Mercurial > hg
annotate tests/test-ancestor.py @ 28573:6a42564081cb
shelve: adds restoring newly created branch (issue5048) (BC)
Before this patch shelve never preserved branch information,
so after applying unshelve branch was the same as it was
on working copy no matter in which branch shelve took place.
This patch makes bare shelving(with no files specified, without
interactive,include and exclude option) remembers information if
the working directory was on newly created branch ,in other words
working directory was on different branch than its first parent.
In this situation unshelving restores branch information to the working
directory.
author | liscju <piotr.listkiewicz@gmail.com> |
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date | Wed, 10 Feb 2016 02:23:27 +0100 |
parents | 4056fdf71aff |
children | 18e738038d78 |
rev | line source |
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1 from __future__ import absolute_import |
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2 |
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3 import binascii |
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4 import getopt |
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5 import math |
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6 import os |
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7 import random |
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8 import sys |
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9 import time |
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10 |
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11 from mercurial.node import nullrev |
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12 from mercurial import ( |
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13 ancestor, |
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14 commands, |
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15 hg, |
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16 ui, |
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17 util, |
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18 ) |
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19 |
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20 def buildgraph(rng, nodes=100, rootprob=0.05, mergeprob=0.2, prevprob=0.7): |
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21 '''nodes: total number of nodes in the graph |
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22 rootprob: probability that a new node (not 0) will be a root |
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23 mergeprob: probability that, excluding a root a node will be a merge |
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24 prevprob: probability that p1 will be the previous node |
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25 |
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26 return value is a graph represented as an adjacency list. |
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27 ''' |
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28 graph = [None] * nodes |
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29 for i in xrange(nodes): |
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30 if i == 0 or rng.random() < rootprob: |
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31 graph[i] = [nullrev] |
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32 elif i == 1: |
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33 graph[i] = [0] |
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34 elif rng.random() < mergeprob: |
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35 if i == 2 or rng.random() < prevprob: |
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36 # p1 is prev |
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37 p1 = i - 1 |
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38 else: |
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39 p1 = rng.randrange(i - 1) |
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40 p2 = rng.choice(range(0, p1) + range(p1 + 1, i)) |
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41 graph[i] = [p1, p2] |
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42 elif rng.random() < prevprob: |
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43 graph[i] = [i - 1] |
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44 else: |
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45 graph[i] = [rng.randrange(i - 1)] |
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46 |
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47 return graph |
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48 |
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49 def buildancestorsets(graph): |
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50 ancs = [None] * len(graph) |
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51 for i in xrange(len(graph)): |
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52 ancs[i] = set([i]) |
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53 if graph[i] == [nullrev]: |
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54 continue |
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55 for p in graph[i]: |
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56 ancs[i].update(ancs[p]) |
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57 return ancs |
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58 |
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59 class naiveincrementalmissingancestors(object): |
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60 def __init__(self, ancs, bases): |
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61 self.ancs = ancs |
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62 self.bases = set(bases) |
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63 def addbases(self, newbases): |
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64 self.bases.update(newbases) |
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65 def removeancestorsfrom(self, revs): |
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66 for base in self.bases: |
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67 if base != nullrev: |
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68 revs.difference_update(self.ancs[base]) |
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69 revs.discard(nullrev) |
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70 def missingancestors(self, revs): |
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71 res = set() |
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72 for rev in revs: |
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73 if rev != nullrev: |
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74 res.update(self.ancs[rev]) |
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75 for base in self.bases: |
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76 if base != nullrev: |
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77 res.difference_update(self.ancs[base]) |
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78 return sorted(res) |
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79 |
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80 def test_missingancestors(seed, rng): |
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81 # empirically observed to take around 1 second |
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82 graphcount = 100 |
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83 testcount = 10 |
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84 inccount = 10 |
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85 nerrs = [0] |
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86 # the default mu and sigma give us a nice distribution of mostly |
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87 # single-digit counts (including 0) with some higher ones |
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88 def lognormrandom(mu, sigma): |
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89 return int(math.floor(rng.lognormvariate(mu, sigma))) |
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90 |
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91 def samplerevs(nodes, mu=1.1, sigma=0.8): |
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92 count = min(lognormrandom(mu, sigma), len(nodes)) |
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93 return rng.sample(nodes, count) |
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94 |
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95 def err(seed, graph, bases, seq, output, expected): |
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96 if nerrs[0] == 0: |
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97 print >> sys.stderr, 'seed:', hex(seed)[:-1] |
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98 if gerrs[0] == 0: |
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99 print >> sys.stderr, 'graph:', graph |
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100 print >> sys.stderr, '* bases:', bases |
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101 print >> sys.stderr, '* seq: ', seq |
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102 print >> sys.stderr, '* output: ', output |
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103 print >> sys.stderr, '* expected:', expected |
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104 nerrs[0] += 1 |
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105 gerrs[0] += 1 |
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106 |
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107 for g in xrange(graphcount): |
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108 graph = buildgraph(rng) |
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109 ancs = buildancestorsets(graph) |
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110 gerrs = [0] |
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111 for _ in xrange(testcount): |
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112 # start from nullrev to include it as a possibility |
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113 graphnodes = range(nullrev, len(graph)) |
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114 bases = samplerevs(graphnodes) |
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115 |
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116 # fast algorithm |
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117 inc = ancestor.incrementalmissingancestors(graph.__getitem__, bases) |
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118 # reference slow algorithm |
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119 naiveinc = naiveincrementalmissingancestors(ancs, bases) |
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120 seq = [] |
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121 revs = [] |
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122 for _ in xrange(inccount): |
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123 if rng.random() < 0.2: |
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124 newbases = samplerevs(graphnodes) |
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125 seq.append(('addbases', newbases)) |
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126 inc.addbases(newbases) |
