python3.2
3.2.2

#include "Python.h"
Go to the source code of this file.
Functions  
static int  _siftdown (PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos) 
static int  _siftup (PyListObject *heap, Py_ssize_t pos) 
static PyObject *  heappush (PyObject *self, PyObject *args) 
PyDoc_STRVAR (heappush_doc,"Push item onto heap, maintaining the heap invariant.")  
static PyObject *  heappop (PyObject *self, PyObject *heap) 
PyDoc_STRVAR (heappop_doc,"Pop the smallest item off the heap, maintaining the heap invariant.")  
static PyObject *  heapreplace (PyObject *self, PyObject *args) 
PyDoc_STRVAR (heapreplace_doc,"Pop and return the current smallest value, and add the new item.\n\ \n\ This is more efficient than heappop() followed by heappush(), and can be\n\ more appropriate when using a fixedsize heap. Note that the value\n\ returned may be larger than item! That constrains reasonable uses of\n\ this routine unless written as part of a conditional replacement:\n\n\ if item > heap[0]:\n\ item = heapreplace(heap, item)\n")  
static PyObject *  heappushpop (PyObject *self, PyObject *args) 
PyDoc_STRVAR (heappushpop_doc,"Push item on the heap, then pop and return the smallest item\n\ from the heap. The combined action runs more efficiently than\n\ heappush() followed by a separate call to heappop().")  
static PyObject *  heapify (PyObject *self, PyObject *heap) 
PyDoc_STRVAR (heapify_doc,"Transform list into a heap, inplace, in O(len(heap)) time.")  
static PyObject *  nlargest (PyObject *self, PyObject *args) 
PyDoc_STRVAR (nlargest_doc,"Find the n largest elements in a dataset.\n\ \n\ Equivalent to: sorted(iterable, reverse=True)[:n]\n")  
static int  _siftdownmax (PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos) 
static int  _siftupmax (PyListObject *heap, Py_ssize_t pos) 
static PyObject *  nsmallest (PyObject *self, PyObject *args) 
PyDoc_STRVAR (nsmallest_doc,"Find the n smallest elements in a dataset.\n\ \n\ Equivalent to: sorted(iterable)[:n]\n")  
PyDoc_STRVAR (module_doc,"Heap queue algorithm (a.k.a. priority queue).\n\ \n\ Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\ all k, counting elements from 0. For the sake of comparison,\n\ nonexisting elements are considered to be infinite. The interesting\n\ property of a heap is that a[0] is always its smallest element.\n\ \n\ Usage:\n\ \n\ heap = [] # creates an empty heap\n\ heappush(heap, item) # pushes a new item on the heap\n\ item = heappop(heap) # pops the smallest item from the heap\n\ item = heap[0] # smallest item on the heap without popping it\n\ heapify(x) # transforms list into a heap, inplace, in linear time\n\ item = heapreplace(heap, item) # pops and returns smallest item, and adds\n\ # new item; the heap size is unchanged\n\ \n\ Our API differs from textbook heap algorithms as follows:\n\ \n\  We use 0based indexing. This makes the relationship between the\n\ index for a node and the indexes for its children slightly less\n\ obvious, but is more suitable since Python uses 0based indexing.\n\ \n\  Our heappop() method returns the smallest item, not the largest.\n\ \n\ These two make it possible to view the heap as a regular Python list\n\ without surprises: heap[0] is the smallest item, and heap.sort()\n\ maintains the heap invariant!\n")  
PyDoc_STRVAR (__about__,"Heap queues\n\ \n\ [explanation by Fran\xc3\xa7ois Pinard]\n\ \n\ Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\ all k, counting elements from 0. For the sake of comparison,\n\ nonexisting elements are considered to be infinite. The interesting\n\ property of a heap is that a[0] is always its smallest element.\n""\n\ The strange invariant above is meant to be an efficient memory\n\ representation for a tournament. The numbers below are `k', not a[k]:\n\ \n\ 0\n\ \n\ 1 2\n\ \n\ 3 4 5 6\n\ \n\ 7 8 9 10 11 12 13 14\n\ \n\ 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n\ \n\ \n\ In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In\n\ an usual binary tournament we see in sports, each cell is the winner\n\ over the two cells it tops, and we can trace the winner down the tree\n\ to see all opponents s/he had. However, in many computer applications\n\ of such tournaments, we do not need to trace the history of a winner.