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given)lxml.html._difflib.SequenceMatcher.set_seq1lxml.html._difflib.SequenceMatcher.set_seqslxml.html._difflib.SequenceMatcher.set_seq2cfunc.to_py.__Pyx_CFunc_8c8a55__4lxml_4html_8_difflib_15SequenceMatcher_double__etc_to_py_4self.wrapArgument '%.200s' has incorrect type (expected %.200s, got %.200s)lxml.html._difflib.SequenceMatcher.__init__dictionary changed size during iterationlxml.html._difflib.SequenceMatcher._SequenceMatcher__chain_b'NoneType' object is not iterabletoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack/usr/include/python3.14/cpython/tupleobject.hlxml.html._difflib.SequenceMatcher.get_opcodeslxml.html._difflib.SequenceMatcher.get_grouped_opcodes'NoneType' object has no attribute '%.30s'lxml.html._difflib.SequenceMatcher.find_longest_match/usr/include/python3.14/cpython/listobject.hlxml.html._difflib.SequenceMatcher.get_matching_blockscan't send non-None value to a just-started generator'NoneType' object is not subscriptablecfunc.to_py.__Pyx_CFunc_8c8a55__4lxml_4html_8_difflib_15SequenceMatcher_double__etc_to_py_4selflxml.html._difflib.SequenceMatcher.real_quick_ratiolxml.html._difflib.SequenceMatcher.ratio.genexprlxml.html._difflib.SequenceMatcher.ratiolocal variable '%s' referenced before assignmentlxml.html._difflib.SequenceMatcher.quick_ratiolxml.html._difflib.SequenceMatcher.__reduce_cython__lxml.html._difflib.SequenceMatcher.__setstate_cython__lxml.html._difflib.get_close_matcheslxml.html._difflib._keep_original_ws.genexprresult == PYGEN_RETURN || result == PYGEN_ERRORlxml.html._difflib._keep_original_wslxml.html._difflib.Differ.__init__lxml.html._difflib.Differ.comparelxml.html._difflib.Differ._dumpcannot fit '%.200s' into an index-sized integerlxml.html._difflib.Differ._plain_replacelxml.html._difflib.Differ._fancy_replacelxml.html._difflib.Differ._fancy_helperlxml.html._difflib.Differ._qformatlxml.html._difflib.IS_LINE_JUNKlxml.html._difflib.IS_CHARACTER_JUNKlxml.html._difflib._format_range_unifiedlxml.html._difflib.unified_diff'%.200s' object is unsliceablelxml.html._difflib._format_range_contextlxml.html._difflib.context_difflxml.html._difflib.context_diff.genexprlxml.html._difflib._check_types while calling a Python objectNULL result without error in PyObject_Calllxml.html._difflib.diff_bytes.decodelxml.html._difflib.__defaults__free variable '%s' referenced before assignment in enclosing scopelxml.html._difflib._mdiff._make_linelxml.html._difflib._mdiff._make_line.record_sub_infolxml.html._difflib._mdiff._line_iterator%.200s object is not an iteratorlxml.html._difflib._mdiff._line_pair_iteratorlxml.html._difflib.HtmlDiff.__init__lxml.html._difflib.HtmlDiff.make_filelxml.html._difflib.HtmlDiff._tab_newline_replace.expand_tabslxml.html._difflib.HtmlDiff._tab_newline_replacelxml.html._difflib.HtmlDiff._split_linelxml.html._difflib.HtmlDiff._line_wrapperlxml.html._difflib.HtmlDiff._collect_lineslxml.html._difflib.HtmlDiff._format_linelxml.html._difflib.HtmlDiff._make_prefixlxml.html._difflib.HtmlDiff._convert_flagslxml.html._difflib.HtmlDiff.make_tablelxml.html._difflib.__pyx_unpickle_SequenceMatcher__set_state__pyx_unpickle_SequenceMatcherlxml.html._difflib.__pyx_unpickle_SequenceMatcher__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__annotations__ must be set to a dict objectInterpreter change detected - this module can only be loaded into one interpreter per process.