U AZ1@sddlmZddlmZmZmZddlmZmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZmZddlmZmZmZmZddlmZmZzddlmZWn e k rddlmZYnXdd l!m"Z"m#Z#m$Z$dd l%m&Z&m'Z'm(Z(m)Z)m*Z*d d l+m,Z,m-Z-m.Z.d ddddddddddddddddddd d!d"d#d$d%d&d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;ddZ2e1fd?dZ3Gd@d,d,e0Z4dAdBZ5dCdZ6edDkre6j7Z8ee Z6e8e6_7dEdZ9dFdZ:dGd$Z;dHd<ZdydLd"Z?dMd:Z@dzdNd;ZAGdOdde0ZBd{dPd7ZCdQd!ZDdRd ZEd|dSdZFd}dTd1ZGdUd2ZHdVd4ZIdWd6ZJdXd5ZKd~dZd+ZLd[dZMdd]d8ZNd^d=ZOdd`d3ZPdadZQe$e"ffdbdZRddcd ZSddddZTded)ZUddfdZVeWfdgd%ZXdhd&ZYdid-ZZdjd9Z[dkd#Z\dldZ]dmdnfdodZ^efdpdZ_Gdqd0d0eZ`Gdrd/d/e0ZaGdsd.d.e0ZbeWfdtdZcdudZdddvd'Zeddwd(ZfdIS))print_function)Counter defaultdictdeque)partialwraps)merge) chaincompresscountcycle dropwhilegroupbyislicerepeat takewhiletee) itemgetterltgtsub)maxsize version_info)Sequence) binary_type string_types text_type)filtermaprangezip zip_longest)consumeflattentakeadjacentalways_iterablealways_reversiblebucketchunkedcircular_shiftscollapsecollateconsecutive_groupsconsumer count_cycle differencedistinct_permutations distributedivide exactly_nfirstgroupby_transformileninterleave_longest interleave intersperseislice_extendediteratelocatelstripmake_decorator map_reduce numeric_rangeonepaddedpeekablerstrip run_lengthseekable SequenceView side_effectsliced sort_togethersplit_at split_after split_beforespystaggerstripunique_to_eachwindowed with_iter zip_offsetcCsttt|t|gS)aBreak *iterable* into lists of length *n*: >>> list(chunked([1, 2, 3, 4, 5, 6], 3)) [[1, 2, 3], [4, 5, 6]] If the length of *iterable* is not evenly divisible by *n*, the last returned list will be shorter: >>> list(chunked([1, 2, 3, 4, 5, 6, 7, 8], 3)) [[1, 2, 3], [4, 5, 6], [7, 8]] To use a fill-in value instead, see the :func:`grouper` recipe. :func:`chunked` is useful for splitting up a computation on a large number of keys into batches, to be pickled and sent off to worker processes. One example is operations on rows in MySQL, which does not implement server-side cursors properly and would otherwise load the entire dataset into RAM on the client. )iterrr%)iterablenrZ5/usr/lib/python3/dist-packages/more_itertools/more.pyr*UscCs<ztt|WStk r6|tkr.td|YSXdS)aReturn the first item of *iterable*, or *default* if *iterable* is empty. >>> first([0, 1, 2, 3]) 0 >>> first([], 'some default') 'some default' If *default* is not provided and there are no items in the iterable, raise ``ValueError``. :func:`first` is useful when you have a generator of expensive-to-retrieve values and want any arbitrary one. It is marginally shorter than ``next(iter(iterable), default)``. zKfirst() was called on an empty iterable, and no default value was provided.N)nextrW StopIteration_marker ValueError)rXdefaultrZrZr[r6ms c@s`eZdZdZddZddZddZdd Zefd d Z d d Z ddZ e Z ddZ ddZdS)rEaWrap an iterator to allow lookahead and prepending elements. Call :meth:`peek` on the result to get the value that will be returned by :func:`next`. This won't advance the iterator: >>> p = peekable(['a', 'b']) >>> p.peek() 'a' >>> next(p) 'a' Pass :meth:`peek` a default value to return that instead of raising ``StopIteration`` when the iterator is exhausted. >>> p = peekable([]) >>> p.peek('hi') 'hi' peekables also offer a :meth:`prepend` method, which "inserts" items at the head of the iterable: >>> p = peekable([1, 2, 3]) >>> p.prepend(10, 11, 12) >>> next(p) 10 >>> p.peek() 11 >>> list(p) [11, 12, 1, 2, 3] peekables can be indexed. Index 0 is the item that will be returned by :func:`next`, index 1 is the item after that, and so on: The values up to the given index will be cached. >>> p = peekable(['a', 'b', 'c', 'd']) >>> p[0] 'a' >>> p[1] 'b' >>> next(p) 'a' Negative indexes are supported, but be aware that they will cache the remaining items in the source iterator, which may require significant storage. To check whether a peekable is exhausted, check its truth value: >>> p = peekable(['a', 'b']) >>> if p: # peekable has items ... list(p) ['a', 'b'] >>> if not p: # peekable is exhaused ... list(p) [] cCst||_t|_dSN)rW_itr_cacheselfrXrZrZr[__init__s zpeekable.__init__cCs|SrarZrerZrZr[__iter__szpeekable.__iter__cCs(z |Wntk r"YdSXdSNFT)peekr]rgrZrZr[__bool__s  zpeekable.__bool__cCs|Sra)rkrgrZrZr[ __nonzero__szpeekable.__nonzero__cCsJ|js@z|jt|jWn"tk r>|tkr6|YSX|jdS)zReturn the item that will be next returned from ``next()``. Return ``default`` if there are no items left. If ``default`` is not provided, raise ``StopIteration``. r)rcappendr\rbr]r^)rer`rZrZr[rjs z peekable.peekcGs|jt|dS)aStack up items to be the next ones returned from ``next()`` or ``self.peek()``. The items will be returned in first in, first out order:: >>> p = peekable([1, 2, 3]) >>> p.prepend(10, 11, 12) >>> next(p) 10 >>> list(p) [11, 12, 1, 2, 3] It is possible, by prepending items, to "resurrect" a peekable that previously raised ``StopIteration``. >>> p = peekable([]) >>> next(p) Traceback (most recent call last): ... StopIteration >>> p.prepend(1) >>> next(p) 1 >>> next(p) Traceback (most recent call last): ... StopIteration N)rc extendleftreversed)reitemsrZrZr[prependszpeekable.prependcCs|jr|jSt|jSra)rcpopleftr\rbrgrZrZr[__next__s zpeekable.__next__cCs|jdkrdn|j}|dkrF|jdkr*dn|j}|jdkr>tn|j}n@|dkr~|jdkr\dn|j}|jdkrvt dn|j}ntd|dks|dkr|j|jn>tt ||dt}t |j}||kr|jt |j||t |j|S)Nr"rzslice step cannot be zero) stepstartstoprr_rcextendrbminmaxlenrlist)reindexrurvrwrY cache_lenrZrZr[ _get_slices zpeekable._get_slicecCsdt|tr||St|j}|dkr6|j|jn$||krZ|jt|j|d||j|SNrr") isinstanceslicerr{rcrxrbr)rer}r~rZrZr[ __getitem__(s   zpeekable.