3 \�I�@s8dZddddddddd d ddgZd dlmZd dlmZedd�Zdd�Z Gdd�d�Z d8dd�Zdd�ZGdd�d�Z d dlZejd�jfdd�Zd9dd�Zdd �Zd:d#d �Zd$d%�Zd;d&d�Zd'd(�Zd" 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. Methods: __init__(isjunk=None, a='', b='') Construct a SequenceMatcher. set_seqs(a, b) Set the two sequences to be compared. set_seq1(a) Set the first sequence to be compared. set_seq2(b) Set the second sequence to be compared. find_longest_match(alo, ahi, blo, bhi) Find longest matching block in a[alo:ahi] and b[blo:bhi]. get_matching_blocks() Return list of triples describing matching subsequences. get_opcodes() Return list of 5-tuples describing how to turn a into b. ratio() Return a measure of the sequences' similarity (float in [0,1]). quick_ratio() Return an upper bound on .ratio() relatively quickly. real_quick_ratio() Return an upper bound on ratio() very quickly. N�TcCs(||_d|_|_||_|j||�dS)a!Construct 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. The elements of a must be hashable. See also .set_seqs() and .set_seq1(). Optional arg b is the second of two sequences to be compared. By default, an empty string. The elements of b must be hashable. See also .set_seqs() and .set_seq2(). Optional arg autojunk should be set to False to disable the "automatic junk heuristic" that treats popular elements as junk (see module documentation for more information). N)�isjunk�a�b�autojunk�set_seqs)�selfrrrrrrr�__init__�s;zSequenceMatcher.__init__cCs|j|�|j|�dS)z�Set the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 N)�set_seq1�set_seq2)rrrrrrr�s zSequenceMatcher.set_seqscCs$||jkrdS||_d|_|_dS)aMSet 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(). N)r�matching_blocks�opcodes)rrrrrr�s zSequenceMatcher.set_seq1cCs2||jkrdS||_d|_|_d|_|j�dS)aMSet 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(). N)rrr � fullbcount�_SequenceMatcher__chain_b)rrrrrr�s zSequenceMatcher.set_seq2cCs�|j}i|_}x*t|�D]\}}|j|g�}|j|�qWt�|_}|j}|r�x"|j�D]}||�r\|j |�q\Wx|D] }||=q|Wt�|_ }t|�} |jr�| dkr�| dd} x*|j �D]\}}t|�| kr�|j |�q�Wx|D] }||=q�WdS)N��d�)r�b2j� enumerate� setdefault�append�set�bjunkr�keys�addZbpopular�lenr�items)rrr&�i�elt�indicesZjunkrZpopular�nZntestZidxsrrrZ __chain_b)s, zSequenceMatcher.__chain_bcCsN|j|j|j|jjf\}}}}||d} } }i}g} x�t||�D]�}|j}i}xn|j||| �D]Z}||krpqb||krzP||dd�d}||<||krb||d||d|} } }qbW|}qBWxb| |k�r*| |k�r*||| d��r*|| d|| dk�r*| d| d|d} } }q�WxX| ||k�r�| ||k�r�||| |��r�|| ||| |k�r�|d7}�q.Wxb| |k�r�| |k�r�||| d��r�|| d|| dk�r�| d| d|d} } }�q�WxV| ||k�r@| ||k�r@||| |��r@|| ||| |k�r@|d}�q�Wt| | |�S)a�Find longest matching block in a[alo:ahi] and b[blo:bhi]. If isjunk is not defined: Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where alo <= i <= i+k <= ahi blo <= j <= j+k <= bhi and for all (i',j',k') meeting those conditions, k >= k' i <= i' and if i == i', j <= j' In other words, of all maximal matching blocks, return one that starts earliest in a, and of all those maximal matching blocks that start earliest in a, return the one that starts earliest in b. >>> s = SequenceMatcher(None, " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=0, b=4, size=5) If isjunk is defined, first the longest