__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 callsShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling/usr/include/python3.14/cpython/unicodeobject.h%s() got multiple values for keyword argument '%U'%.200s() keywords must be stringscached_type == NULL || PyType_Check(cached_type)unbound method %.200S() needs an argument/usr/include/python3.14/cpython/methodobject.h%s() got an unexpected keyword argument '%U'Module '_difflib' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%ddouble (Py_ssize_t, Py_ssize_t)metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesqualified name of the generatorobject being iterated by 'yield from', or Nonesend(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.close() -> raise GeneratorExit inside generator._cython_3_1_4._common_types_metatype_cython_3_1_4.cython_function_or_methodlxml.html._difflib.__pyx_scope_struct____Pyx_CFunc_8c8a55__4lxml_4html_8_difflib_15SequenceMatcher_double__etc_to_py_4selflxml.html._difflib.__pyx_scope_struct_19_restorelxml.html._difflib.__pyx_scope_struct_18__line_wrapperlxml.html._difflib.__pyx_scope_struct_17__tab_newline_replacelxml.html._difflib.__pyx_scope_struct_16__line_pair_iteratorlxml.html._difflib.__pyx_scope_struct_15__line_iteratorlxml.html._difflib.__pyx_scope_struct_14__mdifflxml.html._difflib.__pyx_scope_struct_13_diff_byteslxml.html._difflib.__pyx_scope_struct_12_genexprlxml.html._difflib.__pyx_scope_struct_11_genexprlxml.html._difflib.__pyx_scope_struct_10_context_difflxml.html._difflib.__pyx_scope_struct_9_unified_difflxml.html._difflib.__pyx_scope_struct_8__qformatlxml.html._difflib.__pyx_scope_struct_7__fancy_helperlxml.html._difflib.__pyx_scope_struct_6__fancy_replacelxml.html._difflib.__pyx_scope_struct_5__plain_replacelxml.html._difflib.__pyx_scope_struct_4__dumplxml.html._difflib.__pyx_scope_struct_3_comparelxml.html._difflib.__pyx_scope_struct_2_genexprlxml.html._difflib.__pyx_scope_struct_1_genexprlxml.html._difflib.__pyx_scope_struct__get_grouped_opcodeslxml.html._difflib.__pyx_defaultslxml.html._difflib.SequenceMatcher SequenceMatcher is a flexible class for comparing pairs of sequences of any type, so long as the sequence elements are hashable. 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. __Pyx_Coroutine_get_is_running(gen)invalid vtable found for imported typeraise: 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 BaseException__Pyx_Coroutine_get_is_running(self)delegate_result != PYGEN_ERROR || ret == NULL'%.200s' object is not subscriptablebase 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 typemultiple bases have vtable conflict: '%.200s' and '%.200s'Unable to initialize pickling for %.200sjoin() result is too long for a Python string/usr/include/python3.14/cpython/longintrepr.hPyType_HasFeature(op->ob_base.ob_type, Py_TPFLAGS_LONG_SUBCLASS)__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)__mro_entries__ must return a tuplePyExceptionClass_Check(exc_type)generator raised StopIteration/usr/include/python3.14/cpython/classobject.hPy_TYPE(_PyObject_CAST(ob)) != &PyBool_TypePyExceptionClass_Check(exc_type1)PyExceptionClass_Check(exc_type2)generator ignored GeneratorExit/usr/include/python3.14/cpython/dictobject.htype == NULL || (value != NULL && type == (PyObject*) Py_TYPE(value))iter() returned non-iterator of type '%.200s'/usr/include/python3.14/cpython/floatobject.h(result == PYGEN_RETURN && retval != NULL) || (result == PYGEN_ERROR && retval == NULL)Py_TYPE(ob) != &PyLong_Typeset_seq1exactlysrc/lxml/html/_difflib.pyset_seqsset_seq2wrapMissing