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268 lines
8.0 KiB
268 lines
8.0 KiB
from _typeshed import Incomplete, SupportsLenAndGetItem
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from collections.abc import Sequence
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from typing import (
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Any,
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ClassVar,
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Final,
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Generic,
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Literal as L,
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Self,
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SupportsIndex,
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final,
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overload,
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)
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from typing_extensions import TypeVar
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import numpy as np
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from numpy import _CastingKind
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from numpy._core.multiarray import ravel_multi_index, unravel_index
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from numpy._typing import (
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ArrayLike,
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DTypeLike,
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NDArray,
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_AnyShape,
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_ArrayLike,
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_DTypeLike,
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_FiniteNestedSequence,
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_HasDType,
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_NestedSequence,
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_SupportsArray,
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)
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__all__ = [ # noqa: RUF022
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"ravel_multi_index",
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"unravel_index",
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"mgrid",
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"ogrid",
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"r_",
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"c_",
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"s_",
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"index_exp",
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"ix_",
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"ndenumerate",
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"ndindex",
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"fill_diagonal",
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"diag_indices",
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"diag_indices_from",
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]
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###
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_T = TypeVar("_T")
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_TupleT = TypeVar("_TupleT", bound=tuple[Any, ...])
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_ArrayT = TypeVar("_ArrayT", bound=NDArray[Any])
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_DTypeT = TypeVar("_DTypeT", bound=np.dtype)
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_ScalarT = TypeVar("_ScalarT", bound=np.generic)
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_ScalarT_co = TypeVar("_ScalarT_co", bound=np.generic, default=Any, covariant=True)
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_BoolT_co = TypeVar("_BoolT_co", bound=bool, default=bool, covariant=True)
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_AxisT_co = TypeVar("_AxisT_co", bound=int, default=L[0], covariant=True)
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_MatrixT_co = TypeVar("_MatrixT_co", bound=bool, default=L[False], covariant=True)
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_NDMinT_co = TypeVar("_NDMinT_co", bound=int, default=L[1], covariant=True)
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_Trans1DT_co = TypeVar("_Trans1DT_co", bound=int, default=L[-1], covariant=True)
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###
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class ndenumerate(Generic[_ScalarT_co]):
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@overload
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def __init__(self: ndenumerate[_ScalarT], arr: _FiniteNestedSequence[_SupportsArray[np.dtype[_ScalarT]]]) -> None: ...
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@overload
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def __init__(self: ndenumerate[np.str_], arr: str | _NestedSequence[str]) -> None: ...
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@overload
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def __init__(self: ndenumerate[np.bytes_], arr: bytes | _NestedSequence[bytes]) -> None: ...
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@overload
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def __init__(self: ndenumerate[np.bool], arr: bool | _NestedSequence[bool]) -> None: ...
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@overload
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def __init__(self: ndenumerate[np.intp], arr: int | _NestedSequence[int]) -> None: ...
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@overload
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def __init__(self: ndenumerate[np.float64], arr: float | _NestedSequence[float]) -> None: ...
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@overload
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def __init__(self: ndenumerate[np.complex128], arr: complex | _NestedSequence[complex]) -> None: ...
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@overload
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def __init__(self: ndenumerate[Incomplete], arr: object) -> None: ...
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# The first overload is a (semi-)workaround for a mypy bug (tested with v1.10 and v1.11)
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@overload
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def __next__(
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self: ndenumerate[np.bool | np.number | np.flexible | np.datetime64 | np.timedelta64],
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/,
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) -> tuple[_AnyShape, _ScalarT_co]: ...
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@overload
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def __next__(self: ndenumerate[np.object_], /) -> tuple[_AnyShape, Incomplete]: ...
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@overload
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def __next__(self, /) -> tuple[_AnyShape, _ScalarT_co]: ...
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#
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def __iter__(self) -> Self: ...
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class ndindex:
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@overload
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def __init__(self, shape: tuple[SupportsIndex, ...], /) -> None: ...
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@overload
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def __init__(self, /, *shape: SupportsIndex) -> None: ...
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#
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def __iter__(self) -> Self: ...
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def __next__(self) -> _AnyShape: ...
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class nd_grid(Generic[_BoolT_co]):
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__slots__ = ("sparse",)
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sparse: _BoolT_co
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def __init__(self, sparse: _BoolT_co = ...) -> None: ... # stubdefaulter: ignore[missing-default]
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@overload
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def __getitem__(self: nd_grid[L[False]], key: slice | Sequence[slice]) -> NDArray[Incomplete]: ...
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@overload
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def __getitem__(self: nd_grid[L[True]], key: slice | Sequence[slice]) -> tuple[NDArray[Incomplete], ...]: ...
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@final
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class MGridClass(nd_grid[L[False]]):
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__slots__ = ()
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def __init__(self) -> None: ...
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@final
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class OGridClass(nd_grid[L[True]]):
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__slots__ = ()
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def __init__(self) -> None: ...
