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# Copyright (C) 2005, 2006, 2008 Canonical Ltd
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation; either version 2 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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# You should have received a copy of the GNU General Public License
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# along with this program; if not, write to the Free Software
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# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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"""Topological sorting routines."""
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revision as _mod_revision,
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__all__ = ["topo_sort", "TopoSorter", "merge_sort", "MergeSorter"]
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"""Topological sort a graph.
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graph -- sequence of pairs of node->parents_list.
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The result is a list of node names, such that all parents come before their
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node identifiers can be any hashable object, and are typically strings.
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This function has the same purpose as the TopoSorter class, but uses a
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different algorithm to sort the graph. That means that while both return a
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list with parents before their child nodes, the exact ordering can be
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topo_sort is faster when the whole list is needed, while when iterating
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over a part of the list, TopoSorter.iter_topo_order should be used.
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kg = _mod_graph.KnownGraph(graph)
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class TopoSorter(object):
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def __init__(self, graph):
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"""Topological sorting of a graph.
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:param graph: sequence of pairs of node_name->parent_names_list.
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i.e. [('C', ['B']), ('B', ['A']), ('A', [])]
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For this input the output from the sort or
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iter_topo_order routines will be:
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node identifiers can be any hashable object, and are typically strings.
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If you have a graph like [('a', ['b']), ('a', ['c'])] this will only use
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one of the two values for 'a'.
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The graph is sorted lazily: until you iterate or sort the input is
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not processed other than to create an internal representation.
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iteration or sorting may raise GraphCycleError if a cycle is present
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# store a dict of the graph.
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self._graph = dict(graph)
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"""Sort the graph and return as a list.
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After calling this the sorter is empty and you must create a new one.
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return list(self.iter_topo_order())
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### Useful if fiddling with this code.
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### sorted_names = list(self.iter_topo_order())
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### for index in range(len(sorted_names)):
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### rev = sorted_names[index]
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### for left_index in range(index):
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### if rev in self.original_graph[sorted_names[left_index]]:
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### print "revision in parent list of earlier revision"
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### import pdb;pdb.set_trace()
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def iter_topo_order(self):
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"""Yield the nodes of the graph in a topological order.
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After finishing iteration the sorter is empty and you cannot continue
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visitable = set(graph)
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# this is a stack storing the depth first search into the graph.
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pending_node_stack = []
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# at each level of 'recursion' we have to check each parent. This
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# stack stores the parents we have not yet checked for the node at the
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# matching depth in pending_node_stack
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pending_parents_stack = []
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# this is a set of the completed nodes for fast checking whether a
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# parent in a node we are processing on the stack has already been
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# emitted and thus can be skipped.
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completed_node_names = set()
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# now pick a random node in the source graph, and transfer it to the
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# top of the depth first search stack of pending nodes.
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node_name, parents = graph.popitem()
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pending_node_stack.append(node_name)
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pending_parents_stack.append(list(parents))
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# loop until pending_node_stack is empty
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while pending_node_stack:
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parents_to_visit = pending_parents_stack[-1]
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# if there are no parents left, the revision is done
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if not parents_to_visit:
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# append the revision to the topo sorted list
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# all the nodes parents have been added to the output,
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# now we can add it to the output.
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popped_node = pending_node_stack.pop()
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pending_parents_stack.pop()
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completed_node_names.add(popped_node)
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# recurse depth first into a single parent
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next_node_name = parents_to_visit.pop()
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if next_node_name in completed_node_names:
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# parent was already completed by a child, skip it.
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if next_node_name not in visitable:
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# parent is not a node in the original graph, skip it.
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# transfer it along with its parents from the source graph
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# into the top of the current depth first search stack.
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parents = graph.pop(next_node_name)
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# if the next node is not in the source graph it has
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# already been popped from it and placed into the
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# current search stack (but not completed or we would
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# have hit the continue 6 lines up). this indicates a
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raise errors.GraphCycleError(pending_node_stack)
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pending_node_stack.append(next_node_name)
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pending_parents_stack.append(list(parents))
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def merge_sort(graph, branch_tip, mainline_revisions=None, generate_revno=False):
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"""Topological sort a graph which groups merges.
