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♻️ simplified numpy._typing dtype- and array-like aliases #157

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Merged
jorenham merged 2 commits into from
Feb 19, 2025
Merged

♻️ simplified numpy._typing dtype- and array-like aliases #157

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f109cc7
♻️ simplified `numpy._typing` dtype- and array-like aliases
jorenham Feb 19, 2025
2853e03
🚨 temporarily ignore a pyright error (will fix in #158)
jorenham Feb 19, 2025
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2 changes: 1 addition & 1 deletion src/numpy-stubs/_core/_multiarray_umath.pyi
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Expand Up @@ -1412,7 +1412,7 @@ def dot(a: npt.ArrayLike, b: npt.ArrayLike, out: _ArrayT) -> _ArrayT: ...

#
@overload
def vdot(a: _ArrayLike[_NumericT], b: _ArrayLike[_NumericT], /) -> _NumericT: ... # type: ignore[overload-overlap] # false positive
def vdot(a: _ArrayLike[_NumericT], b: _ArrayLike[_NumericT], /) -> _NumericT: ... # type: ignore[overload-overlap] # pyright: ignore[reportOverlappingOverload]
@overload
def vdot(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, /) -> np.signedinteger: ...
@overload
Expand Down
41 changes: 17 additions & 24 deletions src/numpy-stubs/_typing/_array_like.pyi
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Expand Up @@ -6,7 +6,7 @@ from typing_extensions import Never, TypeVar
import numpy as np
from numpy.dtypes import StringDType

from ._nbit_base import _32Bit, _64Bit
from ._nbit_base import _64Bit
from ._nested_sequence import _NestedSequence
from ._shape import _Shape

Expand All @@ -32,7 +32,7 @@ _UnknownType: TypeAlias = Never
# any and all remaining overloads
@runtime_checkable
class _SupportsArray(Protocol[_DType_co]):
def __array__(self) -> np.ndarray[Any, _DType_co]: ...
def __array__(self, /) -> np.ndarray[Any, _DType_co]: ...

@runtime_checkable
class _SupportsArrayFunc(Protocol):
Expand All @@ -54,38 +54,31 @@ _ArrayLike: TypeAlias = _SupportsArray[np.dtype[_ScalarType]] | _NestedSequence[
# A union representing array-like objects; consists of two typevars:
# One representing types that can be parametrized w.r.t. `np.dtype`
# and another one for the rest
_DualArrayLike: TypeAlias = _SupportsArray[_DType] | _NestedSequence[_SupportsArray[_DType]] | _T | _NestedSequence[_T]
_DualArrayLike: TypeAlias = _SupportsArray[_DType] | _T | _NestedSequence[_T] | _NestedSequence[_SupportsArray[_DType]]

ArrayLike: TypeAlias = _Buffer | _DualArrayLike[np.dtype[Any], bool | int | float | complex | str | bytes]
ArrayLike: TypeAlias = _Buffer | _DualArrayLike[np.dtype[Any], complex | str | bytes]

# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
# given the casting rules `same_kind`
_ArrayLikeBool_co: TypeAlias = _DualArrayLike[np.dtype[np.bool], bool]
_ArrayLikeUInt_co: TypeAlias = _DualArrayLike[np.dtype[np.bool] | np.dtype[np.unsignedinteger], bool]
_ArrayLikeInt_co: TypeAlias = _DualArrayLike[np.dtype[np.bool] | np.dtype[np.integer], bool | int]
_ArrayLikeFloat_co: TypeAlias = _DualArrayLike[
np.dtype[np.bool] | np.dtype[np.integer] | np.dtype[np.floating],
bool | int | float,
]
_ArrayLikeComplex_co: TypeAlias = _DualArrayLike[
(np.dtype[np.bool] | np.dtype[np.integer] | np.dtype[np.floating] | np.dtype[np.complexfloating]),
bool | int | float | complex,
]
_ArrayLikeNumber_co: TypeAlias = _DualArrayLike[np.dtype[np.bool] | np.dtype[np.number], bool | int | float | complex]
_ArrayLikeTD64_co: TypeAlias = _DualArrayLike[np.dtype[np.bool] | np.dtype[np.integer] | np.dtype[np.timedelta64], bool | int]
_ArrayLikeDT64_co: TypeAlias = _SupportsArray[np.dtype[np.datetime64]] | _NestedSequence[_SupportsArray[np.dtype[np.datetime64]]]
_ArrayLikeObject_co: TypeAlias = _SupportsArray[np.dtype[np.object_]] | _NestedSequence[_SupportsArray[np.dtype[np.object_]]]

