* Restore the pure python version of heapq.py.
* Mark the C version as private and only use when available.
This commit is contained in:
Raymond Hettinger
parent
61e40bd897
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# -*- coding: Latin-1 -*-
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"""Heap queue algorithm (a.k.a. priority queue).
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Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
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all k, counting elements from 0. For the sake of comparison,
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non-existing elements are considered to be infinite. The interesting
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property of a heap is that a[0] is always its smallest element.
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Usage:
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heap = [] # creates an empty heap
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heappush(heap, item) # pushes a new item on the heap
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item = heappop(heap) # pops the smallest item from the heap
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item = heap[0] # smallest item on the heap without popping it
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heapify(x) # transforms list into a heap, in-place, in linear time
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item = heapreplace(heap, item) # pops and returns smallest item, and adds
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# new item; the heap size is unchanged
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Our API differs from textbook heap algorithms as follows:
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- We use 0-based indexing. This makes the relationship between the
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index for a node and the indexes for its children slightly less
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obvious, but is more suitable since Python uses 0-based indexing.
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- Our heappop() method returns the smallest item, not the largest.
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These two make it possible to view the heap as a regular Python list
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without surprises: heap[0] is the smallest item, and heap.sort()
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maintains the heap invariant!
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"""
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# Original code by Kevin O'Connor, augmented by Tim Peters
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__about__ = """Heap queues
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[explanation by François Pinard]
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Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
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all k, counting elements from 0. For the sake of comparison,
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non-existing elements are considered to be infinite. The interesting
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property of a heap is that a[0] is always its smallest element.
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The strange invariant above is meant to be an efficient memory
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representation for a tournament. The numbers below are `k', not a[k]:
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0
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1 2
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3 4 5 6
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7 8 9 10 11 12 13 14
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15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
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In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In
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an usual binary tournament we see in sports, each cell is the winner
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over the two cells it tops, and we can trace the winner down the tree
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to see all opponents s/he had. However, in many computer applications
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of such tournaments, we do not need to trace the history of a winner.
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To be more memory efficient, when a winner is promoted, we try to
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replace it by something else at a lower level, and the rule becomes
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that a cell and the two cells it tops contain three different items,
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but the top cell "wins" over the two topped cells.
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If this heap invariant is protected at all time, index 0 is clearly
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the overall winner. The simplest algorithmic way to remove it and
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find the "next" winner is to move some loser (let's say cell 30 in the
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diagram above) into the 0 position, and then percolate this new 0 down
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the tree, exchanging values, until the invariant is re-established.
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This is clearly logarithmic on the total number of items in the tree.
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By iterating over all items, you get an O(n ln n) sort.
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A nice feature of this sort is that you can efficiently insert new
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items while the sort is going on, provided that the inserted items are
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not "better" than the last 0'th element you extracted. This is
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especially useful in simulation contexts, where the tree holds all
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incoming events, and the "win" condition means the smallest scheduled
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time. When an event schedule other events for execution, they are
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scheduled into the future, so they can easily go into the heap. So, a
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heap is a good structure for implementing schedulers (this is what I
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used for my MIDI sequencer :-).
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Various structures for implementing schedulers have been extensively
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studied, and heaps are good for this, as they are reasonably speedy,
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the speed is almost constant, and the worst case is not much different
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than the average case. However, there are other representations which
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are more efficient overall, yet the worst cases might be terrible.
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Heaps are also very useful in big disk sorts. You most probably all
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know that a big sort implies producing "runs" (which are pre-sorted
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sequences, which size is usually related to the amount of CPU memory),
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followed by a merging passes for these runs, which merging is often
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very cleverly organised[1]. It is very important that the initial
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sort produces the longest runs possible. Tournaments are a good way
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to that. If, using all the memory available to hold a tournament, you
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replace and percolate items that happen to fit the current run, you'll
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produce runs which are twice the size of the memory for random input,
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and much better for input fuzzily ordered.
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Moreover, if you output the 0'th item on disk and get an input which
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may not fit in the current tournament (because the value "wins" over
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the last output value), it cannot fit in the heap, so the size of the
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heap decreases. The freed memory could be cleverly reused immediately
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for progressively building a second heap, which grows at exactly the
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same rate the first heap is melting. When the first heap completely
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vanishes, you switch heaps and start a new run. Clever and quite
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effective!