type objectselfat leastat mostsrc/lxml/html/_difflib.cPyTuple_Check(__pyx_args)__init___SequenceMatcher__chain_b__pyx_t_6PyTuple_Check(sequence)PyList_Check(sequence)PyTuple_Check(op)get_grouped_opcodesget__pyx_t_2PyList_Check(op)0 <= index.0genexpr__reduce_cython__name '%U' is not defined__setstate_cython__tupleExpected %s, got %.200sget_close_matches_keep_original_wsgenerator already executingcomparePyTuple_Check(__pyx_t_4)_plain_replace_dump_fancy_replace__pyx_t_10__pyx_t_4__pyx_t_12__pyx_t_1_fancy_helper_qformat__pyx_t_5IS_LINE_JUNK__pyx_t_3IS_CHARACTER_JUNK_format_range_unifiedunified_diff_format_range_contextcontext_diff__pyx_t_14_check_typeslxml.html._difflib.diff_bytesdecodelxml.html._difflib.ndifflxml.html._difflib._mdiff_make_linechange_rerecord_sub_infodiff_lines_iteratorstartswithfrom_lineto_line_line_iterator_line_pair_iteratormake_file_tab_newline_replaceexpand_tabs_split_line_line_wrapper_collect_lines_format_line_make_prefix_convert_flagsmake_tablelxml.html._difflib.restoresetlist__loader__loader__file__origin__package__parent__path__submodule_search_locationsPyTuple_Check(res)data != NULL_cython_3_1_4takes no arguments%.200s() %s (%zd given)takes exactly one argumentBad call flags for CyFunctiontakes no keyword arguments%.200s() %skeywords must be stringsPyTuple_Check(tuple)PyCallable_Check(callable)offset > 0PyCFunction_Check(func_obj)PyTuple_Check(kwds)PyErr_Occurred()builtinscython_runtime__builtins__does not match__debug____pyx_capi___calculate_ratioPyTuple_Check(bases)__orig_bases__init lxml.html._difflib__module__func_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__name of the generatorgi_frameFrame of the generatorgi_runninggi_yieldfromgi_codethrowclose__reduce_ex__aCythonUnboundCMethod_cython_3_1_4.generatorcannot pickle '%.200s' objectiternextneeds an argumentf->f_back == NULLret != NULLcannot import name %SPy_TYPE(ob) != &PyBool_TypePyTuple_Check(substrings)PyUnicode_Check(op)PyUnstable_Long_IsCompact(op)an integer is requiredindex < list->allocatedPyTuple_Check(mro)index < Py_SIZE(tuple)PyMethod_Check(meth)kind == PyUnicode_4BYTE_KINDgen->is_runningPyDict_Check(op)!gen->yieldfrom!gen->yieldfrom_am_sendPyTuple_Check(o)PyFloat_Check(op)!last_one_off || *dpos == '0'index >= 0value <= 0xffUPUUXPVLXTS float__pyx_gb_4lxml_4html_8_difflib_15SequenceMatcher_12generator__pyx_f_4lxml_4html_8_difflib_15SequenceMatcher_get_opcodes__pyx_f_4lxml_4html_8_difflib_15SequenceMatcher_get_matching_blocks__pyx_f_4lxml_4html_8_difflib_15SequenceMatcher_find_longest_match__pyx_pf_4lxml_4html_8_difflib_15SequenceMatcher_8__chain_b__pyx_pw_4lxml_4html_8_difflib_15SequenceMatcher_1__init__Py_SET_SIZEPyList_SET_ITEMPyTuple_SET_ITEMPy_SIZE?wJ?@333333?@,,,,,˅w_F-ɕw_F-ɛ__pyx_gb_4lxml_4html_8_difflib_6Differ_10generator3__pyx_gb_4lxml_4html_8_difflib_6Differ_4generator1__pyx_gb_4lxml_4html_8_difflib_17_keep_original_ws_2generator14__Pyx_Generator_GetInlinedResult__pyx_pf_4lxml_4html_8_difflib_get_close_matchesoW>% U=$ |$d~eL3LL,d8#('__pyx_gb_4lxml_4html_8_difflib_12generator7__pyx_pf_4lxml_4html_8_difflib_4IS_LINE_JUNK__pyx_gb_4lxml_4html_8_difflib_6Differ_19generator6__pyx_gb_4lxml_4html_8_difflib_6Differ_13generator4:T"T TSSSSSsS[[UXWXDXlXXHz{~su8 qW __pyx_gb_4lxml_4html_8_difflib_22generator9__pyx_gb_4lxml_4html_8_difflib_17generator8__pyx_gb_4lxml_4html_8_difflib_12context_diff_5generator16__pyx_gb_4lxml_4html_8_difflib_12context_diff_2generator15 j T 