23.8 kB

	import builtins
	from collections.abc import Callable
	from typing import Any, Literal, overload

	import numpy as np
	from numpy import (
	dtype,
	float64,
	int8,
	int16,
	int32,
	int64,
	int_,
	long,
	uint,
	uint8,
	uint16,
	uint32,
	uint64,
	ulong,
	)
	from numpy._typing import (
	ArrayLike,
	NDArray,
	_ArrayLikeFloat_co,
	_ArrayLikeInt_co,
	_DTypeLikeBool,
	_Int8Codes,
	_Int16Codes,
	_Int32Codes,
	_Int64Codes,
	_IntCodes,
	_LongCodes,
	_ShapeLike,
	_SupportsDType,
	_UInt8Codes,
	_UInt16Codes,
	_UInt32Codes,
	_UInt64Codes,
	_UIntCodes,
	_ULongCodes,
	)
	from numpy.random.bit_generator import BitGenerator

	__all__ = [
	"RandomState",
	"beta",
	"binomial",
	"bytes",
	"chisquare",
	"choice",
	"dirichlet",
	"exponential",
	"f",
	"gamma",
	"geometric",
	"get_bit_generator",
	"get_state",
	"gumbel",
	"hypergeometric",
	"laplace",
	"logistic",
	"lognormal",
	"logseries",
	"multinomial",
	"multivariate_normal",
	"negative_binomial",
	"noncentral_chisquare",
	"noncentral_f",
	"normal",
	"pareto",
	"permutation",
	"poisson",
	"power",
	"rand",
	"randint",
	"randn",
	"random",
	"random_integers",
	"random_sample",
	"ranf",
	"rayleigh",
	"sample",
	"seed",
	"set_bit_generator",
	"set_state",
	"shuffle",
	"standard_cauchy",
	"standard_exponential",
	"standard_gamma",
	"standard_normal",
	"standard_t",
	"triangular",
	"uniform",
	"vonmises",
	"wald",
	"weibull",
	"zipf",
	]

