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mtrand.pyi
from collections.abc import Callable from typing import Any, Union, overload, Literal from numpy import ( bool_, dtype, float32, float64, int8, int16, int32, int64, int_, ndarray, uint, uint8, uint16, uint32, uint64, ) from numpy.random.bit_generator import BitGenerator from numpy._typing import ( ArrayLike, _ArrayLikeFloat_co, _ArrayLikeInt_co, _DoubleCodes, _DTypeLikeBool, _DTypeLikeInt, _DTypeLikeUInt, _Float32Codes, _Float64Codes, _Int8Codes, _Int16Codes, _Int32Codes, _Int64Codes, _IntCodes, _ShapeLike, _SingleCodes, _SupportsDType, _UInt8Codes, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UIntCodes, ) _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 RandomState: _bit_generator: BitGenerator def __init__(self, seed: None | _ArrayLikeInt_co | 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], RandomState], tuple[str], dict[str, Any]]: ... def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ... @overload def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ... @overload def get_state( self, legacy: Literal[True] = ... ) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]: ... def set_state( self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float] ) -> None: ... @overload def random_sample(self, size: None = ...) -> float: ... # type: ignore[misc] @overload def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ... @overload def random(self, size: None = ...) -> float: ... # type: ignore[misc] @overload def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ... @overload def beta(self, a: float, b: float, 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: float = ..., size: None = ...) -> float: ... # type: ignore[misc] @overload def exponential( self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def standard_exponential(self, size: None = ...) -> float: ... # type: ignore[misc] @overload def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ... @overload def tomaxint(self, size: None = ...) -> int: ... # type: ignore[misc] @overload def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]: ... @overload def randint( # type: ignore[misc] self, low: int, high: None | int = ..., ) -> int: ... @overload def randint( # type: ignore[misc] self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeBool = ..., ) -> bool: ... @overload def randint( # type: ignore[misc] self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeInt | _DTypeLikeUInt = ..., ) -> int: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[int_]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: _DTypeLikeBool = ..., ) -> ndarray[Any, dtype[bool_]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., ) -> ndarray[Any, dtype[int8]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., ) -> ndarray[Any, dtype[int16]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., ) -> ndarray[Any, dtype[int32]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., ) -> ndarray[Any, dtype[int64]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., ) -> ndarray[Any, dtype[uint8]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., ) -> ndarray[Any, dtype[uint16]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., ) -> ndarray[Any, dtype[uint32]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., ) -> ndarray[Any, dtype[uint64]]: ... @overload def randint( # 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_]] = ..., ) -> ndarray[Any, dtype[int_]]: ... @overload def randint( # type: ignore[misc] self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., ) -> ndarray[Any, dtype[uint]]: ... def bytes(self, length: int) -> bytes: ... @overload def choice( self, a: int, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., ) -> int: ... @overload def choice( self, a: int, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., ) -> ndarray[Any, dtype[int_]]: ... @overload def choice( self, a: ArrayLike, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., ) -> Any: ... @overload def choice( self, a: ArrayLike, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., ) -> ndarray[Any, Any]: ... @overload def uniform(self, low: float = ..., high: float = ..., 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 rand(self) -> float: ... @overload def rand(self, *args: int) -> ndarray[Any, dtype[float64]]: ... @overload def randn(self) -> float: ... @overload def randn(self, *args: int) -> ndarray[Any, dtype[float64]]: ... @overload def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ... # type: ignore[misc] @overload def random_integers( self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[int_]]: ... @overload def standard_normal(self, size: None = ...) -> float: ... # type: ignore[misc] @overload def standard_normal( # type: ignore[misc] self, size: _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... @overload def normal(self, loc: float = ..., scale: float = ..., 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: float, size: None = ..., ) -> float: ... @overload def standard_gamma( self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., ) -> ndarray[Any, dtype[float64]]: ... @overload def gamma(self, shape: float, scale: float = ..., 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: float, dfden: float, 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: float, dfden: float, nonc: float, 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: float, 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: float, nonc: float, 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: float, 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: float, kappa: float, 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: float, 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: float, 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: float, 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: float = ..., scale: float = ..., 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: float = ..., scale: float = ..., 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: float = ..., scale: float = ..., 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: float = ..., sigma: float = ..., 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: float = ..., 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: float, scale: float, 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: float, mode: float, right: float, 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: float, size: None = ...) -> int: ... # type: ignore[misc] @overload def binomial( self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... @overload def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ... # type: ignore[misc] @overload def negative_binomial( self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... @overload def poisson(self, lam: float = ..., size: None = ...) -> int: ... # type: ignore[misc] @overload def poisson( self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... @overload def zipf(self, a: float, size: None = ...) -> int: ... # type: ignore[misc] @overload def zipf( self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... @overload def geometric(self, p: float, size: None = ...) -> int: ... # type: ignore[misc] @overload def geometric( self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... @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[int_]]: ... @overload def logseries(self, p: float, size: None = ...) -> int: ... # type: ignore[misc] @overload def logseries( self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... def multivariate_normal( self, mean: _ArrayLikeFloat_co, cov: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., check_valid: Literal["warn", "raise", "ignore"] = ..., tol: float = ..., ) -> ndarray[Any, dtype[float64]]: ... def multinomial( self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[int_]]: ... def dirichlet( self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ... ) -> ndarray[Any, dtype[float64]]: ... def shuffle(self, x: ArrayLike) -> None: ... @overload def permutation(self, x: int) -> ndarray[Any, dtype[int_]]: ... @overload def permutation(self, x: ArrayLike) -> ndarray[Any, 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: ...
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