~bzr-pqm/bzr/bzr.dev

# 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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  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())