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donbarbosdonbarbos
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[networkx] Update to 3.6.*
Closes: python#15082 Diff: https://github.com/networkx/networkx/compare/networkx-3.5..networkx-3.6
1 parent 1218f18 commit eea6bcd

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-64
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‎stubs/networkx/METADATA.toml‎

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version = "3.5"
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version = "3.6"
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upstream_repository = "https://github.com/networkx/networkx"
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# requires a version of numpy with a `py.typed` file
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requires = ["numpy>=1.20"]

‎stubs/networkx/networkx/algorithms/__init__.pyi‎

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@@ -102,6 +102,7 @@ from networkx.algorithms.mis import *
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from networkx.algorithms.moral import *
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from networkx.algorithms.non_randomness import *
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from networkx.algorithms.operators import *
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from networkx.algorithms.perfect_graph import *
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from networkx.algorithms.planar_drawing import *
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from networkx.algorithms.planarity import *
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from networkx.algorithms.polynomials import *

‎stubs/networkx/networkx/algorithms/approximation/steinertree.pyi‎

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from _typeshed import Incomplete
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from collections.abc import Iterable
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from typing_extensions import deprecated
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from networkx.classes.graph import Graph, _Node
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from networkx.utils.backends import _dispatchable
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__all__ = ["metric_closure", "steiner_tree"]
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@_dispatchable
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@deprecated(
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"`metric_closure` is deprecated and will be removed in NetworkX 3.8. Use `networkx.all_pairs_shortest_path_length` instead."
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)
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def metric_closure(G: Graph[_Node], weight="weight"): ...
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@_dispatchable
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def steiner_tree(G: Graph[_Node], terminal_nodes: Iterable[Incomplete], weight: str = "weight", method: str | None = None): ...

‎stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness.pyi‎

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@@ -20,6 +20,8 @@ def approximate_current_flow_betweenness_centrality(
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epsilon: float = 0.5,
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kmax: int = 10000,
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seed: int | RandomState | None = None,
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*,
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sample_weight: float = 1,
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) -> dict[Incomplete, float]: ...
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@_dispatchable
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def current_flow_betweenness_centrality(

‎stubs/networkx/networkx/algorithms/centrality/subgraph_alg.pyi‎

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@@ -6,9 +6,9 @@ from networkx.utils.backends import _dispatchable
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__all__ = ["subgraph_centrality_exp", "subgraph_centrality", "communicability_betweenness_centrality", "estrada_index"]
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@_dispatchable
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def subgraph_centrality_exp(G: Graph[_Node]) -> dict[Incomplete, float]: ...
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def subgraph_centrality_exp(G: Graph[_Node], *, normalized: bool = False) -> dict[Incomplete, float]: ...
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@_dispatchable
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def subgraph_centrality(G: Graph[_Node]) -> dict[Incomplete, float]: ...
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def subgraph_centrality(G: Graph[_Node], *, normalized: bool = False) -> dict[Incomplete, float]: ...
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@_dispatchable
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def communicability_betweenness_centrality(G: Graph[_Node]) -> dict[Incomplete, Incomplete]: ...
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@_dispatchable

‎stubs/networkx/networkx/algorithms/cluster.pyi‎

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from _typeshed import Incomplete
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from collections.abc import Iterable
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from collections.abc import Generator, Iterable
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from networkx.classes.graph import Graph, _Node
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from networkx.classes.graph import Graph, _NBunch, _Node
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from networkx.utils.backends import _dispatchable
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__all__ = ["triangles", "average_clustering", "clustering", "transitivity", "square_clustering", "generalized_degree"]
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__all__ = [
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"triangles",
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"all_triangles",
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"average_clustering",
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"clustering",
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"transitivity",
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"square_clustering",
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"generalized_degree",
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]
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@_dispatchable
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def triangles(G: Graph[_Node], nodes=None) -> int | dict[Incomplete, int]: ...
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@_dispatchable
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def all_triangles(G: Graph[_Node], nbunch: _NBunch[_Node] = None) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
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@_dispatchable
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def average_clustering(
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G: Graph[_Node], nodes: Iterable[_Node] | None = None, weight: str | None = None, count_zeros: bool = True
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) -> float: ...

