Add networkx stubs

pyrightconfig.stricter.json

@@ -49,6 +49,7 @@
         "stubs/mysqlclient",
         "stubs/oauthlib",
         "stubs/openpyxl",
+        "stubs/networkx",
         "stubs/passlib",
         "stubs/peewee",
         "stubs/pexpect",

stubs/networkx/METADATA.toml

@@ -0,0 +1,9 @@
+version = "3.1"
+upstream_repository = "https://github.com/networkx/networkx"
+requires = ["numpy"]
+partial_stub = true
+
+[tool.stubtest]
+ignore_missing_stub = true
+# stub_uploader won't allow pandas-stubs in the requires field https://github.com/typeshed-internal/stub_uploader/issues/90
+stubtest_requirements = ["pandas"]

stubs/networkx/networkx/__init__.pyi

@@ -0,0 +1,24 @@
+from networkx.algorithms import *
+from networkx.classes import *
+from networkx.classes import filters as filters
+from networkx.convert import *
+from networkx.convert_matrix import *
+from networkx.drawing import *
+from networkx.exception import *
+from networkx.generators import *
+from networkx.linalg import *
+from networkx.readwrite import *
+from networkx.relabel import *
+
+from . import (
+    algorithms as algorithms,
+    classes as classes,
+    convert as convert,
+    convert_matrix as convert_matrix,
+    drawing as drawing,
+    generators as generators,
+    linalg as linalg,
+    readwrite as readwrite,
+    relabel as relabel,
+    utils as utils,
+)

