Merge networkx from python-type-stubs

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

@@ -30,7 +30,7 @@ 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

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

@@ -23,3 +23,4 @@ def degree_pearson_correlation_coefficient(
 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) -> float: ...
+def attribute_ac(M) -> float: ...

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

@@ -1,4 +1,5 @@
-from collections.abc import Generator
+from _typeshed import Incomplete
+from collections.abc import Generator, Iterable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -7,18 +8,23 @@ __all__ = ["generate_edgelist", "write_edgelist", "parse_edgelist", "read_edgeli
 
 @_dispatchable
 def write_edgelist(
-    G: Graph[_Node], path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8"
+    G: Graph[_Node],
+    path,
+    comments: str = "#",
+    delimiter: str = " ",
+    data: bool | Iterable[Incomplete] = True,
+    encoding: str = "utf-8",
 ) -> None: ...
 @_dispatchable
-def generate_edgelist(G: Graph[_Node], delimiter: str = " ", data: bool = True) -> Generator[str]: ...
+def generate_edgelist(G: Graph[_Node], delimiter: str = " ", data: bool | Iterable[Incomplete] = True) -> Generator[str]: ...
 @_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(
@@ -27,7 +33,7 @@ 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
@@ -11,9 +11,7 @@ def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None =
 @_dispatchable
 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
 

stubs/networkx/networkx/algorithms/bipartite/matrix.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
@@ -9,8 +9,8 @@ __all__ = ["biadjacency_matrix", "from_biadjacency_matrix"]
 @_dispatchable
 def biadjacency_matrix(
     G: Graph[_Node],
-    row_order: Iterable[_Node],
-    column_order: Iterable[Incomplete] | None = None,
+    row_order: Collection[_Node],
+    column_order: Collection[Incomplete] | None = None,
     dtype=None,
     weight: str | None = "weight",
     format="csr",

stubs/networkx/networkx/algorithms/bipartite/projection.pyi

@@ -1,5 +1,5 @@
 from _typeshed import Incomplete
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Collection, Iterable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -15,7 +15,7 @@ __all__ = [
 @_dispatchable
 def projected_graph(B: Graph[_Node], nodes: Iterable[Incomplete], multigraph: bool = False): ...
 @_dispatchable
-def weighted_projected_graph(B: Graph[_Node], nodes: Iterable[Incomplete], ratio: bool = False): ...
+def weighted_projected_graph(B: Graph[_Node], nodes: Collection[Incomplete], ratio: bool = False): ...
 @_dispatchable
 def collaboration_weighted_projected_graph(B: Graph[_Node], nodes: Iterable[Incomplete]): ...
 @_dispatchable

stubs/networkx/networkx/algorithms/boundary.pyi

@@ -13,7 +13,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node], None, None]: ...
@@ -22,7 +22,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node, dict[str, Incomplete]], None, None]: ...
@@ -31,7 +31,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node, dict[str, Incomplete]], None, None]: ...
@@ -40,7 +40,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default: _U | None = None,
 ) -> Generator[tuple[_Node, _Node, dict[str, _U]], None, None]: ...
@@ -49,7 +49,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default: _U | None = None,
 ) -> Generator[tuple[_Node, _Node, dict[str, _U]], None, None]: ...
@@ -58,7 +58,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node, int], None, None]: ...
@@ -67,7 +67,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node, int], None, None]: ...
@@ -76,7 +76,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node, int, dict[str, Incomplete]], None, None]: ...
@@ -85,7 +85,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default=None,
 ) -> Generator[tuple[_Node, _Node, int, dict[str, Incomplete]], None, None]: ...
@@ -94,7 +94,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default: _U | None = None,
 ) -> Generator[tuple[_Node, _Node, int, dict[str, _U]], None, None]: ...
@@ -103,7 +103,7 @@ def edge_boundary(
     G: Graph[_Node],
     nbunch1: Iterable[Incomplete],
     nbunch2: Iterable[Incomplete] | None = None,
-    data=False,
+    data: bool | Incomplete = False,
     keys: bool = False,
     default: _U | None = None,
 ) -> Generator[tuple[_Node, _Node, int, dict[str, _U]], None, None]: ...

stubs/networkx/networkx/algorithms/centrality/closeness.pyi

@@ -1,4 +1,5 @@
-from _typeshed import Incomplete, SupportsGetItem
+from _typeshed import Incomplete
+from collections.abc import Mapping
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -13,7 +14,7 @@ def closeness_centrality(
 def incremental_closeness_centrality(
     G: Graph[_Node],
     edge: tuple[Incomplete],
-    prev_cc: SupportsGetItem[Incomplete, Incomplete] | None = None,
+    prev_cc: Mapping[Incomplete, Incomplete] | None = None,
     insertion: bool | None = True,
     wf_improved: bool | None = True,
 ) -> dict[_Node, float]: ...

