Source code for graphscope.nx.classes.function

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# This file digraph.py is referred and derived from project NetworkX,
#
#  https://github.com/networkx/networkx/blob/master/networkx/classes/digraph.py
#
# which has the following license:
#
# Copyright (C) 2004-2020, NetworkX Developers
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
#
# This file is part of NetworkX.
#
# NetworkX is distributed under a BSD license; see LICENSE.txt for more
# information.
#

import networkx.classes.function as func

from graphscope.nx.classes.cache import get_node_data
from graphscope.nx.utils.compat import patch_docstring

__all__ = [
    "nodes",
    "edges",
    "degree",
    "degree_histogram",
    "neighbors",
    "number_of_nodes",
    "number_of_edges",
    "density",
    "is_directed",
    "info",
    "freeze",
    "is_frozen",
    "subgraph",
    "induced_subgraph",
    "edge_subgraph",
    "to_directed",
    "to_undirected",
    "add_star",
    "add_path",
    "add_cycle",
    "create_empty_copy",
    "all_neighbors",
    "non_neighbors",
    "non_edges",
    "common_neighbors",
    "set_node_attributes",
    "get_node_attributes",
    "set_edge_attributes",
    "get_edge_attributes",
    "is_weighted",
    "is_negatively_weighted",
    "is_empty",
    "selfloop_edges",
    "nodes_with_selfloops",
    "number_of_selfloops",
]


# forward the NetworkX functions
from networkx.classes.function import add_cycle
from networkx.classes.function import add_path
from networkx.classes.function import add_star
from networkx.classes.function import all_neighbors
from networkx.classes.function import common_neighbors
from networkx.classes.function import create_empty_copy
from networkx.classes.function import degree
from networkx.classes.function import degree_histogram
from networkx.classes.function import density
from networkx.classes.function import edges
from networkx.classes.function import freeze
from networkx.classes.function import get_edge_attributes
from networkx.classes.function import get_node_attributes
from networkx.classes.function import info
from networkx.classes.function import is_directed
from networkx.classes.function import is_empty
from networkx.classes.function import is_frozen
from networkx.classes.function import is_negatively_weighted
from networkx.classes.function import is_weighted
from networkx.classes.function import neighbors
from networkx.classes.function import nodes
from networkx.classes.function import nodes_with_selfloops
from networkx.classes.function import non_edges
from networkx.classes.function import non_neighbors
from networkx.classes.function import number_of_edges
from networkx.classes.function import number_of_nodes
from networkx.classes.function import selfloop_edges
from networkx.classes.function import subgraph
from networkx.classes.function import to_directed
from networkx.classes.function import to_undirected


[docs]def induced_subgraph(G, nbunch): """Returns a independent deep copy subgraph induced on nbunch. The induced subgraph of a graph on a set of nodes N is the graph with nodes N and edges from G which have both ends in N. Parameters ---------- G : NetworkX Graph nbunch : node, container of nodes or None (for all nodes) Returns ------- subgraph : SubGraph A independent deep copy of the subgraph in `G` induced by the nodes. Examples -------- >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc >>> H = G.subgraph([0, 1, 2]) >>> list(H.edges) [(0, 1), (1, 2)] """ induced_nodes = G.nbunch_iter(nbunch) return G.subgraph(induced_nodes)
[docs]def edge_subgraph(G, edges): """Returns a independent deep copy subgraph induced by the specified edges. The induced subgraph contains each edge in `edges` and each node incident to any of those edges. Parameters ---------- G : NetworkX Graph edges : iterable An iterable of edges. Edges not present in `G` are ignored. Returns ------- subgraph : SubGraph A edge-induced subgraph of subgraph of `G`. Examples -------- >>> G = nx.path_graph(5) >>> H = G.edge_subgraph([(0, 1), (3, 4)]) >>> list(H.nodes) [0, 1, 3, 4] >>> list(H.edges) [(0, 1), (3, 4)] """ return G.edge_subgraph(edges)
[docs]@patch_docstring(func.number_of_selfloops) def number_of_selfloops(G): if G.is_multigraph(): # we forward the MultiGraph nd MultiDiGraph return sum(1 for _ in selfloop_edges(G)) return G.number_of_selfloops()
@patch_docstring(func.set_node_attributes) def set_node_attributes(G, values, name=None): if G.is_multigraph(): # multigraph forward NetworkX func.set_node_attributes(G, values, name) return # Set node attributes based on type of `values` if name is not None: # `values` must not be a dict of dict try: # `values` is a dict for n, v in values.items(): if n in G: dd = get_node_data(G, n) dd[name] = values[n] G.set_node_data(n, dd) except AttributeError: # `values` is a constant for n in G: dd = get_node_data(G, n) dd[name] = values G.set_node_data(n, dd) else: # `values` must be dict of dict for n, d in values.items(): if n in G: dd = get_node_data(G, n) dd.update(d) G.set_node_data(n, dd) @patch_docstring(func.set_edge_attributes) def set_edge_attributes(G, values, name=None): # noqa: C901 if G.is_multigraph(): # multigraph forward NetworkX func.set_edge_attributes(G, values, name) return if name is not None: # `values` does not contain attribute names try: # if `values` is a dict using `.items()` => {edge: value} for (u, v), value in values.items(): dd = G.get_edge_data(u, v) if dd is not None: dd[name] = value G.set_edge_data(u, v, dd) except AttributeError: # treat `values` as a constant for u, v, data in G.edges(data=True): data[name] = values else: for (u, v), d in values.items(): dd = G.get_edge_data(u, v) if dd is not None: dd.update(d) G.set_edge_data(u, v, dd)