matching block is determined as above, but with the additional restriction that no junk element appears in the block. Then that block is extended as far as possible by matching (only) junk elements on both sides. So the resulting block never matches on junk except as identical junk happens to be adjacent to an "interesting" match. Here's the same example as before, but considering blanks to be junk. That prevents " abcd" from matching the " abcd" at the tail end of the second sequence directly. Instead only the "abcd" can match, and matches the leftmost "abcd" in the second sequence: >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=1, b=0, size=4) If no blocks match, return (alo, blo, 0). >>> s = SequenceMatcher(None, "ab", "c") >>> s.find_longest_match(0, 2, 0, 1) Match(a=0, b=0, size=0) r r%)rrr&r+�__contains__�range�getr)r�alo�ahi�blo�bhirrr&ZisbjunkZbestiZbestjZbestsizeZj2lenZnothingr0Zj2lengetZnewj2len�j�krrr�find_longest_matchPsB8$ "z"SequenceMatcher.find_longest_matchcCs�|jdk r|jSt|j�t|j�}}d|d|fg}g}x�|r�|j�\}}}}|j||||�\} } }}|r:|j|�|| kr�|| kr�|j|| || f�| ||kr:| ||kr:|j| ||| ||f�q:W|j�d} }}g}x^|D]V\}}}| ||k�r|||k�r||7}q�|�r2|j| ||f�|||} }}q�W|�r\|j| ||f�|j||df�tt t j|��|_|jS)aReturn list of triples describing matching subsequences. Each triple is of the form (i, j, n), and means that a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i and in j. New in Python 2.5, it's also guaranteed that if (i, j, n) and (i', j', n') are adjacent triples in the list, and the second is not the last triple in the list, then i+n != i' or j+n != j'. IOW, adjacent triples never describe adjacent equal blocks. The last triple is a dummy, (len(a), len(b), 0), and is the only triple with n==0. >>> s = SequenceMatcher(None, "abxcd", "abcd") >>> list(s.get_matching_blocks()) [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)] Nr )rr.rr�popr=r)�sort�list�mapr�_make)r�la�lbZqueuerr7r8r9r:r0r;r<�x�i1�j1Zk1Znon_adjacent�i2�j2Zk2rrr�get_matching_blocks�s8 z#SequenceMatcher.get_matching_blockscCs�|jdk r|jSd}}g|_}x�|j�D]�\}}}d}||krP||krPd}n||kr^d}n||krjd}|r�|j|||||f�||||}}|r,|jd||||f�q,W|S)a[Return list of 5-tuples describing how to turn a into b. Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the tuple preceding it, and likewise for j1 == the previous j2. The tags are strings, with these meanings: 'replace': a[i1:i2] should be replaced by b[j1:j2] 'delete': a[i1:i2] should be deleted. Note that j1==j2 in this case. 'insert': b[j1:j2] should be inserted at a[i1:i1]. Note that i1==i2 in this case. 'equal': a[i1:i2] == b[j1:j2] >>> a = "qabxcd" >>> b = "abycdf" >>> s = SequenceMatcher(None, a, b) >>> for tag, i1, i2, j1, j2 in s.get_opcodes(): ... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" % ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))) delete a[0:1] (q) b[0:0] () equal a[1:3] (ab) b[0:2] (ab) replace a[3:4] (x) b[2:3] (y) equal a[4:6] (cd) b[3:5] (cd) insert a[6:6] () b[5:6] (f) Nr r�replace�delete�insert�equal)r rJr))rr0r;Zanswer�ai�bj�size�tagrrr�get_opcodess$ zSequenceMatcher.get_opcodes�c csr|j�}|sdg}|dddkrZ|d\}}}}}|t|||�|t|||�|f|d<|dddkr�|d\}}}}}||t|||�|t|||�f|d<||}g} x�|D]�\}}}}}|dko�|||k�r(| j||t|||�|t|||�f�| Vg} t|||�t|||�}}| j|||||f�q�W| �rnt| �dk�ob| dddk�rn| VdS) a� 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)]] rNr r%N)rNr r%r r%��rUrU)rS�max�minr)r.) rr3ZcodesrRrFrHrGrIZnn�grouprrr�get_grouped_opcodes<s(&&((z#SequenceMatcher.get_grouped_opcodescCs0tdd�|j�D��}t|t|j�t|j��S)a�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 css|]}|dVqdS)r%NrUr)�.0Ztriplerrr� �sz(SequenceMatcher.ratio..)