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In a word, heaps are useful memory structures to know. I use them in
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a few applications, and I think it is good to keep a `heap' module
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around. :-)
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--------------------
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[1] The disk balancing algorithms which are current, nowadays, are
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more annoying than clever, and this is a consequence of the seeking
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capabilities of the disks. On devices which cannot seek, like big
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tape drives, the story was quite different, and one had to be very
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clever to ensure (far in advance) that each tape movement will be the
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most effective possible (that is, will best participate at
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"progressing" the merge). Some tapes were even able to read
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backwards, and this was also used to avoid the rewinding time.
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Believe me, real good tape sorts were quite spectacular to watch!
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From all times, sorting has always been a Great Art! :-)
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"""
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__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace']
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def heappush(heap, item):
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"""Push item onto heap, maintaining the heap invariant."""
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heap.append(item)
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_siftdown(heap, 0, len(heap)-1)
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def heappop(heap):
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"""Pop the smallest item off the heap, maintaining the heap invariant."""
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lastelt = heap.pop() # raises appropriate IndexError if heap is empty
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if heap:
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returnitem = heap[0]
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heap[0] = lastelt
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_siftup(heap, 0)
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else:
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returnitem = lastelt
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return returnitem
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def heapreplace(heap, item):
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"""Pop and return the current smallest value, and add the new item.
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This is more efficient than heappop() followed by heappush(), and can be
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more appropriate when using a fixed-size heap. Note that the value
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returned may be larger than item! That constrains reasonable uses of
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this routine.
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"""
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returnitem = heap[0] # raises appropriate IndexError if heap is empty
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heap[0] = item
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_siftup(heap, 0)
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return returnitem
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def heapify(x):
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"""Transform list into a heap, in-place, in O(len(heap)) time."""
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n = len(x)
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# Transform bottom-up. The largest index there's any point to looking at
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# is the largest with a child index in-range, so must have 2*i + 1 < n,
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# or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
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# j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is
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# (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
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for i in reversed(xrange(n//2)):
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_siftup(x, i)
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# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos
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# is the index of a leaf with a possibly out-of-order value. Restore the
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# heap invariant.
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def _siftdown(heap, startpos, pos):
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newitem = heap[pos]
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# Follow the path to the root, moving parents down until finding a place
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# newitem fits.
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while pos > startpos:
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parentpos = (pos - 1) >> 1
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parent = heap[parentpos]
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if parent <= newitem:
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break
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heap[pos] = parent
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pos = parentpos
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heap[pos] = newitem
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# The child indices of heap index pos are already heaps, and we want to make
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# a heap at index pos too. We do this by bubbling the smaller child of
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# pos up (and so on with that child's children, etc) until hitting a leaf,
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# then using _siftdown to move the oddball originally at index pos into place.
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#
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# We *could* break out of the loop as soon as we find a pos where newitem <=
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# both its children, but turns out that's not a good idea, and despite that
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# many books write the algorithm that way. During a heap pop, the last array
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# element is sifted in, and that tends to be large, so that comparing it
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# against values starting from the root usually doesn't pay (= usually doesn't
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# get us out of the loop early). See Knuth, Volume 3, where this is
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# explained and quantified in an exercise.
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#
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# Cutting the # of comparisons is important, since these routines have no
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# way to extract "the priority" from an array element, so that intelligence
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# is likely to be hiding in custom __cmp__ methods, or in array elements
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# storing (priority, record) tuples. Comparisons are thus potentially
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# expensive.
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#
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# On random arrays of length 1000, making this change cut the number of
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# comparisons made by heapify() a little, and those made by exhaustive
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# heappop() a lot, in accord with theory. Here are typical results from 3
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# runs (3 just to demonstrate how small the variance is):
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#
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# Compares needed by heapify Compares needed by 1000 heappops
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# -------------------------- --------------------------------
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# 1837 cut to 1663 14996 cut to 8680
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# 1855 cut to 1659 14966 cut to 8678
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# 1847 cut to 1660 15024 cut to 8703
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#
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# Building the heap by using heappush() 1000 times instead required
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# 2198, 2148, and 2219 compares: heapify() is more efficient, when
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# you can use it.
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#
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# The total compares needed by list.sort() on the same lists were 8627,
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# 8627, and 8632 (this should be compared to the sum of heapify() and
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# heappop() compares): list.sort() is (unsurprisingly!) more efficient
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# for sorting.
