Apply fixes from ruff check

asteroid_filterbanks/griffin_lim.py

@@ -2,7 +2,7 @@
 import math
 from typing import Optional, List
 
-from . import Encoder, Decoder, STFTFB
+from . import Decoder, STFTFB
 from .stft_fb import perfect_synthesis_window
 from . import transforms
 

asteroid_filterbanks/pcen.py

@@ -1,6 +1,5 @@
 from torch import nn
 import torch
-from . import transforms
 from typing import Union, Optional, Tuple
 
 try:
@@ -140,9 +139,9 @@ class PCEN(_PCEN):
             Defaults to 0.04
         n_channels: Number of channels in the time frequency representation.
             Defaults to 1
-        trainable: If True, the parameters (alpha, delta, root and smooth) are trainable. If False, the parameters are fixed.
-            Individual parameters can set to be fixed or trainable by passing a dictionary of booleans, with the key
-            matching the parameter name and the value being either True (trainable) or False (fixed).
+        trainable: If True, the parameters (alpha, delta, root and smooth) are trainable. If False, the parameters are
+            fixed. Individual parameters can set to be fixed or trainable by passing a dictionary of booleans, with the
+            key matching the parameter name and the value being either True (trainable) or False (fixed).
             i.e. ``{"alpha": False, "delta": True, "root": False, "smooth": True}``
             Defaults to False
         per_channel_smoothing: If True, each channel has it's own smoothing coefficient.
@@ -207,9 +206,9 @@ class StatefulPCEN(_PCEN):
             Defaults to 0.04
         n_channels: Number of channels in the time frequency representation.
             Defaults to 1
-        trainable: If True, the parameters (alpha, delta, root and smooth) are trainable. If False, the parameters are fixed.
-            Individual parameters can set to be fixed or trainable by passing a dictionary of booleans, with the key
-            matching the parameter name and the value being either True (trainable) or False (fixed).
+        trainable: If True, the parameters (alpha, delta, root and smooth) are trainable. If False, the parameters are
+            fixed. Individual parameters can set to be fixed or trainable by passing a dictionary of booleans, with the
+            key matching the parameter name and the value being either True (trainable) or False (fixed).
             i.e. ``{"alpha": False, "delta": True, "root": False, "smooth": True}``
             Defaults to False
         per_channel_smoothing: If True, each channel has it's own smoothing coefficient.
@@ -250,7 +249,8 @@ def from_pcen(cls, pcen: PCEN):
     def forward(
         self, mag_spec: torch.Tensor, initial_state: Optional[torch.Tensor] = None
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Computes the PCEN from magnitude spectrum representation, and an optional smoothed version of the filterbank (Equation (2) in [1]).
+        """Computes the PCEN from magnitude spectrum representation, and an optional smoothed version of the filterbank
+           (Equation (2) in [1]).
 
         Args:
             mag_spec: tensor containing an magnitude spectrum representation.

asteroid_filterbanks/transforms.py

@@ -3,7 +3,6 @@
 from typing import Tuple
 
 from .scripting import script_if_tracing
-from .deprecation import mark_deprecated
 
 
 def mul_c(inp, other, dim: int = -2):

tests/pcen_test.py

@@ -1,9 +1,8 @@
 import torch
-from asteroid_filterbanks import Encoder, Decoder, STFTFB, transforms
+from asteroid_filterbanks import Encoder, STFTFB, transforms
 from asteroid_filterbanks.pcen import PCEN, StatefulPCEN, ExponentialMovingAverage
 from torch.testing import assert_allclose
 import pytest
-import re
 
 
 @pytest.mark.parametrize("n_channels", [2, 4])

tests/stft_test.py

@@ -153,4 +153,4 @@ def test_melgram_encoder(n_filters, n_mels, ndim):
     mel_spec = enc(wav)
     assert wav.shape[:-1] == mel_spec.shape[:-2]
     assert mel_spec.shape[-2] == n_mels
-    conf = melgram_fb.get_config()
+    melgram_fb.get_config()