�sumrJrr.rr)rrrrr�rationszSequenceMatcher.ratiocCs�|jdkr8i|_}x"|jD]}|j|d�d||<qW|j}i}|jd}}xH|jD]>}||�rl||}n|j|d�}|d||<|dkrV|d}qVWt|t|j�t|j��S)z�Return an upper bound on ratio() relatively quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute. Nr r%)r!rr6r4rrr.)rr!r1ZavailZavailhasrZnumbrrr�quick_ratio�s zSequenceMatcher.quick_ratiocCs*t|j�t|j�}}tt||�||�S)z�Return an upper bound on ratio() very quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute than either .ratio() or .quick_ratio(). )r.rrrrW)rrCrDrrr�real_quick_ratio�sz SequenceMatcher.real_quick_ratio)NrrT)rT)�__name__� __module__�__qualname__�__doc__rrrrr"r=rJrSrYr]r^r_rrrrr+sj @,'nG7 2rT�333333�?cCs�|dkstd|f��d|ko(dkns 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'] r zn must be > 0: %rgg�?z cutoff must be in [0.0, 1.0]: %rcSsg|]\}}|�qSrr)rZZscorerErrr� �sz%get_close_matches..) � ValueErrorrrrr_r^r]r)� _nlargest)ZwordZ possibilitiesr3�cutoff�result�srErrrr�s cCs4dt|�}}x ||kr.|||kr.|d7}qW|S)z} Return number of `ch` characters at the start of `line`. Example: >>> _count_leading(' abc', ' ') 3 r r%)r.)�line�chr0r3rrr�_count_leading�s rmc@sJeZdZdZddd�Zdd�Zdd�Zd d �Zdd�Zd d�Z dd�Z dS)ra� 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. Methods: __init__(linejunk=None, charjunk=None) Construct a text differencer, with optional filters. compare(a, b) Compare two sequences of lines; generate the resulting delta. NcCs||_||_dS)a� 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. N)�linejunk�charjunk)rrnrorrrrMszDiffer.__init__c cs�t|j||�}x�|j�D]�\}}}}}|dkrD|j||||||�} n\|dkr^|jd|||�} nB|dkrx|jd|||�} n(|dkr�|jd|||�} ntd|f��| Ed HqWd S) a� 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 writeline() 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 rKrL�-rM�+rN� zunknown tag %rN)rrnrS�_fancy_replace�_dumprf) rrr�cruncherrRr7r8r9r:�grrr�comparedszDiffer.compareccs*x$t||�D]}d|||fVqWdS)z4Generate comparison results for a same-tagged range.z%s %sN)r5)rrRrE�lo�hir0rrrrt�szDiffer._dumpc cs�||kr||kst�||||krF|jd|||�}|jd|||�}n |jd|||�}|jd|||�}x||fD]} | EdHqpWdS)Nrqrp)�AssertionErrorrt) rrr7r8rr9r:�first�secondrvrrr�_plain_replace�szDiffer._plain_replaceccsHd\}}t|j�} d\} }x�t||�D]�}||} | j| �xxt||�D]j}||}|| krp| dkrH||} }qH| j|�| j�|krH| j�|krH| j�|krH| j�||}}}qHWq&W||k�r�| dkr�|j||||||�EdHdS| |d}}}nd} |j ||||||�EdH||||}}| dk�rd}}| j ||�x�| j�D]�\}}}}}||||}}|dk�r�|d|7}|d|7}nb|dk�r�|d |7}nJ|d k�r�|d|7}n2|dk�r�|d |7}|d |7}ntd|f��qTW|j ||||�EdHn d|V|j ||d|||d|�EdHdS)aL When replacing one block of lines with another, search the blocks for *similar* lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it. Example: >>> d = Differ() >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ... ['abcdefGhijkl\n'], 0, 1) >>> print(''.join(results), end="") - abcDefghiJkl ? ^ ^ ^ + abcdefGhijkl ? ^ ^ ^ �G�z��?