Source code for ubelt.util_list

"""
Utility functions for manipulating iterables, lists, and sequences.

The :func:`chunks` function splits a list into smaller parts. There are different strategies for how to do this.

The :func:`flatten` function take a list of lists and removees the inner lists. This
only removes one level of nesting.

The :func:`iterable` function checks if an object is iterable or not. Similar to the
:func:`callable` builtin function.

The :func:`argmax`, :func:`argmin`, and :func:`argsort` work similarly to the
analogous :mod:`numpy` functions, except they operate on dictionaries and other
Python builtin types.

The :func:`take` and :func:`compress` are generators, and also similar to their
lesser known, but very useful numpy equivalents.

There are also other numpy inspired functions: :func:`unique`,
:func:`argunique`, :func:`unique_flags`, and :func:`boolmask`.
"""
import itertools as it
import math
import operator
from collections import abc as collections_abc
from itertools import zip_longest
from ubelt import util_const
from ubelt import util_dict

__all__ = [
    'allsame', 'argmax', 'argmin', 'argsort', 'argunique', 'boolmask',
    'chunks', 'compress', 'flatten', 'iter_window', 'iterable', 'peek', 'take',
    'unique', 'unique_flags',
]


[docs] class chunks(object): """ Generates successive n-sized chunks from ``items``. If the last chunk has less than n elements, ``bordermode`` is used to determine fill values. Note: FIXME: When nchunks is given, that's how many chunks we should get but the issue is that chunksize is not well defined in that instance For instance how do we turn a list with 4 elements into 3 chunks where does the extra item go? In ubelt <= 0.10.3 there is a bug when specifying nchunks, where it chooses a chunksize that is too large. Specify ``legacy=True`` to get the old buggy behavior if needed. Notes: This is similar to functionality provided by :func:`more_itertools.chunked`, :func:`more_itertools.chunked_even`, :func:`more_itertools.sliced`, :func:`more_itertools.divide`, Yields: List[T]: subsequent non-overlapping chunks of the input items Attributes: remainder (int): number of leftover items that don't divide cleanly References: .. [SO_434287] http://stackoverflow.com/questions/434287/iterate-over-a-list-in-chunks Example: >>> import ubelt as ub >>> items = '1234567' >>> genresult = ub.chunks(items, chunksize=3) >>> list(genresult) [['1', '2', '3'], ['4', '5', '6'], ['7']] Example: >>> import ubelt as ub >>> items = [1, 2, 3, 4, 5, 6, 7] >>> genresult = ub.chunks(items, chunksize=3, bordermode='none') >>> assert list(genresult) == [[1, 2, 3], [4, 5, 6], [7]] >>> genresult = ub.chunks(items, chunksize=3, bordermode='cycle') >>> assert list(genresult) == [[1, 2, 3], [4, 5, 6], [7, 1, 2]] >>> genresult = ub.chunks(items, chunksize=3, bordermode='replicate') >>> assert list(genresult) == [[1, 2, 3], [4, 5, 6], [7, 7, 7]] Example: >>> import ubelt as ub >>> assert len(list(ub.chunks(range(2), nchunks=2))) == 2 >>> assert len(list(ub.chunks(range(3), nchunks=2))) == 2 >>> # Note: ub.chunks will not do the 2,1,1 split >>> assert len(list(ub.chunks(range(4), nchunks=3))) == 3 >>> assert len(list(ub.chunks([], 2, bordermode='none'))) == 0 >>> assert len(list(ub.chunks([], 2, bordermode='cycle'))) == 0 >>> assert len(list(ub.chunks([], 2, None, bordermode='replicate'))) == 0 Example: >>> from ubelt.util_list import * # NOQA >>> def _check_len(self): ... assert len(self) == len(list(self)) >>> _check_len(chunks(list(range(3)), nchunks=2)) >>> _check_len(chunks(list(range(2)), nchunks=2)) >>> _check_len(chunks(list(range(2)), nchunks=3)) Example: >>> from ubelt.util_list import * # NOQA >>> import pytest >>> assert pytest.raises(ValueError, chunks, range(9)) >>> assert pytest.raises(ValueError, chunks, range(9), chunksize=2, nchunks=2) >>> assert