64583e075f
This unifies all the patterns to be graph based so that they are more uniform and there underlying constraints are more easy to understand (taskflow basically processes graphs). Change-Id: Ib2ab07c1c87165cf40a06508128010887f658391
197 lines
7.1 KiB
Python
197 lines
7.1 KiB
Python
# -*- coding: utf-8 -*-
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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 collections
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import os
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import networkx as nx
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import six
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def _common_format(g, edge_notation):
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lines = []
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lines.append("Name: %s" % g.name)
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lines.append("Type: %s" % type(g).__name__)
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lines.append("Frozen: %s" % nx.is_frozen(g))
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lines.append("Density: %0.3f" % nx.density(g))
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lines.append("Nodes: %s" % g.number_of_nodes())
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for n in g.nodes_iter():
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lines.append(" - %s" % n)
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lines.append("Edges: %s" % g.number_of_edges())
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for (u, v, e_data) in g.edges_iter(data=True):
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if e_data:
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lines.append(" %s %s %s (%s)" % (u, edge_notation, v, e_data))
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else:
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lines.append(" %s %s %s" % (u, edge_notation, v))
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return lines
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class Graph(nx.Graph):
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"""A graph subclass with useful utility functions."""
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def __init__(self, data=None, name=''):
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super(Graph, self).__init__(name=name, data=data)
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self.frozen = False
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def freeze(self):
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"""Freezes the graph so that no more mutations can occur."""
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if not self.frozen:
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nx.freeze(self)
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return self
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def export_to_dot(self):
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"""Exports the graph to a dot format (requires pydot library)."""
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return nx.to_pydot(self).to_string()
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def pformat(self):
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"""Pretty formats your graph into a string."""
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return os.linesep.join(_common_format(self, "<->"))
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class DiGraph(nx.DiGraph):
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"""A directed graph subclass with useful utility functions."""
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def __init__(self, data=None, name=''):
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super(DiGraph, self).__init__(name=name, data=data)
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self.frozen = False
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def freeze(self):
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"""Freezes the graph so that no more mutations can occur."""
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if not self.frozen:
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nx.freeze(self)
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return self
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def get_edge_data(self, u, v, default=None):
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"""Returns a *copy* of the edge attribute dictionary between (u, v).
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NOTE(harlowja): this differs from the networkx get_edge_data() as that
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function does not return a copy (but returns a reference to the actual
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edge data).
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"""
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try:
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return dict(self.adj[u][v])
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except KeyError:
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return default
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def topological_sort(self):
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"""Return a list of nodes in this graph in topological sort order."""
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return nx.topological_sort(self)
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def pformat(self):
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"""Pretty formats your graph into a string.
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This pretty formatted string representation includes many useful
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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 = _common_format(self, "->")
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cycles = list(nx.cycles.recursive_simple_cycles(self))
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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("%s" % (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 os.linesep.join(lines)
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def export_to_dot(self):
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"""Exports the graph to a dot format (requires pydot library)."""
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return nx.to_pydot(self).to_string()
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def is_directed_acyclic(self):
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"""Returns if this graph is a DAG or not."""
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return nx.is_directed_acyclic_graph(self)
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def no_successors_iter(self):
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"""Returns an iterator for all nodes with no successors."""
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for n in self.nodes_iter():
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if not len(self.successors(n)):
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yield n
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def no_predecessors_iter(self):
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"""Returns an iterator for all nodes with no predecessors."""
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for n in self.nodes_iter():
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if not len(self.predecessors(n)):
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yield n
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def bfs_predecessors_iter(self, n):
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"""Iterates breadth first over *all* predecessors of a given node.
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This will go through the nodes predecessors, then the predecessor nodes
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predecessors and so on until no more predecessors are found.
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NOTE(harlowja): predecessor cycles (if they exist) will not be iterated
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over more than once (this prevents infinite iteration).
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"""
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visited = set([n])
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queue = collections.deque(self.predecessors_iter(n))
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while queue:
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pred = queue.popleft()
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if pred not in visited:
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yield pred
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visited.add(pred)
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for pred_pred in self.predecessors_iter(pred):
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if pred_pred not in visited:
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queue.append(pred_pred)
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class OrderedDiGraph(DiGraph):
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"""A directed graph subclass with useful utility functions.
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This derivative retains node, edge, insertation and iteration
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ordering (so that the iteration order matches the insertation
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order).
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"""
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node_dict_factory = collections.OrderedDict
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adjlist_dict_factory = collections.OrderedDict
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edge_attr_dict_factory = collections.OrderedDict
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def merge_graphs(graph, *graphs, **kwargs):
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"""Merges a bunch of graphs into a new graph.
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If no additional graphs are provided the first graph is
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returned unmodified otherwise the merged graph is returned.
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"""
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tmp_graph = graph
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allow_overlaps = kwargs.get('allow_overlaps', False)
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overlap_detector = kwargs.get('overlap_detector')
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if overlap_detector is not None and not six.callable(overlap_detector):
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raise ValueError("Overlap detection callback expected to be callable")
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elif overlap_detector is None:
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overlap_detector = (lambda to_graph, from_graph:
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len(to_graph.subgraph(from_graph.nodes_iter())))
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for g in graphs:
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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 = overlap_detector(graph, g)
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if overlaps:
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raise ValueError("Can not merge graph %s into %s since there "
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"are %s overlapping nodes (and we do not "
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"support merging nodes)" % (g, graph,
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overlaps))
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graph = nx.algorithms.compose(graph, g)
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# Keep the first graphs name.
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if graphs:
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graph.name = tmp_graph.name
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return graph
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