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Files and Folders in: //lib64/python2.7//lib-dynload

NameTypeSizeLast ModifiedActions
Python-2.7.18-py2.7.egg-info File 1525 bytes April 10 2024 04:58:41.
_bisectmodule.so File 14088 bytes April 10 2024 04:58:50.
_bsddb.so File 177920 bytes April 10 2024 04:58:50.
_codecs_cn.so File 151328 bytes April 10 2024 04:58:50.
_codecs_hk.so File 159440 bytes April 10 2024 04:58:50.
_codecs_iso2022.so File 24608 bytes April 10 2024 04:58:50.
_codecs_jp.so File 266040 bytes April 10 2024 04:58:50.
_codecs_kr.so File 138992 bytes April 10 2024 04:58:50.
_codecs_tw.so File 110296 bytes April 10 2024 04:58:50.
_collectionsmodule.so File 37568 bytes April 10 2024 04:58:50.
_cryptmodule.so File 8184 bytes April 10 2024 04:58:50.
_csv.so File 35912 bytes April 10 2024 04:58:50.
_ctypes.so File 134992 bytes April 10 2024 04:58:50.
_curses.so File 88312 bytes April 10 2024 04:58:50.
_curses_panel.so File 17144 bytes April 10 2024 04:58:50.
_elementtree.so File 52232 bytes April 10 2024 04:58:50.
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_heapq.so File 23456 bytes April 10 2024 04:58:50.
_hotshot.so File 32520 bytes April 10 2024 04:58:50.
_io.so File 161560 bytes April 10 2024 04:58:50.
_json.so File 43456 bytes April 10 2024 04:58:50.
_localemodule.so File 21688 bytes April 10 2024 04:58:50.
_lsprof.so File 23312 bytes April 10 2024 04:58:50.
_md5module.so File 19112 bytes April 10 2024 04:58:50.
_multibytecodecmodule.so File 36768 bytes April 10 2024 04:58:50.
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_randommodule.so File 16848 bytes April 10 2024 04:58:50.
_sha256module.so File 22008 bytes April 10 2024 04:58:50.
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_shamodule.so File 17384 bytes April 10 2024 04:58:50.
_socketmodule.so File 88288 bytes April 10 2024 04:58:50.
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_tkinter.so File 70152 bytes April 10 2024 04:58:50.
arraymodule.so File 48400 bytes April 10 2024 04:58:50.
audioop.so File 33520 bytes April 10 2024 04:58:50.
binascii.so File 26272 bytes April 10 2024 04:58:50.
bz2.so File 44256 bytes April 10 2024 04:58:50.
cPickle.so File 84904 bytes April 10 2024 04:58:50.
cStringIO.so File 24392 bytes April 10 2024 04:58:50.
cmathmodule.so File 39680 bytes April 10 2024 04:58:50.
datetime.so File 91512 bytes April 10 2024 04:58:50.
dbm.so File 16864 bytes April 10 2024 04:58:50.
dlmodule.so File 11936 bytes April 10 2024 04:58:50.
fcntlmodule.so File 19840 bytes April 10 2024 04:58:50.
future_builtins.so File 9264 bytes April 10 2024 04:58:50.
gdbmmodule.so File 20320 bytes April 10 2024 04:58:50.
grpmodule.so File 12912 bytes April 10 2024 04:58:50.
imageop.so File 20760 bytes April 10 2024 04:58:50.
itertoolsmodule.so File 62192 bytes April 10 2024 04:58:50.
linuxaudiodev.so File 17112 bytes April 10 2024 04:58:50.
math.so File 38064 bytes April 10 2024 04:58:50.
mmapmodule.so File 26832 bytes April 10 2024 04:58:50.
nismodule.so File 17056 bytes April 10 2024 04:58:50.
operator.so File 47720 bytes April 10 2024 04:58:50.
ossaudiodev.so File 30512 bytes April 10 2024 04:58:50.
parsermodule.so File 55280 bytes April 10 2024 04:58:50.
pyexpat.so File 54608 bytes April 10 2024 04:58:50.
readline.so File 28680 bytes April 10 2024 04:58:50.
resource.so File 16752 bytes April 10 2024 04:58:50.
selectmodule.so File 29536 bytes April 10 2024 04:58:50.
spwdmodule.so File 13424 bytes April 10 2024 04:58:50.
stropmodule.so File 29136 bytes April 10 2024 04:58:50.
syslog.so File 12168 bytes April 10 2024 04:58:50.
termios.so File 26160 bytes April 10 2024 04:58:50.
timemodule.so File 25392 bytes April 10 2024 04:58:50.
timingmodule.so File 7984 bytes April 10 2024 04:58:50.
unicodedata.so File 698736 bytes April 10 2024 04:58:50.
xxsubtype.so File 13608 bytes April 10 2024 04:58:50.
zlibmodule.so File 28784 bytes April 10 2024 04:58:50.

Reading File: //lib64/python2.7//lib-dynload/_heapq.so

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Heap queues

[explanation by Fran�ois 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! :-)
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!
Find the n smallest elements in a dataset.

Equivalent to:  sorted(iterable)[:n]
Find the n largest elements in a dataset.

Equivalent to:  sorted(iterable, reverse=True)[:n]
Transform list into a heap, in-place, in O(len(heap)) time.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().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)
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SILENT KILLER Tool