__getitem__N)__name__ __module__ __qualname____doc__rfrhrkrlr^rjrqrsr\rrrZrZrZr[rEs9 c/s|ddd|dd}t|r&tnttdd}dd |D}d d |D}|r|fd d |D\}}t|Vd d |D}qPdS)zHelper for ``collate()``, called when the user is using the ``reverse`` or ``key`` keyword arguments on Python versions below 3.5. keycSs|SrarZ)arZrZr[:z_collate..reverseFrrcSsg|] }t|qSrZ)rE.0itrZrZr[ >sz_collate..cSsg|] }|r|qSrZrZrprZrZr[r?sc3s|]}||fVqdSra)rjrrrZr[ Asz_collate..cSsg|] }|r|qSrZrZrxrZrZr[rCsN)poprrzryrr\) iterableskwargsrZ min_or_maxZ peekables_rrZrr[_collate5s  rcOs|s t|St||S)a^Return a sorted merge of the items from each of several already-sorted *iterables*. >>> list(collate('ACDZ', 'AZ', 'JKL')) ['A', 'A', 'C', 'D', 'J', 'K', 'L', 'Z', 'Z'] Works lazily, keeping only the next value from each iterable in memory. Use :func:`collate` to, for example, perform a n-way mergesort of items that don't fit in memory. If a *key* function is specified, the iterables will be sorted according to its result: >>> key = lambda s: int(s) # Sort by numeric value, not by string >>> list(collate(['1', '10'], ['2', '11'], key=key)) ['1', '2', '10', '11'] If the *iterables* are sorted in descending order, set *reverse* to ``True``: >>> list(collate([5, 3, 1], [4, 2, 0], reverse=True)) [5, 4, 3, 2, 1, 0] If the elements of the passed-in iterables are out of order, you might get unexpected results. On Python 2.7, this function delegates to :func:`heapq.merge` if neither of the keyword arguments are specified. On Python 3.5+, this function is an alias for :func:`heapq.merge`. )rr)rrrZrZr[r-Fs!)rcstfdd}|S)abDecorator that automatically advances a PEP-342-style "reverse iterator" to its first yield point so you don't have to call ``next()`` on it manually. >>> @consumer ... def tally(): ... i = 0 ... while True: ... print('Thing number %s is %s.' % (i, (yield))) ... i += 1 ... >>> t = tally() >>> t.send('red') Thing number 0 is red. >>> t.send('fish') Thing number 1 is fish. Without the decorator, you would have to call ``next(t)`` before ``t.send()`` could be used. cs||}t||Sra)r\)argsrgenfuncrZr[wrappers zconsumer..wrapper)r)rrrZrr[r/uscCs&tt|ddd}|r"|ddSdS)zReturn the number of items in *iterable*. >>> ilen(x for x in range(1000000) if x % 3 == 0) 333334 This consumes the iterable, so handle with care. r"maxlenr)r enumerate)rXdrZrZr[r8s ccs|V||}qdS)zReturn ``start``, ``func(start)``, ``func(func(start))``, ... >>> from itertools import islice >>> list(islice(iterate(lambda x: 2*x, 1), 10)) [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] NrZ)rrvrZrZr[r=s c cs$|}|D] }|Vq W5QRXdS)a:Wrap an iterable in a ``with`` statement, so it closes once exhausted. For example, this will close the file when the iterator is exhausted:: upper_lines = (line.upper() for line in with_iter(open('foo'))) Any context manager which returns an iterable is a candidate for ``with_iter``. NrZ)Zcontext_managerrXitemrZrZr[rUs NcCsht|}z t|}Wn tk r4|p.tdYnXz t|Wntk rVYnX|pbtd|S)aReturn the first item from *iterable*, which is expected to contain only that item. Raise an exception if *iterable* is empty or has more than one item. :func:`one` is useful for ensuring that an iterable contains only one item. For example, it can be used to retrieve the result of a database query that is expected to return a single row. If *iterable* is empty, ``ValueError`` will be raised. You may specify a different exception with the *too_short* keyword: >>> it = [] >>> one(it) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError: too many items in iterable (expected 1)' >>> too_short = IndexError('too few items') >>> one(it, too_short=too_short) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... IndexError: too few items Similarly, if *iterable* contains more than one item, ``ValueError`` will be raised. You may specify a different exception with the *too_long* keyword: >>> it = ['too', 'many'] >>> one(it) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError: too many items in iterable (expected 1)' >>> too_long = RuntimeError >>> one(it, too_long=too_long) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... RuntimeError Note that :func:`one` attempts to advance *iterable* twice to ensure there is only one item. If there is more than one, both items will be discarded. See :func:`spy` or :func:`peekable` to check iterable contents less destructively. z&too few items in iterable (expected 1)z'too many items in iterable (expected 1))rWr\r]r_)rXZ too_shortZtoo_longrvaluerZrZr[rCs,   cs6fddt|}t|}|dg||dS)awYield successive distinct permutations of the elements in *iterable*. >>> sorted(distinct_permutations([1, 0, 1])) [(0, 1, 1), (1, 0, 1), (1, 1, 0)] Equivalent to ``set(permutations(iterable))``, except duplicates are not generated and thrown away. For larger input sequences this is much more efficient. Duplicate permutations arise when there are duplicated elements in the input iterable. The number of items returned is `n! / (x_1! * x_2! * ... * x_n!)