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-rw-r--r--Lib/heapq.py10
1 files changed, 5 insertions, 5 deletions
diff --git a/Lib/heapq.py b/Lib/heapq.py
index abdad03d63..dfda498af7 100644
--- a/Lib/heapq.py
+++ b/Lib/heapq.py
@@ -13,7 +13,7 @@ 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(heap) # transform list into a heap, in-place, in linear time
+heapify(x) # transforms list into a heap, in-place, in linear time
Our API differs from textbook heap algorithms as follows:
@@ -175,16 +175,16 @@ def heappop(heap):
returnitem = lastelt
return returnitem
-def heapify(heap):
- """Transform list heap into a heap, in-place, in O(len(heap)) time."""
- n = len(heap)
+def heapify(x):
+ """Transform list into a heap, in-place, in O(len(heap)) time."""
+ n = len(x)
# Transform bottom-up. The largest index there's any point to looking at
# is the largest with a child index in-range, so must have 2*i + 1 < n,
# or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
# j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is
# (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
for i in xrange(n//2 - 1, -1, -1):
- _siftdown(heap, i)
+ _siftdown(x, i)
if __name__ == "__main__":
# Simple sanity test