diff --git "a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/more_itertools/more.py" "b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/more_itertools/more.py" deleted file mode 100644--- "a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/more_itertools/more.py" +++ /dev/null @@ -1,4316 +0,0 @@ -import warnings - -from collections import Counter, defaultdict, deque, abc -from collections.abc import Sequence -from functools import partial, reduce, wraps -from heapq import merge, heapify, heapreplace, heappop -from itertools import ( - chain, - compress, - count, - cycle, - dropwhile, - groupby, - islice, - repeat, - starmap, - takewhile, - tee, - zip_longest, -) -from math import exp, factorial, floor, log -from queue import Empty, Queue -from random import random, randrange, uniform -from operator import itemgetter, mul, sub, gt, lt, ge, le -from sys import hexversion, maxsize -from time import monotonic - -from .recipes import ( - consume, - flatten, - pairwise, - powerset, - take, - unique_everseen, -) - -__all__ = [ - 'AbortThread', - 'SequenceView', - 'UnequalIterablesError', - 'adjacent', - 'all_unique', - 'always_iterable', - 'always_reversible', - 'bucket', - 'callback_iter', - 'chunked', - 'chunked_even', - 'circular_shifts', - 'collapse', - 'collate', - 'combination_index', - 'consecutive_groups', - 'consumer', - 'count_cycle', - 'countable', - 'difference', - 'distinct_combinations', - 'distinct_permutations', - 'distribute', - 'divide', - 'duplicates_everseen', - 'duplicates_justseen', - 'exactly_n', - 'filter_except', - 'first', - 'groupby_transform', - 'ichunked', - 'ilen', - 'interleave', - 'interleave_evenly', - 'interleave_longest', - 'intersperse', - 'is_sorted', - 'islice_extended', - 'iterate', - 'last', - 'locate', - 'lstrip', - 'make_decorator', - 'map_except', - 'map_if', - 'map_reduce', - 'mark_ends', - 'minmax', - 'nth_or_last', - 'nth_permutation', - 'nth_product', - 'numeric_range', - 'one', - 'only', - 'padded', - 'partitions', - 'peekable', - 'permutation_index', - 'product_index', - 'raise_', - 'repeat_each', - 'repeat_last', - 'replace', - 'rlocate', - 'rstrip', - 'run_length', - 'sample', - 'seekable', - 'set_partitions', - 'side_effect', - 'sliced', - 'sort_together', - 'split_after', - 'split_at', - 'split_before', - 'split_into', - 'split_when', - 'spy', - 'stagger', - 'strip', - 'strictly_n', - 'substrings', - 'substrings_indexes', - 'time_limited', - 'unique_in_window', - 'unique_to_each', - 'unzip', - 'value_chain', - 'windowed', - 'windowed_complete', - 'with_iter', - 'zip_broadcast', - 'zip_equal', - 'zip_offset', -] - - -_marker = object() - - -def chunked(iterable, n, strict=False): - """Break *iterable* into lists of length *n*: - - >>> list(chunked([1, 2, 3, 4, 5, 6], 3)) - [[1, 2, 3], [4, 5, 6]] - - By the default, the last yielded list will have fewer than *n* elements - if the length of *iterable* is not divisible by *n*: - - >>> 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. - - If the length of *iterable* is not divisible by *n* and *strict* is - ``True``, then ``ValueError`` will be raised before the last - list is yielded. - - """ - iterator = iter(partial(take, n, iter(iterable)), []) - if strict: - if n is None: - raise ValueError('n must not be None when using strict mode.') - - def ret(): - for chunk in iterator: - if len(chunk) != n: - raise ValueError('iterable is not divisible by n.') - yield chunk - - return iter(ret()) - else: - return iterator - - -def first(iterable, default=_marker): - """Return 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)``. - - """ - try: - return next(iter(iterable)) - except StopIteration as e: - if default is _marker: - raise ValueError( - 'first() was called on an empty iterable, and no ' - 'default value was provided.' - ) from e - return default - - -def last(iterable, default=_marker): - """Return the last item of *iterable*, or *default* if *iterable* is - empty. - - >>> last([0, 1, 2, 3]) - 3 - >>> last([], 'some default') - 'some default' - - If *default* is not provided and there are no items in the iterable, - raise ``ValueError``. - """ - try: - if isinstance(iterable, Sequence): - return iterable[-1] - # Work around https://bugs.python.org/issue38525 - elif hasattr(iterable, '__reversed__') and (hexversion != 0x030800F0): - return next(reversed(iterable)) - else: - return deque(iterable, maxlen=1)[-1] - except (IndexError, TypeError, StopIteration): - if default is _marker: - raise ValueError( - 'last() was called on an empty iterable, and no default was ' - 'provided.' - ) - return default - - -def nth_or_last(iterable, n, default=_marker): - """Return the nth or the last item of *iterable*, - or *default* if *iterable* is empty. - - >>> nth_or_last([0, 1, 2, 3], 2) - 2 - >>> nth_or_last([0, 1], 2) - 1 - >>> nth_or_last([], 0, 'some default') - 'some default' - - If *default* is not provided and there are no items in the iterable, - raise ``ValueError``. - """ - return last(islice(iterable, n + 1), default=default) - - -class peekable: - """Wrap 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 exhausted - ... list(p) - [] - - """ - - def __init__(self, iterable): - self._it = iter(iterable) - self._cache = deque() - - def __iter__(self): - return self - - def __bool__(self): - try: - self.peek() - except StopIteration: - return False - return True - - def peek(self, default=_marker): - """Return the item that will be next returned from ``next()``. - - Return ``default`` if there are no items left. If ``default`` is not - provided, raise ``StopIteration``. - - """ - if not self._cache: - try: - self._cache.append(next(self._it)) - except StopIteration: - if default is _marker: - raise - return default - return self._cache[0] - - def prepend(self, *items): - """Stack 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 - - """ - self._cache.extendleft(reversed(items)) - - def __next__(self): - if self._cache: - return self._cache.popleft() - - return next(self._it) - - def _get_slice(self, index): - # Normalize the slice's arguments - step = 1 if (index.step is None) else index.step - if step > 0: - start = 0 if (index.start is None) else index.start - stop = maxsize if (index.stop is None) else index.stop - elif step < 0: - start = -1 if (index.start is None) else index.start - stop = (-maxsize - 1) if (index.stop is None) else index.stop - else: - raise ValueError('slice step cannot be zero') - - # If either the start or stop index is negative, we'll need to cache - # the rest of the iterable in order to slice from the right side. - if (start < 0) or (stop < 0): - self._cache.extend(self._it) - # Otherwise we'll need to find the rightmost index and cache to that - # point. - else: - n = min(max(start, stop) + 1, maxsize) - cache_len = len(self._cache) - if n >= cache_len: - self._cache.extend(islice(self._it, n - cache_len)) - - return list(self._cache)[index] - - def __getitem__(self, index): - if isinstance(index, slice): - return self._get_slice(index) - - cache_len = len(self._cache) - if index < 0: - self._cache.extend(self._it) - elif index >= cache_len: - self._cache.extend(islice(self._it, index + 1 - cache_len)) - - return self._cache[index] - - -def collate(*iterables, **kwargs): - """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 3.5+, this function is an alias for :func:`heapq.merge`. - - """ - warnings.warn( - "collate is no longer part of more_itertools, use heapq.merge", - DeprecationWarning, - ) - return merge(*iterables, **kwargs) - - -def consumer(func): - """Decorator 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. - - """ - - @wraps(func) - def wrapper(*args, **kwargs): - gen = func(*args, **kwargs) - next(gen) - return gen - - return wrapper - - -def ilen(iterable): - """Return 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. - - """ - # This approach was selected because benchmarks showed it's likely the - # fastest of the known implementations at the time of writing. - # See GitHub tracker: #236, #230. - counter = count() - deque(zip(iterable, counter), maxlen=0) - return next(counter) - - -def iterate(func, start): - """Return ``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] - - """ - while True: - yield start - start = func(start) - - -def with_iter(context_manager): - """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``. - - """ - with context_manager as iterable: - yield from iterable - - -def one(iterable, too_short=None, too_long=None): - """Return 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: Expected exactly one item in iterable, but got 'too', - 'many', and perhaps more. - >>> 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. See :func:`spy` or :func:`peekable` to check iterable - contents less destructively. - - """ - it = iter(iterable) - - try: - first_value = next(it) - except StopIteration as e: - raise ( - too_short or ValueError('too few items in iterable (expected 1)') - ) from e - - try: - second_value = next(it) - except StopIteration: - pass - else: - msg = ( - 'Expected exactly one item in iterable, but got {!r}, {!r}, ' - 'and perhaps more.'.format(first_value, second_value) - ) - raise too_long or ValueError(msg) - - return first_value - - -def raise_(exception, *args): - raise exception(*args) - - -def strictly_n(iterable, n, too_short=None, too_long=None): - """Validate that *iterable* has exactly *n* items and return them if - it does. If it has fewer than *n* items, call function *too_short* - with those items. If it has more than *n* items, call function - *too_long* with the first ``n + 1`` items. - - >>> iterable = ['a', 'b', 'c', 'd'] - >>> n = 4 - >>> list(strictly_n(iterable, n)) - ['a', 'b', 'c', 'd'] - - By default, *too_short* and *too_long* are functions that raise - ``ValueError``. - - >>> list(strictly_n('ab', 3)) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError: too few items in iterable (got 2) - - >>> list(strictly_n('abc', 2)) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError: too many items in iterable (got at least 3) - - You can instead supply functions that do something else. - *too_short* will be called with the number of items in *iterable*. - *too_long* will be called with `n + 1`. - - >>> def too_short(item_count): - ... raise RuntimeError - >>> it = strictly_n('abcd', 6, too_short=too_short) - >>> list(it) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - RuntimeError - - >>> def too_long(item_count): - ... print('The boss is going to hear about this') - >>> it = strictly_n('abcdef', 4, too_long=too_long) - >>> list(it) - The boss is going to hear about this - ['a', 'b', 'c', 'd'] - - """ - if too_short is None: - too_short = lambda item_count: raise_( - ValueError, - 'Too few items in iterable (got {})'.format(item_count), - ) - - if too_long is None: - too_long = lambda item_count: raise_( - ValueError, - 'Too many items in iterable (got at least {})'.format(item_count), - ) - - it = iter(iterable) - for i in range(n): - try: - item = next(it) - except StopIteration: - too_short(i) - return - else: - yield item - - try: - next(it) - except StopIteration: - pass - else: - too_long(n + 1) - - -def distinct_permutations(iterable, r=None): - """Yield 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. - - If *r* is given, only the *r*-length permutations are yielded. - - >>> sorted(distinct_permutations([1, 0, 1], r=2)) - [(0, 1), (1, 0), (1, 1)] - >>> sorted(distinct_permutations(range(3), r=2)) - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] - - """ - # Algorithm: https://w.wiki/Qai - def _full(A): - while True: - # Yield the permutation we have - yield tuple(A) - - # Find the largest index i such that A[i] < A[i + 1] - for i in range(size - 2, -1, -1): - if A[i] < A[i + 1]: - break - # If no such index exists, this permutation is the last one - else: - return - - # Find the largest index j greater than j such that A[i] < A[j] - for j in range(size - 1, i, -1): - if A[i] < A[j]: - break - - # Swap the value of A[i] with that of A[j], then reverse the - # sequence from A[i + 1] to form the new permutation - A[i], A[j] = A[j], A[i] - A[i + 1 :] = A[: i - size : -1] # A[i + 1:][::-1] - - # Algorithm: modified from the above - def _partial(A, r): - # Split A into the first r items and the last r items - head, tail = A[:r], A[r:] - right_head_indexes = range(r - 1, -1, -1) - left_tail_indexes = range(len(tail)) - - while True: - # Yield the permutation we have - yield tuple(head) - - # Starting from the right, find the first index of the head with - # value smaller than the maximum value of the tail - call it i. - pivot = tail[-1] - for i in right_head_indexes: - if head[i] < pivot: - break - pivot = head[i] - else: - return - - # Starting from the left, find the first value of the tail - # with a value greater than head[i] and swap. - for j in left_tail_indexes: - if tail[j] > head[i]: - head[i], tail[j] = tail[j], head[i] - break - # If we didn't find one, start from the right and find the first - # index of the head with a value greater than head[i] and swap. - else: - for j in right_head_indexes: - if head[j] > head[i]: - head[i], head[j] = head[j], head[i] - break - - # Reverse head[i + 1:] and swap it with tail[:r - (i + 1)] - tail += head[: i - r : -1] # head[i + 1:][::-1] - i += 1 - head[i:], tail[:] = tail[: r - i], tail[r - i :] - - items = sorted(iterable) - - size = len(items) - if r is None: - r = size - - if 0 < r <= size: - return _full(items) if (r == size) else _partial(items, r) - - return iter(() if r else ((),)) - - -def intersperse(e, iterable, n=1): - """Intersperse 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] - - """ - if n == 0: - raise ValueError('n must be > 0') - elif n == 1: - # interleave(repeat(e), iterable) -> e, x_0, e, x_1, e, x_2... - # islice(..., 1, None) -> x_0, e, x_1, e, x_2... - return islice(interleave(repeat(e), iterable), 1, None) - else: - # interleave(filler, chunks) -> [e], [x_0, x_1], [e], [x_2, x_3]... - # islice(..., 1, None) -> [x_0, x_1], [e], [x_2, x_3]... - # flatten(...) -> x_0, x_1, e, x_2, x_3... - filler = repeat([e]) - chunks = chunked(iterable, n) - return flatten(islice(interleave(filler, chunks), 1, None)) - - -def unique_to_each(*iterables): - """Return 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. - - """ - pool = [list(it) for it in iterables] - counts = Counter(chain.from_iterable(map(set, pool))) - uniques = {element for element in counts if counts[element] == 1} - return [list(filter(uniques.__contains__, it)) for it in pool] - - -def windowed(seq, n, fillvalue=None, step=1): - """Return 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, '!')] - - To slide into the iterable's items, use :func:`chain` to add filler items - to the left: - - >>> iterable = [1, 2, 3, 4] - >>> n = 3 - >>> padding = [None] * (n - 1) - >>> list(windowed(chain(padding, iterable), 3)) - [(None, None, 1), (None, 1, 2), (1, 2, 3), (2, 3, 4)] - """ - if n < 0: - raise ValueError('n must be >= 0') - if n == 0: - yield tuple() - return - if step < 1: - raise ValueError('step must be >= 1') - - window = deque(maxlen=n) - i = n - for _ in map(window.append, seq): - i -= 1 - if not i: - i = step - yield tuple(window) - - size = len(window) - if size < n: - yield tuple(chain(window, repeat(fillvalue, n - size))) - elif 0 < i < min(step, n): - window += (fillvalue,) * i - yield tuple(window) - - -def substrings(iterable): - """Yield all of the substrings of *iterable*. - - >>> [''.join(s) for s in substrings('more')] - ['m', 'o', 'r', 'e', 'mo', 'or', 're', 'mor', 'ore', 'more'] - - Note that non-string iterables can also be subdivided. - - >>> list(substrings([0, 1, 2])) - [(0,), (1,), (2,), (0, 1), (1, 2), (0, 1, 2)] - - """ - # The length-1 substrings - seq = [] - for item in iter(iterable): - seq.append(item) - yield (item,) - seq = tuple(seq) - item_count = len(seq) - - # And the rest - for n in range(2, item_count + 1): - for i in range(item_count - n + 1): - yield seq[i : i + n] - - -def substrings_indexes(seq, reverse=False): - """Yield all substrings and their positions in *seq* - - The items yielded will be a tuple of the form ``(substr, i, j)``, where - ``substr == seq[i:j]``. - - This function only works for iterables that support slicing, such as - ``str`` objects. - - >>> for item in substrings_indexes('more'): - ... print(item) - ('m', 0, 1) - ('o', 1, 2) - ('r', 2, 3) - ('e', 3, 4) - ('mo', 0, 2) - ('or', 1, 3) - ('re', 2, 4) - ('mor', 0, 3) - ('ore', 1, 4) - ('more', 0, 4) - - Set *reverse* to ``True`` to yield the same items in the opposite order. - - - """ - r = range(1, len(seq) + 1) - if reverse: - r = reversed(r) - return ( - (seq[i : i + L], i, i + L) for L in r for i in range(len(seq) - L + 1) - ) - - -class bucket: - """Wrap *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]) # Bucket by 1st character - >>> sorted(list(s)) # Get the keys - ['a', 'b', 'c'] - >>> 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]) - [] - - """ - - def __init__(self, iterable, key, validator=None): - self._it = iter(iterable) - self._key = key - self._cache = defaultdict(deque) - self._validator = validator or (lambda x: True) - - def __contains__(self, value): - if not self._validator(value): - return False - - try: - item = next(self[value]) - except StopIteration: - return False - else: - self._cache[value].appendleft(item) - - return True - - def _get_values(self, value): - """ - 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. - """ - while True: - # If we've cached some items that match the target value, emit - # the first one and evict it from the cache. - if self._cache[value]: - yield self._cache[value].popleft() - # Otherwise we need to advance the parent iterator to search for - # a matching item, caching the rest. - else: - while True: - try: - item = next(self._it) - except StopIteration: - return - item_value = self._key(item) - if item_value == value: - yield item - break - elif self._validator(item_value): - self._cache[item_value].append(item) - - def __iter__(self): - for item in self._it: - item_value = self._key(item) - if self._validator(item_value): - self._cache[item_value].append(item) - - yield from self._cache.keys() - - def __getitem__(self, value): - if not self._validator(value): - return iter(()) - - return self._get_values(value) - - -def spy(iterable, n=1): - """Return 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] - - """ - it = iter(iterable) - head = take(n, it) - - return head.copy(), chain(head, it) - - -def interleave(*iterables): - """Return 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`. - - """ - return chain.from_iterable(zip(*iterables)) - - -def interleave_longest(*iterables): - """Return 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). - - """ - i = chain.from_iterable(zip_longest(*iterables, fillvalue=_marker)) - return (x for x in i if x is not _marker) - - -def interleave_evenly(iterables, lengths=None): - """ - Interleave multiple iterables so that their elements are evenly distributed - throughout the output sequence. - - >>> iterables = [1, 2, 3, 4, 5], ['a', 'b'] - >>> list(interleave_evenly(iterables)) - [1, 2, 'a', 3, 4, 'b', 5] - - >>> iterables = [[1, 2, 3], [4, 5], [6, 7, 8]] - >>> list(interleave_evenly(iterables)) - [1, 6, 4, 2, 7, 3, 8, 5] - - This function requires iterables of known length. Iterables without - ``__len__()`` can be used by manually specifying lengths with *lengths*: - - >>> from itertools import combinations, repeat - >>> iterables = [combinations(range(4), 2), ['a', 'b', 'c']] - >>> lengths = [4 * (4 - 1) // 2, 3] - >>> list(interleave_evenly(iterables, lengths=lengths)) - [(0, 1), (0, 2), 'a', (0, 3), (1, 2), 'b', (1, 3), (2, 3), 'c'] - - Based on Bresenham's algorithm. - """ - if lengths is None: - try: - lengths = [len(it) for it in iterables] - except TypeError: - raise ValueError( - 'Iterable lengths could not be determined automatically. ' - 'Specify them with the lengths keyword.' - ) - elif len(iterables) != len(lengths): - raise ValueError('Mismatching number of iterables and lengths.') - - dims = len(lengths) - - # sort iterables by length, descending - lengths_permute = sorted( - range(dims), key=lambda i: lengths[i], reverse=True - ) - lengths_desc = [lengths[i] for i in lengths_permute] - iters_desc = [iter(iterables[i]) for i in lengths_permute] - - # the longest iterable is the primary one (Bresenham: the longest - # distance along an axis) - delta_primary, deltas_secondary = lengths_desc[0], lengths_desc[1:] - iter_primary, iters_secondary = iters_desc[0], iters_desc[1:] - errors = [delta_primary // dims] * len(deltas_secondary) - - to_yield = sum(lengths) - while to_yield: - yield next(iter_primary) - to_yield -= 1 - # update errors for each secondary iterable - errors = [e - delta for e, delta in zip(errors, deltas_secondary)] - - # those iterables for which the error is negative are yielded - # ("diagonal step" in Bresenham) - for i, e in enumerate(errors): - if e < 0: - yield next(iters_secondary[i]) - to_yield -= 1 - errors[i] += delta_primary - - -def collapse(iterable, base_type=None, levels=None): - """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] - - Binary and text strings 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']] - - """ - - def walk(node, level): - if ( - ((levels is not None) and (level > levels)) - or isinstance(node, (str, bytes)) - or ((base_type is not None) and isinstance(node, base_type)) - ): - yield node - return - - try: - tree = iter(node) - except TypeError: - yield node - return - else: - for child in tree: - yield from walk(child, level + 1) - - yield from walk(iterable, 0) - - -def side_effect(func, iterable, chunk_size=None, before=None, after=None): - """Invoke *func* on each item in *iterable* (or on each *chunk_size* group - of items) before yielding the item. - - `func` must be a function that takes a single argument. Its return value - will be discarded. - - *before* and *after* are optional functions that take no arguments. They - will be executed before iteration starts and after it ends, respectively. - - `side_effect` can be used for logging, updating progress bars, or anything - that is not functionally "pure." - - Emitting a status message: - - >>> 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 - - """ - try: - if before is not None: - before() - - if chunk_size is None: - for item in iterable: - func(item) - yield item - else: - for chunk in chunked(iterable, chunk_size): - func(chunk) - yield from chunk - finally: - if after is not None: - after() - - -def sliced(seq, n, strict=False): - """Yield slices of length *n* from the sequence *seq*. - - >>> list(sliced((1, 2, 3, 4, 5, 6), 3)) - [(1, 2, 3), (4, 5, 6)] - - By the default, the last yielded slice will have fewer than *n* elements - if the length of *seq* is not divisible by *n*: - - >>> list(sliced((1, 2, 3, 4, 5, 6, 7, 8), 3)) - [(1, 2, 3), (4, 5, 6), (7, 8)] - - If the length of *seq* is not divisible by *n* and *strict* is - ``True``, then ``ValueError`` will be raised before the last - slice is yielded. - - This function will only work for iterables that support slicing. - For non-sliceable iterables, see :func:`chunked`. - - """ - iterator = takewhile(len, (seq[i : i + n] for i in count(0, n))) - if strict: - - def ret(): - for _slice in iterator: - if len(_slice) != n: - raise ValueError("seq is not divisible by n.") - yield _slice - - return iter(ret()) - else: - return iterator - - -def split_at(iterable, pred, maxsplit=-1, keep_separator=False): - """Yield lists of items from *iterable*, where each list is delimited by - an item where callable *pred* returns ``True``. - - >>> 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], []] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_at(range(10), lambda n: n % 2 == 1, maxsplit=2)) - [[0], [2], [4, 5, 6, 7, 8, 9]] - - By default, the delimiting items are not included in the output. - The include them, set *keep_separator* to ``True``. - - >>> list(split_at('abcdcba', lambda x: x == 'b', keep_separator=True)) - [['a'], ['b'], ['c', 'd', 'c'], ['b'], ['a']] - - """ - if maxsplit == 0: - yield list(iterable) - return - - buf = [] - it = iter(iterable) - for item in it: - if pred(item): - yield buf - if keep_separator: - yield [item] - if maxsplit == 1: - yield list(it) - return - buf = [] - maxsplit -= 1 - else: - buf.append(item) - yield buf - - -def split_before(iterable, pred, maxsplit=-1): - """Yield lists of items from *iterable*, where each list ends just before - an item for which 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]] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_before(range(10), lambda n: n % 3 == 0, maxsplit=2)) - [[0, 1, 2], [3, 4, 5], [6, 7, 8, 9]] - """ - if maxsplit == 0: - yield list(iterable) - return - - buf = [] - it = iter(iterable) - for item in it: - if pred(item) and buf: - yield buf - if maxsplit == 1: - yield [item] + list(it) - return - buf = [] - maxsplit -= 1 - buf.append(item) - if buf: - yield buf - - -def split_after(iterable, pred, maxsplit=-1): - """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]] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_after(range(10), lambda n: n % 3 == 0, maxsplit=2)) - [[0], [1, 2, 3], [4, 5, 6, 7, 8, 9]] - - """ - if maxsplit == 0: - yield list(iterable) - return - - buf = [] - it = iter(iterable) - for item in it: - buf.append(item) - if pred(item) and buf: - yield buf - if maxsplit == 1: - yield list(it) - return - buf = [] - maxsplit -= 1 - if buf: - yield buf - - -def split_when(iterable, pred, maxsplit=-1): - """Split *iterable* into pieces based on the output of *pred*. - *pred* should be a function that takes successive pairs of items and - returns ``True`` if the iterable should be split in between them. - - For example, to find runs of increasing numbers, split the iterable when - element ``i`` is larger than element ``i + 1``: - - >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], lambda x, y: x > y)) - [[1, 2, 3, 3], [2, 5], [2, 4], [2]] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], - ... lambda x, y: x > y, maxsplit=2)) - [[1, 2, 3, 3], [2, 5], [2, 4, 2]] - - """ - if maxsplit == 0: - yield list(iterable) - return - - it = iter(iterable) - try: - cur_item = next(it) - except StopIteration: - return - - buf = [cur_item] - for next_item in it: - if pred(cur_item, next_item): - yield buf - if maxsplit == 1: - yield [next_item] + list(it) - return - buf = [] - maxsplit -= 1 - - buf.append(next_item) - cur_item = next_item - - yield buf - - -def split_into(iterable, sizes): - """Yield a list of sequential items from *iterable* of length 'n' for each - integer 'n' in *sizes*. - - >>> list(split_into([1,2,3,4,5,6], [1,2,3])) - [[1], [2, 3], [4, 5, 6]] - - If the sum of *sizes* is smaller than the length of *iterable*, then the - remaining items of *iterable* will not be returned. - - >>> list(split_into([1,2,3,4,5,6], [2,3])) - [[1, 2], [3, 4, 5]] - - If the sum of *sizes* is larger than the length of *iterable*, fewer items - will be returned in the iteration that overruns *iterable* and further - lists will be empty: - - >>> list(split_into([1,2,3,4], [1,2,3,4])) - [[1], [2, 3], [4], []] - - When a ``None`` object is encountered in *sizes*, the returned list will - contain items up to the end of *iterable* the same way that itertools.slice - does: - - >>> list(split_into([1,2,3,4,5,6,7,8,9,0], [2,3,None])) - [[1, 2], [3, 4, 5], [6, 7, 8, 9, 0]] - - :func:`split_into` can be useful for grouping a series of items where the - sizes of the groups are not uniform. An example would be where in a row - from a table, multiple columns represent elements of the same feature - (e.g. a point represented by x,y,z) but, the format is not the same for - all columns. - """ - # convert the iterable argument into an iterator so its contents can - # be consumed by islice in case it is a generator - it = iter(iterable) - - for size in sizes: - if size is None: - yield list(it) - return - else: - yield list(islice(it, size)) - - -def padded(iterable, fillvalue=None, n=None, next_multiple=False): - """Yield 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. - - """ - it = iter(iterable) - if n is None: - yield from chain(it, repeat(fillvalue)) - elif n < 1: - raise ValueError('n must be at least 1') - else: - item_count = 0 - for item in it: - yield item - item_count += 1 - - remaining = (n - item_count) % n if next_multiple else n - item_count - for _ in range(remaining): - yield fillvalue - - -def repeat_each(iterable, n=2): - """Repeat each element in *iterable* *n* times. - - >>> list(repeat_each('ABC', 3)) - ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'] - """ - return chain.from_iterable(map(repeat, iterable, repeat(n))) - - -def repeat_last(iterable, default=None): - """After the *iterable* is exhausted, keep yielding its last element. - - >>> list(islice(repeat_last(range(3)), 5)) - [0, 1, 2, 2, 2] - - If the iterable is empty, yield *default* forever:: - - >>> list(islice(repeat_last(range(0), 42), 5)) - [42, 42, 42, 42, 42] - - """ - item = _marker - for item in iterable: - yield item - final = default if item is _marker else item - yield from repeat(final) - - -def distribute(n, iterable): - """Distribute 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`. - - """ - if n < 1: - raise ValueError('n must be at least 1') - - children = tee(iterable, n) - return [islice(it, index, None, n) for index, it in enumerate(children)] - - -def stagger(iterable, offsets=(-1, 0, 1), longest=False, fillvalue=None): - """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. - - """ - children = tee(iterable, len(offsets)) - - return zip_offset( - *children, offsets=offsets, longest=longest, fillvalue=fillvalue - ) - - -class UnequalIterablesError(ValueError): - def __init__(self, details=None): - msg = 'Iterables have different lengths' - if details is not None: - msg += (': index 0 has length {}; index {} has length {}').format( - *details - ) - - super().__init__(msg) - - -def _zip_equal_generator(iterables): - for combo in zip_longest(*iterables, fillvalue=_marker): - for val in combo: - if val is _marker: - raise UnequalIterablesError() - yield combo - - -def _zip_equal(*iterables): - # Check whether the iterables are all the same size. - try: - first_size = len(iterables[0]) - for i, it in enumerate(iterables[1:], 1): - size = len(it) - if size != first_size: - break - else: - # If we didn't break out, we can use the built-in zip. - return zip(*iterables) - - # If we did break out, there was a mismatch. - raise UnequalIterablesError(details=(first_size, i, size)) - # If any one of the iterables didn't have a length, start reading - # them until one runs out. - except TypeError: - return _zip_equal_generator(iterables) - - -def zip_equal(*iterables): - """``zip`` the input *iterables* together, but raise - ``UnequalIterablesError`` if they aren't all the same length. - - >>> it_1 = range(3) - >>> it_2 = iter('abc') - >>> list(zip_equal(it_1, it_2)) - [(0, 'a'), (1, 'b'), (2, 'c')] - - >>> it_1 = range(3) - >>> it_2 = iter('abcd') - >>> list(zip_equal(it_1, it_2)) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - more_itertools.more.UnequalIterablesError: Iterables have different - lengths - - """ - if hexversion >= 0x30A00A6: - warnings.warn( - ( - 'zip_equal will be removed in a future version of ' - 'more-itertools. Use the builtin zip function with ' - 'strict=True instead.' - ), - DeprecationWarning, - ) - - return _zip_equal(*iterables) - - -def zip_offset(*iterables, offsets, longest=False, fillvalue=None): - """``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 some 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. - - """ - if len(iterables) != len(offsets): - raise ValueError("Number of iterables and offsets didn't match") - - staggered = [] - for it, n in zip(iterables, offsets): - if n < 0: - staggered.append(chain(repeat(fillvalue, -n), it)) - elif n > 0: - staggered.append(islice(it, n, None)) - else: - staggered.append(it) - - if longest: - return zip_longest(*staggered, fillvalue=fillvalue) - - return zip(*staggered) - - -def sort_together(iterables, key_list=(0,), key=None, reverse=False): - """Return 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 multiple 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')] - - To sort by a function of the elements of the iterable, pass a *key* - function. Its arguments are the elements of the iterables corresponding to - the key list:: - - >>> names = ('a', 'b', 'c') - >>> lengths = (1, 2, 3) - >>> widths = (5, 2, 1) - >>> def area(length, width): - ... return length * width - >>> sort_together([names, lengths, widths], key_list=(1, 