`, where `n` is the total number of items input, and each `x_i` is the count of a distinct item in the input sequence. c3st|dkrt|Vn\|D]V}||dkr*q|||<||d8<|||dD] }|VqR||d7<qdS)aInternal helper function :arg item_counts: Stores the unique items in ``iterable`` and how many times they are repeated :arg perm: The permutation that is being built for output :arg i: The index of the permutation being modified The output permutations are built up recursively; the distinct items are placed until their repetitions are exhausted. rr"N)tuple) item_countsZpermirrperm_unique_helperrZr[rs   z1distinct_permutations..perm_unique_helperNr")rsumvalues)rXrZlengthrZrr[r2s  cCs^|dkrtdnH|dkr0ttt||ddSt|g}t||}ttt||ddSdS)a6Intersperse filler element *e* among the items in *iterable*, leaving *n* items between each filler element. >>> list(intersperse('!', [1, 2, 3, 4, 5])) [1, '!', 2, '!', 3, '!', 4, '!', 5] >>> list(intersperse(None, [1, 2, 3, 4, 5], n=2)) [1, 2, None, 3, 4, None, 5] rz n must be > 0r"N)r_rr:rr*r$)erXrYZfillerZchunksrZrZr[r;,s    csFdd|D}tttt|fddDfdd|DS)aReturn the elements from each of the input iterables that aren't in the other input iterables. For example, suppose you have a set of packages, each with a set of dependencies:: {'pkg_1': {'A', 'B'}, 'pkg_2': {'B', 'C'}, 'pkg_3': {'B', 'D'}} If you remove one package, which dependencies can also be removed? If ``pkg_1`` is removed, then ``A`` is no longer necessary - it is not associated with ``pkg_2`` or ``pkg_3``. Similarly, ``C`` is only needed for ``pkg_2``, and ``D`` is only needed for ``pkg_3``:: >>> unique_to_each({'A', 'B'}, {'B', 'C'}, {'B', 'D'}) [['A'], ['C'], ['D']] If there are duplicates in one input iterable that aren't in the others they will be duplicated in the output. Input order is preserved:: >>> unique_to_each("mississippi", "missouri") [['p', 'p'], ['o', 'u', 'r']] It is assumed that the elements of each iterable are hashable. cSsg|] }t|qSrZ)r|rrZrZr[rasz"unique_to_each..csh|]}|dkr|qS)r"rZ)relement)countsrZr[ cs z!unique_to_each..csg|]}ttj|qSrZ)r|r __contains__r)uniquesrZr[rds)rr from_iterablerset)rZpoolrZ)rrr[rSFsc cs|dkrtd|dkr$tVdS|dkr4tdt|}tg|}|j}t|D]}|t||qTt|Vd}|D].} || |d|}||dkrzt|Vqz||r|||krt||D] }||qt|VdS)aReturn a sliding window of width *n* over the given iterable. >>> all_windows = windowed([1, 2, 3, 4, 5], 3) >>> list(all_windows) [(1, 2, 3), (2, 3, 4), (3, 4, 5)] When the window is larger than the iterable, *fillvalue* is used in place of missing values:: >>> list(windowed([1, 2, 3], 4)) [(1, 2, 3, None)] Each window will advance in increments of *step*: >>> list(windowed([1, 2, 3, 4, 5, 6], 3, fillvalue='!', step=2)) [(1, 2, 3), (3, 4, 5), (5, 6, '!')] rzn must be >= 0Nr"zstep must be >= 1)r_rrWrrmrr\) seqrY fillvaluerurZwindowrmrrrrZrZr[rTgs.       c@s2eZdZdZd ddZddZddZd d ZdS) r)aWrap *iterable* and return an object that buckets it iterable into child iterables based on a *key* function. >>> iterable = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2', 'b3'] >>> s = bucket(iterable, key=lambda x: x[0]) >>> a_iterable = s['a'] >>> next(a_iterable) 'a1' >>> next(a_iterable) 'a2' >>> list(s['b']) ['b1', 'b2', 'b3'] The original iterable will be advanced and its items will be cached until they are used by the child iterables. This may require significant storage. By default, attempting to select a bucket to which no items belong will exhaust the iterable and cache all values. If you specify a *validator* function, selected buckets will instead be checked against it. >>> from itertools import count >>> it = count(1, 2) # Infinite sequence of odd numbers >>> key = lambda x: x % 10 # Bucket by last digit >>> validator = lambda x: x in {1, 3, 5, 7, 9} # Odd digits only >>> s = bucket(it, key=key, validator=validator) >>> 2 in s False >>> list(s[2]) [] NcCs,t||_||_tt|_|p$dd|_dS)NcSsdS)NTrZrrZrZr[rrz!bucket.__init__..)rWrb_keyrrrc _validator)rerXrZ validatorrZrZr[rfs  zbucket.__init__cCsJ||sdSzt||}Wntk r4YdSX|j||dSri)rr\r]rc appendleft)rerrrZrZr[rs zbucket.__contains__ccs|j|r|j|Vqzt|j}Wntk r@YdSX||}||kr^|Vqq||r|j||qqdS)z Helper to yield items from the parent iterator that match *value*. Items that don't match are stored in the local cache as they are encountered. N)rcrrr\rbr]rrrm)rerrZ item_valuerZrZr[ _get_valuess   zbucket._get_valuescCs||stdS||S)NrZ)rrWr)rerrZrZr[rs zbucket.__getitem__)N)rrrrrfrrrrZrZrZr[r)s   cCs t|}t||}|t||fS)aReturn a 2-tuple with a list containing the first *n* elements of *iterable*, and an iterator with the same items as *iterable*. This allows you to "look ahead" at the items in the iterable without advancing it. There is one item in the list by default: >>> iterable = 'abcdefg' >>> head, iterable = spy(iterable) >>> head ['a'] >>> list(iterable) ['a', 'b', 'c', 'd', 'e', 'f', 'g'] You may use unpacking to retrieve items instead of lists: >>> (head,), iterable = spy('abcdefg') >>> head 'a' >>> (first, second), iterable = spy('abcdefg', 2) >>> first 'a' >>> second 'b' The number of items requested can be larger than the number of items in the iterable: >>> iterable = [1, 2, 3, 4, 5] >>> head, iterable = spy(iterable, 10) >>> head [1, 2, 3, 4, 5] >>> list(iterable) [1, 2, 3, 4, 5] )rWr%r )rXrYrheadrZrZr[rPs% cGstt|S)a4Return a new iterable yielding from each iterable in turn, until the shortest is exhausted. >>> list(interleave([1, 2, 3], [4, 5], [6, 7, 8])) [1, 4, 6, 2, 5, 7] For a version that doesn't terminate after the shortest iterable is exhausted, see :func:`interleave_longest`. )r rr )rrZrZr[r:s cGs"tt|dti}dd|DS)asReturn a new iterable yielding from each iterable in turn, skipping any that are exhausted. >>> list(interleave_longest([1, 2, 3], [4, 5], [6, 7, 8])) [1, 4, 6, 2, 5, 7, 3, 8] This function produces the same output as :func:`roundrobin`, but may perform better for some inputs (in particular when the number of iterables is large). rcss|]}|tk r|VqdSra)r^rrZrZr[r6sz%interleave_longest..)r rr!r^)rrrZrZr[r9)s c#s*fdd|dD] }|VqdS)a.Flatten an iterable with multiple levels of nesting (e.g., a list of lists of tuples) into non-iterable types. >>> iterable = [(1, 2), ([3, 4], [[5], [6]])] >>> list(collapse(iterable)) [1, 2, 3, 4, 5, 6] String types are not considered iterable and will not be collapsed. To avoid collapsing other types, specify *base_type*: >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']] >>> list(collapse(iterable, base_type=tuple)) ['ab', ('cd', 'ef'), 'gh', 'ij'] Specify *levels* to stop flattening after a certain level: >>> iterable = [('a', ['b']), ('c', ['d'])] >>> list(collapse(iterable)) # Fully flattened ['a', 'b', 'c', 'd'] >>> list(collapse(iterable, levels=1)) # Only one level flattened ['a', ['b'], 'c', ['d']] c3sdk r|ks,t|ts,dk r6t|r6|VdSz t|}Wntk r^|VYdSX|D]}||dD] }|VqvqddSNr")rrrW TypeError)ZnodelevelZtreeZchildr base_typelevelswalkrZr[rQs( zcollapse..walkrNrZ)rXrrrrZrr[r,9sccsrz\|dk r||dkr2|D]}|||Vqn(t||D]}|||D] }|VqLq>> from more_itertools import consume >>> func = lambda item: print('Received {}'.format(item)) >>> consume(side_effect(func, range(2))) Received 0 Received 1 Operating on chunks of items: >>> pair_sums = [] >>> func = lambda chunk: pair_sums.append(sum(chunk)) >>> list(side_effect(func, [0, 1, 2, 3, 4, 5], 2)) [0, 1, 2, 3, 4, 5] >>> list(pair_sums) [1, 5, 9] Writing to a file-like object: >>> from io import StringIO >>> from more_itertools import consume >>> f = StringIO() >>> func = lambda x: print(x, file=f) >>> before = lambda: print(u'HEADER', file=f) >>> after = f.close >>> it = [u'a', u'b', u'c'] >>> consume(side_effect(func, it, before=before, after=after)) >>> f.closed True N)r*)rrXZ chunk_sizeZbeforeZafterrchunkrZrZr[rJhs, cs ttfddtdDS)aYield slices of length *n* from the sequence *seq*. >>> list(sliced((1, 2, 3, 4, 5, 6), 3)) [(1, 2, 3), (4, 5, 6)] If the length of the sequence is not divisible by the requested slice length, the last slice will be shorter. >>> list(sliced((1, 2, 3, 4, 5, 6, 7, 8), 3)) [(1, 2, 3), (4, 5, 6), (7, 8)] This function will only work for iterables that support slicing. For non-sliceable iterables, see :func:`chunked`. c3s|]}||VqdSrarZ)rrrYrrZr[rszsliced..r)rboolr )rrYrZrr[rKsccs6g}|D]"}||r |Vg}q||q|VdS)awYield lists of items from *iterable*, where each list is delimited by an item where callable *pred* returns ``True``. The lists do not include the delimiting items. >>> list(split_at('abcdcba', lambda x: x == 'b')) [['a'], ['c', 'd', 'c'], ['a']] >>> list(split_at(range(10), lambda n: n % 2 == 1)) [[0], [2], [4], [6], [8], []] NrmrXpredZbufrrZrZr[rMs  ccs8g}|D]$}||r"|r"|Vg}||q|VdS)aWYield lists of items from *iterable*, where each list starts with an item where callable *pred* returns ``True``: >>> list(split_before('OneTwo', lambda s: s.isupper())) [['O', 'n', 'e'], ['T', 'w', 'o']] >>> list(split_before(range(10), lambda n: n % 3 == 0)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] NrrrZrZr[rOs   ccs<g}|D]$}||||r|r|Vg}q|r8|VdS)a_Yield lists of items from *iterable*, where each list ends with an item where callable *pred* returns ``True``: >>> list(split_after('one1two2', lambda s: s.isdigit())) [['o', 'n', 'e', '1'], ['t', 'w', 'o', '2']] >>> list(split_after(range(10), lambda n: n % 3 == 0)) [[0], [1, 2, 3], [4, 5, 6], [7, 8, 9]] NrrrZrZr[rNs   Fc cst|}|dkr,t|t|D] }|VqnZ|dkr>tdnHd}|D]}|V|d7}qF|rj|||n||}t|D] }|VqzdS)aYield the elements from *iterable*, followed by *fillvalue*, such that at least *n* items are emitted. >>> list(padded([1, 2, 3], '?', 5)) [1, 2, 3, '?', '?'] If *next_multiple* is ``True``, *fillvalue* will be emitted until the number of items emitted is a multiple of *n*:: >>> list(padded([1, 2, 3, 4], n=3, next_multiple=True)) [1, 2, 3, 4, None, None] If *n* is ``None``, *fillvalue* will be emitted indefinitely. Nr"n must be at least 1r)rWr rr_r) rXrrYZ next_multiplerrZ item_countZ remainingrrZrZr[rDs    cs0dkrtdt|}fddt|DS)aDistribute the items from *iterable* among *n* smaller iterables. >>> group_1, group_2 = distribute(2, [1, 2, 3, 4, 5, 6]) >>> list(group_1) [1, 3, 5] >>> list(group_2) [2, 4, 6] If the length of *iterable* is not evenly divisible by *n*, then the length of the returned iterables will not be identical: >>> children = distribute(3, [1, 2, 3, 4, 5, 6, 7]) >>> [list(c) for c in children] [[1, 4, 7], [2, 5], [3, 6]] If the length of *iterable* is smaller than *n*, then the last returned iterables will be empty: >>> children = distribute(5, [1, 2, 3]) >>> [list(c) for c in children] [[1], [2], [3], [], []] This function uses :func:`itertools.tee` and may require significant storage. If you need the order items in the smaller iterables to match the original iterable, see :func:`divide`. r"rcsg|]\}}t||dqSra)r)rr}rrYrZr[r8szdistribute..)r_rr)rYrXchildrenrZrr[r3s rtrr"cCs t|t|}t||||dS)a[Yield tuples whose elements are offset from *iterable*. The amount by which the `i`-th item in each tuple is offset is given by the `i`-th item in *offsets*. >>> list(stagger([0, 1, 2, 3])) [(None, 0, 1), (0, 1, 2), (1, 2, 3)] >>> list(stagger(range(8), offsets=(0, 2, 4))) [(0, 2, 4), (1, 3, 5), (2, 4, 6), (3, 5, 7)] By default, the sequence will end when the final element of a tuple is the last item in the iterable. To continue until the first element of a tuple is the last item in the iterable, set *longest* to ``True``:: >>> list(stagger([0, 1, 2, 3], longest=True)) [(None, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, None), (3, None, None)] By default, ``None`` will be used to replace offsets beyond the end of the sequence. Specify *fillvalue* to use some other value. )offsetslongestr)rr{rV)rXrrrrrZrZr[rQ;scOs|d}|dd}|dd}t|t|kr8tdg}t||D]P\}}|dkrp|tt|| |qF|dkr|t||dqF||qF|rt|d|iSt|S)aG``zip`` the