	class RandomState:
	_bit_generator: BitGenerator
	def __init__(self, seed: _ArrayLikeInt_co \| BitGenerator \| None = ...) -> 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[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ... # noqa: E501
	def seed(self, seed: _ArrayLikeFloat_co \| None = None) -> None: ...
	@overload
	def get_state(self, legacy: Literal[False] = False) -> dict[str, Any]: ...
	@overload
	def get_state(
	self, legacy: Literal[True] = True
	) -> dict[str, Any] \| tuple[str, NDArray[uint32], int, int, float]: ...
	def set_state(
	self, state: dict[str, Any] \| tuple[str, NDArray[uint32], int, int, float]
	) -> None: ...
	@overload
	def random_sample(self, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ...
	@overload
	def random(self, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def random(self, size: _ShapeLike) -> NDArray[float64]: ...
	@overload
	def beta(self, a: float, b: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def beta(
	self,
	a: _ArrayLikeFloat_co,
	b: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def exponential(self, scale: float = 1.0, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def exponential(
	self, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def standard_exponential(self, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ...
	@overload
	def tomaxint(self, size: None = None) -> int: ... # type: ignore[misc]
	@overload
	# Generates long values, but stores it in a 64bit int:
	def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	) -> int: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: type[bool] = ...,
	) -> bool: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: type[np.bool] = ...,
	) -> np.bool: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: type[int] = ...,
	) -> int: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[uint8] \| type[uint8] \| _UInt8Codes \| _SupportsDType[dtype[uint8]] = ..., # noqa: E501
	) -> uint8: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[uint16] \| type[uint16] \| _UInt16Codes \| _SupportsDType[dtype[uint16]] = ..., # noqa: E501
	) -> uint16: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[uint32] \| type[uint32] \| _UInt32Codes \| _SupportsDType[dtype[uint32]] = ..., # noqa: E501
	) -> uint32: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[uint] \| type[uint] \| _UIntCodes \| _SupportsDType[dtype[uint]] = ..., # noqa: E501
	) -> uint: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[ulong] \| type[ulong] \| _ULongCodes \| _SupportsDType[dtype[ulong]] = ..., # noqa: E501
	) -> ulong: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[uint64] \| type[uint64] \| _UInt64Codes \| _SupportsDType[dtype[uint64]] = ..., # noqa: E501
	) -> uint64: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[int8] \| type[int8] \| _Int8Codes \| _SupportsDType[dtype[int8]] = ..., # noqa: E501
	) -> int8: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[int16] \| type[int16] \| _Int16Codes \| _SupportsDType[dtype[int16]] = ..., # noqa: E501
	) -> int16: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[int32] \| type[int32] \| _Int32Codes \| _SupportsDType[dtype[int32]] = ..., # noqa: E501
	) -> int32: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[int_] \| type[int_] \| _IntCodes \| _SupportsDType[dtype[int_]] = ..., # noqa: E501
	) -> int_: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[long] \| type[long] \| _LongCodes \| _SupportsDType[dtype[long]] = ..., # noqa: E501
	) -> long: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: int,
	high: int \| None = None,
	size: None = None,
	dtype: dtype[int64] \| type[int64] \| _Int64Codes \| _SupportsDType[dtype[int64]] = ..., # noqa: E501
	) -> int64: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	) -> NDArray[long]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: _DTypeLikeBool = ...,
	) -> NDArray[np.bool]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[int8] \| type[int8] \| _Int8Codes \| _SupportsDType[dtype[int8]] = ..., # noqa: E501
	) -> NDArray[int8]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[int16] \| type[int16] \| _Int16Codes \| _SupportsDType[dtype[int16]] = ..., # noqa: E501
	) -> NDArray[int16]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[int32] \| type[int32] \| _Int32Codes \| _SupportsDType[dtype[int32]] = ..., # noqa: E501
	) -> NDArray[int32]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[int64] \| type[int64] \| _Int64Codes \| _SupportsDType[dtype[int64]] \| None = ..., # noqa: E501
	) -> NDArray[int64]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[uint8] \| type[uint8] \| _UInt8Codes \| _SupportsDType[dtype[uint8]] = ..., # noqa: E501
	) -> NDArray[uint8]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[uint16] \| type[uint16] \| _UInt16Codes \| _SupportsDType[dtype[uint16]] = ..., # noqa: E501
	) -> NDArray[uint16]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[uint32] \| type[uint32] \| _UInt32Codes \| _SupportsDType[dtype[uint32]] = ..., # noqa: E501
	) -> NDArray[uint32]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[uint64] \| type[uint64] \| _UInt64Codes \| _SupportsDType[dtype[uint64]] = ..., # noqa: E501
	) -> NDArray[uint64]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[long] \| type[int] \| type[long] \| _LongCodes \| _SupportsDType[dtype[long]] = ..., # noqa: E501
	) -> NDArray[long]: ...
	@overload
	def randint( # type: ignore[misc]
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	dtype: dtype[ulong] \| type[ulong] \| _ULongCodes \| _SupportsDType[dtype[ulong]] = ..., # noqa: E501
	) -> NDArray[ulong]: ...
	def bytes(self, length: int) -> builtins.bytes: ...
	@overload
	def choice(
	self,
	a: int,
	size: None = None,
	replace: bool = True,
	p: _ArrayLikeFloat_co \| None = None,
	) -> int: ...
	@overload
	def choice(
	self,
	a: int,
	size: _ShapeLike \| None = None,
	replace: bool = True,
	p: _ArrayLikeFloat_co \| None = None,
	) -> NDArray[long]: ...
	@overload
	def choice(
	self,
	a: ArrayLike,
	size: None = None,
	replace: bool = True,
	p: _ArrayLikeFloat_co \| None = None,
	) -> Any: ...
	@overload
	def choice(
	self,
	a: ArrayLike,
	size: _ShapeLike \| None = None,
	replace: bool = True,
	p: _ArrayLikeFloat_co \| None = None,
	) -> NDArray[Any]: ...
	@overload
	def uniform(
	self, low: float = 0.0, high: float = 1.0, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def uniform(
	self,
	low: _ArrayLikeFloat_co = 0.0,
	high: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def rand(self) -> float: ...
	@overload
	def rand(self, *args: int) -> NDArray[float64]: ...
	@overload
	def randn(self) -> float: ...
	@overload
	def randn(self, *args: int) -> NDArray[float64]: ...
	@overload
	def random_integers(
