|
|
from __future__ import absolute_import
|
|
|
|
|
|
import binascii
|
|
|
import getopt
|
|
|
import math
|
|
|
import os
|
|
|
import random
|
|
|
import sys
|
|
|
import time
|
|
|
|
|
|
from mercurial.node import nullrev
|
|
|
from mercurial import (
|
|
|
ancestor,
|
|
|
commands,
|
|
|
hg,
|
|
|
ui,
|
|
|
util,
|
|
|
)
|
|
|
|
|
|
def buildgraph(rng, nodes=100, rootprob=0.05, mergeprob=0.2, prevprob=0.7):
|
|
|
'''nodes: total number of nodes in the graph
|
|
|
rootprob: probability that a new node (not 0) will be a root
|
|
|
mergeprob: probability that, excluding a root a node will be a merge
|
|
|
prevprob: probability that p1 will be the previous node
|
|
|
|
|
|
return value is a graph represented as an adjacency list.
|
|
|
'''
|
|
|
graph = [None] * nodes
|
|
|
for i in xrange(nodes):
|
|
|
if i == 0 or rng.random() < rootprob:
|
|
|
graph[i] = [nullrev]
|
|
|
elif i == 1:
|
|
|
graph[i] = [0]
|
|
|
elif rng.random() < mergeprob:
|
|
|
if i == 2 or rng.random() < prevprob:
|
|
|
# p1 is prev
|
|
|
p1 = i - 1
|
|
|
else:
|
|
|
p1 = rng.randrange(i - 1)
|
|
|
p2 = rng.choice(range(0, p1) + range(p1 + 1, i))
|
|
|
graph[i] = [p1, p2]
|
|
|
elif rng.random() < prevprob:
|
|
|
graph[i] = [i - 1]
|
|
|
else:
|
|
|
graph[i] = [rng.randrange(i - 1)]
|
|
|
|
|
|
return graph
|
|
|
|
|
|
def buildancestorsets(graph):
|
|
|
ancs = [None] * len(graph)
|
|
|
for i in xrange(len(graph)):
|
|
|
ancs[i] = set([i])
|
|
|
if graph[i] == [nullrev]:
|
|
|
continue
|
|
|
for p in graph[i]:
|
|
|
ancs[i].update(ancs[p])
|
|
|
return ancs
|
|
|
|
|
|
class naiveincrementalmissingancestors(object):
|
|
|
def __init__(self, ancs, bases):
|
|
|
self.ancs = ancs
|
|
|
self.bases = set(bases)
|
|
|
def addbases(self, newbases):
|
|
|
self.bases.update(newbases)
|
|
|
def removeancestorsfrom(self, revs):
|
|
|
for base in self.bases:
|
|
|
if base != nullrev:
|
|
|
revs.difference_update(self.ancs[base])
|
|
|
revs.discard(nullrev)
|
|
|
def missingancestors(self, revs):
|
|
|
res = set()
|
|
|
for rev in revs:
|
|
|
if rev != nullrev:
|
|
|
res.update(self.ancs[rev])
|
|
|
for base in self.bases:
|
|
|
if base != nullrev:
|
|
|
res.difference_update(self.ancs[base])
|
|
|
return sorted(res)
|
|
|
|
|
|
def test_missingancestors(seed, rng):
|
|
|
# empirically observed to take around 1 second
|
|
|
graphcount = 100
|
|
|
testcount = 10
|
|
|
inccount = 10
|
|
|
nerrs = [0]
|
|
|
# the default mu and sigma give us a nice distribution of mostly
|
|
|
# single-digit counts (including 0) with some higher ones
|
|
|
def lognormrandom(mu, sigma):
|
|
|
return int(math.floor(rng.lognormvariate(mu, sigma)))
|
|
|
|
|
|
def samplerevs(nodes, mu=1.1, sigma=0.8):
|
|
|
