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jorenhamjorenham
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🏷️ stats: stub the remaining private modules
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‎scipy-stubs/stats/_ansari_swilk_statistics.pyi

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# defined in scipy/stats/_ansari_swilk_statistics.pyx
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import numpy as np
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import optype.numpy as onp
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def gscale(test: int, other: int) -> tuple[int, onp.Array1D[np.float32], int]: ... # undocumented
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def swilk(
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x: onp.Array1D[np.float64], a: onp.Array1D[np.float64], init: bool = False, n1: int = -1
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) -> tuple[float, float, int]: ... # undocumented

‎scipy-stubs/stats/_constants.pyi

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from typing import Final
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import numpy as np
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_EPS: Final[np.float64] = ... # undocumented
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_XMAX: Final[np.float64] = ... # undocumented
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_LOGXMAX: Final[np.float64] = ... # undocumented
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_XMIN: Final[np.float64] = ... # undocumented
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_LOGXMIN: Final[np.float64] = ... # undocumented
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_EULER: Final[float] = 0.577215664901532860606512090082402431042 # undocumented
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_ZETA3: Final[float] = 1.202056903159594285399738161511449990765 # undocumented
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_SQRT_PI: Final[float] = 1.772453850905516027298167483341145182798 # undocumented
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_SQRT_2_OVER_PI: Final[float] = 0.7978845608028654 # undocumented
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_LOG_PI: Final[float] = 1.1447298858494002 # undocumented
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_LOG_SQRT_2_OVER_PI: Final[float] = -0.22579135264472744 # undocumented

‎scipy-stubs/stats/_finite_differences.pyi

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from collections.abc import Callable
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from typing import overload
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from typing_extensions import TypeVarTuple
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import numpy as np
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import optype.numpy as onp
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_Ts = TypeVarTuple("_Ts")
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###
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def _central_diff_weights(Np: int, ndiv: int = 1) -> onp.Array1D[np.float64]: ... # undocumented
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#
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@overload
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def _derivative(
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func: Callable[[float], onp.ToFloat], x0: float, dx: float = 1.0, n: int = 1, args: tuple[()] = ..., order: int = 3
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) -> np.float64: ...
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@overload
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def _derivative(
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func: Callable[[float, *_Ts], onp.ToFloat], x0: float, dx: float = 1.0, n: int = 1, *, args: tuple[*_Ts], order: int = 3
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) -> np.float64: ... # undocumented

‎scipy-stubs/stats/_qmvnt_cy.pyi

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# defined in scipy/stats/_qmvnt_cy.pyx
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from typing import Final
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import numpy as np
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import optype.numpy as onp
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###
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gammaincinv: Final[np.ufunc] = ... # implicit re-export from `scipy.special`, required by stubtest
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def _qmvn_inner(
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q: onp.Array1D[np.float64],
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rndm: onp.Array2D[np.float64],
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n_qmc_samples: int,
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n_batches: int,
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cho: onp.Array2D[np.float64],
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lo: onp.Array1D[np.float64],
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hi: onp.Array1D[np.float64],
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) -> tuple[float, np.float64, int]: ... # undocumented
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def _qmvt_inner(
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q: onp.Array1D[np.float64],
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rndm: onp.Array2D[np.float64],
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n_qmc_samples: int,
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n_batches: int,
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cho: onp.Array2D[np.float64],
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lo: onp.Array1D[np.float64],
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hi: onp.Array1D[np.float64],
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nu: float,
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) -> tuple[float, np.float64, int]: ... # undocumented

