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MoFin/venv/lib/python3.12/site-packages/numpy/random/_generator.pyi
T
知微 fa45d8aa5f fix: 小果地址统一node122(兼容LAN+EasyTier) 
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
  Privoxy对node122:18003返回500，直连正常
2026-06-30 02:56:35 +08:00

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# Aliases for builtins shadowed by classes to avoid annotations resolving to class members by ty
from builtins import bytes as py_bytes
from collections.abc import Callable, MutableSequence
from typing import Any, Literal, Self, overload
import numpy as np
from numpy._typing import (
ArrayLike,
DTypeLike,
NDArray,
_ArrayLike,
_ArrayLikeFloat_co,
_ArrayLikeInt_co,
_DTypeLike,
_Float32Codes,
_Float64Codes,
_FloatLike_co,
_Int64Codes,
_NestedSequence,
_ShapeLike,
)
from .bit_generator import BitGenerator, SeedSequence
from .mtrand import RandomState
type _ArrayF32 = NDArray[np.float32]
type _ArrayF64 = NDArray[np.float64]
type _DTypeLikeI64 = _DTypeLike[np.int64] | _Int64Codes
type _DTypeLikeF32 = _DTypeLike[np.float32] | _Float32Codes
type _DTypeLikeF64 = type[float] | _DTypeLike[np.float64] | _Float64Codes
# we use `str` to avoid type-checker performance issues because of the many `Literal` variants
type _DTypeLikeFloat = type[float] | _DTypeLike[np.float32 | np.float64] | str
# Similar to `_ArrayLike{}_co`, but rejects scalars
type _NDArrayLikeInt = NDArray[np.generic[int]] | _NestedSequence[int]
type _NDArrayLikeFloat = NDArray[np.generic[float]] | _NestedSequence[float]
type _MethodExp = Literal["zig", "inv"]
###
class Generator:
def __init__(self, bit_generator: BitGenerator) -> None: ...
def __setstate__(self, state: dict[str, Any] | None) -> None: ...
def __reduce__(self) -> tuple[Callable[[BitGenerator], Generator], tuple[BitGenerator], None]: ...
#
@property
def bit_generator(self) -> BitGenerator: ...
def spawn(self, n_children: int) -> list[Self]: ...
def bytes(self, length: int) -> py_bytes: ...
# continuous distributions
#
@overload
def standard_cauchy(self, size: None = None) -> float: ...
@overload
def standard_cauchy(self, size: _ShapeLike) -> _ArrayF64: ...
#
@overload # size=None (default); NOTE: dtype is ignored
def random(self, size: None = None, dtype: _DTypeLikeFloat = ..., out: None = None) -> float: ...
@overload # size=<given>, dtype=f64 (default)
def random(self, size: _ShapeLike, dtype: _DTypeLikeF64 = ..., out: None = None) -> _ArrayF64: ...
@overload # size=<given>, dtype=f32
def random(self, size: _ShapeLike, dtype: _DTypeLikeF32, out: None = None) -> _ArrayF32: ...
@overload # out: f64 array (keyword)
def random[ArrayT: _ArrayF64](self, size: _ShapeLike | None = None, dtype: _DTypeLikeF64 = ..., *, out: ArrayT) -> ArrayT: ...
@overload # dtype: f32 (keyword), out: f64 array
def random[ArrayT: _ArrayF32](self, size: _ShapeLike | None = None, *, dtype: _DTypeLikeF32, out: ArrayT) -> ArrayT: ...
@overload # out: f64 array (positional)
def random[ArrayT: _ArrayF64](self, size: _ShapeLike | None, dtype: _DTypeLikeF64, out: ArrayT) -> ArrayT: ...
@overload # dtype: f32 (positional), out: f32 array
def random[ArrayT: _ArrayF32](self, size: _ShapeLike | None, dtype: _DTypeLikeF32, out: ArrayT) -> ArrayT: ...
#
@overload # size=None (default); NOTE: dtype is ignored
def standard_normal(self, size: None = None, dtype: _DTypeLikeFloat = ..., out: None = None) -> float: ...