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:param graph: sequence of pairs of node->parents_list.
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:param branch_tip: the tip of the branch to graph. Revisions not
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reachable from branch_tip are not included in the
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:param mainline_revisions: If not None this forces a mainline to be
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used rather than synthesised from the graph.
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This must be a valid path through some part
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of the graph. If the mainline does not cover all
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the revisions, output stops at the start of the
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old revision listed in the mainline revisions
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The order for this parameter is oldest-first.
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:param generate_revno: Optional parameter controlling the generation of
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revision number sequences in the output. See the output description of
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the MergeSorter docstring for details.
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:result: See the MergeSorter docstring for details.
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node identifiers can be any hashable object, and are typically strings.
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return MergeSorter(graph, branch_tip, mainline_revisions,
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generate_revno).sorted()
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class MergeSorter(object):
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__slots__ = ['_node_name_stack',
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'_node_merge_depth_stack',
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'_pending_parents_stack',
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'_first_child_stack',
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'_left_subtree_pushed_stack',
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'_mainline_revisions',
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'_revno_to_branch_count',
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'_completed_node_names',
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def __init__(self, graph, branch_tip, mainline_revisions=None,
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generate_revno=False):
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"""Merge-aware topological sorting of a graph.
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:param graph: sequence of pairs of node_name->parent_names_list.
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i.e. [('C', ['B']), ('B', ['A']), ('A', [])]
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For this input the output from the sort or
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iter_topo_order routines will be:
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:param branch_tip: the tip of the branch to graph. Revisions not
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reachable from branch_tip are not included in the
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:param mainline_revisions: If not None this forces a mainline to be
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used rather than synthesised from the graph.
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This must be a valid path through some part
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of the graph. If the mainline does not cover all
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the revisions, output stops at the start of the
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old revision listed in the mainline revisions
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The order for this parameter is oldest-first.
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:param generate_revno: Optional parameter controlling the generation of
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revision number sequences in the output. See the output description
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The result is a list sorted so that all parents come before
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their children. Each element of the list is a tuple containing:
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(sequence_number, node_name, merge_depth, end_of_merge)
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* sequence_number: The sequence of this row in the output. Useful for
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* node_name: The node name: opaque text to the merge routine.
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* merge_depth: How many levels of merging deep this node has been
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* revno_sequence: When requested this field provides a sequence of
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revision numbers for all revisions. The format is:
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(REVNO, BRANCHNUM, BRANCHREVNO). BRANCHNUM is the number of the
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branch that the revno is on. From left to right the REVNO numbers
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are the sequence numbers within that branch of the revision.
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For instance, the graph {A:[], B:['A'], C:['A', 'B']} will get
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the following revno_sequences assigned: A:(1,), B:(1,1,1), C:(2,).
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This should be read as 'A is the first commit in the trunk',
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'B is the first commit on the first branch made from A', 'C is the
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second commit in the trunk'.
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* end_of_merge: When True the next node is part of a different merge.
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node identifiers can be any hashable object, and are typically strings.
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If you have a graph like [('a', ['b']), ('a', ['c'])] this will only use
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one of the two values for 'a'.
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The graph is sorted lazily: until you iterate or sort the input is
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not processed other than to create an internal representation.
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iteration or sorting may raise GraphCycleError if a cycle is present
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Background information on the design:
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-------------------------------------
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definition: the end of any cluster or 'merge' occurs when:
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1 - the next revision has a lower merge depth than we do.
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C, D are the ends of clusters, E might be but we need more data.
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2 - or the next revision at our merge depth is not our left most
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This is required to handle multiple-merges in one commit.
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C is the end of a cluster due to rule 1.
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D is not the end of a cluster from rule 1, but is from rule 2: E
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is not its left most ancestor
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E is the end of a cluster due to rule 1
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F might be but we need more data.
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we show connecting lines to a parent when:
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- The parent is the start of a merge within this cluster.
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That is, the merge was not done to the mainline before this cluster
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was merged to the mainline.
