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The basic algorithm predates, and is a little fancier than, an algorithm published in the late 1980's by Ratcliff and Obershelp under the hyperbolic name "gestalt pattern matching". The basic idea is to find the longest contiguous matching subsequence that contains no "junk" elements (R-O doesn't address junk). The same idea is then applied recursively to the pieces of the sequences to the left and to the right of the matching subsequence. This does not yield minimal edit sequences, but does tend to yield matches that "look right" to people. SequenceMatcher tries to compute a "human-friendly diff" between two sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the longest *contiguous* & junk-free matching subsequence. That's what catches peoples' eyes. The Windows(tm) windiff has another interesting notion, pairing up elements that appear uniquely in each sequence. That, and the method here, appear to yield more intuitive difference reports than does diff. This method appears to be the least vulnerable to syncing up on blocks of "junk lines", though (like blank lines in ordinary text files, or maybe "

" lines in HTML files). That may be because this is the only method of the 3 that has a *concept* of "junk" . Example, comparing two strings, and considering blanks to be "junk": >>> s = SequenceMatcher(lambda x: x == " ", ... "private Thread currentThread;", ... "private volatile Thread currentThread;") >>> .ratio() returns a float in [0, 1], measuring the "similarity" of the sequences. As a rule of thumb, a .ratio() value over 0.6 means the sequences are close matches: >>> print(round(s.ratio(), 3)) 0.866 >>> If you're only interested in where the sequences match, .get_matching_blocks() is handy: >>> for block in s.get_matching_blocks(): ... print("a[%d] and b[%d] match for %d elements" % block) a[0] and b[0] match for 8 elements a[8] and b[17] match for 21 elements a[29] and b[38] match for 0 elements Note that the last tuple returned by .get_matching_blocks() is always a dummy, (len(a), len(b), 0), and this is the only case in which the last tuple element (number of elements matched) is 0. If you want to know how to change the first sequence into the second, use .get_opcodes(): >>> for opcode in s.get_opcodes(): ... print("%6s a[%d:%d] b[%d:%d]" % opcode) equal a[0:8] b[0:8] insert a[8:8] b[8:17] equal a[8:29] b[17:38] See the Differ class for a fancy human-friendly file differencer, which uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. See also function get_close_matches() in this module, which shows how simple code building on SequenceMatcher can be used to do useful work. Timing: Basic R-O is cubic time worst case and quadratic time expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear. lxml.html._difflib.__pyx_defaultslxml.html._difflib.__pyx_scope_struct__get_grouped_opcodeslxml.html._difflib.__pyx_scope_struct_1_genexprlxml.html._difflib.__pyx_scope_struct_2_genexprlxml.html._difflib.__pyx_scope_struct_3_comparelxml.html._difflib.__pyx_scope_struct_4__dumplxml.html._difflib.__pyx_scope_struct_5__plain_replacelxml.html._difflib.__pyx_scope_struct_6__fancy_replacelxml.html._difflib.__pyx_scope_struct_7__fancy_helperlxml.html._difflib.__pyx_scope_struct_8__qformatlxml.html._difflib.__pyx_scope_struct_9_unified_difflxml.html._difflib.__pyx_scope_struct_10_context_difflxml.html._difflib.__pyx_scope_struct_11_genexprlxml.html._difflib.__pyx_scope_struct_12_genexprlxml.html._difflib.__pyx_scope_struct_13_diff_byteslxml.html._difflib.__pyx_scope_struct_14__mdifflxml.html._difflib.__pyx_scope_struct_15__line_iteratorlxml.html._difflib.__pyx_scope_struct_16__line_pair_iteratorlxml.html._difflib.__pyx_scope_struct_17__tab_newline_replacelxml.html._difflib.__pyx_scope_struct_18__line_wrapperlxml.html._difflib.__pyx_scope_struct_19_restorelxml.html._difflib.__pyx_scope_struct____Pyx_CFunc_8c8a55__4lxml_4html_8_difflib_15SequenceMatcher_double__etc_to_py_4selfdouble (Py_ssize_t, Py_ssize_t)_calculate_ratio_difflibInterpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsModule '_difflib' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__lxml.html._difflib__orig_bases__init lxml.html._difflibname '%U' is not defined%s() got multiple values for keyword argument '%U' while calling a Python objectNULL result without error in PyObject_CallCythonUnboundCMethod%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'at leastat mostexactlys%.200s() takes %.8s %zd positional argument%.1s (%zd given)Missing type objectArgument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%U_cython_3_1_4Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling__module___cython_3_1_4._common_types_metatypekeywords