@overload # size=<given>, dtype: f64 (default)
def standard_normal(self, size: _ShapeLike, dtype: _DTypeLikeF64 = ..., out: None = None) -> _ArrayF64: ...
@overload # size=<given>, dtype: f32
def standard_normal(self, size: _ShapeLike, dtype: _DTypeLikeF32, *, out: None = None) -> _ArrayF32: ...
@overload # dtype: f64 (default), out: f64 array (keyword)
def standard_normal[ArrayT: _ArrayF64](
self, size: _ShapeLike | None = None, dtype: _DTypeLikeF64 = ..., *, out: ArrayT
) -> ArrayT: ...
@overload # dtype: f32 (keyword), out: f32 array
def standard_normal[ArrayT: _ArrayF32](
self, size: _ShapeLike | None = None, *, dtype: _DTypeLikeF32, out: ArrayT
) -> ArrayT: ...
@overload # dtype: f32 (positional), out: f32 array
def standard_normal[ArrayT: _ArrayF32](self, size: _ShapeLike | None, dtype: _DTypeLikeF32, out: ArrayT) -> ArrayT: ...
#
@overload # size=None (default); NOTE: dtype is ignored
def standard_exponential(
self, size: None = None, dtype: _DTypeLikeFloat = ..., method: _MethodExp = "zig", out: None = None
) -> float: ...
@overload # size=<given>, dtype: f64 (default)
def standard_exponential(
self, size: _ShapeLike, dtype: _DTypeLikeF64 = ..., method: _MethodExp = "zig", out: None = None
) -> _ArrayF64: ...
@overload # size=<given>, dtype: f32 (default)
def standard_exponential(
self, size: _ShapeLike, dtype: _DTypeLikeF32, method: _MethodExp = "zig", out: None = None
) -> _ArrayF32: ...
@overload # dtype: f64 (default), out: f64 array (keyword)
def standard_exponential[ArrayT: _ArrayF64](
self, size: _ShapeLike | None = None, dtype: _DTypeLikeF64 = ..., method: _MethodExp = "zig", *, out: ArrayT
) -> ArrayT: ...
@overload # dtype: f32 (keyword), out: f32 array
def standard_exponential[ArrayT: _ArrayF32](
self, size: _ShapeLike | None = None, *, dtype: _DTypeLikeF32, method: _MethodExp = "zig", out: ArrayT
) -> ArrayT: ...
@overload # dtype: f32 (positional), out: f32 array (keyword)
def standard_exponential[ArrayT: _ArrayF32](
self, size: _ShapeLike | None, dtype: _DTypeLikeF32, method: _MethodExp = "zig", *, out: ArrayT
) -> ArrayT: ...
#
@overload # 0d, size=None (default); NOTE: dtype is ignored
def standard_gamma(
self, shape: _FloatLike_co, size: None = None, dtype: _DTypeLikeFloat = ..., out: None = None
) -> float: ...
@overload # >0d, dtype: f64 (default)
def standard_gamma(
self, shape: _NDArrayLikeFloat, size: None = None, dtype: _DTypeLikeF64 = ..., out: None = None
) -> _ArrayF64: ...
@overload # >0d, dtype: f32 (keyword)
def standard_gamma(
self, shape: _NDArrayLikeFloat, size: None = None, *, dtype: _DTypeLikeF32, out: None = None
) -> _ArrayF32: ...
@overload # >=0d, dtype: f64 (default)
def standard_gamma(
self, shape: _ArrayLikeFloat_co, size: None = None, dtype: _DTypeLikeF64 = ..., out: None = None
) -> _ArrayF64 | Any: ...
@overload # >=0d, dtype: f32 (keyword)
def standard_gamma(
self, shape: _ArrayLikeFloat_co, size: None = None, *, dtype: _DTypeLikeF32, out: None = None
) -> _ArrayF32 | Any: ...
@overload # >=0d, size=<given>, dtype: f64 (default)
def standard_gamma(
self, shape: _ArrayLikeFloat_co, size: _ShapeLike, dtype: _DTypeLikeF64 = ..., out: None = None
) -> _ArrayF64: ...
