Merge networkx from python-type-stubs

stubs/networkx/networkx/algorithms/approximation/clique.pyi

@@ -1,12 +1,14 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["clique_removal", "max_clique", "large_clique_size", "maximum_independent_set"]
 
 @_dispatchable
-def maximum_independent_set(G: Graph[_Node]): ...
+def maximum_independent_set(G: Graph[_Node]) -> set[Incomplete]: ...
 @_dispatchable
-def max_clique(G: Graph[_Node]): ...
+def max_clique(G: Graph[_Node]) -> set[Incomplete]: ...
 @_dispatchable
 def clique_removal(G: Graph[_Node]): ...
 @_dispatchable

stubs/networkx/networkx/algorithms/approximation/clustering_coefficient.pyi

@@ -5,4 +5,4 @@ from numpy.random import RandomState
 __all__ = ["average_clustering"]
 
 @_dispatchable
-def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None): ...
+def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None) -> float: ...

stubs/networkx/networkx/algorithms/approximation/connectivity.pyi

@@ -11,4 +11,6 @@ def local_node_connectivity(G: Graph[_Node], source: _Node, target: _Node, cutof
 @_dispatchable
 def node_connectivity(G: Graph[_Node], s: _Node | None = None, t: _Node | None = None): ...
 @_dispatchable
-def all_pairs_node_connectivity(G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None): ...
+def all_pairs_node_connectivity(
+    G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None
+) -> dict[Incomplete, dict[Incomplete, Incomplete]]: ...

stubs/networkx/networkx/algorithms/approximation/dominating_set.pyi

@@ -1,9 +1,11 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"]
 
 @_dispatchable
-def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None): ...
+def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None) -> set[Incomplete]: ...
 @_dispatchable
-def min_edge_dominating_set(G: Graph[_Node]): ...
+def min_edge_dominating_set(G: Graph[_Node]) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/approximation/kcomponents.pyi

@@ -1,7 +1,10 @@
+from _typeshed import Incomplete
+from collections import defaultdict
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["k_components"]
 
 @_dispatchable
-def k_components(G: Graph[_Node], min_density: float = 0.95): ...
+def k_components(G: Graph[_Node], min_density: float = 0.95) -> defaultdict[Incomplete, list[Incomplete]]: ...

stubs/networkx/networkx/algorithms/approximation/matching.pyi

@@ -1,7 +1,9 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["min_maximal_matching"]
 
 @_dispatchable
-def min_maximal_matching(G: Graph[_Node]): ...
+def min_maximal_matching(G: Graph[_Node]) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/approximation/maxcut.pyi

@@ -1,4 +1,5 @@
 from _typeshed import Incomplete
+from collections.abc import Iterable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -12,5 +13,8 @@ def randomized_partitioning(
 ): ...
 @_dispatchable
 def one_exchange(
-    G: Graph[_Node], initial_cut: set[Incomplete] | None = None, seed: int | RandomState | None = None, weight: str | None = None
+    G: Graph[_Node],
+    initial_cut: Iterable[Incomplete] | None = None,
+    seed: int | RandomState | None = None,
+    weight: str | None = None,
 ): ...

stubs/networkx/networkx/algorithms/approximation/traveling_salesman.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete, SupportsLenAndGetItem
-from collections.abc import Callable, Mapping
-from typing import Any, TypeVar
+from collections.abc import Callable, Iterable, Mapping
+from typing import Any, Literal, TypeVar
 
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
@@ -15,13 +15,12 @@ __all__ = [
     "simulated_annealing_tsp",
     "threshold_accepting_tsp",
 ]
-
 _SupportsLenAndGetItemT = TypeVar("_SupportsLenAndGetItemT", bound=SupportsLenAndGetItem[Any])
 
 def swap_two_nodes(soln: _SupportsLenAndGetItemT, seed) -> _SupportsLenAndGetItemT: ...
 def move_one_node(soln: _SupportsLenAndGetItemT, seed) -> _SupportsLenAndGetItemT: ...
 @_dispatchable
-def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None): ...
+def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None) -> list[Incomplete]: ...
 @_dispatchable
 def traveling_salesman_problem(
     G: Graph[_Node],
@@ -30,7 +29,7 @@ def traveling_salesman_problem(
     cycle: bool = True,
     method: Callable[..., Incomplete] | None = None,
     **kwargs,
-): ...
+) -> list[Incomplete]: ...
 @_dispatchable
 def asadpour_atsp(
     G: DiGraph[_Node], weight: str | None = "weight", seed: int | RandomState | None = None, source: str | None = None
@@ -57,7 +56,7 @@ def simulated_annealing_tsp(
 @_dispatchable
 def threshold_accepting_tsp(
     G: Graph[_Node],
-    init_cycle,
+    init_cycle: Literal["greedy"] | Iterable[Incomplete],
     weight: str | None = "weight",
     source=None,
     threshold: int | None = 1,

stubs/networkx/networkx/algorithms/approximation/vertex_cover.pyi

@@ -1,7 +1,9 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["min_weighted_vertex_cover"]
 