��?Ng�?rrK�^rLrprMrqrNrrzunknown tag %rz r%)r~r)NN)rror5rrr_r^r]r}� _fancy_helperrrSrf�_qformat)rrr7r8rr9r:Z best_ratiorhruZeqiZeqjr;rPr0rOZbest_iZbest_jZaeltZbelt�atags�btagsrRZai1Zai2Zbj1Zbj2rCrDrrrrs�sX zDiffer._fancy_replaceccsbg}||kr<||kr*|j||||||�}qT|jd|||�}n||krT|jd|||�}|EdHdS)Nrprq)rsrt)rrr7r8rr9r:rvrrrr��szDiffer._fancy_helperccs�tt|d�t|d��}t|t|d|�d��}t|t|d|�d��}||d�j�}||d�j�}d|V|r�dd||fVd|V|r�dd||fVdS)a� Format "?" output and deal with leading tabs. Example: >>> d = Differ() >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n', ... ' ^ ^ ^ ', ' ^ ^ ^ ') >>> for line in results: print(repr(line)) ... '- \tabcDefghiJkl\n' '? \t ^ ^ ^\n' '+ \tabcdefGhijkl\n' '? \t ^ ^ ^\n' � Nrrz- z? %s%s z+ )rWrm�rstrip)rZalineZbliner�r��commonrrrr� s zDiffer._qformat)NN)r`rarbrcrrwrtr}rsr�r�rrrrr�s\ )^Nz \s*(?:#\s*)?$cCs||�dk S)z� Return 1 for ignorable line: iff `line` is blank or contains a single '#'. Examples: >>> IS_LINE_JUNK('\n') True >>> IS_LINE_JUNK(' # \n') True >>> IS_LINE_JUNK('hello\n') False Nr)rkZpatrrrr>s� cCs||kS)z� Return 1 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 r)rlZwsrrrrNscCs:|d}||}|dkr"dj|�S|s.|d8}dj||�S)z Convert range to the "ed" formatr%z{}z{},{})�format)�start�stop� beginningrrrr�_format_range_unifiedes r�r� ccsht|||||||�d}�xHtd||�j|�D�]0} |s�d}|rJdj|�nd} |r\dj|�nd}dj|| |�Vdj|||�V| d| d}} t|d | d �}t|d| d�}d j|||�Vx�| D]�\}}}}}|dk�rx|||�D]}d|Vq�Wq�|dk�r2x |||�D]}d|V�qW|dkr�x |||�D]}d|V�qHWq�Wq.WdS)a� 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 FNTz {}rz --- {}{}{}z +++ {}{}{}r r%�rT�z@@ -{} +{} @@{}rNrrrKrLrprMrqrU>rLrK>rMrK)�_check_typesrrYr�r�)rr�fromfile�tofile�fromfiledate� tofiledater3�lineterm�startedrX�fromdate�todater{�last�file1_range�file2_rangerRrFrHrGrIrkrrrr ps0) cCsB|d}||}|s|d8}|dkr.dj|�Sdj|||d�S)z Convert range to the "ed" formatr%z{}z{},{})r�)r�r�r�rrrr�_format_range_context�s r�ccs�t|||||||�tddddd�}d} �xztd||�j|�D�]b} | s�d} |rZd j|�nd }|rld j|�nd }dj|||�Vdj|||�V| d | d} }d|Vt| d|d�}dj||�Vtdd�| D��r&xD| D]<\}}}}}|dkr�x$|||�D]}|||V�q Wq�Wt| d|d�}dj||�Vtdd�| D��r>xH| D]@\}}}}}|dk�r^x$|||�D]}|||V�q�W�q^Wq>WdS)ah 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 z+ z- z! z )rMrLrKrNFNTz {}rz *** {}{}{}z --- {}{}{}r r%z***************r�z *** {} ****{}css |]\}}}}}|dkVqdS)rKrLN>rLrKr)rZrR�_rrrr[szcontext_diff..rMrTr�z --- {} ----{}css |]\}}}}}|dkVqdS)rKrMN>rMrKr)rZrRr�rrrr[srLrU)r��dictrrYr�r��any)rrr�r�r�r�r3r��prefixr�rXr�r�r{r�r�rRrFrHr�rkr�rGrIrrrr�s4, cGs�|r2t|dt�r2tdt|d�j|df��|rdt|dt�rdtdt|d�j|df��x$|D]}t|t�sjtd|f��qjWdS)Nr z)lines to compare must be str, not %s (%r)z"all arguments must be str, not: %r)� isinstance�str� TypeError�typer`)rr�args�argrrrr�s r�� c cs�dd�} tt| |��}tt| |��}| |�}| |�}| |�}| |�}| |�}|||||||||�} x| D]}|jdd�VqhWdS)a� 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(). 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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 )rrw)rrrnrorrrr@s#c#s�ddl}|jd��t||||��ddgf�fdd� ��fdd��fdd �}|�}|dkrj|EdH�n.|d 7}d}�xddg|} } d}xR|dkr�yt|�\}} }Wntk r�dSX| |}|| |f| |<| d 7} q�W| |k�r�dV|}n| }d} x.