# heap, also called priority queue
# heap common operations: heapify, top, heappush, heappop, heappushpop, heapreplace, _siftup/_siftdown,
# nlargest/nsmallest, merge
# heapq in Python, there is only min heap, if you want to a max heap, you should reverse the key or implement __lt__.
#
# Time: O(1) to find, O(log(n)) to push/pop/sift, O(nlog(n)) to heapify, O(n) to merge
# Space: O(n)
#
# from Queue import PriorityQueue is alternative way, which is a thread-safe class
import heapq
def heap_operations():
heap = [4, 2, 1, 3]
# heapify
heapq.heapify(heap)
# top
top = heap[0]
# heappop
top = heapq.heappop(heap)
# heappush
heapq.heappush(heap, 5)
# heappushpop = push + pop
heapq.heappushpop(heap, 0)
# heapreplace = pop + push
heapq.heapreplace(heap, 0)
data = [10, 5, 18, 2, 37, 3, 8, 7, 19, 1]
heapq.heapify(data)
old, new = 8, 22 # increase the 8 to 22
i = data.index(old)
data[i] = new
# _siftup, from root to leaf, when increase
heapq._siftup(data, i)
old, new = 10, 4 # decrease the 10 to 4
i = data.index(old)
data[i] = new
# _siftdown, from leaf to root, when decrease
heapq._siftdown(data, 0, i)
# find n largest by queue
heapq.nlargest(data, 3)
# find n smallest by queue
heapq.nsmallest(data, 3)
# Merge multiple sorted inputs into a single sorted output
# e.g. merge timestamped entries from multiple log files
heapq.merge([1, 3, 5, 7], [0, 2, 4, 8], [5, 10, 15, 20], [], [25])