pytest.raises(TypeError, len, chunks((_ for _ in range(2)), 2)) Example: >>> from ubelt.util_list import * # NOQA >>> import ubelt as ub >>> basis = { >>> 'legacy': [False, True], >>> 'chunker': [{'nchunks': 3}, {'nchunks': 4}, {'nchunks': 5}, {'nchunks': 7}, {'chunksize': 3}], >>> 'items': [range(2), range(4), range(5), range(7), range(9)], >>> 'bordermode': ['none', 'cycle', 'replicate'], >>> } >>> grid_items = list(ub.named_product(basis)) >>> rows = [] >>> for grid_item in ub.ProgIter(grid_items): >>> chunker = grid_item.get('chunker') >>> grid_item.update(chunker) >>> kw = ub.dict_diff(grid_item, {'chunker'}) >>> self = chunk_iter = ub.chunks(**kw) >>> chunked = list(chunk_iter) >>> chunk_lens = list(map(len, chunked)) >>> row = ub.dict_union(grid_item, {'chunk_lens': chunk_lens, 'chunks': chunked}) >>> row['chunker'] = str(row['chunker']) >>> if not row['legacy'] and 'nchunks' in kw: >>> assert kw['nchunks'] == row['nchunks'] >>> row.update(chunk_iter.__dict__) >>> rows.append(row) >>> # xdoctest: +SKIP >>> import pandas as pd >>> df = pd.DataFrame(rows) >>> for _, subdf in df.groupby('chunker'): >>> print(subdf) """ def __init__(self, items, chunksize=None, nchunks=None, total=None, bordermode='none', legacy=False): """ Args: items (Iterable): input to iterate over chunksize (int | None): size of each sublist yielded nchunks (int | None): number of chunks to create ( cannot be specified if chunksize is specified) bordermode (str): determines how to handle the last case if the length of the input is not divisible by chunksize valid values are: {'none', 'cycle', 'replicate'} total (int | None): hints about the length of the input legacy (bool): if True use old behavior, defaults to False. This will be removed in the future. """ if nchunks is not None and chunksize is not None: # nocover raise ValueError('Cannot specify both chunksize and nchunks') if nchunks is None and chunksize is None: # nocover raise ValueError('Must specify either chunksize or nchunks') if total is None: try: total = len(items) except TypeError: pass # iterators dont know len if bordermode is None: # nocover bordermode = 'none' if nchunks is None: if total is not None: nchunks = int(math.ceil(total / chunksize)) remainder = 0 else: if total is None: raise ValueError( 'Need to specify total to use nchunks on an iterable ' 'without length hints') if legacy: chunksize: int = int(math.ceil(total / nchunks)) remainder = 0 else: if bordermode == 'none': # I feel like this could be simpler chunksize: int = max(int(math.floor(total / nchunks)), 1) nchunks: int = min(int(math.ceil(total / chunksize)), nchunks) chunked_total: int = chunksize * nchunks remainder: int = total - chunked_total else: # not working chunksize: int = max(int(math.ceil(total / nchunks)), 1) # Can artificially extend the size in this case # total = chunksize * nchunks remainder = 0 self.legacy = legacy self.remainder: int = remainder self.items = items self.total = total self.nchunks = nchunks self.chunksize = chunksize self.bordermode = bordermode def __len__(self): if self.nchunks is None: raise TypeError('length is unknown') return self.nchunks def __iter__(self): bordermode = self.bordermode items = self.items chunksize = self.chunksize if not self.legacy and self.nchunks is not None: return self._new_iterator() else: if bordermode is None or bordermode == 'none': return self.noborder(items, chunksize) elif bordermode == 'cycle': return self.cycle(items, chunksize) elif bordermode == 'replicate': return self.replicate(items, chunksize) else: raise ValueError('unknown bordermode=%r' % (bordermode,))