\n\ To be more memory efficient, when a winner is promoted, we try to\n\ replace it by something else at a lower level, and the rule becomes\n\ that a cell and the two cells it tops contain three different items,\n\ but the top cell \"wins\" over the two topped cells.\n""\n\ If this heap invariant is protected at all time, index 0 is clearly\n\ the overall winner. The simplest algorithmic way to remove it and\n\ find the \"next\" winner is to move some loser (let's say cell 30 in the\n\ diagram above) into the 0 position, and then percolate this new 0 down\n\ the tree, exchanging values, until the invariant is reestablished.\n\ This is clearly logarithmic on the total number of items in the tree.\n\ By iterating over all items, you get an O(n ln n) sort.\n""\n\ A nice feature of this sort is that you can efficiently insert new\n\ items while the sort is going on, provided that the inserted items are\n\ not \"better\" than the last 0'th element you extracted. This is\n\ especially useful in simulation contexts, where the tree holds all\n\ incoming events, and the \"win\" condition means the smallest scheduled\n\ time. When an event schedule other events for execution, they are\n\ scheduled into the future, so they can easily go into the heap. So, a\n\ heap is a good structure for implementing schedulers (this is what I\n\ used for my MIDI sequencer :).\n""\n\ Various structures for implementing schedulers have been extensively\n\ studied, and heaps are good for this, as they are reasonably speedy,\n\ the speed is almost constant, and the worst case is not much different\n\ than the average case. However, there are other representations which\n\ are more efficient overall, yet the worst cases might be terrible.\n""\n\ Heaps are also very useful in big disk sorts. You most probably all\n\ know that a big sort implies producing \"runs\" (which are presorted\n\ sequences, which size is usually related to the amount of CPU memory),\n\ followed by a merging passes for these runs, which merging is often\n\ very cleverly organised[1]. It is very important that the initial\n\ sort produces the longest runs possible. Tournaments are a good way\n\ to that. If, using all the memory available to hold a tournament, you\n\ replace and percolate items that happen to fit the current run, you'll\n\ produce runs which are twice the size of the memory for random input,\n\ and much better for input fuzzily ordered.\n""\n\ Moreover, if you output the 0'th item on disk and get an input which\n\ may not fit in the current tournament (because the value \"wins\" over\n\ the last output value), it cannot fit in the heap, so the size of the\n\ heap decreases. The freed memory could be cleverly reused immediately\n\ for progressively building a second heap, which grows at exactly the\n\ same rate the first heap is melting. When the first heap completely\n\ vanishes, you switch heaps and start a new run. Clever and quite\n\ effective!\n\ \n\ In a word, heaps are useful memory structures to know. I use them in\n\ a few applications, and I think it is good to keep a `heap' module\n\ around. :)\n""\n\ \n\ [1] The disk balancing algorithms which are current, nowadays, are\n\ more annoying than clever, and this is a consequence of the seeking\n\ capabilities of the disks. On devices which cannot seek, like big\n\ tape drives, the story was quite different, and one had to be very\n\ clever to ensure (far in advance) that each tape movement will be the\n\ most effective possible (that is, will best participate at\n\ \"progressing\" the merge). Some tapes were even able to read\n\ backwards, and this was also used to avoid the rewinding time.\n\ Believe me, real good tape sorts were quite spectacular to watch!\n\ From all times, sorting has always been a Great Art! :)\n")  
PyMODINIT_FUNC  PyInit__heapq (void) 
Variables  
static PyMethodDef  heapq_methods [] 
static struct  PyModuleDef 
static int _siftdown  (  PyListObject *  heap, 
Py_ssize_t  startpos,  
Py_ssize_t  pos  
)  [static] 
Definition at line 12 of file _heapqmodule.c.
