asd

venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.pyi

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+from __future__ import annotations
+import numpy as np
+from typing import (overload, Callable, NamedTuple, Protocol)
+import numpy.typing as npt
+from scipy._lib._util import SeedType
+import scipy.stats as stats
+
+
+ArrayLike0D = bool | int | float | complex | str | bytes | np.generic
+
+
+__all__: list[str]
+
+
+class UNURANError(RuntimeError):
+    ...
+
+
+class Method:
+    @overload
+    def rvs(self, size: None = ...) -> float | int: ...  # type: ignore[overload-overlap]
+    @overload
+    def rvs(self, size: int | tuple[int, ...] = ...) -> np.ndarray: ...
+    def set_random_state(self, random_state: SeedType) -> None: ...
+
+
+class TDRDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def dpdf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+
+class TransformedDensityRejection(Method):
+    def __init__(self,
+                 dist: TDRDist,
+                 *,
+                 mode: None | float = ...,
+                 center: None | float = ...,
+                 domain: None | tuple[float, float] = ...,
+                 c: float = ...,
+                 construction_points: int | npt.ArrayLike = ...,
+                 use_dars: bool = ...,
+                 max_squeeze_hat_ratio: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @property
+    def squeeze_hat_ratio(self) -> float: ...
+    @property
+    def squeeze_area(self) -> float: ...
+    @overload
+    def ppf_hat(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf_hat(self, u: npt.ArrayLike) -> np.ndarray: ...
+
+
+class SROUDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+
+class SimpleRatioUniforms(Method):
+    def __init__(self,
+                 dist: SROUDist,
+                 *,
+                 mode: None | float = ...,
+                 pdf_area: float = ...,
+                 domain: None | tuple[float, float] = ...,
+                 cdf_at_mode: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+
+
+class UError(NamedTuple):
+    max_error: float
+    mean_absolute_error: float
+
+class PINVDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def cdf(self) -> Callable[..., float]: ...
+    @property
+    def logpdf(self) -> Callable[..., float]: ...
+
+
+class NumericalInversePolynomial(Method):
+    def __init__(self,
+                 dist: PINVDist,
+                 *,
+                 mode: None | float = ...,
+                 center: None | float = ...,
+                 domain: None | tuple[float, float] = ...,
+                 order: int = ...,
+                 u_resolution: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @property
+    def intervals(self) -> int: ...
+    @overload
+    def ppf(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf(self, u: npt.ArrayLike) -> np.ndarray: ...
+    @overload
+    def cdf(self, x: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def cdf(self, x: npt.ArrayLike) -> np.ndarray: ...
+    def u_error(self, sample_size: int = ...) -> UError: ...
+    def qrvs(self,
+             size: None | int | tuple[int, ...] = ...,
+             d: None | int = ...,
+             qmc_engine: None | stats.qmc.QMCEngine = ...) -> npt.ArrayLike: ...
+
+
+class HINVDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def cdf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+
+class NumericalInverseHermite(Method):
+    def __init__(self,
+                 dist: HINVDist,
+                 *,
+                 domain: None | tuple[float, float] = ...,
+                 order: int= ...,
+                 u_resolution: float = ...,
+                 construction_points: None | npt.ArrayLike = ...,
+                 max_intervals: int = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @property
+    def intervals(self) -> int: ...
+    @overload
+    def ppf(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf(self, u: npt.ArrayLike) -> np.ndarray: ...
+    def qrvs(self,
+             size: None | int | tuple[int, ...] = ...,
+             d: None | int = ...,
+             qmc_engine: None | stats.qmc.QMCEngine = ...) -> npt.ArrayLike: ...
+    def u_error(self, sample_size: int = ...) -> UError: ...
+
+
+class DAUDist(Protocol):
+    @property
+    def pmf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+class DiscreteAliasUrn(Method):
+    def __init__(self,
+                 dist: npt.ArrayLike | DAUDist,
+                 *,
+                 domain: None | tuple[float, float] = ...,
+                 urn_factor: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+
+
+class DGTDist(Protocol):
+    @property
+    def pmf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+class DiscreteGuideTable(Method):
+    def __init__(self,
+                 dist: npt.ArrayLike | DGTDist,
+                 *,
+                 domain: None | tuple[float, float] = ...,
+                 guide_factor: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @overload
+    def ppf(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf(self, u: npt.ArrayLike) -> np.ndarray: ...