annotate mercurial/similar.py @ 31187:6b8e1a08ef1d

patchbomb: add config knob to generate flags by template (issue5354) This can be used to flag patches by branch or topic automatically. Flags optionally given by --flag option are exported as {flags} template keyword, so you can add --flag V2.
author Yuya Nishihara <yuya@tcha.org>
date Sat, 25 Feb 2017 18:41:00 +0900
parents 8614546154cb
children e1d035905b2e
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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1 # similar.py - mechanisms for finding similar files
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2 #
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3 # Copyright 2005-2007 Matt Mackall <mpm@selenic.com>
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4 #
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5 # This software may be used and distributed according to the terms of the
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6 # GNU General Public License version 2 or any later version.
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7
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8 from __future__ import absolute_import
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9
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10 import hashlib
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11
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12 from .i18n import _
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13 from . import (
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14 bdiff,
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15 mdiff,
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16 )
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17
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18 def _findexactmatches(repo, added, removed):
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19 '''find renamed files that have no changes
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20
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21 Takes a list of new filectxs and a list of removed filectxs, and yields
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22 (before, after) tuples of exact matches.
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23 '''
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24 numfiles = len(added) + len(removed)
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25
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26 # Get hashes of removed files.
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27 hashes = {}
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28 for i, fctx in enumerate(removed):
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29 repo.ui.progress(_('searching for exact renames'), i, total=numfiles,
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30 unit=_('files'))
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31 h = hashlib.sha1(fctx.data()).digest()
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32 hashes[h] = fctx
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33
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34 # For each added file, see if it corresponds to a removed file.
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35 for i, fctx in enumerate(added):
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36 repo.ui.progress(_('searching for exact renames'), i + len(removed),
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37 total=numfiles, unit=_('files'))
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38 h = hashlib.sha1(fctx.data()).digest()
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39 if h in hashes:
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40 yield (hashes[h], fctx)
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41
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42 # Done
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43 repo.ui.progress(_('searching for exact renames'), None)
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44
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45 def _ctxdata(fctx):
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46 # lazily load text
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47 orig = fctx.data()
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48 return orig, mdiff.splitnewlines(orig)
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49
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50 def _score(fctx, otherdata):
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51 orig, lines = otherdata
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52 text = fctx.data()
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53 # bdiff.blocks() returns blocks of matching lines
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54 # count the number of bytes in each
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55 equal = 0
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56 matches = bdiff.blocks(text, orig)
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57 for x1, x2, y1, y2 in matches:
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58 for line in lines[y1:y2]:
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59 equal += len(line)
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60
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61 lengths = len(text) + len(orig)
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62 return equal * 2.0 / lengths
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63
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64 def score(fctx1, fctx2):
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65 return _score(fctx1, _ctxdata(fctx2))
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66
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67 def _findsimilarmatches(repo, added, removed, threshold):
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68 '''find potentially renamed files based on similar file content
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69
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70 Takes a list of new filectxs and a list of removed filectxs, and yields
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71 (before, after, score) tuples of partial matches.
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72 '''
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73 copies = {}
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74 for i, r in enumerate(removed):
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75 repo.ui.progress(_('searching for similar files'), i,
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76 total=len(removed), unit=_('files'))
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77
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78 data = None
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79 for a in added:
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80 bestscore = copies.get(a, (None, threshold))[1]
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81 if data is None:
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82 data = _ctxdata(r)
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83 myscore = _score(a, data)
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84 if myscore >= bestscore:
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85 copies[a] = (r, myscore)
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86 repo.ui.progress(_('searching'), None)
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87
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88 for dest, v in copies.iteritems():
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89 source, bscore = v
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90 yield source, dest, bscore
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91
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92 def findrenames(repo, added, removed, threshold):
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93 '''find renamed files -- yields (before, after, score) tuples'''
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94 parentctx = repo['.']
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95 workingctx = repo[None]
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96
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97 # Zero length files will be frequently unrelated to each other, and
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98 # tracking the deletion/addition of such a file will probably cause more
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99 # harm than good. We strip them out here to avoid matching them later on.
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100 addedfiles = set([workingctx[fp] for fp in added
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101 if workingctx[fp].size() > 0])
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102 removedfiles = set([parentctx[fp] for fp in removed
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103 if fp in parentctx and parentctx[fp].size() > 0])
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104
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105 # Find exact matches.
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106 for (a, b) in _findexactmatches(repo,
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107 sorted(addedfiles), sorted(removedfiles)):
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108 addedfiles.remove(b)
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109 yield (a.path(), b.path(), 1.0)
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110
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111 # If the user requested similar files to be matched, search for them also.
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112 if threshold < 1.0:
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113 for (a, b, score) in _findsimilarmatches(repo,
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114 sorted(addedfiles), sorted(removedfiles), threshold):
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115 yield (a.path(), b.path(), score)