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lib
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__init__.py
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__init__.pyi
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__pycache__
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_datasource.py
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_iotools.py
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_version.py
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_version.pyi
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arraypad.py
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arraypad.pyi
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arraysetops.py
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arraysetops.pyi
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arrayterator.py
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arrayterator.pyi
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format.py
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format.pyi
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function_base.py
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function_base.pyi
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histograms.py
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histograms.pyi
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index_tricks.py
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index_tricks.pyi
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mixins.py
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mixins.pyi
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nanfunctions.py
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nanfunctions.pyi
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npyio.py
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npyio.pyi
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polynomial.py
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polynomial.pyi
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recfunctions.py
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scimath.py
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scimath.pyi
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setup.py
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shape_base.py
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shape_base.pyi
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stride_tricks.py
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stride_tricks.pyi
(1.71 KB)
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tests
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twodim_base.py
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twodim_base.pyi
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type_check.py
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type_check.pyi
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ufunclike.py
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ufunclike.pyi
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user_array.py
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utils.py
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utils.pyi
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Editing: index_tricks.pyi
from collections.abc import Sequence from typing import ( Any, TypeVar, Generic, overload, Literal, SupportsIndex, ) from numpy import ( # Circumvent a naming conflict with `AxisConcatenator.matrix` matrix as _Matrix, ndenumerate as ndenumerate, ndindex as ndindex, ndarray, dtype, integer, str_, bytes_, bool_, int_, float_, complex_, intp, _OrderCF, _ModeKind, ) from numpy._typing import ( # Arrays ArrayLike, _NestedSequence, _FiniteNestedSequence, NDArray, _ArrayLikeInt, # DTypes DTypeLike, _SupportsDType, # Shapes _ShapeLike, ) from numpy.core.multiarray import ( unravel_index as unravel_index, ravel_multi_index as ravel_multi_index, ) _T = TypeVar("_T") _DType = TypeVar("_DType", bound=dtype[Any]) _BoolType = TypeVar("_BoolType", Literal[True], Literal[False]) _TupType = TypeVar("_TupType", bound=tuple[Any, ...]) _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: list[str] @overload def ix_(*args: _FiniteNestedSequence[_SupportsDType[_DType]]) -> tuple[ndarray[Any, _DType], ...]: ... @overload def ix_(*args: str | _NestedSequence[str]) -> tuple[NDArray[str_], ...]: ... @overload def ix_(*args: bytes | _NestedSequence[bytes]) -> tuple[NDArray[bytes_], ...]: ... @overload def ix_(*args: bool | _NestedSequence[bool]) -> tuple[NDArray[bool_], ...]: ... @overload def ix_(*args: int | _NestedSequence[int]) -> tuple[NDArray[int_], ...]: ... @overload def ix_(*args: float | _NestedSequence[float]) -> tuple[NDArray[float_], ...]: ... @overload def ix_(*args: complex | _NestedSequence[complex]) -> tuple[NDArray[complex_], ...]: ... class nd_grid(Generic[_BoolType]): sparse: _BoolType def __init__(self, sparse: _BoolType = ...) -> None: ... @overload def __getitem__( self: nd_grid[Literal[False]], key: slice | Sequence[slice], ) -> NDArray[Any]: ... @overload def __getitem__( self: nd_grid[Literal[True]], key: slice | Sequence[slice], ) -> list[NDArray[Any]]: ... class MGridClass(nd_grid[Literal[False]]): def __init__(self) -> None: ... mgrid: MGridClass class OGridClass(nd_grid[Literal[True]]): def __init__(self) -> None: ... ogrid: OGridClass class AxisConcatenator: axis: int matrix: bool ndmin: int trans1d: int def __init__( self, axis: int = ..., matrix: bool = ..., ndmin: int = ..., trans1d: int = ..., ) -> None: ... @staticmethod @overload def concatenate( # type: ignore[misc] *a: ArrayLike, axis: SupportsIndex = ..., out: None = ... ) -> NDArray[Any]: ... @staticmethod @overload def concatenate( *a: ArrayLike, axis: SupportsIndex = ..., out: _ArrayType = ... ) -> _ArrayType: ... @staticmethod def makemat( data: ArrayLike, dtype: DTypeLike = ..., copy: bool = ... ) -> _Matrix[Any, Any]: ... # TODO: Sort out this `__getitem__` method def __getitem__(self, key: Any) -> Any: ... class RClass(AxisConcatenator): axis: Literal[0] matrix: Literal[False] ndmin: Literal[1] trans1d: Literal[-1] def __init__(self) -> None: ... r_: RClass class CClass(AxisConcatenator): axis: Literal[-1] matrix: Literal[False] ndmin: Literal[2] trans1d: Literal[0] def __init__(self) -> None: ... c_: CClass class IndexExpression(Generic[_BoolType]): maketuple: _BoolType def __init__(self, maketuple: _BoolType) -> None: ... @overload def __getitem__(self, item: _TupType) -> _TupType: ... # type: ignore[misc] @overload def __getitem__(self: IndexExpression[Literal[True]], item: _T) -> tuple[_T]: ... @overload def __getitem__(self: IndexExpression[Literal[False]], item: _T) -> _T: ... index_exp: IndexExpression[Literal[True]] s_: IndexExpression[Literal[False]] def fill_diagonal(a: ndarray[Any, Any], val: Any, wrap: bool = ...) -> None: ... def diag_indices(n: int, ndim: int = ...) -> tuple[NDArray[int_], ...]: ... def diag_indices_from(arr: ArrayLike) -> tuple[NDArray[int_], ...]: ... # NOTE: see `numpy/__init__.pyi` for `ndenumerate` and `ndindex`
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