must be strings__name__ must be set to a string object__qualname__ must be set to a string objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__annotations__ must be set to a dict object%.200s() %s (%zd given)%.200s() %sfunc_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutine__dictoffset____vectorcalloffset____weaklistoffset____reduce__Bad call flags for CyFunctiontakes no argumentstakes exactly one argumenttakes no keyword argumentsunbound method %.200S() needs an argumentneeds an argument_cython_3_1_4.cython_function_or_methodneed more than %zd value%.1s to unpacktoo many values to unpack (expected %zd)dictionary changed size during iterationset changed size during iterationgenerator raised StopIterationlocal variable '%s' referenced before assignmentExpected %s, got %.200sraise: arg 3 must be a traceback or Noneinstance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionexception causes must derive from BaseExceptiongenerator already executinggenerator ignored GeneratorExitthrowcannot pickle '%.200s' objectsendsend(arg) -> send 'arg' into generator, return next yielded value or raise StopIteration.throw(typ[,val[,tb]]) -> raise exception in generator, return next yielded value or raise StopIteration.closeclose() -> raise GeneratorExit inside generator.__reduce_ex__gi_yieldfromobject being iterated by 'yield from', or Nonegi_codename of the generatorqualified name of the generatorgi_frameFrame of the generatorgi_running_cython_3_1_4.generatoriter() returned non-iterator of type '%.200s'cannot fit '%.200s' into an index-sized integer'%.200s' object is not subscriptablejoin() result is too long for a Python string'%.200s' object is unsliceablefree variable '%s' referenced before assignment in enclosing scope%.200s object is not an iteratorcannot import name %Shasattr(): attribute name must be stringbase class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typeinvalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'Unable to initialize pickling for %.200smetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases__mro_entries__ must return a tupledoes not matchcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%d__pyx_capi____int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)an integer is requiredlxml.html._difflib+file1_rangejtabsizemake_file%si1*,j& M *D 5Qj 1 A AazyAt1 4q Daq  4aq  :T WIXQ AB! Eas!1 xq3a2RqWAQ$cIQc!+4ASaq 9Cq b>b %Q bQa q 4xquHAV#=QV#=QV#=QU*AU! Generate one of the two sequences that generated a delta. Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract lines originating from file 1 or 2 (parameter `which`), stripping off line prefixes. Examples: >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True), ... 'ore\ntree\nemu\n'.splitlines(keepends=True)) >>> diff = list(diff) >>> print(''.join(restore(diff, 1)), end="") one two three >>> print(''.join(restore(diff, 2)), end="") ore tree emu _nlargest_qformatws -__pyx_stateupdatedecodeAclosegrouplength!  3cDiffer._qformat rstriprestore (line 2076)atagsunified_diffReturns HTML table of side by side comparison with change highlights Arguments: fromlines -- list of "from" lines tolines -- list of "to" lines fromdesc -- "from" file column header string todesc -- "to" file column header string context -- set to True for contextual differences (defaults to False which shows full differences). numlines -- number of context lines. When context is set True, controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the "next" link anchors before the next change (so click of "next" link jumps to just before the change). Set the second sequence to be compared. The first sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq2("abcd") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq1(). Generate comparison results for a same-tagged range._tabsizecline_in_tracebackcountenablewhichalineformat_keyfrom%d_a;a )1Ja__chain_bHtmlDiff._split_line.wrap__setstate_cython__contextmap
blinemsg_table_template Module difflib -- helpers for computing deltas between objects. Function get_close_matches(word, possibilities, n=3, cutoff=0.6): Use SequenceMatcher to return list of the best "good enough" matches. Function context_diff(a, b): For two lists of strings, return a delta in context diff format. Function ndiff(a, b): Return a delta: the difference between `a` and `b` (lists of strings). Function restore(delta, which): Return one of the two sequences that generated an ndiff delta. Function unified_diff(a, b): For two lists of strings, return a delta in unified diff format. Class SequenceMatcher: A flexible class for comparing pairs of sequences of any type. Class Differ: For producing human-readable deltas from sequences of lines of text. Class HtmlDiff: For producing HTML side by side comparison with change highlights. --+aConvert range to the "ed" formatImportError_linejunk<linen must be > 0: diff_lines_iterator
