networkx: Add symbols from python-type-stubs (#14084)

Author: Avasam

stubs/networkx/@tests/stubtest_allowlist.txt

@@ -40,7 +40,4 @@ networkx(\.algorithms)?(\.tree)?(\.mst)?\.SpanningTreeIterator\.Partition\._DT
 networkx(\.algorithms)?(\.tree)?(\.branchings)?\.ArborescenceIterator\.Partition\._DT
 
 # variable differs from runtime type abc.ABCMeta
-networkx.classes.reportviews.EdgeView.dataview
-networkx.classes.reportviews.InEdgeView.dataview
-networkx.classes.reportviews.OutEdgeView.dataview
-networkx.classes.reportviews.OutMultiEdgeView.dataview
+networkx\.classes\.reportviews\.\w*EdgeView\.dataview

stubs/networkx/networkx/__init__.pyi

@@ -1,3 +1,5 @@
+from typing import Final
+
 from networkx.algorithms import *
 from networkx.classes import *
 from networkx.classes import filters as filters
@@ -24,3 +26,5 @@ from . import (
     relabel as relabel,
     utils as utils,
 )
+
+__version__: Final[str]

stubs/networkx/networkx/algorithms/__init__.pyi

@@ -118,6 +118,7 @@ from networkx.algorithms.structuralholes import *
 from networkx.algorithms.summarization import *
 from networkx.algorithms.swap import *
 from networkx.algorithms.time_dependent import *
+from networkx.algorithms.tournament import is_tournament as is_tournament
 from networkx.algorithms.traversal import *
 from networkx.algorithms.tree.branchings import (
     ArborescenceIterator as ArborescenceIterator,

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

@@ -1,5 +1,6 @@
-from _typeshed import Incomplete
-from collections.abc import Callable
+from _typeshed import Incomplete, SupportsLenAndGetItem
+from collections.abc import Callable, Mapping
+from typing import Any, TypeVar
 
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
@@ -15,6 +16,10 @@ __all__ = [
     "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): ...
 @_dispatchable
@@ -31,6 +36,10 @@ def asadpour_atsp(
     G: DiGraph[_Node], weight: str | None = "weight", seed: int | RandomState | None = None, source: str | None = None
 ): ...
 @_dispatchable
+def held_karp_ascent(G: Graph[_Node], weight="weight"): ...
+@_dispatchable
+def spanning_tree_distribution(G: Graph[_Node], z: Mapping[Incomplete, Incomplete]) -> dict[Incomplete, Incomplete]: ...
+@_dispatchable
 def greedy_tsp(G: Graph[_Node], weight: str | None = "weight", source=None): ...
 @_dispatchable
 def simulated_annealing_tsp(

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

@@ -15,3 +15,7 @@ class MinDegreeHeuristic:
 
     def __init__(self, graph) -> None: ...
     def best_node(self, graph): ...
+
+def min_fill_in_heuristic(graph) -> Incomplete | None: ...
+@_dispatchable
+def treewidth_decomp(G: Graph[_Node], heuristic=...) -> tuple[int, Graph[_Node]]: ...

stubs/networkx/networkx/algorithms/asteroidal.pyi

@@ -1,3 +1,5 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
@@ -7,3 +9,5 @@ __all__ = ["is_at_free", "find_asteroidal_triple"]
 def find_asteroidal_triple(G: Graph[_Node]): ...
 @_dispatchable
 def is_at_free(G: Graph[_Node]) -> bool: ...
+@_dispatchable
+def create_component_structure(G: Graph[_Node]) -> dict[Incomplete, Incomplete]: ...

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

@@ -1,11 +1,17 @@
 from _typeshed import Incomplete
-from collections.abc import Iterable
+from collections.abc import Callable, Iterable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["clustering", "average_clustering", "latapy_clustering", "robins_alexander_clustering"]
 
+def cc_dot(nu, nv) -> float: ...
+def cc_max(nu, nv) -> float: ...
+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"): ...
 

stubs/networkx/networkx/algorithms/clique.pyi

@@ -37,3 +37,15 @@ def node_clique_number(G: Graph[_Node], nodes=None, cliques: Iterable[Incomplete
 def number_of_cliques(G, nodes=None, cliques=None) -> int | dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def max_weight_clique(G, weight="weight") -> tuple[Incomplete, Incomplete]: ...
+
+class MaxWeightClique:
+    G: Graph[Incomplete]
+    incumbent_nodes: list[Incomplete]
+    incumbent_weight: int
+    node_weights: dict[Incomplete, int]
+    def __init__(self, G: Graph[_Node], weight): ...
+    def update_incumbent_if_improved(self, C, C_weight): ...
+    def greedily_find_independent_set(self, P): ...
+    def find_branching_nodes(self, P, target): ...
+    def expand(self, C, C_weight, P): ...
+    def find_max_weight_clique(self): ...

stubs/networkx/networkx/algorithms/coloring/equitable_coloring.pyi

@@ -11,5 +11,13 @@ __all__ = ["equitable_color"]
 def is_coloring(G: Graph[_Node], coloring: SupportsGetItem[Incomplete, Incomplete]) -> bool: ...
 @_dispatchable
 def is_equitable(G: Graph[_Node], coloring: Mapping[Incomplete, Incomplete], num_colors: SupportsIndex | None = None) -> bool: ...
+def make_C_from_F(F): ...
+def make_N_from_L_C(L, C): ...
+def make_H_from_C_N(C, N): ...
+def change_color(u, X, Y, N, H, F, C, L): ...
+def move_witnesses(src_color, dst_color, N, H, F, C, T_cal, L): ...
+@_dispatchable
+def pad_graph(G: Graph[_Node], num_colors): ...
+def procedure_P(V_minus, V_plus, N, H, F, C, L, excluded_colors=None): ...
 @_dispatchable
 def equitable_color(G: Graph[_Node], num_colors): ...

stubs/networkx/networkx/algorithms/coloring/greedy_coloring.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
-from collections.abc import Generator
+from collections.abc import Callable, Generator
+from typing import Final
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -32,5 +33,8 @@ def strategy_connected_sequential_dfs(G, colors): ...
 def strategy_connected_sequential(G, colors, traversal: str = "bfs") -> Generator[Incomplete, None, None]: ...
 @_dispatchable
 def strategy_saturation_largest_first(G, colors) -> Generator[Incomplete, None, Incomplete]: ...
+
+STRATEGIES: Final[dict[str, Callable[..., Incomplete]]]
+
 @_dispatchable
 def greedy_color(G: Graph[_Node], strategy="largest_first", interchange: bool = False): ...