@overload # >=0d, size=<given>, dtype: f32
def standard_gamma(
self, shape: _ArrayLikeFloat_co, size: _ShapeLike, dtype: _DTypeLikeF32, *, out: None = None
) -> _ArrayF32: ...
@overload # >=0d, dtype: f64 (default), out: f64 array (keyword)
def standard_gamma[ArrayT: _ArrayF64](
self, shape: _ArrayLikeFloat_co, size: _ShapeLike | None = None, dtype: _DTypeLikeF64 = ..., *, out: ArrayT
) -> ArrayT: ...
@overload # >=0d, dtype: f32 (keyword), out: f32 array
def standard_gamma[ArrayT: _ArrayF32](
self, shape: _ArrayLikeFloat_co, size: _ShapeLike | None = None, *, dtype: _DTypeLikeF32, out: ArrayT
) -> ArrayT: ...
@overload # >=0d, dtype: f32 (positional), out: f32 array
def standard_gamma[ArrayT: _ArrayF32](
self, shape: _ArrayLikeFloat_co, size: _ShapeLike | None, dtype: _DTypeLikeF32, out: ArrayT
) -> ArrayT: ...
#
@overload # 0d
def power(self, /, a: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def power(self, /, a: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def power(self, /, a: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def power(self, /, a: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d
def pareto(self, /, a: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def pareto(self, /, a: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def pareto(self, /, a: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def pareto(self, /, a: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d
def weibull(self, /, a: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def weibull(self, /, a: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def weibull(self, /, a: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def weibull(self, /, a: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d
def standard_t(self, /, df: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def standard_t(self, /, df: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def standard_t(self, /, df: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def standard_t(self, /, df: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d
def chisquare(self, /, df: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def chisquare(self, /, df: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def chisquare(self, /, df: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def chisquare(self, /, df: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d (default)
def exponential(self, /, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (keyword)
def exponential(self, /, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # size=<given> (positional)
def exponential(self, /, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def exponential(self, /, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def exponential(self, /, scale: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d (default)
def rayleigh(self, /, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (keyword)
def rayleigh(self, /, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # size=<given> (positional)
def rayleigh(self, /, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >0d
def rayleigh(self, /, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d
def rayleigh(self, /, scale: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d
def noncentral_chisquare(self, /, df: _FloatLike_co, nonc: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def noncentral_chisquare(self, /, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d
def noncentral_chisquare(self, /, df: _ArrayLikeFloat_co, nonc: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def noncentral_chisquare(self, /, df: _NDArrayLikeFloat, nonc: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d
def noncentral_chisquare(self, /, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d
def f(self, /, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def f(self, /, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d
def f(self, /, dfnum: _ArrayLikeFloat_co, dfden: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def f(self, /, dfnum: _NDArrayLikeFloat, dfden: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d (fallback)
def f(self, /, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d
def vonmises(self, /, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def vonmises(self, /, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d
def vonmises(self, /, mu: _ArrayLikeFloat_co, kappa: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def vonmises(self, /, mu: _NDArrayLikeFloat, kappa: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d (fallback)
def vonmises(self, /, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d
def wald(self, /, mean: _FloatLike_co, scale: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def wald(self, /, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d
def wald(self, /, mean: _ArrayLikeFloat_co, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def wald(self, /, mean: _NDArrayLikeFloat, scale: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d (fallback)
def wald(self, /, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d
def beta(self, /, a: _FloatLike_co, b: _FloatLike_co, size: None = None) -> float: ...
