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"""Unittests for heapq."""
from test.test_support import verify, vereq, verbose, TestFailed
from heapq import heappush, heappop, heapify
import random
def check_invariant(heap):
# Check the heap invariant.
for pos, item in enumerate(heap):
if pos: # pos 0 has no parent
parentpos = (pos-1) >> 1
verify(heap[parentpos] <= item)
# An iterator returning a heap's elements, smallest-first.
class heapiter(object):
def __init__(self, heap):
self.heap = heap
def next(self):
try:
return heappop(self.heap)
except IndexError:
raise StopIteration
def __iter__(self):
return self
def test_main():
# 1) Push 100 random numbers and pop them off, verifying all's OK.
heap = []
data = []
check_invariant(heap)
for i in range(256):
item = random.random()
data.append(item)
heappush(heap, item)
check_invariant(heap)
results = []
while heap:
item = heappop(heap)
check_invariant(heap)
results.append(item)
data_sorted = data[:]
data_sorted.sort()
vereq(data_sorted, results)
# 2) Check that the invariant holds for a sorted array
check_invariant(results)
# 3) Naive "N-best" algorithm
heap = []
for item in data:
heappush(heap, item)
if len(heap) > 10:
heappop(heap)
heap.sort()
vereq(heap, data_sorted[-10:])
# 4) Test heapify.
for size in range(30):
heap = [random.random() for dummy in range(size)]
heapify(heap)
check_invariant(heap)
# 5) Less-naive "N-best" algorithm, much faster (if len(data) is big
# enough <wink>) than sorting all of data. However, if we had a max
# heap instead of a min heap, it could go faster still via
# heapify'ing all of data (linear time), then doing 10 heappops
# (10 log-time steps).
heap = data[:10]
heapify(heap)
for item in data[10:]:
if item > heap[0]: # this gets rarer the longer we run
heappop(heap) # we know heap[0] isn't in best 10 anymore
heappush(heap, item)
vereq(list(heapiter(heap)), data_sorted[-10:])
# Make user happy
if verbose:
print "All OK"
if __name__ == "__main__":
test_main()
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