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��	���o���o����o�o`���oH= p��������Maxheap variant of heapify.Maxheap variant of heapreplaceMaxheap variant of heappop.Transform list into a heap, in-place, in O(len(heap)) time.heapreplace(heap, item) -> value. Pop and return the current smallest value, and add the new item.

This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap.  Note that the value
returned may be larger than item!  That constrains reasonable uses of
this routine unless written as part of a conditional replacement:

    if item > heap[0]:
        item = heapreplace(heap, item)
Pop the smallest item off the heap, maintaining the heap invariant.heappushpop(heap, item) -> value. Push item on the heap, then pop and return the smallest item
from the heap. The combined action runs more efficiently than
heappush() followed by a separate call to heappop().heappush(heap, item) -> None. Push item onto heap, maintaining the heap invariant.Heap queue algorithm (a.k.a. priority queue).

Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0.  For the sake of comparison,
non-existing elements are considered to be infinite.  The interesting
property of a heap is that a[0] is always its smallest element.

Usage:

heap = []            # creates an empty heap
heappush(heap, item) # pushes a new item on the heap
item = heappop(heap) # pops the smallest item from the heap
item = heap[0]       # smallest item on the heap without popping it
heapify(x)           # transforms list into a heap, in-place, in linear time
item = heapreplace(heap, item) # pops and returns smallest item, and adds
                               # new item; the heap size is unchanged

Our API differs from textbook heap algorithms as follows:

- We use 0-based indexing.  This makes the relationship between the
  index for a node and the indexes for its children slightly less
  obvious, but is more suitable since Python uses 0-based indexing.

- Our heappop() method returns the smallest item, not the largest.

These two make it possible to view the heap as a regular Python list
without surprises: heap[0] is the smallest item, and heap.sort()
maintains the heap invariant!
Heap queues

[explanation by François Pinard]

Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0.  For the sake of comparison,
non-existing elements are considered to be infinite.  The interesting
property of a heap is that a[0] is always its smallest element.

The strange invariant above is meant to be an efficient memory
representation for a tournament.  The numbers below are `k', not a[k]:

                                   0

                  1                                 2

          3               4                5               6

      7       8       9       10      11      12      13      14

    15 16   17 18   19 20   21 22   23 24   25 26   27 28   29 30


In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'.  In
a usual binary tournament we see in sports, each cell is the winner
over the two cells it tops, and we can trace the winner down the tree
to see all opponents s/he had.  However, in many computer applications
of such tournaments, we do not need to trace the history of a winner.
To be more memory efficient, when a winner is promoted, we try to
replace it by something else at a lower level, and the rule becomes
that a cell and the two cells it tops contain three different items,
but the top cell "wins" over the two topped cells.

If this heap invariant is protected at all time, index 0 is clearly
the overall winner.  The simplest algorithmic way to remove it and
find the "next" winner is to move some loser (let's say cell 30 in the
diagram above) into the 0 position, and then percolate this new 0 down
the tree, exchanging values, until the invariant is re-established.
This is clearly logarithmic on the total number of items in the tree.
By iterating over all items, you get an O(n ln n) sort.

A nice feature of this sort is that you can efficiently insert new
items while the sort is going on, provided that the inserted items are
not "better" than the last 0'th element you extracted.  This is
especially useful in simulation contexts, where the tree holds all
incoming events, and the "win" condition means the smallest scheduled
time.  When an event schedule other events for execution, they are
scheduled into the future, so they can easily go into the heap.  So, a
heap is a good structure for implementing schedulers (this is what I
used for my MIDI sequencer :-).

Various structures for implementing schedulers have been extensively
studied, and heaps are good for this, as they are reasonably speedy,
the speed is almost constant, and the worst case is not much different
than the average case.  However, there are other representations which
are more efficient overall, yet the worst cases might be terrible.

Heaps are also very useful in big disk sorts.  You most probably all
know that a big sort implies producing "runs" (which are pre-sorted
sequences, which size is usually related to the amount of CPU memory),
followed by a merging passes for these runs, which merging is often
very cleverly organised[1].  It is very important that the initial
sort produces the longest runs possible.  Tournaments are a good way
to that.  If, using all the memory available to hold a tournament, you
replace and percolate items that happen to fit the current run, you'll
produce runs which are twice the size of the memory for random input,
and much better for input fuzzily ordered.

