up follow livre

venv/lib/python3.13/site-packages/scipy/spatial/distance.pyi

@@ -0,0 +1,210 @@
+from typing import (overload, Any, SupportsFloat, Literal, Protocol, SupportsIndex)
+
+import numpy as np
+from numpy.typing import ArrayLike, NDArray
+
+# Anything that can be parsed by `np.float64.__init__` and is thus
+# compatible with `ndarray.__setitem__` (for a float64 array)
+_FloatValue = None | str | bytes | SupportsFloat | SupportsIndex
+
+class _MetricCallback1(Protocol):
+    def __call__(
+        self, __XA: NDArray[Any], __XB: NDArray[Any]
+    ) -> _FloatValue: ...
+
+class _MetricCallback2(Protocol):
+    def __call__(
+        self, __XA: NDArray[Any], __XB: NDArray[Any], **kwargs: Any
+    ) -> _FloatValue: ...
+
+# TODO: Use a single protocol with a parameter specification variable
+# once available (PEP 612)
+_MetricCallback = _MetricCallback1 | _MetricCallback2
+
+_MetricKind = Literal[
+    'braycurtis',
+    'canberra',
+    'chebychev', 'chebyshev', 'cheby', 'cheb', 'ch',
+    'cityblock', 'cblock', 'cb', 'c',
+    'correlation', 'co',
+    'cosine', 'cos',
+    'dice',
+    'euclidean', 'euclid', 'eu', 'e',
+    'hamming', 'hamm', 'ha', 'h',
+    'minkowski', 'mi', 'm', 'pnorm',
+    'jaccard', 'jacc', 'ja', 'j',
+    'jensenshannon', 'js',
+    'kulczynski1',
+    'mahalanobis', 'mahal', 'mah',
+    'rogerstanimoto',
+    'russellrao',
+    'seuclidean', 'se', 's',
+    'sokalmichener',
+    'sokalsneath',
+    'sqeuclidean', 'sqe', 'sqeuclid',
+    'yule',
+]
+
+# Function annotations
+
+def braycurtis(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def canberra(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+# TODO: Add `metric`-specific overloads
+# Returns a float64 or float128 array, depending on the input dtype
+@overload
+def cdist(
+    XA: ArrayLike,
+    XB: ArrayLike,
+    metric: _MetricKind = ...,
+    *,
+    out: None | NDArray[np.floating[Any]] = ...,
+    p: float = ...,
+    w: ArrayLike | None = ...,
+    V: ArrayLike | None = ...,
+    VI: ArrayLike | None = ...,
+) -> NDArray[np.floating[Any]]: ...
+@overload
+def cdist(
+    XA: ArrayLike,
+    XB: ArrayLike,
+    metric: _MetricCallback,
+    *,
+    out: None | NDArray[np.floating[Any]] = ...,
+    **kwargs: Any,
+) -> NDArray[np.floating[Any]]: ...
+
+# TODO: Wait for dtype support; the return type is
+# dependent on the input arrays dtype
+def chebyshev(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> Any: ...
+
+# TODO: Wait for dtype support; the return type is
+# dependent on the input arrays dtype
+def cityblock(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> Any: ...
+
+def correlation(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ..., centered: bool = ...
+) -> np.float64: ...
+
+def cosine(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def dice(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> float: ...
+
+def directed_hausdorff(
+    u: ArrayLike, v: ArrayLike, seed: int | None = ...
+) -> tuple[float, int, int]: ...
+
+def euclidean(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> float: ...
+
+def hamming(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def is_valid_dm(
+    D: ArrayLike,
+    tol: float = ...,
+    throw: bool = ...,
+    name: str | None = ...,
+    warning: bool = ...,
+) -> bool: ...
+
+def is_valid_y(
+    y: ArrayLike,
+    warning: bool = ...,
+    throw: bool = ...,
+    name: str | None = ...,
+) -> bool: ...
+
+def jaccard(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def jensenshannon(
+    p: ArrayLike, q: ArrayLike, base: float | None = ...
+) -> np.float64: ...
+
+def kulczynski1(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def mahalanobis(
+    u: ArrayLike, v: ArrayLike, VI: ArrayLike
+) -> np.float64: ...
+
+def minkowski(
+    u: ArrayLike, v: ArrayLike, p: float = ..., w: ArrayLike | None = ...
+) -> float: ...
+
+def num_obs_dm(d: ArrayLike) -> int: ...
+
+def num_obs_y(Y: ArrayLike) -> int: ...
+
+# TODO: Add `metric`-specific overloads
+@overload
+def pdist(
+    X: ArrayLike,
+    metric: _MetricKind = ...,
+    *,
+    out: None | NDArray[np.floating[Any]] = ...,
+    p: float = ...,
+    w: ArrayLike | None = ...,
+    V: ArrayLike | None = ...,
+    VI: ArrayLike | None = ...,
+) -> NDArray[np.floating[Any]]: ...
+@overload
+def pdist(
+    X: ArrayLike,
+    metric: _MetricCallback,
+    *,
+    out: None | NDArray[np.floating[Any]] = ...,
+    **kwargs: Any,
+) -> NDArray[np.floating[Any]]: ...
+
+def seuclidean(
+    u: ArrayLike, v: ArrayLike, V: ArrayLike
+) -> float: ...
+
+def sokalmichener(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> float: ...
+
+def sokalsneath(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def sqeuclidean(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> np.float64: ...
+
+def squareform(
+    X: ArrayLike,
+    force: Literal["no", "tomatrix", "tovector"] = ...,
+    checks: bool = ...,
+) -> NDArray[Any]: ...
+
+def rogerstanimoto(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> float: ...
+
+def russellrao(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> float: ...
+
+def yule(
+    u: ArrayLike, v: ArrayLike, w: ArrayLike | None = ...
+) -> float: ...