2), key=area) - [('c', 'b', 'a'), (3, 2, 1), (1, 2, 5)] - - 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')] - - """ - if key is None: - # if there is no key function, the key argument to sorted is an - # itemgetter - key_argument = itemgetter(*key_list) - else: - # if there is a key function, call it with the items at the offsets - # specified by the key function as arguments - key_list = list(key_list) - if len(key_list) == 1: - # if key_list contains a single item, pass the item at that offset - # as the only argument to the key function - key_offset = key_list[0] - key_argument = lambda zipped_items: key(zipped_items[key_offset]) - else: - # if key_list contains multiple items, use itemgetter to return a - # tuple of items, which we pass as *args to the key function - get_key_items = itemgetter(*key_list) - key_argument = lambda zipped_items: key( - *get_key_items(zipped_items) - ) - - return list( - zip(*sorted(zip(*iterables), key=key_argument, reverse=reverse)) - ) - - -def unzip(iterable): - """The inverse of :func:`zip`, this function disaggregates the elements - of the zipped *iterable*. - - The ``i``-th iterable contains the ``i``-th element from each element - of the zipped iterable. The first element is used to to determine the - length of the remaining elements. - - >>> iterable = [('a', 1), ('b', 2), ('c', 3), ('d', 4)] - >>> letters, numbers = unzip(iterable) - >>> list(letters) - ['a', 'b', 'c', 'd'] - >>> list(numbers) - [1, 2, 3, 4] - - This is similar to using ``zip(*iterable)``, but it avoids reading - *iterable* into memory. Note, however, that this function uses - :func:`itertools.tee` and thus may require significant storage. - - """ - head, iterable = spy(iter(iterable)) - if not head: - # empty iterable, e.g. zip([], [], []) - return () - # spy returns a one-length iterable as head - head = head[0] - iterables = tee(iterable, len(head)) - - def itemgetter(i): - def getter(obj): - try: - return obj[i] - except IndexError: - # basically if we have an iterable like - # iter([(1, 2, 3), (4, 5), (6,)]) - # the second unzipped iterable would fail at the third tuple - # since it would try to access tup[1] - # same with the third unzipped iterable and the second tuple - # to support these "improperly zipped" iterables, - # we create a custom itemgetter - # which just stops the unzipped iterables - # at first length mismatch - raise StopIteration - - return getter - - return tuple(map(itemgetter(i), it) for i, it in enumerate(iterables)) - - -def divide(n, iterable): - """Divide 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. - - """ - if n < 1: - raise ValueError('n must be at least 1') - - try: - iterable[:0] - except TypeError: - seq = tuple(iterable) - else: - seq = iterable - - q, r = divmod(len(seq), n) - - ret = [] - stop = 0 - for i in range(1, n + 1): - start = stop - stop += q + 1 if i <= r else q - ret.append(iter(seq[start:stop])) - - return ret - - -def always_iterable(obj, base_type=(str, bytes)): - """If *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'] - """ - if obj is None: - return iter(()) - - if (base_type is not None) and isinstance(obj, base_type): - return iter((obj,)) - - try: - return iter(obj) - except TypeError: - return iter((obj,)) - - -def adjacent(predicate, iterable, distance=1): - """Return 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. - - """ - # Allow distance=0 mainly for testing that it reproduces results with map() - if distance < 0: - raise ValueError('distance must be at least 0') - - i1, i2 = tee(iterable) - padding = [False] * distance - selected = chain(padding, map(predicate, i1), padding) - adjacent_to_selected = map(any, windowed(selected, 2 * distance + 1)) - return zip(adjacent_to_selected, i2) - - -def groupby_transform(iterable, keyfunc=None, valuefunc=None, reducefunc=None): - """An extension of :func:`itertools.groupby` that can apply transformations - to the grouped data. - - * *keyfunc* is a function computing a key value for each item in *iterable* - * *valuefunc* is a function that transforms the individual items from - *iterable* after grouping - * *reducefunc* is a function that transforms each group of items - - >>> iterable = 'aAAbBBcCC' - >>> keyfunc = lambda k: k.upper() - >>> valuefunc = lambda v: v.lower() - >>> reducefunc = lambda g: ''.join(g) - >>> list(groupby_transform(iterable, keyfunc, valuefunc, reducefunc)) - [('A', 'aaa'), ('B', 'bbb'), ('C', 'ccc')] - - Each optional argument defaults to an identity function if 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. - - """ - ret = groupby(iterable, keyfunc) - if valuefunc: - ret = ((k, map(valuefunc, g)) for k, g in ret) - if reducefunc: - ret = ((k, reducefunc(g)) for k, g in ret) - - return ret - - -class numeric_range(abc.Sequence, abc.Hashable): - """An 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. - - ``datetime.datetime`` objects can be used for *start* and *stop*, if *step* - is a ``datetime.timedelta`` object: - - >>> import datetime - >>> start = datetime.datetime(2019, 1, 1) - >>> stop = datetime.datetime(2019, 1, 3) - >>> step = datetime.timedelta(days=1) - >>> items = iter(numeric_range(start, stop, step)) - >>> next(items) - datetime.datetime(2019, 1, 1, 0, 0) - >>> next(items) - datetime.datetime(2019, 1, 2, 0, 0) - - """ - - _EMPTY_HASH = hash(range(0, 0)) - - def __init__(self, *args): - argc = len(args) - if argc == 1: - (self._stop,) = args - self._start = type(self._stop)(0) - self._step = type(self._stop - self._start)(1) - elif argc == 2: - self._start, self._stop = args - self._step = type(self._stop - self._start)(1) - elif argc == 3: - self._start, self._stop, self._step = args - elif argc == 0: - raise TypeError( - 'numeric_range expected at least ' - '1 argument, got {}'.format(argc) - ) - else: - raise TypeError( - 'numeric_range expected at most ' - '3 arguments, got {}'.format(argc) - ) - - self._zero = type(self._step)(0) - if self._step == self._zero: - raise ValueError('numeric_range() arg 3 must not be zero') - self._growing = self._step > self._zero - self._init_len() - - def __bool__(self): - if self._growing: - return self._start < self._stop - else: - return self._start > self._stop - - def __contains__(self, elem): - if self._growing: - if self._start <= elem < self._stop: - return (elem - self._start) % self._step == self._zero - else: - if self._start >= elem > self._stop: - return (self._start - elem) % (-self._step) == self._zero - - return False - - def __eq__(self, other): - if isinstance(other, numeric_range): - empty_self = not bool(self) - empty_other = not bool(other) - if empty_self or empty_other: - return empty_self and empty_other # True if both empty - else: - return ( - self._start == other._start - and self._step == other._step - and self._get_by_index(-1) == other._get_by_index(-1) - ) - else: - return False - - def __getitem__(self, key): - if isinstance(key, int): - return self._get_by_index(key) - elif isinstance(key, slice): - step = self._step if key.step is None else key.step * self._step - - if key.start is None or key.start <= -self._len: - start = self._start - elif key.start >= self._len: - start = self._stop - else: # -self._len < key.start < self._len - start = self._get_by_index(key.start) - - if key.stop is None or key.stop >= self._len: - stop = self._stop - elif key.stop <= -self._len: - stop = self._start - else: # -self._len < key.stop < self._len - stop = self._get_by_index(key.stop) - - return numeric_range(start, stop, step) - else: - raise TypeError( - 'numeric range indices must be ' - 'integers or slices, not {}'.format(type(key).__name__) - ) - - def __hash__(self): - if self: - return hash((self._start, self._get_by_index(-1), self._step)) - else: - return self._EMPTY_HASH - - def __iter__(self): - values = (self._start + (n * self._step) for n in count()) - if self._growing: - return takewhile(partial(gt, self._stop), values) - else: - return takewhile(partial(lt, self._stop), values) - - def __len__(self): - return self._len - - def _init_len(self): - if self._growing: - start = self._start - stop = self._stop - step = self._step - else: - start = self._stop - stop = self._start - step = -self._step - distance = stop - start - if distance <= self._zero: - self._len = 0 - else: # distance > 0 and step > 0: regular euclidean division - q, r = divmod(distance, step) - self._len = int(q) + int(r != self._zero) - - def __reduce__(self): - return numeric_range, (self._start, self._stop, self._step) - - def __repr__(self): - if self._step == 1: - return "numeric_range({}, {})".format( - repr(self._start), repr(self._stop) - ) - else: - return "numeric_range({}, {}, {})".format( - repr(self._start), repr(self._stop), repr(self._step) - ) - - def __reversed__(self): - return iter( - numeric_range( - self._get_by_index(-1), self._start - self._step, -self._step - ) - ) - - def count(self, value): - return int(value in self) - - def index(self, value): - if self._growing: - if self._start <= value < self._stop: - q, r = divmod(value - self._start, self._step) - if r == self._zero: - return int(q) - else: - if self._start >= value > self._stop: - q, r = divmod(self._start - value, -self._step) - if r == self._zero: - return int(q) - - raise ValueError("{} is not in numeric range".format(value)) - - def _get_by_index(self, i): - if i < 0: - i += self._len - if i < 0 or i >= self._len: - raise IndexError("numeric range object index out of range") - return self._start + i * self._step - - -def count_cycle(iterable, n=None): - """Cycle 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')] - - """ - iterable = tuple(iterable) - if not iterable: - return iter(()) - counter = count() if n is None else range(n) - return ((i, item) for i in counter for item in iterable) - - -def mark_ends(iterable): - """Yield 3-tuples of the form ``(is_first, is_last, item)``. - - >>> list(mark_ends('ABC')) - [(True, False, 'A'), (False, False, 'B'), (False, True, 'C')] - - Use this when looping over an iterable to take special action on its first - and/or last items: - - >>> iterable = ['Header', 100, 200, 'Footer'] - >>> total = 0 - >>> for is_first, is_last, item in mark_ends(iterable): - ... if is_first: - ... continue # Skip the header - ... if is_last: - ... continue # Skip the footer - ... total += item - >>> print(total) - 300 - """ - it = iter(iterable) - - try: - b = next(it) - except StopIteration: - return - - try: - for i in count(): - a = b - b = next(it) - yield i == 0, False, a - - except StopIteration: - yield i == 0, True, a - - -def locate(iterable, pred=bool, window_size=None): - """Yield 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] - - If *window_size* is given, then the *pred* function will be called with - that many items. This enables searching for sub-sequences: - - >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] - >>> pred = lambda *args: args == (1, 2, 3) - >>> list(locate(iterable, pred=pred, window_size=3)) - [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 - - """ - if window_size is None: - return compress(count(), map(pred, iterable)) - - if window_size < 1: - raise ValueError('window size must be at least 1') - - it = windowed(iterable, window_size, fillvalue=_marker) - return compress(count(), starmap(pred, it)) - - -def lstrip(iterable, pred): - """Yield 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`. - - """ - return dropwhile(pred, iterable) - - -def rstrip(iterable, pred): - """Yield 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`. - - """ - cache = [] - cache_append = cache.append - cache_clear = cache.clear - for x in iterable: - if pred(x): - cache_append(x) - else: - yield from cache - cache_clear() - yield x - - -def strip(iterable, pred): - """Yield 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`. - - """ - return rstrip(lstrip(iterable, pred), pred) - - -class islice_extended: - """An 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] - - You can also use slice notation directly: - - >>> iterable = map(str, count()) - >>> it = islice_extended(iterable)[10:20:2] - >>> list(it) - ['10', '12', '14', '16', '18'] - - """ - - def __init__(self, iterable, *args): - it = iter(iterable) - if args: - self._iterable = _islice_helper(it, slice(*args)) - else: - self._iterable = it - - def __iter__(self): - return self - - def __next__(self): - return next(self._iterable) - - def __getitem__(self, key): - if isinstance(key, slice): - return islice_extended(_islice_helper(self._iterable, key)) - - raise TypeError('islice_extended.__getitem__ argument must be a slice') - - -def _islice_helper(it, s): - start = s.start - stop = s.stop - if s.step == 0: - raise ValueError('step argument must be a non-zero integer or None.') - step = s.step or 1 - - if step > 0: - start = 0 if (start is None) else start - - if start < 0: - # Consume all but the last -start items - cache = deque(enumerate(it, 1), maxlen=-start) - len_iter = cache[-1][0] if cache else 0 - - # Adjust start to be positive - i = max(len_iter + start, 0) - - # Adjust stop to be positive - if stop is None: - j = len_iter - elif stop >= 0: - j = min(stop, len_iter) - else: - j = max(len_iter + stop, 0) - - # Slice the cache - n = j - i - if n <= 0: - return - - for index, item in islice(cache, 0, n, step): - yield item - elif (stop is not None) and (stop < 0): - # Advance to the start position - next(islice(it, start, start), None) - - # When stop is negative, we have to carry -stop items while - # iterating - cache = deque(islice(it, -stop), maxlen=-stop) - - for index, item in enumerate(it): - cached_item = cache.popleft() - if index % step == 0: - yield cached_item - cache.append(item) - else: - # When both start and stop are positive we have the normal case - yield from islice(it, start, stop, step) - else: - start = -1 if (start is None) else start - - if (stop is not None) and (stop < 0): - # Consume all but the last items - n = -stop - 1 - cache = deque(enumerate(it, 1), maxlen=n) - len_iter = cache[-1][0] if cache else 0 - - # If start and stop are both negative they are comparable and - # we can just slice. Otherwise we can adjust start to be negative - # and then slice. - if start < 0: - i, j = start, stop - else: - i, j = min(start - len_iter, -1), None - - for index, item in list(cache)[i:j:step]: - yield item - else: - # Advance to the stop position - if stop is not None: - m = stop + 1 - next(islice(it, m, m), None) - - # stop is positive, so if start is negative they are not comparable - # and we need the rest of the items. - if start < 0: - i = start - n = None - # stop is None and start is positive, so we just need items up to - # the start index. - elif stop is None: - i = None - n = start + 1 - # Both stop and start are positive, so they are comparable. - else: - i = None - n = start - stop - if n <= 0: - return - - cache = list(islice(it, n)) - - yield from cache[i::step] - - -def always_reversible(iterable): - """An 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. - """ - try: - return reversed(iterable) - except TypeError: - return reversed(list(iterable)) - - -def consecutive_groups(iterable, ordering=lambda x: x): - """Yield 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'] - - Each group of consecutive items is an iterator that shares it source with - *iterable*. When an an output group is advanced, the previous group is - no longer available unless its elements are copied (e.g., into a ``list``). - - >>> iterable = [1, 2, 11, 12, 21, 22] - >>> saved_groups = [] - >>> for group in consecutive_groups(iterable): - ... saved_groups.append(list(group)) # Copy group elements - >>> saved_groups - [[1, 2], [11, 12], [21, 22]] - - """ - for k, g in groupby( - enumerate(iterable), key=lambda x: x[0] - ordering(x[1]) - ): - yield map(itemgetter(1), g) - - -def difference(iterable, func=sub, *, initial=None): - """This function is the inverse of :func:`itertools.accumulate`. By default - it will compute the first difference of *iterable* using - :func:`operator.sub`: - - >>> from itertools import accumulate - >>> iterable = accumulate([0, 1, 2, 3, 4]) # produces 0, 1, 3, 6, 10 - >>> list(difference(iterable)) - [0, 1, 2, 3, 4] - - *func* defaults to :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] - >>> func = lambda x, y: x // y - >>> list(difference(iterable, func)) - [1, 2, 3, 4, 5] - - If the *initial* keyword is set, the first element will be skipped when - computing successive differences. - - >>> it = [10, 11, 13, 16] # from accumulate([1, 2, 3], initial=10) - >>> list(difference(it, initial=10)) - [1, 2, 3] - - """ - a, b = tee(iterable) - try: - first = [next(b)] - except StopIteration: - return iter([]) - - if initial is not None: - first = [] - - return chain(first, starmap(func, zip(b, a))) - - -class SequenceView(Sequence): - """Return a read-only view of the sequence object *target*. - - :class:`SequenceView` objects are analogous 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. - - """ - - def __init__(self, target): - if not isinstance(target, Sequence): - raise TypeError - self._target = target - - def __getitem__(self, index): - return self._target[index] - - def __len__(self): - return len(self._target) - - def __repr__(self): - return '{}({})'.format(self.__class__.__name__, repr(self._target)) - - -class seekable: - """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') - - Call :meth:`peek` to look ahead one item without advancing the iterator: - - >>> it = seekable('1234') - >>> it.peek() - '1' - >>> list(it) - ['1', '2', '3', '4'] - >>> it.peek(default='empty') - 'empty' - - Before the iterator is at its end, calling :func:`bool` on it will return - ``True``. After it will return ``False``: - - >>> it = seekable('5678') - >>> bool(it) - True - >>> list(it) - ['5', '6', '7', '8'] - >>> bool(it) - False - - 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']) - - By default, the cache grows as the source iterable progresses, so beware of - wrapping very large or infinite iterables. Supply *maxlen* to limit the - size of the cache (this of course limits how far back you can seek). - - >>> from itertools import count - >>> it = seekable((str(n) for n in count()), maxlen=2) - >>> next(it), next(it), next(it), next(it) - ('0', '1', '2', '3') - >>> list(it.elements()) - ['2', '3'] - >>> it.seek(0) - >>> next(it), next(it), next(it), next(it) - ('2', '3', '4', '5') - >>> next(it) - '6' - - """ - - def __init__(self, iterable, maxlen=None): - self._source = iter(iterable) - if maxlen is None: - self._cache = [] - else: - self._cache = deque([], maxlen) - self._index = None - - def __iter__(self): - return self - - def __next__(self): - if self._index is not None: - try: - item = self._cache[self._index] - except IndexError: - self._index = None - else: - self._index += 1 - return item - - item = next(self._source) - self._cache.append(item) - return item - - def __bool__(self): - try: - self.peek() - except StopIteration: - return False - return True - - def peek(self, default=_marker): - try: - peeked = next(self) - except StopIteration: - if default is _marker: - raise - return default - if self._index is None: - self._index = len(self._cache) - self._index -= 1 - return peeked - - def elements(self): - return SequenceView(self._cache) - - def seek(self, index): - self._index = index - remainder = index - len(self._cache) - if remainder > 0: - consume(self, remainder) - - -class run_length: - """ - :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'] - - """ - - @staticmethod - def encode(iterable): - return ((k, ilen(g)) for k, g in groupby(iterable)) - - @staticmethod - def decode(iterable): - return chain.from_iterable(repeat(k, n) for k, n in iterable) - - -def exactly_n(iterable, n, predicate=bool): - """Return ``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. - - """ - return len(take(n + 1, filter(predicate, iterable))) == n - - -def circular_shifts(iterable): - """Return 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)] - """ - lst = list(iterable) - return take(len(lst), windowed(cycle(lst), len(lst))) - - -def make_decorator(wrapping_func, result_index=0): - """Return 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' - - """ - # See https://sites.google.com/site/bbayles/index/decorator_factory for - # notes on how this works. - def decorator(*wrapping_args, **wrapping_kwargs): - def outer_wrapper(f): - def inner_wrapper(*args, **kwargs): - result = f(*args, **kwargs) - wrapping_args_ = list(wrapping_args) - wrapping_args_.insert(result_index, result) - return wrapping_func(*wrapping_args_, **wrapping_kwargs) - - return inner_wrapper - - return outer_wrapper - - return decorator - - -def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None): - """Return 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 - procedure: - - >>> 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. - - """ - valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc - - ret = defaultdict(list) - for item in iterable: - key = keyfunc(item) - value = valuefunc(item) - ret[key].append(value) - - if reducefunc is not None: - for key, value_list in ret.items(): - ret[key] = reducefunc(value_list) - - ret.default_factory = None - return ret - - -def rlocate(iterable, pred=bool, window_size=None): - """Yield the index of each item in *iterable* for which *pred* returns - ``True``, starting from the right and moving left. - - *pred* defaults to :func:`bool`, which will select truthy items: - - >>> list(rlocate([0, 1, 1, 0, 1, 0, 0])) # Truthy at 1, 2, and 4 - [4, 2, 1] - - Set *pred* to a custom function to, e.g., find the indexes for a particular - item: - - >>> iterable = iter('abcb') - >>> pred = lambda x: x == 'b' - >>> list(rlocate(iterable, pred)) - [3, 1] - - If *window_size* is given, then the *pred* function will be called with - that many items. This enables searching for sub-sequences: - - >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] - >>> pred = lambda *args: args == (1, 2, 3) - >>> list(rlocate(iterable, pred=pred, window_size=3)) - [9, 5, 1] - - Beware, this function won't return anything for infinite iterables. - If *iterable* is reversible, ``rlocate`` will reverse it and search from - the right. Otherwise, it will search from the left and return the results - in reverse order. - - See :func:`locate` to for other example applications. - - """ - if window_size is None: - try: - len_iter = len(iterable) - return (len_iter - i - 1 for i in locate(reversed(iterable), pred)) - except TypeError: - pass - - return reversed(list(locate(iterable, pred, window_size))) - - -def replace(iterable, pred, substitutes, count=None, window_size=1): - """Yield the items from *iterable*, replacing the items for which *pred* - returns ``True`` with the items from the iterable *substitutes*. - - >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1] - >>> pred = lambda x: x == 0 - >>> substitutes = (2, 3) - >>> list(replace(iterable, pred, substitutes)) - [1, 1, 2, 3, 1, 1, 2, 3, 1, 1] - - If *count* is given, the number of replacements will be limited: - - >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1, 0] - >>> pred = lambda x: x == 0 - >>> substitutes = [None] - >>> list(replace(iterable, pred, substitutes, count=2)) - [1, 1, None, 1, 1, None, 1, 1, 0] - - Use *window_size* to control the number of items passed as arguments to - *pred*. This allows for locating and replacing subsequences. - - >>> iterable = [0, 1, 2, 5, 0, 1, 2, 5] - >>> window_size = 3 - >>> pred = lambda *args: args == (0, 1, 2) # 3 items passed to pred - >>> substitutes = [3, 4] # Splice in these items - >>> list(replace(iterable, pred, substitutes, window_size=window_size)) - [3, 4, 5, 3, 4, 5] - - """ - if window_size < 1: - raise ValueError('window_size must be at least 1') - - # Save the substitutes iterable, since it's used more than once - substitutes = tuple(substitutes) - - # Add padding such that the number of windows matches the length of the - # iterable - it = chain(iterable, [_marker] * (window_size - 1)) - windows = windowed(it, window_size) - - n = 0 - for w in windows: - # If the current window matches our predicate (and we haven't hit - # our maximum number of replacements), splice in the substitutes - # and then consume the following windows that overlap with this one. - # For example, if the iterable is (0, 1, 2, 3, 4...) - # and the window size is 2, we have (0, 1), (1, 2), (2, 3)... - # If the predicate matches on (0, 1), we need to zap (0, 1) and (1, 2) - if pred(*w): - if (count is None) or (n < count): - n += 1 - yield from substitutes - consume(windows, window_size - 1) - continue - - # If there was no match (or we've reached the replacement limit), - # yield the first item from the window. - if w and (w[0] is not _marker): - yield w[0] - - -def partitions(iterable): - """Yield all possible order-preserving partitions of *iterable*. - - >>> iterable = 'abc' - >>> for part in partitions(iterable): - ... print([''.join(p) for p in part]) - ['abc'] - ['a', 'bc'] - ['ab', 'c'] - ['a', 'b', 'c'] - - This is unrelated to :func:`partition`. - - """ - sequence = list(iterable) - n = len(sequence) - for i in powerset(range(1, n)): - yield [sequence[i:j] for i, j in zip((0,) + i, i + (n,))] - - -def set_partitions(iterable, k=None): - """ - Yield the set partitions of *iterable* into *k* parts. Set partitions are - not order-preserving. - - >>> iterable = 'abc' - >>> for part in set_partitions(iterable, 2): - ... print([''.join(p) for p in part]) - ['a', 'bc'] - ['ab', 'c'] - ['b', 'ac'] - - - If *k* is not given, every set partition is generated. - - >>> iterable = 'abc' - >>> for part in set_partitions(iterable): - ... print([''.join(p) for p in part]) - ['abc'] - ['a', 'bc'] - ['ab', 'c'] - ['b', 'ac'] - ['a', 'b', 'c'] - - """ - L = list(iterable) - n = len(L) - if k is not None: - if k < 1: - raise ValueError( - "Can't partition in a negative or zero number of groups" - ) - elif k > n: - return - - def set_partitions_helper(L, k): - n = len(L) - if k == 1: - yield [L] - elif n == k: - yield [[s] for s in L] - else: - e, *M = L - for p in set_partitions_helper(M, k - 1): - yield [[e], *p] - for p in set_partitions_helper(M, k): - for i in range(len(p)): - yield p[:i] + [[e] + p[i]] + p[i + 1 :] - - if k is None: - for k in range(1, n + 1): - yield from set_partitions_helper(L, k) - else: - yield from set_partitions_helper(L, k) - - -class time_limited: - """ - Yield items from *iterable* until *limit_seconds* have passed. - If the time limit expires before all items have been yielded, the - ``timed_out`` parameter will be set to ``True``. - - >>> from time import sleep - >>> def generator(): - ... yield 1 - ... yield 2 - ... sleep(0.2) - ... yield 3 - >>> iterable = time_limited(0.1, generator()) - >>> list(iterable) - [1, 2] - >>> iterable.timed_out - True - - Note that the time is checked before each item is yielded, and iteration - stops if the time elapsed is greater than *limit_seconds*. If your time - limit is 1 second, but it takes 2 seconds to generate the first item from - the iterable, the function will run for 2 seconds and not yield anything. - - """ - - def __init__(self, limit_seconds, iterable): - if limit_seconds < 0: - raise ValueError('limit_seconds must be positive') - self.limit_seconds = limit_seconds - self._iterable = iter(iterable) - self._start_time = monotonic() - self.timed_out = False - - def __iter__(self): - return self - - def __next__(self): - item = next(self._iterable) - if monotonic() - self._start_time > self.limit_seconds: - self.timed_out = True - raise StopIteration - - return item - - -def only(iterable, default=None, too_long=None): - """If *iterable* has only one item, return it. - If it has zero items, return *default*. - If it has more than one item, raise the exception given by *too_long*, - which is ``ValueError`` by default. - - >>> only([], default='missing') - 'missing' - >>> only([1]) - 1 - >>> only([1, 2]) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError: Expected exactly one item in iterable, but got 1, 2, - and perhaps more.' - >>> only([1, 2], too_long=TypeError) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - TypeError - - Note that :func:`only` attempts to advance *iterable* twice to ensure there - is only one item. See :func:`spy` or :func:`peekable` to check - iterable contents less destructively. - """ - it = iter(iterable) - first_value = next(it, default) - - try: - second_value = next(it) - except StopIteration: - pass - else: - msg = ( - 'Expected exactly one item in iterable, but got {!r}, {!r}, ' - 'and perhaps more.'.format(first_value, second_value) - ) - raise too_long or ValueError(msg) - - return first_value - - -def ichunked(iterable, n): - """Break *iterable* into sub-iterables with *n* elements each. - :func:`ichunked` is like :func:`chunked`, but it yields iterables - instead of lists. - - If the sub-iterables are read in order, the elements of *iterable* - won't be stored in memory. - If they are read out of order, :func:`itertools.tee` is used to cache - elements as necessary. - - >>> from itertools import count - >>> all_chunks = ichunked(count(), 4) - >>> c_1, c_2, c_3 = next(all_chunks), next(all_chunks), next(all_chunks) - >>> list(c_2) # c_1's elements have been cached; c_3's haven't been - [4, 5, 6, 7] - >>> list(c_1) - [0, 1, 2, 3] - >>> list(c_3) - [8, 9, 10, 11] - - """ - source = iter(iterable) - - while True: - # Check to see whether we're at the end of the source iterable - item = next(source, _marker) - if item is _marker: - return - - # Clone the source and yield an n-length slice - source, it = tee(chain([item], source)) - yield islice(it, n) - - # Advance the source iterable - consume(source, n) - - -def distinct_combinations(iterable, r): - """Yield the distinct combinations of *r* items taken from *iterable*. - - >>> list(distinct_combinations([0, 0, 1], 2)) - [(0, 0), (0, 1)] - - Equivalent to ``set(combinations(iterable))``, except duplicates are not - generated and thrown away. For larger input sequences this is much more - efficient. - - """ - if r < 0: - raise ValueError('r must be non-negative') - elif r == 0: - yield () - return - pool = tuple(iterable) - generators = [unique_everseen(enumerate(pool), key=itemgetter(1))] - current_combo = [None] * r - level = 0 - while generators: - try: - cur_idx, p = next(generators[-1]) - except StopIteration: - generators.pop() - level -= 1 - continue - current_combo[level] = p - if level + 1 == r: - yield tuple(current_combo) - else: - generators.append( - unique_everseen( - enumerate(pool[cur_idx + 1 :], cur_idx + 1), - key=itemgetter(1), - ) - ) - level += 1 - - -def filter_except(validator, iterable, *exceptions): - """Yield the items from *iterable* for which the *validator* function does - not raise one of the specified *exceptions*. - - *validator* is called for each item in *iterable*. - It should be a function that accepts one argument and raises an exception - if that item is not valid. - - >>> iterable = ['1', '2', 'three', '4', None] - >>> list(filter_except(int, iterable, ValueError, TypeError)) - ['1', '2', '4'] - - If an exception other than one given by *exceptions* is raised by - *validator*, it is raised like normal. - """ - for item in iterable: - try: - validator(item) - except exceptions: - pass - else: - yield item - - -def map_except(function, iterable, *exceptions): - """Transform each item from *iterable* with *function* and yield the - result, unless *function* raises one of the specified *exceptions*. - - *function* is called to transform each item in *iterable*. - It should accept one argument. - - >>> iterable = ['1', '2', 'three', '4', None] - >>> list(map_except(int, iterable, ValueError, TypeError)) - [1, 2, 4] - - If an exception other than one given by *exceptions* is raised by - *function*, it is raised like normal. - """ - for item in iterable: - try: - yield function(item) - except exceptions: - pass - - -def map_if(iterable, pred, func, func_else=lambda x: x): - """Evaluate each item from *iterable* using *pred*. If the result is - equivalent to ``True``, transform the item with *func* and yield it. - Otherwise, transform the item with *func_else* and yield it. - - *pred*, *func*, and *func_else* should each be functions that accept - one argument. By default, *func_else* is the identity function. - - >>> from math import sqrt - >>> iterable = list(range(-5, 5)) - >>> iterable - [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4] - >>> list(map_if(iterable, lambda x: x > 3, lambda x: 'toobig')) - [-5, -4, -3, -2, -1, 0, 1, 2, 3, 'toobig'] - >>> list(map_if(iterable, lambda x: x >= 0, - ... lambda x: f'{sqrt(x):.2f}', lambda