diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/__init__.pyi | 75 | ||||
| -rw-r--r-- | numpy/core/multiarray.py | 18 | ||||
| -rw-r--r-- | numpy/core/multiarray.pyi | 378 | ||||
| -rw-r--r-- | numpy/core/numeric.pyi | 17 | ||||
| -rw-r--r-- | numpy/lib/index_tricks.pyi | 33 | ||||
| -rw-r--r-- | numpy/typing/_ufunc.pyi | 26 | ||||
| -rw-r--r-- | numpy/typing/tests/data/fail/array_constructors.py | 4 | ||||
| -rw-r--r-- | numpy/typing/tests/data/fail/multiarray.py | 32 | ||||
| -rw-r--r-- | numpy/typing/tests/data/pass/multiarray.py | 84 | ||||
| -rw-r--r-- | numpy/typing/tests/data/reveal/array_constructors.py | 42 | ||||
| -rw-r--r-- | numpy/typing/tests/data/reveal/multiarray.py | 80 |
11 files changed, 621 insertions, 168 deletions
diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index a83e5d9c7..066ac2451 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -294,11 +294,33 @@ from numpy.core.einsumfunc import ( einsum_path as einsum_path, ) +from numpy.core.multiarray import ( + array as array, + empty_like as empty_like, + empty as empty, + zeros as zeros, + concatenate as concatenate, + inner as inner, + where as where, + lexsort as lexsort, + can_cast as can_cast, + min_scalar_type as min_scalar_type, + result_type as result_type, + dot as dot, + vdot as vdot, + bincount as bincount, + copyto as copyto, + putmask as putmask, + packbits as packbits, + unpackbits as unpackbits, + shares_memory as shares_memory, + may_share_memory as may_share_memory, +) + from numpy.core.numeric import ( zeros_like as zeros_like, ones as ones, ones_like as ones_like, - empty_like as empty_like, full as full, full_like as full_like, count_nonzero as count_nonzero, @@ -868,13 +890,9 @@ def busday_offset( busdaycal=..., out=..., ): ... -def can_cast(from_, to, casting=...): ... def compare_chararrays(a, b, cmp_op, rstrip): ... -def concatenate(__a, axis=..., out=..., dtype=..., casting=...): ... -def copyto(dst, src, casting=..., where=...): ... def datetime_as_string(arr, unit=..., timezone=..., casting=...): ... def datetime_data(__dtype): ... -def dot(a, b, out=...): ... def frombuffer(buffer, dtype=..., count=..., offset=..., *, like=...): ... def fromfile( file, dtype=..., count=..., sep=..., offset=..., *, like=... @@ -883,24 +901,12 @@ def fromiter(iter, dtype, count=..., *, like=...): ... def frompyfunc(func, nin, nout, * identity): ... def fromstring(string, dtype=..., count=..., sep=..., *, like=...): ... def geterrobj(): ... -def inner(a, b): ... def is_busday( dates, weekmask=..., holidays=..., busdaycal=..., out=... ): ... -def lexsort(keys, axis=...): ... -def may_share_memory(a, b, max_work=...): ... -def min_scalar_type(a): ... def nested_iters(*args, **kwargs): ... # TODO: Sort out parameters def promote_types(type1, type2): ... -def putmask(a, mask, values): ... -def result_type(*arrays_and_dtypes): ... def seterrobj(errobj): ... -def shares_memory(a, b, max_work=...): ... -def vdot(a, b): ... -@overload -def where(__condition): ... -@overload -def where(__condition, __x, __y): ... _NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray) _DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic) @@ -1616,7 +1622,7 @@ _BufferType = Union[ndarray, bytes, bytearray, memoryview] _T = TypeVar("_T") _T_co = TypeVar("_T_co", covariant=True) _2Tuple = Tuple[_T, _T] -_Casting = L["no", "equiv", "safe", "same_kind", "unsafe"] +_CastingKind = L["no", "equiv", "safe", "same_kind", "unsafe"] _DTypeLike = Union[ dtype[_ScalarType], @@ -1866,7 +1872,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): self, dtype: _DTypeLike[_ScalarType], order: _OrderKACF = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., subok: bool = ..., copy: bool = ..., ) -> NDArray[_ScalarType]: ... @@ -1875,7 +1881,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): self, dtype: DTypeLike, order: _OrderKACF = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., subok: bool = ..., copy: bool = ..., ) -> NDArray[Any]: ... @@ -2886,7 +2892,7 @@ class generic(_ArrayOrScalarCommon): self, dtype: _DTypeLike[_ScalarType], order: _OrderKACF = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., subok: bool = ..., copy: bool = ..., ) -> _ScalarType: ... @@ -2895,7 +2901,7 @@ class generic(_ArrayOrScalarCommon): self, dtype: DTypeLike, order: _OrderKACF = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., subok: bool = ..., copy: bool = ..., ) -> Any: ... @@ -3430,31 +3436,6 @@ class str_(character, str): unicode_ = str_ str0 = str_ -def array( - object: object, - dtype: DTypeLike = ..., - *, - copy: bool = ..., - order: _OrderKACF = ..., - subok: bool = ..., - ndmin: int = ..., - like: ArrayLike = ..., -) -> ndarray: ... -def zeros( - shape: _ShapeLike, - dtype: DTypeLike = ..., - order: _OrderCF = ..., - *, - like: ArrayLike = ..., -) -> ndarray: ... -def empty( - shape: _ShapeLike, - dtype: DTypeLike = ..., - order: _OrderCF = ..., - *, - like: ArrayLike = ..., -) -> ndarray: ... - # # Constants # diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index b7a3a8d67..78aa7a65c 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -249,7 +249,7 @@ def concatenate(arrays, axis=None, out=None, *, dtype=None, casting=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.inner) def inner(a, b): """ - inner(a, b) + inner(a, b, /) Inner product of two arrays. @@ -341,7 +341,7 @@ def inner(a, b): @array_function_from_c_func_and_dispatcher(_multiarray_umath.where) def where(condition, x=None, y=None): """ - where(condition, [x, y]) + where(condition, [x, y], /) Return elements chosen from `x` or `y` depending on `condition`. @@ -613,7 +613,7 @@ def can_cast(from_, to, casting=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.min_scalar_type) def min_scalar_type(a): """ - min_scalar_type(a) + min_scalar_type(a, /) For scalar ``a``, returns the data type with the smallest size and smallest scalar kind which can hold its value. For non-scalar @@ -825,7 +825,7 @@ def dot(a, b, out=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot) def vdot(a, b): """ - vdot(a, b) + vdot(a, b, /) Return the dot product of two vectors. @@ -883,7 +883,7 @@ def vdot(a, b): @array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount) def bincount(x, weights=None, minlength=None): """ - bincount(x, weights=None, minlength=0) + bincount(x, /, weights=None, minlength=0) Count number of occurrences of each value in array of non-negative ints. @@ -1151,7 +1151,7 @@ def putmask(a, mask, values): @array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits) def packbits(a, axis=None, bitorder='big'): """ - packbits(a, axis=None, bitorder='big') + packbits(a, /, axis=None, bitorder='big') Packs the elements of a binary-valued array into bits in a uint8 array. @@ -1209,7 +1209,7 @@ def packbits(a, axis=None, bitorder='big'): @array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits) def unpackbits(a, axis=None, count=None, bitorder='big'): """ - unpackbits(a, axis=None, count=None, bitorder='big') + unpackbits(a, /, axis=None, count=None, bitorder='big') Unpacks elements of a uint8 array into a binary-valued output array. @@ -1293,7 +1293,7 @@ def unpackbits(a, axis=None, count=None, bitorder='big'): @array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory) def shares_memory(a, b, max_work=None): """ - shares_memory(a, b, max_work=None) + shares_memory(a, b, /, max_work=None) Determine if two arrays share memory. @@ -1368,7 +1368,7 @@ def shares_memory(a, b, max_work=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.may_share_memory) def may_share_memory(a, b, max_work=None): """ - may_share_memory(a, b, max_work=None) + may_share_memory(a, b, /, max_work=None) Determine if two arrays might share memory diff --git a/numpy/core/multiarray.pyi b/numpy/core/multiarray.pyi new file mode 100644 index 000000000..70048d0e6 --- /dev/null +++ b/numpy/core/multiarray.pyi @@ -0,0 +1,378 @@ +# TODO: Sort out any and all missing functions in this namespace + +import sys +from typing import ( + Any, + Optional, + overload, + TypeVar, + List, + Type, + Union, + Sequence, + Tuple, +) + +from numpy import ( + busdaycalendar as busdaycalendar, + ndarray, + dtype, + str_, + bool_, + uint8, + intp, + float64, + timedelta64, + generic, + unsignedinteger, + signedinteger, + floating, + complexfloating, + _OrderKACF, + _OrderCF, + _CastingKind, + _ModeKind, +) + +from numpy.typing import ( + # Shapes + _ShapeLike, + + # DTypes + DTypeLike, + _SupportsDType, + + # Arrays + NDArray, + ArrayLike, + _SupportsArray, + _NestedSequence, + _ArrayLikeBool_co, + _ArrayLikeUInt_co, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeTD64_co, + _ArrayLikeObject_co, + _IntLike_co, +) + +if sys.version_info >= (3, 8): + from typing import SupportsIndex, Literal as L +else: + from typing_extensions import SupportsIndex, Literal as L + +_SCT = TypeVar("_SCT", bound=generic) +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) + +_DTypeLike = Union[ + dtype[_SCT], + Type[_SCT], + _SupportsDType[dtype[_SCT]], +] +_ArrayLike = _NestedSequence[_SupportsArray[dtype[_SCT]]] + +__all__: List[str] + +@overload +def empty_like( + prototype: _ArrayLike[_SCT], + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[_SCT]: ... +@overload +def empty_like( + prototype: object, + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[Any]: ... +@overload +def empty_like( + prototype: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[_SCT]: ... +@overload +def empty_like( + prototype: Any, + dtype: DTypeLike, + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[Any]: ... + +@overload +def array( + object: _ArrayLike[_SCT], + dtype: None = ..., + *, + copy: bool = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def array( + object: object, + dtype: None = ..., + *, + copy: bool = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def array( + object: Any, + dtype: _DTypeLike[_SCT], + *, + copy: bool = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def array( + object: Any, + dtype: DTypeLike, + *, + copy: bool = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def zeros( + shape: _ShapeLike, + dtype: None = ..., + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def zeros( + shape: _ShapeLike, + dtype: _DTypeLike[_SCT], + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def zeros( + shape: _ShapeLike, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def empty( + shape: _ShapeLike, + dtype: None = ..., + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def empty( + shape: _ShapeLike, + dtype: _DTypeLike[_SCT], + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def empty( + shape: _ShapeLike, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def unravel_index( # type: ignore[misc] + indices: _IntLike_co, + shape: _ShapeLike, + order: _OrderCF = ..., +) -> Tuple[intp, ...]: ... +@overload +def unravel_index( + indices: _ArrayLikeInt_co, + shape: _ShapeLike, + order: _OrderCF = ..., +) -> Tuple[NDArray[intp], ...]: ... + +@overload +def ravel_multi_index( # type: ignore[misc] + multi_index: Sequence[_IntLike_co], + dims: Sequence[SupportsIndex], + mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., + order: _OrderCF = ..., +) -> intp: ... +@overload +def ravel_multi_index( + multi_index: Sequence[_ArrayLikeInt_co], + dims: Sequence[SupportsIndex], + mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., + order: _OrderCF = ..., +) -> NDArray[intp]: ... + +@overload +def concatenate( # type: ignore[misc] + __arrays: _ArrayLike[_SCT], + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: None = ..., + casting: Optional[_CastingKind] = ... +) -> NDArray[_SCT]: ... +@overload +def concatenate( # type: ignore[misc] + __arrays: ArrayLike, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: None = ..., + casting: Optional[_CastingKind] = ... +) -> NDArray[Any]: ... +@overload +def concatenate( # type: ignore[misc] + __arrays: ArrayLike, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: _DTypeLike[_SCT], + casting: Optional[_CastingKind] = ... +) -> NDArray[_SCT]: ... +@overload +def concatenate( # type: ignore[misc] + __arrays: ArrayLike, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: DTypeLike, + casting: Optional[_CastingKind] = ... +) -> NDArray[Any]: ... +@overload +def concatenate( + __arrays: ArrayLike, + axis: Optional[SupportsIndex] = ..., + out: _ArrayType = ..., + *, + dtype: DTypeLike = ..., + casting: Optional[_CastingKind] = ... +) -> _ArrayType: ... + +def inner( + __a: ArrayLike, + __b: ArrayLike, +) -> Any: ... + +@overload +def where( + __condition: ArrayLike, +) -> Tuple[NDArray[intp], ...]: ... +@overload +def where( + __condition: ArrayLike, + __x: ArrayLike, + __y: ArrayLike, +) -> NDArray[Any]: ... + +def lexsort( + keys: ArrayLike, + axis: Optional[SupportsIndex] = ..., +) -> Any: ... + +def can_cast( + from_: Union[ArrayLike, DTypeLike], + to: DTypeLike, + casting: Optional[_CastingKind] = ..., +) -> bool: ... + +def min_scalar_type( + __a: ArrayLike, +) -> dtype[Any]: ... + +def result_type( + *arrays_and_dtypes: Union[ArrayLike, DTypeLike], +) -> dtype[Any]: ... + +@overload +def dot(a: ArrayLike, b: ArrayLike, out: None = ...) -> Any: ... +@overload +def dot(a: ArrayLike, b: ArrayLike, out: _ArrayType) -> _ArrayType: ... + +@overload +def vdot(__a: _ArrayLikeBool_co, __b: _ArrayLikeBool_co) -> bool_: ... # type: ignore[misc] +@overload +def vdot(__a: _ArrayLikeUInt_co, __b: _ArrayLikeUInt_co) -> unsignedinteger[Any]: ... # type: ignore[misc] +@overload +def vdot(__a: _ArrayLikeInt_co, __b: _ArrayLikeInt_co) -> signedinteger[Any]: ... # type: ignore[misc] +@overload +def vdot(__a: _ArrayLikeFloat_co, __b: _ArrayLikeFloat_co) -> floating[Any]: ... # type: ignore[misc] +@overload +def vdot(__a: _ArrayLikeComplex_co, __b: _ArrayLikeComplex_co) -> complexfloating[Any, Any]: ... # type: ignore[misc] +@overload +def vdot(__a: _ArrayLikeTD64_co, __b: _ArrayLikeTD64_co) -> timedelta64: ... +@overload +def vdot(__a: _ArrayLikeObject_co, __b: Any) -> Any: ... +@overload +def vdot(__a: Any, __b: _ArrayLikeObject_co) -> Any: ... + +def bincount( + __x: ArrayLike, + weights: Optional[ArrayLike] = ..., + minlength: SupportsIndex = ..., +) -> NDArray[intp]: ... + +def copyto( + dst: NDArray[Any], + src: ArrayLike, + casting: Optional[_CastingKind] = ..., + where: Optional[_ArrayLikeBool_co] = ..., +) -> None: ... + +def putmask( + a: NDArray[Any], + mask: _ArrayLikeBool_co, + values: ArrayLike, +) -> None: ... + +def packbits( + __a: _ArrayLikeInt_co, + axis: Optional[SupportsIndex] = ..., + bitorder: L["big", "little"] = ..., +) -> NDArray[uint8]: ... + +def unpackbits( + __a: _ArrayLike[uint8], + axis: Optional[SupportsIndex] = ..., + count: Optional[SupportsIndex] = ..., + bitorder: L["big", "little"] = ..., +) -> NDArray[uint8]: ... + +def shares_memory( + __a: object, + __b: object, + max_work: Optional[int] = ..., +) -> bool: ... + +def may_share_memory( + __a: object, + __b: object, + max_work: Optional[int] = ..., +) -> bool: ... diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi index f57951434..3c2b553ec 100644 --- a/numpy/core/numeric.pyi +++ b/numpy/core/numeric.pyi @@ -67,23 +67,6 @@ def ones_like( shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... -@overload -def empty_like( - a: _ArrayType, - dtype: None = ..., - order: _OrderKACF = ..., - subok: Literal[True] = ..., - shape: None = ..., -) -> _ArrayType: ... -@overload -def empty_like( - a: ArrayLike, - dtype: DTypeLike = ..., - order: _OrderKACF = ..., - subok: bool = ..., - shape: Optional[_ShapeLike] = ..., -) -> ndarray: ... - def full( shape: _ShapeLike, fill_value: Any, diff --git a/numpy/lib/index_tricks.pyi b/numpy/lib/index_tricks.pyi index