‎stubs/networkx/networkx/algorithms/dag.pyi‎

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@@ -21,7 +21,6 @@ __all__ = [
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"dag_longest_path",
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"dag_longest_path_length",
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"dag_to_branching",
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"compute_v_structures",
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]
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@_dispatchable
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def dag_longest_path_length(G: DiGraph[_Node], weight: str | None = "weight", default_weight: int | None = 1) -> int: ...
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@_dispatchable
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def dag_to_branching(G: DiGraph[_Node]) -> DiGraph[_Node]: ...
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@_dispatchable
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def compute_v_structures(G: DiGraph[_Node]) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...

‎stubs/networkx/networkx/algorithms/distance_regular.pyi‎

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@@ -9,8 +9,8 @@ __all__ = ["is_distance_regular", "is_strongly_regular", "intersection_array", "
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@_dispatchable
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def is_distance_regular(G: Graph[_Node]) -> bool: ...
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@_dispatchable
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def global_parameters(b, c) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
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def global_parameters(b: list[Incomplete], c: list[Incomplete]) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
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@_dispatchable
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def intersection_array(G: Graph[_Node]): ...
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def intersection_array(G: Graph[_Node]) -> tuple[list[Incomplete], list[Incomplete]]: ...
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@_dispatchable
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def is_strongly_regular(G: Graph[_Node]) -> bool: ...

‎stubs/networkx/networkx/algorithms/isomorphism/ismags.pyi‎

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from _typeshed import Incomplete
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from collections.abc import Generator
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from collections.abc import Callable, Generator, Hashable, Iterable
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from typing import Any
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__all__ = ["ISMAGS"]
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def are_all_equal(iterable: Iterable[Any]) -> bool: ...
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def make_partition(
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items: Iterable[Hashable], test: Callable[[Hashable, Hashable], bool], check: bool = True
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) -> list[set[Incomplete]]: ...
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def node_to_part_ID_dict(partition: Iterable[Iterable[Incomplete]]) -> dict[Incomplete, int]: ...
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def color_degree_by_node(G, n_colors, e_colors): ...
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class EdgeLookup:
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edge_dict: Incomplete
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def __init__(self, edge_dict) -> None: ...
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def __getitem__(self, edge): ...
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def items(self): ...
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class ISMAGS:
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graph: Incomplete
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subgraph: Incomplete
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node_equality: Incomplete
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edge_equality: Incomplete
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def __init__(self, graph, subgraph, node_match=None, edge_match=None, cache=None) -> None: ...
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def create_aligned_partitions(self, thing_matcher, sg_things, g_things): ...
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def find_isomorphisms(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, Incomplete]: ...
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def largest_common_subgraph(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, None]: ...
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def analyze_symmetry(self, graph, node_partitions, edge_colors): ...
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def analyze_subgraph_symmetry(self) -> dict[Hashable, set[Hashable]]: ...
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def is_isomorphic(self, symmetry: bool = False) -> bool: ...
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def subgraph_is_isomorphic(self, symmetry: bool = False) -> bool: ...
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def isomorphisms_iter(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, None]: ...

‎stubs/networkx/networkx/algorithms/non_randomness.pyi‎

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@@ -4,4 +4,4 @@ from networkx.utils.backends import _dispatchable
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__all__ = ["non_randomness"]
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@_dispatchable
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def non_randomness(G: Graph[_Node], k: int | None = None, weight: str | None = "weight"): ...
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def non_randomness(G: Graph[_Node], k: int | None = None, weight: str | None = "weight") -> tuple[float, float]: ...

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