stubs/networkx/networkx/algorithms/centrality/eigenvector.pyi

@@ -1,4 +1,5 @@
-from _typeshed import Incomplete, SupportsGetItem
+from _typeshed import Incomplete
+from collections.abc import Mapping
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -10,7 +11,7 @@ def eigenvector_centrality(
     G: Graph[_Node],
     max_iter: int | None = 100,
     tol: float | None = 1e-06,
-    nstart: SupportsGetItem[Incomplete, Incomplete] | None = None,
+    nstart: Mapping[Incomplete, Incomplete] | None = None,
     weight: str | None = None,
 ) -> dict[Incomplete, float]: ...
 @_dispatchable

stubs/networkx/networkx/algorithms/centrality/katz.pyi

@@ -1,4 +1,5 @@
 from _typeshed import Incomplete, SupportsGetItem
+from collections.abc import Iterable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -12,7 +13,7 @@ def katz_centrality(
     beta: float | SupportsGetItem[Incomplete, Incomplete] | None = 1.0,
     max_iter: int | None = 1000,
     tol: float | None = 1e-06,
-    nstart: SupportsGetItem[Incomplete, Incomplete] | None = None,
+    nstart: Iterable[Incomplete] | None = None,
     normalized: bool | None = True,
     weight: str | None = None,
 ) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/centrality/percolation.pyi

@@ -1,4 +1,5 @@
-from _typeshed import Incomplete, SupportsGetItem
+from _typeshed import Incomplete
+from collections.abc import Mapping
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -9,6 +10,6 @@ __all__ = ["percolation_centrality"]
 def percolation_centrality(
     G: Graph[_Node],
     attribute: str | None = "percolation",
-    states: SupportsGetItem[Incomplete, Incomplete] | None = None,
+    states: Mapping[Incomplete, Incomplete] | None = None,
     weight: str | None = None,
 ) -> dict[Incomplete, float]: ...

stubs/networkx/networkx/algorithms/clique.pyi

@@ -29,11 +29,13 @@ def make_clique_bipartite(
     G: Graph[_Node], fpos: bool | None = None, create_using: Graph[_Node] | None = None, name=None
 ) -> Graph[_Node]: ...
 @overload
-def node_clique_number(
-    G: Graph[_Node], nodes=None, cliques: Iterable[Incomplete] | None = None, separate_nodes=False
+def node_clique_number(  # type: ignore[overload-overlap]
+    G: Graph[_Node], nodes: Iterable[_Node] | None = None, cliques: Iterable[Incomplete] | None = None, separate_nodes=False
 ) -> dict[_Node, int]: ...
 @overload
-def node_clique_number(G: Graph[_Node], nodes=None, cliques: Iterable[Incomplete] | None = None, separate_nodes=False) -> int: ...
+def node_clique_number(
+    G: Graph[_Node], nodes: _Node, cliques: Iterable[Incomplete] | None = None, separate_nodes=False
+) -> int: ...
 def number_of_cliques(G: Graph[_Node], nodes=None, cliques=None) -> int | dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def max_weight_clique(G: Graph[_Node], weight="weight") -> tuple[list[Incomplete], int]: ...

stubs/networkx/networkx/algorithms/community/kernighan_lin.pyi

@@ -9,7 +9,7 @@ __all__ = ["kernighan_lin_bisection"]
 @_dispatchable
 def kernighan_lin_bisection(
     G: Graph[_Node],
-    partition: tuple[Incomplete] | None = None,
+    partition: tuple[Incomplete, Incomplete] | None = None,
     max_iter: int = 10,
     weight: str = "weight",
     seed: int | RandomState | None = None,

stubs/networkx/networkx/algorithms/flow/mincost.pyi

@@ -12,10 +12,10 @@ def min_cost_flow_cost(
 @_dispatchable
 def min_cost_flow(
     G: Graph[_Node], demand: str = "demand", capacity: str = "capacity", weight: str = "weight"
-) -> dict[Incomplete, dict[Incomplete, Incomplete]]: ...
+) -> tuple[int | Incomplete, dict[Incomplete, dict[Incomplete, Incomplete]]]: ...
 @_dispatchable
 def cost_of_flow(G: Graph[_Node], flowDict: SupportsGetItem[Incomplete, Incomplete], weight: str = "weight") -> int | float: ...
 @_dispatchable
 def max_flow_min_cost(
     G: Graph[_Node], s: str, t: str, capacity: str = "capacity", weight: str = "weight"
-) -> dict[Incomplete, dict[Incomplete, Incomplete]]: ...
+) -> tuple[int | Incomplete, dict[Incomplete, dict[Incomplete, Incomplete]]]: ...

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

@@ -1,6 +1,8 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
+from ...classes.graph import Graph, _Node
+
 __all__ = ["ISMAGS"]
 
 class ISMAGS:
@@ -9,10 +11,10 @@ class ISMAGS:
     node_equality: Incomplete
     edge_equality: Incomplete
 
-    def __init__(self, graph, subgraph, node_match=None, edge_match=None, cache=None) -> None: ...
+    def __init__(self, graph: Graph[_Node], subgraph: Graph[_Node], node_match=None, edge_match=None, cache=None) -> None: ...
     def find_isomorphisms(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, Incomplete]: ...
     def largest_common_subgraph(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, None]: ...
-    def analyze_symmetry(self, graph, node_partitions, edge_colors): ...
+    def analyze_symmetry(self, graph: Graph[_Node], node_partitions, edge_colors): ...
     def is_isomorphic(self, symmetry: bool = False) -> bool: ...
     def subgraph_is_isomorphic(self, symmetry: bool = False) -> bool: ...
     def isomorphisms_iter(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, None]: ...