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# Copyright (C) 2005, 2008 Canonical Ltd
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
def max_distance(node, ancestors, distances, root_descendants):
"""Calculate the max distance to an ancestor.
Return None if not all possible ancestors have known distances"""
best = None
if node in distances:
best = distances[node]
for ancestor in ancestors[node]:
# skip ancestors we will never traverse:
if root_descendants is not None and ancestor not in root_descendants:
continue
# An ancestor which is not listed in ancestors will never be in
# distances, so we pretend it never existed.
if ancestor not in ancestors:
continue
if ancestor not in distances:
return None
if best is None or distances[ancestor]+1 > best:
best = distances[ancestor] + 1
return best
def node_distances(graph, ancestors, start, root_descendants=None):
"""Produce a list of nodes, sorted by distance from a start node.
This is an algorithm devised by Aaron Bentley, because applying Dijkstra
backwards seemed too complicated.
For each node, we walk its descendants. If all the descendant's ancestors
have a max-distance-to-start, (excluding ones that can never reach start),
we calculate their max-distance-to-start, and schedule their descendants.
So when a node's last parent acquires a distance, it will acquire a
distance on the next iteration.
Once we know the max distances for all nodes, we can return a list sorted
by distance, farthest first.
"""
distances = {start: 0}
lines = set([start])
while len(lines) > 0:
new_lines = set()
for line in lines:
line_descendants = graph[line]
for descendant in line_descendants:
distance = max_distance(descendant, ancestors, distances,
root_descendants)
if distance is None:
continue
distances[descendant] = distance
new_lines.add(descendant)
lines = new_lines
return distances
def nodes_by_distance(distances):
"""Return a list of nodes sorted by distance"""
def by_distance(n):
return distances[n],n
node_list = distances.keys()
node_list.sort(key=by_distance, reverse=True)
return node_list
def select_farthest(distances, common):
"""Return the farthest common node, or None if no node qualifies."""
node_list = nodes_by_distance(distances)
for node in node_list:
if node in common:
return node
def all_descendants(descendants, start):
"""Produce a set of all descendants of the start node.
The input is a map of node->list of descendants for a graph encompassing
start.
"""
result = set()
lines = set([start])
while len(lines) > 0:
new_lines = set()
for line in lines:
if line not in descendants:
continue
for descendant in descendants[line]:
if descendant in result:
continue
result.add(descendant)
new_lines.add(descendant)
lines = new_lines
return result
class Graph(object):
"""A graph object which can memoise and cache results for performance."""
def __init__(self):
super(Graph, self).__init__()
self.roots = set([])
self.ghosts = set([])
self._graph_ancestors = {}
self._graph_descendants = {}
def add_ghost(self, node_id):
"""Add a ghost to the graph."""
self.ghosts.add(node_id)
self._ensure_descendant(node_id)
def add_node(self, node_id, parent_ids):
"""Add node_id to the graph with parent_ids as its parents."""
if len(parent_ids) == 0:
self.roots.add(node_id)
self._graph_ancestors[node_id] = list(parent_ids)
self._ensure_descendant(node_id)
for parent in parent_ids:
self._ensure_descendant(parent)
self._graph_descendants[parent][node_id] = 1
def _ensure_descendant(self, node_id):
"""Ensure that a descendant lookup for node_id will work."""
if not node_id in self._graph_descendants:
self._graph_descendants[node_id] = {}
def get_ancestors(self):
"""Return a dictionary of graph node:ancestor_list entries."""
return dict(self._graph_ancestors.items())
def get_ancestry(self, node_id, topo_sorted=True):
"""Return the inclusive ancestors of node_id in topological order."""
# maybe optimise this ?
from bzrlib.tsort import topo_sort
result = {}
pending = set([node_id])
while len(pending):
current = pending.pop()
parents = self._graph_ancestors[current]
parents = [parent for parent in parents if parent not in self.ghosts]
result[current] = parents
for parent in parents:
if parent not in result and parent not in pending:
pending.add(parent)
if not topo_sorted:
return result.keys()
return topo_sort(result.items())
def get_descendants(self):
"""Return a dictionary of graph node:child_node:distance entries."""
return dict(self._graph_descendants.items())
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