x: None)) - [None, None, None, None, None, '0.00', '1.00', '1.41', '1.73', '2.00'] - """ - for item in iterable: - yield func(item) if pred(item) else func_else(item) - - -def _sample_unweighted(iterable, k): - # Implementation of "Algorithm L" from the 1994 paper by Kim-Hung Li: - # "Reservoir-Sampling Algorithms of Time Complexity O(n(1+log(N/n)))". - - # Fill up the reservoir (collection of samples) with the first `k` samples - reservoir = take(k, iterable) - - # Generate random number that's the largest in a sample of k U(0,1) numbers - # Largest order statistic: https://en.wikipedia.org/wiki/Order_statistic - W = exp(log(random()) / k) - - # The number of elements to skip before changing the reservoir is a random - # number with a geometric distribution. Sample it using random() and logs. - next_index = k + floor(log(random()) / log(1 - W)) - - for index, element in enumerate(iterable, k): - - if index == next_index: - reservoir[randrange(k)] = element - # The new W is the largest in a sample of k U(0, `old_W`) numbers - W *= exp(log(random()) / k) - next_index += floor(log(random()) / log(1 - W)) + 1 - - return reservoir - - -def _sample_weighted(iterable, k, weights): - # Implementation of "A-ExpJ" from the 2006 paper by Efraimidis et al. : - # "Weighted random sampling with a reservoir". - - # Log-transform for numerical stability for weights that are small/large - weight_keys = (log(random()) / weight for weight in weights) - - # Fill up the reservoir (collection of samples) with the first `k` - # weight-keys and elements, then heapify the list. - reservoir = take(k, zip(weight_keys, iterable)) - heapify(reservoir) - - # The number of jumps before changing the reservoir is a random variable - # with an exponential distribution. Sample it using random() and logs. - smallest_weight_key, _ = reservoir[0] - weights_to_skip = log(random()) / smallest_weight_key - - for weight, element in zip(weights, iterable): - if weight >= weights_to_skip: - # The notation here is consistent with the paper, but we store - # the weight-keys in log-space for better numerical stability. - smallest_weight_key, _ = reservoir[0] - t_w = exp(weight * smallest_weight_key) - r_2 = uniform(t_w, 1) # generate U(t_w, 1) - weight_key = log(r_2) / weight - heapreplace(reservoir, (weight_key, element)) - smallest_weight_key, _ = reservoir[0] - weights_to_skip = log(random()) / smallest_weight_key - else: - weights_to_skip -= weight - - # Equivalent to [element for weight_key, element in sorted(reservoir)] - return [heappop(reservoir)[1] for _ in range(k)] - - -def sample(iterable, k, weights=None): - """Return a *k*-length list of elements chosen (without replacement) - from the *iterable*. Like :func:`random.sample`, but works on iterables - of unknown length. - - >>> iterable = range(100) - >>> sample(iterable, 5) # doctest: +SKIP - [81, 60, 96, 16, 4] - - An iterable with *weights* may also be given: - - >>> iterable = range(100) - >>> weights = (i * i + 1 for i in range(100)) - >>> sampled = sample(iterable, 5, weights=weights) # doctest: +SKIP - [79, 67, 74, 66, 78] - - The algorithm can also be used to generate weighted random permutations. - The relative weight of each item determines the probability that it - appears late in the permutation. - - >>> data = "abcdefgh" - >>> weights = range(1, len(data) + 1) - >>> sample(data, k=len(data), weights=weights) # doctest: +SKIP - ['c', 'a', 'b', 'e', 'g', 'd', 'h', 'f'] - """ - if k == 0: - return [] - - iterable = iter(iterable) - if weights is None: - return _sample_unweighted(iterable, k) - else: - weights = iter(weights) - return _sample_weighted(iterable, k, weights) - - -def is_sorted(iterable, key=None, reverse=False, strict=False): - """Returns ``True`` if the items of iterable are in sorted order, and - ``False`` otherwise. *key* and *reverse* have the same meaning that they do - in the built-in :func:`sorted` function. - - >>> is_sorted(['1', '2', '3', '4', '5'], key=int) - True - >>> is_sorted([5, 4, 3, 1, 2], reverse=True) - False - - If *strict*, tests for strict sorting, that is, returns ``False`` if equal - elements are found: - - >>> is_sorted([1, 2, 2]) - True - >>> is_sorted([1, 2, 2], strict=True) - False - - The function returns ``False`` after encountering the first out-of-order - item. If there are no out-of-order items, the iterable is exhausted. - """ - - compare = (le if reverse else ge) if strict else (lt if reverse else gt) - it = iterable if key is None else map(key, iterable) - return not any(starmap(compare, pairwise(it))) - - -class AbortThread(BaseException): - pass - - -class callback_iter: - """Convert a function that uses callbacks to an iterator. - - Let *func* be a function that takes a `callback` keyword argument. - For example: - - >>> def func(callback=None): - ... for i, c in [(1, 'a'), (2, 'b'), (3, 'c')]: - ... if callback: - ... callback(i, c) - ... return 4 - - - Use ``with callback_iter(func)`` to get an iterator over the parameters - that are delivered to the callback. - - >>> with callback_iter(func) as it: - ... for args, kwargs in it: - ... print(args) - (1, 'a') - (2, 'b') - (3, 'c') - - The function will be called in a background thread. The ``done`` property - indicates whether it has completed execution. - - >>> it.done - True - - If it completes successfully, its return value will be available - in the ``result`` property. - - >>> it.result - 4 - - Notes: - - * If the function uses some keyword argument besides ``callback``, supply - *callback_kwd*. - * If it finished executing, but raised an exception, accessing the - ``result`` property will raise the same exception. - * If it hasn't finished executing, accessing the ``result`` - property from within the ``with`` block will raise ``RuntimeError``. - * If it hasn't finished executing, accessing the ``result`` property from - outside the ``with`` block will raise a - ``more_itertools.AbortThread`` exception. - * Provide *wait_seconds* to adjust how frequently the it is polled for - output. - - """ - - def __init__(self, func, callback_kwd='callback', wait_seconds=0.1): - self._func = func - self._callback_kwd = callback_kwd - self._aborted = False - self._future = None - self._wait_seconds = wait_seconds - self._executor = __import__("concurrent.futures").futures.ThreadPoolExecutor(max_workers=1) - self._iterator = self._reader() - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - self._aborted = True - self._executor.shutdown() - - def __iter__(self): - return self - - def __next__(self): - return next(self._iterator) - - @property - def done(self): - if self._future is None: - return False - return self._future.done() - - @property - def result(self): - if not self.done: - raise RuntimeError('Function has not yet completed') - - return self._future.result() - - def _reader(self): - q = Queue() - - def callback(*args, **kwargs): - if self._aborted: - raise AbortThread('canceled by user') - - q.put((args, kwargs)) - - self._future = self._executor.submit( - self._func, **{self._callback_kwd: callback} - ) - - while True: - try: - item = q.get(timeout=self._wait_seconds) - except Empty: - pass - else: - q.task_done() - yield item - - if self._future.done(): - break - - remaining = [] - while True: - try: - item = q.get_nowait() - except Empty: - break - else: - q.task_done() - remaining.append(item) - q.join() - yield from remaining - - -def windowed_complete(iterable, n): - """ - Yield ``(beginning, middle, end)`` tuples, where: - - * Each ``middle`` has *n* items from *iterable* - * Each ``beginning`` has the items before the ones in ``middle`` - * Each ``end`` has the items after the ones in ``middle`` - - >>> iterable = range(7) - >>> n = 3 - >>> for beginning, middle, end in windowed_complete(iterable, n): - ... print(beginning, middle, end) - () (0, 1, 2) (3, 4, 5, 6) - (0,) (1, 2, 3) (4, 5, 6) - (0, 1) (2, 3, 4) (5, 6) - (0, 1, 2) (3, 4, 5) (6,) - (0, 1, 2, 3) (4, 5, 6) () - - Note that *n* must be at least 0 and most equal to the length of - *iterable*. - - This function will exhaust the iterable and may require significant - storage. - """ - if n < 0: - raise ValueError('n must be >= 0') - - seq = tuple(iterable) - size = len(seq) - - if n > size: - raise ValueError('n must be <= len(seq)') - - for i in range(size - n + 1): - beginning = seq[:i] - middle = seq[i : i + n] - end = seq[i + n :] - yield beginning, middle, end - - -def all_unique(iterable, key=None): - """ - Returns ``True`` if all the elements of *iterable* are unique (no two - elements are equal). - - >>> all_unique('ABCB') - False - - If a *key* function is specified, it will be used to make comparisons. - - >>> all_unique('ABCb') - True - >>> all_unique('ABCb', str.lower) - False - - The function returns as soon as the first non-unique element is - encountered. Iterables with a mix of hashable and unhashable items can - be used, but the function will be slower for unhashable items. - """ - seenset = set() - seenset_add = seenset.add - seenlist = [] - seenlist_add = seenlist.append - for element in map(key, iterable) if key else iterable: - try: - if element in seenset: - return False - seenset_add(element) - except TypeError: - if element in seenlist: - return False - seenlist_add(element) - return True - - -def nth_product(index, *args): - """Equivalent to ``list(product(*args))[index]``. - - The products of *args* can be ordered lexicographically. - :func:`nth_product` computes the product at sort position *index* without - computing the previous products. - - >>> nth_product(8, range(2), range(2), range(2), range(2)) - (1, 0, 0, 0) - - ``IndexError`` will be raised if the given *index* is invalid. - """ - pools = list(map(tuple, reversed(args))) - ns = list(map(len, pools)) - - c = reduce(mul, ns) - - if index < 0: - index += c - - if not 0 <= index < c: - raise IndexError - - result = [] - for pool, n in zip(pools, ns): - result.append(pool[index % n]) - index //= n - - return tuple(reversed(result)) - - -def nth_permutation(iterable, r, index): - """Equivalent to ``list(permutations(iterable, r))[index]``` - - The subsequences of *iterable* that are of length *r* where order is - important can be ordered lexicographically. :func:`nth_permutation` - computes the subsequence at sort position *index* directly, without - computing the previous subsequences. - - >>> nth_permutation('ghijk', 2, 5) - ('h', 'i') - - ``ValueError`` will be raised If *r* is negative or greater than the length - of *iterable*. - ``IndexError`` will be raised if the given *index* is invalid. - """ - pool = list(iterable) - n = len(pool) - - if r is None or r == n: - r, c = n, factorial(n) - elif not 0 <= r < n: - raise ValueError - else: - c = factorial(n) // factorial(n - r) - - if index < 0: - index += c - - if not 0 <= index < c: - raise IndexError - - if c == 0: - return tuple() - - result = [0] * r - q = index * factorial(n) // c if