AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/scipy/spatial/_ckdtree.pyi

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]: ...