stubs/networkx/networkx/algorithms/__init__.pyi

@@ -0,0 +1,135 @@
+from networkx.algorithms import (
+    approximation as approximation,
+    assortativity as assortativity,
+    bipartite as bipartite,
+    centrality as centrality,
+    chordal as chordal,
+    clique as clique,
+    cluster as cluster,
+    coloring as coloring,
+    community as community,
+    components as components,
+    connectivity as connectivity,
+    flow as flow,
+    isomorphism as isomorphism,
+    link_analysis as link_analysis,
+    lowest_common_ancestors as lowest_common_ancestors,
+    node_classification as node_classification,
+    operators as operators,
+    shortest_paths as shortest_paths,
+    tournament as tournament,
+    traversal as traversal,
+    tree as tree,
+)
+from networkx.algorithms.assortativity import *
+from networkx.algorithms.asteroidal import *
+from networkx.algorithms.bipartite import (
+    complete_bipartite_graph as complete_bipartite_graph,
+    is_bipartite as is_bipartite,
+    projected_graph as projected_graph,
+)
+from networkx.algorithms.boundary import *
+from networkx.algorithms.bridges import *
+from networkx.algorithms.centrality import *
+from networkx.algorithms.chains import *
+from networkx.algorithms.chordal import *
+from networkx.algorithms.clique import *
+from networkx.algorithms.cluster import *
+from networkx.algorithms.coloring import *
+from networkx.algorithms.communicability_alg import *
+from networkx.algorithms.components import *
+from networkx.algorithms.connectivity import (
+    all_node_cuts as all_node_cuts,
+    all_pairs_node_connectivity as all_pairs_node_connectivity,
+    average_node_connectivity as average_node_connectivity,
+    edge_connectivity as edge_connectivity,
+    edge_disjoint_paths as edge_disjoint_paths,
+    is_k_edge_connected as is_k_edge_connected,
+    k_components as k_components,
+    k_edge_augmentation as k_edge_augmentation,
+    k_edge_components as k_edge_components,
+    k_edge_subgraphs as k_edge_subgraphs,
+    minimum_edge_cut as minimum_edge_cut,
+    minimum_node_cut as minimum_node_cut,
+    node_connectivity as node_connectivity,
+    node_disjoint_paths as node_disjoint_paths,
+    stoer_wagner as stoer_wagner,
+)
+from networkx.algorithms.core import *
+from networkx.algorithms.covering import *
+from networkx.algorithms.cuts import *
+from networkx.algorithms.cycles import *
+from networkx.algorithms.d_separation import *
+from networkx.algorithms.dag import *
+from networkx.algorithms.distance_measures import *
+from networkx.algorithms.distance_regular import *
+from networkx.algorithms.dominance import *
+from networkx.algorithms.dominating import *
+from networkx.algorithms.efficiency_measures import *
+from networkx.algorithms.euler import *
+from networkx.algorithms.flow import (
+    capacity_scaling as capacity_scaling,
+    cost_of_flow as cost_of_flow,
+    gomory_hu_tree as gomory_hu_tree,
+    max_flow_min_cost as max_flow_min_cost,
+    maximum_flow as maximum_flow,
+    maximum_flow_value as maximum_flow_value,
+    min_cost_flow as min_cost_flow,
+    min_cost_flow_cost as min_cost_flow_cost,
+    minimum_cut as minimum_cut,
+    minimum_cut_value as minimum_cut_value,
+    network_simplex as network_simplex,
+)
+from networkx.algorithms.graph_hashing import *
+from networkx.algorithms.graphical import *
+from networkx.algorithms.hierarchy import *
+from networkx.algorithms.hybrid import *
+from networkx.algorithms.isolate import *
+from networkx.algorithms.isomorphism import (
+    could_be_isomorphic as could_be_isomorphic,
+    fast_could_be_isomorphic as fast_could_be_isomorphic,
+    faster_could_be_isomorphic as faster_could_be_isomorphic,
+    is_isomorphic as is_isomorphic,
+)
+from networkx.algorithms.isomorphism.vf2pp import *
+from networkx.algorithms.link_analysis import *
+from networkx.algorithms.link_prediction import *
+from networkx.algorithms.lowest_common_ancestors import *
+from networkx.algorithms.matching import *
+from networkx.algorithms.minors import *
+from networkx.algorithms.mis import *
+from networkx.algorithms.moral import *
+from networkx.algorithms.non_randomness import *
+from networkx.algorithms.operators import *
+from networkx.algorithms.planar_drawing import *
+from networkx.algorithms.planarity import *
+from networkx.algorithms.polynomials import *
+from networkx.algorithms.reciprocity import *
+from networkx.algorithms.regular import *
+from networkx.algorithms.richclub import *
+from networkx.algorithms.shortest_paths import *
+from networkx.algorithms.similarity import *
+from networkx.algorithms.simple_paths import *
+from networkx.algorithms.smallworld import *
+from networkx.algorithms.smetric import *
+from networkx.algorithms.sparsifiers import *
+from networkx.algorithms.structuralholes import *
+from networkx.algorithms.summarization import *
+from networkx.algorithms.swap import *
+from networkx.algorithms.traversal import *
+from networkx.algorithms.tree.branchings import (
+    ArborescenceIterator as ArborescenceIterator,
+    maximum_branching as maximum_branching,
+    maximum_spanning_arborescence as maximum_spanning_arborescence,
+    minimum_branching as minimum_branching,
+    minimum_spanning_arborescence as minimum_spanning_arborescence,
+)
+from networkx.algorithms.tree.coding import *
+from networkx.algorithms.tree.decomposition import *
+from networkx.algorithms.tree.mst import *
+from networkx.algorithms.tree.operations import *
+from networkx.algorithms.tree.recognition import *
+from networkx.algorithms.triads import *
+from networkx.algorithms.vitality import *
+from networkx.algorithms.voronoi import *
+from networkx.algorithms.wiener import *

stubs/networkx/networkx/algorithms/approximation/__init__.pyi

@@ -0,0 +1,13 @@
+from networkx.algorithms.approximation.clique import *
+from networkx.algorithms.approximation.clustering_coefficient import *
+from networkx.algorithms.approximation.connectivity import *
+from networkx.algorithms.approximation.distance_measures import *
+from networkx.algorithms.approximation.dominating_set import *
+from networkx.algorithms.approximation.kcomponents import *
+from networkx.algorithms.approximation.matching import *
+from networkx.algorithms.approximation.maxcut import *
+from networkx.algorithms.approximation.ramsey import *
+from networkx.algorithms.approximation.steinertree import *
+from networkx.algorithms.approximation.traveling_salesman import *
+from networkx.algorithms.approximation.treewidth import *
+from networkx.algorithms.approximation.vertex_cover import *

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

@@ -0,0 +1,4 @@
+def maximum_independent_set(G): ...
+def max_clique(G): ...
+def clique_removal(G): ...
+def large_clique_size(G): ...

