Bump networkx to 3.2.1

stubs/networkx/@tests/stubtest_allowlist.txt

@@ -0,0 +1,21 @@
+# overloading a class-decorated method crashes stubtest
+networkx\.(algorithms\.)?(boundary\.)?edge_boundary
+networkx\.(algorithms\.)?(bridges\.)?local_bridges
+networkx\.(algorithms\.)?(clique\.)?node_clique_number
+networkx\.(algorithms\.)?(shortest_paths\.)?(generic\.)?shortest_path
+networkx\.(convert_matrix\.)?from_numpy_array
+networkx\.(convert_matrix\.)?from_pandas_adjacency
+networkx\.(convert_matrix\.)?from_pandas_edgelist
+networkx\.(generators\.)?(random_clustered\.)?random_clustered_graph
+networkx\.(relabel\.)?relabel_nodes
+
+# Stubtest doesn't understand aliases of class-decorated methods
+networkx\.(algorithms\.)?(centrality\.)?(current_flow_closeness\.)?information_centrality
+networkx\.(generators\.)?(random_graphs\.)?binomial_graph
+networkx\.(generators\.)?(random_graphs\.)?erdos_renyi_graph
+
+# Stubtest says: "runtime argument "backend" has a default value of type None, which is
+#   incompatible with stub argument type builtins.str. This is often caused by overloads
+#   failing to account for explicitly passing in the default value."
+# Which is true, but would require some way of concatenating `backend` to ParamSpec.kwargs
+networkx\.(utils\.backends\.)?_dispatch\.__call__

stubs/networkx/@tests/test_cases/check_dispatch_decorator.py

@@ -0,0 +1,23 @@
+from typing_extensions import assert_type
+
+from networkx.utils.backends import _dispatch
+
+
+@_dispatch
+def some_method(int_p: int, str_p: str) -> float:
+    return 0.0
+
+
+# Wrong param / order
+some_method("", 0)  # type: ignore
+# backend is kw-only
+some_method(0, "", None)  # type: ignore
+# No backend means no **backend_kwargs allowed
+some_method(0, "", backend_specific_kwarg="")  # type: ignore
+some_method(0, "", backend=None, backend_specific_kwarg="")  # type: ignore
+
+# Correct usage
+assert_type(some_method(0, ""), float)
+# type system doesn't allow this yet (see comment in networkx/utils/backends.pyi)
+# assert_type(some_method(0, "", backend=None), float)
+assert_type(some_method(0, "", backend="custom backend", backend_specific_kwarg=""), float)

stubs/networkx/METADATA.toml

@@ -1,4 +1,4 @@
-version = "3.1"
+version = "3.2.1"
 upstream_repository = "https://github.com/networkx/networkx"
 requires = ["numpy"]
 partial_stub = true

stubs/networkx/networkx/__init__.pyi

@@ -6,9 +6,11 @@ from networkx.convert_matrix import *
 from networkx.drawing import *
 from networkx.exception import *
 from networkx.generators import *
+from networkx.lazy_imports import _lazy_import as _lazy_import
 from networkx.linalg import *
 from networkx.readwrite import *
 from networkx.relabel import *
+from networkx.utils.backends import _dispatch as _dispatch
 
 from . import (
     algorithms as algorithms,

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

@@ -1,4 +1,10 @@
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def maximum_independent_set(G): ...
+@_dispatch
 def max_clique(G): ...
+@_dispatch
 def clique_removal(G): ...
+@_dispatch
 def large_clique_size(G): ...

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

@@ -1,3 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def average_clustering(G, trials: int = 1000, seed: Incomplete | None = None): ...

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

@@ -1,5 +1,10 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def local_node_connectivity(G, source, target, cutoff: Incomplete | None = None): ...
+@_dispatch
 def node_connectivity(G, s: Incomplete | None = None, t: Incomplete | None = None): ...
+@_dispatch
 def all_pairs_node_connectivity(G, nbunch: Incomplete | None = None, cutoff: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/distance_measures.pyi

@@ -1,3 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def diameter(G, seed: Incomplete | None = None): ...