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This can be detected thus:
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* The parent has a higher merge depth and is the next revision in
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The next revision in the list constraint is needed for this case:
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B 1 [C, F] # we do not want to show a line to F which is depth 2
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C 1 [H] # note that this is a long line to show back to the
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ancestor - see the end of merge rules.
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- Part of this merges 'branch':
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The parent has the same merge depth and is our left most parent and we
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are not the end of the cluster.
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A 0 [C, B] lines: [B, C]
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B 1 [E, C] lines: [C]
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D 0 [F, E] lines: [E, F]
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- The end of this merge/cluster:
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we can ONLY have multiple parents at the end of a cluster if this
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branch was previously merged into the 'mainline'.
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- if we have one and only one parent, show it
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Note that this may be to a greater merge depth - for instance if
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this branch continued from a deeply nested branch to add something
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- if we have more than one parent - show the second oldest (older ==
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further down the list) parent with
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an equal or lower merge depth
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XXXX revisit when awake. ddaa asks about the relevance of each one
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- maybe more than one parent is relevant
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self._generate_revno = generate_revno
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# a dict of the graph.
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self._graph = dict(graph)
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# if there is an explicit mainline, alter the graph to match. This is
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# easier than checking at every merge whether we are on the mainline and
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# if so which path to take.
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if mainline_revisions is None:
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self._mainline_revisions = []
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self._stop_revision = None
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self._mainline_revisions = list(mainline_revisions)
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self._stop_revision = self._mainline_revisions[0]
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# skip the first revision, its what we reach and its parents are
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# therefore irrelevant
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for index, revision in enumerate(self._mainline_revisions[1:]):
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# NB: index 0 means self._mainline_revisions[1]
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# if the mainline matches the graph, nothing to do.
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parent = self._mainline_revisions[index]
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# end of mainline_revisions history
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graph_parent_ids = self._graph[revision]
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if not graph_parent_ids:
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# We ran into a ghost, skip over it, this is a workaround for
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# bug #243536, the _graph has had ghosts stripped, but the
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# mainline_revisions have not
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if graph_parent_ids[0] == parent:
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# remove it from its prior spot
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self._graph[revision].remove(parent)
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# insert it into the start of the mainline
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self._graph[revision].insert(0, parent)
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# we need to do a check late in the process to detect end-of-merges
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# which requires the parents to be accessible: its easier for now
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# to just keep the original graph around.
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self._original_graph = dict(self._graph.items())
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# we need to know the revision numbers of revisions to determine
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# the revision numbers of their descendants
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# this is a graph from node to [revno_tuple, first_child]
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# where first_child is True if no other children have seen this node
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# and revno_tuple is the tuple that was assigned to the node.
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# we dont know revnos to start with, so we start it seeded with
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self._revnos = dict((revision, [None, True])
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for revision in self._graph)
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# Each mainline revision counts how many child branches have spawned from it.
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self._revno_to_branch_count = {}
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# this is a stack storing the depth first search into the graph.
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self._node_name_stack = []
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# at each level of recursion we need the merge depth this node is at:
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self._node_merge_depth_stack = []
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# at each level of 'recursion' we have to check each parent. This
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# stack stores the parents we have not yet checked for the node at the
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# matching depth in _node_name_stack
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self._pending_parents_stack = []
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# When we first look at a node we assign it a seqence number from its
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self._first_child_stack = []
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# this is a set of the nodes who have been completely analysed for fast
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# membership checking
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self._completed_node_names = set()
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# this is the scheduling of nodes list.
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# Nodes are scheduled
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# from the bottom left of the tree: in the tree
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# the scheduling order is: F, E, D, C, B, A
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# that is - 'left subtree, right subtree, node'
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# which would mean that when we schedule A we can emit the entire tree.
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self._scheduled_nodes = []
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# This records for each node when we have processed its left most
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# unmerged subtree. After this subtree is scheduled, all other subtrees
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# have their merge depth increased by one from this nodes merge depth.
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# it contains tuples - name, merge_depth
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self._left_subtree_pushed_stack = []
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# seed the search with the tip of the branch
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if (branch_tip is not None and
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branch_tip != _mod_revision.NULL_REVISION):
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parents = self._graph.pop(branch_tip)
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self._push_node(branch_tip, 0, parents)
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"""Sort the graph and return as a list.
