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Add type hinting for numeric.py #38
Code cleanup using Black
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,155 +1,111 @@ | ||
| from types import ModuleType | ||
| from typing import ( | ||
| Sequence, | ||
| Optional, | ||
| TypeVar, | ||
| Union, | ||
| Tuple, | ||
| List, | ||
| Iterable, | ||
| Callable, | ||
| Any, | ||
| ) | ||
|
|
||
| from numpy import ndarray, dtype, _ArrayLike, _ShapeLike | ||
|
|
||
| T = TypeVar("T") | ||
|
|
||
| def zeros_like( | ||
| a: _ArrayLike, | ||
| dtype: Optional[dtype] = None, | ||
| order: str = "K", | ||
| subok: bool = True, | ||
| shape: Optional[Union[int, Sequence[int]]] = None, | ||
| ) -> ndarray[int]: ... | ||
| def ones( | ||
| shape: _ShapeLike, dtype: Optional[dtype] = None, order: str = "C" | ||
| ) -> ndarray[int]: ... | ||
| def ones_like( | ||
| a: _ArrayLike, | ||
| dtype: Optional[dtype] = None, | ||
| order: str = "K", | ||
| subok: bool = True, | ||
| shape: Optional[_ShapeLike] = None, | ||
| ) -> ndarray[int]: ... | ||
| def full( | ||
| shape: _ShapeLike, fill_value: T, dtype: Optional[dtype] = None, order: str = "C" | ||
| ) -> ndarray[T]: ... | ||
| def full_like( | ||
| a: _ArrayLike, | ||
| fill_value: T, | ||
| dtype: Optional[dtype] = None, | ||
| order: str = "K", | ||
| subok: bool = True, | ||
| shape: Optional[_ShapeLike] = None, | ||
| ) -> ndarray[T]: ... | ||
| def count_nonzero( | ||
| a: _ArrayLike, axis: Optional[Union[int, Tuple[int], Tuple[int, int]]] = None | ||
| ) -> Union[int, ndarray[int]]: ... | ||
| def isfortran(a: ndarray) -> bool: ... | ||
| def argwhere(a: _ArrayLike) -> ndarray: ... | ||
| def flatnonzero(a: _ArrayLike) -> ndarray: ... | ||
| def correlate(a: _ArrayLike, v: _ArrayLike, mode: str = "valid") -> ndarray: ... | ||
| def convolve(a: _ArrayLike, v: _ArrayLike, mode: str = "full") -> ndarray: ... | ||
| def outer(a: _ArrayLike, b: _ArrayLike, out: ndarray = None) -> ndarray: ... | ||
| def tensordot( | ||
| a: _ArrayLike, | ||
| b: _ArrayLike, | ||
| axes: Union[ | ||
| int, Tuple[int, int], Tuple[Tuple[int, int], ...], Tuple[List[int, int], ...] | ||
| ] = 2, | ||
| ) -> ndarray: ... | ||
| def roll( | ||
| a: _ArrayLike, | ||
| shift: Union[int, Tuple[int, ...]], | ||
| axis: Optional[Union[int, Tuple[int, ...]]] = None, | ||
| ) -> T: ... | ||
| def rollaxis(a: _ArrayLike, axis: int, start: int = 0) -> ndarray: ... | ||
| def normalize_axis_tuple( | ||
| axis: Union[int, Iterable[int]], | ||
| ndim: int, | ||
| argname: Optional[str] = None, | ||
| allow_duplicate: bool = False, | ||
| ) -> Tuple[int, ...]: ... | ||
| def moveaxis( | ||
| a: ndarray, | ||
| source: Union[int, Sequence[int]], | ||
| destination: Union[int, Sequence[int]], | ||
| ) -> ndarray: ... | ||
| def cross( | ||
| a: _ArrayLike, | ||
| b: _ArrayLike, | ||
| axisa: int = -1, | ||
| axisb: int = -1, | ||
| axisc: int = -1, | ||
| axis: Optional[int] = None, | ||
| ) -> ndarray: ... | ||
| def indices( | ||
| dimensions: Sequence[int], dtype: dtype = int, sparse: bool = False | ||
| ) -> Union[ndarray, Tuple[ndarray, ...]]: ... | ||
| def fromfunction(function: Callable, shape: Tuple[int, int], **kwargs) -> Any: ... | ||
| def isscalar(element: Any) -> bool: ... | ||
| def binary_repr(num: int, width: Optional[int] = None) -> str: ... | ||
| def base_repr(number: int, base: int = 2, padding: int = 0) -> str: ... | ||
| def identity(n: int, dtype: Optional[dtype] = None) -> ndarray: ... | ||
| def allclose( | ||
| a: _ArrayLike, | ||
| b: _ArrayLike, | ||
| rtol: float = 1.0e-5, | ||
| atol: float = 1.0e-8, | ||
| equal_nan: bool = False, | ||
| ) -> bool: ... | ||
| def isclose( | ||
| a: _ArrayLike, | ||
| b: _ArrayLike, | ||
| rtol: float = 1.0e-5, | ||
| atol: float = 1.0e-8, | ||
| equal_nan: bool = False, | ||
| ) -> _ArrayLike: ... | ||
| def array_equal(a1: _ArrayLike, a2: _ArrayLike) -> bool: ... | ||
| def array_equiv(a1: _ArrayLike, a2: _ArrayLike) -> bool: ... | ||
| def extend_all(module: ModuleType): ... |
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Is there a meaningful difference between
TypeVar("_ArrayLike"),TypeVar("_ArrayLike", object)and what you wrote here?I agree that it's a good idea to clearly identify "array like" arguments, but I'm not certain this the right annotation. Note that in most cases NumPy is fine with absolutely any type of object in cases where it needs an "array like" argument
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You have a good point. I was already hesitating on how to create this TypeVar, but since an
array_likecan be just about anything, it makes better sense to just declare it as_ArrayLike = TypeVar("_ArrayLike")Best thing is, we can always change this TypeVar if we come to another solution.