totext_convert_flagsdiffs Return True for ignorable character: iff `ch` is a space or tab. Examples: >>> IS_CHARACTER_JUNK(' ') True >>> IS_CHARACTER_JUNK('\t') True >>> IS_CHARACTER_JUNK('\n') False >>> IS_CHARACTER_JUNK('x') False __setstate___dict__pyx_unpickle_SequenceMatcher__main__valueHtmlDiff._make_prefixprefixes Compare `a` and `b` (lists of strings); return a `Differ`-style delta. Optional keyword parameters `linejunk` and `charjunk` are for filter functions, or can be None: - linejunk: A function that should accept a single string argument and return true iff the string is junk. The default is None, and is recommended; the underlying SequenceMatcher class has an adaptive notion of "noise" lines. - charjunk: A function that accepts a character (string of length 1), and returns true iff the character is junk. The default is the module-level function IS_CHARACTER_JUNK, which filters out whitespace characters (a blank or tab; note: it's a bad idea to include newline in this!). Tools/scripts/ndiff.py is a command-line front-end to this function. Example: >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True), ... 'ore\ntree\nemu\n'.splitlines(keepends=True)) >>> print(''.join(diff), end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu context_diffjunkSequenceMatcher.set_seq2 (line 241)fromlist_keep_original_ws..genexprfromDifftype_name-QfA Compare two sequences of lines; generate the delta as a unified diff. Unified diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with ---, +++, or @@) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The unidiff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. Example: >>> for line in unified_diff('one two three four'.split(), ... 'zero one tree four'.split(), 'Original', 'Current', ... '2005-01-26 23:30:50', '2010-04-02 10:20:52', ... lineterm=''): ... print(line) # doctest: +NORMALIZE_WHITESPACE --- Original 2005-01-26 23:30:50 +++ Current 2010-04-02 10:20:52 @@ -1,4 +1,4 @@ +zero one -two -three +tree four __pyx_result Diff comparison %(table)s%(legend)s ! __qualname__numlinesHtmlDiff instance initializer Arguments: tabsize -- tab stop spacing, defaults to 8. wrapcolumn -- column number where lines are broken and wrapped, defaults to None where lines are not wrapped. linejunk,charjunk -- keyword arguments passed into ndiff() (used by HtmlDiff() to generate the side by side HTML differences). See ndiff() documentation for argument default values and descriptions. _line_wrapperenumeratechange_recrqr^__reduce_cython____module__@@ -{} +{} @@{}``` [K 0 ` pi e `P`8 `X``! `A`S 0`pH  {  1 X `` ``Х``XT``Z`@H`x`p3``@`@T` ` `@ X ` @  ``  `pTb`Y   ``X"`$```$`P/ (`! п`H$ ؐ`_$ `@$ pN DN p 8` `  P `T`M `   A    ȟ  `i << f н     T   t D  x  X l Legends
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lineterm_mdiff.._line_iteratorg Isolate change clusters by eliminating ranges with no changes. Return a generator of groups with up to n lines of context. Each group is in the same format as returned by get_opcodes(). >>> from pprint import pprint >>> a = list(map(str, range(1,40))) >>> b = a[:] >>> b[8:8] = ['i'] # Make an insertion >>> b[20] += 'x' # Make a replacement >>> b[23:28] = [] # Make a deletion >>> b[30] += 'y' # Make another replacement >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes())) [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)], [('equal', 16, 19, 17, 20), ('replace', 19, 20, 20, 21), ('equal', 20, 22, 21, 23), ('delete', 22, 27, 23, 23), ('equal', 27, 30, 23, 26)], [('equal', 31, 34, 27, 30), ('replace', 34, 35, 30, 31), ('equal', 35, 38, 31, 34)]] _mdiff.._make_line_line_iterator__dict__,M!$ $A 4q 4q $k+Yj"*)9A A )/waq_tab_newline_replaceSequenceMatcher_initializingfromfile tolistsub+5:t2Sj0t4s*Aj?q QqYaqQ !1 1 S7! L3gQ F#S '1F$aYas! 1Bd'aSequenceMatcher.set_seq2--?+next^ratioall arguments must be str, not: contextLinesValueErrorIS_LINE_JUNKA Return True for ignorable line: if `line` is blank or contains a single '#'. Examples: >>> IS_LINE_JUNK('\n') True >>> IS_LINE_JUNK(' # \n') True >>> IS_LINE_JUNK('hello\n') False _format_range_unified Differ is a class for comparing sequences of lines of text, and producing human-readable differences or deltas. Differ uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. Each line of a Differ delta begins with a two-letter code: '- ' line unique to sequence 1 '+ ' line unique to sequence 2 ' ' line common to both sequences '? ' line not present in either input sequence Lines beginning with '? ' attempt to guide the eye to