{ PyObject *newitem, *parent; int cmp; Py_ssize_t parentpos; assert(PyList_Check(heap)); if (pos >= PyList_GET_SIZE(heap)) { PyErr_SetString(PyExc_IndexError, "index out of range"); return 1; } newitem = PyList_GET_ITEM(heap, pos); Py_INCREF(newitem); /* Follow the path to the root, moving parents down until finding a place newitem fits. */ while (pos > startpos){ parentpos = (pos  1) >> 1; parent = PyList_GET_ITEM(heap, parentpos); cmp = PyObject_RichCompareBool(newitem, parent, Py_LT); if (cmp == 1) { Py_DECREF(newitem); return 1; } if (cmp == 0) break; Py_INCREF(parent); Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, parent); pos = parentpos; } Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, newitem); return 0; }
static int _siftdownmax  (  PyListObject *  heap, 
Py_ssize_t  startpos,  
Py_ssize_t  pos  
)  [static] 
Definition at line 352 of file _heapqmodule.c.
{ PyObject *newitem, *parent; int cmp; Py_ssize_t parentpos; assert(PyList_Check(heap)); if (pos >= PyList_GET_SIZE(heap)) { PyErr_SetString(PyExc_IndexError, "index out of range"); return 1; } newitem = PyList_GET_ITEM(heap, pos); Py_INCREF(newitem); /* Follow the path to the root, moving parents down until finding a place newitem fits. */ while (pos > startpos){ parentpos = (pos  1) >> 1; parent = PyList_GET_ITEM(heap, parentpos); cmp = PyObject_RichCompareBool(parent, newitem, Py_LT); if (cmp == 1) { Py_DECREF(newitem); return 1; } if (cmp == 0) break; Py_INCREF(parent); Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, parent); pos = parentpos; } Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, newitem); return 0; }
static int _siftup  (  PyListObject *  heap, 
Py_ssize_t  pos  
)  [static] 
Definition at line 49 of file _heapqmodule.c.
{ Py_ssize_t startpos, endpos, childpos, rightpos; int cmp; PyObject *newitem, *tmp; assert(PyList_Check(heap)); endpos = PyList_GET_SIZE(heap); startpos = pos; if (pos >= endpos) { PyErr_SetString(PyExc_IndexError, "index out of range"); return 1; } newitem = PyList_GET_ITEM(heap, pos); Py_INCREF(newitem); /* Bubble up the smaller child until hitting a leaf. */ childpos = 2*pos + 1; /* leftmost child position */ while (childpos < endpos) { /* Set childpos to index of smaller child. */ rightpos = childpos + 1; if (rightpos < endpos) { cmp = PyObject_RichCompareBool( PyList_GET_ITEM(heap, childpos), PyList_GET_ITEM(heap, rightpos), Py_LT); if (cmp == 1) { Py_DECREF(newitem); return 1; } if (cmp == 0) childpos = rightpos; } /* Move the smaller child up. */ tmp = PyList_GET_ITEM(heap, childpos); Py_INCREF(tmp); Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, tmp); pos = childpos; childpos = 2*pos + 1; } /* The leaf at pos is empty now. Put newitem there, and and bubble it up to its final resting place (by sifting its parents down). */ Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, newitem); return _siftdown(heap, startpos, pos); }
static int _siftupmax  (  PyListObject *  heap, 
Py_ssize_t  pos  
)  [static] 
Definition at line 389 of file _heapqmodule.c.