[docs] def _new_iterator(self): chunksize = self.chunksize nchunks = self.nchunks chunksize = self.chunksize remainder = self.remainder if self.bordermode == 'cycle': iterator = it.cycle(iter(self.items)) elif self.bordermode == 'replicate': def replicator(items): for item in items: yield item while True: yield item iterator = replicator(iter(self.items)) elif self.bordermode == 'none': iterator = iter(self.items) else: raise KeyError(self.bordermode) # Build an iterator that describes how big each chunk will be if remainder: # TODO: # handle replicate and cycle border modes # TODO: # benchmark different methods chunksize_iter = it.chain( it.repeat(chunksize + 1, remainder), it.repeat(chunksize, nchunks - remainder) ) else: chunksize_iter = it.repeat(chunksize, nchunks) for _chunksize in chunksize_iter: chunk = list(it.islice(iterator, _chunksize)) # if chunk: yield chunk
[docs] @staticmethod def noborder(items, chunksize): # feed the same iter to zip_longest multiple times, this causes it to # consume successive values of the same sequence sentinel = object() copied_iters = [iter(items)] * chunksize chunks_with_sentinals = zip_longest(*copied_iters, fillvalue=sentinel) # Dont fill empty space in the last chunk, just return it as is for chunk in chunks_with_sentinals: yield [item for item in chunk if item is not sentinel]
[docs] @staticmethod def cycle(items, chunksize): sentinel = object() copied_iters = [iter(items)] * chunksize chunks_with_sentinals = zip_longest(*copied_iters, fillvalue=sentinel) # Fill empty space in the last chunk with values from the beginning bordervalues = it.cycle(iter(items)) for chunk in chunks_with_sentinals: yield [item if item is not sentinel else next(bordervalues) for item in chunk]
[docs] @staticmethod def replicate(items, chunksize): sentinel = object() copied_iters = [iter(items)] * chunksize # Fill empty space in the last chunk by replicating the last value chunks_with_sentinals = zip_longest(*copied_iters, fillvalue=sentinel) for chunk in chunks_with_sentinals: filt_chunk = [item for item in chunk if item is not sentinel] if len(filt_chunk) == chunksize: yield filt_chunk else: sizediff = (chunksize - len(filt_chunk)) padded_chunk = filt_chunk + [filt_chunk[-1]] * sizediff yield padded_chunk
[docs] def iterable(obj, strok=False): """ Checks if the input implements the iterator interface. An exception is made for strings, which return False unless ``strok`` is True Args: obj (object): a scalar or iterable input strok (bool, default=False): if True allow strings to be interpreted as iterable Returns: bool: True if the input is iterable Example: >>> import ubelt as ub >>> obj_list = [3, [3], '3', (3,), [3, 4, 5], {}] >>> result = [ub.iterable(obj) for obj in obj_list] >>> assert result == [False, True, False, True, True, True] >>> result = [ub.iterable(obj, strok=True) for obj in obj_list] >>> assert result == [False, True, True, True, True, True] """ try: iter(obj) except Exception: return False else: return strok or not isinstance(obj, str)
[docs] def take(items, indices, default=util_const.NoParam): """ Lookup a subset of an indexable object using a sequence of indices. The ``items`` input is usually a list or dictionary. When ``items`` is a list, this should be a sequence of integers. When ``items`` is a dict, this is a list of keys to lookup in that dictionary. For dictionaries, a default may be specified as a placeholder to use if a key from ``indices`` is not in ``items``. Args: items (Sequence[VT] | Mapping[KT, VT]): An indexable object to select items from. indices (Iterable[int | KT]): A sequence of indexes into ``items``. default (Any, default=NoParam): if specified ``items`` must support the ``get`` method. Yields: VT: a selected item within the list SeeAlso: :func:`ubelt.dict_subset` Note: ``ub.take(items, indices)`` is equivalent to ``(items[i] for i in indices)`` when ``default`` is unspecified. Notes: This is based on the :func:`numpy.take` function, but written in pure python. Do not confuse this with :func:`more_itertools.take`, the behavior is very different. Example: >>> import ubelt as ub >>> items = [0, 1, 2, 