intraline differences, and were not present in either input sequence. These lines can be confusing if the sequences contain tab characters. Note that Differ makes no claim to produce a *minimal* diff. To the contrary, minimal diffs are often counter-intuitive, because they synch up anywhere possible, sometimes accidental matches 100 pages apart. Restricting synch points to contiguous matches preserves some notion of locality, at the occasional cost of producing a longer diff. Example: Comparing two texts. First we set up the texts, sequences of individual single-line strings ending with newlines (such sequences can also be obtained from the `readlines()` method of file-like objects): >>> text1 = ''' 1. Beautiful is better than ugly. ... 2. Explicit is better than implicit. ... 3. Simple is better than complex. ... 4. Complex is better than complicated. ... '''.splitlines(keepends=True) >>> len(text1) 4 >>> text1[0][-1] '\n' >>> text2 = ''' 1. Beautiful is better than ugly. ... 3. Simple is better than complex. ... 4. Complicated is better than complex. ... 5. Flat is better than nested. ... '''.splitlines(keepends=True) Next we instantiate a Differ object: >>> d = Differ() Note that when instantiating a Differ object we may pass functions to filter out line and character 'junk'. See Differ.__init__ for details. Finally, we compare the two: >>> result = list(d.compare(text1, text2)) 'result' is a list of strings, so let's pretty-print it: >>> from pprint import pprint as _pprint >>> _pprint(result) [' 1. Beautiful is better than ugly.\n', '- 2. Explicit is better than implicit.\n', '- 3. Simple is better than complex.\n', '+ 3. Simple is better than complex.\n', '? ++\n', '- 4. Complex is better than complicated.\n', '? ^ ---- ^\n', '+ 4. Complicated is better than complex.\n', '? ++++ ^ ^\n', '+ 5. Flat is better than nested.\n'] As a single multi-line string it looks like this: >>> print(''.join(result), end="") 1. Beautiful is better than ugly. - 2. Explicit is better than implicit. - 3. Simple is better than complex. + 3. Simple is better than complex. ? ++ - 4. Complex is better than complicated. ? ^ ---- ^ + 4. Complicated is better than complex. ? ++++ ^ ^ + 5. Flat is better than nested. _make_lineinput must be a sequence of strings, not %sc"HtmlDiff.make_filelegend_collect_lines id="difflib_chg_cqr--- {}{}{}resultwrap(self: 'SequenceMatcher') -> floatrangeA 4xq !3as!1ASAQSq Bhiq q4q 12Rq1E!;3iq qQh&K1"!A 4q q aq  1 q1A9AQ 4xq Qe?r(@1yy !A  A[t81!;avTzSet the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75  2U! data_rows Compare two sequences of lines; generate the resulting delta. Each sequence must contain individual single-line strings ending with newlines. Such sequences can be obtained from the `readlines()` method of file-like objects. The delta generated also consists of newline- terminated strings, ready to be printed as-is via the writelines() method of a file-like object. Example: >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True), ... 'ore\ntree\nemu\n'.splitlines(True))), ... end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu Replace whitespace with the original whitespace characters in `s`__reduce__namedtupleDiffer._plain_replacebj1AttributeErrorcharsetHtmlDiff._collect_linesmake_table_is_coroutinebest_ratioencode :root {color-scheme: light dark} table.diff { font-family: Menlo, Consolas, Monaco, Liberation Mono, Lucida Console, monospace; border: medium; } .diff_header { background-color: #e0e0e0; font-weight: bold; } td.diff_header { text-align: right; padding: 0 8px; } .diff_next { background-color: #c0c0c0; padding: 4px 0; } .diff_add {background-color:palegreen} .diff_chg {background-color:#ffff77} .diff_sub {background-color:#ffaaaa} table.diff[summary="Legends"] { margin-top: 20px; border: 1px solid #ccc; } table.diff[summary="Legends"] th { background-color: #e0e0e0; padding: 4px 8px; } table.diff[summary="Legends"] td { padding: 4px 8px; } @media (prefers-color-scheme: dark) { .diff_header {background-color:#666} .diff_next {background-color:#393939} .diff_add {background-color:darkgreen} .diff_chg {background-color:#847415} .diff_sub {background-color:darkred} table.diff[summary="Legends"] {border-color:#555} table.diff[summary="Legends"] th{background-color:#666} }cutoffBuilds list of text lines by splitting text lines at wrap point This function will determine if the input text line needs to be wrapped (split) into separate lines. If so, the first wrap point will be determined and the first line appended to the output text line list. This function is used recursively to handle the second part of the split line to further split it. to_line__spec___namedtupleisspaceobject--++fromfiledatebhiexpandtabstnum_linesbloIS_CHARACTER_JUNK (line 1082)>equalstartlasttableb U"At1Qwct7!1 7'+Zr!