count = min(lognormrandom(mu, sigma), len(nodes))
|
|
|
return rng.sample(nodes, count)
|
|
|
|
|
|
def err(seed, graph, bases, seq, output, expected):
|
|
|
if nerrs[0] == 0:
|
|
|
print >> sys.stderr, 'seed:', hex(seed)[:-1]
|
|
|
if gerrs[0] == 0:
|
|
|
print >> sys.stderr, 'graph:', graph
|
|
|
print >> sys.stderr, '* bases:', bases
|
|
|
print >> sys.stderr, '* seq: ', seq
|
|
|
print >> sys.stderr, '* output: ', output
|
|
|
print >> sys.stderr, '* expected:', expected
|
|
|
nerrs[0] += 1
|
|
|
gerrs[0] += 1
|
|
|
|
|
|
for g in xrange(graphcount):
|
|
|
graph = buildgraph(rng)
|
|
|
ancs = buildancestorsets(graph)
|
|
|
gerrs = [0]
|
|
|
for _ in xrange(testcount):
|
|
|
# start from nullrev to include it as a possibility
|
|
|
graphnodes = range(nullrev, len(graph))
|
|
|
bases = samplerevs(graphnodes)
|
|
|
|
|
|
# fast algorithm
|
|
|
inc = ancestor.incrementalmissingancestors(graph.__getitem__, bases)
|
|
|
# reference slow algorithm
|
|
|
naiveinc = naiveincrementalmissingancestors(ancs, bases)
|
|
|
seq = []
|
|
|
revs = []
|
|
|
for _ in xrange(inccount):
|
|
|
if rng.random() < 0.2:
|
|
|
newbases = samplerevs(graphnodes)
|
|
|
seq.append(('addbases', newbases))
|
|
|
inc.addbases(newbases)
|
|
|
naiveinc.addbases(newbases)
|
|
|
if rng.random() < 0.4:
|
|
|
# larger set so that there are more revs to remove from
|
|
|
revs = samplerevs(graphnodes, mu=1.5)
|
|
|
seq.append(('removeancestorsfrom', revs))
|
|
|
hrevs = set(revs)
|
|
|
rrevs = set(revs)
|
|
|
inc.removeancestorsfrom(hrevs)
|
|
|
naiveinc.removeancestorsfrom(rrevs)
|
|
|
if hrevs != rrevs:
|
|
|
err(seed, graph, bases, seq, sorted(hrevs),
|
|
|
sorted(rrevs))
|
|
|
else:
|
|
|
revs = samplerevs(graphnodes)
|
|
|
seq.append(('missingancestors', revs))
|
|
|
h = inc.missingancestors(revs)
|
|
|
r = naiveinc.missingancestors(revs)
|
|
|
if h != r:
|
|
|
err(seed, graph, bases, seq, h, r)
|
|
|
|
|
|
# graph is a dict of child->parent adjacency lists for this graph:
|
|
|
# o 13
|
|
|
# |
|
|
|
# | o 12
|
|
|
# | |
|
|
|
# | | o 11
|
|
|
# | | |\
|
|
|
# | | | | o 10
|
|
|
# | | | | |
|
|
|
# | o---+ | 9
|
|
|
# | | | | |
|
|
|
# o | | | | 8
|
|
|
# / / / /
|
|
|
# | | o | 7
|
|
|
# | | | |
|
|
|
# o---+ | 6
|
|
|
# / / /
|
|
|
# | | o 5
|
|
|
# | |/
|
|
|
# | o 4
|
|
|
# | |
|
|
|
# o | 3
|
|
|
# | |
|
|
|
# | o 2
|
|
|
# |/
|
|
|
# o 1
|
|
|
# |
|
|
|
# o 0
|
|
|
|
|
|
graph = {0: [-1], 1: [0], 2: [1], 3: [1], 4: [2], 5: [4], 6: [4],
|
|
|
7: [4], 8: [-1], 9: [6, 7], 10: [5], 11: [3, 7], 12: [9],
|
|
|
13: [8]}
|
|
|
|
|
|
def genlazyancestors(revs, stoprev=0, inclusive=False):
|
|
|
print ("%% lazy ancestor set for %s, stoprev = %s, inclusive = %s" %
|
|
|
(revs, stoprev, inclusive))