	self, low: int, high: int \| None = None, size: None = None
	) -> int: ... # type: ignore[misc]
	@overload
	def random_integers(
	self,
	low: _ArrayLikeInt_co,
	high: _ArrayLikeInt_co \| None = None,
	size: _ShapeLike \| None = None,
	) -> NDArray[long]: ...
	@overload
	def standard_normal(self, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def standard_normal( # type: ignore[misc]
	self, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def normal(
	self, loc: float = 0.0, scale: float = 1.0, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def normal(
	self,
	loc: _ArrayLikeFloat_co = 0.0,
	scale: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def standard_gamma( # type: ignore[misc]
	self,
	shape: float,
	size: None = None,
	) -> float: ...
	@overload
	def standard_gamma(
	self,
	shape: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def gamma(self, shape: float, scale: float = 1.0, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def gamma(
	self,
	shape: _ArrayLikeFloat_co,
	scale: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def f(self, dfnum: float, dfden: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def f(
	self,
	dfnum: _ArrayLikeFloat_co,
	dfden: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def noncentral_f(
	self, dfnum: float, dfden: float, nonc: float, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def noncentral_f(
	self,
	dfnum: _ArrayLikeFloat_co,
	dfden: _ArrayLikeFloat_co,
	nonc: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def chisquare(self, df: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def chisquare(
	self, df: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def noncentral_chisquare(
	self, df: float, nonc: float, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def noncentral_chisquare(
	self,
	df: _ArrayLikeFloat_co,
	nonc: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def standard_t(self, df: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def standard_t(
	self, df: _ArrayLikeFloat_co, size: None = None
	) -> NDArray[float64]: ...
	@overload
	def standard_t(
	self, df: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def vonmises(self, mu: float, kappa: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def vonmises(
	self,
	mu: _ArrayLikeFloat_co,
	kappa: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def pareto(self, a: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def pareto(
	self, a: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def weibull(self, a: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def weibull(
	self, a: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def power(self, a: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def power(
	self, a: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def standard_cauchy(self, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def standard_cauchy(self, size: _ShapeLike \| None = None) -> NDArray[float64]: ...
	@overload
	def laplace(
	self, loc: float = 0.0, scale: float = 1.0, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def laplace(
	self,
	loc: _ArrayLikeFloat_co = 0.0,
	scale: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def gumbel(
	self, loc: float = 0.0, scale: float = 1.0, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def gumbel(
	self,
	loc: _ArrayLikeFloat_co = 0.0,
	scale: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def logistic(
	self, loc: float = 0.0, scale: float = 1.0, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def logistic(
	self,
	loc: _ArrayLikeFloat_co = 0.0,
	scale: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def lognormal(
	self, mean: float = 0.0, sigma: float = 1.0, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def lognormal(
	self,
	mean: _ArrayLikeFloat_co = 0.0,
	sigma: _ArrayLikeFloat_co = 1.0,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def rayleigh(self, scale: float = 1.0, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def rayleigh(
	self, scale: _ArrayLikeFloat_co = 1.0, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def wald(self, mean: float, scale: float, size: None = None) -> float: ... # type: ignore[misc]
	@overload
	def wald(
	self,
	mean: _ArrayLikeFloat_co,
	scale: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	@overload
	def triangular(
	self, left: float, mode: float, right: float, size: None = None
	) -> float: ... # type: ignore[misc]
	@overload
	def triangular(
	self,
	left: _ArrayLikeFloat_co,
	mode: _ArrayLikeFloat_co,
	right: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None,
	) -> NDArray[float64]: ...
	@overload
	def binomial(
	self, n: int, p: float, size: None = None
	) -> int: ... # type: ignore[misc]
	@overload
	def binomial(
	self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	@overload
	def negative_binomial(
	self, n: float, p: float, size: None = None
	) -> int: ... # type: ignore[misc]
	@overload
	def negative_binomial(
	self,
	n: _ArrayLikeFloat_co,
	p: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	@overload
	def poisson(
	self, lam: float = 1.0, size: None = None
	) -> int: ... # type: ignore[misc]
	@overload
	def poisson(
	self, lam: _ArrayLikeFloat_co = 1.0, size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	@overload
	def zipf(self, a: float, size: None = None) -> int: ... # type: ignore[misc]
	@overload
	def zipf(
	self, a: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	@overload
	def geometric(self, p: float, size: None = None) -> int: ... # type: ignore[misc]
	@overload
	def geometric(
	self, p: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	@overload
	def hypergeometric(
	self, ngood: int, nbad: int, nsample: int, size: None = None
	) -> int: ... # type: ignore[misc]
	@overload
	def hypergeometric(
	self,
	ngood: _ArrayLikeInt_co,
	nbad: _ArrayLikeInt_co,
	nsample: _ArrayLikeInt_co,
	size: _ShapeLike \| None = None,
	) -> NDArray[long]: ...
	@overload
	def logseries(self, p: float, size: None = None) -> int: ... # type: ignore[misc]
	@overload
	def logseries(
	self, p: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	def multivariate_normal(
	self,
	mean: _ArrayLikeFloat_co,
	cov: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None,
	check_valid: Literal["warn", "raise", "ignore"] = "warn",
	tol: float = 1e-8,
	) -> NDArray[float64]: ...
	def multinomial(
	self, n: _ArrayLikeInt_co,
	pvals: _ArrayLikeFloat_co,
	size: _ShapeLike \| None = None
	) -> NDArray[long]: ...
	def dirichlet(
	self, alpha: _ArrayLikeFloat_co, size: _ShapeLike \| None = None
	) -> NDArray[float64]: ...
	def shuffle(self, x: ArrayLike) -> None: ...
	@overload
	def permutation(self, x: int) -> NDArray[long]: ...
	@overload
	def permutation(self, x: ArrayLike) -> NDArray[Any]: ...