input *iterables* together, but offset the `i`-th iterable by the `i`-th item in *offsets*. >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1))) [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e')] This can be used as a lightweight alternative to SciPy or pandas to analyze data sets in which somes series have a lead or lag relationship. By default, the sequence will end when the shortest iterable is exhausted. To continue until the longest iterable is exhausted, set *longest* to ``True``. >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1), longest=True)) [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e'), (None, 'f')] By default, ``None`` will be used to replace offsets beyond the end of the sequence. Specify *fillvalue* to use some other value. rrFrNz,Number of iterables and offsets didn't matchr) getr{r_r rmr rrr!)rrrrrZ staggeredrrYrZrZr[rVWs   rcCstttt|t||dS)aReturn the input iterables sorted together, with *key_list* as the priority for sorting. All iterables are trimmed to the length of the shortest one. This can be used like the sorting function in a spreadsheet. If each iterable represents a column of data, the key list determines which columns are used for sorting. By default, all iterables are sorted using the ``0``-th iterable:: >>> iterables = [(4, 3, 2, 1), ('a', 'b', 'c', 'd')] >>> sort_together(iterables) [(1, 2, 3, 4), ('d', 'c', 'b', 'a')] Set a different key list to sort according to another iterable. Specifying mutliple keys dictates how ties are broken:: >>> iterables = [(3, 1, 2), (0, 1, 0), ('c', 'b', 'a')] >>> sort_together(iterables, key_list=(1, 2)) [(2, 3, 1), (0, 0, 1), ('a', 'c', 'b')] Set *reverse* to ``True`` to sort in descending order. >>> sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True) [(3, 2, 1), ('a', 'b', 'c')] )rr)r|r sortedr)rZkey_listrrZrZr[rLs c Cs|dkrtdt|}tt||\}}g}t|D]V}||||krL|n|}|d||d|krp|dn|}|t|||q6|S)aDivide the elements from *iterable* into *n* parts, maintaining order. >>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6]) >>> list(group_1) [1, 2, 3] >>> list(group_2) [4, 5, 6] If the length of *iterable* is not evenly divisible by *n*, then the length of the returned iterables will not be identical: >>> children = divide(3, [1, 2, 3, 4, 5, 6, 7]) >>> [list(c) for c in children] [[1, 2, 3], [4, 5], [6, 7]] If the length of the iterable is smaller than n, then the last returned iterables will be empty: >>> children = divide(5, [1, 2, 3]) >>> [list(c) for c in children] [[1], [2], [3], [], []] This function will exhaust the iterable before returning and may require significant storage. If order is not important, see :func:`distribute`, which does not first pull the iterable into memory. r"r)r_rdivmodr{rrmrW) rYrXrqrretrrvrwrZrZr[r4s $cCsZ|dkrtdS|dk r,t||r,t|fSz t|WStk rTt|fYSXdS)axIf *obj* is iterable, return an iterator over its items:: >>> obj = (1, 2, 3) >>> list(always_iterable(obj)) [1, 2, 3] If *obj* is not iterable, return a one-item iterable containing *obj*:: >>> obj = 1 >>> list(always_iterable(obj)) [1] If *obj* is ``None``, return an empty iterable: >>> obj = None >>> list(always_iterable(None)) [] By default, binary and text strings are not considered iterable:: >>> obj = 'foo' >>> list(always_iterable(obj)) ['foo'] If *base_type* is set, objects for which ``isinstance(obj, base_type)`` returns ``True`` won't be considered iterable. >>> obj = {'a': 1} >>> list(always_iterable(obj)) # Iterate over the dict's keys ['a'] >>> list(always_iterable(obj, base_type=dict)) # Treat dicts as a unit [{'a': 1}] Set *base_type* to ``None`` to avoid any special handling and treat objects Python considers iterable as iterable: >>> obj = 'foo' >>> list(always_iterable(obj, base_type=None)) ['f', 'o', 'o'] NrZ)rWrr)objrrZrZr[r's)  cCsZ|dkrtdt|\}}dg|}t|t|||}ttt|d|d}t||S)asReturn an iterable over `(bool, item)` tuples where the `item` is drawn from *iterable* and the `bool` indicates whether that item satisfies the *predicate* or is adjacent to an item that does. For example, to find whether items are adjacent to a ``3``:: >>> list(adjacent(lambda x: x == 3, range(6))) [(False, 0), (False, 1), (True, 2), (True, 3), (True, 4), (False, 5)] Set *distance* to change what counts as adjacent. For example, to find whether items are two places away from a ``3``: >>> list(adjacent(lambda x: x == 3, range(6), distance=2)) [(False, 0), (True, 1), (True, 2), (True, 3), (True, 4), (True, 5)] This is useful for contextualizing the results of a search function. For example, a code comparison tool might want to identify lines that have changed, but also surrounding lines to give the viewer of the diff context. The predicate function will only be called once for each item in the iterable. See also :func:`groupby_transform`, which can be used with this function to group ranges of items with the same `bool` value. rzdistance must be at least 0Fr")r_rr ranyrTr ) predicaterXZdistanceZi1Zi2ZpaddingZselectedZadjacent_to_selectedrZrZr[r&s  cs,dkrddnfddt||DS)aAn extension of :func:`itertools.groupby` that transforms the values of *iterable* after grouping them. *keyfunc* is a function used to compute a grouping key for each item. *valuefunc* is a function for transforming the items after grouping. >>> iterable = 'AaaABbBCcA' >>> keyfunc = lambda x: x.upper() >>> valuefunc = lambda x: x.lower() >>> grouper = groupby_transform(iterable, keyfunc, valuefunc) >>> [(k, ''.join(g)) for k, g in grouper] [('A', 