66P7h777777745__pyx_gb_4lxml_4html_8_difflib_6_mdiff_7generator18__pyx_pf_4lxml_4html_8_difflib_6_mdiff__make_line__pyx_pf_4lxml_4html_8_difflib_23ndiff8tswop ܕÕTԘԙx_F- ,lzdN8Yields 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. Yields 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. Returns line of text with user's change markup and line formatting. lines -- list of lines from the ndiff generator to produce a line of text from. When producing the line of text to return, the lines used are removed from this list. format_key -- '+' return first line in list with "add" markup around the entire line. '-' return first line in list with "delete" markup around the entire line. '?' return first line in list with add/delete/change intraline markup (indices obtained from second line) None return first line in list with no markup side -- indice into the num_lines list (0=from,1=to) num_lines -- from/to current line number. This is NOT intended to be a passed parameter. It is present as a keyword argument to maintain memory of the current line numbers between calls of this function. 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. __pyx_gb_4lxml_4html_8_difflib_8HtmlDiff_10generator11_PyLong_CompactValue__pyx_gb_4lxml_4html_8_difflib_27generator10 o Y =<<<<< %s%s%s%s totexttoprefixtolisttolinestolinetofiledatetofiletodesctodatetodatato_lineto_diffto%d_throw
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%(header_row)s %(data_rows)s
table_tab_newline_replacesurrogateescapesumsub_infosub_stylesstylesstripstopstatestartedstartsrc/lxml/html/_difflib.py_split_line__spec__spansizeside__setstate_cython____setstate__set_seqsset_seq2set_seq1__set_name__sendselfsecondscoresrstrip :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} }reversedresultrestore (line 2076)restorereplace__reduce_ex____reduce_cython____reduce__record_sub_inforeratio..genexprratiorange__qualname___qformat__pyx_vtable____pyx_unpickle_SequenceMatcher__pyx_type__pyx_state__pyx_result__pyx_checksum__pyx_PickleError__prepare__prefixesprefix_prefixpossibilitiespopularpop_plain_replacepicklepatobjectnumlinesnum_linesnum_chgnum_blanks_to_yieldnum_blanks_pendingntestnnnlargest_nlargestnext_idnext_hrefnext__new__ndiff (line 1330)ndiff _namedtuplenamedtuple__name__n must be > 0: ">nnmsg__mro_entries____module____metaclass___mdiff.._make_line..record_sub_info_mdiff.._make_line_mdiff.._line_pair_iterator_mdiff.._line_iterator_mdiffmaxmatchesmatch_objectmarkersmarkmapmake_table_make_prefix_make_linemake_file_make__main__lxml.html._difflib<lolinetermlines_to_writelines to compare must be str, not lineslinenum_linejunklinejunk_line_wrapperline_pair_iterator_line_pair_iteratorline_numline_iterator_line_iteratorline2line1linelength_legendlegendlblastlakeyskey_keep_original_ws..genexpr_keep_original_wsjunkj2j1jitemsisspaceisjunkisenabled_is_coroutineinsertinput must be a sequence of strings, not %s_initializing__init__indicesindexin_changeidxs id="difflib_chg_id id="i2i1ihiheapqheader_row>group__getstate__get_grouped_opcodesget_close_matches (line 686)get_close_matchesgetgenexprgcgfunc__func__fromtextfromprefixfromlistfromlinesfromlinefromfiledatefromfilefromdescfromdatefromdatafrom_linefrom%d_fromDifffound_diff_format_range_unified_format_range_context_format_lineformat_keyformatfmtflaglistflagfirst_file_templatefile2_rangefile1_range_fancy_replace_fancy_helperexpandtabsexpand_tabserrequalenumerateendencodeenableeltdump_jdump_i_dump__doc__disablediffsdiff_lines_iteratordiff_bytes..decodediff_bytes_dict__dict__dfuncdeltadelete_default_prefixdecodedata_rowsdata_listd%dcutoff