xheap

XHEAP <https://pypi.python.org/pypi/xheap>_

It's like heapq <https://docs.python.org/3.5/library/heapq.html>_ (blazingly fast) but object-oriented + more features.

Read more here for the background story <http://srkunze.blogspot.com/2016/01/fast-object-oriented-heap-implementation.html>_.

Why?

Less code.

What are heaps good for anyway?

When you need the smallest item of a large list—fast and with no overhead.

How?

Let's suppose you have a heap, you can use pop to get its smallest item.

.. code:: python

from xheap import Heap

heap = Heap(['H', 'D', 'B', 'A', 'E', 'C', 'L', 'J', 'I'])
heap.pop()   # returns A
heap.pop()   # returns B
heap.pop()   # returns C
heap.pop()   # returns D

Heapsort <https://en.wikipedia.org/wiki/Heapsort>_ works this way.

Can I insert an item?

Indeed and it's as fast as pop. Use push for insertion.

.. code:: python

heap = Heap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.push('Z')

Can I remove an item from the middle of a heap?

Yes, that's what RemovalHeap.remove is supposed to do.

.. code:: python

from xheap import RemovalHeap

heap = RemovalHeap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.remove('L')

Can I specify the order of the heap?

Just imagine two heaps of the very same set of items but you need different sorting for each heap. Or you need a max-heap instead of a min-heap. That is what OrderHeap is designed for:

.. code:: python

from xheap import OrderHeap

items = [date(2016, 1, 1), date(2016, 1, 2),  date(2016, 1, 3),  date(2016, 1, 4)]

day_heap = OrderHeap(items, key=lambda date: date.day)
day_heap.peek()      # returns date(2016, 1, 1)

weekday_heap = OrderHeap(items, key=lambda date: date.weekday())
weekday_heap.peek()  # returns date(2016, 1, 4)

What about both remove+order?

No problem. Use XHeap.

If you wonder why there are 4 distinct heap implementations, it's a matter of speed. Each additional feature slows a heap down. Thus, you could always use XHeap but beware of the slowdown.

Checking Heap Invariant

A heap is just a list. So, if you tinker with it, you can check whether its invariant still holds:

.. code:: python

heap = Heap([4, 3, 7, 6, 1, 2, 9, 8, 5])
heap[3] = 10           # I know what I am doing here
heap.check_invariant() # but better check... ooops

Conclusion

Good

• uses C implementation if available (i.e. fast)
• object-oriented
• no slowdown if you don't need more than a simple heap
• removal possible
• custom orders possible
• works with Python2 and Python3

Bad

• no drawbacks discovered so far ;-)

• needs fix/work:

  • item wrapper which allows duplicate items
  • decrease-key+increase-key: another missing use-case of heapq <https://docs.python.org/3.5/library/heapq.html>_
  • merge heaps

• ideas are welcome :-)