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

@@ -0,0 +1,3 @@
+from _typeshed import Incomplete
+
+def average_clustering(G, trials: int = 1000, seed: Incomplete | None = None): ...

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

@@ -0,0 +1,5 @@
+from _typeshed import Incomplete
+
+def local_node_connectivity(G, source, target, cutoff: Incomplete | None = None): ...
+def node_connectivity(G, s: Incomplete | None = None, t: Incomplete | None = None): ...
+def all_pairs_node_connectivity(G, nbunch: Incomplete | None = None, cutoff: Incomplete | None = None): ...

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

@@ -0,0 +1,3 @@
+from _typeshed import Incomplete
+
+def diameter(G, seed: Incomplete | None = None): ...

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

@@ -0,0 +1,4 @@
+from _typeshed import Incomplete
+
+def min_weighted_dominating_set(G, weight: Incomplete | None = None): ...
+def min_edge_dominating_set(G): ...

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

@@ -0,0 +1 @@
+def k_components(G, min_density: float = 0.95): ...

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

@@ -0,0 +1 @@
+def min_maximal_matching(G): ...

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

@@ -0,0 +1,4 @@
+from _typeshed import Incomplete
+
+def randomized_partitioning(G, seed: Incomplete | None = None, p: float = 0.5, weight: Incomplete | None = None): ...
+def one_exchange(G, initial_cut: Incomplete | None = None, seed: Incomplete | None = None, weight: Incomplete | None = None): ...

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

@@ -0,0 +1 @@
+def ramsey_R2(G): ...

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

@@ -0,0 +1,4 @@
+from _typeshed import Incomplete
+
+def metric_closure(G, weight: str = "weight"): ...
+def steiner_tree(G, terminal_nodes, weight: str = "weight", method: Incomplete | None = None): ...

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

@@ -0,0 +1,33 @@
+from _typeshed import Incomplete
+
+def christofides(G, weight: str = "weight", tree: Incomplete | None = None): ...
+def traveling_salesman_problem(
+    G, weight: str = "weight", nodes: Incomplete | None = None, cycle: bool = True, method: Incomplete | None = None
+): ...
+def asadpour_atsp(G, weight: str = "weight", seed: Incomplete | None = None, source: Incomplete | None = None): ...
+def greedy_tsp(G, weight: str = "weight", source: Incomplete | None = None): ...
+def simulated_annealing_tsp(
+    G,
+    init_cycle,
+    weight: str = "weight",
+    source: Incomplete | None = None,
+    # docstring says int, but it can be a float and does become a float mid-equation if alpha is also a float
+    temp: float = 100,
+    move: str = "1-1",
+    max_iterations: int = 10,
+    N_inner: int = 100,
+    alpha: float = 0.01,
+    seed: Incomplete | None = None,
+): ...
+def threshold_accepting_tsp(
+    G,
+    init_cycle,
+    weight: str = "weight",
+    source: Incomplete | None = None,
+    threshold: float = 1,
+    move: str = "1-1",
+    max_iterations: int = 10,
+    N_inner: int = 100,
+    alpha: float = 0.1,
+    seed: Incomplete | None = None,
+): ...

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

@@ -0,0 +1,11 @@
+from _typeshed import Incomplete
+
+__all__ = ["treewidth_min_degree", "treewidth_min_fill_in"]
+
+def treewidth_min_degree(G): ...
+def treewidth_min_fill_in(G): ...
+
+class MinDegreeHeuristic:
+    count: Incomplete
+    def __init__(self, graph) -> None: ...
+    def best_node(self, graph): ...