$A--- {} ----{}get_grouped_opcodes__all__Use SequenceMatcher to return list of the best "good enough" matches. word is a sequence for which close matches are desired (typically a string). possibilities is a list of sequences against which to match word (typically a list of strings). Optional arg n (default 3) is the maximum number of close matches to return. n must be > 0. Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities that don't score at least that similar to word are ignored. The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first. >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"]) ['apple', 'ape'] >>> import keyword as _keyword >>> get_close_matches("wheel", _keyword.kwlist) ['while'] >>> get_close_matches("Apple", _keyword.kwlist) [] >>> get_close_matches("accept", _keyword.kwlist) ['except'] &SequenceMatcher.set_seqsbest_ichSet the first sequence to be compared. The second sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq1("bcde") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq2(). _wrapcolumn Return True for ignorable character: iff `ch` is a space or tab. Examples: >>> IS_CHARACTER_JUNK(' ') True >>> IS_CHARACTER_JUNK('\t') True >>> IS_CHARACTER_JUNK('\n') False >>> IS_CHARACTER_JUNK('x') False sumDiffer._dumpappendpopularA 4q WBiq  s!1d! EEe2TqQd#Q WBiq  A Aqb$d"Ba t1Cs!Qt1AQQcAQqQQ BaAQ 1 F"A E5! )1 L4q0123456789abcdef0123456789ABCDEFaelt Format "?" output and deal with tabs. Example: >>> d = Differ() >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n', ... ' ^ ^ ^ ', ' ^ ^ ^ ') >>> for line in results: print(repr(line)) ... '- \tabcDefghiJkl\n' '? \t ^ ^ ^\n' '+ \tabcdefGhijkl\n' '? \t ^ ^ ^\n' aequivSequenceMatcher.set_seq1 (line 215)TTDDd($kQUUbbffoostFFJJK G1F,avWA!qt3gU#TG54uGSXX[[__ffmmrruuyzDDKKPPSSWWccjjoorrvv~~EEJJMMQQbbiinnqquu~~EEFq0AWKwa0AWKqbegin %(header_row)s %(data_rows)s
compilestylesndiff (line 1330)HtmlDiff._tab_newline_replace..expand_tabscollectionsexpand_tabs00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899tolines__doc__Match__class_getitem__*** {}{}{} Format "?" output and deal with tabs. Example: >>> d = Differ() >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n', ... ' ^ ^ ^ ', ' ^ ^ ^ ') >>> for line in results: print(repr(line)) ... '- \tabcDefghiJkl\n' '? \t ^ ^ ^\n' '+ \tabcdefGhijkl\n' '? \t ^ ^ ^\n' _line_pair_iteratorflaglisti2 (SequenceMatcher.__chain_bgcSequenceMatcher.set_seq1in_change__set_name__nnReturns HTML markup of "from" / "to" text lines side -- 0 or 1 indicating "from" or "to" text flag -- indicates if difference on line linenum -- line number (used for line number column) text -- line text to be marked up  Empty File SequenceMatcher.__reduce_cython__insertmarkersSet the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 Differ.comparecodes_plain_replacea( L LrT1AQd!iqQaq;aqrT1AQd!iqQaq;aqz#QiqFaARqz#QiqFaARqq 4z%q )1DAset_seq1-end__new___prefixadd_noteN!!!1adeltan Return True for ignorable line: if `line` is blank or contains a single '#'. Examples: >>> IS_LINE_JUNK('\n') True >>> IS_LINE_JUNK(' # \n') True >>> IS_LINE_JUNK('hello\n') False _charjunkreplaceitems Generate one of the two sequences that generated a delta. Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract lines originating from file 1 or 2 (parameter `which`), stripping off line prefixes. Examples: >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True), ... 'ore\ntree\nemu\n'.splitlines(keepends=True)) >>> diff = list(diff) >>> print(''.join(restore(diff, 1)), end="") one two three >>> print(''.join(restore(diff, 2)), end="") ore tree emu header_rowdcfunc.to_pyreAq__name__wrapcolumn+ 1_makeIS_LINE_JUNK (line 1061) When replacing one block of lines with another, search the blocks for *similar* lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it. Example: >>> d = Differ() >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ... ['abcdefGhijkl\n'], 0, 1) >>> print(''.join(results), end="") - abcDefghiJkl ? ^ ^ ^ + abcdefGhijkl ? ^ ^ ^ id~Qerrb U"Awct7!1t1Q 7'+Qline_iteratorStopIterationnum_blanks_pending_SequenceMatcher__chain_b_mdiff-?+?#Note that Cython is deliberately stricter than PEP-484 and rejects subclasses of builtin types. If you need to pass subclasses then set the 'annotation_typing' directive to False.num_blanks_to_yieldHtmlDiff-+?dump_i_default_prefix Compare two sequences of lines; generate the delta as a unified diff. Unified diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with ---, +++, or @@) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The unidiff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. Example: >>> for line in unified_diff('one two three four'.split(), ... 