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127 naiveinc.addbases(newbases) |
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128 if rng.random() < 0.4: |
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129 # larger set so that there are more revs to remove from |
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130 revs = samplerevs(graphnodes, mu=1.5) |
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131 seq.append(('removeancestorsfrom', revs)) |
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132 hrevs = set(revs) |
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133 rrevs = set(revs) |
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134 inc.removeancestorsfrom(hrevs) |
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135 naiveinc.removeancestorsfrom(rrevs) |
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136 if hrevs != rrevs: |
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137 err(seed, graph, bases, seq, sorted(hrevs), |
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138 sorted(rrevs)) |
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139 else: |
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140 revs = samplerevs(graphnodes) |
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141 seq.append(('missingancestors', revs)) |
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142 h = inc.missingancestors(revs) |
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143 r = naiveinc.missingancestors(revs) |
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144 if h != r: |
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145 err(seed, graph, bases, seq, h, r) |
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146 |
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147 # graph is a dict of child->parent adjacency lists for this graph: |
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148 # o 13 |
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149 # | |
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150 # | o 12 |
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151 # | | |
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152 # | | o 11 |
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153 # | | |\ |
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154 # | | | | o 10 |
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155 # | | | | | |
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156 # | o---+ | 9 |
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157 # | | | | | |
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158 # o | | | | 8 |
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159 # / / / / |
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160 # | | o | 7 |
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161 # | | | | |
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162 # o---+ | 6 |
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163 # / / / |
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164 # | | o 5 |
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165 # | |/ |
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166 # | o 4 |
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167 # | | |
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168 # o | 3 |
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169 # | | |
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170 # | o 2 |
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171 # |/ |
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172 # o 1 |
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173 # | |
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174 # o 0 |
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175 |
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176 graph = {0: [-1], 1: [0], 2: [1], 3: [1], 4: [2], 5: [4], 6: [4], |
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177 7: [4], 8: [-1], 9: [6, 7], 10: [5], 11: [3, 7], 12: [9], |
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178 13: [8]} |
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179 |
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180 def genlazyancestors(revs, stoprev=0, inclusive=False): |
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181 print ("%% lazy ancestor set for %s, stoprev = %s, inclusive = %s" % |
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182 (revs, stoprev, inclusive)) |
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183 return ancestor.lazyancestors(graph.get, revs, stoprev=stoprev, |
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184 inclusive=inclusive) |
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185 |
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186 def printlazyancestors(s, l): |
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187 print 'membership: %r' % [n for n in l if n in s] |
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188 print 'iteration: %r' % list(s) |
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189 |
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190 def test_lazyancestors(): |
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191 # Empty revs |
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192 s = genlazyancestors([]) |
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193 printlazyancestors(s, [3, 0, -1]) |
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194 |
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195 # Standard example |
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196 s = genlazyancestors([11, 13]) |
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197 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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198 |
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199 # Standard with ancestry in the initial set (1 is ancestor of 3) |
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200 s = genlazyancestors([1, 3]) |
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201 printlazyancestors(s, [1, -1, 0]) |
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202 |
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203 # Including revs |
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204 s = genlazyancestors([11, 13], inclusive=True) |
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205 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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206 |
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207 # Test with stoprev |
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208 s = genlazyancestors([11, 13], stoprev=6) |
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209 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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210 s = genlazyancestors([11, 13], stoprev=6, inclusive=True) |
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211 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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212 |
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213 |
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214 # The C gca algorithm requires a real repo. These are textual descriptions of |
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215 # DAGs that have been known to be problematic. |
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216 dagtests = [ |
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217 '+2*2*2/*3/2', |
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218 '+3*3/*2*2/*4*4/*4/2*4/2*2', |
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219 ] |
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220 def test_gca(): |
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221 u = ui.ui() |
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222 for i, dag in enumerate(dagtests): |
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223 repo = hg.repository(u, 'gca%d' % i, create=1) |
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224 cl = repo.changelog |
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225 if not util.safehasattr(cl.index, 'ancestors'): |
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226 # C version not available |
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227 return |
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228 |
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229 commands.debugbuilddag(u, repo, dag) |
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230 # Compare the results of the Python and C versions. This does not |
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231 # include choosing a winner when more than one gca exists -- we make |
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232 # sure both return exactly the same set of gcas. |
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233 for a in cl: |
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234 for b in cl: |
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235 cgcas = sorted(cl.index.ancestors(a, b)) |
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236 pygcas = sorted(ancestor.ancestors(cl.parentrevs, a, b)) |
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237 if cgcas != pygcas: |
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238 print "test_gca: for dag %s, gcas for %d, %d:" % (dag, a, b) |
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239 print " C returned: %s" % cgcas |
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240 print " Python returned: %s" % pygcas |
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241 |
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242 def main(): |
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243 seed = None |
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244 opts, args = getopt.getopt(sys.argv[1:], 's:', ['seed=']) |
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245 for o, a in opts: |
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246 if o in ('-s', '--seed'): |
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247 seed = long(a, base=0) # accepts base 10 or 16 strings |
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248 |
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249 if seed is None: |
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250 try: |
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251 seed = long(binascii.hexlify(os.urandom(16)), 16) |
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252 except AttributeError: |
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253 seed = long(time.time() * 1000) |
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254 |
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255 rng = random.Random(seed) |
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256 test_missingancestors(seed, rng) |
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257 test_lazyancestors() |
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258 test_gca() |
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259 |
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260 if __name__ == '__main__': |
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261 main() |