3] >>> indices = [2, 0] >>> list(ub.take(items, indices)) [2, 0] Example: >>> import ubelt as ub >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> result = list(ub.take(dict_, keys, None)) >>> assert result == ['a', 'b', 'c', None, None] Example: >>> import ubelt as ub >>> dict_ = {1: 'a', 2: 'b', 3: 'c'} >>> keys = [1, 2, 3, 4, 5] >>> try: >>> print(list(ub.take(dict_, keys))) >>> raise AssertionError('did not get key error') >>> except KeyError: >>> print('correctly got key error') """ if default is util_const.NoParam: for index in indices: yield items[index] else: for index in indices: yield items.get(index, default)
[docs] def compress(items, flags): """ Selects from ``items`` where the corresponding value in ``flags`` is True. Args: items (Iterable[Any]): a sequence to select items from flags (Iterable[bool]): corresponding sequence of bools Returns: Iterable[Any]: a subset of masked items Notes: This function is based on :func:`numpy.compress`, but is pure Python and swaps the condition and array argument to be consistent with :func:`ubelt.take`. This is equivalent to :func:`itertools.compress`. Example: >>> import ubelt as ub >>> items = [1, 2, 3, 4, 5] >>> flags = [False, True, True, False, True] >>> list(ub.compress(items, flags)) [2, 3, 5] """ return it.compress(items, flags)
[docs] def flatten(nested): """ Transforms a nested iterable into a flat iterable. Args: nested (Iterable[Iterable[Any]]): list of lists Returns: Iterable[Any]: flattened items Notes: Equivalent to :func:`more_itertools.flatten` and :func:`itertools.chain.from_iterable`. Example: >>> import ubelt as ub >>> nested = [['a', 'b'], ['c', 'd']] >>> list(ub.flatten(nested)) ['a', 'b', 'c', 'd'] """ return it.chain.from_iterable(nested)
[docs] def unique(items, key=None): """ Generates unique items in the order they appear. Args: items (Iterable[T]): list of items key (Callable[[T], Any] | None, default=None): custom normalization function. If specified returns items where ``key(item)`` is unique. Yields: T: a unique item from the input sequence Notes: Functionally equivalent to :func:`more_itertools.unique_everseen`. Example: >>> import ubelt as ub >>> items = [4, 6, 6, 0, 6, 1, 0, 2, 2, 1] >>> unique_items = list(ub.unique(items)) >>> assert unique_items == [4, 6, 0, 1, 2] Example: >>> import ubelt as ub >>> items = ['A', 'a', 'b', 'B', 'C', 'c', 'D', 'e', 'D', 'E'] >>> unique_items = list(ub.unique(items, key=str.lower)) >>> assert unique_items == ['A', 'b', 'C', 'D', 'e'] >>> unique_items = list(ub.unique(items)) >>> assert unique_items == ['A', 'a', 'b', 'B', 'C', 'c', 'D', 'e', 'E'] """ seen = set() if key is None: for item in items: if item not in seen: seen.add(item) yield item else: for item in items: norm = key(item) if norm not in seen: seen.add(norm) yield item
[docs] def argunique(items, key=None): """ Returns indices corresponding to the first instance of each unique item. Args: items (Sequence[VT]): indexable collection of items key (Callable[[VT], Any] | None, default=None): custom normalization function. If specified returns items where ``key(item)`` is unique. Returns: Iterator[int] : indices of the unique items Example: >>> import ubelt as ub >>> items = [0, 2, 5, 1, 1, 0, 2, 4] >>> indices = list(ub.argunique(items)) >>> assert indices == [0, 1, 2, 3, 7] >>> indices = list(ub.argunique(items, key=lambda x: x % 2 == 0)) >>> assert indices == [0, 2] """ if key is None: return unique(range(len(items)), key=lambda i: items[i]) else: return unique(range(len(items)), key=lambda i: key(items[i]))