_ArrayLikeVoid_co: TypeAlias = _SupportsArray[np.dtype[np.void]] | _NestedSequence[_SupportsArray[np.dtype[np.void]]]
_ArrayLikeUInt_co: TypeAlias = _DualArrayLike[np.dtype[np.unsignedinteger | np.bool], bool]
_ArrayLikeInt_co: TypeAlias = _DualArrayLike[np.dtype[np.integer | np.bool], int]
_ArrayLikeFloat_co: TypeAlias = _DualArrayLike[np.dtype[np.floating | np.integer | np.bool], float]
_ArrayLikeComplex_co: TypeAlias = _DualArrayLike[np.dtype[np.bool | np.number], complex]
_ArrayLikeNumber_co: TypeAlias = _ArrayLikeComplex_co
_ArrayLikeTD64_co: TypeAlias = _DualArrayLike[np.dtype[np.timedelta64 | np.integer | np.bool], int]
_ArrayLikeDT64_co: TypeAlias = _ArrayLike[np.datetime64]
_ArrayLikeObject_co: TypeAlias = _ArrayLike[np.object_]
_ArrayLikeVoid_co: TypeAlias = _ArrayLike[np.void]
_ArrayLikeStr_co: TypeAlias = _DualArrayLike[np.dtype[np.str_], str]
_ArrayLikeBytes_co: TypeAlias = _DualArrayLike[np.dtype[np.bytes_], bytes]
_ArrayLikeString_co: TypeAlias = _DualArrayLike[StringDType, str]
_ArrayLikeAnyString_co: TypeAlias = _ArrayLikeStr_co | _ArrayLikeBytes_co | _ArrayLikeString_co
_ArrayLikeAnyString_co: TypeAlias = _DualArrayLike[StringDType | np.dtype[np.character], bytes | str]

__Float64_co: TypeAlias = np.floating[_64Bit] | np.float32 | np.float16 | np.integer | np.bool
__Complex128_co: TypeAlias = np.number[_64Bit] | np.number[_32Bit] | np.float16 | np.integer | np.bool
_ArrayLikeFloat64_co: TypeAlias = _DualArrayLike[np.dtype[__Float64_co], float | int]
_ArrayLikeComplex128_co: TypeAlias = _DualArrayLike[np.dtype[__Complex128_co], complex | float | int]
__Complex128_co: TypeAlias = np.complexfloating[_64Bit] | np.complex64 | __Float64_co
_ArrayLikeFloat64_co: TypeAlias = _DualArrayLike[np.dtype[__Float64_co], float]
_ArrayLikeComplex128_co: TypeAlias = _DualArrayLike[np.dtype[__Complex128_co], complex]

# NOTE: This includes `builtins.bool`, but not `numpy.bool`.
_ArrayLikeInt: TypeAlias = _DualArrayLike[np.dtype[np.integer], int]
Expand Down
184 changes: 40 additions & 144 deletions src/numpy-stubs/_typing/_dtype_like.pyi
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@@ -1,104 +1,69 @@
from _typeshed import Incomplete
from collections.abc import Sequence
from typing import Any, Protocol, TypeAlias, TypedDict, runtime_checkable
from typing_extensions import TypeVar
from typing_extensions import Required, TypeVar

import numpy as np

from ._char_codes import (
_BoolCodes,
_ByteCodes,
_BytesCodes,
_CDoubleCodes,
_CLongDoubleCodes,
_CSingleCodes,
_Complex64Codes,
_Complex128Codes,
_ComplexFloatingCodes,
_DT64Codes,
_DoubleCodes,
_Float16Codes,
_Float32Codes,
_Float64Codes,
_HalfCodes,
_Int8Codes,
_Int16Codes,
_Int32Codes,
_Int64Codes,
_IntCCodes,
_IntCodes,
_IntPCodes,
_LongCodes,
_LongDoubleCodes,
_LongLongCodes,
_FloatingCodes,
_NumberCodes,
_ObjectCodes,
_ShortCodes,
_SingleCodes,
_SignedIntegerCodes,
_StrCodes,
_TD64Codes,
_UByteCodes,
_UInt8Codes,
_UInt16Codes,
_UInt32Codes,
_UInt64Codes,
_UIntCCodes,
_UIntCodes,
_UIntPCodes,
_ULongLongCodes,
_UShortCodes,
_UnsignedIntegerCodes,
_VoidCodes,
)
from ._shape import _ShapeLike

_SCT = TypeVar("_SCT", bound=np.generic)
_DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any])
_ScalarT = TypeVar("_ScalarT", bound=np.generic)
_DTypeT_co = TypeVar("_DTypeT_co", covariant=True, bound=np.dtype[Any])

_DTypeLikeNested: TypeAlias = Any
# TODO(jorenham): Actually annotate this
_DTypeLikeNested: TypeAlias = Incomplete