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

@@ -1,4 +1,8 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def min_weighted_dominating_set(G, weight: Incomplete | None = None): ...
+@_dispatch
 def min_edge_dominating_set(G): ...

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

@@ -1 +1,4 @@
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def k_components(G, min_density: float = 0.95): ...

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

@@ -1 +1,4 @@
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def min_maximal_matching(G): ...

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

@@ -1,4 +1,8 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def randomized_partitioning(G, seed: Incomplete | None = None, p: float = 0.5, weight: Incomplete | None = None): ...
+@_dispatch
 def one_exchange(G, initial_cut: Incomplete | None = None, seed: Incomplete | None = None, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/ramsey.pyi

@@ -1 +1,4 @@
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def ramsey_R2(G): ...

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

@@ -1,4 +1,8 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def metric_closure(G, weight: str = "weight"): ...
+@_dispatch
 def steiner_tree(G, terminal_nodes, weight: str = "weight", method: Incomplete | None = None): ...

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

@@ -1,11 +1,18 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def christofides(G, weight: str = "weight", tree: Incomplete | None = None): ...
+@_dispatch
 def traveling_salesman_problem(
     G, weight: str = "weight", nodes: Incomplete | None = None, cycle: bool = True, method: Incomplete | None = None
 ): ...
+@_dispatch
 def asadpour_atsp(G, weight: str = "weight", seed: Incomplete | None = None, source: Incomplete | None = None): ...
+@_dispatch
 def greedy_tsp(G, weight: str = "weight", source: Incomplete | None = None): ...
+@_dispatch
 def simulated_annealing_tsp(
     G,
     init_cycle,
@@ -19,6 +26,7 @@ def simulated_annealing_tsp(
     alpha: float = 0.01,
     seed: Incomplete | None = None,
 ): ...
+@_dispatch
 def threshold_accepting_tsp(
     G,
     init_cycle,

stubs/networkx/networkx/algorithms/approximation/treewidth.pyi

@@ -1,8 +1,12 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
 __all__ = ["treewidth_min_degree", "treewidth_min_fill_in"]
 
+@_dispatch
 def treewidth_min_degree(G): ...
+@_dispatch
 def treewidth_min_fill_in(G): ...
 
 class MinDegreeHeuristic:

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

@@ -1,3 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def min_weighted_vertex_cover(G, weight: Incomplete | None = None): ...

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

@@ -1,5 +1,8 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def average_degree_connectivity(
     G, source: str = "in+out", target: str = "in+out", nodes: Incomplete | None = None, weight: Incomplete | None = None
 ): ...

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

@@ -1,10 +1,16 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def degree_assortativity_coefficient(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
 ): ...
+@_dispatch
 def degree_pearson_correlation_coefficient(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
 ): ...
+@_dispatch
 def attribute_assortativity_coefficient(G, attribute, nodes: Incomplete | None = None): ...
+@_dispatch
 def numeric_assortativity_coefficient(G, attribute, nodes: Incomplete | None = None): ...

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

@@ -1,12 +1,18 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def attribute_mixing_dict(G, attribute, nodes: Incomplete | None = None, normalized: bool = False): ...
+@_dispatch
 def attribute_mixing_matrix(
     G, attribute, nodes: Incomplete | None = None, mapping: Incomplete | None = None, normalized: bool = True
 ): ...
+@_dispatch
 def degree_mixing_dict(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None, normalized: bool = False
 ): ...
+@_dispatch
 def degree_mixing_matrix(
     G,
     x: str = "out",
@@ -16,4 +22,5 @@ def degree_mixing_matrix(
     normalized: bool = True,
     mapping: Incomplete | None = None,
 ): ...
+@_dispatch
 def mixing_dict(xy, normalized: bool = False): ...

