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rhelmot
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Add networkx stubs (#10544)
Co-authored-by: Avasam <[email protected]> Co-authored-by: Audrey Dutcher <[email protected]>
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‎pyrightconfig.stricter.json

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"stubs/mysqlclient",
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"stubs/oauthlib",
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"stubs/openpyxl",
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"stubs/networkx",
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"stubs/passlib",
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"stubs/peewee",
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"stubs/pexpect",

‎stubs/networkx/METADATA.toml

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

‎stubs/networkx/networkx/__init__.pyi

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from networkx.algorithms import *
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from networkx.classes import *
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from networkx.classes import filters as filters
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from networkx.convert import *
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from networkx.convert_matrix import *
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from networkx.drawing import *
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from networkx.exception import *
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from networkx.generators import *
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from networkx.linalg import *
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from networkx.readwrite import *
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from networkx.relabel import *
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from . import (
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algorithms as algorithms,
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classes as classes,
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convert as convert,
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convert_matrix as convert_matrix,
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drawing as drawing,
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generators as generators,
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linalg as linalg,
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readwrite as readwrite,
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relabel as relabel,
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utils as utils,
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)

‎stubs/networkx/networkx/algorithms/__init__.pyi

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

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

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from networkx.algorithms.approximation.clique import *
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from networkx.algorithms.approximation.clustering_coefficient import *
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from networkx.algorithms.approximation.connectivity import *
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from networkx.algorithms.approximation.distance_measures import *
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from networkx.algorithms.approximation.dominating_set import *
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from networkx.algorithms.approximation.kcomponents import *
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from networkx.algorithms.approximation.matching import *
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from networkx.algorithms.approximation.maxcut import *
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from networkx.algorithms.approximation.ramsey import *
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from networkx.algorithms.approximation.steinertree import *
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from networkx.algorithms.approximation.traveling_salesman import *
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from networkx.algorithms.approximation.treewidth import *
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from networkx.algorithms.approximation.vertex_cover import *

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

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def maximum_independent_set(G): ...
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def max_clique(G): ...
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def clique_removal(G): ...
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def large_clique_size(G): ...

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

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from _typeshed import Incomplete
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def average_clustering(G, trials: int = 1000, seed: Incomplete | None = None): ...

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

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from _typeshed import Incomplete
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def local_node_connectivity(G, source, target, cutoff: Incomplete | None = None): ...
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def node_connectivity(G, s: Incomplete | None = None, t: Incomplete | None = None): ...
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def all_pairs_node_connectivity(G, nbunch: Incomplete | None = None, cutoff: Incomplete | None = None): ...

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

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from _typeshed import Incomplete
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def diameter(G, seed: Incomplete | None = None): ...

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

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from _typeshed import Incomplete
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def min_weighted_dominating_set(G, weight: Incomplete | None = None): ...
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def min_edge_dominating_set(G): ...

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

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def k_components(G, min_density: float = 0.95): ...

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

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def min_maximal_matching(G): ...

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

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from _typeshed import Incomplete
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def randomized_partitioning(G, seed: Incomplete | None = None, p: float = 0.5, weight: Incomplete | None = None): ...
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def one_exchange(G, initial_cut: Incomplete | None = None, seed: Incomplete | None = None, weight: Incomplete | None = None): ...

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

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def ramsey_R2(G): ...

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

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from _typeshed import Incomplete
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def metric_closure(G, weight: str = "weight"): ...
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def steiner_tree(G, terminal_nodes, weight: str = "weight", method: Incomplete | None = None): ...

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

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

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

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

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

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from _typeshed import Incomplete
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def min_weighted_vertex_cover(G, weight: Incomplete | None = None): ...

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

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from networkx.algorithms.assortativity.connectivity import *
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from networkx.algorithms.assortativity.correlation import *
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from networkx.algorithms.assortativity.mixing import *
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from networkx.algorithms.assortativity.neighbor_degree import *
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from networkx.algorithms.assortativity.pairs import *

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

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from _typeshed import Incomplete
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def average_degree_connectivity(
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G, source: str = "in+out", target: str = "in+out", nodes: Incomplete | None = None, weight: Incomplete | None = None
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): ...

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

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

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

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

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

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from _typeshed import Incomplete
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def average_neighbor_degree(
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G, source: str = "out", target: str = "out", nodes: Incomplete | None = None, weight: Incomplete | None = None
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): ...

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

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from _typeshed import Incomplete
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from collections.abc import Generator
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def node_attribute_xy(G, attribute, nodes: Incomplete | None = None) -> Generator[Incomplete, None, None]: ...
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def node_degree_xy(
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G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
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) -> Generator[Incomplete, None, None]: ...

‎stubs/networkx/networkx/algorithms/asteroidal.pyi

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def find_asteroidal_triple(G): ...
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def is_at_free(G): ...

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

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from networkx.algorithms.bipartite.basic import *
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from networkx.algorithms.bipartite.centrality import *
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from networkx.algorithms.bipartite.cluster import *
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from networkx.algorithms.bipartite.covering import *
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from networkx.algorithms.bipartite.edgelist import *
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from networkx.algorithms.bipartite.generators import *
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from networkx.algorithms.bipartite.matching import *
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from networkx.algorithms.bipartite.matrix import *
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from networkx.algorithms.bipartite.projection import *
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from networkx.algorithms.bipartite.redundancy import *
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from networkx.algorithms.bipartite.spectral import *

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

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

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

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def degree_centrality(G, nodes): ...
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def betweenness_centrality(G, nodes): ...
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def closeness_centrality(G, nodes, normalized: bool = True): ...

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

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from _typeshed import Incomplete
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def latapy_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
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clustering = latapy_clustering
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def average_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
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def robins_alexander_clustering(G): ...

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

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from _typeshed import Incomplete
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def min_edge_cover(G, matching_algorithm: Incomplete | None = None): ...

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