# Mandatory keys
class _DTypeDictBase(TypedDict):
names: Sequence[str]
formats: Sequence[_DTypeLikeNested]

# Mandatory + optional keys
class _DTypeDict(_DTypeDictBase, total=False):
# Only `str` elements are usable as indexing aliases,
# but `titles` can in principle accept any object
offsets: Sequence[int]
titles: Sequence[Any]
class _DTypeDict(TypedDict, total=False):
names: Required[Sequence[str]]
formats: Required[Sequence[_DTypeLikeNested]]
itemsize: int
aligned: bool
offsets: Sequence[int]
# Only `str` elements are usable as indexing aliases,
# but `titles` can in principle accept any object
titles: Sequence[str | Any]

# A protocol for anything with the dtype attribute
@runtime_checkable
class _SupportsDType(Protocol[_DType_co]):
class _SupportsDType(Protocol[_DTypeT_co]):
@property
def dtype(self) -> _DType_co: ...
def dtype(self) -> _DTypeT_co: ...

# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
_DTypeLike: TypeAlias = np.dtype[_SCT] | type[_SCT] | _SupportsDType[np.dtype[_SCT]]
_DTypeLike: TypeAlias = type[_ScalarT] | np.dtype[_ScalarT] | _SupportsDType[np.dtype[_ScalarT]]

# Would create a dtype[np.void]
_VoidDTypeLike: TypeAlias = (
tuple[_DTypeLikeNested, int]
| tuple[_DTypeLikeNested, _ShapeLike]
| list[Any]
| _DTypeDict
| tuple[_DTypeLikeNested, _DTypeLikeNested]
)
_VoidDTypeLike: TypeAlias = tuple[_DTypeLikeNested, int | _ShapeLike | _DTypeLikeNested] | list[Any] | _DTypeDict

# Anything that can be coerced into numpy.dtype.
# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
DTypeLike: TypeAlias = (
np.dtype[Any]
# array-scalar types and generic types
| type[Any] # NOTE: We're stuck with `type[Any]` due to object dtypes
# anything with a dtype attribute
| _SupportsDType[np.dtype[Any]]
# character codes, type strings or comma-separated fields, e.g., 'float64'
| str
| _VoidDTypeLike
# default data type (float64)
| None
)
# Aliases for commonly used dtype-like objects.
# Note that the precision of `np.number` subclasses is ignored herein.
_DTypeLikeBool: TypeAlias = type[bool] | _DTypeLike[np.bool] | _BoolCodes
_DTypeLikeUInt: TypeAlias = _DTypeLike[np.unsignedinteger] | _UnsignedIntegerCodes
_DTypeLikeInt: TypeAlias = type[int] | _DTypeLike[np.signedinteger] | _SignedIntegerCodes
_DTypeLikeFloat: TypeAlias = type[float] | _DTypeLike[np.floating] | _FloatingCodes
_DTypeLikeComplex: TypeAlias = type[complex] | _DTypeLike[np.complexfloating] | _ComplexFloatingCodes
_DTypeLikeDT64: TypeAlias = _DTypeLike[np.timedelta64] | _TD64Codes
_DTypeLikeTD64: TypeAlias = _DTypeLike[np.datetime64] | _DT64Codes
_DTypeLikeStr: TypeAlias = type[str] | _DTypeLike[np.str_] | _StrCodes
_DTypeLikeBytes: TypeAlias = type[bytes] | _DTypeLike[np.bytes_] | _BytesCodes
_DTypeLikeVoid: TypeAlias = _DTypeLike[np.void] | _VoidCodes | _VoidDTypeLike
_DTypeLikeObject: TypeAlias = type | _DTypeLike[np.object_] | _ObjectCodes

_DTypeLikeComplex_co: TypeAlias = type[complex] | _DTypeLike[np.number] | _NumberCodes | _BoolCodes

# NOTE: while it is possible to provide the dtype as a dict of
# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
Expand All @@ -107,75 +72,6 @@ DTypeLike: TypeAlias = (
#
# See https://github.com/numpy/numpy/issues/16891 for more details.