'aaaa'), ('B', 'bbb'), ('C', 'cc'), ('A', 'a')] *keyfunc* and *valuefunc* default to identity functions if they are not specified. :func:`groupby_transform` is useful when grouping elements of an iterable using a separate iterable as the key. To do this, :func:`zip` the iterables and pass a *keyfunc* that extracts the first element and a *valuefunc* that extracts the second element:: >>> from operator import itemgetter >>> keys = [0, 0, 1, 1, 1, 2, 2, 2, 3] >>> values = 'abcdefghi' >>> iterable = zip(keys, values) >>> grouper = groupby_transform(iterable, itemgetter(0), itemgetter(1)) >>> [(k, ''.join(g)) for k, g in grouper] [(0, 'ab'), (1, 'cde'), (2, 'fgh'), (3, 'i')] Note that the order of items in the iterable is significant. Only adjacent items are grouped together, so if you don't want any duplicate groups, you should sort the iterable by the key function. NcSs|SrarZrrZrZr[rMrz#groupby_transform..c3s |]\}}|t|fVqdSra)rrkg valuefuncrZr[rNsz$groupby_transform..r)rXkeyfuncrrZrr[r7+s"cst|}|dkr(|\}t|ddn<|dkr>|\}dn&|dkrR|\}nd}t||fddtD}dkrttt||Sdkrttt||St dd S) aRAn extension of the built-in ``range()`` function whose arguments can be any orderable numeric type. With only *stop* specified, *start* defaults to ``0`` and *step* defaults to ``1``. The output items will match the type of *stop*: >>> list(numeric_range(3.5)) [0.0, 1.0, 2.0, 3.0] With only *start* and *stop* specified, *step* defaults to ``1``. The output items will match the type of *start*: >>> from decimal import Decimal >>> start = Decimal('2.1') >>> stop = Decimal('5.1') >>> list(numeric_range(start, stop)) [Decimal('2.1'), Decimal('3.1'), Decimal('4.1')] With *start*, *stop*, and *step* specified the output items will match the type of ``start + step``: >>> from fractions import Fraction >>> start = Fraction(1, 2) # Start at 1/2 >>> stop = Fraction(5, 2) # End at 5/2 >>> step = Fraction(1, 2) # Count by 1/2 >>> list(numeric_range(start, stop, step)) [Fraction(1, 2), Fraction(1, 1), Fraction(3, 2), Fraction(2, 1)] If *step* is zero, ``ValueError`` is raised. Negative steps are supported: >>> list(numeric_range(3, -1, -1.0)) [3.0, 2.0, 1.0, 0.0] Be aware of the limitations of floating point numbers; the representation of the yielded numbers may be surprising. r"rrrz/numeric_range takes at most 3 arguments, got {}c3s|]}|VqdSrarZ)rrYrvrurZr[rsz numeric_range..z$numeric_range arg 3 must not be zeroN) r{typerformatr rrrrr_)rZargcrwerr_msgrrZrr[rBQs$&  cs<tstdS|dkr"tnt|}fdd|DS)aCycle through the items from *iterable* up to *n* times, yielding the number of completed cycles along with each item. If *n* is omitted the process repeats indefinitely. >>> list(count_cycle('AB', 3)) [(0, 'A'), (0, 'B'), (1, 'A'), (1, 'B'), (2, 'A'), (2, 'B')] rZNc3s |]}D]}||fVq qdSrarZ)rrrrXrZr[rszcount_cycle..)rrWr r)rXrYZcounterrZrr[r0s cCsttt||S)aYield the index of each item in *iterable* for which *pred* returns ``True``. *pred* defaults to :func:`bool`, which will select truthy items: >>> list(locate([0, 1, 1, 0, 1, 0, 0])) [1, 2, 4] Set *pred* to a custom function to, e.g., find the indexes for a particular item: >>> list(locate(['a', 'b', 'c', 'b'], lambda x: x == 'b')) [1, 3] Use with :func:`windowed` to find the indexes of a sub-sequence: >>> from more_itertools import windowed >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] >>> sub = [1, 2, 3] >>> pred = lambda w: w == tuple(sub) # windowed() returns tuples >>> list(locate(windowed(iterable, len(sub)), pred=pred)) [1, 5, 9] Use with :func:`seekable` to find indexes and then retrieve the associated items: >>> from itertools import count >>> from more_itertools import seekable >>> source = (3 * n + 1 if (n % 2) else n // 2 for n in count()) >>> it = seekable(source) >>> pred = lambda x: x > 100 >>> indexes = locate(it, pred=pred) >>> i = next(indexes) >>> it.seek(i) >>> next(it) 106 )r r rrXrrZrZr[r>s'cCs t||S)aYield the items from *iterable*, but strip any from the beginning for which *pred* returns ``True``. For example, to remove a set of items from the start of an iterable: >>> iterable = (None, False, None, 1, 2, None, 3, False, None) >>> pred = lambda x: x in {None, False, ''} >>> list(lstrip(iterable, pred)) [1, 2, None, 3, False, None] This function is analogous to to :func:`str.lstrip`, and is essentially an wrapper for :func:`itertools.dropwhile`. )r rrZrZr[r?sccsJg}|j}|D]6}||r$||q|D] }|Vq(|dd=|VqdS)aYield the items from *iterable*, but strip any from the end for which *pred* returns ``True``. For example, to remove a set of items from the end of an iterable: >>> iterable = (None, False, None, 1, 2, None, 3, False, None) >>> pred = lambda x: x in {None, False, ''} >>> list(rstrip(iterable, pred)) [None, False, None, 1, 2, None, 3] This function is analogous to :func:`str.rstrip`. Nr)rXrcacheZ cache_appendryrZrZr[rFs  cCstt|||S)aYield the items from *iterable*, but strip any from the beginning and end for which *pred* returns ``True``. For example, to remove a set of items from both ends of an iterable: >>> iterable = (None, False, None, 1, 2, None, 3, False, None) >>> pred = lambda x: x in {None, False, ''} >>> list(strip(iterable, pred)) [1, 2, None, 3] This function is analogous to :func:`str.strip`. )rFr?rrZrZr[rRscgst|}|j}|j}|jdkr&td|jp.d}t|}|dkr|dkrNdn|}|dkrtt|d| d}|r~|ddnd}t||d} |dkr|} n"|dkrt ||} nt||d} | | } | dkrdSt |d| |D]\} } | Vqn|dk rl|dkrlt t |||dtt || | d}t|D]0\} } | }| |dkr\|V| | q8nt ||||D] } | Vqzn<|dkrdn|}|dk r4|dkr4| d} tt|d| d}|r|ddnd}|dkr||} } nt ||dd} } t|| | |D]\} } | Vq n|dk