[docs] def unique_flags(items, key=None): """ Returns a list of booleans corresponding to the first instance of each unique item. Args: items (Sequence[VT]): indexable collection of items key (Callable[[VT], Any] | None, default=None): custom normalization function. If specified returns items where ``key(item)`` is unique. Returns: List[bool] : flags the items that are unique Example: >>> import ubelt as ub >>> items = [0, 2, 1, 1, 0, 9, 2] >>> flags = ub.unique_flags(items) >>> assert flags == [True, True, True, False, False, True, False] >>> flags = ub.unique_flags(items, key=lambda x: x % 2 == 0) >>> assert flags == [True, False, True, False, False, False, False] """ len_ = len(items) if key is None: item_to_index = dict(zip(reversed(items), reversed(range(len_)))) indices = item_to_index.values() else: indices = argunique(items, key=key) flags = boolmask(indices, len_) return flags
[docs] def boolmask(indices, maxval=None): """ Constructs a list of booleans where an item is True if its position is in ``indices`` otherwise it is False. Args: indices (List[int]): list of integer indices maxval (int | None): length of the returned list. If not specified this is inferred using ``max(indices)`` Returns: List[bool]: mask - a list of booleans. mask[idx] is True if idx in indices Note: In the future the arg ``maxval`` may change its name to ``shape`` Example: >>> import ubelt as ub >>> indices = [0, 1, 4] >>> mask = ub.boolmask(indices, maxval=6) >>> assert mask == [True, True, False, False, True, False] >>> mask = ub.boolmask(indices) >>> assert mask == [True, True, False, False, True] """ if maxval is None: indices = list(indices) maxval = max(indices) + 1 mask = [False] * maxval for index in indices: mask[index] = True return mask
[docs] def iter_window(iterable, size=2, step=1, wrap=False): """ Iterates through iterable with a window size. This is essentially a 1D sliding window. Args: iterable (Iterable[T]): an iterable sequence size (int, default=2): sliding window size step (int, default=1): sliding step size wrap (bool, default=False): wraparound flag Returns: Iterable[T]: returns a possibly overlapping windows in a sequence Notes: Similar to :func:`more_itertools.windowed`, Similar to :func:`more_itertools.pairwise`, Similar to :func:`more_itertools.triplewise`, Similar to :func:`more_itertools.sliding_window` Example: >>> import ubelt as ub >>> iterable = [1, 2, 3, 4, 5, 6] >>> size, step, wrap = 3, 1, True >>> window_iter = ub.iter_window(iterable, size, step, wrap) >>> window_list = list(window_iter) >>> print('window_list = %r' % (window_list,)) window_list = [(1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6), (5, 6, 1), (6, 1, 2)] Example: >>> import ubelt as ub >>> iterable = [1, 2, 3, 4, 5, 6] >>> size, step, wrap = 3, 2, True >>> window_iter = ub.iter_window(iterable, size, step, wrap) >>> window_list = list(window_iter) >>> print('window_list = {!r}'.format(window_list)) window_list = [(1, 2, 3), (3, 4, 5), (5, 6, 1)] Example: >>> import ubelt as ub >>> iterable = [1, 2, 3, 4, 5, 6] >>> size, step, wrap = 3, 2, False >>> window_iter = ub.iter_window(iterable, size, step, wrap) >>> window_list = list(window_iter) >>> print('window_list = {!r}'.format(window_list)) window_list = [(1, 2, 3), (3, 4, 5)] Example: >>> import ubelt as ub >>> iterable = [] >>> size, step, wrap = 3, 2, False >>> window_iter = ub.iter_window(iterable, size, step, wrap) >>> window_list = list(window_iter) >>> print('window_list = {!r}'.format(window_list)) window_list = [] """ # it.tee may be slow, but works on all iterables iter_list = it.tee(iterable, size) if wrap: # Secondary iterables need to be cycled for wraparound iter_list = [iter_list[0]] + list(map(it.cycle, iter_list[1:])) # Step each iterator the appropriate number of times try: for count, iter_ in enumerate(iter_list[1:], start=1): for _ in range(count): next(iter_) except StopIteration: return iter(()) else: _window_iter = zip(*iter_list) # Account for the step size window_iter = it.islice(_window_iter, 0, None, step) return window_iter