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class AxisConcatenator(Generic[_AxisT_co, _MatrixT_co, _NDMinT_co, _Trans1DT_co]):
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__slots__ = "axis", "matrix", "ndmin", "trans1d"
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makemat: ClassVar[type[np.matrix[tuple[int, int], np.dtype]]]
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axis: _AxisT_co
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matrix: _MatrixT_co
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ndmin: _NDMinT_co
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trans1d: _Trans1DT_co
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# NOTE: mypy does not understand that these default values are the same as the
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# TypeVar defaults. Since the workaround would require us to write 16 overloads,
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# we ignore the assignment type errors here.
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def __init__(
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self,
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/,
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axis: _AxisT_co = 0, # type: ignore[assignment]
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matrix: _MatrixT_co = False, # type: ignore[assignment]
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ndmin: _NDMinT_co = 1, # type: ignore[assignment]
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trans1d: _Trans1DT_co = -1, # type: ignore[assignment]
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) -> None: ...
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# TODO(jorenham): annotate this
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def __getitem__(self, key: Incomplete, /) -> Incomplete: ...
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def __len__(self, /) -> L[0]: ...
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# Keep in sync with _core.multiarray.concatenate
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@staticmethod
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@overload
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def concatenate(
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arrays: _ArrayLike[_ScalarT],
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/,
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axis: SupportsIndex | None = 0,
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out: None = None,
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*,
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dtype: None = None,
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casting: _CastingKind | None = "same_kind",
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) -> NDArray[_ScalarT]: ...
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@staticmethod
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@overload
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def concatenate(
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arrays: SupportsLenAndGetItem[ArrayLike],
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/,
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axis: SupportsIndex | None = 0,
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out: None = None,
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*,
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dtype: _DTypeLike[_ScalarT],
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casting: _CastingKind | None = "same_kind",
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) -> NDArray[_ScalarT]: ...
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@staticmethod
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@overload
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def concatenate(
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arrays: SupportsLenAndGetItem[ArrayLike],
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/,
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axis: SupportsIndex | None = 0,
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out: None = None,
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*,
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dtype: DTypeLike | None = None,
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casting: _CastingKind | None = "same_kind",
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) -> NDArray[Incomplete]: ...
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@staticmethod
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@overload
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def concatenate(
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arrays: SupportsLenAndGetItem[ArrayLike],
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/,
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axis: SupportsIndex | None = 0,
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*,
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out: _ArrayT,
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dtype: DTypeLike | None = None,
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casting: _CastingKind | None = "same_kind",
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) -> _ArrayT: ...
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@staticmethod
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@overload
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def concatenate(
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arrays: SupportsLenAndGetItem[ArrayLike],
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/,
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axis: SupportsIndex | None,
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out: _ArrayT,
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*,
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dtype: DTypeLike | None = None,
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casting: _CastingKind | None = "same_kind",
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) -> _ArrayT: ...
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@final
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class RClass(AxisConcatenator[L[0], L[False], L[1], L[-1]]):
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__slots__ = ()
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def __init__(self, /) -> None: ...
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@final
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class CClass(AxisConcatenator[L[-1], L[False], L[2], L[0]]):
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__slots__ = ()
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def __init__(self, /) -> None: ...
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class IndexExpression(Generic[_BoolT_co]):
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__slots__ = ("maketuple",)
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maketuple: _BoolT_co
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def __init__(self, maketuple: _BoolT_co) -> None: ...
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@overload
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def __getitem__(self, item: _TupleT) -> _TupleT: ...
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@overload
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def __getitem__(self: IndexExpression[L[True]], item: _T) -> tuple[_T]: ...
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@overload
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def __getitem__(self: IndexExpression[L[False]], item: _T) -> _T: ...
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@overload
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def ix_(*args: _FiniteNestedSequence[_HasDType[_DTypeT]]) -> tuple[np.ndarray[_AnyShape, _DTypeT], ...]: ...
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@overload
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def ix_(*args: str | _NestedSequence[str]) -> tuple[NDArray[np.str_], ...]: ...
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@overload
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def ix_(*args: bytes | _NestedSequence[bytes]) -> tuple[NDArray[np.bytes_], ...]: ...
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@overload
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def ix_(*args: bool | _NestedSequence[bool]) -> tuple[NDArray[np.bool], ...]: ...
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@overload
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def ix_(*args: int | _NestedSequence[int]) -> tuple[NDArray[np.intp], ...]: ...
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@overload
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def ix_(*args: float | _NestedSequence[float]) -> tuple[NDArray[np.float64], ...]: ...
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@overload
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def ix_(*args: complex | _NestedSequence[complex]) -> tuple[NDArray[np.complex128], ...]: ...
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#
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def fill_diagonal(a: NDArray[Any], val: object, wrap: bool = False) -> None: ...
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#
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def diag_indices(n: int, ndim: int = 2) -> tuple[NDArray[np.intp], ...]: ...
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def diag_indices_from(arr: ArrayLike) -> tuple[NDArray[np.intp], ...]: ...
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#
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mgrid: Final[MGridClass] = ...
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ogrid: Final[OGridClass] = ...
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r_: Final[RClass] = ...
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c_: Final[CClass] = ...
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index_exp: Final[IndexExpression[L[True]]] = ...
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s_: Final[IndexExpression[L[False]]] = ...
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