Extract NetworkX types from docstrings (#13458)

Author: darabos

stubs/networkx/networkx/algorithms/__init__.pyi

@@ -30,6 +30,7 @@ from networkx.algorithms.bipartite import (
 )
 from networkx.algorithms.boundary import *
 from networkx.algorithms.bridges import *
+from networkx.algorithms.broadcasting import *
 from networkx.algorithms.centrality import *
 from networkx.algorithms.chains import *
 from networkx.algorithms.chordal import *
@@ -116,6 +117,7 @@ from networkx.algorithms.sparsifiers import *
 from networkx.algorithms.structuralholes import *
 from networkx.algorithms.summarization import *
 from networkx.algorithms.swap import *
+from networkx.algorithms.time_dependent import *
 from networkx.algorithms.traversal import *
 from networkx.algorithms.tree.branchings import (
     ArborescenceIterator as ArborescenceIterator,
@@ -132,4 +134,5 @@ from networkx.algorithms.tree.recognition import *
 from networkx.algorithms.triads import *
 from networkx.algorithms.vitality import *
 from networkx.algorithms.voronoi import *
+from networkx.algorithms.walks import *
 from networkx.algorithms.wiener import *

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

@@ -1,10 +1,11 @@
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def maximum_independent_set(G): ...
+def maximum_independent_set(G: Graph[_Node]): ...
 @_dispatchable
-def max_clique(G): ...
+def max_clique(G: Graph[_Node]): ...
 @_dispatchable
-def clique_removal(G): ...
+def clique_removal(G: Graph[_Node]): ...
 @_dispatchable
-def large_clique_size(G): ...
+def large_clique_size(G: Graph[_Node]): ...

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

@@ -1,6 +1,6 @@
-from _typeshed import Incomplete
-
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
+from numpy.random import RandomState
 
 @_dispatchable
-def average_clustering(G, trials: int = 1000, seed: Incomplete | None = None): ...
+def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None): ...

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

@@ -1,10 +1,12 @@
 from _typeshed import Incomplete
+from collections.abc import Iterable
 
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def local_node_connectivity(G, source, target, cutoff: Incomplete | None = None): ...
+def local_node_connectivity(G: Graph[_Node], source: _Node, target: _Node, cutoff: int | None = None): ...
 @_dispatchable
-def node_connectivity(G, s: Incomplete | None = None, t: Incomplete | None = None): ...
+def node_connectivity(G: Graph[_Node], s: _Node | None = None, t: _Node | None = None): ...
 @_dispatchable
-def all_pairs_node_connectivity(G, nbunch: Incomplete | None = None, cutoff: Incomplete | None = None): ...
+def all_pairs_node_connectivity(G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None): ...

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

@@ -1,6 +1,6 @@
-from _typeshed import Incomplete
-
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
+from numpy.random import RandomState
 
 @_dispatchable
-def diameter(G, seed: Incomplete | None = None): ...
+def diameter(G: Graph[_Node], seed: int | RandomState | None = None): ...

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

@@ -1,8 +1,7 @@
-from _typeshed import Incomplete
-
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def min_weighted_dominating_set(G, weight: Incomplete | None = None): ...
+def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None): ...
 @_dispatchable
-def min_edge_dominating_set(G): ...
+def min_edge_dominating_set(G: Graph[_Node]): ...

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

@@ -1,4 +1,5 @@
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def k_components(G, min_density: float = 0.95): ...
+def k_components(G: Graph[_Node], min_density: float = 0.95): ...

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

@@ -1,4 +1,5 @@
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def min_maximal_matching(G): ...
+def min_maximal_matching(G: Graph[_Node]): ...

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

@@ -1,8 +1,14 @@
 from _typeshed import Incomplete
 
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
+from numpy.random import RandomState
 
 @_dispatchable
-def randomized_partitioning(G, seed: Incomplete | None = None, p: float = 0.5, weight: Incomplete | None = None): ...
+def randomized_partitioning(
+    G: Graph[_Node], seed: int | RandomState | None = None, p: float = 0.5, weight: str | None = None
+): ...
 @_dispatchable
-def one_exchange(G, initial_cut: Incomplete | None = None, seed: Incomplete | None = None, weight: Incomplete | None = None): ...
+def one_exchange(
+    G: Graph[_Node], initial_cut: set[Incomplete] | None = None, seed: int | RandomState | None = None, weight: str | None = None
+): ...

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

@@ -1,4 +1,5 @@
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def ramsey_R2(G): ...
+def ramsey_R2(G: Graph[_Node]): ...