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Task/Priority-queue/Python/priority-queue-4.py

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+>>> help('heapq')
+Help on module heapq:
+
+NAME
+    heapq - Heap queue algorithm (a.k.a. priority queue).
+
+DESCRIPTION
+    Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
+    all k, counting elements from 0.  For the sake of comparison,
+    non-existing elements are considered to be infinite.  The interesting
+    property of a heap is that a[0] is always its smallest element.
+
+    Usage:
+
+    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(x)           # transforms list into a heap, in-place, in linear time
+    item = heapreplace(heap, item) # pops and returns smallest item, and adds
+                                   # new item; the heap size is unchanged
+
+    Our API differs from textbook heap algorithms as follows:
+
+    - We use 0-based indexing.  This makes the relationship between the
+      index for a node and the indexes for its children slightly less
+      obvious, but is more suitable since Python uses 0-based indexing.
+
+    - Our heappop() method returns the smallest item, not the largest.
+
+    These two make it possible to view the heap as a regular Python list
+    without surprises: heap[0] is the smallest item, and heap.sort()
+    maintains the heap invariant!
+
+FUNCTIONS
+    heapify(...)
+        Transform list into a heap, in-place, in O(len(heap)) time.
+
+    heappop(...)
+        Pop the smallest item off the heap, maintaining the heap invariant.
+
+    heappush(...)
+        Push item onto heap, maintaining the heap invariant.
+
+    heappushpop(...)
+        Push item on the heap, then pop and return the smallest item
+        from the heap. The combined action runs more efficiently than
+        heappush() followed by a separate call to heappop().
+
+    heapreplace(...)
+        Pop and return the current smallest value, and add the new item.
+
+        This is more efficient than heappop() followed by heappush(), and can be
+        more appropriate when using a fixed-size heap.  Note that the value
+        returned may be larger than item!  That constrains reasonable uses of
+        this routine unless written as part of a conditional replacement:
+
+            if item > heap[0]:
+                item = heapreplace(heap, item)
+
+    merge(*iterables)
+        Merge multiple sorted inputs into a single sorted output.
+
+        Similar to sorted(itertools.chain(*iterables)) but returns a generator,
+        does not pull the data into memory all at once, and assumes that each of
+        the input streams is already sorted (smallest to largest).
+
+        >>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
+        [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]
+
+    nlargest(n, iterable, key=None)
+        Find the n largest elements in a dataset.
+
+        Equivalent to:  sorted(iterable, key=key, reverse=True)[:n]
+
+    nsmallest(n, iterable, key=None)
+        Find the n smallest elements in a dataset.
+
+        Equivalent to:  sorted(iterable, key=key)[:n]
+
+DATA
+    __about__ = 'Heap queues\n\n[explanation by François Pinard]\n\nH... t...
+    __all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge', '...
+
+FILE
+    c:\python32\lib\heapq.py
+
+
+>>>