Remove ImmutableArray and MutableArray.

These classes were added to support type checking, but they are not necessary now that Final and Literal have landed, and in fact can cause some pretty serious performance regressions by failing to use numpy's C implementations of the various ufuncs. Removing these should help general performance quite a lot.

PiperOrigin-RevId: 656033273

connectomics/common/array.py

@@ -25,9 +25,8 @@
 
 from collections import abc
 import numbers
-from typing import Any, List, Tuple, TypeVar, Type, Union
+from typing import Any, List, Tuple, TypeVar, Union
 
-from connectomics.common import array_mixins
 import numpy as np
 import numpy.typing as npt
 
@@ -43,12 +42,11 @@
 IndexExpOrPointLookups = Union[ArbitrarySlice, PointLookups]
 CanonicalSliceOrPointLookups = Union[CanonicalSlice, PointLookups]
 
-ArrayLike = Union[npt.ArrayLike, 'ImmutableArray', 'MutableArray']
+ArrayLike = npt.ArrayLike
 Tuple3f = Tuple[float, float, float]
 Tuple3i = Tuple[int, int, int]
 Tuple4i = Tuple[int, int, int, int]
-ArrayLike3d = Union[npt.ArrayLike, 'ImmutableArray', 'MutableArray', Tuple3f,
-                    Tuple3i]
+ArrayLike3d = Union[npt.ArrayLike, Tuple3f, Tuple3i]
 
 
 def is_point_lookup(ind: IndexExpOrPointLookups) -> bool:
@@ -120,75 +118,12 @@ def process_slice_ind(slice_ind: ArbitrarySlice, limit: int) -> slice:
   return slice(*slice_ind.indices(limit))
 
 
-# TODO(timblakely): Make these typed by using Generic[T]
-class ImmutableArray(array_mixins.ImmutableArrayMixin, np.ndarray):
-  """Strongly typed, immutable NumPy NDArray."""
-
-  def __new__(cls: Type['ImmutableArray'],
-              input_array: 'ArrayLike',
-              *args,
-              zero_copy=False,
-              **kwargs) -> 'ImmutableArray':
-    if zero_copy:
-      obj = np.asanyarray(input_array, *args, **kwargs).view(cls)
-    else:
-      obj = np.array(input_array, *args, **kwargs).view(cls)
-    obj.flags.writeable = False
-    return obj
-
-  def __init__(self, *args, zero_copy=False, **kwargs):
-    # Needed for mixin construction.
-    super().__init__()  # pylint: disable=no-value-for-parameter
-
-  def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
-    out = kwargs.get('out', ())
-
-    # Defer to the implementation of the ufunc on unwrapped values.
-    inputs = tuple(_to_np_compatible(x) for x in inputs)
-    if out:
-      kwargs['out'] = tuple(_to_np_compatible(x) for x in out)
-    result = getattr(ufunc, method)(*inputs, **kwargs)
-
-    if method == 'at':
-      # no return value
-      return None
-
-    return MutableArray(result)
-
-  def copy(self, *args, **kwargs) -> 'MutableArray':
-    return MutableArray(np.asarray(self).copy(*args, **kwargs))
-
-  def __str__(self):
-    return np.ndarray.__repr__(self)
-
-
-class MutableArray(array_mixins.MutableArrayMixin, ImmutableArray):
-  """Strongly typed mutable version of np.ndarray."""
-
-  def __new__(cls: Type['MutableArray'],
-              input_array: 'ArrayLike',
-              *args,
-              zero_copy=False,
-              **kwargs) -> 'MutableArray':
-    if zero_copy:
-      obj = np.asanyarray(input_array, *args, **kwargs).view(cls)
-    else:
-      obj = np.array(input_array, *args, **kwargs).view(cls)
-    obj.flags.writeable = True
-    return obj
-
-
 def is_arraylike(obj):
   # Technically sequences, but this is intended to check for numeric sequences.
   if isinstance(obj, str) or isinstance(obj, bytes):
     return False
-  return isinstance(obj, abc.Sequence) or isinstance(
-      obj, np.ndarray) or isinstance(obj, ImmutableArray) or isinstance(
-          obj, MutableArray)
+  return isinstance(obj, abc.Sequence) or isinstance(obj, np.ndarray)
 
 
 def _to_np_compatible(array_like) -> np.ndarray:
-  if isinstance(array_like, ImmutableArray) or isinstance(
-      array_like, MutableArray):
-    return np.asarray(array_like)
-  return array_like
+  return np.asarray(array_like)