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

@@ -0,0 +1,3 @@
+from _typeshed import Incomplete
+
+def min_weighted_vertex_cover(G, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/assortativity/__init__.pyi

@@ -0,0 +1,5 @@
+from networkx.algorithms.assortativity.connectivity import *
+from networkx.algorithms.assortativity.correlation import *
+from networkx.algorithms.assortativity.mixing import *
+from networkx.algorithms.assortativity.neighbor_degree import *
+from networkx.algorithms.assortativity.pairs import *

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

@@ -0,0 +1,5 @@
+from _typeshed import Incomplete
+
+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

@@ -0,0 +1,10 @@
+from _typeshed import Incomplete
+
+def degree_assortativity_coefficient(
+    G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
+): ...
+def degree_pearson_correlation_coefficient(
+    G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
+): ...
+def attribute_assortativity_coefficient(G, attribute, nodes: Incomplete | None = None): ...
+def numeric_assortativity_coefficient(G, attribute, nodes: Incomplete | None = None): ...

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

@@ -0,0 +1,19 @@
+from _typeshed import Incomplete
+
+def attribute_mixing_dict(G, attribute, nodes: Incomplete | None = None, normalized: bool = False): ...
+def attribute_mixing_matrix(
+    G, attribute, nodes: Incomplete | None = None, mapping: Incomplete | None = None, normalized: bool = True
+): ...
+def degree_mixing_dict(
+    G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None, normalized: bool = False
+): ...
+def degree_mixing_matrix(
+    G,
+    x: str = "out",
+    y: str = "in",
+    weight: Incomplete | None = None,
+    nodes: Incomplete | None = None,
+    normalized: bool = True,
+    mapping: Incomplete | None = None,
+): ...
+def mixing_dict(xy, normalized: bool = False): ...

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

@@ -0,0 +1,5 @@
+from _typeshed import Incomplete
+
+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

@@ -0,0 +1,7 @@
+from _typeshed import Incomplete
+from collections.abc import Generator
+
+def node_attribute_xy(G, attribute, nodes: Incomplete | None = None) -> Generator[Incomplete, None, None]: ...
+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

@@ -0,0 +1,2 @@
+def find_asteroidal_triple(G): ...
+def is_at_free(G): ...

stubs/networkx/networkx/algorithms/bipartite/__init__.pyi

@@ -0,0 +1,11 @@
+from networkx.algorithms.bipartite.basic import *
+from networkx.algorithms.bipartite.centrality import *
+from networkx.algorithms.bipartite.cluster import *
+from networkx.algorithms.bipartite.covering import *
+from networkx.algorithms.bipartite.edgelist import *
+from networkx.algorithms.bipartite.generators import *
+from networkx.algorithms.bipartite.matching import *
+from networkx.algorithms.bipartite.matrix import *
+from networkx.algorithms.bipartite.projection import *
+from networkx.algorithms.bipartite.redundancy import *
+from networkx.algorithms.bipartite.spectral import *

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

@@ -0,0 +1,8 @@
+from _typeshed import Incomplete
+
+def color(G): ...
+def is_bipartite(G): ...
+def is_bipartite_node_set(G, nodes): ...
+def sets(G, top_nodes: Incomplete | None = None): ...
+def density(B, nodes): ...
+def degrees(B, nodes, weight: Incomplete | None = None): ...

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

@@ -0,0 +1,3 @@
+def degree_centrality(G, nodes): ...
+def betweenness_centrality(G, nodes): ...
+def closeness_centrality(G, nodes, normalized: bool = True): ...

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

@@ -0,0 +1,8 @@
+from _typeshed import Incomplete
+
+def latapy_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
+
+clustering = latapy_clustering
+
+def average_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
+def robins_alexander_clustering(G): ...

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

@@ -0,0 +1,3 @@
+from _typeshed import Incomplete
+
+def min_edge_cover(G, matching_algorithm: Incomplete | None = None): ...