a3bfef6b6..0f9ae94a9 100644 --- a/numpy/lib/index_tricks.pyi +++ b/numpy/lib/index_tricks.pyi @@ -44,6 +44,11 @@ from numpy.typing import ( _ShapeLike, ) +from numpy.core.multiarray import ( + unravel_index as unravel_index, + ravel_multi_index as ravel_multi_index, +) + if sys.version_info >= (3, 8): from typing import Literal, SupportsIndex else: @@ -58,34 +63,6 @@ _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: List[str] @overload -def unravel_index( # type: ignore[misc] - indices: Union[int, integer[Any]], - shape: _ShapeLike, - order: _OrderCF = ... -) -> Tuple[intp, ...]: ... -@overload -def unravel_index( - indices: _ArrayLikeInt, - shape: _ShapeLike, - order: _OrderCF = ... -) -> Tuple[NDArray[intp], ...]: ... - -@overload -def ravel_multi_index( # type: ignore[misc] - multi_index: Sequence[Union[int, integer[Any]]], - dims: _ShapeLike, - mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., - order: _OrderCF = ... -) -> intp: ... -@overload -def ravel_multi_index( - multi_index: Sequence[_ArrayLikeInt], - dims: _ShapeLike, - mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., - order: _OrderCF = ... -) -> NDArray[intp]: ... - -@overload def ix_(*args: _NestedSequence[_SupportsDType[_DType]]) -> Tuple[ndarray[Any, _DType], ...]: ... @overload def ix_(*args: _NestedSequence[str]) -> Tuple[NDArray[str_], ...]: ... diff --git a/numpy/typing/_ufunc.pyi b/numpy/typing/_ufunc.pyi index f4fead504..be1e654c2 100644 --- a/numpy/typing/_ufunc.pyi +++ b/numpy/typing/_ufunc.pyi @@ -16,7 +16,7 @@ from typing import ( TypeVar, ) -from numpy import ufunc, _Casting, _OrderKACF +from numpy import ufunc, _CastingKind, _OrderKACF from numpy.typing import NDArray from ._shape import _ShapeLike @@ -81,7 +81,7 @@ class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): out: None = ..., *, where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -95,7 +95,7 @@ class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): out: None | NDArray[Any] | Tuple[NDArray[Any]] = ..., *, where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -133,7 +133,7 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): out: None = ..., *, where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -148,7 +148,7 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): out: None | NDArray[Any] | Tuple[NDArray[Any]] = ..., *, where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -200,7 +200,7 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): *, out: None = ..., where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -215,7 +215,7 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): *, out: None | NDArray[Any] | Tuple[NDArray[Any]] = ..., where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -257,7 +257,7 @@ class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): __out2: None = ..., *, where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -273,7 +273,7 @@ class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): *, out: _2Tuple[NDArray[Any]] = ..., where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -316,7 +316,7 @@ class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): __out2: None = ..., *, where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -333,7 +333,7 @@ class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): *, out: _2Tuple[NDArray[Any]] = ..., where: None | _ArrayLikeBool_co = ..., - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -378,7 +378,7 @@ class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): __x2: ArrayLike, out: None = ..., *, - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., @@ -393,7 +393,7 @@ class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): __x2: ArrayLike, out: NDArray[Any] | Tuple[NDArray[Any]], *, - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., diff --git a/numpy/typing/tests/data/fail/array_constructors.py b/numpy/typing/tests/data/fail/array_constructors.py