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After calling this the sorter is empty and you must create a new one.
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return list(self.iter_topo_order())
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def iter_topo_order(self):
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"""Yield the nodes of the graph in a topological order.
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After finishing iteration the sorter is empty and you cannot continue
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# These are safe to offload to local variables, because they are used
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# as a stack and modified in place, never assigned to.
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node_name_stack = self._node_name_stack
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node_merge_depth_stack = self._node_merge_depth_stack
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pending_parents_stack = self._pending_parents_stack
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left_subtree_pushed_stack = self._left_subtree_pushed_stack
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completed_node_names = self._completed_node_names
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scheduled_nodes = self._scheduled_nodes
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graph_pop = self._graph.pop
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def push_node(node_name, merge_depth, parents,
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node_name_stack_append=node_name_stack.append,
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node_merge_depth_stack_append=node_merge_depth_stack.append,
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left_subtree_pushed_stack_append=left_subtree_pushed_stack.append,
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pending_parents_stack_append=pending_parents_stack.append,
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first_child_stack_append=self._first_child_stack.append,
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"""Add node_name to the pending node stack.
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Names in this stack will get emitted into the output as they are popped
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This inlines a lot of self._variable.append functions as local
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node_name_stack_append(node_name)
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node_merge_depth_stack_append(merge_depth)
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left_subtree_pushed_stack_append(False)
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pending_parents_stack_append(list(parents))
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# as we push it, check if it is the first child
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# node has parents, assign from the left most parent.
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parent_info = revnos[parents[0]]
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first_child = parent_info[1]
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parent_info[1] = False
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# We don't use the same algorithm here, but we need to keep the
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first_child_stack_append(first_child)
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def pop_node(node_name_stack_pop=node_name_stack.pop,
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node_merge_depth_stack_pop=node_merge_depth_stack.pop,
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first_child_stack_pop=self._first_child_stack.pop,
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left_subtree_pushed_stack_pop=left_subtree_pushed_stack.pop,
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pending_parents_stack_pop=pending_parents_stack.pop,
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original_graph=self._original_graph,
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completed_node_names_add=self._completed_node_names.add,
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scheduled_nodes_append=scheduled_nodes.append,
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revno_to_branch_count=self._revno_to_branch_count,
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"""Pop the top node off the stack
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The node is appended to the sorted output.
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# we are returning from the flattened call frame:
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# pop off the local variables
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node_name = node_name_stack_pop()
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merge_depth = node_merge_depth_stack_pop()
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first_child = first_child_stack_pop()
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# remove this node from the pending lists:
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left_subtree_pushed_stack_pop()
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pending_parents_stack_pop()
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parents = original_graph[node_name]
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# node has parents, assign from the left most parent.
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parent_revno = revnos[parents[0]][0]
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# not the first child, make a new branch
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base_revno = parent_revno[0]
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branch_count = revno_to_branch_count.get(base_revno, 0)
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revno_to_branch_count[base_revno] = branch_count
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revno = (parent_revno[0], branch_count, 1)
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# revno = (parent_revno[0], branch_count, parent_revno[-1]+1)
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# as the first child, we just increase the final revision
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revno = parent_revno[:-1] + (parent_revno[-1] + 1,)
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# no parents, use the root sequence
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root_count = revno_to_branch_count.get(0, -1)
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revno = (0, root_count, 1)
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revno_to_branch_count[0] = root_count
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# store the revno for this node for future reference
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revnos[node_name][0] = revno
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completed_node_names_add(node_name)
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scheduled_nodes_append((node_name, merge_depth, revno))
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while node_name_stack:
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# loop until this call completes.
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parents_to_visit = pending_parents_stack[-1]
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# if all parents are done, the revision is done
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if not parents_to_visit:
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# append the revision to the topo sorted scheduled list:
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# all the nodes parents have been scheduled added, now
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# we can add it to the output.