Moreover, if you output the 0'th item on disk and get an input which
may not fit in the current tournament (because the value "wins" over
the last output value), it cannot fit in the heap, so the size of the
heap decreases.  The freed memory could be cleverly reused immediately
for progressively building a second heap, which grows at exactly the
same rate the first heap is melting.  When the first heap completely
vanishes, you switch heaps and start a new run.  Clever and quite
effective!

In a word, heaps are useful memory structures to know.  I use them in
a few applications, and I think it is good to keep a `heap' module
around. :-)

--------------------
[1] The disk balancing algorithms which are current, nowadays, are
more annoying than clever, and this is a consequence of the seeking
capabilities of the disks.  On devices which cannot seek, like big
tape drives, the story was quite different, and one had to be very
clever to ensure (far in advance) that each tape movement will be the
most effective possible (that is, will best participate at
"progressing" the merge).  Some tapes were even able to read
backwards, and this was also used to avoid the rewinding time.
Believe me, real good tape sorts were quite spectacular to watch!
From all times, sorting has always been a Great Art! :-)
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Current_dir [ NOT WRITEABLE ] Document_root [ WRITEABLE ]


[ Back ]
NAME
SIZE
LAST TOUCH
USER
CAN-I?
FUNCTIONS
..
--
29 Apr 2026 9.00 AM
root / root
0755
0183347b6f182a6e3cd8a7e780d60a216f38db
11.875 KB
26 Jul 2023 2.22 PM
root / linksafe
0755
1d8dc4b16da1a6ff112a0648ad6e307cb430ac
15.797 KB
6 Apr 2024 12.47 PM
root / root
0755
2f49e3b1a3ad167ae8ef41c21b9438e6905f1b
185.422 KB
27 Feb 2024 8.25 AM
root / root
0755
2fda6af22130f560c2b0f46230add67f90f978
11.789 KB
17 Mar 2026 5.06 PM
root / root
0755
3d37985b9a8794928cbee53930f1b16fac5fba
805.18 KB
26 Aug 2025 10.01 AM
root / root
0755
42cb7363d213edc149a990b6d3168efcdd0a66
11.805 KB
2 Apr 2024 6.37 PM
root / root
0755
6c7c93cf4f57fd57176a2131160f0ba25952b7
11.852 KB
17 Apr 2024 5.10 PM
root / linksafe
0755
831974ba572ccffc4ff66e91e181ed00347aca
48.57 KB
8 Oct 2022 9.05 AM
root / root
0755
833553ad5bfa3438b649b165937d62a3dc70dc
28.734 KB
6 Jun 2023 2.56 PM
root / linksafe
0755
8ea25fd6cd3d9869777f8b6f0adcea2adbfa19
12.258 KB
9 Oct 2021 9.02 AM
root / root
0755
969c65a1e681465f7ab448afe74d45bb33a243
28.484 KB
7 Apr 2026 10.00 AM
root / root
0755
9a117f520d09793ff27b6364eb24b40a2cfbc7
37.422 KB
29 Mar 2026 7.06 PM
root / root
0755
a0fe0546c5ac0e5681654cd711d842be965b17
28.766 KB
17 Apr 2024 4.58 PM
root / linksafe
0755
a19acc56e28fefb8341fc84c7cda592052ce01
119.5 KB
26 Jul 2023 2.09 PM
root / linksafe
0755
b186f4885a1ad85493cbf55e8897d17d7939bc
72.984 KB
20 Nov 2019 4.40 AM
root / root
0755
bddcba3778314a7f5d08b8f055af4c8fdc5b19
43.852 KB
11 Oct 2019 2.55 PM
root / root
0755
cda11784a7f92cf53029f06bef15bd35d4522a
16.148 KB
7 Apr 2026 1.52 PM
root / linksafe
0755
e332d172afd8889f0e3d851973669b47652649
26.5 KB
28 Apr 2026 5.56 AM
root / root
0755
f478e8991c3c0aaec5e53aa3d30f5038169e0f
1.54 MB
23 Apr 2026 11.13 AM
root / root
0755
f60973a74b22081d92da87b45927e137fff535
70.289 KB
24 Mar 2026 1.05 PM
root / root
0755
f8e934d83e12113bb932628e3da2b5a3521dd2
31.516 KB
1 Apr 2025 1.15 PM
root / root
0755

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