index f13fdacb2..eb57e5c00 100644 --- a/numpy/typing/tests/data/fail/array_constructors.py +++ b/numpy/typing/tests/data/fail/array_constructors.py @@ -7,12 +7,12 @@ np.require(a, requirements=1) # E: No overload variant np.require(a, requirements="TEST") # E: incompatible type np.zeros("test") # E: incompatible type -np.zeros() # E: Missing positional argument +np.zeros() # E: require at least one argument np.ones("test") # E: incompatible type np.ones() # E: Missing positional argument -np.array(0, float, True) # E: Too many positional +np.array(0, float, True) # E: No overload variant np.linspace(None, 'bob') # E: No overload variant np.linspace(0, 2, num=10.0) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/multiarray.py b/numpy/typing/tests/data/fail/multiarray.py new file mode 100644 index 000000000..e931e9d5c --- /dev/null +++ b/numpy/typing/tests/data/fail/multiarray.py @@ -0,0 +1,32 @@ +from typing import List +import numpy as np +import numpy.typing as npt + +i8: np.int64 + +AR_b: npt.NDArray[np.bool_] +AR_u1: npt.NDArray[np.uint8] +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_M: npt.NDArray[np.datetime64] + +AR_LIKE_f: List[float] + +np.where(AR_b, 1) # E: No overload variant + +np.can_cast(AR_f8, 1) # E: incompatible type + +np.vdot(AR_M, AR_M) # E: incompatible type + +np.copyto(AR_LIKE_f, AR_f8) # E: incompatible type + +np.putmask(AR_LIKE_f, [True, True, False], 1.5) # E: incompatible type + +np.packbits(AR_f8) # E: incompatible type +np.packbits(AR_u1, bitorder=">") # E: incompatible type + +np.unpackbits(AR_i8) # E: incompatible type +np.unpackbits(AR_u1, bitorder=">") # E: incompatible type + +np.shares_memory(1, 1, max_work=i8) # E: incompatible type +np.may_share_memory(1, 1, max_work=i8) # E: incompatible type diff --git a/numpy/typing/tests/data/pass/multiarray.py b/numpy/typing/tests/data/pass/multiarray.py index e2b5d16a0..e5d33c673 100644 --- a/numpy/typing/tests/data/pass/multiarray.py +++ b/numpy/typing/tests/data/pass/multiarray.py @@ -1,37 +1,77 @@ -from __future__ import annotations - from typing import Any import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] = np.array([1.0]) +AR_i4 = np.array([1], dtype=np.int32) +AR_u1 = np.array([1], dtype=np.uint8) -AR_f8: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0]) -AR_i8: np.ndarray[Any, np.dtype[np.int_]] = np.array([1]) +AR_LIKE_f = [1.5] +AR_LIKE_i = [1] b_f8 = np.broadcast(AR_f8) -b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8) +b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8) next(b_f8) -next(b_i8_f8_f8) - b_f8.reset() -b_i8_f8_f8.reset() - b_f8.index -b_i8_f8_f8.index - b_f8.iters -b_i8_f8_f8.iters - b_f8.nd -b_i8_f8_f8.nd - b_f8.ndim -b_i8_f8_f8.ndim - b_f8.numiter -b_i8_f8_f8.numiter - b_f8.shape -b_i8_f8_f8.shape - b_f8.size -b_i8_f8_f8.size + +next(b_i4_f8_f8) +b_i4_f8_f8.reset() +b_i4_f8_f8.ndim +b_i4_f8_f8.index +b_i4_f8_f8.iters +b_i4_f8_f8.nd +b_i4_f8_f8.numiter +b_i4_f8_f8.shape +b_i4_f8_f8.size + +np.inner(AR_f8, AR_i4) + +np.where([True, True, False]) +np.where([True, True, False], 1, 0) + +np.lexsort([0, 1, 2]) + +np.can_cast(np.dtype("i8"), int) +np.can_cast(AR_f8, "f8") +np.can_cast(AR_f8, np.complex128, casting="unsafe") + +np.min_scalar_type([1]) +np.min_scalar_type(AR_f8) + +np.result_type(int, AR_i4) +np.result_type(AR_f8, AR_u1) +np.result_type(AR_f8, np.complex128) + +np.dot(AR_LIKE_f, AR_i4) +np.dot(AR_u1, 1) +np.dot(1.5j, 1) +np.dot(AR_u1, 1, out=AR_f8) + +np.vdot(AR_LIKE_f, AR_i4) +np.vdot(AR_u1, 1) +np.vdot(1.5j, 1) + +np.bincount(AR_i4) + +np.copyto(AR_f8, [1.6]) + +np.putmask(AR_f8, [True], 1.5) + +np.packbits(AR_i4) +np.packbits(AR_u1) + +np.unpackbits(AR_u1) + +np.shares_memory(1, 2) +np.shares_memory(AR_f8, AR_f8, max_work=1) + +np.may_share_memory(1, 2) +np.may_share_memory(AR_f8, AR_f8, max_work=1) diff --git a/numpy/typing/tests/data/reveal/array_constructors.py b/numpy/typing/tests/data/reveal/array_constructors.py