It's like heapq (blazingly fast) but object-oriented + more features.
Read more here for the background story.
Less code.
When you need the smallest item of a large list—fast and with no overhead.
Let's suppose you have a heap, you can use pop
to get its smallest item.
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 works this way.
Indeed and it's as fast as pop. Use push
for insertion.
heap = Heap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.push('Z')
Yes, that's what RemovalHeap.remove
is supposed to do.
from xheap import RemovalHeap
heap = RemovalHeap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.remove('L')
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:
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)
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.
A heap is just a list. So, if you tinker with it, you can check whether its invariant still holds:
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
- 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
- no drawbacks discovered so far ;-)
- needs fix/work:
- item wrapper which allows duplicate items
- decrease-key+increase-key: another missing use-case of heapq
- merge heaps
- ideas are welcome :-)