[docs] def allsame(iterable, eq=operator.eq): """ Determine if all items in a sequence are the same Args: iterable (Iterable[T]): items to determine if they are all the same eq (Callable[[T, T], bool], default=operator.eq): function used to test for equality Returns: bool: True if all items are equal, otherwise False Notes: Similar to :func:`more_itertools.all_equal` Example: >>> import ubelt as ub >>> ub.allsame([1, 1, 1, 1]) True >>> ub.allsame([]) True >>> ub.allsame([0, 1]) False >>> iterable = iter([0, 1, 1, 1]) >>> next(iterable) >>> ub.allsame(iterable) True >>> ub.allsame(range(10)) False >>> ub.allsame(range(10), lambda a, b: True) True """ iter_ = iter(iterable) try: first = next(iter_) except StopIteration: return True return all(eq(first, item) for item in iter_)
[docs] def argsort(indexable, key=None, reverse=False): """ Returns the indices that would sort a indexable object. This is similar to :func:`numpy.argsort`, but it is written in pure python and works on both lists and dictionaries. Args: indexable (Iterable[VT] | Mapping[KT, VT]): indexable to sort by key (Callable[[VT], VT] | None, default=None): customizes the ordering of the indexable reverse (bool, default=False): if True returns in descending order Returns: List[int] | List[KT]: indices - list of indices that sorts the indexable Example: >>> import ubelt as ub >>> # argsort works on dicts by returning keys >>> dict_ = {'a': 3, 'b': 2, 'c': 100} >>> indices = ub.argsort(dict_) >>> assert list(ub.take(dict_, indices)) == sorted(dict_.values()) >>> # argsort works on lists by returning indices >>> indexable = [100, 2, 432, 10] >>> indices = ub.argsort(indexable) >>> assert list(ub.take(indexable, indices)) == sorted(indexable) >>> # Can use iterators, but be careful. It exhausts them. >>> indexable = reversed(range(100)) >>> indices = ub.argsort(indexable) >>> assert indices[0] == 99 >>> # Can use key just like sorted >>> indexable = [[0, 1, 2], [3, 4], [5]] >>> indices = ub.argsort(indexable, key=len) >>> assert indices == [2, 1, 0] >>> # Can use reverse just like sorted >>> indexable = [0, 2, 1] >>> indices = ub.argsort(indexable, reverse=True) >>> assert indices == [1, 2, 0] """ # Create an iterator of value/key pairs if isinstance(indexable, collections_abc.Mapping): vk_iter = ((v, k) for k, v in indexable.items()) else: vk_iter = ((v, k) for k, v in enumerate(indexable)) # Sort by values and extract the indices if key is None: indices = [k for v, k in sorted(vk_iter, reverse=reverse)] else: # If key is provided, call it using the value as input indices = [k for v, k in sorted(vk_iter, key=lambda vk: key(vk[0]), reverse=reverse)] return indices
[docs] def argmax(indexable, key=None): """ Returns index / key of the item with the largest value. This is similar to :func:`numpy.argmax`, but it is written in pure python and works on both lists and dictionaries. Args: indexable (Iterable[VT] | Mapping[KT, VT]): indexable to sort by key (Callable[[VT], Any] | None, default=None): customizes the ordering of the indexable Returns: int | KT: the index of the item with the maximum value. Example: >>> import ubelt as ub >>> assert ub.argmax({'a': 3, 'b': 2, 'c': 100}) == 'c' >>> assert ub.argmax(['a', 'c', 'b', 'z', 'f']) == 3 >>> assert ub.argmax([[0, 1], [2, 3, 4], [5]], key=len) == 1 >>> assert ub.argmax({'a': 3, 'b': 2, 3: 100, 4: 4}) == 3 >>> assert ub.argmax(iter(['a', 'c', 'b', 'z', 'f'])) == 3 """ if key is None and isinstance(indexable, collections_abc.Mapping): return max(indexable.items(), key=operator.itemgetter(1))[0] elif hasattr(indexable, 'index'): if key is None: return indexable.index(max(indexable)) else: return indexable.index(max(indexable, key=key)) else: # less efficient, but catch all solution return argsort(indexable, key=key)[-1]
