reconnect moved files to git repo

venv/lib/python3.11/site-packages/scipy/spatial/_ckdtree.pyi

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+from __future__ import annotations
+from typing import (
+    Any,
+    Generic,
+    overload,
+    TypeVar,
+)
+
+import numpy as np
+import numpy.typing as npt
+from scipy.sparse import coo_matrix, dok_matrix
+
+from typing import Literal
+
+# TODO: Replace `ndarray` with a 1D float64 array when possible
+_BoxType = TypeVar("_BoxType", None, npt.NDArray[np.float64])
+
+# Copied from `numpy.typing._scalar_like._ScalarLike`
+# TODO: Expand with 0D arrays once we have shape support
+_ArrayLike0D = bool | int | float | complex | str | bytes | np.generic
+
+_WeightType = npt.ArrayLike | tuple[npt.ArrayLike | None, npt.ArrayLike | None]
+
+class cKDTreeNode:
+    @property
+    def data_points(self) -> npt.NDArray[np.float64]: ...
+    @property
+    def indices(self) -> npt.NDArray[np.intp]: ...
+
+    # These are read-only attributes in cython, which behave like properties
+    @property
+    def level(self) -> int: ...
+    @property
+    def split_dim(self) -> int: ...
+    @property
+    def children(self) -> int: ...
+    @property
+    def start_idx(self) -> int: ...
+    @property
+    def end_idx(self) -> int: ...
+    @property
+    def split(self) -> float: ...
+    @property
+    def lesser(self) -> cKDTreeNode | None: ...
+    @property
+    def greater(self) -> cKDTreeNode | None: ...
+
+class cKDTree(Generic[_BoxType]):
+    @property
+    def n(self) -> int: ...
+    @property
+    def m(self) -> int: ...
+    @property
+    def leafsize(self) -> int: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def tree(self) -> cKDTreeNode: ...
+
+    # These are read-only attributes in cython, which behave like properties
+    @property
+    def data(self) -> npt.NDArray[np.float64]: ...
+    @property
+    def maxes(self) -> npt.NDArray[np.float64]: ...
+    @property
+    def mins(self) -> npt.NDArray[np.float64]: ...
+    @property
+    def indices(self) -> npt.NDArray[np.float64]: ...
+    @property
+    def boxsize(self) -> _BoxType: ...
+
+    # NOTE: In practice `__init__` is used as constructor, not `__new__`.
+    # The latter gives us more flexibility in setting the generic parameter
+    # though.
+    @overload
+    def __new__(  # type: ignore[overload-overlap]
+        cls,
+        data: npt.ArrayLike,
+        leafsize: int = ...,
+        compact_nodes: bool = ...,
+        copy_data: bool = ...,
+        balanced_tree: bool = ...,
+        boxsize: None = ...,
+    ) -> cKDTree[None]: ...
+    @overload
+    def __new__(
+        cls,
+        data: npt.ArrayLike,
+        leafsize: int = ...,
+        compact_nodes: bool = ...,
+        copy_data: bool = ...,
+        balanced_tree: bool = ...,
+        boxsize: npt.ArrayLike = ...,
+    ) -> cKDTree[npt.NDArray[np.float64]]: ...
+
+    # TODO: returns a 2-tuple of scalars if `x.ndim == 1` and `k == 1`,
+    # returns a 2-tuple of arrays otherwise
+    def query(
+        self,
+        x: npt.ArrayLike,
+        k: npt.ArrayLike = ...,
+        eps: float = ...,
+        p: float = ...,
+        distance_upper_bound: float = ...,
+        workers: int | None = ...,
+    ) -> tuple[Any, Any]: ...
+
+    # TODO: returns a list scalars if `x.ndim <= 1`,
+    # returns an object array of lists otherwise
+    def query_ball_point(
+        self,
+        x: npt.ArrayLike,
+        r: npt.ArrayLike,
+        p: float = ...,
+        eps: float = ...,
+        workers: int | None = ...,
+        return_sorted: bool | None = ...,
+        return_length: bool = ...
+    ) -> Any: ...
+
+    def query_ball_tree(
+        self,
+        other: cKDTree,
+        r: float,
+        p: float,
+        eps: float = ...,
+    ) -> list[list[int]]: ...
+
+    @overload
+    def query_pairs(  # type: ignore[overload-overlap]
+        self,
+        r: float,
+        p: float = ...,
+        eps: float = ...,
+        output_type: Literal["set"] = ...,
+    ) -> set[tuple[int, int]]: ...
+    @overload
+    def query_pairs(
+        self,
+        r: float,
+        p: float = ...,
+        eps: float = ...,
+        output_type: Literal["ndarray"] = ...,
+    ) -> npt.NDArray[np.intp]: ...
+
+    @overload
+    def count_neighbors(  # type: ignore[overload-overlap]
+        self,
+        other: cKDTree,
+        r: _ArrayLike0D,
+        p: float = ...,
+        weights: None | tuple[None, None] = ...,
+        cumulative: bool = ...,
+    ) -> int: ...
+    @overload
+    def count_neighbors(  # type: ignore[overload-overlap]
+        self,
+        other: cKDTree,
+        r: _ArrayLike0D,
+        p: float = ...,
+        weights: _WeightType = ...,
+        cumulative: bool = ...,
+    ) -> np.float64: ...
+    @overload
+    def count_neighbors(  # type: ignore[overload-overlap]
+        self,
+        other: cKDTree,
+        r: npt.ArrayLike,
+        p: float = ...,
+        weights: None | tuple[None, None] = ...,
+        cumulative: bool = ...,
+    ) -> npt.NDArray[np.intp]: ...
+    @overload
+    def count_neighbors(
+        self,
+        other: cKDTree,
+        r: npt.ArrayLike,
+        p: float = ...,
+        weights: _WeightType = ...,
+        cumulative: bool = ...,
+    ) -> npt.NDArray[np.float64]: ...
+
+    @overload
+    def sparse_distance_matrix(  # type: ignore[overload-overlap]
+        self,
+        other: cKDTree,
+        max_distance: float,
+        p: float = ...,
+        output_type: Literal["dok_matrix"] = ...,
+    ) -> dok_matrix: ...
+    @overload
+    def sparse_distance_matrix(  # type: ignore[overload-overlap]
+        self,
+        other: cKDTree,
+        max_distance: float,
+        p: float = ...,
+        output_type: Literal["coo_matrix"] = ...,
+    ) -> coo_matrix: ...
+    @overload
+    def sparse_distance_matrix(  # type: ignore[overload-overlap]
+        self,
+        other: cKDTree,
+        max_distance: float,
+        p: float = ...,
+        output_type: Literal["dict"] = ...,
+    ) -> dict[tuple[int, int], float]: ...
+    @overload
+    def sparse_distance_matrix(
+        self,
+        other: cKDTree,
+        max_distance: float,
+        p: float = ...,
+        output_type: Literal["ndarray"] = ...,
+    ) -> npt.NDArray[np.void]: ...