index 2e803a365..775b0e1e0 100644 --- a/numpy/typing/tests/data/reveal/array_constructors.py +++ b/numpy/typing/tests/data/reveal/array_constructors.py @@ -1,21 +1,46 @@ from typing import List, Any import numpy as np +import numpy.typing as npt class SubClass(np.ndarray): ... i8: np.int64 -A: np.ndarray +A: npt.NDArray[np.float64] B: SubClass C: List[int] def func(i: int, j: int, **kwargs: Any) -> SubClass: ... +reveal_type(np.empty_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.empty_like([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.empty_like(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.empty_like(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.array(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.array([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.array(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.array(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.zeros([1, 5, 6])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.zeros([1, 5, 6], dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.zeros([1, 5, 6], dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.empty([1, 5, 6])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.empty([1, 5, 6], dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.empty([1, 5, 6], dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.concatenate(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.concatenate([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.concatenate(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] +reveal_type(np.concatenate(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.concatenate([1, 1.0], out=A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] + reveal_type(np.asarray(A)) # E: numpy.ndarray[Any, Any] reveal_type(np.asarray(B)) # E: numpy.ndarray[Any, Any] reveal_type(np.asarray(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asanyarray(A)) # E: numpy.ndarray[Any, Any] +reveal_type(np.asanyarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] reveal_type(np.asanyarray(B)) # E: SubClass reveal_type(np.asanyarray(B, dtype=int)) # E: numpy.ndarray[Any, Any] reveal_type(np.asanyarray(C)) # E: numpy.ndarray[Any, Any] @@ -28,7 +53,7 @@ reveal_type(np.asfortranarray(A)) # E: numpy.ndarray[Any, Any] reveal_type(np.asfortranarray(B)) # E: numpy.ndarray[Any, Any] reveal_type(np.asfortranarray(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(A)) # E: numpy.ndarray[Any, Any] +reveal_type(np.require(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] reveal_type(np.require(B)) # E: SubClass reveal_type(np.require(B, requirements=None)) # E: SubClass reveal_type(np.require(B, dtype=int)) # E: numpy.ndarray[Any, Any] @@ -45,22 +70,17 @@ reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray[Any, Any reveal_type(np.logspace(0, 10)) # E: numpy.ndarray[Any, Any] reveal_type(np.geomspace(1, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.zeros_like(A)) # E: numpy.ndarray[Any, Any] +reveal_type(np.zeros_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] reveal_type(np.zeros_like(C)) # E: numpy.ndarray[Any, Any] reveal_type(np.zeros_like(B)) # E: SubClass reveal_type(np.zeros_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ones_like(A)) # E: numpy.ndarray[Any, Any] +reveal_type(np.ones_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] reveal_type(np.ones_like(C)) # E: numpy.ndarray[Any, Any] reveal_type(np.ones_like(B)) # E: SubClass reveal_type(np.ones_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, Any] -reveal_type(np.empty_like(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.empty_like(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.empty_like(B)) # E: SubClass -reveal_type(np.empty_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.full_like(A, i8)) # E: numpy.ndarray[Any, Any] +reveal_type(np.full_like(A, i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] reveal_type(np.full_like(C, i8)) # E: numpy.ndarray[Any, Any] reveal_type(np.full_like(B, i8)) # E: SubClass reveal_type(np.full_like(B, i8, dtype=np.int64)) # E: numpy.ndarray[Any, Any] diff --git a/numpy/typing/tests/data/reveal/multiarray.py b/numpy/typing/tests/data/reveal/multiarray.py index 33e9ede7c..2811b1f35 100644 --- a/numpy/typing/tests/data/reveal/multiarray.py +++ b/numpy/typing/tests/data/reveal/multiarray.py @@ -1,35 +1,77 @@ -from typing import Any +from typing import Any, List import numpy as np +import numpy.typing as npt -AR_f8: np.ndarray[Any, np.dtype[np.float64]] -AR_i8: np.ndarray[Any, np.dtype[np.int64]] +AR_f8: npt.NDArray[np.float64] +AR_i8: npt.NDArray[np.int64] +AR_u1: npt.NDArray[np.uint8] + +AR_LIKE_f: List[float] +AR_LIKE_i: List[int] b_f8 = np.broadcast(AR_f8) b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8) reveal_type(next(b_f8)) # E: tuple[Any] -reveal_type(next(b_i8_f8_f8)) # E: tuple[Any] - reveal_type(b_f8.reset()) # E: None -reveal_type(b_i8_f8_f8.reset()) # E: None - reveal_type(b_f8.index) # E: int -reveal_type(b_i8_f8_f8.index) # E: int - reveal_type(b_f8.iters) # E: tuple[numpy.flatiter[Any]] -reveal_type(b_i8_f8_f8.iters) # E: tuple[numpy.flatiter[Any]] - reveal_type(b_f8.nd) # E: int -reveal_type(b_i8_f8_f8.nd) # E: int - reveal_type(b_f8.ndim) # E: int -reveal_type(b_i8_f8_f8.ndim) # E: int - reveal_type(b_f8.numiter) # E: int -reveal_type(b_i8_f8_f8.numiter) # E: int - reveal_type(b_f8.shape) # E: tuple[builtins.int] -reveal_type(b_i8_f8_f8.shape) # E: tuple[builtins.int] - reveal_type(b_f8.size) # E: int + +reveal_type(next(b_i8_f8_f8)) # E: tuple[Any] +reveal_type(b_i8_f8_f8.reset()) # E: None +reveal_type(b_i8_f8_f8.index) # E: int +reveal_type(b_i8_f8_f8.iters) # E: tuple[numpy.flatiter[Any]] +reveal_type(b_i8_f8_f8.nd) # E: int +reveal_type(b_i8_f8_f8.ndim) # E: int +reveal_type(b_i8_f8_f8.numiter) # E: int +reveal_type(b_i8_f8_f8.shape) # E: tuple[builtins.int] reveal_type(b_i8_f8_f8.size) # E: int + +reveal_type(np.inner(AR_f8, AR_i8)) # E: Any + +reveal_type(np.where([True, True, False])) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.where([True, True, False], 1, 0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] + +reveal_type(np.lexsort([0, 1, 2])) # E: Any + +reveal_type(np.can_cast(np.dtype("i8"), int)) # E: bool +reveal_type(np.can_cast(AR_f8, "f8")) # E: bool +reveal_type(np.can_cast(AR_f8, np.complex128, casting="unsafe")) # E: bool + +reveal_type(np.min_scalar_type([1])) # E: numpy.dtype[Any] +reveal_type(np.min_scalar_type(AR_f8)) # E: numpy.dtype[Any] + +reveal_type(np.result_type(int, [1])) # E: numpy.dtype[Any] +reveal_type(np.result_type(AR_f8, AR_u1)) # E: numpy.dtype[Any] +reveal_type(np.result_type(AR_f8, np.complex128)) # E: numpy.dtype[Any] + +reveal_type(np.dot(AR_LIKE_f, AR_i8)) # E: Any +reveal_type(np.dot(AR_u1, 1)) # E: Any +reveal_type(np.dot(1.5j, 1)) # E: Any +reveal_type(np.dot(AR_u1, 1, out=AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] + +reveal_type(np.vdot(AR_LIKE_f, AR_i8)) # E: numpy.floating[Any] +reveal_type(np.vdot(AR_u1, 1)) # E: numpy.signedinteger[Any] +reveal_type(np.vdot(1.5j, 1)) # E: numpy.complexfloating[Any, Any] + +reveal_type(np.bincount(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] + +reveal_type(np.copyto(AR_f8, [1., 1.5, 1.6])) # E: None + +reveal_type(np.putmask(AR_f8, [True, True, False], 1.5)) # E: None + +reveal_type(np.packbits(AR_i8)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] +reveal_type(np.packbits(AR_u1)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] + +reveal_type(np.unpackbits(AR_u1)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] + +reveal_type(np.shares_memory(1, 2)) # E: bool +reveal_type(np.shares_memory(AR_f8, AR_f8, max_work=1)) # E: bool + +reveal_type(np.may_share_memory(1, 2)) # E: bool +reveal_type(np.may_share_memory(AR_f8, AR_f8, max_work=1)) # E: bool |