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Mini Shell
Mini Shell
import sys
from typing import (
Any,
Dict,
Generic,
List,
Optional,
overload,
Set,
Tuple,
TypeVar,
Union,
)
import numpy as np
import numpy.typing as npt
from scipy.sparse import coo_matrix, dok_matrix
if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions 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 = Union[
bool,
int,
float,
complex,
str,
bytes,
np.generic,
]
_WeightType = Union[
npt.ArrayLike,
Tuple[Optional[npt.ArrayLike], Optional[npt.ArrayLike]],
]
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) -> Optional[cKDTreeNode]: ...
@property
def greater(self) -> Optional[cKDTreeNode]: ...
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[misc]
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: Optional[int] = ...,
) -> 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: Optional[int] = ...,
return_sorted: Optional[bool] = ...,
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[misc]
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[misc]
self,
other: cKDTree,
r: _ArrayLike0D,
p: float = ...,
weights: None | Tuple[None, None] = ...,
cumulative: bool = ...,
) -> int: ...
@overload
def count_neighbors( # type: ignore[misc]
self,
other: cKDTree,
r: _ArrayLike0D,
p: float = ...,
weights: _WeightType = ...,
cumulative: bool = ...,
) -> np.float64: ...
@overload
def count_neighbors( # type: ignore[misc]
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[misc]
self,
other: cKDTree,
max_distance: float,
p: float = ...,
output_type: Literal["dok_matrix"] = ...,
) -> dok_matrix: ...
@overload
def sparse_distance_matrix( # type: ignore[misc]
self,
other: cKDTree,
max_distance: float,
p: float = ...,
output_type: Literal["coo_matrix"] = ...,
) -> coo_matrix: ...
@overload
def sparse_distance_matrix( # type: ignore[misc]
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]: ...
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