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�H��H��t5L�� 1�H��1�L���L��H��H�q���H�5qH��H���m�H��[���H��H���index out of rangeheap argument must be a listheappushheappushpopheapreplace__about__heappopheapify_heappop_max_heapreplace_max_heapify_max_heapqlist changed size during iteration;��������0��Y����x���*�4g����$���k���� ��0�������H��� �8`�������������������$zRx�$����FJw�?:*3$"D����H\���F�B�B �B(�A0�A8�D`H
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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 [ NOT WRITEABLE ]


[ Back ]
NAME
SIZE
LAST TOUCH
USER
CAN-I?
FUNCTIONS
..
--
24 Mar 2026 9.59 AM
root / root
0755
14dc711f9ab6f7555acde17e53c9e99daa8da6
11.461 KB
26 Jun 2024 1.55 PM
root / linksafe
0755
16589f45b7e9235b205e28931d7ac93c38d48d
16.789 KB
5 Nov 2024 2.34 AM
root / root
0755
38868007829e9440b976ea0a16f3b119ba29a4
36.258 KB
28 Apr 2025 5.36 PM
root / linksafe
0755
4059ed8756647a4f99a14bfa8d6c4a889d3711
19.305 KB
6 May 2020 2.26 PM
root / root
0755
5dee0f913e93875f9eecfbde80e9994e2e53c9
11.602 KB
27 Jan 2026 4.01 PM
root / linksafe
0755
63136d47d30d1e2b9f5d2c194e76ca5ec70fe4
11.789 KB
2 Apr 2024 6.37 PM
root / root
0755
64d02af54c83a8437f3824b08ab4e2c5cd3110
15.75 KB
13 Feb 2026 12.41 PM
root / root
0755
6b497bbcf99a29eaf0b27952637398720cc9b5
11.719 KB
26 Aug 2025 10.01 AM
root / root
0755
82c0f4c21eb21f1ab903dd096a49b1abc7426b
7.398 KB
26 Jul 2023 2.31 PM
root / linksafe
0755
93367a39aa2ee95bdaaced8f23ea278fc9116f
11.75 KB
5 Apr 2023 3.24 PM
root / root
0755
94fa878d54dbf26fc936e4443ba4154f264e6a
17.242 KB
17 Apr 2024 4.58 PM
root / linksafe
0755
9cd9bfbfd2ce7aa5ebab92fa9423ef193d8e16
16.047 KB
2 Apr 2024 6.37 PM
root / root
0755
9d0f81469fdcaa9990e885645598568167b3e5
465.508 KB
6 Nov 2025 7.45 AM
root / root
0755
9db40599417da046f7e02ab3d4bd96146d5d18
15.805 KB
26 Jun 2024 1.55 PM
root / linksafe
0755
b04e49fb35382b37f98905f76280af0a203831
26.516 KB
2 Apr 2026 3.01 PM
root / root
0755
b908048309e487c66bf389ca0beadf7e99cd71
267.625 KB
6 Jun 2023 2.56 PM
root / linksafe
0755
c184a9c4cd99f86eb39e034ea2ea5106c07775
7.391 KB
26 Jul 2023 2.22 PM
root / linksafe
0755
c2d1c4ac2669450bf65afee9d863caae711e68
15.5 KB
26 Jul 2023 2.39 PM
root / linksafe
0755
c988fa21ba4eb54fea7d0b28ed2e2eda057b1f
63.859 KB
1 Apr 2025 1.15 PM
root / root
0755
d3cc913439a826bba29ea0f14e591df41eef1a
16.078 KB
5 Apr 2023 3.24 PM
root / root
0755
d83594009899563964cd9e5b71ec37696ab2ff
313.547 KB
13 Mar 2026 3.20 PM
root / linksafe
0755
d963bd617d889878a80dfe3d1b489799ff0373
683.836 KB
3 Apr 2026 4.54 PM
root / root
0755
dcc8b2f6c1cd3cc01c0666eabdec2dee972e0e
11.539 KB
26 Jul 2023 3.03 PM
root / linksafe
0755
e9bf03385a135c7650a00279b214f04a794fd1
15.648 KB
26 Jun 2024 1.55 PM
root / linksafe
0755
f0ba4314c8fb056c7e6fe597da0cdf754eb90c
25.867 KB
3 Aug 2020 7.24 PM
root / root
0755
f7772f7e19c36c8b278e3191385c0ae6e6d7d9
58.344 KB
8 Mar 2026 11.05 PM
root / root
0755

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