144 lines
5.1 KiB
ReStructuredText
144 lines
5.1 KiB
ReStructuredText
:mod:`bisect` --- Array bisection algorithm
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===========================================
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.. module:: bisect
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:synopsis: Array bisection algorithms for binary searching.
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.. sectionauthor:: Fred L. Drake, Jr. <fdrake@acm.org>
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.. example based on the PyModules FAQ entry by Aaron Watters <arw@pythonpros.com>
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This module provides support for maintaining a list in sorted order without
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having to sort the list after each insertion. For long lists of items with
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expensive comparison operations, this can be an improvement over the more common
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approach. The module is called :mod:`bisect` because it uses a basic bisection
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algorithm to do its work. The source code may be most useful as a working
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example of the algorithm (the boundary conditions are already right!).
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The following functions are provided:
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.. function:: bisect_left(a, x, lo=0, hi=len(a))
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Locate the proper insertion point for *x* in *a* to maintain sorted order.
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The parameters *lo* and *hi* may be used to specify a subset of the list
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which should be considered; by default the entire list is used. If *x* is
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already present in *a*, the insertion point will be before (to the left of)
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any existing entries. The return value is suitable for use as the first
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parameter to ``list.insert()``. This assumes that *a* is already sorted.
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.. function:: bisect_right(a, x, lo=0, hi=len(a))
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bisect(a, x, lo=0, hi=len(a))
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Similar to :func:`bisect_left`, but returns an insertion point which comes
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after (to the right of) any existing entries of *x* in *a*.
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.. function:: insort_left(a, x, lo=0, hi=len(a))
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Insert *x* in *a* in sorted order. This is equivalent to
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``a.insert(bisect.bisect_left(a, x, lo, hi), x)``. This assumes that *a* is
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already sorted.
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Also note that while the fast search step is O(log n), the slower insertion
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step is O(n), so the overall operation is slow.
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.. function:: insort_right(a, x, lo=0, hi=len(a))
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insort(a, x, lo=0, hi=len(a))
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Similar to :func:`insort_left`, but inserting *x* in *a* after any existing
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entries of *x*.
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Also note that while the fast search step is O(log n), the slower insertion
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step is O(n), so the overall operation is slow.
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Searching Sorted Lists
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----------------------
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The above :func:`bisect` functions are useful for finding insertion points, but
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can be tricky or awkward to use for common searching tasks. The following three
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functions show how to transform them into the standard lookups for sorted
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lists::
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def find(a, key):
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'''Find leftmost item exact equal to the key.
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Raise ValueError if no such item exists.
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'''
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i = bisect_left(a, key)
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if i < len(a) and a[i] == key:
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return a[i]
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raise ValueError('No item found with key equal to: %r' % (key,))
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def find_le(a, key):
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'''Find largest item less-than or equal to key.
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Raise ValueError if no such item exists.
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If multiple keys are equal, return the leftmost.
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'''
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i = bisect_left(a, key)
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if i < len(a) and a[i] == key:
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return a[i]
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if i == 0:
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raise ValueError('No item found with key at or below: %r' % (key,))
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return a[i-1]
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def find_ge(a, key):
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'''Find smallest item greater-than or equal to key.
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Raise ValueError if no such item exists.
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If multiple keys are equal, return the leftmost.
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'''
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i = bisect_left(a, key)
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if i == len(a):
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raise ValueError('No item found with key at or above: %r' % (key,))
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return a[i]
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Other Examples
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--------------
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.. _bisect-example:
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The :func:`bisect` function is generally useful for categorizing numeric data.
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This example uses :func:`bisect` to look up a letter grade for an exam total
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(say) based on a set of ordered numeric breakpoints: 85 and up is an 'A', 75..84
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is a 'B', etc.
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>>> grades = "FEDCBA"
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>>> breakpoints = [30, 44, 66, 75, 85]
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>>> from bisect import bisect
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>>> def grade(total):
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... return grades[bisect(breakpoints, total)]
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...
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>>> grade(66)
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'C'
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>>> map(grade, [33, 99, 77, 44, 12, 88])
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['E', 'A', 'B', 'D', 'F', 'A']
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Unlike the :func:`sorted` function, it does not make sense for the :func:`bisect`
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functions to have *key* or *reversed* arguments because that would lead to an
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inefficent design (successive calls to bisect functions would not "remember"
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all of the previous key lookups).
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Instead, it is better to search a list of precomputed keys to find the index
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of the record in question::
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>>> data = [('red', 5), ('blue', 1), ('yellow', 8), ('black', 0)]
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>>> data.sort(key=lambda r: r[1])
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>>> keys = [r[1] for r in data] # precomputed list of keys
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>>> data[bisect_left(keys, 0)]
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('black', 0)
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>>> data[bisect_left(keys, 1)]
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('blue', 1)
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>>> data[bisect_left(keys, 5)]
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('red', 5)
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>>> data[bisect_left(keys, 8)]
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('yellow', 8)
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.. seealso::
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`SortedCollection recipe
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<http://code.activestate.com/recipes/577197-sortedcollection/>`_ that
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encapsulates precomputed keys, allowing straight-forward insertion and
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searching using a *key* function.
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