fix lint.

src/ptwt/_util.py

@@ -1,9 +1,5 @@
 """Utility methods to compute wavelet decompositions from a dataset."""
-<<<<<<< HEAD
-from typing import Any, Callable, Dict, List, Optional, Protocol, Sequence, Tuple, Union
-=======
 from typing import Any, Callable, List, Optional, Protocol, Sequence, Tuple, Union
->>>>>>> ac76b4478066c7891b94e00ed661712991214d5c
 
 import numpy as np
 import pywt

src/ptwt/conv_transform.py

@@ -250,7 +250,7 @@ def _waverec_fold_channels_1d_list(
 def _preprocess_tensor_dec1d(
     data: torch.Tensor,
 ) -> Tuple[torch.Tensor, Union[List[int], None]]:
-    """Preprocess input tensor dimensions
+    """Preprocess input tensor dimensions.
 
     Args:
         data (torch.Tensor): An input tensor of any shape.

src/ptwt/conv_transform_3.py

@@ -259,34 +259,18 @@ def waverec3(
         if len(axes) != 3:
             raise ValueError("3D transforms work with two axes")
         else:
-<<<<<<< HEAD
-            _check_axes_argument(axes)
-=======
             _check_axes_argument(list(axes))
->>>>>>> ac76b4478066c7891b94e00ed661712991214d5c
             swap_axes_fn = partial(_swap_axes, axes=list(axes))
             coeffs = _map_result(coeffs, swap_axes_fn)
 
     wavelet = _as_wavelet(wavelet)
     ds = None
     # the Union[tensor, dict] idea is coming from pywt. We don't change it here.
-<<<<<<< HEAD
-    res_lll = coeffs[0]
-    if not isinstance(res_lll, torch.Tensor):
-        raise ValueError(
-            "First element of coeffs must be the approximation coefficient tensor."
-        )
-
-    if len(res_lll.shape) >= 5:
-        coeffs, ds = _waverec3d_fold_channels_3d_list(coeffs)
-        res_lll = coeffs[0]  # TODO: Check if this is tensor.
-=======
     res_lll = _check_if_tensor(coeffs[0])
 
     if len(res_lll.shape) >= 5:
         coeffs, ds = _waverec3d_fold_channels_3d_list(coeffs)
         res_lll = _check_if_tensor(coeffs[0])
->>>>>>> ac76b4478066c7891b94e00ed661712991214d5c
 
     torch_device = res_lll.device
     torch_dtype = res_lll.dtype

src/ptwt/matmul_transform.py

@@ -177,7 +177,7 @@ def __init__(
         wavelet: Union[Wavelet, str],
         level: Optional[int] = None,
         boundary: str = "qr",
-        axis: int = -1,
+        axis: Optional[int] = -1,
     ) -> None:
         """Create a matrix-fwt object.
 
@@ -313,11 +313,11 @@ def __call__(self, input_signal: torch.Tensor) -> List[torch.Tensor]:
 
         Args:
             input_signal (torch.Tensor): Batched input data.
-            An example shape could be ``[batch_size, time]``.
-            Inputs can have any dimension.
-            This transform affects the last axis by default.
-            Use the axis argument in the constructor to choose
-            another axis.
+                An example shape could be ``[batch_size, time]``.
+                Inputs can have any dimension.
+                This transform affects the last axis by default.
+                Use the axis argument in the constructor to choose
+                another axis.
 
         Returns:
             List[torch.Tensor]: A list with the coefficients for each scale.

tests/test_matrix_fwt.py

@@ -213,6 +213,7 @@ def test_matrix1d_batch_channel(size):
 
 @pytest.mark.parametrize("axis", (0, 1, 2, 3, 4))
 def test_axis_1d(axis):
+    """Ensure the axis argument is supported correctly."""
     data = torch.randn(24, 24, 24, 24, 24).type(torch.float64)
     matrix_wavedec = MatrixWavedec(wavelet="haar", level=3, axis=axis)
     coeff = matrix_wavedec(data)