@overload # size=<given>
def beta(self, /, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d
def beta(self, /, a: _ArrayLikeFloat_co, b: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def beta(self, /, a: _NDArrayLikeFloat, b: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d (fallback)
def beta(self, /, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d (default)
def gamma(self, /, shape: _FloatLike_co, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (positional)
def gamma(self, /, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # size=<given> (keyword)
def gamma(self, /, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d
def gamma(self, /, shape: _ArrayLikeFloat_co, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def gamma(self, /, shape: _NDArrayLikeFloat, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d (fallback)
def gamma(self, /, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d (default), 0d (default)
def uniform(self, /, low: _FloatLike_co = 0.0, high: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # >=0d, >=0d, size=<given> (positional)
def uniform(self, /, low: _ArrayLikeFloat_co, high: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (positional)
def uniform(self, /, low: _ArrayLikeFloat_co, high: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d, size=<given> (keyword)
def uniform(self, /, low: _ArrayLikeFloat_co = 0.0, high: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (keyword)
def uniform(self, /, low: _ArrayLikeFloat_co = 0.0, *, high: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def uniform(self, /, low: _NDArrayLikeFloat, high: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d (fallback)
def uniform(self, /, low: _ArrayLikeFloat_co = 0.0, high: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d (default), 0d (default)
def normal(self, /, loc: _FloatLike_co = 0.0, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (positional)
def normal(self, /, loc: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (positional)
def normal(self, /, loc: _ArrayLikeFloat_co, scale: _NDArrayLikeFloat, size: None) -> _ArrayF64: ...
@overload # size=<given> (keyword)
def normal(self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (keyword)
def normal(self, /, loc: _ArrayLikeFloat_co = 0.0, *, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def normal(self, /, loc: _NDArrayLikeFloat, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d
def normal(self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d (default), 0d (default)
def gumbel(self, /, loc: _FloatLike_co = 0.0, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (positional)
def gumbel(self, /, loc: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (positional)
def gumbel(self, /, loc: _ArrayLikeFloat_co, scale: _NDArrayLikeFloat, size: None) -> _ArrayF64: ...
@overload # size=<given> (keyword)
def gumbel(self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (keyword)
def gumbel(self, /, loc: _ArrayLikeFloat_co = 0.0, *, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def gumbel(self, /, loc: _NDArrayLikeFloat, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d
def gumbel(self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64 | Any: ...
#
@overload # 0d (default), 0d (default)
def logistic(self, /, loc: _FloatLike_co = 0.0, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (positional)
def logistic(self, /, loc: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (positional)
def logistic(self, /, loc: _ArrayLikeFloat_co, scale: _NDArrayLikeFloat, size: None) -> _ArrayF64: ...
@overload # size=<given> (keyword)
def logistic(self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (keyword)
def logistic(self, /, loc: _ArrayLikeFloat_co = 0.0, *, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def logistic(self, /, loc: _NDArrayLikeFloat, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d
def logistic(
self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: None = None
) -> _ArrayF64 | Any: ...
#
@overload # 0d (default), 0d (default)
def laplace(self, /, loc: _FloatLike_co = 0.0, scale: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (positional)
def laplace(self, /, loc: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (positional)
def laplace(self, /, loc: _ArrayLikeFloat_co, scale: _NDArrayLikeFloat, size: None) -> _ArrayF64: ...
@overload # size=<given> (keyword)
def laplace(self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (keyword)
def laplace(self, /, loc: _ArrayLikeFloat_co = 0.0, *, scale: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def laplace(self, /, loc: _NDArrayLikeFloat, scale: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d
def laplace(
self, /, loc: _ArrayLikeFloat_co = 0.0, scale: _ArrayLikeFloat_co = 1.0, size: None = None
) -> _ArrayF64 | Any: ...
#
@overload # 0d (default), 0d (default)
def lognormal(self, /, mean: _FloatLike_co = 0.0, sigma: _FloatLike_co = 1.0, size: None = None) -> float: ...
@overload # size=<given> (positional)
def lognormal(self, /, mean: _ArrayLikeFloat_co, sigma: _ArrayLikeFloat_co, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (positional)
def lognormal(self, /, mean: _ArrayLikeFloat_co, sigma: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # size=<given> (keyword)
def lognormal(self, /, mean: _ArrayLikeFloat_co = 0.0, sigma: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> _ArrayF64: ...
@overload # >=0d, >0d (keyword)
def lognormal(self, /, mean: _ArrayLikeFloat_co = 0.0, *, sigma: _NDArrayLikeFloat, size: None = None) -> _ArrayF64: ...