{ Py_ssize_t startpos, endpos, childpos, rightpos; int cmp; PyObject *newitem, *tmp; assert(PyList_Check(heap)); endpos = PyList_GET_SIZE(heap); startpos = pos; if (pos >= endpos) { PyErr_SetString(PyExc_IndexError, "index out of range"); return 1; } newitem = PyList_GET_ITEM(heap, pos); Py_INCREF(newitem); /* Bubble up the smaller child until hitting a leaf. */ childpos = 2*pos + 1; /* leftmost child position */ while (childpos < endpos) { /* Set childpos to index of smaller child. */ rightpos = childpos + 1; if (rightpos < endpos) { cmp = PyObject_RichCompareBool( PyList_GET_ITEM(heap, rightpos), PyList_GET_ITEM(heap, childpos), Py_LT); if (cmp == 1) { Py_DECREF(newitem); return 1; } if (cmp == 0) childpos = rightpos; } /* Move the smaller child up. */ tmp = PyList_GET_ITEM(heap, childpos); Py_INCREF(tmp); Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, tmp); pos = childpos; childpos = 2*pos + 1; } /* The leaf at pos is empty now. Put newitem there, and and bubble it up to its final resting place (by sifting its parents down). */ Py_DECREF(PyList_GET_ITEM(heap, pos)); PyList_SET_ITEM(heap, pos, newitem); return _siftdownmax(heap, startpos, pos); }
Definition at line 241 of file _heapqmodule.c.
{ Py_ssize_t i, n; if (!PyList_Check(heap)) { PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); return NULL; } n = PyList_GET_SIZE(heap); /* Transform bottomup. The largest index there's any point to looking at is the largest with a child index inrange, so must have 2*i + 1 < n, or i < (n1)/2. If n is even = 2*j, this is (2*j1)/2 = j1/2 so j1 is the largest, which is n//2  1. If n is odd = 2*j+1, this is (2*j+11)/2 = j so j1 is the largest, and that's again n//21. */ for (i=n/21 ; i>=0 ; i) if(_siftup((PyListObject *)heap, i) == 1) return NULL; Py_INCREF(Py_None); return Py_None; }
Definition at line 124 of file _heapqmodule.c.
{ PyObject *lastelt, *returnitem; Py_ssize_t n; if (!PyList_Check(heap)) { PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); return NULL; } /* # raises appropriate IndexError if heap is empty */ n = PyList_GET_SIZE(heap); if (n == 0) { PyErr_SetString(PyExc_IndexError, "index out of range"); return NULL; } lastelt = PyList_GET_ITEM(heap, n1) ; Py_INCREF(lastelt); PyList_SetSlice(heap, n1, n, NULL); n; if (!n) return lastelt; returnitem = PyList_GET_ITEM(heap, 0); PyList_SET_ITEM(heap, 0, lastelt); if (_siftup((PyListObject *)heap, 0) == 1) { Py_DECREF(returnitem); return NULL; } return returnitem; }
Definition at line 99 of file _heapqmodule.c.
{ PyObject *heap, *item; if (!PyArg_UnpackTuple(args, "heappush", 2, 2, &heap, &item)) return NULL; if (!PyList_Check(heap)) { PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); return NULL; } if (PyList_Append(heap, item) == 1) return NULL; if (_siftdown((PyListObject *)heap, 0, PyList_GET_SIZE(heap)1) == 1) return NULL; Py_INCREF(Py_None); return Py_None; }
static PyObject* heappushpop  (  PyObject *  self, 
PyObject *  args  
)  [static] 
Definition at line 199 of file _heapqmodule.c.
{ PyObject *heap, *item, *returnitem; int cmp; if (!PyArg_UnpackTuple(args, "heappushpop", 2, 2, &heap, &item)) return NULL; if (!PyList_Check(heap)) { PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); return NULL; } if (PyList_GET_SIZE(heap) < 1) { Py_INCREF(item); return item; } cmp = PyObject_RichCompareBool(PyList_GET_ITEM(heap, 0), item, Py_LT); if (cmp == 1) return NULL; if (cmp == 0) { Py_INCREF(item); return item; } returnitem = PyList_GET_ITEM(heap, 0); Py_INCREF(item); PyList_SET_ITEM(heap, 0, item); if (_siftup((PyListObject *)heap, 0) == 1) { Py_DECREF(returnitem); return NULL; } return returnitem; }
static PyObject* heapreplace  (  PyObject *  self, 
PyObject *  args  
)  [static] 
Definition at line 161 of file _heapqmodule.c.
