2dc03b7333
Reasons are useful for later analysis when running as to why the edge between two nodes was created so when linking items in the graph it would be nice to assign a reason. Change-Id: I2185cf5fb3c2c07c0f5536d3b210080c6f61d5dd
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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# vim: tabstop=4 shiftwidth=4 softtabstop=4
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# Copyright (C) 2012 Yahoo! Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import six
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import networkx as nx
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from networkx import algorithms
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def get_edge_attrs(graph, u, v):
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"""Gets the dictionary of edge attributes between u->v (or none)."""
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if not graph.has_edge(u, v):
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return None
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return dict(graph.adj[u][v])
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def merge_graphs(graphs, allow_overlaps=False):
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if not graphs:
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return None
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graph = graphs[0]
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for g in graphs[1:]:
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# This should ensure that the nodes to be merged do not already exist
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# in the graph that is to be merged into. This could be problematic if
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# there are duplicates.
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if not allow_overlaps:
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# Attempt to induce a subgraph using the to be merged graphs nodes
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# and see if any graph results.
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overlaps = graph.subgraph(g.nodes_iter())
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if len(overlaps):
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raise ValueError("Can not merge graph %s into %s since there "
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"are %s overlapping nodes" (g, graph,
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len(overlaps)))
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# Keep the target graphs name.
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name = graph.name
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graph = algorithms.compose(graph, g)
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graph.name = name
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return graph
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def get_no_successors(graph):
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"""Returns an iterator for all nodes with no successors"""
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for n in graph.nodes_iter():
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if not len(graph.successors(n)):
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yield n
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def get_no_predecessors(graph):
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"""Returns an iterator for all nodes with no predecessors"""
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for n in graph.nodes_iter():
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if not len(graph.predecessors(n)):
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yield n
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def pformat(graph):
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"""Pretty formats your graph into a string representation that includes
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details about your graph, including; name, type, frozeness, node count,
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nodes, edge count, edges, graph density and graph cycles (if any).
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"""
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lines = []
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lines.append("Name: %s" % graph.name)
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lines.append("Type: %s" % type(graph).__name__)
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lines.append("Frozen: %s" % nx.is_frozen(graph))
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lines.append("Nodes: %s" % graph.number_of_nodes())
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for n in graph.nodes_iter():
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lines.append(" - %s" % n)
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lines.append("Edges: %s" % graph.number_of_edges())
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for (u, v, e_data) in graph.edges_iter(data=True):
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if e_data:
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lines.append(" %s -> %s (%s)" % (u, v, e_data))
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else:
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lines.append(" %s -> %s" % (u, v))
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lines.append("Density: %0.3f" % nx.density(graph))
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cycles = list(nx.cycles.recursive_simple_cycles(graph))
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lines.append("Cycles: %s" % len(cycles))
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for cycle in cycles:
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buf = six.StringIO()
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buf.write(str(cycle[0]))
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for i in range(1, len(cycle)):
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buf.write(" --> %s" % (cycle[i]))
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buf.write(" --> %s" % (cycle[0]))
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lines.append(" %s" % buf.getvalue())
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return "\n".join(lines)
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def export_graph_to_dot(graph):
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"""Exports the graph to a dot format (requires pydot library)"""
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return nx.to_pydot(graph).to_string()
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