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def _siftup(heap, pos):
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endpos = len(heap)
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startpos = pos
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newitem = heap[pos]
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# Bubble up the smaller child until hitting a leaf.
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childpos = 2*pos + 1 # leftmost child position
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while childpos < endpos:
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# Set childpos to index of smaller child.
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rightpos = childpos + 1
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if rightpos < endpos and heap[rightpos] <= heap[childpos]:
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childpos = rightpos
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# Move the smaller child up.
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heap[pos] = heap[childpos]
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pos = childpos
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childpos = 2*pos + 1
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# The leaf at pos is empty now. Put newitem there, and bubble it up
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# to its final resting place (by sifting its parents down).
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heap[pos] = newitem
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_siftdown(heap, startpos, pos)
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# If available, use C implementation
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try:
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from _heapq import heappush, heappop, heapify, heapreplace
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except ImportError:
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pass
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if __name__ == "__main__":
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# Simple sanity test
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heap = []
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data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
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for item in data:
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heappush(heap, item)
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sort = []
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while heap:
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sort.append(heappop(heap))
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print sort
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@ -0,0 +1,364 @@
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/* Drop in replacement for heapq.py
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C implementation derived directly from heapq.py in Py2.3
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which was written by Kevin O'Connor, augmented by Tim Peters,
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annotated by François Pinard, and converted to C by Raymond Hettinger.
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*/
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#include "Python.h"
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static int
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_siftdown(PyListObject *heap, int startpos, int pos)
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{
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PyObject *newitem, *parent;
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int cmp, parentpos;
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assert(PyList_Check(heap));
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if (pos >= PyList_GET_SIZE(heap)) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return -1;
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}
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newitem = PyList_GET_ITEM(heap, pos);
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Py_INCREF(newitem);
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/* Follow the path to the root, moving parents down until finding
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a place newitem fits. */
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while (pos > startpos){
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parentpos = (pos - 1) >> 1;
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parent = PyList_GET_ITEM(heap, parentpos);
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cmp = PyObject_RichCompareBool(parent, newitem, Py_LE);
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if (cmp == -1)
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return -1;
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if (cmp == 1)
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break;
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Py_INCREF(parent);
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, parent);
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pos = parentpos;
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}
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, newitem);
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return 0;
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}
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static int
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_siftup(PyListObject *heap, int pos)
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{
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int startpos, endpos, childpos, rightpos;
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int cmp;
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PyObject *newitem, *tmp;
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assert(PyList_Check(heap));
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endpos = PyList_GET_SIZE(heap);