‎scipy-stubs/stats/_stats.pyi

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# defined in scipy/stats/_stats.pyx
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from collections.abc import Callable
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from typing import Final, Literal, TypeAlias, TypedDict, overload, type_check_only
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from typing_extensions import CapsuleType, ReadOnly
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import numpy as np
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import optype.numpy as onp
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import optype.numpy.compat as npc
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###
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# matches the `ctypedef fused ordered`
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_Ordered: TypeAlias = np.int32 | np.int64 | np.float32 | np.float64
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# matches the `ctypedef fused real`
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_Real: TypeAlias = np.float32 | np.float64 | np.longdouble
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# castable to `_Real`
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_AsReal: TypeAlias = npc.floating | npc.integer | np.bool_
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# castable to a real distance matrix
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_Dist2D: TypeAlias = onp.Array2D[_AsReal]
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# the (presumed) type of the `global_corr` parameters
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_GlobalCorr: TypeAlias = Literal["mgc", "mantel", "biased", "rank"]
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###
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@type_check_only
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class _CApiDict(TypedDict):
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_geninvgauss_pdf: ReadOnly[CapsuleType]
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_studentized_range_cdf: ReadOnly[CapsuleType]
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_studentized_range_cdf_asymptotic: ReadOnly[CapsuleType]
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_studentized_range_pdf: ReadOnly[CapsuleType]
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_studentized_range_pdf_asymptotic: ReadOnly[CapsuleType]
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_studentized_range_moment: ReadOnly[CapsuleType]
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_genhyperbolic_pdf: ReadOnly[CapsuleType]
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_genhyperbolic_logpdf: ReadOnly[CapsuleType]
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###
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__pyx_capi__: Final[_CApiDict] = ... # undocumented
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def von_mises_cdf(k_obj: onp.ToFloatND, x_obj: onp.ToFloatND) -> onp.ArrayND[np.float64]: ... # undocumented
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def _kendall_dis(x: onp.Array1D[np.intp], y: onp.Array1D[np.intp]) -> int: ... # undocumented
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def _toint64(x: onp.ToIntND) -> onp.Array1D[np.int64]: ... # undocumented
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def _weightedrankedtau(
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x: onp.Array1D[_Ordered],
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y: onp.Array1D[_Ordered],
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rank: onp.Array1D[np.intp],
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weigher: Callable[[float], onp.ToFloat],
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additive: bool,
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) -> np.float64: ... # undocumented
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def _rank_distance_matrix(distx: onp.Array2D[npc.floating | npc.integer]) -> onp.Array2D[np.intp]: ... # undocumented
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#
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@overload
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def _center_distance_matrix(
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distx: _Dist2D, global_corr: _GlobalCorr = "mgc", is_ranked: onp.ToTrue = True
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) -> tuple[onp.Array2D[np.float64], onp.Array2D[np.intp]]: ...
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@overload
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def _center_distance_matrix(
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distx: _Dist2D, global_corr: _GlobalCorr, is_ranked: onp.ToFalse
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) -> tuple[onp.Array2D[np.float64], onp.Array1D[np.float64]]: ...
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@overload
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def _center_distance_matrix(
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distx: _Dist2D, global_corr: _GlobalCorr = "mgc", *, is_ranked: onp.ToFalse
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) -> tuple[onp.Array2D[np.float64], onp.Array1D[np.float64]]: ... # undocumented
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#
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@overload
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def _transform_distance_matrix(
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distx: _Dist2D, disty: _Dist2D, global_corr: _GlobalCorr = "mgc", is_ranked: onp.ToTrue = True
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) -> dict[str, onp.Array2D[np.float64] | onp.Array2D[np.intp]]: ...
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@overload
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def _transform_distance_matrix(
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distx: _Dist2D, disty: _Dist2D, global_corr: _GlobalCorr, is_ranked: onp.ToFalse
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) -> dict[str, onp.Array2D[np.float64] | onp.Array1D[np.float64]]: ...
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@overload
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def _transform_distance_matrix(
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distx: _Dist2D, disty: _Dist2D, global_corr: _GlobalCorr = "mgc", *, is_ranked: onp.ToFalse
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) -> dict[str, onp.Array2D[np.float64] | onp.Array1D[np.float64]]: ... # undocumented
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#
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def _local_covariance(
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distx: _Dist2D, disty: _Dist2D, rank_distx: onp.ArrayND[_AsReal], rank_disty: onp.ArrayND[_AsReal]
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) -> onp.Array2D[np.float64]: ... # undocumented
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def _local_correlations(
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distx: _Dist2D, disty: _Dist2D, global_corr: _GlobalCorr = "mgc"
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) -> onp.Array2D[np.float64]: ... # undocumented
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def geninvgauss_logpdf(x: float, p: float, b: float) -> float: ... # undocumented
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def _studentized_range_cdf_logconst(k: float, df: float) -> float: ... # undocumented
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def _studentized_range_pdf_logconst(k: float, df: float) -> float: ... # undocumented
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def genhyperbolic_pdf(x: float, p: float, a: float, b: float) -> float: ... # undocumented
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def genhyperbolic_logpdf(x: float, p: float, a: float, b: float) -> float: ... # undocumented
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# keep in sync with `gaussian_kernel_estimate_log`
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@overload
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def gaussian_kernel_estimate( # type: ignore[overload-overlap]
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points: onp.Array2D[_AsReal],
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values: onp.Array2D[_Real],
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xi: onp.Array2D[_AsReal],
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cho_cov: onp.Array2D[_AsReal],
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dtype: onp.AnyFloat32DType,
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_: _Real | float = 0.0,
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) -> onp.Array2D[np.float32]: ...
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@overload
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def gaussian_kernel_estimate(
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points: onp.Array2D[_AsReal],
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values: onp.Array2D[_Real],
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xi: onp.Array2D[_AsReal],
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cho_cov: onp.Array2D[_AsReal],
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dtype: onp.AnyFloat64DType,
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_: _Real | float = 0.0,
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) -> onp.Array2D[np.float64]: ...
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@overload
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def gaussian_kernel_estimate(
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points: onp.Array2D[_AsReal],
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values: onp.Array2D[_Real],
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xi: onp.Array2D[_AsReal],
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cho_cov: onp.Array2D[_AsReal],
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dtype: onp.AnyLongDoubleDType,
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_: _Real | float = 0.0,
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) -> onp.Array2D[np.longdouble]: ... # undocumented
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# keep in sync with `gaussian_kernel_estimate`
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@overload
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def gaussian_kernel_estimate_log( # type: ignore[overload-overlap]
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points: onp.Array2D[_AsReal],
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values: onp.Array2D[_Real],
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xi: onp.Array2D[_AsReal],
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cho_cov: onp.Array2D[_AsReal],
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dtype: onp.AnyFloat32DType,
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_: _Real | float = 0.0,
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) -> onp.Array2D[np.float32]: ...
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@overload
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def gaussian_kernel_estimate_log(
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points: onp.Array2D[_AsReal],
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values: onp.Array2D[_Real],
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xi: onp.Array2D[_AsReal],
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cho_cov: onp.Array2D[_AsReal],
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dtype: onp.AnyFloat64DType,
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_: _Real | float = 0.0,
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) -> onp.Array2D[np.float64]: ...
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@overload
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def gaussian_kernel_estimate_log(
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points: onp.Array2D[_AsReal],
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values: onp.Array2D[_Real],
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xi: onp.Array2D[_AsReal],
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cho_cov: onp.Array2D[_AsReal],
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dtype: onp.AnyLongDoubleDType,
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_: _Real | float = 0.0,
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) -> onp.Array2D[np.longdouble]: ... # undocumented

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