	_rand: RandomState

	beta = _rand.beta
	binomial = _rand.binomial
	bytes = _rand.bytes
	chisquare = _rand.chisquare
	choice = _rand.choice
	dirichlet = _rand.dirichlet
	exponential = _rand.exponential
	f = _rand.f
	gamma = _rand.gamma
	get_state = _rand.get_state
	geometric = _rand.geometric
	gumbel = _rand.gumbel
	hypergeometric = _rand.hypergeometric
	laplace = _rand.laplace
	logistic = _rand.logistic
	lognormal = _rand.lognormal
	logseries = _rand.logseries
	multinomial = _rand.multinomial
	multivariate_normal = _rand.multivariate_normal
	negative_binomial = _rand.negative_binomial
	noncentral_chisquare = _rand.noncentral_chisquare
	noncentral_f = _rand.noncentral_f
	normal = _rand.normal
	pareto = _rand.pareto
	permutation = _rand.permutation
	poisson = _rand.poisson
	power = _rand.power
	rand = _rand.rand
	randint = _rand.randint
	randn = _rand.randn
	random = _rand.random
	random_integers = _rand.random_integers
	random_sample = _rand.random_sample
	rayleigh = _rand.rayleigh
	seed = _rand.seed
	set_state = _rand.set_state
	shuffle = _rand.shuffle
	standard_cauchy = _rand.standard_cauchy
	standard_exponential = _rand.standard_exponential
	standard_gamma = _rand.standard_gamma
	standard_normal = _rand.standard_normal
	standard_t = _rand.standard_t
	triangular = _rand.triangular
	uniform = _rand.uniform
	vonmises = _rand.vonmises
	wald = _rand.wald
	weibull = _rand.weibull
	zipf = _rand.zipf
	# Two legacy that are trivial wrappers around random_sample
	sample = _rand.random_sample
	ranf = _rand.random_sample

	def set_bit_generator(bitgen: BitGenerator) -> None: ...

	def get_bit_generator() -> BitGenerator: ...

Xet Storage Details

Size:: 23.8 kB
Xet hash:: 6b53777257256d3103882d342ea8f39e9be85ec90b901002f8cab7e2782544d0

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.