{ PyObject *heap, *item, *returnitem; if (!PyArg_UnpackTuple(args, "heapreplace", 2, 2, &heap, &item)) return NULL; if (!PyList_Check(heap)) { PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); return NULL; } if (PyList_GET_SIZE(heap) < 1) { PyErr_SetString(PyExc_IndexError, "index out of range"); return NULL; } returnitem = PyList_GET_ITEM(heap, 0); Py_INCREF(item); PyList_SET_ITEM(heap, 0, item); if (_siftup((PyListObject *)heap, 0) == 1) { Py_DECREF(returnitem); return NULL; } return returnitem; }
Definition at line 269 of file _heapqmodule.c.
{ PyObject *heap=NULL, *elem, *iterable, *sol, *it, *oldelem; Py_ssize_t i, n; int cmp; if (!PyArg_ParseTuple(args, "nO:nlargest", &n, &iterable)) return NULL; it = PyObject_GetIter(iterable); if (it == NULL) return NULL; heap = PyList_New(0); if (heap == NULL) goto fail; for (i=0 ; i<n ; i++ ){ elem = PyIter_Next(it); if (elem == NULL) { if (PyErr_Occurred()) goto fail; else goto sortit; } if (PyList_Append(heap, elem) == 1) { Py_DECREF(elem); goto fail; } Py_DECREF(elem); } if (PyList_GET_SIZE(heap) == 0) goto sortit; for (i=n/21 ; i>=0 ; i) if(_siftup((PyListObject *)heap, i) == 1) goto fail; sol = PyList_GET_ITEM(heap, 0); while (1) { elem = PyIter_Next(it); if (elem == NULL) { if (PyErr_Occurred()) goto fail; else goto sortit; } cmp = PyObject_RichCompareBool(sol, elem, Py_LT); if (cmp == 1) { Py_DECREF(elem); goto fail; } if (cmp == 0) { Py_DECREF(elem); continue; } oldelem = PyList_GET_ITEM(heap, 0); PyList_SET_ITEM(heap, 0, elem); Py_DECREF(oldelem); if (_siftup((PyListObject *)heap, 0) == 1) goto fail; sol = PyList_GET_ITEM(heap, 0); } sortit: if (PyList_Sort(heap) == 1) goto fail; if (PyList_Reverse(heap) == 1) goto fail; Py_DECREF(it); return heap; fail: Py_DECREF(it); Py_XDECREF(heap); return NULL; }
Definition at line 439 of file _heapqmodule.c.
{ PyObject *heap=NULL, *elem, *iterable, *los, *it, *oldelem; Py_ssize_t i, n; int cmp; if (!PyArg_ParseTuple(args, "nO:nsmallest", &n, &iterable)) return NULL; it = PyObject_GetIter(iterable); if (it == NULL) return NULL; heap = PyList_New(0); if (heap == NULL) goto fail; for (i=0 ; i<n ; i++ ){ elem = PyIter_Next(it); if (elem == NULL) { if (PyErr_Occurred()) goto fail; else goto sortit; } if (PyList_Append(heap, elem) == 1) { Py_DECREF(elem); goto fail; } Py_DECREF(elem); } n = PyList_GET_SIZE(heap); if (n == 0) goto sortit; for (i=n/21 ; i>=0 ; i) if(_siftupmax((PyListObject *)heap, i) == 1) goto fail; los = PyList_GET_ITEM(heap, 0); while (1) { elem = PyIter_Next(it); if (elem == NULL) { if (PyErr_Occurred()) goto fail; else goto sortit; } cmp = PyObject_RichCompareBool(elem, los, Py_LT); if (cmp == 1) { Py_DECREF(elem); goto fail; } if (cmp == 0) { Py_DECREF(elem); continue; } oldelem = PyList_GET_ITEM(heap, 0); PyList_SET_ITEM(heap, 0, elem); Py_DECREF(oldelem); if (_siftupmax((PyListObject *)heap, 0) == 1) goto fail; los = PyList_GET_ITEM(heap, 0); } sortit: if (PyList_Sort(heap) == 1) goto fail; Py_DECREF(it); return heap; fail: Py_DECREF(it); Py_XDECREF(heap); return NULL; }
PyDoc_STRVAR  (  heappush_doc  , 
"Push item onto  heap,  
maintaining the heap invariant."  