rX|d}t t |||d|dkrl|} d} n2|dkrd} |d} nd} ||} | dkrdStt || }|| d|D] } | VqdS)aHAn extension of :func:`itertools.islice` that supports negative values for *stop*, *start*, and *step*. >>> iterable = iter('abcdefgh') >>> list(islice_extended(iterable, -4, -1)) ['e', 'f', 'g'] Slices with negative values require some caching of *iterable*, but this function takes care to minimize the amount of memory required. For example, you can use a negative step with an infinite iterator: >>> from itertools import count >>> list(islice_extended(count(), 110, 99, -2)) [110, 108, 106, 104, 102, 100] rz1step argument must be a non-zero integer or None.r"Nrrt)rrvrwrur_rWrrrzryrr\rrrmr|)rXrsrvrwrurrZlen_iterrjrYr}rZ cached_itemmrZrZr[r<sv              cCs0z t|WStk r*tt|YSXdS)aAn extension of :func:`reversed` that supports all iterables, not just those which implement the ``Reversible`` or ``Sequence`` protocols. >>> print(*always_reversible(x for x in range(3))) 2 1 0 If the iterable is already reversible, this function returns the result of :func:`reversed()`. If the iterable is not reversible, this function will cache the remaining items in the iterable and yield them in reverse order, which may require significant storage. N)rorr|rrZrZr[r({s  cCs|SrarZrrZrZr[rrrc#s6tt|fdddD]\}}ttd|VqdS)aYield groups of consecutive items using :func:`itertools.groupby`. The *ordering* function determines whether two items are adjacent by returning their position. By default, the ordering function is the identity function. This is suitable for finding runs of numbers: >>> iterable = [1, 10, 11, 12, 20, 30, 31, 32, 33, 40] >>> for group in consecutive_groups(iterable): ... print(list(group)) [1] [10, 11, 12] [20] [30, 31, 32, 33] [40] For finding runs of adjacent letters, try using the :meth:`index` method of a string of letters: >>> from string import ascii_lowercase >>> iterable = 'abcdfgilmnop' >>> ordering = ascii_lowercase.index >>> for group in consecutive_groups(iterable, ordering): ... print(list(group)) ['a', 'b', 'c', 'd'] ['f', 'g'] ['i'] ['l', 'm', 'n', 'o', 'p'] cs|d|dSrrZrorderingrZr[rrz$consecutive_groups..rr"N)rrrr)rXrrrrZrr[r.s  csVt|\}}z t|}Wntk r4tgYSXt|gtfddt||S)aBy default, compute the first difference of *iterable* using :func:`operator.sub`. >>> iterable = [0, 1, 3, 6, 10] >>> list(difference(iterable)) [0, 1, 2, 3, 4] This is the opposite of :func:`accumulate`'s default behavior: >>> from more_itertools import accumulate >>> iterable = [0, 1, 2, 3, 4] >>> list(accumulate(iterable)) [0, 1, 3, 6, 10] >>> list(difference(accumulate(iterable))) [0, 1, 2, 3, 4] By default *func* is :func:`operator.sub`, but other functions can be specified. They will be applied as follows:: A, B, C, D, ... --> A, func(B, A), func(C, B), func(D, C), ... For example, to do progressive division: >>> iterable = [1, 2, 6, 24, 120] # Factorial sequence >>> func = lambda x, y: x // y >>> list(difference(iterable, func)) [1, 2, 3, 4, 5] cs|d|dS)Nr"rrZrrrZr[rrzdifference..)rr\r]rWr rr )rXrrbrrZrr[r1s   c@s0eZdZdZddZddZddZdd Zd S) rIaSReturn a read-only view of the sequence object *target*. :class:`SequenceView` objects are analagous to Python's built-in "dictionary view" types. They provide a dynamic view of a sequence's items, meaning that when the sequence updates, so does the view. >>> seq = ['0', '1', '2'] >>> view = SequenceView(seq) >>> view SequenceView(['0', '1', '2']) >>> seq.append('3') >>> view SequenceView(['0', '1', '2', '3']) Sequence views support indexing, slicing, and length queries. They act like the underlying sequence, except they don't allow assignment: >>> view[1] '1' >>> view[1:-1] ['1', '2'] >>> len(view) 4 Sequence views are useful as an alternative to copying, as they don't require (much) extra storage. cCst|tst||_dSra)rrr_target)retargetrZrZr[rfs zSequenceView.__init__cCs |j|Sra)r)rer}rZrZr[rszSequenceView.__getitem__cCs t|jSra)r{rrgrZrZr[__len__szSequenceView.__len__cCsd|jjt|jS)Nz{}({}))r __class__rreprrrgrZrZr[__repr__szSequenceView.__repr__N)rrrrrfrrrrZrZrZr[rIs c@s<eZdZdZddZddZddZeZdd Zd d Z d S) rHa Wrap an iterator to allow for seeking backward and forward. This progressively caches the items in the source iterable so they can be re-visited. Call :meth:`seek` with an index to seek to that position in the source iterable. To "reset" an iterator, seek to ``0``: >>> from itertools import count >>> it = seekable((str(n) for n in count())) >>> next(it), next(it), next(it) ('0', '1', '2') >>> it.seek(0) >>> next(it), next(it), next(it) ('0', '1', '2') >>> next(it) '3' You can also seek forward: >>> it = seekable((str(n) for n in range(20))) >>> it.seek(10) >>> next(it) '10' >>> it.seek(20) # Seeking past the end of the source isn't a problem >>> list(it) [] >>> it.seek(0) # Resetting works even after hitting the end >>> next(it), next(it), next(it) ('0', '1', '2') The cache grows as the source iterable progresses, so beware of wrapping very large or infinite iterables. You may view the contents of the cache with the :meth:`elements` method. That returns a :class:`SequenceView`, a view that updates automatically: >>> it = seekable((str(n) for n in range(10))) >>> next(it), next(it), next(it) ('0', '1', '2') >>> elements = it.elements() >>> elements SequenceView(['0', '1', '2']) >>> next(it) '3' >>> elements SequenceView(['0', '1', '2', '3']) cCst||_g|_d|_dSra)rW_sourcerc_indexrdrZrZr[rf8s zseekable.__init__cCs|SrarZrgrZrZr[rh=szseekable.__iter__cCsb|jdk rHz|j|j}Wntk r4d|_YnX|jd7_|St|j}|j||Sr)rrc IndexErrorr\rrm)rerrZrZr[rs@s    zseekable.