# Aliases for commonly used dtype-like objects.
# Note that the precision of `np.number` subclasses is ignored herein.
_DTypeLikeBool: TypeAlias = type[bool] | type[np.bool] | np.dtype[np.bool] | _SupportsDType[np.dtype[np.bool]] | _BoolCodes
_DTypeLikeUInt: TypeAlias = (
type[np.unsignedinteger[Any]]
| np.dtype[np.unsignedinteger[Any]]
| _SupportsDType[np.dtype[np.unsignedinteger[Any]]]
| _UInt8Codes
| _UInt16Codes
| _UInt32Codes
| _UInt64Codes
| _UByteCodes
| _UShortCodes
| _UIntCCodes
| _LongCodes
| _ULongLongCodes
| _UIntPCodes
| _UIntCodes
)
_DTypeLikeInt: TypeAlias = (
type[int]
| type[np.signedinteger[Any]]
| np.dtype[np.signedinteger[Any]]
| _SupportsDType[np.dtype[np.signedinteger[Any]]]
| _Int8Codes
| _Int16Codes
| _Int32Codes
| _Int64Codes
| _ByteCodes
| _ShortCodes
| _IntCCodes
| _LongCodes
| _LongLongCodes
| _IntPCodes
| _IntCodes
)
_DTypeLikeFloat: TypeAlias = (
type[float]
| type[np.floating[Any]]
| np.dtype[np.floating[Any]]
| _SupportsDType[np.dtype[np.floating[Any]]]
| _Float16Codes
| _Float32Codes
| _Float64Codes
| _HalfCodes
| _SingleCodes
| _DoubleCodes
| _LongDoubleCodes
)
_DTypeLikeComplex: TypeAlias = (
type[complex]
| type[np.complexfloating[Any]]
| np.dtype[np.complexfloating[Any]]
| _SupportsDType[np.dtype[np.complexfloating[Any]]]
| _Complex64Codes
| _Complex128Codes
| _CSingleCodes
| _CDoubleCodes
| _CLongDoubleCodes
)
_DTypeLikeDT64: TypeAlias = (
type[np.timedelta64] | np.dtype[np.timedelta64] | _SupportsDType[np.dtype[np.timedelta64]] | _TD64Codes
)
_DTypeLikeTD64: TypeAlias = type[np.datetime64] | np.dtype[np.datetime64] | _SupportsDType[np.dtype[np.datetime64]] | _DT64Codes
_DTypeLikeStr: TypeAlias = type[str] | type[np.str_] | np.dtype[np.str_] | _SupportsDType[np.dtype[np.str_]] | _StrCodes
_DTypeLikeBytes: TypeAlias = (
type[bytes] | type[np.bytes_] | np.dtype[np.bytes_] | _SupportsDType[np.dtype[np.bytes_]] | _BytesCodes
)
_DTypeLikeVoid: TypeAlias = type[np.void] | np.dtype[np.void] | _SupportsDType[np.dtype[np.void]] | _VoidCodes | _VoidDTypeLike
_DTypeLikeObject: TypeAlias = type | np.dtype[np.object_] | _SupportsDType[np.dtype[np.object_]] | _ObjectCodes

_DTypeLikeComplex_co: TypeAlias = _DTypeLikeBool | _DTypeLikeUInt | _DTypeLikeInt | _DTypeLikeFloat | _DTypeLikeComplex
# Anything that can be coerced into numpy.dtype.
# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
DTypeLike: TypeAlias = str | _DTypeLike[Any] | _VoidDTypeLike | None
16 changes: 7 additions & 9 deletions src/numpy-stubs/_typing/_scalars.pyi
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Expand Up @@ -4,20 +4,18 @@ import numpy as np

# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and
# `np.bytes_` are already subclasses of their builtin counterpart

_CharLike_co: TypeAlias = str | bytes

# The 6 `<X>Like_co` type-aliases below represent all scalars that can be
# coerced into `<X>` (with the casting rule `same_kind`)
_BoolLike_co: TypeAlias = bool | np.bool
_UIntLike_co: TypeAlias = np.unsignedinteger[Any] | _BoolLike_co
_IntLike_co: TypeAlias = int | np.integer | _BoolLike_co
_FloatLike_co: TypeAlias = float | np.floating | _IntLike_co
_ComplexLike_co: TypeAlias = complex | np.complexfloating | _FloatLike_co
_TD64Like_co: TypeAlias = np.timedelta64 | _IntLike_co

_NumberLike_co: TypeAlias = int | float | complex | np.number | np.bool
_ScalarLike_co: TypeAlias = int | float | complex | str | bytes | np.generic
_UIntLike_co: TypeAlias = bool | np.unsignedinteger | np.bool
_IntLike_co: TypeAlias = int | np.integer | np.bool
_FloatLike_co: TypeAlias = float | np.floating | np.integer | np.bool
_ComplexLike_co: TypeAlias = complex | np.number | np.bool
_NumberLike_co = _ComplexLike_co
_TD64Like_co: TypeAlias = int | np.timedelta64 | np.integer | np.bool
_ScalarLike_co: TypeAlias = complex | bytes | str | np.generic

# `_VoidLike_co` is technically not a scalar, but it's close enough
_VoidLike_co: TypeAlias = tuple[Any, ...] | np.void
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