) 
PyDoc_STRVAR  (  heappop_doc  , 
"Pop the smallest item off the  heap,  
maintaining the heap invariant."  
) 
PyDoc_STRVAR  (  heapreplace_doc  , 
"Pop and return the current smallest  value,  
and add the new item.\n\\n\This is more efficient than  heappop) followed by heappush(,  
and can be\n\more appropriate when using a fixedsize heap.Note that the value\n\returned may be larger than item!That constrains reasonable uses of\n\this routine unless written as part of a conditional replacement:\n\n\if  item  
) 
PyDoc_STRVAR  (  heappushpop_doc  , 
"Push item on the  heap,  
then pop and return the smallest item\n\from the heap.The combined action runs more efficiently than\n\heappush() followed by a separate call to heappop()."  
) 
PyDoc_STRVAR  (  heapify_doc  , 
"Transform list into a  heap,  
in  place,  
in O(len(heap)) time."  
) 
PyDoc_STRVAR  (  nlargest_doc  , 
"Find the n largest elements in a dataset.\n\\n\Equivalent to: sorted(iterable, reverse=True)\n"  [:n]  
) 
PyDoc_STRVAR  (  nsmallest_doc  , 
"Find the n smallest elements in a dataset.\n\\n\Equivalent to: sorted(iterable)\n"  [:n]  
) 
PyDoc_STRVAR  (  module_doc  , 
"Heap queue algorithm (a.k.a. priority queue).\n\\n\Heaps are arrays for which a <= a and a <= a for\n\all  k[k][2 *k+1][k][2 *k+2],  
counting elements from 0.For the sake of  comparison,  
\n\nonexisting elements are considered to be infinite.The interesting\n\property of a heap is that a is always its smallest element.\n\\n\Usage:\n\\n\  heap[0] = [] # creates an empty heap\n\heappush(heap, item) # pushes a new item on the heap\n\item = heappop(heap) # pops the smallest item from the heap\n\item = heap[0] # smallest item on the heap without popping it\n\heapify(x) # transforms list into a heap , 

in  place,  
in linear time\n\  item = heapreplace(heap, item) # pops and returns smallest item , 

and adds\n\#new item;the heap size is unchanged\n\\n\Our API differs from textbook heap algorithms as follows:\n\\n\We use 0based indexing.This makes the relationship between the\n\index for a node and the indexes for its children slightly less\n\  obvious,  
but is more suitable since Python uses 0based indexing.\n\\n\Our heappop() method returns the smallest  item,  
not the largest.\n\\n\These two make it possible to view the heap as a regular Python list\n\without surprises:heap is the smallest  item[0],  
and heap.sort()\n\maintains the heap invariant!\n"  
) 
PyDoc_STRVAR  (  __about__  , 
"Heap queues\n\\n\\n\\n\Heaps are arrays for which a <= a and a <= a for\n\all  k[explanation by Fran\xc3\xa7ois Pinard][k][2 *k+1][k][2 *k+2],  
counting elements from 0.For the sake of  comparison,  
\n\nonexisting elements are considered to be infinite.The interesting\n\property of a heap is that a is always its smallest element.\n""\n\The strange invariant above is meant to be an efficient memory\n\representation for a tournament.The numbers below are`k'  [0],  
not a:\n\\n\0\n\\n\1 2\n\\n\3 4 5 6\n\\n\7 8 9 10 11 12 13 14\n\\n\15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n\\n\\n\In the tree  above[k],  
each cell`k'is topping`2 *k+1'and`2 *k+2'.In\n\an usual binary tournament we see in  sports,  
each cell is the winner\n\over the two cells it  tops,  
and we can trace the winner down the tree\n\to see all opponents s/he had.  However,  
in many computer applications\n\of such  tournaments,  
we do not need to trace the history of a winner.\n\To be more memory  efficient,  
when a winner is  promoted,  
we try to\n\replace it by something else at a lower  level,  
and the rule becomes\n\that a cell and the two cells it tops contain three different  items,  
\n\but the top cell\"wins\" over the two topped cells.\n""\n\If this heap invariant is protected at all  time,  