__next__cCs t|jSra)rIrcrgrZrZr[elementsPszseekable.elementscCs*||_|t|j}|dkr&t||dS)Nr)rr{rcr#)rer}Z remainderrZrZr[seekSsz seekable.seekN) rrrrrfrhrsr\rrrZrZrZr[rHs3c@s(eZdZdZeddZeddZdS)rGa :func:`run_length.encode` compresses an iterable with run-length encoding. It yields groups of repeated items with the count of how many times they were repeated: >>> uncompressed = 'abbcccdddd' >>> list(run_length.encode(uncompressed)) [('a', 1), ('b', 2), ('c', 3), ('d', 4)] :func:`run_length.decode` decompresses an iterable that was previously compressed with run-length encoding. It yields the items of the decompressed iterable: >>> compressed = [('a', 1), ('b', 2), ('c', 3), ('d', 4)] >>> list(run_length.decode(compressed)) ['a', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd', 'd'] cCsddt|DS)Ncss|]\}}|t|fVqdSra)r8rrZrZr[rpsz$run_length.encode..rrrZrZr[encodenszrun_length.encodecCstdd|DS)Ncss|]\}}t||VqdSra)r)rrrYrZrZr[rtsz$run_length.decode..)r rrrZrZr[decoderszrun_length.decodeN)rrrr staticmethodrrrZrZrZr[rGZs  cCstt|dt|||kS)aReturn ``True`` if exactly ``n`` items in the iterable are ``True`` according to the *predicate* function. >>> exactly_n([True, True, False], 2) True >>> exactly_n([True, True, False], 1) False >>> exactly_n([0, 1, 2, 3, 4, 5], 3, lambda x: x < 3) True The iterable will be advanced until ``n + 1`` truthy items are encountered, so avoid calling it on infinite iterables. r")r{r%r)rXrYrrZrZr[r5wscCs$t|}tt|tt|t|S)zReturn a list of circular shifts of *iterable*. >>> circular_shifts(range(4)) [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)] )r|r%r{rTr )rXZlstrZrZr[r+scsfdd}|S)aReturn a decorator version of *wrapping_func*, which is a function that modifies an iterable. *result_index* is the position in that function's signature where the iterable goes. This lets you use itertools on the "production end," i.e. at function definition. This can augment what the function returns without changing the function's code. For example, to produce a decorator version of :func:`chunked`: >>> from more_itertools import chunked >>> chunker = make_decorator(chunked, result_index=0) >>> @chunker(3) ... def iter_range(n): ... return iter(range(n)) ... >>> list(iter_range(9)) [[0, 1, 2], [3, 4, 5], [6, 7, 8]] To only allow truthy items to be returned: >>> truth_serum = make_decorator(filter, result_index=1) >>> @truth_serum(bool) ... def boolean_test(): ... return [0, 1, '', ' ', False, True] ... >>> list(boolean_test()) [1, ' ', True] The :func:`peekable` and :func:`seekable` wrappers make for practical decorators: >>> from more_itertools import peekable >>> peekable_function = make_decorator(peekable) >>> @peekable_function() ... def str_range(*args): ... return (str(x) for x in range(*args)) ... >>> it = str_range(1, 20, 2) >>> next(it), next(it), next(it) ('1', '3', '5') >>> it.peek() '7' >>> next(it) '7' csfdd}|S)Ncsfdd}|S)Ncs(||}t}|||Sra)r|insert)rrresultZwrapping_args_)f result_index wrapping_args wrapping_funcwrapping_kwargsrZr[ inner_wrappers  zOmake_decorator..decorator..outer_wrapper..inner_wrapperrZ)rr)rrrr)rr[ outer_wrappersz8make_decorator..decorator..outer_wrapperrZ)rrr rr)rrr[ decorators z!make_decorator..decoratorrZ)rrr rZr r[r@s2 c Cst|dkrddn|}tt}|D]"}||}||}|||q |dk rj|D]\}}||||<qTd|_|S)aReturn a dictionary that maps the items in *iterable* to categories defined by *keyfunc*, transforms them with *valuefunc*, and then summarizes them by category with *reducefunc*. *valuefunc* defaults to the identity function if it is unspecified. If *reducefunc* is unspecified, no summarization takes place: >>> keyfunc = lambda x: x.upper() >>> result = map_reduce('abbccc', keyfunc) >>> sorted(result.items()) [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])] Specifying *valuefunc* transforms the categorized items: >>> keyfunc = lambda x: x.upper() >>> valuefunc = lambda x: 1 >>> result = map_reduce('abbccc', keyfunc, valuefunc) >>> sorted(result.items()) [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])] Specifying *reducefunc* summarizes the categorized items: >>> keyfunc = lambda x: x.upper() >>> valuefunc = lambda x: 1 >>> reducefunc = sum >>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc) >>> sorted(result.items()) [('A', 1), ('B', 2), ('C', 3)] You may want to filter the input iterable before applying the map/reduce proecdure: >>> all_items = range(30) >>> items = [x for x in all_items if 10 <= x <= 20] # Filter >>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1 >>> categories = map_reduce(items, keyfunc=keyfunc) >>> sorted(categories.items()) [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])] >>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum) >>> sorted(summaries.items()) [(0, 90), (1, 75)] Note that all items in the iterable are gathered into a list before the summarization step, which may require significant storage. The returned object is a :obj:`collections.defaultdict` with the ``default_factory`` set to ``None``, such that it behaves like a normal dictionary. NcSs|SrarZrrZrZr[rrzmap_reduce..)rr|rmrpdefault_factory) rXrrZ reducefuncrrrrZ value_listrZrZr[rAs3)NN)r")Nr")r")NN)NNN)NNF)rFN)rF)r")NN)N)r)NN)gZ __future__r collectionsrrr functoolsrrheapqr itertoolsr r r r r rrrrroperatorrrrrsysrrZcollections.abcr ImportErrorZsixrrrZ six.movesrrrr r!Zrecipesr#r$r%__all__objectr^r*r6rErr-rZ_collate_docstringr/r8r=rUrCr2r;rSrTr)rPr:r9r,rJrKrMrOrNrDr3rQrVrLr4r'r&r7rBr0rr>r?rFrRr<r(r.r1rIrHrGr5r+r@rArZrZrZr[s  0 4 +)   =. ! 4U + / > !# + !,5 ' &=  *v% &,V  A