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_generator.pyi
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from collections.abc import Callable from typing import Any, Union, overload, TypeVar, Literal from numpy import ( bool_, dtype, float32, float64, int8, int16, int32, int64, int_, ndarray, uint, uint8, uint16, uint32, uint64, ) from numpy.random import BitGenerator, SeedSequence from numpy._typing import ( ArrayLike, _ArrayLikeFloat_co, _ArrayLikeInt_co, _DoubleCodes, _DTypeLikeBool, _DTypeLikeInt, _DTypeLikeUInt, _Float32Codes, _Float64Codes, _FloatLike_co, _Int8Codes, _Int16Codes, _Int32Codes, _Int64Codes, _IntCodes, _ShapeLike, _SingleCodes, _SupportsDType, _UInt8Codes, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UIntCodes, ) _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) _DTypeLikeFloat32 = Union[ dtype[float32], _SupportsDType[dtype[float32]], type[float32], _Float32Codes, _SingleCodes, ] _DTypeLikeFloat64 = Union[ dtype[float64], _SupportsDType[dtype[float64]], type[float], type[float64], _Float64Codes, _DoubleCodes, ] class Generator: def __init__(self, bit_generator: BitGenerator) -> None: ... def __repr__(self) -> str: ... def __str__(self) -> str: ... def __getstate__(self) -> dict[str, Any]: ... def __setstate__(self, state: dict[str, Any]) -> None: ... def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ... @property def bit_generator(self) -> BitGenerator: ... def spawn(self, n_children: int) -> list[Generator]: ... def bytes(self, length: int) -> bytes: ... @overload def standard_normal( # type: ignore[misc] self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ..., ) -> float: ... @overload def standard_normal( # type: ignore[misc] self, size: _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_normal( # type: ignore[misc] self, *, out: ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_normal( # type: ignore[misc] self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ..., ) -> ndarray[Any, dtype[float32]]: ... @overload def standard_normal( # type: ignore[misc] self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ... @overload def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]: ... @overload def standard_exponential( # type: ignore[misc] self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., method: Literal["zig", "inv"] = ..., out: None = ..., ) -> float: ... @overload def standard_exponential( self, size: _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_exponential( self, *, out: ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_exponential( self, size: _ShapeLike = ..., *, method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_exponential( self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float32]] = ..., ) -> ndarray[Any, dtype[float32]]: ... @overload def standard_exponential( self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def random( # type: ignore[misc] self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ..., ) -> float: ... @overload def random( self, *, out: ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def random( self, size: _ShapeLike = ..., *, out: None | ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def random( self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ..., ) -> ndarray[Any, dtype[float32]]: ... @overload def random( self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def beta( self, a: _FloatLike_co, b: _FloatLike_co, size: None = ..., ) -> float: ... # type: ignore[misc] @overload def beta( self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc] @overload def exponential( self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def integers( # type: ignore[misc] self, low: int, high: None | int = ..., ) -> int: ... @overload def integers( # type: ignore[misc] self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeBool = ..., endpoint: bool = ..., ) -> bool: ... @overload def integers( # type: ignore[misc] self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeInt | _DTypeLikeUInt = ..., endpoint: bool = ..., ) -> int: ... @overload def integers( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[int64]]: ... @overload def integers( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: _DTypeLikeBool = ..., endpoint: bool = ..., ) -> ndarray[Any, dtype[bool_]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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]]: ... @overload def integers( # type: ignore[misc] 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_]]: ... @overload def integers( # type: ignore[misc] 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]]: ... # TODO: Use a TypeVar _T here to get away from Any output? Should be int->ndarray[Any,dtype[int64]], ArrayLike[_T] -> _T | ndarray[Any,Any] @overload def choice( self, a: int, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ..., ) -> int: ... @overload def choice( self, a: int, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ..., ) -> ndarray[Any, dtype[int64]]: ... @overload def choice( self, a: ArrayLike, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ..., ) -> Any: ... @overload def choice( self, a: ArrayLike, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ..., ) -> ndarray[Any, Any]: ... @overload def uniform( self, low: _FloatLike_co = ..., high: _FloatLike_co = ..., size: None = ..., ) -> float: ... # type: ignore[misc] @overload def uniform( self, low: _ArrayLikeFloat_co = ..., high: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def normal( self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ..., ) -> float: ... # type: ignore[misc] @overload def normal( self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_gamma( # type: ignore[misc] self, shape: _FloatLike_co, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ..., ) -> float: ... @overload def standard_gamma( self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_gamma( self, shape: _ArrayLikeFloat_co, *, out: ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_gamma( self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ..., ) -> ndarray[Any, dtype[float32]]: ... @overload def standard_gamma( self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc] @overload def gamma( self, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def f( self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def noncentral_f( self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def chisquare( self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def noncentral_chisquare( self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def standard_t( self, df: _ArrayLikeFloat_co, size: None = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_t( self, df: _ArrayLikeFloat_co, size: _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def vonmises( self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def pareto( self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def weibull( self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def power(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] @overload def power( self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc] @overload def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ... @overload def laplace( self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ..., ) -> float: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @overload def poisson( self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int64]]: ... @overload def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] @overload def zipf( self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int64]]: ... @overload def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ... # type: ignore[misc] @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: ...