index 0 is clearly\n\the overall winner.The simplest algorithmic way to remove it and\n\find the\"next\" winner is to move some loser (let's say cell 30 in the\n\diagram above) into the 0  position,  
and then percolate this new 0 down\n\the  tree,  
exchanging  values,  
until the invariant is reestablished.\n\This is clearly logarithmic on the total number of items in the tree.\n\By iterating over all  items,  
you get an O(n ln n) sort.\n""\n\A nice feature of this sort is that you can efficiently insert new\n\items while the sort is going  on,  
provided that the inserted items are\n\not\"better\" than the last 0'th element you extracted. This is\n\especially useful in simulation  contexts,  
where the tree holds all\n\incoming  events,  
and the\"win\" condition means the smallest scheduled\n\time. When an event schedule other events for  execution,  
they are\n\scheduled into the  future,  
so they can easily go into the heap.  So,  
a\n\heap is a good structure for implementing schedulers(this is what I\n\used for my MIDI sequencer:).\n""\n\Various structures for implementing schedulers have been extensively\n\  studied,  
and heaps are good for  this,  
as they are reasonably  speedy,  
\n\the speed is almost  constant,  
and the worst case is not much different\n\than the average case.  However,  
there are other representations which\n\are more efficient  overall,  
yet the worst cases might be terrible.\n""\n\Heaps are also very useful in big disk sorts.You most probably all\n\know that a big sort implies producing\"runs\"  which are presorted\n\sequences, which size is usually related to the amount of CPU memory,  
\n\followed by a merging passes for these  runs,  
which merging is often\n\very cleverly organised.It is very important that the initial\n\sort produces the longest runs possible.Tournaments are a good way\n\to that.  If[1],  
using all the memory available to hold a  tournament,  
you\n\replace and percolate items that happen to fit the current  run,  
you'll\n\produce runs which are twice the size of the memory for random  input,  
\n\and much better for input fuzzily ordered.\n""\n\  Moreover,  
if you output the 0'th item on disk and get an input which\n\may not fit in the current  tournamentbecause the value\"wins\" over\n\the last output value,  
it cannot fit in the  heap,  
so the size of the\n\heap decreases.The freed memory could be cleverly reused immediately\n\for progressively building a second  heap,  
which grows at exactly the\n\same rate the first heap is melting.When the first heap completely\n\  vanishes,  
you switch heaps and start a new run.Clever and quite\n\effective!\n\\n\In a  word,  
heaps are useful memory structures to know.I use them in\n\a few  applications,  
and I think it is good to keep a`heap'module\n\around.:  
) 
Definition at line 679 of file _heapqmodule.c.
{ PyObject *m, *about; m = PyModule_Create(&_heapqmodule); if (m == NULL) return NULL; about = PyUnicode_DecodeUTF8(__about__, strlen(__about__), NULL); PyModule_AddObject(m, "__about__", about); return m; }
PyMethodDef heapq_methods[] [static] 
{ {"heappush", (PyCFunction)heappush, METH_VARARGS, heappush_doc}, {"heappushpop", (PyCFunction)heappushpop, METH_VARARGS, heappushpop_doc}, {"heappop", (PyCFunction)heappop, METH_O, heappop_doc}, {"heapreplace", (PyCFunction)heapreplace, METH_VARARGS, heapreplace_doc}, {"heapify", (PyCFunction)heapify, METH_O, heapify_doc}, {"nlargest", (PyCFunction)nlargest, METH_VARARGS, nlargest_doc}, {"nsmallest", (PyCFunction)nsmallest, METH_VARARGS, nsmallest_doc}, {NULL, NULL} }
Definition at line 522 of file _heapqmodule.c.
struct PyModuleDef [static] 
{ PyModuleDef_HEAD_INIT, "_heapq", module_doc, 1, heapq_methods, NULL, NULL, NULL, NULL }
Definition at line 666 of file _heapqmodule.c.