Remove ndarray_like code

Author: dstansby

numcodecs/compat.py

@@ -2,11 +2,12 @@
 import codecs
 
 import numpy as np
+import numpy.typing as npt
 
-from .ndarray_like import NDArrayLike, is_ndarray_like
+from .ndarray_like import is_ndarray_like
 
 
-def ensure_ndarray_like(buf) -> NDArrayLike:
+def ensure_ndarray_like(buf) -> npt.NDArray:
     """Convenience function to coerce `buf` to ndarray-like array.
 
     Parameters
@@ -38,7 +39,7 @@ def ensure_ndarray_like(buf) -> NDArrayLike:
             mem = memoryview(buf)
             # instantiate array from memoryview, ensures no copy
             buf = np.array(mem, copy=False)
-    return buf
+    return np.asanyarray(buf, copy=False)
 
 
 def ensure_ndarray(buf) -> np.ndarray:
@@ -63,7 +64,7 @@ def ensure_ndarray(buf) -> np.ndarray:
     return np.array(ensure_ndarray_like(buf), copy=False)
 
 
-def ensure_contiguous_ndarray_like(buf, max_buffer_size=None, flatten=True) -> NDArrayLike:
+def ensure_contiguous_ndarray_like(buf, max_buffer_size=None, flatten=True) -> npt.ArrayLike:
     """Convenience function to coerce `buf` to ndarray-like array.
     Also ensures that the returned value exports fully contiguous memory,
     and supports the new-style buffer interface. If the optional max_buffer_size is
@@ -174,7 +175,7 @@ def ensure_text(s, encoding="utf-8"):
     return s
 
 
-def ndarray_copy(src, dst) -> NDArrayLike:
+def ndarray_copy(src, dst) -> npt.NDArray:
     """Copy the contents of the array from `src` to `dst`."""
 
     if dst is None:

numcodecs/ndarray_like.py

@@ -1,65 +1,6 @@
-from typing import Any, ClassVar, Protocol, runtime_checkable
-
-
-class _CachedProtocolMeta(Protocol.__class__):  # type: ignore[name-defined]
-    """Custom implementation of @runtime_checkable
-
-    The native implementation of @runtime_checkable is slow,
-    see <https://github.com/zarr-developers/numcodecs/issues/379>.
-
-    This metaclass keeps an unbounded cache of the result of
-    isinstance checks using the object's class as the cache key.
-    """
-
-    _instancecheck_cache: ClassVar[dict[tuple[type, type], bool]] = {}
-
-    def __instancecheck__(self, instance):
-        key = (self, instance.__class__)
-        ret = self._instancecheck_cache.get(key)
-        if ret is None:
-            ret = super().__instancecheck__(instance)
-            self._instancecheck_cache[key] = ret
-        return ret
-
-
-@runtime_checkable
-class DType(Protocol, metaclass=_CachedProtocolMeta):
-    itemsize: int
-    name: str
-    kind: str
-
-
-@runtime_checkable
-class FlagsObj(Protocol, metaclass=_CachedProtocolMeta):
-    c_contiguous: bool
-    f_contiguous: bool
-    owndata: bool
-
-
-@runtime_checkable
-class NDArrayLike(Protocol, metaclass=_CachedProtocolMeta):
-    dtype: DType
-    shape: tuple[int, ...]
-    strides: tuple[int, ...]
-    ndim: int
-    size: int
-    itemsize: int
-    nbytes: int
-    flags: FlagsObj
-
-    def __len__(self) -> int: ...  # pragma: no cover
-
-    def __getitem__(self, key) -> Any: ...  # pragma: no cover
-
-    def __setitem__(self, key, value): ...  # pragma: no cover
-
-    def tobytes(self, order: str | None = ...) -> bytes: ...  # pragma: no cover
-
-    def reshape(self, *shape: int, order: str = ...) -> "NDArrayLike": ...  # pragma: no cover
-
-    def view(self, dtype: DType = ...) -> "NDArrayLike": ...  # pragma: no cover
+import numpy.typing as npt
 
 
 def is_ndarray_like(obj: object) -> bool:
     """Return True when `obj` is ndarray-like"""
-    return isinstance(obj, NDArrayLike)
+    return isinstance(obj, npt.ArrayLike)