'zero one tree four'.split(), 'Original', 'Current', ... '2005-01-26 23:30:50', '2010-04-02 10:20:52', ... lineterm=''): ... print(line) # doctest: +NORMALIZE_WHITESPACE --- Original 2005-01-26 23:30:50 +++ Current 2010-04-02 10:20:52 @@ -1,4 +1,4 @@ +zero one -two -three +tree four __pyx_checksumkeysdiff_bytes..decodeHtmlDiff._tab_newline_replacefound_diffcruncher6ar. )1 ;c IQfEQb ;c *EQd%t1A q  T*! V881At1BfAT#Qd!6Qd!4q 4q 5ARq t1q 5+RuBa  &line_pair_iterator(\++|\-+|\^+)+++ {}{}{}sendHtmlDiff._convert_flagsget_dumpA Zr82XQ$A Kq 1_mdiff.._make_line..record_sub_infotodescbtagsDiffer._fancy_replace (line 913)xmlcharrefreplacentestfrom_linetodata__pyx_vtable__alo{}HtmlDiff.__init__dfuncahi__pyx_typereversedset_seq2Differ._fancy_helperxReturns iterator that splits (wraps) mdiff text linessizematch_objectpatXidxsautojunktext__test__stopindex Compare two sequences of lines; generate the delta as a context diff. Context diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with *** or ---) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The context diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. If not specified, the strings default to blanks. Example: >>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(True), ... 'zero\none\ntree\nfour\n'.splitlines(True), 'Original', 'Current')), ... end="") *** Original --- Current *************** *** 1,4 **** one ! two ! three four --- 1,4 ---- + zero one ! tree four tofileMakes list of "next" linkswrapmax num_chgisenabledDiffercompare?unknown delta choice (must be 1 or 2): %rReturns HTML file of side by side comparison with change highlights Arguments: fromlines -- list of "from" lines tolines -- list of "to" lines fromdesc -- "from" file column header string todesc -- "to" file column header string context -- set to True for contextual differences (defaults to False which shows full differences). numlines -- number of context lines. When context is set True, controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the "next" link anchors before the next change (so click of "next" link jumps to just before the change). charset -- charset of the HTML document __getstate__- )context_diff (line 1201)isjunkkeygenexpri_legend_stylesPickleErrorset_seqsuse_setstatediff_bytesget_close_matchesindices<unknown tag _keep_original_wscrfirstConvert range to the "ed" formatto_diffA  3c IWHA qM"F!gQd-q&qgQa G1Ayqutf-8+-SequenceMatcher.ratio (line 614)SequenceMatcher.ratiodump_jsrc/lxml/html/_difflib.pyline1 Compare two sequences of lines; generate the delta as a context diff. Context diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with *** or ---) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The context diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. If not specified, the strings default to blanks. Example: >>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(True), ... 'zero\none\ntree\nfour\n'.splitlines(True), 'Original', 'Current')), ... end="") *** Original --- Current *************** *** 1,4 **** one ! two ! three four --- 1,4 ---- + zero one ! tree four __func__file2_range Compare `a` and `b` (lists of strings); return a `Differ`-style delta. Optional keyword parameters `linejunk` and `charjunk` are for filter functions, or can be None: - linejunk: A function that should accept a single string argument and return true iff the string is junk. The default is None, and is recommended; the underlying SequenceMatcher class has an adaptive notion of "noise" lines. - charjunk: A function that accepts a character (string of length 1), and returns true iff the character is junk. The default is the module-level function IS_CHARACTER_JUNK, which filters out whitespace characters (a blank or tab; note: it's a bad idea to include newline in this!). Tools/scripts/ndiff.py is a command-line front-end to this function. Example: >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True), ... 'ore\ntree\nemu\n'.splitlines(keepends=True)) >>> print(''.join(diff), end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu SequenceMatcher.get_grouped_opcodes (line 564)fromlinespoptolinefromprefixYields from/to lines of text with a change indication. This function is an iterator. It itself pulls lines from a differencing iterator, processes them and yields them. When it can it yields both a "from" and a "to" line, otherwise it will yield one or the other. In addition to yielding the lines of from/to text, a boolean flag is yielded to indicate if the text line(s) have differences in them. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. j 0 L O1 M M{},{}hkA ^1!"~~@@A?