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startpos = pos;
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if (pos >= endpos) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return -1;
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}
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newitem = PyList_GET_ITEM(heap, pos);
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Py_INCREF(newitem);
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/* Bubble up the smaller child until hitting a leaf. */
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childpos = 2*pos + 1; /* leftmost child position */
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while (childpos < endpos) {
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/* Set childpos to index of smaller child. */
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rightpos = childpos + 1;
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if (rightpos < endpos) {
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cmp = PyObject_RichCompareBool(
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PyList_GET_ITEM(heap, rightpos),
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PyList_GET_ITEM(heap, childpos),
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Py_LE);
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if (cmp == -1)
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return -1;
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if (cmp == 1)
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childpos = rightpos;
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}
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/* Move the smaller child up. */
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tmp = PyList_GET_ITEM(heap, childpos);
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Py_INCREF(tmp);
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, tmp);
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pos = childpos;
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childpos = 2*pos + 1;
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}
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/* The leaf at pos is empty now. Put newitem there, and and bubble
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it up to its final resting place (by sifting its parents down). */
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Py_DECREF(PyList_GET_ITEM(heap, pos));
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PyList_SET_ITEM(heap, pos, newitem);
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return _siftdown(heap, startpos, pos);
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}
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static PyObject *
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heappush(PyObject *self, PyObject *args)
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{
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PyObject *heap, *item;
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if (!PyArg_UnpackTuple(args, "heappush", 2, 2, &heap, &item))
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return NULL;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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if (PyList_Append(heap, item) == -1)
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return NULL;
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if (_siftdown((PyListObject *)heap, 0, PyList_GET_SIZE(heap)-1) == -1)
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return NULL;
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Py_INCREF(Py_None);
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return Py_None;
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}
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PyDoc_STRVAR(heappush_doc,
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"Push item onto heap, maintaining the heap invariant.");
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static PyObject *
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heappop(PyObject *self, PyObject *heap)
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{
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PyObject *lastelt, *returnitem;
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int n;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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/* # raises appropriate IndexError if heap is empty */
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n = PyList_GET_SIZE(heap);
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if (n == 0) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return NULL;
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}
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lastelt = PyList_GET_ITEM(heap, n-1) ;
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Py_INCREF(lastelt);
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PyList_SetSlice(heap, n-1, n, NULL);
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n--;
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if (!n)
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return lastelt;
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returnitem = PyList_GET_ITEM(heap, 0);
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PyList_SET_ITEM(heap, 0, lastelt);
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if (_siftup((PyListObject *)heap, 0) == -1) {
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Py_DECREF(returnitem);
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return NULL;
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}
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return returnitem;
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}
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PyDoc_STRVAR(heappop_doc,
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"Pop the smallest item off the heap, maintaining the heap invariant.");
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static PyObject *