must be in [0.0, 1.0]: cutoffcrunchercrqrcrcqrcount_convert_flagscontext_linescontext_diff..genexprcontext_diff (line 1201)context_diffcontextLinescontextcompilecomparecollections_collect_linescodesclosecline_in_traceback__class_getitem___check_typescharset_charjunkcharjunkchange_re__chain_bchcfunc.to_pycbtagsbloblinebj2bj1bhibest_ratiobest_jbest_ibeltbeginningbeginb2jbautojunkatagsasyncio.coroutinesasciiargsargarangeappend&aloall arguments must be str, not: all arguments must be bytes, not __all__alineai2ai1ahiaequivaeltadd_notetf- (\++|\-+|\^+)+-+---?+-+?--+--?+--++-?+? ?) (--- {} ----{}*** {} ****{}****************** {}{}{}! @@ -{} +{} @@{}+++ {}{}{}--- {}{}{} {}{},{}{} #+ X 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 ? ^ ^ ^ WINDOWValueErrorUse 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'] TypeErrorStopIterationSet the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 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(). 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(). SequenceMatcher.set_seqs (line 203)SequenceMatcher.set_seqsSequenceMatcher.set_seq2 (line 241)SequenceMatcher.set_seq2SequenceMatcher.set_seq1 (line 215)SequenceMatcher.set_seq1SequenceMatcher.ratio (line 614)SequenceMatcher.ratioSequenceMatcher.get_grouped_opcodes (line 564)SequenceMatcher.get_grouped_opcodes_SequenceMatcher__chain_bSequenceMatcher.__setstate_cython__SequenceMatcher.__reduce_cython__SequenceMatcher.__chain_bSequenceMatcherReturn 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 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 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 __Pyx_CFunc_8c8a55__4lxml_4html_8_difflib_15SequenceMatcher_double__etc_to_py_4self..wrapPickleErrorNote 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.MatchKeyError 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)]] Incompatible checksums (0x%x vs (0x69089d0, 0xfefe063, 0xd40abce) = (a, autojunk, b, b2j, bjunk, bpopular, fullbcount, isjunk, matching_blocks, opcodes))ImportErrorIS_LINE_JUNK (line 1061)IS_LINE_JUNKIS_CHARACTER_JUNK (line 1082)IS_CHARACTER_JUNKHtmlDiff.make_tableHtmlDiff.make_fileHtmlDiff._tab_newline_replace..expand_tabsHtmlDiff._tab_newline_replaceHtmlDiff._split_lineHtmlDiff._make_prefixHtmlDiff._line_wrapperHtmlDiff._format_lineHtmlDiff._convert_flagsHtmlDiff._collect_linesHtmlDiff.__init__HtmlDiffGenericAlias 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 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' For 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. 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. Differ.compare (line 853)Differ.compareDiffer._qformat (line 1017)Differ._qformatDiffer._plain_replaceDiffer._fancy_replace (line 913)Differ._fancy_replaceDiffer._fancy_helperDiffer._dumpDiffer.__init__Differ Diff comparison %(table)s%(legend)s 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 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 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 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 AttributeErrorAssertionError_ 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 Returns 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). Makes list of "next" linksCreate unique anchor prefixesReturns 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 Collects mdiff output into separate lists Before storing the mdiff from/to data into a list, it is converted into a single line of text with HTML markup. Returns iterator that splits (wraps) mdiff text linesBuilds 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. Returns 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. Returns 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 HtmlDiff 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. 