(!1|7!12DNRS 1 Construct a text differencer, with optional filters. The two optional keyword parameters are for filter functions: - `linejunk`: A function that should accept a single string argument, and return true iff the string is junk. The module-level function `IS_LINE_JUNK` may be used to filter out lines without visible characters, except for at most one splat ('#'). It is recommended to leave linejunk None; the underlying SequenceMatcher class has an adaptive notion of "noise" lines that's better than any static definition the author has ever been able to craft. - `charjunk`: A function that should accept a string of length 1. The module-level function `IS_CHARACTER_JUNK` may be used to filter out whitespace characters (a blank or tab; **note**: bad idea to include newline in this!). Use of IS_CHARACTER_JUNK is recommended. arangeflagdeleteReturns from/to line lists with tabs expanded and newlines removed. Instead of tab characters being replaced by the number of spaces needed to fill in to the next tab stop, this function will fill the space with tab characters. This is done so that the difference algorithms can identify changes in a file when tabs are replaced by spaces and vice versa. At the end of the HTML generation, the tab characters will be replaced with a nonbreakable space. matches__pyx_PickleErrorlines_to_writeline_numbeltbest_j %s%s%s%s }A"/a*** {} ****{}_fancy_helperfromdatandiff&A D G6 Cwiq cAU! 7!1 IZqQ 1 s%q6!AQ qCq L Q Cq 4zRs! BcRq XSa3avRq4q qCqa b sizefromtext.__metaclass__HtmlDiff._line_wrapperTypeErrorUse SequenceMatcher to return list of the best "good enough" matches. word is a sequence for which close matches are desired (typically a string). possibilities is a list of sequences against which to match word (typically a list of strings). Optional arg n (default 3) is the maximum number of close matches to return. n must be > 0. Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities that don't score at least that similar to word are ignored. The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first. >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"]) ['apple', 'ape'] >>> import keyword as _keyword >>> get_close_matches("wheel", _keyword.kwlist) ['while'] >>> get_close_matches("Apple", _keyword.kwlist) [] >>> get_close_matches("accept", _keyword.kwlist) ['except'] Set the second sequence to be compared. The first sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq2("abcd") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq1(). SequenceMatcher.set_seqs (line 203)Set the first sequence to be compared. The second sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq1("bcde") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq2(). sideSequenceMatcher.__setstate_cython__fromdateai1unified_diff (line 1116)b {}lbA. #WAq ATRs!4q+--ratio..genexprargsfword No Differences Found j1mark_mdiff.._line_pair_iteratorA* 2SA  E t;a N! Jatypesdisablestate 4xqA 4{!4q 4xqA 4xqT__init___check_typesprefixReturn a measure of the sequences' similarity (float in [0,1]). Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is 1 if the sequences are identical, and 0 if they have nothing in common. .ratio() is expensive to compute if you haven't already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.quick_ratio() 0.75 >>> s.real_quick_ratio() 1.0 _Differ._fancy_replacepickleA IQa IQa_split_line">nIncompatible checksums (0x%x vs (0x69089d0, 0xfefe063, 0xd40abce) = (a, autojunk, b, b2j, bjunk, bpopular, fullbcount, isjunk, matching_blocks, opcodes)) When replacing one block of lines with another, search the blocks for *similar* lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it. Example: >>> d = Differ() >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ... ['abcdefGhijkl\n'], 0, 1) >>> print(''.join(results), end="") - abcDefghiJkl ? ^ ^ ^ + abcdefGhijkl ? ^ ^ ^ striplo asyncio.coroutinesfmt_fancy_replacespancutoff must be in [0.0, 1.0]: funcGenericAliasqt3at6Cq 3avWAtoprefix__reduce_ex__KeyErrorlacharjunk_make_prefixstagscorebj2fromline id="Returns generator yielding marked up from/to side by side differences. Arguments: fromlines -- list of text lines to compared to tolines tolines -- list of text lines to be compared to fromlines context -- number of context lines to display on each side of difference, if None, all from/to text lines will be generated. linejunk -- passed on to ndiff (see ndiff documentation) charjunk -- passed on to ndiff (see ndiff documentation) This function returns an iterator which returns a tuple: (from line tuple, to line tuple, boolean flag) from/to line tuple -- (line num, line text) line num -- integer or None (to indicate a context separation) line text -- original line text with following markers inserted: '\0+' -- marks start of added text '\0-' -- marks start of deleted text '\0^' -- marks start of changed text '\1' -- marks end of added/deleted/changed text boolean flag -- None indicates context separation, True indicates either "from" or "to" line contains a change, otherwise False. This function/iterator was originally developed to generate side by side file difference for making HTML pages (see HtmlDiff class for example usage). Note, this function utilizes the ndiff function to generate the side by side difference markup. Optional ndiff arguments may be passed to this function and they in turn will be passed to ndiff. ? formatfromdescheapq}Kq Qthrow tripleDiffer.