|�r.| |}| d 7} | |V|d 8}�qW|d }yDx>|�rxt|�\}} }|�r`|d }n|d 8}|| |fV�q._make_line..record_sub_info��rr)r>�sub�reversed)r�Z format_key�sideZ num_lines�textZmarkersr�r��keyZbegin�end)� change_rerr� _make_line�s 4z_mdiff.._make_linec3s�g}d\}}�xlx t|�dkr0|jt�d��qWdjdd�|D��}|jd�rX|}�n�|jd�r��|dd��|dd �d fVq�n�|jd�r�|d 8}�|dd�d d fVq�nl|jd�rވ|dd�d }}|d d}}�n>|jd��r�|d d��|dd �d fVq�n|jd��r8�|dd��|d d �d fVqn�|jd��rd|d 8}�|dd�d d fVqn�|jd��r�|d 7}d �|dd �d fVqn�|jd��r�d �|dd �}}|d d}}n^|jd��r�|d 7}d �|dd �d fVqn2|jd��r�|d d �d d��|d d �dfVqx|dk�r:|d 7}dV�qWx|dk�rZ|d 8}d V�q>W|jd��rld S||d fVqWd S)!a�Yields from/to lines of text with a change indication. This function is an iterator. 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Legends

Colors
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Links
(f)irst change
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Nr�rTr rr%r��>)r)r.r��count�_split_line)rZ data_listZline_numr�rQrVr0r3ZmarkZline1Zline2rrrr�s8 zHtmlDiff._split_lineccs�x�|D]�\}}}|dkr&|||fVq||\}}\}}gg} } |j| ||�|j| ||�x@| sh| r�| rx| jd�}nd}| r�| jd�}nd}|||fVq`WqWdS)z5Returns iterator that splits (wraps) mdiff text linesNr rrr)rrr)rrr)r�r>)r�diffs�fromdata�todata�flagZfromlineZfromtextZtolineZtotext�fromlist�tolistrrr� _line_wrapper;s zHtmlDiff._line_wrappercCs�ggg}}}xz|D]r\}}}y4|j|jd|f|��|j|jd|f|��Wn(tk r||jd�|jd�YnX|j|�qW|||fS)z�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. r r%N)r)�_format_liner�)rr�r�r��flaglistr�r�r�rrr�_collect_linesWs zHtmlDiff._collect_linescCsryd|}d|j||f}Wntk r6d}YnX|jdd�jdd�jdd �}|jd d�j�}d|||fS) aReturns 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 z%dz id="%s%s"r�&z&r�z>�%s%s)�_prefixr�rKr�)rr�r�Zlinenumr��idrrrr�ls zHtmlDiff._format_linecCs0dtj}dtj}tjd7_||g|_dS)zCreate unique anchor prefixeszfrom%d_zto%d_r%N)r�_default_prefixr�)rZ fromprefix�toprefixrrr�_make_prefix�s zHtmlDiff._make_prefixcCs�|jd}dgt|�}dgt|�}d\} } d}xbt|�D]V\}} | r�| s�d} |}td||g�}d|| f||<| d7} d|| f||<qnz2 No Differences Found z( Empty File z!fz#t)r F)r�r.r'rV)rr�r�r�r�r�r�next_id� next_hrefZnum_chgZ in_changer�r0r�rrr�_convert_flags�s: zHtmlDiff._convert_flagsc CsV|j�|j||�\}}|r"|}nd}t||||j|jd�}|jrL|j|�}|j|�\} } }|j| | |||�\} } }}} g}d}x`t t |��D]P}||dkr�|dkr�|jd�q�|j|| |||| |||| |f�q�W|s�|�rddd |dd |f}nd }|jt d j|�||jdd�}|jd d�jdd�jdd�jdd�jdd�S)a�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. 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z+%srr%)Z data_rows� header_rowr�z+zz-zz^zr�zr�z zV %s%s%s%s )rr�r�r�r�r�r�r�rr5r.r)�_table_templater�r�r�rK)rr�r�r�r�r�r�Z context_linesr�r�r�r�rrrjZfmtr0rr�rrrr��sJ zHtmlDiff.make_table)rrFr�)rrFr�)r`rarbrcr�r�rr�r�rrr�r�r�r�r�r�rrr�rrrrr�s&7/ccspyddd�t|�}Wn tk r6td|��YnXd|f}x*|D]"}|dd�|krF|dd�VqFWdS)a0 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 z- z+ )r%r�z)unknown delta choice (must be 1 or 2): %rz Nr�)�int�KeyErrorrf)ZdeltaZwhichrR�prefixesrkrrrrs cCsddl}ddl}|j|�S)Nr )�doctest�difflibZtestmod)r rrrr�_test,sr�__main__)rTrd)r�)rrrrrTr�)rrrrrTr�)r�r�r�r�rTr�)$rc�__all__�heapqrrg�collectionsrZ_namedtuplerrrrrmrr�r��matchrrr�r r�rr�r rr�r�r�rr��objectrrrr`rrrr�s^ 0O H K %! a