[docs] def argmin(indexable, key=None): """ Returns index / key of the item with the smallest value. This is similar to :func:`numpy.argmin`, but it is written in pure python and works on both lists and dictionaries. Args: indexable (Iterable[VT] | Mapping[KT, VT]): indexable to sort by key (Callable[[VT], VT] | None, default=None): customizes the ordering of the indexable Returns: int | KT: the index of the item with the minimum value. Example: >>> import ubelt as ub >>> assert ub.argmin({'a': 3, 'b': 2, 'c': 100}) == 'b' >>> assert ub.argmin(['a', 'c', 'b', 'z', 'f']) == 0 >>> assert ub.argmin([[0, 1], [2, 3, 4], [5]], key=len) == 2 >>> assert ub.argmin({'a': 3, 'b': 2, 3: 100, 4: 4}) == 'b' >>> assert ub.argmin(iter(['a', 'c', 'A', 'z', 'f'])) == 2 """ if key is None and isinstance(indexable, collections_abc.Mapping): return min(indexable.items(), key=operator.itemgetter(1))[0] elif hasattr(indexable, 'index'): if key is None: return indexable.index(min(indexable)) else: return indexable.index(min(indexable, key=key)) else: # less efficient, but catch all solution return argsort(indexable, key=key)[0]
[docs] def peek(iterable, default=util_const.NoParam): """ Look at the first item of an iterable. If the input is an iterator, then the next element is exhausted (i.e. a pop operation). Args: iterable (Iterable[T]): an iterable default (T): default item to return if the iterable is empty, otherwise a StopIteration error is raised Returns: T: item - the first item of ordered sequence, a popped item from an iterator, or an arbitrary item from an unordered collection. Notes: Similar to :func:`more_itertools.peekable` Example: >>> import ubelt as ub >>> data = [0, 1, 2] >>> ub.peek(data) 0 >>> iterator = iter(data) >>> print(ub.peek(iterator)) 0 >>> print(ub.peek(iterator)) 1 >>> print(ub.peek(iterator)) 2 >>> ub.peek(range(3)) 0 >>> ub.peek([], 3) 3 """ if default is util_const.NoParam: return next(iter(iterable)) else: return next(iter(iterable), default)
# Stubs for potential future object oriented wrappers class IterableMixin: """ """ unique = unique # chunks = chunks histogram = util_dict.dict_hist duplicates = util_dict.find_duplicates group = util_dict.group_items def chunks(self, size=None, num=None, bordermode='none'): return chunks(self, chunksize=size, nchunks=num, total=len(self), bordermode=bordermode) # def histogram(self, weights=None, ordered=False, labels=None): # util_dict.dict_hist.__doc__ # return util_dict.dict_hist(self, weights=weights, ordered=ordered) # def duplicates(self, k=2, key=None): # util_dict.find_duplicates.__doc__ # return util_dict.find_duplicates(self, k=k, key=key) # def group(self, key): # util_dict.group_items.__doc__ # return util_dict.group_items(self, key=key) class OrderedIterableMixin(IterableMixin): compress = compress argunique = argunique window = iter_window class UList(list, OrderedIterableMixin): """ An extended list class that features additional helper methods. Example: >>> from ubelt.util_list import UList >>> self = UList() >>> self.append(1) >>> self += UList([1, 2, 3]) >>> self += UList([5, 7]) >>> # >>> print(f'unique: {list(self.unique())}') >>> print(f'argunique: {list(self.argunique())}') >>> # >>> print(f'chunks: {list(self.chunks(num=2))}') >>> print(f'chunks: {list(self.chunks(size=2))}') >>> # >>> print(f'window: {list(self.window(3))}') >>> # >>> print(f'take: {list(self.take([0, 2, 3]))}') >>> print(f'compress: {list(self.compress([0, 1, 0, 1]))}') >>> # >>> print(f'argsort: {self.argsort()}') >>> print(f'argmax: {self.argmax()}') >>> print(f'argmin: {self.argmin()}') >>> print(f'flatten: {list(UList([self, [2, 3, 3]]).flatten())}') >>> print(f'allsame: {self.allsame()}') >>> print(f'peek: {self.peek()}') >>> print(f'histogram: {self.histogram()}') >>> print(f'group: {self.group(key=lambda x: x % 2)}') >>> print(f'duplicates: {self.duplicates()}') """ peek = peek take = take flatten = flatten allsame = allsame argsort = argsort argmax = argmax argmin = argmin # class USet(set, IterableMixin): # ... # class Set(set, IterableMixin): # ...