r < n else index - for d in range(1, n + 1): - q, i = divmod(q, d) - if 0 <= n - d < r: - result[n - d] = i - if q == 0: - break - - return tuple(map(pool.pop, result)) - - -def value_chain(*args): - """Yield all arguments passed to the function in the same order in which - they were passed. If an argument itself is iterable then iterate over its - values. - - >>> list(value_chain(1, 2, 3, [4, 5, 6])) - [1, 2, 3, 4, 5, 6] - - Binary and text strings are not considered iterable and are emitted - as-is: - - >>> list(value_chain('12', '34', ['56', '78'])) - ['12', '34', '56', '78'] - - - Multiple levels of nesting are not flattened. - - """ - for value in args: - if isinstance(value, (str, bytes)): - yield value - continue - try: - yield from value - except TypeError: - yield value - - -def product_index(element, *args): - """Equivalent to ``list(product(*args)).index(element)`` - - The products of *args* can be ordered lexicographically. - :func:`product_index` computes the first index of *element* without - computing the previous products. - - >>> product_index([8, 2], range(10), range(5)) - 42 - - ``ValueError`` will be raised if the given *element* isn't in the product - of *args*. - """ - index = 0 - - for x, pool in zip_longest(element, args, fillvalue=_marker): - if x is _marker or pool is _marker: - raise ValueError('element is not a product of args') - - pool = tuple(pool) - index = index * len(pool) + pool.index(x) - - return index - - -def combination_index(element, iterable): - """Equivalent to ``list(combinations(iterable, r)).index(element)`` - - The subsequences of *iterable* that are of length *r* can be ordered - lexicographically. :func:`combination_index` computes the index of the - first *element*, without computing the previous combinations. - - >>> combination_index('adf', 'abcdefg') - 10 - - ``ValueError`` will be raised if the given *element* isn't one of the - combinations of *iterable*. - """ - element = enumerate(element) - k, y = next(element, (None, None)) - if k is None: - return 0 - - indexes = [] - pool = enumerate(iterable) - for n, x in pool: - if x == y: - indexes.append(n) - tmp, y = next(element, (None, None)) - if tmp is None: - break - else: - k = tmp - else: - raise ValueError('element is not a combination of iterable') - - n, _ = last(pool, default=(n, None)) - - # Python versiosn below 3.8 don't have math.comb - index = 1 - for i, j in enumerate(reversed(indexes), start=1): - j = n - j - if i <= j: - index += factorial(j) // (factorial(i) * factorial(j - i)) - - return factorial(n + 1) // (factorial(k + 1) * factorial(n - k)) - index - - -def permutation_index(element, iterable): - """Equivalent to ``list(permutations(iterable, r)).index(element)``` - - The subsequences of *iterable* that are of length *r* where order is - important can be ordered lexicographically. :func:`permutation_index` - computes the index of the first *element* directly, without computing - the previous permutations. - - >>> permutation_index([1, 3, 2], range(5)) - 19 - - ``ValueError`` will be raised if the given *element* isn't one of the - permutations of *iterable*. - """ - index = 0 - pool = list(iterable) - for i, x in zip(range(len(pool), -1, -1), element): - r = pool.index(x) - index = index * i + r - del pool[r] - - return index - - -class countable: - """Wrap *iterable* and keep a count of how many items have been consumed. - - The ``items_seen`` attribute starts at ``0`` and increments as the iterable - is consumed: - - >>> iterable = map(str, range(10)) - >>> it = countable(iterable) - >>> it.items_seen - 0 - >>> next(it), next(it) - ('0', '1') - >>> list(it) - ['2', '3', '4', '5', '6', '7', '8', '9'] - >>> it.items_seen - 10 - """ - - def __init__(self, iterable): - self._it = iter(iterable) - self.items_seen = 0 - - def __iter__(self): - return self - - def __next__(self): - item = next(self._it) - self.items_seen += 1 - - return item - - -def chunked_even(iterable, n): - """Break *iterable* into lists of approximately length *n*. - Items are distributed such the lengths of the lists differ by at most - 1 item. - - >>> iterable = [1, 2, 3, 4, 5, 6, 7] - >>> n = 3 - >>> list(chunked_even(iterable, n)) # List lengths: 3, 2, 2 - [[1, 2, 3], [4, 5], [6, 7]] - >>> list(chunked(iterable, n)) # List lengths: 3, 3, 1 - [[1, 2, 3], [4, 5, 6], [7]] - - """ - - len_method = getattr(iterable, '__len__', None) - - if len_method is None: - return _chunked_even_online(iterable, n) - else: - return _chunked_even_finite(iterable, len_method(), n) - - -def _chunked_even_online(iterable, n): - buffer = [] - maxbuf = n + (n - 2) * (n - 1) - for x in iterable: - buffer.append(x) - if len(buffer) == maxbuf: - yield buffer[:n] - buffer = buffer[n:] - yield from _chunked_even_finite(buffer, len(buffer), n) - - -def _chunked_even_finite(iterable, N, n): - if N < 1: - return - - # Lists are either size `full_size <= n` or `partial_size = full_size - 1` - q, r = divmod(N, n) - num_lists = q + (1 if r > 0 else 0) - q, r = divmod(N, num_lists) - full_size = q + (1 if r > 0 else 0) - partial_size = full_size - 1 - num_full = N - partial_size * num_lists - num_partial = num_lists - num_full - - buffer = [] - iterator = iter(iterable) - - # Yield num_full lists of full_size - for x in iterator: - buffer.append(x) - if len(buffer) == full_size: - yield buffer - buffer = [] - num_full -= 1 - if num_full <= 0: - break - - # Yield num_partial lists of partial_size - for x in iterator: - buffer.append(x) - if len(buffer) == partial_size: - yield buffer - buffer = [] - num_partial -= 1 - - -def zip_broadcast(*objects, scalar_types=(str, bytes), strict=False): - """A version of :func:`zip` that "broadcasts" any scalar - (i.e., non-iterable) items into output tuples. - - >>> iterable_1 = [1, 2, 3] - >>> iterable_2 = ['a', 'b', 'c'] - >>> scalar = '_' - >>> list(zip_broadcast(iterable_1, iterable_2, scalar)) - [(1, 'a', '_'), (2, 'b', '_'), (3, 'c', '_')] - - The *scalar_types* keyword argument determines what types are considered - scalar. It is set to ``(str, bytes)`` by default. Set it to ``None`` to - treat strings and byte strings as iterable: - - >>> list(zip_broadcast('abc', 0, 'xyz', scalar_types=None)) - [('a', 0, 'x'), ('b', 0, 'y'), ('c', 0, 'z')] - - If the *strict* keyword argument is ``True``, then - ``UnequalIterablesError`` will be raised if any of the iterables have - different lengthss. - """ - - def is_scalar(obj): - if scalar_types and isinstance(obj, scalar_types): - return True - try: - iter(obj) - except TypeError: - return True - else: - return False - - size = len(objects) - if not size: - return - - iterables, iterable_positions = [], [] - scalars, scalar_positions = [], [] - for i, obj in enumerate(objects): - if is_scalar(obj): - scalars.append(obj) - scalar_positions.append(i) - else: - iterables.append(iter(obj)) - iterable_positions.append(i) - - if len(scalars) == size: - yield tuple(objects) - return - - zipper = _zip_equal if strict else zip - for item in zipper(*iterables): - new_item = [None] * size - - for i, elem in zip(iterable_positions, item): - new_item[i] = elem - - for i, elem in zip(scalar_positions, scalars): - new_item[i] = elem - - yield tuple(new_item) - - -def unique_in_window(iterable, n, key=None): - """Yield the items from *iterable* that haven't been seen recently. - *n* is the size of the lookback window. - - >>> iterable = [0, 1, 0, 2, 3, 0] - >>> n = 3 - >>> list(unique_in_window(iterable, n)) - [0, 1, 2, 3, 0] - - The *key* function, if provided, will be used to determine uniqueness: - - >>> list(unique_in_window('abAcda', 3, key=lambda x: x.lower())) - ['a', 'b', 'c', 'd', 'a'] - - The items in *iterable* must be hashable. - - """ - if n <= 0: - raise ValueError('n must be greater than 0') - - window = deque(maxlen=n) - uniques = set() - use_key = key is not None - - for item in iterable: - k = key(item) if use_key else item - if k in uniques: - continue - - if len(uniques) == n: - uniques.discard(window[0]) - - uniques.add(k) - window.append(k) - - yield item - - -def duplicates_everseen(iterable, key=None): - """Yield duplicate elements after their first appearance. - - >>> list(duplicates_everseen('mississippi')) - ['s', 'i', 's', 's', 'i', 'p', 'i'] - >>> list(duplicates_everseen('AaaBbbCccAaa', str.lower)) - ['a', 'a', 'b', 'b', 'c', 'c', 'A', 'a', 'a'] - - This function is analagous to :func:`unique_everseen` and is subject to - the same performance considerations. - - """ - seen_set = set() - seen_list = [] - use_key = key is not None - - for element in iterable: - k = key(element) if use_key else element - try: - if k not in seen_set: - seen_set.add(k) - else: - yield element - except TypeError: - if k not in seen_list: - seen_list.append(k) - else: - yield element - - -def duplicates_justseen(iterable, key=None): - """Yields serially-duplicate elements after their first appearance. - - >>> list(duplicates_justseen('mississippi')) - ['s', 's', 'p'] - >>> list(duplicates_justseen('AaaBbbCccAaa', str.lower)) - ['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a'] - - This function is analagous to :func:`unique_justseen`. - - """ - return flatten( - map( - lambda group_tuple: islice_extended(group_tuple[1])[1:], - groupby(iterable, key), - ) - ) - - -def minmax(iterable_or_value, *others, key=None, default=_marker): - """Returns both the smallest and largest items in an iterable - or the largest of two or more arguments. - - >>> minmax([3, 1, 5]) - (1, 5) - - >>> minmax(4, 2, 6) - (2, 6) - - If a *key* function is provided, it will be used to transform the input - items for comparison. - - >>> minmax([5, 30], key=str) # '30' sorts before '5' - (30, 5) - - If a *default* value is provided, it will be returned if there are no - input items. - - >>> minmax([], default=(0, 0)) - (0, 0) - - Otherwise ``ValueError`` is raised. - - This function is based on the - `recipe `__ by - Raymond Hettinger and takes care to minimize the number of comparisons - performed. - """ - iterable = (iterable_or_value, *others) if others else iterable_or_value - - it = iter(iterable) - - try: - lo = hi = next(it) - except StopIteration as e: - if default is _marker: - raise ValueError( - '`minmax()` argument is an empty iterable. ' - 'Provide a `default` value to suppress this error.' - ) from e - return default - - # Different branches depending on the presence of key. This saves a lot - # of unimportant copies which would slow the "key=None" branch - # significantly down. - if key is None: - for x, y in zip_longest(it, it, fillvalue=lo): - if y < x: - x, y = y, x - if x < lo: - lo = x - if hi < y: - hi = y - - else: - lo_key = hi_key = key(lo) - - for x, y in zip_longest(it, it, fillvalue=lo): - - x_key, y_key = key(x), key(y) - - if y_key < x_key: - x, y, x_key, y_key = y, x, y_key, x_key - if x_key < lo_key: - lo, lo_key = x, x_key - if hi_key < y_key: - hi, hi_key = y, y_key - - return lo, hi