@overload # >0d, >=0d
def lognormal(self, /, mean: _NDArrayLikeFloat, sigma: _ArrayLikeFloat_co = 1.0, size: None = None) -> _ArrayF64: ...
@overload # >=0d, >=0d
def lognormal(
self, /, mean: _ArrayLikeFloat_co = 0.0, sigma: _ArrayLikeFloat_co = 1.0, size: None = None
) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d, 0d
def triangular(self, /, left: _FloatLike_co, mode: _FloatLike_co, right: _FloatLike_co, size: None = None) -> float: ...
@overload # >=0d, >=0d, >=0d, size=<given>
def triangular(
self, /, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: _ShapeLike
) -> _ArrayF64: ...
@overload # >=0d, >=0d, >0d
def triangular(
self, /, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _NDArrayLikeFloat, size: None = None
) -> _ArrayF64: ...
@overload # >=0d, >0d, >=0d
def triangular(
self, /, left: _ArrayLikeFloat_co, mode: _NDArrayLikeFloat, right: _ArrayLikeFloat_co, size: None = None
) -> _ArrayF64: ...
@overload # >0d, >=0d, >=0d
def triangular(
self, /, left: _NDArrayLikeFloat, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: None = None
) -> _ArrayF64: ...
@overload # >=0d, >=0d, >=0d (fallback)
def triangular(
self, /, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: None = None
) -> _ArrayF64 | Any: ...
#
@overload # 0d, 0d, 0d
def noncentral_f(self, /, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = None) -> float: ...
@overload # >=0d, >=0d, >=0d, size=<given>
def noncentral_f(
self, /, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: _ShapeLike
) -> _ArrayF64: ...
@overload # >=0d, >=0d, >0d
def noncentral_f(
self, /, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _NDArrayLikeFloat, size: None = None
) -> _ArrayF64: ...
@overload # >=0d, >0d, >=0d
def noncentral_f(
self, /, dfnum: _ArrayLikeFloat_co, dfden: _NDArrayLikeFloat, nonc: _ArrayLikeFloat_co, size: None = None
) -> _ArrayF64: ...
@overload # >0d, >=0d, >=0d
def noncentral_f(
self, /, dfnum: _NDArrayLikeFloat, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None = None
) -> _ArrayF64: ...
@overload # >=0d, >=0d, >=0d (fallback)
def noncentral_f(
self, /, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None = None
) -> _ArrayF64 | Any: ...
###
# discrete
#
@overload # 0d bool | int
def integers[AnyIntT: (bool, int)](
self, low: int, high: int | None = None, size: None = None, *, dtype: type[AnyIntT], endpoint: bool = False
) -> AnyIntT: ...
@overload # 0d integer dtype
def integers[ScalarT: np.integer | np.bool](
self, low: int, high: int | None = None, size: None = None, *, dtype: _DTypeLike[ScalarT], endpoint: bool = False
) -> ScalarT: ...
@overload # 0d int64 (default)
def integers(
self, low: int, high: int | None = None, size: None = None, dtype: _DTypeLikeI64 = ..., endpoint: bool = False
) -> np.int64: ...
@overload # 0d unknown
def integers(
self, low: int, high: int | None = None, size: None = None, dtype: DTypeLike | None = ..., endpoint: bool = False
) -> Any: ...
@overload # integer dtype, size=<given>
def integers[ScalarT: np.integer | np.bool](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: _ShapeLike,
dtype: _DTypeLike[ScalarT],
endpoint: bool = False,
) -> NDArray[ScalarT]: ...
@overload # int64 (default), size=<given>
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: _ShapeLike,
dtype: _DTypeLikeI64 = ...,
endpoint: bool = False,
) -> NDArray[np.int64]: ...
@overload # unknown, size=<given>
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
*,
size: _ShapeLike,
dtype: DTypeLike | None = ...,
endpoint: bool = False,
) -> np.ndarray: ...
@overload # >=0d, integer dtype
def integers[ScalarT: np.integer | np.bool](
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
size: _ShapeLike | None = None,
*,
dtype: _DTypeLike[ScalarT],
endpoint: bool = False,
) -> NDArray[ScalarT] | Any: ...
