Makes function signatures as explicit as possible

src/__init__.pyi

@@ -26,6 +26,16 @@ _TLabel = tp.Union[
         tp.Tuple['_TLabel', ...],
 ]
 
+_TVShape = tp.TypeVar("_TVShape")
+_1D = tp.Tuple[tp.Any]
+_2D = tp.Tuple[tp.Any, tp.Any]
+_TVDtypeAny = tp.TypeVar('_TVDtypeAny', bound=np.dtype)
+_TVNDArrayAny = tp.TypeVar("_TVNDArrayAny", np.ndarray)
+_T1DArrayInt64 = np.ndarray[_1D, np.dtype[np.int64]]
+_T1DArrayBool = np.ndarray[_1D, np.dtype[np.bool_]]
+_T1DArrayObject = np.ndarray[_1D, np.dtype[np.object_]]
+_T2DArrayBool = np.ndarray[_2D, np.dtype[np.bool_]]
+
 class ErrorInitTypeBlocks(RuntimeError):
     def __init__(self, *args: tp.Any, **kwargs: tp.Any) -> None: ...
     def with_traceback(self, tb: Exception) -> Exception: ...
@@ -119,7 +129,7 @@ class FrozenAutoMap:
     def __init__(self, labels: tp.Iterable[_TLabel] | np.ndarray = (), /,) -> None: ...
     def get(self, __key: _TLabel, /,) -> int: ...
     def keys(self) -> tp.Iterator[_TLabel]: ...
-    def items(self) -> tp.Iterator[tuple[_TLabel, int]]: ...
+    def items(self) -> tp.Iterator[tp.Tuple[_TLabel, int]]: ...
     def values(self) -> tp.Iterator[int]: ...
     def get_all(self, __key: list[_TLabel] | np.ndarray) -> np.ndarray: ...
     def get_any(self, __key: list[_TLabel] | np.ndarray) -> list[int]: ...
@@ -186,26 +196,34 @@ def split_after_count(
 
 def count_iteration(__iterable: tp.Iterable) -> int: ...
 
-def immutable_filter(__array: np.ndarray) -> np.ndarray: ...
+def immutable_filter(__array: _TVNDArrayAny) -> _TVNDArrayAny: ...
 def mloc(__array: np.ndarray) -> int: ...
 def name_filter(__name: _TLabel) -> _TLabel: ...
-def shape_filter(__array: np.ndarray) -> np.ndarray: ...
-def column_2d_filter(__array: np.ndarray) -> np.ndarray: ...
-def column_1d_filter(__array: np.ndarray) -> np.ndarray: ...
-def row_1d_filter(__array: np.ndarray) -> np.ndarray: ...
-def array_deepcopy(__array: np.ndarray, memo: tp.Optional[tp.Dict[int, tp.Any]]) -> np.ndarray: ...
+
+def shape_filter(__array: np.ndarray[_1D, _TVDtypeAny] | np.ndarray[_2D, _TVDtypeAny]) -> np.ndarray[_2D, _TVDtypeAny]: ...
+def column_2d_filter(__array: np.ndarray[_1D, _TVDtypeAny] | np.ndarray[_2D, _TVDtypeAny]) -> np.ndarray[_2D, _TVDtypeAny]: ...
+def column_1d_filter(__array: np.ndarray[_1D, _TVDtypeAny] | np.ndarray[_2D, _TVDtypeAny]) -> np.ndarray[_1D, _TVDtypeAny]: ...
+def row_1d_filter(__array: np.ndarray[_1D, _TVDtypeAny] | np.ndarray[_2D, _TVDtypeAny]) -> np.ndarray[_1D, _TVDtypeAny]: ...
+
+def array_deepcopy(__array: _TVNDArrayAny, memo: tp.Optional[tp.Dict[int, tp.Any]]) -> _TVNDArrayAny: ...
 def resolve_dtype(__d1: np.dtype, __d2: np.dtype) -> np.dtype: ...
 def resolve_dtype_iter(__dtypes: tp.Iterable[np.dtype]) -> np.dtype: ...
 def isna_element(__value: tp.Any, include_none: bool = True) -> bool: ...
 def dtype_from_element(__value: tp.Optional[_TLabel]) -> np.dtype: ...
-def get_new_indexers_and_screen(indexers: np.ndarray, positions: np.ndarray) -> tp.Tuple[np.ndarray, np.ndarray]: ...
+def get_new_indexers_and_screen(indexers: _T1DArrayInt64, positions: _T1DArrayInt64) -> tp.Tuple[_T1DArrayInt64, _T1DArrayInt64]: ...
 
-def first_true_1d(__array: np.ndarray, *, forward: bool) -> int: ...
-def first_true_2d(__array: np.ndarray, *, forward: bool, axis: int) -> np.ndarray: ...
-def nonzero_1d(__array: np.ndarray, /) -> np.ndarray: ...
+def first_true_1d(__array: _T1DArrayBool, *, forward: bool) -> int: ...
+def first_true_2d(__array: _T2DArrayBool, *, forward: bool, axis: int) -> _T1DArrayInt64: ...
+def nonzero_1d(__array: _T1DArrayBool, /) -> _T1DArrayInt64: ...
 def is_objectable_dt64(__array: np.ndarray, /) -> bool: ...
 def is_objectable(__array: np.ndarray, /) -> bool: ...
-def astype_array(__array: np.ndarray, __dtype: np.dtype | None, /) -> np.ndarray: ...
+
+@tp.overload
+def astype_array(__array: np.ndarray[_TVShape, tp.Any], __dtype: _TVDtypeAny, /) -> np.ndarray[_TVShape, _TVDtypeAny]: ...
+@tp.overload
+def astype_array(__array: np.ndarray[_TVShape, tp.Any], __dtype: None, /) -> np.ndarray[_TVShape, np.int64]: ...
+def astype_array(__array: np.ndarray[_TVShape, tp.Any], __dtype: _TVDtypeAny | None, /) -> np.ndarray[_TVShape, _TVDtypeAny] | np.ndarray[_TVShape, np.int64]: ...
+
 def slice_to_ascending_slice(__slice: slice, __size: int) -> slice: ...
-def array_to_tuple_array(__array: np.ndarray) -> np.ndarray: ...
+def array_to_tuple_array(__array: np.ndarray) -> _T1DArrayObject: ...
 def array_to_tuple_iter(__array: np.ndarray) -> tp.Iterator[tp.Tuple[tp.Any, ...]]: ...

src/methods.c

@@ -870,7 +870,7 @@ get_new_indexers_and_screen(PyObject *Py_UNUSED(m), PyObject *args, PyObject *kw
     }
 
     if (PyArray_TYPE(indexers) != NPY_INT64) {
-        PyErr_SetString(PyExc_ValueError, "Array must be of type np.int64");
+        PyErr_SetString(PyExc_ValueError, "indexers must be of type np.int64");
         return NULL;
     }