mirror of
https://github.com/davidhalter/typeshed.git
synced 2026-06-24 09:48:39 +08:00
networkx: complete the layout module (#14580)
This commit is contained in:
Ali Hamdan
@@ -1,13 +1,29 @@
|
||||
# Stub-only module, can't be imported at runtime.
|
||||
|
||||
from typing import TypeVar
|
||||
from typing_extensions import TypeAlias
|
||||
import sys
|
||||
from collections.abc import Collection
|
||||
from typing import Any, Protocol, type_check_only
|
||||
from typing_extensions import TypeAlias, TypeVar
|
||||
|
||||
import numpy as np
|
||||
|
||||
_G = TypeVar("_G", bound=np.generic)
|
||||
_ScalarT = TypeVar("_ScalarT", bound=bool | int | float | complex | str | bytes | np.generic)
|
||||
_GenericT = TypeVar("_GenericT", bound=np.generic)
|
||||
_GenericT_co = TypeVar("_GenericT_co", bound=np.generic, covariant=True)
|
||||
_ShapeT_co = TypeVar("_ShapeT_co", bound=tuple[int, ...], default=Any, covariant=True)
|
||||
|
||||
# numpy aliases
|
||||
Array1D: TypeAlias = np.ndarray[tuple[int], np.dtype[_G]]
|
||||
Array2D: TypeAlias = np.ndarray[tuple[int, int], np.dtype[_G]]
|
||||
if sys.version_info >= (3, 10):
|
||||
@type_check_only
|
||||
class SupportsArray(Protocol[_GenericT_co, _ShapeT_co]):
|
||||
def __array__(self) -> np.ndarray[_ShapeT_co, np.dtype[_GenericT_co]]: ...
|
||||
|
||||
ArrayLike1D: TypeAlias = Collection[_ScalarT] | SupportsArray[_GenericT, tuple[int]]
|
||||
else:
|
||||
# networkx does not support Python 3.9 but pyright still runs on 3.9 in CI
|
||||
# See https://github.com/python/typeshed/issues/10722
|
||||
ArrayLike1D: TypeAlias = Collection[_ScalarT] | np.ndarray[tuple[int], np.dtype[_GenericT]]
|
||||
|
||||
Array1D: TypeAlias = np.ndarray[tuple[int], np.dtype[_GenericT]]
|
||||
Array2D: TypeAlias = np.ndarray[tuple[int, int], np.dtype[_GenericT]]
|
||||
Seed: TypeAlias = int | np.random.Generator | np.random.RandomState
|
||||
|
||||
@@ -1,7 +1,12 @@
|
||||
from _typeshed import Incomplete
|
||||
from collections.abc import Collection, Mapping
|
||||
from typing import Any, Literal
|
||||
from typing_extensions import TypeAlias
|
||||
|
||||
import numpy as np
|
||||
from networkx._typing import Array1D, Array2D, ArrayLike1D, Seed
|
||||
from networkx.classes.graph import Graph, _Node
|
||||
from networkx.utils.backends import _dispatchable
|
||||
from numpy.random import RandomState
|
||||
from numpy.typing import NDArray
|
||||
|
||||
__all__ = [
|
||||
"bipartite_layout",
|
||||
@@ -22,99 +27,134 @@ __all__ = [
|
||||
"arf_layout",
|
||||
]
|
||||
|
||||
_FloatArrayLike1D: TypeAlias = ArrayLike1D[float, np.number[Any]] # Any because we don't care about the bit base
|
||||
|
||||
def random_layout(
|
||||
G, center=None, dim: int = 2, seed: int | RandomState | None = None, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
G: Graph[_Node],
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
dim: int = 2,
|
||||
seed: Seed | None = None,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[_Node, Array1D[np.float32]]: ...