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

@@ -1,5 +1,8 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def average_neighbor_degree(
     G, source: str = "out", target: str = "out", nodes: Incomplete | None = None, weight: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/assortativity/pairs.pyi

@@ -1,7 +1,11 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def node_attribute_xy(G, attribute, nodes: Incomplete | None = None) -> Generator[Incomplete, None, None]: ...
+@_dispatch
 def node_degree_xy(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
 ) -> Generator[Incomplete, None, None]: ...

stubs/networkx/networkx/algorithms/asteroidal.pyi

@@ -1,2 +1,6 @@
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def find_asteroidal_triple(G): ...
+@_dispatch
 def is_at_free(G): ...

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

@@ -1,8 +1,16 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def color(G): ...
+@_dispatch
 def is_bipartite(G): ...
+@_dispatch
 def is_bipartite_node_set(G, nodes): ...
+@_dispatch
 def sets(G, top_nodes: Incomplete | None = None): ...
+@_dispatch
 def density(B, nodes): ...
+@_dispatch
 def degrees(B, nodes, weight: Incomplete | None = None): ...

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

@@ -1,3 +1,8 @@
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def degree_centrality(G, nodes): ...
+@_dispatch
 def betweenness_centrality(G, nodes): ...
+@_dispatch
 def closeness_centrality(G, nodes, normalized: bool = True): ...

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

@@ -1,8 +1,13 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def latapy_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
 
 clustering = latapy_clustering
 
+@_dispatch
 def average_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
+@_dispatch
 def robins_alexander_clustering(G): ...

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

@@ -1,3 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def min_edge_cover(G, matching_algorithm: Incomplete | None = None): ...

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

@@ -1,8 +1,13 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def write_edgelist(G, path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8") -> None: ...
+@_dispatch
 def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[Incomplete, None, None]: ...
+@_dispatch
 def parse_edgelist(
     lines,
     comments: str = "#",
@@ -11,6 +16,7 @@ def parse_edgelist(
     nodetype: Incomplete | None = None,
     data: bool = True,
 ): ...
+@_dispatch
 def read_edgelist(
     path,
     comments: str = "#",

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

@@ -1,10 +1,20 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def complete_bipartite_graph(n1, n2, create_using: Incomplete | None = None): ...
+@_dispatch
 def configuration_model(aseq, bseq, create_using: Incomplete | None = None, seed: Incomplete | None = None): ...
+@_dispatch
 def havel_hakimi_graph(aseq, bseq, create_using: Incomplete | None = None): ...
+@_dispatch
 def reverse_havel_hakimi_graph(aseq, bseq, create_using: Incomplete | None = None): ...
+@_dispatch
 def alternating_havel_hakimi_graph(aseq, bseq, create_using: Incomplete | None = None): ...
+@_dispatch
 def preferential_attachment_graph(aseq, p, create_using: Incomplete | None = None, seed: Incomplete | None = None): ...
+@_dispatch
 def random_graph(n, m, p, seed: Incomplete | None = None, directed: bool = False): ...
+@_dispatch
 def gnmk_random_graph(n, m, k, seed: Incomplete | None = None, directed: bool = False): ...

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

@@ -1,9 +1,15 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def hopcroft_karp_matching(G, top_nodes: Incomplete | None = None): ...
+@_dispatch
 def eppstein_matching(G, top_nodes: Incomplete | None = None): ...
+@_dispatch
 def to_vertex_cover(G, matching, top_nodes: Incomplete | None = None): ...
 
 maximum_matching = hopcroft_karp_matching
 
+@_dispatch
 def minimum_weight_full_matching(G, top_nodes: Incomplete | None = None, weight: str = "weight"): ...

stubs/networkx/networkx/algorithms/bipartite/matrix.pyi

@@ -1,5 +1,8 @@
 from _typeshed import Incomplete
 
+from networkx.utils.backends import _dispatch
+
+@_dispatch
 def biadjacency_matrix(
     G,
     row_order,
@@ -8,4 +11,5 @@ def biadjacency_matrix(
     weight: str = "weight",
     format: str = "csr",
 ): ...
+@_dispatch
 def from_biadjacency_matrix(A, create_using: Incomplete | None = None, edge_attribute: str = "weight"): ...