470 lines
19 KiB
Python
470 lines
19 KiB
Python
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"""
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This type stub file was generated by pyright.
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"""
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from collections.abc import Callable
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from typing import Any, Literal, TypeVar, Union, overload
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from numpy import bool_, dtype, float32, float64, int16, int32, int64, int8, int_, ndarray, uint, uint16, uint32, uint64, uint8
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from numpy.random import BitGenerator, SeedSequence
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from numpy._typing import ArrayLike, _ArrayLikeFloat_co, _ArrayLikeInt_co, _DTypeLikeBool, _DTypeLikeInt, _DTypeLikeUInt, _DoubleCodes, _Float32Codes, _Float64Codes, _FloatLike_co, _Int16Codes, _Int32Codes, _Int64Codes, _Int8Codes, _IntCodes, _ShapeLike, _SingleCodes, _SupportsDType, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UInt8Codes, _UIntCodes
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_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
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_DTypeLikeFloat32 = Union[dtype[float32], _SupportsDType[dtype[float32]], type[float32], _Float32Codes, _SingleCodes,]
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_DTypeLikeFloat64 = Union[dtype[float64], _SupportsDType[dtype[float64]], type[float], type[float64], _Float64Codes, _DoubleCodes,]
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class Generator:
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def __init__(self, bit_generator: BitGenerator) -> None:
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...
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def __repr__(self) -> str:
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...
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def __str__(self) -> str:
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...
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def __getstate__(self) -> dict[str, Any]:
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...
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def __setstate__(self, state: dict[str, Any]) -> None:
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...
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def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]:
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...
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@property
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def bit_generator(self) -> BitGenerator:
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...
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def spawn(self, n_children: int) -> list[Generator]:
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...
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def bytes(self, length: int) -> bytes:
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...
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@overload
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def standard_normal(self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ...) -> float:
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...
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@overload
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def standard_normal(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_normal(self, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_normal(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def standard_normal(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]:
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...
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@overload
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def standard_exponential(self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., method: Literal["zig", "inv"] = ..., out: None = ...) -> float:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_exponential(self, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ..., *, method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ...) -> float:
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...
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@overload
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def random(self, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: _ShapeLike = ..., *, out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def random(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def beta(self, a: _FloatLike_co, b: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def beta(self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def exponential(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def integers(self, low: int, high: None | int = ...) -> int:
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...
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@overload
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def integers(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeBool = ..., endpoint: bool = ...) -> bool:
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...
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@overload
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def integers(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeInt | _DTypeLikeUInt = ..., endpoint: bool = ...) -> int:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: _DTypeLikeBool = ..., endpoint: bool = ...) -> ndarray[Any, dtype[bool_]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int8]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int16]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int32]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint8]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint16]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint32]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint64]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int_]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint]]:
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...
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@overload
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def choice(self, a: int, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> int:
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...
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@overload
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def choice(self, a: int, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def choice(self, a: ArrayLike, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> Any:
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...
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@overload
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def choice(self, a: ArrayLike, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> ndarray[Any, Any]:
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...
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@overload
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def uniform(self, low: _FloatLike_co = ..., high: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def uniform(self, low: _ArrayLikeFloat_co = ..., high: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def normal(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def normal(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_gamma(self, shape: _FloatLike_co, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ...) -> float:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def gamma(self, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def f(self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def noncentral_f(self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def chisquare(self, df: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def chisquare(self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def noncentral_chisquare(self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_t(self, df: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def standard_t(self, df: _ArrayLikeFloat_co, size: None = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_t(self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def vonmises(self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def pareto(self, a: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def pareto(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def weibull(self, a: _FloatLike_co, size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def weibull(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def power(self, a: _FloatLike_co, size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def power(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def standard_cauchy(self, size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def laplace(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def laplace(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def gumbel(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def gumbel(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def logistic(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def logistic(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def lognormal(self, mean: _FloatLike_co = ..., sigma: _FloatLike_co = ..., size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def lognormal(self, mean: _ArrayLikeFloat_co = ..., sigma: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def rayleigh(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def wald(self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def wald(self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def triangular(self, left: _FloatLike_co, mode: _FloatLike_co, right: _FloatLike_co, size: None = ...) -> float:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def triangular(self, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def binomial(self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def negative_binomial(self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def negative_binomial(self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def poisson(self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def zipf(self, a: _FloatLike_co, size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def zipf(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def geometric(self, p: _FloatLike_co, size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def geometric(self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def hypergeometric(self, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def logseries(self, p: _FloatLike_co, size: None = ...) -> int:
|
||
|
|
...
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def logseries(self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
def multivariate_normal(self, mean: _ArrayLikeFloat_co, cov: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., check_valid: Literal["warn", "raise", "ignore"] = ..., tol: float = ..., *, method: Literal["svd", "eigh", "cholesky"] = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
def multinomial(self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
def multivariate_hypergeometric(self, colors: _ArrayLikeInt_co, nsample: int, size: None | _ShapeLike = ..., method: Literal["marginals", "count"] = ...) -> ndarray[Any, dtype[int64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
def dirichlet(self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||
|
|
...
|
||
|
|
|
||
|
|
def permuted(self, x: ArrayLike, *, axis: None | int = ..., out: None | ndarray[Any, Any] = ...) -> ndarray[Any, Any]:
|
||
|
|
...
|
||
|
|
|
||
|
|
def shuffle(self, x: ArrayLike, axis: int = ...) -> None:
|
||
|
|
...
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def default_rng(seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator = ...) -> Generator:
|
||
|
|
...
|
||
|
|
|