|
||||
def circular_layout(
|
||||
G, scale: float = 1, center=None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
G: Graph[_Node], scale: float = 1, center: _FloatArrayLike1D | None = None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def shell_layout(
|
||||
G, nlist=None, rotate=None, scale: float = 1, center=None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
def bipartite_layout(
|
||||
G,
|
||||
nodes=None,
|
||||
align: str = "vertical",
|
||||
G: Graph[_Node],
|
||||
nlist: Collection[Collection[_Node]] | None = None,
|
||||
rotate: float | None = None,
|
||||
scale: float = 1,
|
||||
center=None,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
dim: int = 2,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def bipartite_layout(
|
||||
G: Graph[_Node],
|
||||
nodes: Collection[_Node] | None = None,
|
||||
align: Literal["vertical", "horizontal"] = "vertical",
|
||||
scale: float = 1,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
aspect_ratio: float = ...,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def spring_layout(
|
||||
G,
|
||||
k=None,
|
||||
pos=None,
|
||||
fixed=None,
|
||||
G: Graph[_Node],
|
||||
k: float | None = None,
|
||||
pos: Mapping[_Node, Collection[float]] | None = None,
|
||||
fixed: Collection[_Node] | None = None,
|
||||
iterations: int = 50,
|
||||
threshold: float = 0.0001,
|
||||
weight: str = "weight",
|
||||
scale: float = 1,
|
||||
center=None,
|
||||
weight: str | None = "weight",
|
||||
scale: float | None = 1,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
dim: int = 2,
|
||||
seed: int | RandomState | None = None,
|
||||
seed: Seed | None = None,
|
||||
store_pos_as: str | None = None,
|
||||
*,
|
||||
method: str = "auto",
|
||||
method: Literal["auto", "force", "energy"] = "auto",
|
||||
gravity: float = 1.0,
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
|
||||
fruchterman_reingold_layout = spring_layout
|
||||
|
||||
def kamada_kawai_layout(
|
||||
G, dist=None, pos=None, weight: str = "weight", scale: float = 1, center=None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
def spectral_layout(
|
||||
G, weight: str = "weight", scale: float = 1, center=None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
def planar_layout(
|
||||
G, scale: float = 1, center=None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
def spiral_layout(
|
||||
G,
|
||||
G: Graph[_Node],
|
||||
dist: Mapping[_Node, Mapping[_Node, float]] | None = None,
|
||||
pos: Mapping[_Node, Collection[float]] | None = None,
|
||||
weight: str | None = "weight",
|
||||
scale: float = 1,
|
||||
center=None,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
dim: int = 2,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def spectral_layout(
|
||||
G: Graph[_Node],
|
||||
weight: str | None = "weight",
|
||||
scale: float = 1,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
dim: int = 2,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def planar_layout(
|
||||
G: Graph[_Node], scale: float = 1, center: _FloatArrayLike1D | None = None, dim: int = 2, store_pos_as: str | None = None
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def spiral_layout(
|
||||
G: Graph[_Node],
|
||||
scale: float = 1,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
dim: int = 2,
|
||||
resolution: float = 0.35,
|
||||
equidistant: bool = False,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def multipartite_layout(
|
||||
G, subset_key: str = "subset", align: str = "vertical", scale: float = 1, center=None, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
G: Graph[_Node],
|
||||
subset_key: str | Mapping[Any, Collection[_Node]] = "subset", # layers can be "any" hashable
|
||||
align: Literal["vertical", "horizontal"] = "vertical",
|
||||
scale: float = 1,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def arf_layout(
|
||||
G,
|
||||
pos=None,
|
||||
G: Graph[_Node],
|
||||
pos: Mapping[_Node, Collection[float]] | None = None,
|
||||
scaling: float = 1,
|
||||
a: float = 1.1,
|
||||
etol: float = 1e-06,
|
||||
dt: float = 0.001,
|
||||
max_iter: int = 1000,
|
||||
*,
|
||||
seed: int | RandomState | None = None,
|
||||
seed: Seed | None = None,
|
||||
store_pos_as: str | None = None,
|
||||
): ...
|
||||
) -> dict[_Node, Array1D[np.float32]]: ...
|
||||
@_dispatchable
|
||||
def forceatlas2_layout(
|
||||
G,
|
||||
pos=None,
|
||||
G: Graph[_Node],
|
||||
pos: Mapping[_Node, Collection[float]] | None = None,
|
||||
*,
|
||||
max_iter=100,
|
||||
jitter_tolerance=1.0,
|
||||
scaling_ratio=2.0,
|
||||
max_iter: int = 100,
|
||||
jitter_tolerance: float = 1.0,
|
||||
scaling_ratio: float = 2.0,
|
||||
gravity: float = 1.0,
|
||||
distributed_action=False,
|
||||
strong_gravity=False,
|
||||
node_mass=None,
|
||||
node_size=None,
|
||||
weight=None,
|
||||
dissuade_hubs=False,
|
||||
linlog=False,
|
||||
seed: int | RandomState | None = None,
|
||||
distributed_action: bool = False,
|
||||
strong_gravity: bool = False,
|
||||
node_mass: Mapping[_Node, float] | None = None,
|
||||
node_size: Mapping[_Node, float] | None = None,
|
||||
weight: str | None = None,
|
||||
dissuade_hubs: bool = False,
|
||||
linlog: bool = False,
|
||||
seed: Seed | None = None,
|
||||
dim: int = 2,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
def rescale_layout(pos, scale: float = 1): ...
|
||||
def rescale_layout_dict(pos, scale: float = 1): ...
|
||||
) -> dict[_Node, Array1D[np.float32]]: ...
|
||||
def rescale_layout(pos: NDArray[np.number[Any]], scale: float = 1) -> Array2D[np.float64]: ... # ignore the bit base
|
||||
def rescale_layout_dict(pos: Mapping[_Node, Collection[float]], scale: float = 1) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
def bfs_layout(
|
||||
G, start, *, align="vertical", scale=1, center=None, store_pos_as: str | None = None
|
||||
) -> dict[Incomplete, Incomplete]: ...
|
||||
G: Graph[_Node],
|
||||
start: _Node,
|
||||
*,
|
||||
align: Literal["vertical", "horizontal"] = "vertical",
|
||||
scale: float = 1,
|
||||
center: _FloatArrayLike1D | None = None,
|
||||
store_pos_as: str | None = None,
|
||||
) -> dict[_Node, Array1D[np.float64]]: ...
|
||||
|
||||
Reference in New Issue
Block a user