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while pending_parents_stack[-1]:
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if not left_subtree_pushed_stack[-1]:
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# recurse depth first into the primary parent
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next_node_name = pending_parents_stack[-1].pop(0)
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is_left_subtree = True
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left_subtree_pushed_stack[-1] = True
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# place any merges in right-to-left order for scheduling
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# which gives us left-to-right order after we reverse
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# the scheduled queue. XXX: This has the effect of
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# allocating common-new revisions to the right-most
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# subtree rather than the left most, which will
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# display nicely (you get smaller trees at the top
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# of the combined merge).
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next_node_name = pending_parents_stack[-1].pop()
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is_left_subtree = False
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if next_node_name in completed_node_names:
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# this parent was completed by a child on the
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# call stack. skip it.
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# otherwise transfer it from the source graph into the
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# top of the current depth first search stack.
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parents = graph_pop(next_node_name)
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# if the next node is not in the source graph it has
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# already been popped from it and placed into the
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# current search stack (but not completed or we would
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# have hit the continue 4 lines up.
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# this indicates a cycle.
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if next_node_name in self._original_graph:
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raise errors.GraphCycleError(node_name_stack)
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# This is just a ghost parent, ignore it
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# a new child branch from name_stack[-1]
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node_merge_depth_stack[-1] + next_merge_depth)
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# and do not continue processing parents until this 'call'
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# We have scheduled the graph. Now deliver the ordered output:
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stop_revision = self._stop_revision
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generate_revno = self._generate_revno
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original_graph = self._original_graph
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while scheduled_nodes:
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node_name, merge_depth, revno = scheduled_nodes.pop()
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if node_name == stop_revision:
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if not len(scheduled_nodes):
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# last revision is the end of a merge
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elif scheduled_nodes[-1][1] < merge_depth:
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# the next node is to our left
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elif (scheduled_nodes[-1][1] == merge_depth and
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(scheduled_nodes[-1][0] not in
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original_graph[node_name])):
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# the next node was part of a multiple-merge.
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yield (sequence_number, node_name, merge_depth, revno, end_of_merge)
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yield (sequence_number, node_name, merge_depth, end_of_merge)
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def _push_node(self, node_name, merge_depth, parents):
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"""Add node_name to the pending node stack.
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Names in this stack will get emitted into the output as they are popped
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self._node_name_stack.append(node_name)
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self._node_merge_depth_stack.append(merge_depth)
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self._left_subtree_pushed_stack.append(False)
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self._pending_parents_stack.append(list(parents))
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# as we push it, figure out if this is the first child
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parents = self._original_graph[node_name]
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# node has parents, assign from the left most parent.
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parent_info = self._revnos[parents[0]]
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first_child = parent_info[1]
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parent_info[1] = False
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# We don't use the same algorithm here, but we need to keep the
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self._first_child_stack.append(first_child)
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"""Pop the top node off the stack
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The node is appended to the sorted output.
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# we are returning from the flattened call frame:
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# pop off the local variables
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node_name = self._node_name_stack.pop()
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merge_depth = self._node_merge_depth_stack.pop()
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first_child = self._first_child_stack.pop()
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# remove this node from the pending lists:
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self._left_subtree_pushed_stack.pop()
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self._pending_parents_stack.pop()
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parents = self._original_graph[node_name]
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# node has parents, assign from the left most parent.
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parent_revno = self._revnos[parents[0]][0]
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# not the first child, make a new branch
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base_revno = parent_revno[0]
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branch_count = self._revno_to_branch_count.get(base_revno, 0)
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self._revno_to_branch_count[base_revno] = branch_count
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revno = (parent_revno[0], branch_count, 1)
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# revno = (parent_revno[0], branch_count, parent_revno[-1]+1)
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# as the first child, we just increase the final revision
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revno = parent_revno[:-1] + (parent_revno[-1] + 1,)
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# no parents, use the root sequence
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root_count = self._revno_to_branch_count.get(0, 0)
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root_count = self._revno_to_branch_count.get(0, -1)
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revno = (0, root_count, 1)
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self._revno_to_branch_count[0] = root_count
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# store the revno for this node for future reference
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self._revnos[node_name][0] = revno
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self._completed_node_names.add(node_name)
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self._scheduled_nodes.append((node_name, merge_depth, self._revnos[node_name][0]))
added
removed
bzrlib/tsort.py
