Minor.

resampler/__init__.py

@@ -6,7 +6,7 @@
 from __future__ import annotations
 
 __docformat__ = 'google'
-__version__ = '0.8.7'
+__version__ = '0.8.8'
 __version_info__ = tuple(int(num) for num in __version__.split('.'))
 
 from collections.abc import Callable, Iterable, Sequence
@@ -52,7 +52,7 @@ def _noop_decorator(*args: Any, **kwargs: Any) -> Any:
   import torch
 
   _DType = np.dtype[Any]  # (Requires Python 3.9 or TYPE_CHECKING.)
-  _NDArray = np.ndarray[Any, Any]
+  _NDArray = numpy.typing.NDArray[Any]
   _DTypeLike = numpy.typing.DTypeLike
   _ArrayLike = numpy.typing.ArrayLike
   _TensorflowTensor: typing.TypeAlias = tf.Tensor
@@ -394,6 +394,7 @@ def make_sparse_matrix(
 
 class _NumpyArraylib(_Arraylib[_NDArray]):
   """Numpy implementation of the array abstraction."""
+  # pylint: disable=missing-function-docstring
 
   def __init__(self, array: _NDArray) -> None:
     super().__init__(arraylib='numpy', array=np.asarray(array))
@@ -587,11 +588,9 @@ def make_sparse_matrix(
     return tf.sparse.SparseTensor(indices, data, shape)
 
 
-# pylint: disable=missing-function-docstring
-
-
 class _TorchArraylib(_Arraylib[_TorchTensor]):
   """Torch implementation of the array abstraction."""
+  # pylint: disable=missing-function-docstring
 
   def __init__(self, array: _NDArray) -> None:
     import torch
@@ -704,9 +703,6 @@ def make_sparse_matrix(
     # .coalesce() is unnecessary because indices/data are already merged.
 
 
-# pylint: enable=missing-function-docstring
-
-
 class _JaxArraylib(_Arraylib[_JaxArray]):
   """Jax implementation of the array abstraction."""
 

resampler_notebook.py

@@ -339,6 +339,7 @@
 import matplotlib.pyplot as plt
 import mediapy as media  # https://github.com/google/mediapy
 import numpy as np
+import numpy.typing
 import pdoc
 import scipy.signal
 import skimage
@@ -359,8 +360,8 @@
 # pylint: disable=protected-access, missing-function-docstring
 # mypy: allow-incomplete-defs, allow-untyped-defs
 
-_ArrayLike = resampler._ArrayLike
-_NDArray = resampler._NDArray
+_ArrayLike = numpy.typing.ArrayLike
+_NDArray = numpy.typing.NDArray[Any]
 _TensorflowTensor = resampler._TensorflowTensor
 _TorchTensor = resampler._TorchTensor
 _JaxArray = resampler._JaxArray
@@ -479,8 +480,8 @@ def must_be_int(x: _ArrayLike) -> _NDArray:
 # %%
 def get_rms(a: _ArrayLike, b: _ArrayLike) -> float:
   """Return the root-mean-square difference between two arrays."""
-  a2: _NDArray = media.to_float01(a)
-  b2: _NDArray = media.to_float01(b)
+  a2 = media.to_float01(a)
+  b2 = media.to_float01(b)
   rms: float = np.sqrt(np.mean(np.square(a2 - b2))).item()
   return rms
 

test_resampler.py

@@ -16,12 +16,13 @@
 import warnings
 
 import numpy as np
+import numpy.typing
 import scipy
 
 import resampler
 
-_ArrayLike = Any
-_NDArray = Any
+_ArrayLike = numpy.typing.ArrayLike
+_NDArray = numpy.typing.NDArray[Any]
 _TensorflowTensor = Any
 
 # pylint: disable=protected-access, missing-function-docstring, too-many-public-methods