 @_dispatchable
-def min_weighted_vertex_cover(G: Graph[_Node], weight: str | None = None): ...
+def min_weighted_vertex_cover(G: Graph[_Node], weight: str | None = None) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/assortativity/connectivity.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 from collections.abc import Iterable
+from typing import Literal
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -8,5 +9,9 @@ __all__ = ["average_degree_connectivity"]
 
 @_dispatchable
 def average_degree_connectivity(
-    G: Graph[_Node], source="in+out", target="in+out", nodes: Iterable[Incomplete] | None = None, weight: str | None = None
-): ...
+    G: Graph[_Node],
+    source: Literal["in+out", "out", "in"] = "in+out",
+    target: Literal["in+out", "out", "in"] = "in+out",
+    nodes: Iterable[Incomplete] | None = None,
+    weight: str | None = None,
+) -> dict[Incomplete, int | float]: ...

stubs/networkx/networkx/algorithms/assortativity/correlation.pyi

@@ -14,12 +14,13 @@ __all__ = [
 @_dispatchable
 def degree_assortativity_coefficient(
     G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
-): ...
+) -> float: ...
 @_dispatchable
 def degree_pearson_correlation_coefficient(
     G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
-): ...
+) -> float: ...
 @_dispatchable
-def attribute_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None): ...
+def attribute_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None) -> float: ...
 @_dispatchable
-def numeric_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None): ...
+def numeric_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None) -> float: ...
+def attribute_ac(M) -> float: ...

stubs/networkx/networkx/algorithms/assortativity/mixing.pyi

@@ -9,7 +9,7 @@ __all__ = ["attribute_mixing_matrix", "attribute_mixing_dict", "degree_mixing_ma
 @_dispatchable
 def attribute_mixing_dict(
     G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None, normalized: bool = False
-): ...
+) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def attribute_mixing_matrix(
     G: Graph[_Node],
@@ -21,7 +21,7 @@ def attribute_mixing_matrix(
 @_dispatchable
 def degree_mixing_dict(
     G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes=None, normalized: bool = False
-): ...
+) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def degree_mixing_matrix(
     G: Graph[_Node],
@@ -33,4 +33,4 @@ def degree_mixing_matrix(
     mapping: SupportsGetItem[Incomplete, Incomplete] | None = None,
 ): ...
 @_dispatchable
-def mixing_dict(xy, normalized: bool = False): ...
+def mixing_dict(xy, normalized: bool = False) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/assortativity/neighbor_degree.pyi

@@ -13,4 +13,4 @@ def average_neighbor_degree(
     target: str | None = "out",
     nodes: Iterable[Incomplete] | None = None,
     weight: str | None = None,
-): ...
+) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/asteroidal.pyi

@@ -6,7 +6,7 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["is_at_free", "find_asteroidal_triple"]
 
 @_dispatchable
-def find_asteroidal_triple(G: Graph[_Node]): ...
+def find_asteroidal_triple(G: Graph[_Node]) -> list[Incomplete] | None: ...
 @_dispatchable
 def is_at_free(G: Graph[_Node]) -> bool: ...
 @_dispatchable

stubs/networkx/networkx/algorithms/bipartite/basic.pyi

@@ -7,14 +7,14 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["is_bipartite", "is_bipartite_node_set", "color", "sets", "density", "degrees"]
 
 @_dispatchable
-def color(G: Graph[_Node]): ...
+def color(G: Graph[_Node]) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def is_bipartite(G: Graph[_Node]) -> bool: ...
 @_dispatchable
 def is_bipartite_node_set(G: Graph[_Node], nodes: Iterable[Incomplete]) -> bool: ...
 @_dispatchable
-def sets(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None): ...
+def sets(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None) -> tuple[set[Incomplete], set[Incomplete]]: ...
 @_dispatchable
-def density(B: Graph[_Node], nodes): ...
+def density(B: Graph[_Node], nodes) -> float: ...
 @_dispatchable
-def degrees(B: Graph[_Node], nodes, weight: str | None = None): ...
+def degrees(B: Graph[_Node], nodes, weight: str | None = None) -> tuple[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/bipartite/centrality.pyi

@@ -1,11 +1,13 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["degree_centrality", "betweenness_centrality", "closeness_centrality"]
 
 @_dispatchable
-def degree_centrality(G: Graph[_Node], nodes): ...
+def degree_centrality(G: Graph[_Node], nodes) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def betweenness_centrality(G: Graph[_Node], nodes): ...
+def betweenness_centrality(G: Graph[_Node], nodes) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def closeness_centrality(G: Graph[_Node], nodes, normalized: bool | None = True): ...
+def closeness_centrality(G: Graph[_Node], nodes, normalized: bool | None = True) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/bipartite/cluster.pyi

@@ -13,11 +13,13 @@ def cc_min(nu, nv) -> float: ...
 modes: dict[str, Callable[[Incomplete, Incomplete], float]]
 
 @_dispatchable
-def latapy_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...
+def latapy_clustering(
+    G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"
+) -> dict[Incomplete, Incomplete]: ...
 