@overload # >=0d, int64 (default)
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
size: _ShapeLike | None = None,
dtype: _DTypeLikeI64 = ...,
endpoint: bool = False,
) -> NDArray[np.int64] | Any: ...
@overload # >=0d, unknown
def integers(
self,
low: _ArrayLikeInt_co,
high: _ArrayLikeInt_co | None = None,
size: _ShapeLike | None = None,
dtype: DTypeLike | None = ...,
endpoint: bool = False,
) -> np.ndarray | Any: ...
#
@overload # 0d
def zipf(self, /, a: _FloatLike_co, size: None = None) -> int: ...
@overload # size=<given>
def zipf(self, /, a: _ArrayLikeFloat_co, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # >0d
def zipf(self, /, a: _NDArrayLikeFloat, size: None = None) -> NDArray[np.int64]: ...
@overload # >=0d
def zipf(self, /, a: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64] | Any: ...
#
@overload # 0d
def geometric(self, /, p: _FloatLike_co, size: None = None) -> int: ...
@overload # size=<given>
def geometric(self, /, p: _ArrayLikeFloat_co, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # >0d
def geometric(self, /, p: _NDArrayLikeFloat, size: None = None) -> NDArray[np.int64]: ...
@overload # >=0d
def geometric(self, /, p: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64] | Any: ...
#
@overload # 0d
def logseries(self, /, p: _FloatLike_co, size: None = None) -> int: ...
@overload # size=<given>
def logseries(self, /, p: _ArrayLikeFloat_co, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # >0d
def logseries(self, /, p: _NDArrayLikeFloat, size: None = None) -> NDArray[np.int64]: ...
@overload # >=0d
def logseries(self, /, p: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64] | Any: ...
#
@overload # 0d (default)
def poisson(self, /, lam: _FloatLike_co = 1.0, size: None = None) -> int: ...
@overload # size=<given> (keyword)
def poisson(self, /, lam: _ArrayLikeFloat_co = 1.0, *, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # size=<given> (positional)
def poisson(self, /, lam: _ArrayLikeFloat_co, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # >0d
def poisson(self, /, lam: _NDArrayLikeFloat, size: None = None) -> NDArray[np.int64]: ...
@overload # >=0d
def poisson(self, /, lam: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64] | Any: ...
#
@overload # 0d, 0d
def binomial(self, /, n: int, p: _FloatLike_co, size: None = None) -> int: ...
@overload # size=<given>
def binomial(self, /, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # >=0d, >0d
def binomial(self, /, n: _ArrayLikeInt_co, p: _NDArrayLikeFloat, size: None = None) -> NDArray[np.int64]: ...
@overload # >0d, >=0d
def binomial(self, /, n: _NDArrayLikeInt, p: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64]: ...
@overload # >=0d, >=0d
def binomial(self, /, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64] | Any: ...
#
@overload # 0d, 0d
def negative_binomial(self, /, n: _FloatLike_co, p: _FloatLike_co, size: None = None) -> int: ...
@overload # size=<given>
def negative_binomial(self, /, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: _ShapeLike) -> NDArray[np.int64]: ...
@overload # >=0d, >0d
def negative_binomial(self, /, n: _ArrayLikeFloat_co, p: _NDArrayLikeFloat, size: None = None) -> NDArray[np.int64]: ...
@overload # >0d, >=0d
def negative_binomial(self, /, n: _NDArrayLikeFloat, p: _ArrayLikeFloat_co, size: None = None) -> NDArray[np.int64]: ...
@overload # >=0d, >=0d
def negative_binomial(
self, /, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None = None
) -> NDArray[np.int64] | Any: ...
#
@overload # 0d, 0d, 0d
def hypergeometric(self, /, ngood: int, nbad: int, nsample: int, size: None = None) -> int: ...
@overload # size=<given>
def hypergeometric(
self, /, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: _ShapeLike
) -> NDArray[np.int64]: ...
@overload # >=0d, >=0d, >0d
def hypergeometric(
self, /, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _NDArrayLikeInt, size: None = None
) -> NDArray[np.int64] | Any: ...
