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"""Unittests for heapq."""
from heapq import heappush, heappop, heapify, heapreplace, nlargest, nsmallest
import random
import unittest
from test import test_support
import sys
def heapiter(heap):
# An iterator returning a heap's elements, smallest-first.
try:
while 1:
yield heappop(heap)
except IndexError:
pass
class TestHeap(unittest.TestCase):
def test_push_pop(self):
# 1) Push 256 random numbers and pop them off, verifying all's OK.
heap = []
data = []
self.check_invariant(heap)
for i in range(256):
item = random.random()
data.append(item)
heappush(heap, item)
self.check_invariant(heap)
results = []
while heap:
item = heappop(heap)
self.check_invariant(heap)
results.append(item)
data_sorted = data[:]
data_sorted.sort()
self.assertEqual(data_sorted, results)
# 2) Check that the invariant holds for a sorted array
self.check_invariant(results)
def check_invariant(self, heap):
# Check the heap invariant.
for pos, item in enumerate(heap):
if pos: # pos 0 has no parent
parentpos = (pos-1) >> 1
self.assert_(heap[parentpos] <= item)
def test_heapify(self):
for size in range(30):
heap = [random.random() for dummy in range(size)]
heapify(heap)
self.check_invariant(heap)
def test_naive_nbest(self):
data = [random.randrange(2000) for i in range(1000)]
heap = []
for item in data:
heappush(heap, item)
if len(heap) > 10:
heappop(heap)
heap.sort()
self.assertEqual(heap, sorted(data)[-10:])
def test_nbest(self):
# 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).
data = [random.randrange(2000) for i in range(1000)]
heap = data[:10]
heapify(heap)
for item in data[10:]:
if item > heap[0]: # this gets rarer the longer we run
heapreplace(heap, item)
self.assertEqual(list(heapiter(heap)), sorted(data)[-10:])
def test_heapsort(self):
# Exercise everything with repeated heapsort checks
for trial in xrange(100):
size = random.randrange(50)
data = [random.randrange(25) for i in range(size)]
if trial & 1: # Half of the time, use heapify
heap = data[:]
heapify(heap)
else: # The rest of the time, use heappush
heap = []
for item in data:
heappush(heap, item)
heap_sorted = [heappop(heap) for i in range(size)]
self.assertEqual(heap_sorted, sorted(data))
def test_nsmallest(self):
data = [random.randrange(2000) for i in range(1000)]
for n in (0, 1, 2, 10, 100, 400, 999, 1000, 1100):
self.assertEqual(nsmallest(n, data), sorted(data)[:n])
def test_largest(self):
data = [random.randrange(2000) for i in range(1000)]
for n in (0, 1, 2, 10, 100, 400, 999, 1000, 1100):
self.assertEqual(nlargest(n, data), sorted(data, reverse=True)[:n])
def test_main(verbose=None):
test_classes = [TestHeap]
test_support.run_unittest(*test_classes)
# verify reference counting
if verbose and hasattr(sys, "gettotalrefcount"):
import gc
counts = [None] * 5
for i in xrange(len(counts)):
test_support.run_unittest(*test_classes)
gc.collect()
counts[i] = sys.gettotalrefcount()
print counts
if __name__ == "__main__":
test_main(verbose=True)
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