Significantly revamped the base project to use more robust static checking including benchmarks and integration test support

typings/numpy.pyi

@@ -0,0 +1,54 @@
+"""Type stubs for NumPy."""
+
+from typing import Any, Self
+
+# Basic numpy types
+dtype = Any
+float64 = Any
+float32 = Any
+int64 = Any
+int32 = Any
+bool_ = Any
+
+class ndarray:
+    """NumPy array type stub."""
+    def __init__(self, data: Any) -> None: ...
+    @property
+    def shape(self) -> tuple[int, ...]: ...
+    @property
+    def dtype(self) -> dtype: ...
+    @property
+    def size(self) -> int: ...
+    def reshape(self, *args: Any) -> Self: ...
+    def astype(self, dtype: Any) -> Self: ...
+    def __getitem__(self, key: Any) -> Any: ...
+    def __setitem__(self, key: Any, value: Any) -> None: ...
+    def mean(self, axis: int | None = None) -> Any: ...
+    def sum(self, axis: int | None = None) -> Any: ...
+    def max(self, axis: int | None = None) -> Any: ...
+    def min(self, axis: int | None = None) -> Any: ...
+
+def array(data: Any, dtype: dtype | None = None) -> ndarray: ...
+def zeros(shape: int | tuple[int, ...], dtype: dtype | None = None) -> ndarray: ...
+def ones(shape: int | tuple[int, ...], dtype: dtype | None = None) -> ndarray: ...
+def empty(shape: int | tuple[int, ...], dtype: dtype | None = None) -> ndarray: ...
+def arange(
+    start: Any,
+    stop: Any | None = None,
+    step: Any | None = None,
+    dtype: dtype | None = None,
+) -> ndarray: ...
+def concatenate(arrays: list[ndarray], axis: int | None = None) -> ndarray: ...
+def stack(arrays: list[ndarray], axis: int = 0) -> ndarray: ...
+def split(ary: ndarray, indices_or_sections: Any, axis: int = 0) -> list[ndarray]: ...
+def reshape(a: ndarray, newshape: int | tuple[int, ...]) -> ndarray: ...
+def transpose(a: ndarray, axes: tuple[int, ...] | None = None) -> ndarray: ...
+def random() -> Any: ...
+def sqrt(x: Any) -> Any: ...
+def exp(x: Any) -> Any: ...
+def log(x: Any) -> Any: ...
+def sin(x: Any) -> Any: ...
+def cos(x: Any) -> Any: ...
+def maximum(x1: Any, x2: Any) -> Any: ...
+def minimum(x1: Any, x2: Any) -> Any: ...
+def clip(a: Any, a_min: Any, a_max: Any) -> Any: ...

typings/numpy/__init__.pyi

@@ -0,0 +1,27 @@
+from typing import Any, Generic, TypeVar
+
+T = TypeVar("T")
+
+class dtype:
+    float32: dtype
+    float64: dtype
+    int32: dtype
+    int64: dtype
+    bool: dtype
+
+class ndarray(Generic[T]):
+    dtype: dtype
+    shape: tuple[int, ...]
+    ndim: int
+    size: int
+
+    def __init__(self, shape: tuple[int, ...], dtype: dtype = None) -> None: ...
+    def __getitem__(self, key: Any) -> ndarray[T]: ...
+    def __setitem__(self, key: Any, value: Any) -> None: ...
+    def reshape(self, *shape: int) -> ndarray[T]: ...
+    def flatten(self) -> ndarray[T]: ...
+
+def array(object: Any, dtype: dtype = None) -> ndarray[Any]: ...
+def zeros(shape: tuple[int, ...], dtype: dtype = None) -> ndarray[Any]: ...
+def ones(shape: tuple[int, ...], dtype: dtype = None) -> ndarray[Any]: ...
+def empty(shape: tuple[int, ...], dtype: dtype = None) -> ndarray[Any]: ...