__init__00010203040506071011121314151617202122232425262730313233343536374041424344454647505152535455565760616263646566677071727374757677+Create unique anchor prefixes Compare `a` and `b`, two sequences of lines represented as bytes rather than str. This is a wrapper for `dfunc`, which is typically either unified_diff() or context_diff(). Inputs are losslessly converted to strings so that `dfunc` only has to worry about strings, and encoded back to bytes on return. This is necessary to compare files with unknown or inconsistent encoding. All other inputs (except `n`) must be bytes rather than str. ***************possibilitiestodateto%d_ai2j2eltb2j Compare two sequences of lines; generate the resulting delta. Each sequence must contain individual single-line strings ending with newlines. Such sequences can be obtained from the `readlines()` method of file-like objects. The delta generated also consists of newline- terminated strings, ready to be printed as-is via the writelines() method of a file-like object. Example: >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True), ... 'ore\ntree\nemu\n'.splitlines(True))), ... end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu _format_lineHtmlDiff.make_tablesecondcontext_diff..genexprlines to compare must be str, not Yields from/to lines of text with a change indication. This function is an iterator. It itself pulls lines from the line iterator. Its difference from that iterator is that this function always yields a pair of from/to text lines (with the change indication). If necessary it will collect single from/to lines until it has a matching pair from/to pair to yield. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. Return a measure of the sequences' similarity (float in [0,1]). Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is 1 if the sequences are identical, and 0 if they have nothing in common. .ratio() is expensive to compute if you haven't already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.quick_ratio() 0.75 >>> s.real_quick_ratio() 1.0 AssertionErrorA e2Q cXQfA  T$hhat7(!4q t81D qXQzip Isolate change clusters by eliminating ranges with no changes. Return a generator of groups with up to n lines of context. Each group is in the same format as returned by get_opcodes(). >>> from pprint import pprint >>> a = list(map(str, range(1,40))) >>> b = a[:] >>> b[8:8] = ['i'] # Make an insertion >>> b[20] += 'x' # Make a replacement >>> b[23:28] = [] # Make a deletion >>> b[30] += 'y' # Make another replacement >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes())) [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)], [('equal', 16, 19, 17, 20), ('replace', 19, 20, 20, 21), ('equal', 20, 22, 21, 23), ('delete', 22, 27, 23, 23), ('equal', 27, 30, 23, 26)], [('equal', 31, 34, 27, 30), ('replace', 34, 35, 30, 31), ('equal', 35, 38, 31, 34)]] >tag_c%dSequenceMatcher.get_grouped_opcodes(F 6*IXQclinejunkFor producing HTML side by side comparison with change highlights. This class can be used to create an HTML table (or a complete HTML file containing the table) showing a side by side, line by line comparison of text with inter-line and intra-line change highlights. The table can be generated in either full or contextual difference mode. The following methods are provided for HTML generation: make_table -- generates HTML for a single side by side table make_file -- generates complete HTML file with a single side by side table See Doc/includes/diff.py for an example usage of this class. tag_sget_close_matches (line 686)333333??@@`# (08Oh X##4# _t)?+kz,}0 GCTL.text$mn6.text$mn$00.text$x.idata$58.00cfg@.CRT$XCAH.CRT$XCZP.CRT$XIAX.CRT$XIZ`.CRT$XPAh.CRT$XPZp.CRT$XTAx.CRT$XTZ.rdata#l.rdata$voltmd#\.rdata$zzzdbgH&.rtc$IAAP&.rtc$IZZX&.rtc$TAA`&.rtc$TZZh&.xdata0B`.edataBP.idata$2B.idata$3B.idata$4J0.idata$6`6.dataX#.bss$.pdata`.rsrc$01`.rsrc$02dT4p!hh&!h&d 4 r p& 4 p`P  p`P0 @ p`P0 dT4p4 p `P!'!'# P!::h (!t d!4"`!x!P'!t `!x!P'!t `!x!P'!`!x!P'!`!x!P'!h t d!4"`!x!P' ( !"d&T'#$t%4, .A. (! .A. 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