|
|
|
return ancestor.lazyancestors(graph.get, revs, stoprev=stoprev,
|
|
|
inclusive=inclusive)
|
|
|
|
|
|
def printlazyancestors(s, l):
|
|
|
print 'membership: %r' % [n for n in l if n in s]
|
|
|
print 'iteration: %r' % list(s)
|
|
|
|
|
|
def test_lazyancestors():
|
|
|
# Empty revs
|
|
|
s = genlazyancestors([])
|
|
|
printlazyancestors(s, [3, 0, -1])
|
|
|
|
|
|
# Standard example
|
|
|
s = genlazyancestors([11, 13])
|
|
|
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
|
|
|
|
|
|
# Standard with ancestry in the initial set (1 is ancestor of 3)
|
|
|
s = genlazyancestors([1, 3])
|
|
|
printlazyancestors(s, [1, -1, 0])
|
|
|
|
|
|
# Including revs
|
|
|
s = genlazyancestors([11, 13], inclusive=True)
|
|
|
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
|
|
|
|
|
|
# Test with stoprev
|
|
|
s = genlazyancestors([11, 13], stoprev=6)
|
|
|
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
|
|
|
s = genlazyancestors([11, 13], stoprev=6, inclusive=True)
|
|
|
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
|
|
|
|
|
|
|
|
|
# The C gca algorithm requires a real repo. These are textual descriptions of
|
|
|
# DAGs that have been known to be problematic.
|
|
|
dagtests = [
|
|
|
'+2*2*2/*3/2',
|
|
|
'+3*3/*2*2/*4*4/*4/2*4/2*2',
|
|
|
]
|
|
|
def test_gca():
|
|
|
u = ui.ui()
|
|
|
for i, dag in enumerate(dagtests):
|
|
|
repo = hg.repository(u, 'gca%d' % i, create=1)
|
|
|
cl = repo.changelog
|
|
|
if not util.safehasattr(cl.index, 'ancestors'):
|
|
|
# C version not available
|
|
|
return
|
|
|
|
|
|
commands.debugbuilddag(u, repo, dag)
|
|
|
# Compare the results of the Python and C versions. This does not
|
|
|
# include choosing a winner when more than one gca exists -- we make
|
|
|
# sure both return exactly the same set of gcas.
|
|
|
for a in cl:
|
|
|
for b in cl:
|
|
|
cgcas = sorted(cl.index.ancestors(a, b))
|
|
|
pygcas = sorted(ancestor.ancestors(cl.parentrevs, a, b))
|
|
|
if cgcas != pygcas:
|
|
|
print "test_gca: for dag %s, gcas for %d, %d:" % (dag, a, b)
|
|
|
print " C returned: %s" % cgcas
|
|
|
print " Python returned: %s" % pygcas
|
|
|
|
|
|
def main():
|
|
|
seed = None
|
|
|
opts, args = getopt.getopt(sys.argv[1:], 's:', ['seed='])
|
|
|
for o, a in opts:
|
|
|
if o in ('-s', '--seed'):
|
|
|
seed = long(a, base=0) # accepts base 10 or 16 strings
|
|
|
|
|
|
if seed is None:
|
|
|
try:
|
|
|
seed = long(binascii.hexlify(os.urandom(16)), 16)
|
|
|
except AttributeError:
|
|
|
seed = long(time.time() * 1000)
|
|
|
|
|
|
rng = random.Random(seed)
|
|
|
test_missingancestors(seed, rng)
|
|
|
test_lazyancestors()
|
|
|
test_gca()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
main()
|
|
|
|