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heapreplace(PyObject *self, PyObject *args)
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{
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PyObject *heap, *item, *returnitem;
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if (!PyArg_UnpackTuple(args, "heapreplace", 2, 2, &heap, &item))
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return NULL;
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if (!PyList_Check(heap)) {
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PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
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return NULL;
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}
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if (PyList_GET_SIZE(heap) < 1) {
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PyErr_SetString(PyExc_IndexError, "index out of range");
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return NULL;
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}
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returnitem = PyList_GET_ITEM(heap, 0);
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Py_INCREF(item);
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PyList_SET_ITEM(heap, 0, item);
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if (_siftup((PyListObject *)heap, 0) == -1) {
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Py_DECREF(returnitem);
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return NULL;
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}
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return returnitem;
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}
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PyDoc_STRVAR(heapreplace_doc,
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"Pop and return the current smallest value, and add the new item.\n\
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\n\
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This is more efficient than heappop() followed by heappush(), and can be\n\
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more appropriate when using a fixed-size heap. Note that the value\n\
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returned may be larger than item! That constrains reasonable uses of\n\
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this routine.\n");
|
||||
|
||||
static PyObject *
|
||||
heapify(PyObject *self, PyObject *heap)
|
||||
{
|
||||
int i, n;
|
||||
|
||||
if (!PyList_Check(heap)) {
|
||||
PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
|
||||
return NULL;
|
||||
}
|
||||
|
||||
n = PyList_GET_SIZE(heap);
|
||||
/* Transform bottom-up. The largest index there's any point to
|
||||
looking at is the largest with a child index in-range, so must
|
||||
have 2*i + 1 < n, or i < (n-1)/2. If n is even = 2*j, this is
|
||||
(2*j-1)/2 = j-1/2 so j-1 is the largest, which is n//2 - 1. If
|
||||
n is odd = 2*j+1, this is (2*j+1-1)/2 = j so j-1 is the largest,
|
||||
and that's again n//2-1.
|
||||
*/
|
||||
for (i=n/2-1 ; i>=0 ; i--)
|
||||
if(_siftup((PyListObject *)heap, i) == -1)
|
||||
return NULL;
|
||||
Py_INCREF(Py_None);
|
||||
return Py_None;
|
||||
}
|
||||
|
||||
PyDoc_STRVAR(heapify_doc,
|
||||
"Transform list into a heap, in-place, in O(len(heap)) time.");
|
||||
|
||||
static PyMethodDef heapq_methods[] = {
|
||||
{"heappush", (PyCFunction)heappush,
|
||||
METH_VARARGS, heappush_doc},
|
||||
{"heappop", (PyCFunction)heappop,
|
||||
METH_O, heappop_doc},
|
||||
{"heapreplace", (PyCFunction)heapreplace,
|
||||
METH_VARARGS, heapreplace_doc},
|
||||
{"heapify", (PyCFunction)heapify,
|
||||
METH_O, heapify_doc},
|
||||
{NULL, NULL} /* sentinel */
|
||||
};
|
||||
|
||||
PyDoc_STRVAR(module_doc,
|
||||
"Heap queue algorithm (a.k.a. priority queue).\n\
|
||||
\n\
|
||||
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
|
||||
all k, counting elements from 0. For the sake of comparison,\n\
|
||||
non-existing elements are considered to be infinite. The interesting\n\
|
||||
property of a heap is that a[0] is always its smallest element.\n\
|
||||
\n\
|
||||
Usage:\n\
|
||||
\n\
|
||||
heap = [] # creates an empty heap\n\
|
||||
heappush(heap, item) # pushes a new item on the heap\n\
|
||||
item = heappop(heap) # pops the smallest item from the heap\n\
|
||||
item = heap[0] # smallest item on the heap without popping it\n\
|
||||
heapify(x) # transforms list into a heap, in-place, in linear time\n\
|
||||
item = heapreplace(heap, item) # pops and returns smallest item, and adds\n\
|
||||
# new item; the heap size is unchanged\n\
|
||||
\n\
|
||||
Our API differs from textbook heap algorithms as follows:\n\
|
||||
\n\
|
||||
- We use 0-based indexing. This makes the relationship between the\n\
|
||||
index for a node and the indexes for its children slightly less\n\
|
||||
obvious, but is more suitable since Python uses 0-based indexing.\n\
|
||||
\n\
|
||||
- Our heappop() method returns the smallest item, not the largest.\n\
|
||||
\n\
|
||||
These two make it possible to view the heap as a regular Python list\n\
|
||||
without surprises: heap[0] is the smallest item, and heap.sort()\n\
|
||||
maintains the heap invariant!\n");
|
||||
|
||||
|
||||
PyDoc_STRVAR(__about__,
|
||||
"Heap queues\n\
|
||||
\n\
|
||||
[explanation by François Pinard]\n\
|
||||
\n\
|
||||
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
|
||||
all k, counting elements from 0. For the sake of comparison,\n\
|
||||
non-existing elements are considered to be infinite. The interesting\n\
|
||||
property of a heap is that a[0] is always its smallest element.\n"
|
||||
"\n\
|
||||
The strange invariant above is meant to be an efficient memory\n\
|
||||
representation for a tournament. The numbers below are `k', not a[k]:\n\
|
||||
\n\
|
||||
0\n\
|
||||
\n\
|
||||
1 2\n\
|
||||
\n\
|
||||
3 4 5 6\n\
|
||||
\n\
|
||||
7 8 9 10 11 12 13 14\n\
|
||||
\n\
|
||||
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n\
|
||||
\n\
|
||||
\n\
|
||||
