Module heapq
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Module heapq

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!

Functions [hide private]
 
_siftdown(heap, startpos, pos)
 
_siftup(heap, pos)
 
heappush(...)
Push item onto heap, maintaining the heap invariant.
 
heappop(...)
Pop the smallest item off the heap, maintaining the heap invariant.
 
heapify(...)
Transform list into a heap, in-place, in O(len(heap)) time.
 
heapreplace(...)
Pop and return the current smallest value, and add the new item.
 
nsmallest(n, iterable, key=None)
Find the n smallest elements in a dataset.
 
nlargest(n, iterable, key=None)
Find the n largest elements in a dataset.
Variables [hide private]
  __about__ = 'Heap queues\n\n[explanation by Fran\xe7ois Pinard...

Imports: islice, repeat, count, imap, izip, tee, itemgetter, bisect, _nsmallest, _nlargest


Function Details [hide private]

heapreplace(...)

 
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)

nsmallest(n, iterable, key=None)

 

Find the n smallest elements in a dataset.

Equivalent to: sorted(iterable, key=key)[:n]

nlargest(n, iterable, key=None)

 

Find the n largest elements in a dataset.

Equivalent to: sorted(iterable, key=key, reverse=True)[:n]


Variables Details [hide private]

__about__

Value:
'''Heap queues

[explanation by Fran\xe7ois 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.
...