 clustering = latapy_clustering
 
 @_dispatchable
-def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...
+def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot") -> float: ...
 @_dispatchable
-def robins_alexander_clustering(G: Graph[_Node]): ...
+def robins_alexander_clustering(G: Graph[_Node]) -> float: ...

stubs/networkx/networkx/algorithms/bipartite/covering.pyi

@@ -7,4 +7,4 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["min_edge_cover"]
 
 @_dispatchable
-def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None): ...
+def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/bipartite/edgelist.pyi

@@ -1,23 +1,32 @@
 from _typeshed import Incomplete
-from collections.abc import Generator
+from collections.abc import Generator, Iterable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["generate_edgelist", "write_edgelist", "parse_edgelist", "read_edgelist"]
 
 @_dispatchable
-def write_edgelist(G, path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8") -> None: ...
+def write_edgelist(
+    G: Graph[_Node],
+    path,
+    comments: str = "#",
+    delimiter: str = " ",
+    data: bool | Iterable[Incomplete] = True,
+    encoding: str = "utf-8",
+) -> None: ...
 @_dispatchable
-def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[Incomplete, None, None]: ...
+def generate_edgelist(
+    G: Graph[_Node], delimiter: str = " ", data: bool | Iterable[Incomplete] = True
+) -> Generator[str, None, None]: ...
 @_dispatchable
 def parse_edgelist(
     lines,
     comments: str | None = "#",
     delimiter: str | None = None,
     create_using: Graph[_Node] | None = None,
     nodetype=None,
-    data=True,
+    data: bool | Iterable[Incomplete] = True,
 ): ...
 @_dispatchable
 def read_edgelist(
@@ -26,7 +35,7 @@ def read_edgelist(
     delimiter: str | None = None,
     create_using=None,
     nodetype=None,
-    data=True,
+    data: bool | Iterable[Incomplete] = True,
     edgetype=None,
     encoding: str | None = "utf-8",
 ): ...

stubs/networkx/networkx/algorithms/bipartite/generators.pyi

@@ -1,5 +1,5 @@
 from _typeshed import Incomplete
-from collections.abc import Iterable
+from collections.abc import Collection
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -20,24 +20,24 @@ __all__ = [
 def complete_bipartite_graph(n1, n2, create_using: Graph[_Node] | None = None): ...
 @_dispatchable
 def configuration_model(
-    aseq: Iterable[Incomplete],
-    bseq: Iterable[Incomplete],
+    aseq: Collection[Incomplete],
+    bseq: Collection[Incomplete],
     create_using: Graph[_Node] | None = None,
     seed: int | RandomState | None = None,
 ): ...
 @_dispatchable
-def havel_hakimi_graph(aseq: Iterable[Incomplete], bseq: Iterable[Incomplete], create_using: Graph[_Node] | None = None): ...
+def havel_hakimi_graph(aseq: Collection[Incomplete], bseq: Collection[Incomplete], create_using: Graph[_Node] | None = None): ...
 @_dispatchable
 def reverse_havel_hakimi_graph(
-    aseq: Iterable[Incomplete], bseq: Iterable[Incomplete], create_using: Graph[_Node] | None = None
+    aseq: Collection[Incomplete], bseq: Collection[Incomplete], create_using: Graph[_Node] | None = None
 ): ...
 @_dispatchable
 def alternating_havel_hakimi_graph(
-    aseq: Iterable[Incomplete], bseq: Iterable[Incomplete], create_using: Graph[_Node] | None = None
+    aseq: Collection[Incomplete], bseq: Collection[Incomplete], create_using: Graph[_Node] | None = None
 ): ...
 @_dispatchable
 def preferential_attachment_graph(
-    aseq: Iterable[Incomplete], p: float, create_using: Graph[_Node] | None = None, seed: int | RandomState | None = None
+    aseq: Collection[Incomplete], p: float, create_using: Graph[_Node] | None = None, seed: int | RandomState | None = None
 ): ...
 @_dispatchable
 def random_graph(n: int, m: int, p: float, seed: int | RandomState | None = None, directed: bool | None = False): ...

stubs/networkx/networkx/algorithms/bipartite/matching.pyi

@@ -1,4 +1,4 @@
-from _typeshed import Incomplete, SupportsGetItem
+from _typeshed import Incomplete
 from collections.abc import Iterable
 
 from networkx.classes.graph import Graph, _Node
@@ -7,17 +7,15 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["maximum_matching", "hopcroft_karp_matching", "eppstein_matching", "to_vertex_cover", "minimum_weight_full_matching"]
 
 @_dispatchable
-def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None = None): ...
+def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None = None) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def eppstein_matching(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None): ...
+def eppstein_matching(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def to_vertex_cover(
-    G: Graph[_Node], matching: SupportsGetItem[Incomplete, Incomplete], top_nodes: Iterable[Incomplete] | None = None
-): ...
+def to_vertex_cover(G: Graph[_Node], matching: Iterable[Incomplete], top_nodes: Iterable[Incomplete] | None = None): ...
 
 maximum_matching = hopcroft_karp_matching
 
 @_dispatchable
 def minimum_weight_full_matching(
     G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None, weight: str | None = "weight"
-): ...
+) -> dict[Incomplete, Incomplete]: ...