing the wavelet coefficients.
+        WaveletCoeffDetailTuple2d: A tuple containing the wavelet coefficients.
         The coefficients are in pywt order. That is::
 
             [cAs, (cHs, cVs, cDs), … (cH1, cV1, cD1)] .
@@ -248,8 +247,9 @@ def waverec2(
     or forward transform by running transposed convolutions.
 
     Args:
-        coeffs (WaveletTransformReturn2d): The wavelet coefficient tupl produced by wavedec2.
-            The coefficients must be in pywt order. That is::
+        coeffs (WaveletCoeffDetailTuple2d): The wavelet coefficient tuple
+            produced by wavedec2. The coefficients must be in pywt order.
+            That is::
 
             [cAs, (cHs, cVs, cDs), … (cH1, cV1, cD1)] .
 

src/ptwt/conv_transform_3.py

@@ -127,7 +127,7 @@ def wavedec3(
             instead of the last three. Defaults to (-3, -2, -1).
 
     Returns:
-        WaveletTransformReturn3d: A tuple with the lll coefficients and
+        WaveletCoeffDetailDict: A tuple with the lll coefficients and
         dictionaries with the filter order strings::
 
             ("aad", "ada", "add", "daa", "dad", "dda", "ddd")
@@ -236,7 +236,8 @@ def waverec3(
     """Reconstruct a signal from wavelet coefficients.
 
     Args:
-        coeffs (WaveletTransformReturn3d): The wavelet coefficient tuple produced by wavedec3.
+        coeffs (WaveletCoeffDetailDict): The wavelet coefficient tuple
+            produced by wavedec3.
         wavelet (Wavelet or str): A pywt wavelet compatible object or
             the name of a pywt wavelet.
         axes (tuple[int, int, int]): Transform these axes instead of the

src/ptwt/matmul_transform.py

@@ -8,7 +8,7 @@
 """
 
 import sys
-from collections import Sequence
+from collections.abc import Sequence
 from typing import Optional, Union
 
 import numpy as np
@@ -600,8 +600,8 @@ def __call__(self, coefficients: Sequence[torch.Tensor]) -> torch.Tensor:
         """Run the synthesis or inverse matrix fwt.
 
         Args:
-            coefficients (Sequence[torch.Tensor]): The coefficients produced by the forward
-                transform.
+            coefficients (Sequence[torch.Tensor]): The coefficients produced
+                by the forward transform.
 
         Returns:
             torch.Tensor: The input signal reconstruction.

src/ptwt/matmul_transform_2.py

@@ -4,7 +4,6 @@
 """
 
 import sys
-from collections.abc import Sequence
 from functools import partial
 from typing import Optional, Union, cast
 
@@ -430,8 +429,8 @@ def __call__(self, input_signal: torch.Tensor) -> WaveletCoeffDetailTuple2d:
                 This transform affects the last two dimensions.
 
         Returns:
-            (WaveletTransformReturn2d): The resulting coefficients per level are stored in
-            a pywt style tuple. The tuple is ordered as::
+            (WaveletCoeffDetailTuple2d): The resulting coefficients per level
+                are stored in a pywt style tuple. The tuple is ordered as::
 
                 (ll, (lh, hl, hh), ...)
 
@@ -729,7 +728,7 @@ def __call__(
         """Compute the inverse matrix 2d fast wavelet transform.
 
         Args:
-            coefficients (WaveletTransformReturn2d): The coefficient tuple as returned
+            coefficients (WaveletCoeffDetailTuple2d): The coefficient tuple as returned
                 by the `MatrixWavedec2`-Object.
 
         Returns:

src/ptwt/matmul_transform_3.py

@@ -167,7 +167,7 @@ def __call__(self, input_signal: torch.Tensor) -> WaveletCoeffDetailDict:
             ValueError: If the input dimensions don't work.
 
         Returns:
-            WaveletTransformReturn3d:
+            WaveletCoeffDetailDict:
                 A tuple with the approximation coefficients,
                 and a coefficient dict for each scale.
         """
@@ -390,7 +390,7 @@ def __call__(self, coefficients: WaveletCoeffDetailDict) -> torch.Tensor:
         """Reconstruct a batched 3d-signal from its coefficients.
 
         Args:
-            coefficients (WaveletTransformReturn3d):
+            coefficients (WaveletCoeffDetailDict):
                 The output from MatrixWavedec3, consisting of a tuple
                 of the approximation coefficients and a dict with the
                 detail coefficients for each scale.

src/ptwt/packets.py

@@ -4,7 +4,7 @@
 from collections.abc import Sequence
 from functools import partial
 from itertools import product
-from typing import TYPE_CHECKING, Callable, Optional, Union, cast
+from typing import TYPE_CHECKING, Callable, Optional, Union
 
 import numpy as np
 import pywt

src/ptwt/separable_conv_transform.py

@@ -11,13 +11,11 @@
 from typing import Optional, Union
 
 import numpy as np
-import pywt
 import torch
 
 from ._util import (
     Wavelet,
     WaveletCoeffDetailDict,
-    WaveletCoeffDetailTuple2d,
     _as_wavelet,
     _check_axes_argument,
     _check_if_tensor,
@@ -127,7 +125,7 @@ def _separable_conv_wavedecn(
         level (int): The desired decomposition level.
 
     Returns:
-        WaveletTransformReturn3d: A tuple with the approximation coefficients,
+        WaveletCoeffDetailDict: A tuple with the approximation coefficients,
             and a coefficient dict for each scale.
     """
     result: list[dict[str, torch.Tensor]] = []
@@ -156,7 +154,7 @@ def _separable_conv_waverecn(
     """Separable n-dimensional wavelet synthesis transform.
 
     Args:
-        coeffs (WaveletTransformReturn3d):
+        coeffs (WaveletCoeffDetailDict):
             The output as produced by `_separable_conv_wavedecn`.
         wavelet (Wavelet or str): A pywt wavelet compatible object or
             the name of a pywt wavelet, as used by `_separable_conv_wavedecn`.
@@ -208,7 +206,7 @@ def fswavedec2(
         ValueError: If the data is not a batched 2D signal.
 
     Returns:
-        WaveletTransformReturn3d:
+        WaveletCoeffDetailDict:
         A tuple with the lll coefficients and dictionaries
         with the filter order strings::
 
@@ -277,7 +275,7 @@ def fswavedec3(
         ValueError: If the input is not a batched 3D signal.
 
     Returns:
-        WaveletTransformReturn3d:
+        WaveletCoeffDetailDict:
         A tuple with the lll coefficients and dictionaries
         with the filter order strings::
 
@@ -332,7 +330,7 @@ def fswaverec2(
     the hood.
 
     Args:
-        coeffs (WaveletTransformReturn3d):
+        coeffs (WaveletCoeffDetailDict):
             The wavelet coefficients as computed by `fswavedec2`.
         wavelet (Wavelet or str): The wavelet to use for the
             synthesis transform.
@@ -396,7 +394,7 @@ def fswaverec3(
     """Compute a fully separable 3D-padded synthesis wavelet transform.
 
     Args:
-        coeffs (WaveletTransformReturn3d):
+        coeffs (WaveletCoeffDetailDict):
             The wavelet coefficients as computed by `fswavedec3`.
         wavelet (Wavelet or str): The wavelet to use for the
             synthesis transform.