ok
Mini Shell
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: ...
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