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. 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 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. 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 Convert range to the "ed" format 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 Convert range to the "ed" format 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 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 "?" 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' 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 ? ^ ^ ^ Generate comparison results for a same-tagged range. 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 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. Replace whitespace with the original whitespace characters in `s`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'] 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 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)]] 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(). 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(). Set the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 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. __Pyx_CyFunction_init_defaults__Pyx_FetchCommonTypeFromSpec__Pyx_RejectUnknownKeyword__Pyx_ParseKeywordsTuplePyCFunction_GET_SELF__Pyx_PyVectorcall_Function_PyUnicode_NONCOMPACT_DATAPyUnicode_READ__Pyx_PyErr_ExceptionMatchesTuplehkA ^1!"~~@@A?(!1|7!12DNRS 1C1*,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!4A 4xq !3as!1ASAQSq Bhiq q4q 12Rq1E!;3iq qQh&K1"!A 4q q aq  1 q1A9AQ 4xq Qe?r(@1yy !3A Zr82XQ$A Kq 10A e2Q cXQfA  T$hhat7(!4q t81D qXQ1}WA  3c IWHA qM"F!gQd-q&qgQa G1Ayq2hA 4q WBiq  s!1d! EEe2TqQd#Q WBiq  A Aqb$d"Ba t1Cs!Qt1AQQcAQqQQ BaAQ 1 F"A E5! )1 L4q2gA  A[t81!;avTz1&,M!$ $A 4q 4q $k+Yj"*)9A A )/waq'2vj 0 L O1 M ME1Ҷ(F 6*IXQc12rT1AQd!iqQaq;aqrT1AQd!iqQaq;aqz#QiqFaARqz#QiqFaARqq 4z%q )1DAqd b U"At1Qwct7!1 7'+Zr!1b U"Awct7!1t1Q 7'+Q1Q4!  3c0:qt3at6Cq 3avWA0%a( L L0>s 2U! 1+5:t2Sj0t4s*Aj?q QqYaqQ !1 1 S7! L3gQ F#S '1F$aYas! 1Bd'aD2 -QfA0TTDDd($kQUUbbffoostFFJJK G1F,avWA!qt3gU#TG54uGSXX[[__ffmmrruuyzDDKKPPSSWWccjjoorrvv~~EEJJMMQQbbiinnqquu~~EEFq0AWKwa0AWKq1A. #WAq ATRs!4q1fA D G6 Cwiq cAU! 7!1 IZqQ 1 s%q6!AQ qCq L Q Cq 4zRs! BcRq XSa3avRq4q qCq3 A* 2SA  E t;a N! Ja00A* 2SA  E t;a0PA IQa IQa0`B1(2L' 4xqA 4{!4q 4xqA 4xqTA06ar. )1 ;c IQfEQb ;c *EQd%t1A q  T*! V881At1BfAT#Qd!6Qd!4q 4q 5ARq t1q 5+RuBa  &32&1% 6aqV1Bas,e1|6!0~Q5WA 1G1IQ! >a;a )1Ja0N!!!1aI3}A"/a60$10$}Kq Q5\E1#2#G8#2#1~#A2U#0"A@0}"$A24RAqA0CConstruct a SequenceMatcher. Optional arg isjunk is None (the default), or a one-argument function that takes a sequence element and returns true iff the element is junk. None is equivalent to passing "lambda x: 0", i.e. no elements are considered to be junk. For example, pass lambda x: x in " \t" if you're comparing lines as sequences of characters, and don't want to synch up on blanks or hard tabs. Optional arg a is the first of two sequences to be compared. By default, an empty string. 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