@overload # >=0d, >0d, >=0d
def hypergeometric(
self, /, ngood: _ArrayLikeInt_co, nbad: _NDArrayLikeInt, nsample: _ArrayLikeInt_co, size: None = None
) -> NDArray[np.int64] | Any: ...
@overload # >0d, >=0d, >=0d
def hypergeometric(
self, /, ngood: _NDArrayLikeInt, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: None = None
) -> NDArray[np.int64] | Any: ...
@overload # >=0d, >=0d, >=0d
def hypergeometric(
self, /, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: None = None
) -> NDArray[np.int64] | Any: ...
###
# multivariate
#
def dirichlet(self, /, alpha: _ArrayLikeFloat_co, size: _ShapeLike | None = None) -> _ArrayF64: ...
#
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,
*,
method: Literal["svd", "eigh", "cholesky"] = "svd",
) -> _ArrayF64: ...
#
def multinomial(
self, /, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: _ShapeLike | None = None
) -> NDArray[np.int64]: ...
#
def multivariate_hypergeometric(
self,
/,
colors: _ArrayLikeInt_co,
nsample: int,
size: _ShapeLike | None = None,
method: Literal["marginals", "count"] = "marginals",
) -> NDArray[np.int64]: ...
###
# resampling
# axis must be 0 for MutableSequence
@overload
def shuffle(self, /, x: np.ndarray, axis: int = 0) -> None: ...
@overload
def shuffle(self, /, x: MutableSequence[Any], axis: Literal[0] = 0) -> None: ...
#
@overload
def permutation(self, /, x: int, axis: int = 0) -> NDArray[np.int64]: ...
@overload
def permutation(self, /, x: ArrayLike, axis: int = 0) -> np.ndarray: ...
#
@overload
def permuted[ArrayT: np.ndarray](self, /, x: ArrayT, *, axis: int | None = None, out: None = None) -> ArrayT: ...
@overload
def permuted(self, /, x: ArrayLike, *, axis: int | None = None, out: None = None) -> np.ndarray: ...
@overload
def permuted[ArrayT: np.ndarray](self, /, x: ArrayLike, *, axis: int | None = None, out: ArrayT) -> ArrayT: ...
#
@overload # >=0d int, size=None (default)
def choice(
self,
/,
a: int | _NestedSequence[int],
size: None = None,
replace: bool = True,
p: _ArrayLikeFloat_co | None = None,
axis: int = 0,
shuffle: bool = True,
) -> int: ...
@overload # >=0d known, size=None (default)
def choice[ScalarT: np.generic](
self,
/,
a: _ArrayLike[ScalarT],
size: None = None,
replace: bool = True,
p: _ArrayLikeFloat_co | None = None,
axis: int = 0,
shuffle: bool = True,
) -> ScalarT: ...
@overload # >=0d unknown, size=None (default)
def choice(
self,
/,
a: ArrayLike,
size: None = None,
replace: bool = True,
p: _ArrayLikeFloat_co | None = None,
axis: int = 0,
shuffle: bool = True,
) -> Any: ...
@overload # >=0d int, size=<given>
def choice(
self,
/,
a: int | _NestedSequence[int],
size: _ShapeLike,
replace: bool = True,
p: _ArrayLikeFloat_co | None = None,
axis: int = 0,
shuffle: bool = True,
) -> NDArray[np.int64]: ...
@overload # >=0d known, size=<given>
def choice[ScalarT: np.generic](
self,
/,
a: _ArrayLike[ScalarT],
size: _ShapeLike,
replace: bool = True,
p: _ArrayLikeFloat_co | None = None,
axis: int = 0,
shuffle: bool = True,
) -> NDArray[ScalarT]: ...
@overload # >=0d unknown, size=<given>
def choice(
self,
/,
a: ArrayLike,
size: _ShapeLike,
replace: bool = True,
p: _ArrayLikeFloat_co | None = None,
axis: int = 0,
shuffle: bool = True,
) -> np.ndarray: ...
def default_rng(seed: _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator | RandomState | None = None) -> Generator: ...
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