In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In\n\
|
||||
an usual binary tournament we see in sports, each cell is the winner\n\
|
||||
over the two cells it tops, and we can trace the winner down the tree\n\
|
||||
to see all opponents s/he had. However, in many computer applications\n\
|
||||
of such tournaments, we do not need to trace the history of a winner.\n\
|
||||
To be more memory efficient, when a winner is promoted, we try to\n\
|
||||
replace it by something else at a lower level, and the rule becomes\n\
|
||||
that a cell and the two cells it tops contain three different items,\n\
|
||||
but the top cell \"wins\" over the two topped cells.\n"
|
||||
"\n\
|
||||
If this heap invariant is protected at all time, index 0 is clearly\n\
|
||||
the overall winner. The simplest algorithmic way to remove it and\n\
|
||||
find the \"next\" winner is to move some loser (let's say cell 30 in the\n\
|
||||
diagram above) into the 0 position, and then percolate this new 0 down\n\
|
||||
the tree, exchanging values, until the invariant is re-established.\n\
|
||||
This is clearly logarithmic on the total number of items in the tree.\n\
|
||||
By iterating over all items, you get an O(n ln n) sort.\n"
|
||||
"\n\
|
||||
A nice feature of this sort is that you can efficiently insert new\n\
|
||||
items while the sort is going on, provided that the inserted items are\n\
|
||||
not \"better\" than the last 0'th element you extracted. This is\n\
|
||||
especially useful in simulation contexts, where the tree holds all\n\
|
||||
incoming events, and the \"win\" condition means the smallest scheduled\n\
|
||||
time. When an event schedule other events for execution, they are\n\
|
||||
scheduled into the future, so they can easily go into the heap. So, a\n\
|
||||
heap is a good structure for implementing schedulers (this is what I\n\
|
||||
used for my MIDI sequencer :-).\n"
|
||||
"\n\
|
||||
Various structures for implementing schedulers have been extensively\n\
|
||||
studied, and heaps are good for this, as they are reasonably speedy,\n\
|
||||
the speed is almost constant, and the worst case is not much different\n\
|
||||
than the average case. However, there are other representations which\n\
|
||||
are more efficient overall, yet the worst cases might be terrible.\n"
|
||||
"\n\
|
||||
Heaps are also very useful in big disk sorts. You most probably all\n\
|
||||
know that a big sort implies producing \"runs\" (which are pre-sorted\n\
|
||||
sequences, which size is usually related to the amount of CPU memory),\n\
|
||||
followed by a merging passes for these runs, which merging is often\n\
|
||||
very cleverly organised[1]. It is very important that the initial\n\
|
||||
sort produces the longest runs possible. Tournaments are a good way\n\
|
||||
to that. If, using all the memory available to hold a tournament, you\n\
|
||||
replace and percolate items that happen to fit the current run, you'll\n\
|
||||
produce runs which are twice the size of the memory for random input,\n\
|
||||
and much better for input fuzzily ordered.\n"
|
||||
"\n\
|
||||
Moreover, if you output the 0'th item on disk and get an input which\n\
|
||||
may not fit in the current tournament (because the value \"wins\" over\n\
|
||||
the last output value), it cannot fit in the heap, so the size of the\n\
|
||||
heap decreases. The freed memory could be cleverly reused immediately\n\
|
||||
for progressively building a second heap, which grows at exactly the\n\
|
||||
same rate the first heap is melting. When the first heap completely\n\
|
||||
vanishes, you switch heaps and start a new run. Clever and quite\n\
|
||||
effective!\n\
|
||||
\n\
|
||||
In a word, heaps are useful memory structures to know. I use them in\n\
|
||||
a few applications, and I think it is good to keep a `heap' module\n\
|
||||
around. :-)\n"
|
||||
"\n\
|
||||
--------------------\n\
|
||||
[1] The disk balancing algorithms which are current, nowadays, are\n\
|
||||
more annoying than clever, and this is a consequence of the seeking\n\
|
||||
capabilities of the disks. On devices which cannot seek, like big\n\
|
||||
tape drives, the story was quite different, and one had to be very\n\
|
||||
clever to ensure (far in advance) that each tape movement will be the\n\
|
||||
most effective possible (that is, will best participate at\n\
|
||||
\"progressing\" the merge). Some tapes were even able to read\n\
|
||||
backwards, and this was also used to avoid the rewinding time.\n\
|
||||
Believe me, real good tape sorts were quite spectacular to watch!\n\
|
||||
From all times, sorting has always been a Great Art! :-)\n");
|
||||
|
||||
PyMODINIT_FUNC
|
||||
init_heapq(void)
|
||||
{
|
||||
PyObject *m;
|
||||
|
||||
m = Py_InitModule3("_heapq", heapq_methods, module_doc);
|
||||
PyModule_AddObject(m, "__about__", PyString_FromString(__about__));
|
||||
}
|
||||
|
||||
|
|
@ -47,7 +47,7 @@ extern void initzipimport(void);
|
|||
extern void init_random(void);
|
||||
extern void inititertools(void);
|
||||
extern void initcollections(void);
|
||||
extern void initheapq(void);
|
||||
extern void init_heapq(void);
|
||||
extern void init_bisect(void);
|
||||
extern void init_symtable(void);
|
||||
extern void initmmap(void);
|
||||
|
|
@ -135,7 +135,7 @@ struct _inittab _PyImport_Inittab[] = {
|
|||
{"_hotshot", init_hotshot},
|
||||
{"_random", init_random},
|
||||
{"_bisect", init_bisect},
|
||||
{"heapq", initheapq},
|
||||
{"_heapq", init_heapq},
|
||||
{"itertools", inititertools},
|
||||
{"collections", initcollections},
|
||||
{"_symtable", init_symtable},
|
||||
|
|
|
|||
2
setup.py
2
setup.py
|
|
@ -327,7 +327,7 @@ class PyBuildExt(build_ext):
|
|||
# bisect
|
||||
exts.append( Extension("_bisect", ["_bisectmodule.c"]) )
|
||||
# heapq
|
||||
exts.append( Extension("heapq", ["heapqmodule.c"]) )
|
||||
exts.append( Extension("_heapq", ["_heapqmodule.c"]) )
|
||||
# operator.add() and similar goodies
|
||||
exts.append( Extension('operator', ['operator.c']) )
|
||||
# Python C API test module
|
||||
|
|
|
|||
Loading…
Reference in New Issue